1. science
2. ecology
3. pollution

Interaction of Land Use and Land Cover Change
and Climate Change and Driving Forces: A Case
Study of Jiangxi Province, China
Thesis submitted in partial fulfilment of the requirements of
the degree Doctor rer. nat. of the
Faculty of Forest and Environmental Sciences,
Albert-Ludwigs-Universität
Freiburg im Breisgau, Germany
by
Qi Wang
Freiburg im Breisgau, Germany
2012
Name of Dean:
Prof. Dr. Jürgen Bauhus
Name of Supervisor:
Prof. Dr. Rüdiger Glaser
Name of 2nd Reviewer: Prof. Dr. Andreas Matzarakis
Date of thesis’ defence: 20.06.2012
Acknowledgements
This PhD work would have not been possible without the help and support
provided by my advisory committee.
I owe sincere and earnest thankfulness to my advisor, Prof. Dr. Rüdiger Glaser, for his guidance, enduring support and great patience during my Germany
graduate studies. His mentorship was paramount in providing a well-rounded
experience consistent with my long-term career goals. He encouraged me to
not only grow as a scholar but also as an instructor and an independent thinker.
I am grateful to those members of my doctoral committee in the institute of
geography for their input, valuable discussions and accessibility; to Prof. Dr.
Jörg Stadelbauer for his support; to Prof. Dr. Andreas Matzarakis for his help.
I am obliged to many of my colleagues from the institute of physical geography who supported me. In particular, I gratefully acknowledge the contribution
of my colleague Dr. Dirk Riemann who has been instrumental in helping with
Matlab scripts and gave me valuable advice for my research. I would also like
to thank Dr. Steffen Vogt for his assistance and guidance in getting my graduate career started on the right foot and providing me with the foundation of
remote sensing and geographical information system. Many thanks go to Ghazi
Al Dyab, Dr. Philipp Weckenbrock, Dr. Johannes Schönbein, Tian Zhang, Dr.
Jian-feng, Liu, Angelika Schuler and Dr. Helmut Saurer who supported and
boosted me morally. I would like to thank the support of DAAD in helping me
reaching my goal through scholarship program.
I would like to show my gratitude to my German family: Renate Ries. Special thanks to her, for her kindness, constant support and encouragement.
Last but not least, I would like to thank to my family, relatives and friends
have provided relentless support that has been a constant source of motivation
for me.
Zusammenfassung
Die Veränderung der Landoberfläche im Sinne einer veränderten Bodennutzung und Vegetationsbedeckung (land use and land cover change LULC) stellt
einen bedeutenden Faktor in der Klimaforschung dar, wird jedoch wiederum
selbst durch den Klimawandel beeinflusst.
Ziel der vorgelegten Arbeit ist (1) die Ableitung von Oberflächentemperatur- und Niederschlagstrends der Provinz Jiangxi, China für den Zeitraum von
1951-1999 basierend auf homogenisierten Beobachtungsdaten unter Anwendung von Standard Normal Homogenitätstest (SNHT) sowie ein Vergleich der
Ergebnisse mit den aus den Datensätzen des Global Historical Climatology
Network (GHCN) berechneten Trends. Des Weiteren wurde (2) unter Verwendung der Observation Minus Reanalyse (OMR)-Methode der kleinräumige
Einfluss der Beschaffenheit und Topographie der Landoberfläche auf Veränderungen von Oberflächentemperatur und Niederschlag untersucht. (3) Für die
Arbeit wurde ein auf Integrativen Prozessen und unter Berücksichtigung quantitativer Verfahrensweisen wie Fernerkundung, Geographischer Informationssysteme (GIS) oder statistischen Methoden basierender Ansatz verwendet
sowie die Antriebsmechanismen des Landnutzungswandels von Ackerland und
bebauter Fläche in Jiangxi (1995-2005) analysiert.
Die Ergebnisse nach der Homogenisierung der Oberflächentemperatur- und
Niederschlagsdaten zeigen gegenüber der Verwendung von Rohdaten eine deutlich abgeschwächte Erwärmung der winterlichen Temperaturen in Jiangxi .
Auch die räumliche Abkühlung während der Sommermonate wird durch nicht
bereinigte Daten übertrieben. Der Vergleich zwischen den Datensätzen der
GHCN und den in der hier vorgestellten Analyse verwendeten, bereinigten
Temperaturdaten zeigen eine nur geringfüge Abweichung der jeweils resultierenden Klimatrends. Die Niederschlagserie erweisen sich als homogen. Als
klimatische Besonderheit des Untersuchungsgebiets wurden eine Erwärmung
der Wintermonate sowie eine Abkühlung während der Sommermonate und
zunehmend trockene Verhältnisse während der Frühlingsmonate im Gegensatz
zu einer Verstärkung der Humidität während des Sommers nachgewiesen. Als
geographisches Zentrum der sommerlichen Abkühlung konnte das Einzugsgebiet des Poyang-Sees identifiziert werden.
Der Vergleich von OMR-Trends mit dem Typ der Landoberfläche zeigt eine
besonders deutliche Erwärmung über vegetationsloser Landoberfläche im Vergleich zu Grünlandflächen. 81.1% der Erwärmung über Landoberflächen mit
Vegetationsbedeckung lassen sich auf LULC-Änderungen zusammen mit
topographischen und regionalen Merkmalen zurückführen. Die Aussagekraft
der LULC-Änderung nimmt mit zunehmender Vegetationsbedeckung der
Landoberfläche kontinuierlich ab. Die OMR-Trends der Niederschlagsdatensätze zeigen eine schwache Abhängigkeit von der Art der Landoberfläche.
Zu der Veränderung der Nutzung der Ackerflächen in Jiangxi trugen mehrere
miteinander interagierende Mechanismen, einschließlich politischer und sozioökonomischer Kräfte als unmittelbarer Faktoren, sowie biophysikalische Notwendigkeiten ursächlich bei. Der Übergang von Ackerland zu Bauland ist in
hohem Maße auf eine Kombination sozio-ökonomischer Faktoren zurück zu
führen.
Die hier vorgestellte Arbeit leistet einen wichtigen Beitrag zur Überwachung und Modellierung klimatischer Prozesse. Veränderungen der LULC
sollten zusammen mit Treibhausgasen als wichtige Faktoren bei der lokalen
und regionalen Klimamodellierung Berücksichtigung finden.
Schlüsselwörter: Landnutzung; Klimatrends; Antrieb
List of Tables
Table 2.1 Changes of observation specification in China since 1960s .....................................................65
Table 2.2 Changes of mean temperature level have taken place gradually as a linear trend
starting and ending at arbitrary points of time, a and b respectively. Population data are
given for the cities or counties where the stations are located. The change of mean
temperature level is Qb  Qa . ...........................................................................................................66
Appendix Table1. Thirty-six meteorological stations in Jiangxi and its contiguous provinces ..................83
Table 3.1 Correlation coefficients (r) between temperature/precipitation and corresponding land
cover types ................................................................................................................................... 102
Table 3.2 Summary for regression models of different land cover types ................................................ 103
Table 3.3 Decadal trends of temperature (°C/decade) and precipitation (mm/decade) response to
surface vegetation types and the number of 1°×1°grids for the calculation of correlation
between vegetation types and temperature/precipitation trend ....................................................... 104
Table 4.1 List of the selected biophysical and socio-economic factors affecting land use ...................... 132
Table 4.2 Change matrix of each land use type and transition rates in 1995 and 2005 ........................... 133
Table 4.3 Annual change rates of biophysical and socio-economic indicators........................................ 134
Table 4.4 Summary for regression models of cropland with explanatory variable, POLICY
(1995-2005) ................................................................................................................................. 135
Table 4.5 Coefficients for the final regression model of cropland (1995-2005)b .................................... 135
Table 4.6 Summary for regression models of built-up land (1995-2005) ............................................... 136
Table 4.7 Coefficients for the final regression model of built-up land (1995-2005)a .............................. 136
1
List of Figures
Figure 2.1 Location of the study area in China (above) and locations of 36 meteorological
stations in Jiangxi and its contiguous 6 provinces used in this study (below) are shown.
(Overlaid on a 90m DEM, station numbers see Table 1 in Appendix) ..............................................67
Figure 2.2 Plotting for the discontinuities detected in homogeneity tests in the final test round.
The histogram shows the seasonal frequency for discontinuities on decadal level for the
period of 1951-2000. The frequency of winter, spring, summer and autumn are denoted by
blue, red, green and violet columns respectively. ............................................................................68
Figure 2.3 Q-values on temperature series in December from Jiujiang station (1951-2000). Blue
solid line denotes the results for the original data and pink diamond-shaped point for the
adjusted data. .................................................................................................................................69
Figure 2.4 T-values from performing the single shift SNHT on temperature seres in December
from Jiujiang station (1951-2000). Blue solid line denotes the results for the original data
and pink diamond-shaped point for the adjusted data. Significant levels of 90% and 95%
are also marked. .............................................................................................................................69
Figure 2.5 Q-values on temperature series in February from Guangchang station (1951-2000).
Blue column denotes the results for the original data and pink diamond-shaped point for
the adjusted data. ............................................................................................................................70
Figure 2.6 T-values from performing the single shift SNHT on temperature series in February
from Guangchang station (1951-2000).Blue solid line denotes the results for the original
data. Significant levels of 90% and 95% are also marked. ...............................................................70
Figure 2.7 Q-values on precipitation series in June from Jiujiang station (1951-2000). Blue solid
line denotes the results for the original............................................................................................71
Figure 2.8 T-values from performing the single shift SNHT on precipitation series in June from
Jiujiang station (1951-2000). Blue solid line denotes the results for the original data.
Significant levels of 90% and 95% are also marked. .......................................................................71
Figure 2.9 Anomalies of the mean temperature over Jiangxi province for adjusted data (19512000) relative to 1961-1990 (A) adjusted winter, (B) adjusted spring, (C) adjusted summer,
(D) adjusted autumn, and (E) adjusted annual mean. Annual mean (dashed line) 5-year
running average (solid line). P > 0.05. ............................................................................................72
Figure 2.10 Surface temperature change based on local linear trends over Jiangxi for our
analysis (1951-2000), from the top to the bottom: annual mean, winter, spring, summer and
autumn mean temperature respectively. ..........................................................................................73
Figure 2.11 The same as Figure 2.10, but for unadjusted data. ................................................................74
2
Figure 2.12 Surface air temperature anomalies relative to 1961-1990 mean for (A) Jiangxi (B)
Northern hemisphere (C) Globe is denoted by dotted line for annual mean, solid line for 5year running average. P > 0.05 .......................................................................................................75
Figure 2.13 Surface air temperature anomaly relative to 1961-1990 averaged over Jiangxi
province based on unadjusted raw and adjusted data (1951-2000). Dashed line denotes
unadjusted annual mean, dotted line (adjusted annual mean), crossed-solid line (adjusted 5year running average), solid line (unadjusted 5-year running average) and dashed-dotted
line (trend line). P > 0.05. ...............................................................................................................76
Figure 2.14 Anomalies of the mean temperature over Jiangxi province for unadjusted data
(1951-2000) relative to 1961-1990 (A) unadjusted winter, (B) unadjusted spring, (C)
unadjusted summer, and (D) unadjusted autumn. Annual mean (dashed line) 5-year running
average (solid line). P > 0.05. .........................................................................................................77
Figure 2.15 Surface air temperature anomaly relative to 1961-1990 averaged over Jiangxi
(1951-1990). Triangle-dashed line denotes annual mean for GHCN homogenized data,
dotted line (annual mean for adjusted data), crossed-solid line (5-year running average for
GHCN homogenized data) and solid line (5-year running average for adjusted data). P >
0.05................................................................................................................................................78
Figure 2.16 Surface air temperature change based on local linear trends over Jiangxi for our
analysis (1951-1990), from the top to the bottom: annual mean, winter, spring, summer and
autumn mean temperature respectively. ..........................................................................................79
Figure 2.17 Surface air temperature change based on local linear trends over Jiangxi for GHCN
analysis (1951-1990), from the top to the bottom: annual mean, winter, spring, summer and
autumn mean temperature respectively. ..........................................................................................80
Figure 2.18 Anomalies of the mean precipitation over Jiangxi province for homogenized data
(1951-2000) relative to 1961-1990. Homogeneous (A) winter, (B) spring, (C) summer, (D)
autumn, and (E) annual mean. Annual mean (dashed line) 5-year running average (solid
line). P > 0.05. ................................................................................................................................81
Figure 2.19 Precipitation change based on local linear trends over Jiangxi for our analysis
(1951-2000), from the top to the bottom: annual mean, winter, spring, summer and autumn
mean respectively. ..........................................................................................................................82
Figure 3.1 Monthly mean surface air temperature anomalies (observation [dashed red] and
NCEP-NCAR reanalysis (NNR) [solid blue]) (in ℃) over Jiangxi relative to 1961-1990.
Five decades (1950s-1990s) are compared. TOMR is observation minus reanalysis
temperature. r denotes correlation coefficient between NNR and surface observations. ................. 105
Figure 3.2 Decadal trends of monthly mean temperature. The Observation Minus Reanalysis
(OMR) trend per decade (in ℃) at each grid point was obtained by the average of the„90s
3
minus 80s‟and „80s minus 70s‟ temperatures. The mean value of the decadal trend is
denoted in each panel on the left. a observation; b NNR and c OMR. ............................................ 106
Figure 3.3 Daily mean precipitation anomalies (observation [dashed red] and NCEP-NCAR
reanalysis (NNR) [solid blue]) (in mm) over Jiangxi relative to 1961-1990. Five decades
(1950s-1990s) are compared. TOMR is observation minus reanalysis precipitation. r
denotes correlation coefficient between NNR and surface observations......................................... 107
Figure 3.4 Decadal trend of daily precipitation averaged at stations over Jiangxi. The
Observation Minus Reanalysis (OMR) trend per decade (in mm) at each grid point was
obtained by the average of the „90s minus 80s‟ and „80s minus 70s‟. The mean value of the
decadal trend is denoted in each panel on the left. a observation; b NNR and c OMR. ................... 108
Figure 3.5 Vegetation index map derived from NDVI. a Annual mean; b Winter (DJF); c Spring
(MAM); d Summer (JJA) and e Autumn (SON). ........................................................................... 109
Figure 4.1 Integrated method for studying driving forces of LULC changes ......................................... 137
Figure 4.2 Change pattern of cropland in Jiangxi in 1995 and 2005 ...................................................... 138
Figure 4.3 Change pattern of built-up land in Jiangxi in 1995 and 2005 ................................................ 138
Figure 4.4 Land use and land cover map of Jiangxi in 1995 and 2005 ................................................... 139
Figure 4.5 Spatial patterns of cropland transition and driving factors (1995-2005). (The left
upper is the thematic map of cropland transition rate and the right upper is the thematic
map of rural population (R-POP) change rate. The left bottom is the thematic map of gross
product value-added of primary industry (GDPP) change rate and the right bottom is the
thematic map of rural income (TIRI) change rate. ......................................................................... 140
Figure 4.6 Spatial patterns of built-up land transition and driving factors (1995-2005). (The
upper is the thematic map of built-up land transition rate. The left middle is the thematic
map of gross domestic product value (GDP) change rate and the right middle is the
thematic map of gross product value-added of tertiary industry (GDPT) change rate. The
left bottom is the thematic map of gross product value-added of secondary industry
(GDPS) change rate and the right bottom is the thematic map of Engel‟s coefficient (EC)
change rate. .................................................................................................................................. 141
4
Abstract
Land use and land cover (LULC) change is an important climate forcing, and
climate change also affects LULC processes. We aim to assess the regional scale interaction of LULC change and climate change. Driving forces of LULC
change are also examined. Jiangxi Province, China is used as a case study. (1)
To obtain reliable climate trends, we apply Standard Normal Homogeneity
Test (SNHT) in surface temperature and precipitation data for the period of
1951-1999. We also compare the temperature trend computed from Global
Historical Climatology Network (GHCN) datasets and from our analysis. (2)
To assess the regional impacts of land surface type on surface temperature and
precipitation change integrating regional topographic characteristics, we use
the Observation Minus Reanalysis (OMR) method. (3) To analyze the driving
mechanisms of cropland and built-up land changes over Jiangxi, an integrative
approach with quantitative policy effect involving remote sensing, geographical information system (GIS) and statistical techniques is applied.
Precipitation series are found to be homogeneous. The comparison between
GHCN and our analysis on adjusted temperatures indicates that the resulting
climate trends vary slightly from datasets to datasets. A feature of warming winter versus cooling summer and spring drying versus summer wetting is revealed. Poyang Lake watershed is the center of summer cooling.
OMR trends associated with land surface type present that strong surface
warming response to land barrenness and weak warming response to land greenness. 81.1% of the surface warming over vegetation index areas (0~0.2) attributes to LULC change incorporating regional topographic characteristics.
The contribution capability of LULC change decreases as land cover greenness
increases. OMR trends of precipitation have a weak dependence on the type of
land-surface.
We find that the cropland transition in Jiangxi has been achieved through
multiple interacting mechanisms including policy and socio-economic forces
as the proximate factors and biophysical factor as the underlying cause. The
pathways leading to built-up land transition rely to various degrees and combinations on socio-economic factors.
5
This study has important implications in the monitoring and modeling processes of climate. We suggest LULC change should be considered along with
greenhouse gas as a forcing in local and regional climate modeling.
Keywords: land use land cover; climate trends; driving forces
6
Table of Contents
List of Tables ....................................................................................................................1
List of Figures ..................................................................................................................2
Abstract ............................................................................................................................5
1
Introduction: Global Land Use Processes and Climate Change ............................ 10
1.1
1.1.1
Causes of LULC change .............................................................................. 11
1.1.2
Consequences/impacts of LULC change ...................................................... 15
1.1.3
Future perspectives/Sustainable land management .......................................23
1.2
2
Overview: Land Use Science .............................................................................. 10
Global Climate Change ....................................................................................... 25
1.2.1
Causes of global climate change ..................................................................26
1.2.2
Consequences of global climate change ....................................................... 28
1.2.3
Future climates-the great uncertainty ........................................................... 33
Regional temperature and precipitation trends based on homogenized time
series in Jiangxi Province, China ....................................................................................... 34
2.1
Introduction ........................................................................................................34
2.2
Materials and Methods ........................................................................................ 42
2.2.1
Study site .....................................................................................................42
2.2.2
Data .............................................................................................................43
2.2.3
Homogenization........................................................................................... 44
2.2.4
Spatial interpolation ..................................................................................... 49
2.3
Results ................................................................................................................ 49
2.3.1
Abrupt discontinuities .................................................................................. 50
2.3.2
Trend non-homogeneities .............................................................................52
2.3.3
Outliers ........................................................................................................ 53
2.3.4
Examples of non-homogeneities .................................................................. 53
2.3.5
Temperature trend ........................................................................................ 56
2.3.6
Precipitation trend ........................................................................................ 59
2.4
Discussions ......................................................................................................... 60
7
2.5
Conclusions ........................................................................................................64
2.6 Appendix ................................................................................................................ 83
3
Regional scale impacts of land use and land cover changes on climate change
in Jiangxi Province, China ................................................................................................. 84
3.1
Introduction ........................................................................................................84
3.2
Data and methodology ........................................................................................ 88
3.2.1
Data .............................................................................................................88
3.2.2
Data analysis................................................................................................ 89
3.3
Results ................................................................................................................ 90
3.3.1
Temperature trends of observation and reanalysis ........................................90
3.3.2
Precipitation trends of observation and reanalysis ........................................92
3.3.3
NDVI trend ..................................................................................................93
3.3.4
Surface temperature and precipitation trends with respect to LULC
changes
3.3.5
.................................................................................................................... 93
Temperature/precipitation change as a function of different land cover
types incorporating regional characteristics ................................................................... 95
4
3.4
Discussions ......................................................................................................... 97
3.5
Conclusions ........................................................................................................97
Driving forces of regional cropland and built-up land transition in Jiangxi
Province, China ................................................................................................................ 109
4.1
Introduction ...................................................................................................... 109
4.2
Methodologies .................................................................................................. 115
4.2.1
Overview of interdisciplinary methodology on LULC change research ...... 115
4.2.2
The integrated method to examine cropland and built-up land transition .... 116
4.2.3
The cropland returning policy, biophysical and socio-economic data.......... 117
4.2.4
Detecting the cropland and built-up land dynamics from remote sensing
images
4.3
.................................................................................................................. 119
Results .............................................................................................................. 120
4.3.1
Land use change and spatial pattern 1995-2005.......................................... 120
4.3.2
Regression analysis of the driving forces and interactions of cropland
1995-2005 .................................................................................................................. 121
4.3.3
Regression analysis of the driving forces and interactions of built-up
land 1995-2005............................................................................................................ 123
8
5
4.4
Discussions ....................................................................................................... 125
4.5
Conclusions ...................................................................................................... 130
Conclusions ............................................................................................................ 141
References .................................................................................................................... 143
9
1. Introduction: Global Land Use Processes and Climate Change
1 Introduction: Global Land Use Processes and Climate Change
1.1 Overview: Land Use Science
Land change science has emerged as a fundamental component of global environmental change and sustainability research. This interdisciplinary field seeks
to understand the dynamics of land use/land cover (LULC) as a coupled human-environment system to address theory, concepts, models, and applications
relevant to environmental and societal problems, including the intersec-tion of
the two (Turner II et al., 2007a). The study of LULC change has a long history
dating to ancient times. Early attention focused on how human activities transformed and degraded landscapes, a theme that has resurfaced at different times
(Marsh, 1864; Thomas, 1956) and currently is embedded within the larger concept of global environmental change and earth system science (Meyer and
Turner, 1994; Steffen et al., 2002). With the realization that land surface processes influence climate, the research of global environmental change had paid
more attention to LULC change (National Research Council, 2005). In the
mid-1970s, it was recognized that land cover change modifies surface albedo
and thus surface atmosphere energy exchanges, which affect regional climate
(Charney and Stone, 1975; Otterman, 1974; Sagan et al., 1979). In the early
1980s, terrestrial ecosystems as sources and sinks of carbon were highlighted
(Houghton et al., 1985; Lambin and Geist, 2006; Woodwell et al., 1983). Reducing the uncertainty of these terrestrial sources and sinks of carbon remains a
serious challenge today. Subsequently, a much broader range of impacts of
LULC change on ecosystem goods and services were further found. Of primary concern are impacts on biotic diversity worldwide (Sala et al., 2000), soil
degradation (Trimble and Crosson, 2000), and the ability of biological systems
to support human needs (Vitousek et al., 1997).
Current rates, extents and intensities of LULC are far greater than ever in
history, driving unprecedented changes in ecosystems and environmental processes at local, regional and global scales. Monitoring and mediating the
10
1. Introduction: Global Land Use Processes and Climate Change
negative consequences of LULC while sustaining the production of essential
resources has therefore become a major priority of researchers and policymakers around the world.
Land cover refers to the attributes of the earth‟s land surface and immediate subsurface (e.g. forest, grassland). Land use has been defined as the
purposes for which humans exploit the land cover. It involves both the manner
in which biophysical attributes of the land are manipulated and the intent underlying that manipulation (e.g. cropping, ranching) (Lambin, 2003; Turner
and Ali, 1995). Land cover and changes are visible in remotely-sensed data
from satellite platforms or directly in the field, although it requires interpretation and ground truthing. Observations of land use and its changes generally
require the integration of natural and social scientific methods (expert
knowledge, interviews with land managers) to determine which human activities are occurring in different parts of the landscape, even when land cover
appears to be the same. As a result, scientific investigation of the causes and
consequences of LULC requires an interdisciplinary approach integrating both
natural and social scientific methods.
1.1.1
Causes of LULC change
Great changes have taken place on land surface due to the expansion of land
use systems across the world over the past 300 years. Human actions rather
than natural forces are the source of most contemporary change in the states
and flows of the biosphere (Lambin and Geist, 2006). Understanding these actions and the driving forces is thus of crucial importance for understanding,
modeling, and predicting global environment change and for managing and responding to such change. This process is by no means simple for a number of
reasons. Driving forces on LULC can include almost any facet that affects human activity, including local culture (food preference, etc.), economics
(demand for specific products, financial incentives), environmental conditions
(soil quality, terrain, moisture availability), land policy and development programs (agricultural programs, road building, zoning), and interactions and
feedbacks between these factors, including past human activity on the land
(land degradation, irrigation and roads).
11
1. Introduction: Global Land Use Processes and Climate Change
i. Biophysical drivers
Biophysical factors refer to the natural capacity or predisposing environmental
conditions for land use change, with the set of abiotic and biotic factors-climate,
soils, lithology, topography, relief, hydrology and vegetation-varying among
localities and regions and across time (Lambin et al., 2001b). The variability in
biophysical drivers and natural environmental changes interact with the human
causes of land change. For example, biophysical limitation such as steep slopes
and difficulty of access can provide considerable but not necessarily sufficient
protection for a forest. Natural variability may also induce socio-economic unsustainability, for example when abnormal large amount of precipitation alters
the surface in drought risks and results in over wetting conditions on rangelands. When normal dry condition returns, the livestock management practices
are poorly applied and cause land degradation (Lambin and Geist, 2006). Land
use conversion, such as cropland expansion in dryland, may also increase the
vulnerability of human-environment systems to climatic fluctuations and thus
lead to land degradation (Okin, 2002).
In tropical forests, land characteristics or the biophysical triggers are pointed to be the contributing factors of deforestation. For example, fires as a
causative factor of land cover change in boreal region appeared in 18% of the
deforestation cases (Kasischke et al., 2002). In dryland zones, climate factors
can influ-ence land cover in the form of prolonged period of droughts (Nicholson et al., 1998). Land use can also be indirectly altered by biophysical factors
through the variation in precipitation (Nicholson, 2002).The remarkable changes in soil type priorities can be caused by precipitation changes at landscape
level (Reenberg, 1994; Reenberg et al., 1998). In terms of farmland, decreasing
fertility and soil erosion influence the concrete location of agricultural practices,
lead to land use change (e.g., abandonment and relocation of cultivation). Climate factors, primarily changes in precipitation, were noted in over 25% of the
cases of agricultural intensification in farmland (McConnell and Keys, 2005).
ii. Socio-economic and demographic drivers
Global economy increased nearly sevenfold during the last 50 years (while
global population doubled in roughly the past 40 years) (Lambin and Geist,
2006). Therefore, the demand for ecosystem goods and services is surging.
12
1. Introduction: Global Land Use Processes and Climate Change
Opportunities and constraints for land uses practices are determined by markets
and policies and are increasingly influenced by global factors. Economic factors are defined as a range of variables that directly affect the decision making
by land managers, such as taxes, subsidies, input and output prices, trade, production and transportation costs, capital flows and investments, credit access,
technology etc. (Barbier, 1997). Economic factors and associated policies encompass distinct processes that need individual treatment.
The reallocation of land among uses is influenced by factors related to
production and consumption activities. Stoll et al. (1984) estimated land use
chan-ges among cropland, pasture, and forest land for the South-Central United
States indicating incomes from land-based enterprises drive land use changes
in a majority of cases. Land use change can also be driven by demographic and
economic factors. For example, political and economic failure as well as the
two-year-drought in 2000 and 2001 in Tajikistan triggered severe food shortages. The population was forced to cultivate former abandoned land or pasture in
order to secure food supply (FAO, 2005). Population and personal income has
been major explanatory drivers for major land uses in the United States, and
are negatively correlated with changes in percentages of the land base occupied
by agriculture and forestry. Increases in population and personal income tend
to intensify development pressure in urbanization reducing forest area (Alig
and Nealy, 1987). Population pressure has resulted in sub-division of the parcels to very small sizes with the consequence that past practices such as shifting cultivation, crop rotation and fallow cropping which used to improve soil
fertility are no longer practiced (Kamoni and Makokha, 2010).
Land use change is also a complicated process. Since the end of the Second World War, strong trend in Europe (rural-urban migrations;
metropolisation/accelerated growth of large cities; strong process of territorial
contraction-n/polarisation in the countries of Central and Eastern Europe after
the economic transition) have occurred (Robert, 2007). Metropolitan growth is
a major driver of the development in knowledge economy and technological
innovation. The internationalization of the economy and the emergence of the
globalization process have been leading to accelerated growth of large cities,
now well-known as “metropolisation process”. The development of the infor13
1. Introduction: Global Land Use Processes and Climate Change
mation economy and of the knowledge society is strengthening this process.
European cities are particularly affected by the competition in advanced sectors
(Lise and Huriot, 2002) and in manufacturing activities from new emerging
economies (China, India, and Brazil etc.). The new potentials of rural areas are
being intensively exploited (e.g. renewable energy, raw materials for the industry, growing worldwide demand of food products). These aspects have
promoted the rural development across the world and thus land use has been altered. At smaller scale, industrial development triggers dispersal tendencies in
rural areas where migration flows are intensifying. Greater food production and
assoc-iated water consumption and nutrient and pesticide release, by which the
pattern of land use can be changed, would have significant influences on regional climate, land quality and biodiversity. Moreover, the development of
soft tourism, residential economy (migration of retirees out of cities) and regional airports driven by low-cost companies, to some extent, has affected the
plans and consequences of land use.
In addition, the preceding presentation of demographic, economic and
technological drivers makes it clear that it is also important to understand political driver and their interplays with individual decision-making (Agrawal and
Yadama, 1997; Ostrom et al., 1999; Young, 2002a). In particular, government
policy plays an important role in land change, either directly causative or in
mediating fashion (Lambin and Geist, 2006). Political factors reflecting government programs designed to improve soil conservation and encourage tree
planting, such as the Soil Bank Program in the late 1950‟s and early 1960‟s,
had a significant increment in the amount of miscellaneous private forest and a
similar decrease in cropland and rangeland (Alig et al., 1988).
iii. Cultural drivers
Cultural drivers also have an impact on decision making on land use and these
cultural conditions cannot be isolated from underlying political and economic
background such as ethnic minorities, public attitudes, values and beliefs. The
decision is made by land managers, who have various motivations, collective
memories, personal experience and it is their attitudes, beliefs and individual
perceptions that affect land use decision. For example, cattle‟s ranching is a
significant cause for deforestation reported almost exclusively for humid low14
1. Introduction: Global Land Use Processes and Climate Change
land cases from mainland Latin America. The cultural preference for cattle
ranching comes from colonial Iberian experiences in the 17th and 18th century
in the Americas. Why cattle ranching is so prevalent in land poor Central
America as well as in land rich South America is explained to some degree by
this common cultural legacy (Geist and Lambin, 2002). In agricultural areas
cultural and religious factors often cause strong preferences for staple crops or
for particular cropping practices and restrict the use of certain parts of the land.
(e.g. tobacco and coffee) (McConnell, 2002).
1.1.2
Consequences/impacts of LULC change
LULC change as a “forcing function in global environmental change” (Turner,
2006) conforms generally to the development of human societies and civilizations and is the direct and indirect consequence of human actions to secure
essential resources. Between one-third and one-half of the land surface has
been transformed by human action; the carbon dioxide concentration in the atmosphere has increased by nearly 30% since the onset of the Industrial Revolution (Turner II et al., 2007b). More recently, industrialization has encouraged
the concentration of human populations within urban areas and the depopulation of rural areas, accompanied by the intensification of agriculture in the
most productive lands and the abandonment of marginal lands. Some influences are positive such as continuing increases in food and fiber production,
resource use efficiency, wealth, livelihood security, welfare and human wellbeing (Lambin et al., 2003a; Turner II et al., 2007b). Some influences on climate and ecosystem services and conditions can clearly be undesirable or
negative. Altering ecosystems services-i.e., the benefits people obtain from
ecosystems such as provision services (e.g. food and water), regulating services
(e.g. flood and disease control), cultural services (e.g. spiritual and recreational
benefits), and supporting services (e.g. nutrient cycling) that maintain the conditions for life on earth (Millennium Assessment 2003)-, affects the ability of
biological systems to sustain human demands (Cassman et al., 2005; Odum,
1989; Ojima et al., 1994; Vitousek et al., 1997). Local changes in LULC are so
pervasive that, when aggregated globally, they may significantly affect central
aspects of the earth system functioning and thus life support functions and hu15
1. Introduction: Global Land Use Processes and Climate Change
man livelihoods (Lambin and Geist, 2006). We face the challenge of managing
trade-offs between immediate human needs and maintaining the capacity of the
biosphere to provide goods and services in long term.
The impacts of LULC change are scale-dependent in that some affect the
local environment (e.g. local water quality), while other impacts extend far beyond the location where they arise (e.g., carbon cycle and climate change)
(Mustard and Fisher, 2004). Further, the rate of land modifications across the
world varies from region to region (Richards, 1990). Not all the land changes
have global impacts and are irreversible. Moreover, multiple impacts may
overlap and reinforce each other. This implies the existence of various multidirectional impacts on both ecosystems and people, with biodiversity loss and
soil degradation probably being the sole truly irreversible global environmental
change impacts (Lambin and Geist, 2006). The impacts of LULC change on (a)
climate change; (b) biodiversity loss; (c) soil degradation; (d) water resources;
(e) food provision; and (f) human health are described below.
Climate Change
LULC, as a first-order anthropogenic climate forcing, had an important impact
on the development of contemporary atmosphere and they continue (National
Research Council, 2005; Scholes et al., 2003). However, LULC change, in
combination with fossil fuel combustion, has triggered major and globally significant variation of the naturally evolved synergies over the last 1000 years
(Mann et al., 1999), but particularly in the last 200 to 250 years (Crowley,
2000). The consequence of these human actions can, at least partially, be responsible for the increases in atmospheric greenhouse gases and aerosol load,
thereby driving global warming (Foley et al., 2003; Penner et al., 2001; Prather
et al., 2001). The anthropogenic era is generally thought to have begun 150 to
200 years ago, when the industrial revolution began producing carbon dioxide
and methane at rates sufficient to alter their compositions in the atmosphere.
The multiple influences and interplays between land use, atmosphere and other
climatic elements work at various spatial and temporal scales (Pielke et al.,
1998).
Not only climate effects in the regions where LULC change occurs are to
be assessed, but also their role in altering hemispheric and global atmospheric
16
1. Introduction: Global Land Use Processes and Climate Change
and ocean circulations at large distances from the location of LULC change. At
the global scale, changes in land surface properties in response to vegetation
changes can affect continental and global atmospheric circulation. LULC can
also increase the release of carbon dioxide to the atmosphere by disturbance of
terrestrial soils and vegetation, and this change mainly results in deforestation
especially when followed by agriculture. The expansion of cropland and pastures is at the expense of the detriment of forests. Agriculture causes the release
of soil carbon with respect to the disturbance by tillage. Agricultural land uses
are assessed to contribute to 20% of current annual greenhouse gases forcing
potential (Folland et al., 2001). The conversion of grassland into croplands and
ecosystem degradation is prevalent with the accelerated growth of population
and development of metropolisation process. These dramatic changes in land
use with widespread decrease of forest and grasslands have increased carbon
emission in arid and semi-arid lands of east and central Asia (Chuluun and
Ojima, 2002). Emission from biomass burning has great contribution to the
pollution into the atmosphere, with greenhouse gases and carbonaceous aerosols influence (Crutzen and Andreae, 1990; Eva and Lambin, 2000; Stolle et al.,
2003). Land use and land cover changes are also behind major changes in terrestrial emissions of other greenhouse gases, especially methane (altered
surface hydrology: wetland drainage and rice paddies; cattle grazing), and nitrous oxide (agriculture: input of inorganic nitrogen fertilizers; irrigation;
cultivation of nitrogen fixing plants; biomass combustion). Methane is one of
the most potent contributors to the atmospheric greenhouse effect and plays a
key role in tropospheric chemistry (Lambin and Geist, 2006).
LULC changes can alter land surface albedo. The precise contribution of
this effect to climate change remains a controversial but increasing concern.
The impact of albedo changes on regional climates is also a promising area for
research, especially climate changes associated with changes in dense vegetation cover and urban areas. These variations change surface heat balance not
only by altering surface albedo, but also by altering evaporative heat transfer
resulted from evapotranspiration from vegetation (highest in closed canopy
forest), and by changes in surface roughness, which alter heat transfer between
the relatively stagnant air layer at earth surface (the boundary layer) and the
17
1. Introduction: Global Land Use Processes and Climate Change
tropos-phere. An example of this is the warmer temperatures observed within
urban areas versus rural areas, known as the urban heat island effect (Ellis and
Pontius, 2011).
To summarize, the above discussion clearly identify the importance of
LULC change in the climate system. Therefore, it is essential that we detect
LULC change accurately, at appropriate scales, and in a timely manner so as to
better understand their impacts on climate and provide improved prediction of
future climate.
Biodiversity loss
Global cropland, pastures, plantations, and urban areas have expanded in recent
decades, accompanied by large increases in energy, water, and fertilizer consumption, along with considerable losses of biodiversity. Biodiversity is conventionally interpreted as diversity in genetics, population, species and the ecosystem. Biodiversity, in fact a property of the natural ecosystem, is a product of
complex historical interactions among physical, biological and social systems
over time (McNeely, 1994; Pei and Sajise, 1993). Biodiversity loss is often occurred dramatically by LULC changes. When land conversion is made from a
primary forest to agricultural use, the richness of forest species within deforested areas is reduced. Even when unaccompanied by apparent changes in land
cover, similar influences are found whenever relatively undisturbed lands are
converted to more intensive uses, including livestock grazing, selective tree
harvest and even fire prevention.
The combination of causes lead to the processes of habitat destruction,
degradation and fragmentation which are the most important points resulting in
the decline and extinction of species (Heywood, 1995; Magurran and May,
1999; Van Laake and Sanchez-Azofeifa, 2004). The habitat suitability of forests and other ecosystems surrounding those under intensive use are also
affected by habitat fragmentation, which exposes forest edges to external influences and decreases core habitat area. Smaller habitats generally can support
fewer species and fragmentation can trigger local and even general extinction
for species requiring undisturbed core habitat (Ellis and Pontius, 2011). For the
past 300 years, in which most rapid land-cover changes have been assessed to
lead to most of today‟s landscape configurations. Recorded extinctions for sev18
1. Introduction: Global Land Use Processes and Climate Change
eral groups of organisms reveal rates at least several hundred times the rate expected on the basis of geological record (Pimm and Brooks, 2000). Study also
suggests that species invasions by non-native plants, animals and diseases may
be more ready in areas exposed by land use and land cover change, especially
in the vicinity of human settlements (Ellis and Pontius, 2011).
Soil degradation
Global demand for the products of the land is likely to continue accelerating
for the foreseeable future. The capacity of the land to sustain the demand will
remain a considerably important issue. Soil degradation is defined as an anthropogenic process that reduces the capability of soils to support life on earth
(Oldeman et al., 1991). It is a biophysical process driven by socio-economic
(land tenure, marketing, institutional support) and political (incentives, government stability) factors. The level of disturbance or perturbation being any
stress on soil reflects the possibility that much land use change in some sense
constitutes land degradation (Meyer, 1994). Human activities such as logging,
urban and industrial development and agricultural practices can be interpreted
as disturbance (Seybold et al., 1999). If a disturbance is too dramatic (e.g., subsidence or terrain deformation through gully erosion or mass movement), or if
the soil is inherently fragile (e.g. shallow soils on steep slopes), the soil can
undergo severe degradation (Lambin and Geist, 2006). Soil degradation is also
a challenge to the soil-related inherent constraints to agricultural productivity.
Over 14% of the estimated earth land area (total 134 million km2) is found to
have been degraded by anthropogenic actions (UNEP (United Nations Environment Programme), 2002). FAO (1996b) has reported five major causes of
human-induced soil degradation at global scale: overgrazing, deforestation, agricultural mismanagement, fuel wood consumption and urbanization.
The stripping of soils of vegetation (e.g. overgrazing, land clearing for
agriculture and urban use and large scale logging) exposes soils vulnerable to
increases in wind and water erosion, especially on steep terrain. Poor land
management strips the soil of vegetation cover resulting in erosion. Further,
salinization and waterlogging are caused by poor drainage of irrigated land,
whereas soil nutrient loss occurs through bush burning and continuous cropping with little or no fertilizers to replenish the soil. Fuel wood consumption is
19
1. Introduction: Global Land Use Processes and Climate Change
high in areas where firewood and charcoal are the primary source of energy.
Urbanization is predominant in Asia and the USA. Expansion of built-up areas,
urban sprawl, extractive and other industrial activities causes the loss of valuable agricultural land (Lambin and Geist, 2006).
Water erosion, wind erosion, chemical erosion and physical erosion are
the major types of soil degradation. Water erosion is the most frequent type
and deforestation is deemed as the primary resulting mechanism of soil degradation across world (FAO, 2000a). During the process of erosion, not only soil
fertility is degraded over time, decreasing the land suitability for future agricultural use, but also large amount of phosphorus, nitrogen, and sediments is
released to streams and other aquatic ecosystems, leading to various negative
effects (increased sedimentation, turbidity, eutrophication and coastal hypoxia).
Industrial activities can produce even greater impacts by heavy metal and radioactive pollution, including toxic pollution being left in soil. Modern
agricultural practices including intensive inputs of fertilizers and the concentration of livestock and their manures within small areas have substantially inincreased the surface water erosion. Fertilizer reduction is the most significant
form of chemical degradation (Lambin and Geist, 2006). Other agricultural
chemicals, including herbicides and pesticides are also released to the ground
and surface waters by agricultural practices and remain as contaminants in soil.
In short, it is important to take actions for monitoring soil degradation and
modify the ongoing unsustainable land use practices to prevent further soil
degradation.
Water resources
Land use can modify or disrupt surface water balance, soil infiltration, runoff,
water yields, evapotranspiration rates, snow accumulation, and interception
losses by different plant species. Surface runoff and river discharge generally
increase when natural vegetation is cleared, as the case study in Brazil that reported increased river discharge is coincident with expanding agriculture
(Foley et al., 2005).
Water demands associated with land use practices, especially irrigation,
directly influence water supply through withdrawals and diversions. Irrigation
farming or the consumptive and non-recoverable use of water by irrigated
20
1. Introduction: Global Land Use Processes and Climate Change
crops is a major component of the water balance. Further, the availability of
freshwater is a crucial factor for intensification and expansion of agriculture
(Mustard and Fisher, 2004). In most developed countries, irrigation has remained a major off-stream use of both surface and ground water resources. For
example, in the Western U.S., ground water withdrawals for irrigation have
been among the most significant impacts of agricultural practices, contributing
to dramatic increases in evapotranspiration, excessive declines in water tables
(Eshleman, 2004). A common feature of land use irrigation zones in arid and
semi-arid regions has been identified as the abandonment of irrigated agricultural land due to soil and water degradation (Geist, 2005; Steffen et al., 2004).
Water quality is often degraded by the processes of land use practices. Intensive agriculture increases erosion and sediment load, and leaches nutrients and
agricultural chemicals to groundwater, streams and river leading to water contamination. Growing evidence suggests that agriculture has become the largest
source of excess nitrogen and phosphorus to waterways and coastal zones (Foley et al., 2005). Deterioration of water quality also results from urbanization,
particularly lack of waste water treatment. The causative water degradation in
inland and coastal areas can cause oxygen depletion, eutrophication, and micro-pollutants.
Food provision
Land use conversion has shifted between regions over time reflects the general
development of civilizations, economies, and the growth of populations (FAO,
2004b; Richards, 1990). Many of the transformed or modified ecosystems (e.g.
cropland and pasture) are intimately linked to increases in resource use. Resource use imposing pressure on land is intimately related to population and its
standards of consumption, and this interacts with socio-economic and cultural
structures.
Land use change has clear implications for land-dependent livelihoods
and various activities of livelihood may be affected by land use change. Shift
from forests to agro-industrial agriculture, for example, may result in fewer
opportunities for wild food collection, hunting and forest-dependent activities.
Altered land use practices have been successful in increasing food production
and caused double world grain harvests in the past four decades (Mann, 1999).
21
1. Introduction: Global Land Use Processes and Climate Change
Some of this increase can be due to a 12% increase in world cropland area
(Matson et al., 1997; Wood et al., 2000). Reducing productive capacity of the
land may impoverish land-dependent livelihoods, just as increasing productive
capacity may lead to enrichment (Barbier, 2000a, 2000b; Barrett et al., 2001).
Poverty can cause short-sighted land management decisions proposed by individuals, households, communities and even states.
Human health
Human health is considerably complicated among the types of global change
impacts on societies and it is of crucial significance to understand the impacts
and improve the level of public health. There is a long history of anthropogenic,
mainly land-use driven changes to the environment which are either favorable
to human health, or threatened the health of creature and ecosystem as well as
human well-being. Health is closely related to the services and functions of
ecosystem (such as food supply or self-regulating for disturbance).
In the past, negative influences such as those on water and air quality
conditions were certain limitation to local and regional scales. For example, the
“Black Death” did not occur at global scale but had an impact on a number of
Mediterranean countries at the same time in 1347 (Lambin and Geist, 2006).
Accumulating evidence shows that local land use actions in conjunction with
other underlying factors can lead to the mitigation of risk of deadly diseases.
For example, „urban heat island‟ (mentioned above) exacerbates heat waves,
which can pose health challenge. Land use change (habitat modification, road
and dam construction, irrigation, and the concentration or expansion of urban
environments) can influence the transmission of infectious diseases and can
lead to outbreaks and emergence episodes (Foley et al., 2005). For example,
increasing tropical deforestation agrees with an upsurge of malaria and its vectors in Africa, Asia, and Latin America (Patz et al., 2004; Vittor et al., 2006).
Furthermore, the combined effects of land use and extreme climatic events can
also have great influences, both on direct health outcomes (e.g. heat mortality,
injury and fatalities) and on ecologically mediated diseases. For instance, areas
with extensive deforestation and settlements on degraded hillsides or floodplains suffer the severe morbidity and mortality (Cockburn et al., 1999).
22
1. Introduction: Global Land Use Processes and Climate Change
Indirect land use effect on health is another concern. The land use intensification at larger scale and the growth of agricultural output are brought by
the worldwide application of biocides (pesticides, fungicides, insecticides and
larvacides) (Lambin and Geist, 2006). Food demand is thus fulfilled in most
regions of the world over the past 50 years. However, associated drug resistance, chemical remaining and negative impacts on environment and human
health have likewise been brought. Furthermore, lots of concerns on the influences of climate change and biodiversity losses have aggravated the negative
effects imposed by the more local impacts of LULC change on health. For example, study has found that forest fragmentation, urban sprawl, and
biodiversity loss could be associated with the increased risk of Lyme disease in
the north-eastern United States (Schmidt and Ostfeld, 2001).
1.1.3
Future perspectives/Sustainable land management
Current trends in land use make humans to occupy an ever-larger fraction of
goods and services of the earth ecosystem whereas simultaneously reduce the
capacity of ecosystem to sustain food production, maintain natural resources,
regulate climate and air quality, and mediate human health. To be exactly,
modern land use practices may weaken a myriad of long-term ecosystem services and functioning with the increment of the short-term supplies of material
goods. Potentially negative consequences in the form of climate change, biodiversity loss and pollution are produced by land management for procuring
resources.
Facing the environmental challenge of land use, inherent trade-offs between the fulfillment of immediate human needs and the maintenance of the
capacity of ecosystems to provide goods and services in the future require being estimated and managed. The estimation of trade-offs should be linked to
social and economic impacts of land use, which probably results in long-term
decrease of human well-being through altered ecosystem functioning. Sustainable land management is necessary to enhance the resilience of different land
use practices. Increasing the resilience of managed ecosystems can prevent
from the disturbance such as diseases, climatic extremes, invasive species and
toxic releases and have the stronger ability to recover.
23
1. Introduction: Global Land Use Processes and Climate Change
Decision-making and policy implementation increasingly require multiple geographic scales and multiple ecological dimensions. Local and regional
alteration of land use can potentially cause local, regional and global ecological,
social and economic benefits or degradations. At the global scale, the Kyoto
Protocol offers an example of international efforts to reduce greenhouse gas
emissions from land. It offers incentives, such as a trade in carbon credits that
encourage land use practices which promote the storage of carbon on land, including the planting of trees, perennial crops, the return of crop residues to
soils, and no-till agriculture. The Protocol also promotes practices that reduce
emissions of methane and nitrous oxide from agricultural land. Regional efforts
to improve land use practices have been in progress in many areas of the world
such as the Chesapeake Bay Program in the USA and Tai Lake Program in
China, which have reduced nonpoint pollution of air and water. Currently welldeveloped land use strategies have been available to protect streams and other
aquatic ecosystems from the excessive runoff and flooding as a result of the
construction of impervious surfaces (buildings and roads) in the developed urban and peri-urban areas (Ellis and Pontius, 2011).
Land management in support of biodiversity covers a wide range of policies and practices. To set aside existing biodiversity habitats as conservation
reserves excluding humans, to establish preserves and parks, and to preserve
lands with biodiversity are the basic actions. More recently, practices are being
made to recover habitats on lands stripped of their original habitats and to
manage existing agricultural and urban landscapes to enhance their suitability
as habitat by the plantation of native plants and the restoration of habitat patches within intensified used lands. The establishment of corridors of habitat
between existing patches of habitat distributed across landscapes is another
new land use practice, which has built larger effective habitats by the combination of smaller patches and increase of species migrations. This will be an
important practice in response to future climate changes. Protection of productive agricultural land has been given a closer attention in many regions of the
world. Many national and international programs have made efforts to avoid
land degradation caused by land transformations and incentive programs. In
developing countries with dense populations such as China, land demand for
24
1. Introduction: Global Land Use Processes and Climate Change
industrial and residential use is forcing the transformation of some of the most
productive agricultural land. Policy efforts also take effective to avoid this loss
of production. However, economic demand often outweighs policy efforts.
“Smart growth” and other programs have been developed in these areas to encourage more efficient and desirable land use and to protect agricultural land
(Ellis and Pontius, 2011).
On a whole, sustainable land management is a huge challenge. Greater efforts and new methods are needed to reduce negative environmental impacts of
land use while maintaining positive benefits, if we are to sustain current and
future human populations under desirable conditions.
1.2 Global Climate Change
What factors are responsible for global climate change and what are the geophysical, biological, economic, legal, and cultural consequences of such
changes? The climate of planet earth is unstable. Our evolutionary origins lie in
the warm, relatively benign climate of equatorial Africa, but our ancestors battled the cold, harsh, and unforgiving climate of the last ice age in order to
spread across the planet. Some 10,000 years ago, however, the ice age ended.
We developed agriculture, civilization, industry, and technology generally in a
global climate that was warm, pleasant, and mostly predictable. Fossil fuels are
a legacy bequeathed to us by the biosphere of the distant past. Since the 1800s,
vast quantities of these fossil fuels combustion and use of land source power
our developing technological and global civilization. As a result, the greenhouse gases trapped in the fuels is released back into the atmosphere in the
form of energy-rich organic molecules. Greenhouse gases have a significant
property of absorbing thermal radiation. This is called the greenhouse effect.
The other major component gases of Earth‟s atmosphere, nitrogen (comprising
78% of the dry atmosphere) and oxygen (comprising 21%), exert almost no
greenhouse effect. Adding more of a greenhouse gas, such as carbon dioxide,
to the atmosphere intensifies the greenhouse effect, thus warming Earth‟s climate.
The overwhelming majority of scientists agree that our globe is undergoing major climate change. They also agree that the concentration of greenhouse
25
1. Introduction: Global Land Use Processes and Climate Change
gases in the atmosphere is increasing significantly. We can see from satellite
images and research that the ice caps are melting faster, our sea levels are rising, and weather patterns are changing. We are experiencing more water
shortages and we will witness hurricanes, typhoons and cyclones increasing in
ferocity and frequency. The deserts will expand and the world will ultimately
have difficulty growing enough food. Without doubt, we are entering a period
of climate change.
1.2.1
Causes of global climate change
During the twentieth century, the earth‟s surface warmed by about 1.4 °F.
There are a variety of potential causes for global climate change, including
both natural and anthropogenic mechanisms. Science has made great strides recently in determining which potential causes are actually responsible for the
climate change that occurred during the twentieth century. Some of natural
changes in solar radiation and volcanic activity can influence the earth‟s climate. Emissions of black carbon (soot) may also lead to the warming. Emissions of reflective sulfate aerosols have been associated with a net cooling effect.
A new finding shows that the chilling winter might be linked to stratospheric
change. Strong evidence exhibits that greenhouse gases released to the atmosphere, as well as land-use change, by human activities are the major causes of
contemporary global warming. The impact of land use change on climate
change has been described in 1.1.2.
Greenhouse Gases
The greenhouse gas in the atmosphere is a natural component of the climate
system and helps to maintain the Earth as a habitable planet. Greenhouse gases
allow the incoming solar radiation to pass through the atmosphere to the surface of the Earth. The solar energy is then absorbed by the Earth‟s surface, or
emitted back to the atmosphere as infrared radiation. Some of the emitted radiation goes through the atmosphere and travels back to space, but some is
absorbed by greenhouse gas molecules and then re-emitted in all directions.
The greenhouse effect warms the Earth‟s surface and the lower atmosphere.
Water vapor (H2O) and carbon dioxide are the two largest contributors to this
effect. Other greenhouse gases, e.g. methane, nitrous oxide and chlorofluoro26
1. Introduction: Global Land Use Processes and Climate Change
carbons are only in trace amounts, but still exert an effective warming effect
due to their heat-trapping properties. The level of greenhouse gases, especially
carbon dioxide, has risen over the past two centuries. Concentrations of methane have also increased due to the cultivation of rice, cattle production and
release from landfills. Nearly one-third of anthropogenic nitrous oxide release
is triggered by industrial processes and automobile emissions (National Academy of Sciences (NAS), 2001; Prather et al., 2001). Additional important
feedback mechanisms involve clouds. Clouds are effective at absorbing infrared radiation and have a large greenhouse effect, thus warming the Earth.
Clouds are also effective at reflecting away incoming solar radiation, thus cooling the Earth. A change in almost any facet of clouds, such as their type, location, water content, cloud altitude, particle size and shape, or lifetimes, affects
the degree to which clouds warm or cool the Earth (Christensen et al., 2007).
Sulfate aerosols and black carbon
Sulfate aerosols and black carbon are two important additional examples of anthropogenic forcings. Sulfate aerosols are tiny airborne particles that reflect
sunlight back to space. They travel to the atmosphere through natural processes
of volcanic eruptions. Their concentration in the atmosphere increases primarily through the combustion of fossil fuels containing sulphur during industrial
activities. Anthropogenic release of sulfate aerosols has been associated with a
net cooling effect (Prather et al., 2001). Aerosols affect the Earth‟s temperature
and climate by altering the radiative properties of the atmosphere. A large positive component of this radiative forcing from aerosols is due to black carbonsoot. Black carbon is generated from the burning of fossil fuel and biomass,
and, to a lesser extent, natural fires, but the exact forcing is affected by how
black carbon is mixed with other aerosol constituents. Black carbon is formed
by incomplete combustion especially of coal, diesel fuels, biofuels and outdoor
biomass burning. Black carbon exists in one of several possible mixing states;
distinct from other aerosol particles (externally mixed) or incorporated within
them (internally mixed), or a black-carbon core could be surrounded by a wellmixed shell. But so far it has been assumed that aerosols exist predominantly
as an external mixture. Soot particles absorb sunlight, both heating the air and
reducing the amount of sunlight reaching the ground. The warming effect from
27
1. Introduction: Global Land Use Processes and Climate Change
black carbon may nearly balance the net cooling effect of other anthropogenic
aerosol constituents. The amplitude of the direct radiative forcing from black
carbon itself exceeds that due to methane (Jacobson, 2001).
Stratospheric change
The sun‟s activity waxes and wanes on an 11-year cycle and over this cycle the
amount of ultraviolet (UV) light the sun emits changes a lot more than does the
total amount of energy. The stratosphere, the part of the Earth‟s atmosphere
which does most to absorb UV, might thus be expected to be particularly sensitive to the cycle (The Economist, 2011). Sarah Ineson et al. (2011) find that the
stratosphere at low UV levels in the tropics is cooler, because less UV is for the
stratosphere to absorb. This indicates that the difference in temperature between the tropical stratosphere and the polar stratosphere shrinks. The way of
atmosphere circulation is thus changed. As those changes spread down into the
lower atmosphere, they made it easier for cold surface air from the Arctic to
come south in winter, freezing chunks of northern Europe.
1.2.2
Consequences of global climate change
Global climate change has altered and will continue to alter many facets, such
as water, agriculture, ecosystems, human health and society and environment.
These impacts are in regional disparity.
Water resources
Observational records and climate projections provide abundant evidence that
freshwater resources are vulnerable and have the potential to be strongly affected by climate change, with wide-ranging consequences for human societies
and ecosystems. Observed warming over several decades has been linked to
changes in the large-scale hydrological cycle such as: increasing atmospheric
water vapor content, changing precipitation patterns, intensity and extremes,
reduced snow cover and widespread melting of ice; and changes in soil moisture and runoff (Bates et al., 2008).
An acceleration of the global hydrological cycle is anticipated as rising
temperatures increase the rate of evaporation. Precipitation will increase in the
tropics and higher latitudes, but decrease in already dry semi-arid to mid-arid
latitudes and in the interior of large continents. A greater frequency in droughts
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1. Introduction: Global Land Use Processes and Climate Change
and floods will need to be planned for but already, water scarce areas of the
world are expected to become drier and hotter. The increasing frequency of
drought can be expected to encourage further development of available
groundwater to guarantee agricultural production. And the loss of glaciers will
eventually reduce water availability during warm and dry periods in regions
supplied by melt water from major mountain ranges, where more than onesixth of the world‟s population currently live (FAO, 2011). Further, climate
change will stress water resources and place additional burdens on already
stressed water systems. Floods and degraded water quality are likely to be amplified by climate change. A warmer climate increases water evaporation from
land and sea, and allows more moisture to be held in the atmosphere. For every
1°F rise in temperature, the water holding capacity of the atmosphere increases
by about 4% (Karl et al., 2009). In a word, many aspects of the environment,
economy and society are dependent on water resources, and changes in the hydrological resource base have the powerful potential to affect environmental
quality, economic development and social well-being (Arnell, 1999).
Agriculture
Climate change clearly affects agriculture, climate is also affected by agriculture, which contributes 13.5% of all human-induced greenhouse gas emissions
globally (Karl et al., 2009). Agricultural productivity hinges on the climate and
land resources. Climate change can have both positive and negative impacts on
plants.
Crop responses in a changing climate reflect the interaction among three
factors: increasing temperatures, changing water resources, and rising carbon
dioxide concentrations (Karl et al., 2009). Elevated carbon dioxide and low
warming effect have positive impacts on many crops, but growth and yields often show a negative response to higher levels of warming. Both of the responses are dependent upon the physiological characteristics of different plants, e.g.
photosynthesis and respiration. Increased air pollution influences crop yields.
For example, plants are sensitive to ozone pollution and crop yields reduce as
ozone levels increase. Agriculture is highly sensitive to climate extremes. Extreme events such as heavy downpours and droughts are likely to decrease crop
yields because water excesses or deficits have negative impacts on plant grow29
1. Introduction: Global Land Use Processes and Climate Change
th. Changes in season length are also important. Weeds, diseases, and insect
pests benefit from warming, and weeds also benefit from a higher carbon dioxide concentration. Crop plants therefore face an increasing stress and more
focus is required to pest and weed control. Warming and moisture increase the
possibility of proliferation and survival of parasites and disease pathogens, thus
reducing livestock productivity. Forage quantity increases as atmospheric carbon dioxide concentration rises, it has negative impacts on forage quality
because plant nitrogen and protein concentrations often decline with higher
concentrations of carbon dioxide. Decreases in forage quality could reduce the
land‟s ability to supply adequate livestock feed.
Social and environmental impact
Climate change will interact with many social and environmental stresses.
Climate change will combine with pollution, population growth, overuse of resources, urbanization, and other social, economic, and environmental stresses
to create larger impacts than from any of these factors alone (Karl et al., 2009).
Many impacts of climate change on society and environment, such as natural
ecosystems and health, are covered in other sections.
Climate is one of the key factors to choose the location to live in. As the
population grows, urban and coastal areas are concentrated more preferences,
society is faced with additional challenges. Climate change is likely to exacerbate these challenges as changes in temperature, precipitation, sea levels, and
extreme weather events increasingly affect homes, communities, water supplies,
land resources, transportation, urban infrastructure, and regional characteristics
that people have come to value and depend on. Vulnerabilities to climate
change are not only location- but also its circumstance-dependent. Vulnerable
groups that generally have few resources and few choices include the very
young, the very old, the sick, and the poor. These groups represent a more significant portion of the total population in some regions and localities than othothers. For example, the elderly more often cite a warm climate as motivating
their choice of where to live and thus make up a larger share of the population
in warmer areas (Sussman et al., 2008). City residents and city infrastructure
have unique vulnerabilities to climate change. The impacts of climate change
on cities are compounded by aging infrastructure, buildings, and populations,
30
1. Introduction: Global Land Use Processes and Climate Change
as well as air pollution and population growth. Further, infrastructure designed
to handle past climate variations is inadequate to handle future changes (Wilbanks et al., 2008). Human communities are intimately associated with
resources both locally and beyond their geographical boundaries. Thus, communities will be vulnerable to the potential impacts of climate change on
climate-sensitive resources. Moreover, insurance - the world‟s largest industry
- is one of the primary mechanisms through which the costs of climate change
are distributed across society (Mills, 2006). Insurance is particularly vulnerable
to the increasing extreme weather events such as floods, but it can also help society manage the risks.
Ecosystems
The natural function of ecosystems provides us both goods and services. Anthropogenic climate change, in combination with other natural stresses, is
affecting natural environments and biodiversity, and these influences are generally expected to increase with higher level of warming (Janetos et al., 2008).
Ecosystem processes that control growth and decomposition have been affected by climate change. Such as photosynthesis, the process by which plants
capture carbon dioxide from the atmosphere and obtain new growth; the plant
and soil processes that recycle nutrients from decomposition and maintain soil
fertility; and the processes by which plants draw water from soils and return
water to the atmosphere. Climate change has impacts on animal and plant species. The timing of the seasons affects plants bud and animal migration. For a
suitable habitat and food supply, geographic distribution of species is also
changed as climate change. The trends are very likely to continue. Interplay
among factors of climate change and other stressors can even lead to species
extinction. Increased drying and hot contribute to a variety of changes that exacerbate a cycle of desertification. Increased droughts raise the risk of death of
perennial plants due to water stress and increased susceptibility to plant diseases. Alien species are prone to invade this region. As these species increase in
abundance, further losses of native vegetation are occurred. When heavy downpours fall, water erosion increases due to less vegetation cover to protect the
soil. Increased air temperatures and reduced soil moisture further exacerbate
erosion. Moreover, coastal and near-shore marine ecosystems are vulnerable to
31
1. Introduction: Global Land Use Processes and Climate Change
many climate change-linked impacts including the increase of air and water
temperatures, ocean acidification, changes in land runoff , sea-level rise, and
altered currents (Karl et al., 2009). Climate change will exacerbate these impacts. Perhaps most vulnerable of all to the impacts of warming are Arctic
ecosystems that rely on sea ice, which is undergoing the loss of summer sea ice
within this century. The ice provides an indispensible platform for icedependent animals to give birth, nurse their pups, and rest. Continued warming
will inevitably pose a threat to the survival of animals including seals, walruses,
and polar bears, and as such.
Human health
Climate change has substantial impacts on human health. Organization (WHO)
estimates that by 2000, the global burden of disease from climate change had
exceeded 150, 000 excess deaths per year (Patz et al., 2005). Climate change
can trigger potentially harmful health effects in a number of ways. There are
direct health impacts from heat waves and severe storms, ailments caused or
exacerbated by air pollution and airborne allergens, and many climate-sensitive
infectious diseases (Ebi et al., 2008). Extremes increase the risk of illness and
death. A study of climate change impacts in California presents that, by the
2090s, annual heat-related deaths in Los Angeles would increase by two to
three times under a lower emissions scenario and by five to seven times under a
higher emissions scenario, compared to a 1990s baseline of about 165 deaths
(Hayhoe et al., 2004). Heavy rains can lead to flooding, which can cause health
impacts including direct injuries as well as increased incidence of waterborne
diseases due to pathogens such as Cryptosporidium and Giardia (Ebi et al.,
2008). Some diseases transmitted by food, water, and insects are likely to increase due to changing climatic conditions such as increasing temperature,
precipitation, and extreme weather events. Further, higher temperature and carbon dioxide concentration raise pollen production and prolong the pollen
season in a number of plants with highly allergenic pollen, presenting a health
risk.
32
1. Introduction: Global Land Use Processes and Climate Change
1.2.3
Future climates-the great uncertainty
Are we witnessing the end of the long period of benign climate since the last
ice age? Will the climate change for the worse due to human activities? In fact,
no one knows for sure. Most atmospheric scientists assume that global warming is at least partially attributable to fossil fuel use, but what that means to
humans and natural ecosystems is largely unknown. The climate is vastly complex and strongly affected by a myriad of factors rather than greenhouse gas
concentrations alone. It is thus extremely difficult to associate any climatic
events with a single cause. Climate change needs to be treated in context of the
many other global challenges such as population growth, urbanization and land
use change. As a result, controversy exists as to the amplitude and danger of
global warming caused by greenhouse gases. Many scientists take the issue
very seriously and support efforts to slow or reverse the atmospheric build-up
with the expectation that global warming will mitigate. Others, however, argue
that current global warming is part of natural, long-term climatic cycles. The
responses to climate change involve reducing emissions to limit future warming, and adapting to the changes that are unavoidable. In summary, future
climate change and its impacts depend on choices we made today.
33
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
2 Regional temperature and precipitation trends based
on homogenized time series in Jiangxi Province, China
2.1 Introduction
The requirement of meteorological data covering a long period is currently increasing with climate change: the more we can describe past changes, the more
we can validate climate models and trust models simulations (Moisselin and
O.Mestre, 2002). Global pattern of climate change is by now well documented,
and is no longer scientifically controversial (Oreskes, 2004). On a regional
scale, however, there is considerable variation in the rates of climate change
and its hydrological and ecological impacts (Cohen, 1990).
Climate variation is in a regional disparity. This variation is caused by
the uneven distribution of solar heating, the individual responses of the atmosphere, oceans and land surface, the interactions between these, and the physical
characteristics of the regions. The perturbations of the atmospheric constituents
that lead to global changes affect certain aspects of these complex interactions.
Some human-induced factors that affect climate („forcings‟) are global in nature, while others differ from one region to another. For example, carbon dioxide, which causes warming, is distributed evenly around the globe, regardless
of where the emissions originate, whereas sulphate aerosols (small particles)
that offset some of the warming tend to be regional in their distribution. Furthermore, the response to forcings is partly governed by feedback processes
that may operate in different regions from those in which the forcing is greatest
(Christensen et al., 2007).
IPCC (2007) points out that observed warming since 1979 has been
0.27°C per decade for the globe, but 0.33°C and 0.13°C per decade for the NH
(Northern Hemisphere) and SH (Southern Hemisphere), respectively. Observed
prec-ipitation trend (1951-2005) ranges from -7 to +2 mm per decade and 5 to
95% error bars range from 3.2 to 5.3 mm per decade for the globe. Although
the global land mean is an indicator of a crucial part of the global hydrological
34
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
cycle, it is difficult to interpret as it is often made up of large regional anomalies of opposite sign. Observed changes in regional temperature and
precipitation can often be physically related to one another (IPCC, 2007). Increasingly reliable regional climate change projections are now available for
many regions owe to advances in modeling and understanding of the physical
processes of the climate system. A myriad of important themes have emerged:
In the regional projections for Asia, warming is likely to be well above the
global mean in central Asia, the Tibetan Plateau and northern Asia, above the
global mean in eastern Asia and South Asia, and similar to the global mean in
Southeast Asia. Model agreement for precipitation is seen over more and larger
regions. Precipitation in boreal winter is very likely to increase in northern
Asia and the Tibetan Plateau, and likely to increase in eastern Asia and the
southern parts of Southeast Asia. Precipitation in summer is likely to increase
in northern Asia, East Asia, South Asia and most of Southeast Asia, but is likely to decrease in central Asia (Christensen et al., 2007).
For long-term climate analyses - particularly climate variability and
change analyses - to be accurate, the climate data used must be homogeneous
(Peterson et al., 1998). Climate change study using unhomogenized long-term
clima-tological time series is hazardous due to many non-climatic factors
caused by errant print, displacement of meteorological stations, replacement of
sensors, modifications of the local environment, etc. (e.g. Bradley and Jones,
1985; Brohan et al., 2006; Peterson and Vose, 1997; Tuomenvirta, 2001).
Some cha-nges cause sharp discontinuities while other changes, particularly
change in the environment in the vicinity of the station, can cause gradual biases in the data. All of these non-homogeneities could hide or even distort the
true climatic signals and patterns, and thus potentially bias the conclusions of
climate studies. Easterling et al. (1996) have shown that differences between
homogenization results from adjusted and unadjusted data become smaller as
the area of averaging becomes larger (e.g. from a region to the globe), These
kinds of data problems have clear implications for uncertainties in regional estimates of climate changes. Homogenization is thus important in climate
change study, especially in regional climate change (Alexandersson and
35
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
Moberg, 1997; Christy et al., 2006; Coats, 2010; Fall et al., 2010b; Tuomenvirta, 2001).
Several basic concepts and general issues concerning the homogenization of climate are introduced. A homogeneous climatic time series has been
defined where all variations are only due to variations in weather and climate
(Conrad and Pollack, 1950). The concept of relative climatic homogeneity was
introdu-ced by Conrad and Pollack (1950). They assume that within a geographical region, climatic patterns will be identical and that observations from
all stations within the region will reveal this identical pattern. The data derived
from all of the stations within this region should be in good correlation, in
similar pattern of variation, and differ only by scaling factors and random sampling variability. Data from a station that demonstrate more than a random
deviation from the regional climate is considered to be inhomogeneous and the
discontinuities of the climate time series is referred to non-homogeneities.
Non-homogeneities may occur either as trends due to gradual changes of the
surroundings of the observation station, as in the case of urbanization, or they
occur as abrupt discontinuities. Abrupt discontinuities are generally caused by
station relocations, instrument changes, or changes in averaging methods, observers and observing practices, etc. (Bradley and Jones, 1985; Moberg and
Alexandersson, 1997).
The most commonly used information about non-climatic influences
comes from records of station relocations, changes in instrumentation, problems with instrumentation, sensor calibration and maintenance logs, changes in
surrounding environmental characteristics and structures, observing practices,
and other similar features. The composite of the information is generally referred to as station metadata (Guttman, 1998). Metadata could provide us with
detailed knowledge when possible non-homogeneities in observation records
are ident-ified as well as the application of the adjustments and the recognition
of the exact cause of the non-homogeneities. The role of metadata also becomes important when station network is sparse. A simple example is a case
where there are only two series available and one discontinuity is detected. Statistical tests cannot distinguish which of the series should be adjusted. In such
kinds of situations, metadata is applied to steer the procedures of testing and
36
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
adjusting. The homogeneity test is ideally meant for detecting one break at a
time. Therefore, metadata are used to divide time series into overlapping periods containi-ng at most one potential discontinuity. However, no station has
absolutely complete and accurate metadata.
Outliers are defined to be the marginal values of a climate time series,
which are very distant from the mean value (Eischeid et al., 1995). They can be
due to measurement errors or extreme meteorological events. Climatic trends
are very sensitive to outliers. When outliers are considered to be undoubtedly
erroneous measurements the problem can be converted into the treatment of
missing data. However, when outliers have a physical background whether
they should be corrected or not (Barnett and Lewis, 1994). Extreme values carry valuable climatological information that should not be dismissed. On the
other hand, outliers can affect the estimation of sample statistics (Lanzante,
1996). Resis-tant and non-resistant methods are the most commonly used for
outlier removal. Resistant method would allow moving all outliers at the expense of losing int-eresting climatological information. Barnett and Lewis
(1994) and Gonza´lez-Rouco et al. (2000) have noted that non-resistant methods would censor outliers by means of replacing them by some threshold value
that keeps the inform-ation of an extreme event and yet does not have such an
important influence on non-resistant statistics. In order to keep the information
of an extreme event but not have great influence on non-resistant statistics, outlier removal in our work is performed based on the non-resistant method of
Gonza´lez-Rouco et al. (2001).
The procedures that are most commonly used for the homogenization of
cli-mate time series are reviewed. Descriptions and references are obtained
from Costa and Soares (2009), Peterson et al. (1998), Ducré-Robitaille et al.
(2003) and Reeves et al. (2007). Several techniques have been developed for
non-cli-matic non-homogeneities detection and adjustment. The approaches
underlying the homogenization are quite different and typically depend on the
type of climate elements (temperature, precipitation, pressure, evaporation,
etc.), the temporal resolution of the observations (annual, seasonal, monthly, or
sub-monthly), length and completeness of the data, the availability of metadata
(station history information) and the monitoring station density etc. According
37
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
to the different requirements and missions different algorithms with respect to
data adjustments have been developed. Decisions are made based on subjective
experiences with the specific data and available metadata involved. A 95 or
99% confidence level generated by a test statistic is to be required before making an adjustment in time series (Peterson et al., 1998).
Most of the analysis techniques and adjustment methods that have been
proposed to identify and remove non-homogeneities are appropriate for the appli-cation on data with moderate and coarse temporal resolution (i.e., monthly
or annually) (Aguilar et al., 2003; Costa and Soares, 2009). Homogeneity testing techniques can be classified into two groups and are usually referred to as
„direct‟ and „indirect‟ methods. Direct methods depend on metadata and the
stud-ies of the effects of specific changes in instrument. Indirect methods use a
variety of statistical and graphical techniques to determine non-homogeneities.
Both approaches are worthwhile and valid and they both have shortcomings.
The advantages of direct methods are that specific information contained is
very relevant and it can provide the researcher with precise knowledge of when
the discontinuity occurred and what it is caused by. Unfortunately, metadata
are often incomplete, missing or sometimes actually erroneous. Sometimes
there are also problems with the interpretation of the metadata appropriately.
Indirect method includes subjective and objective methods. Subjective judgment by an experienced climatologist has been an important tool in lots of
adjustment methods because it can modify the weight given to various inputs
based on a number of factors too laborious to allocate (Jones et al., 1986c;
Jones et al., 1985; Plummer et al., 1995). Subjective judgment can be particularly helpful in an initial inspection of the stations‟ data and when the reliability of metadata varies (Peterson et al., 1998). However, it is impossible to determine whether the detected discontinuity occurred at the candidate or reference station and it would not work without metadata. Objective method not only estimates the position and amplitude of non-homogeneities, but the corresponding confidence intervals as well. The most ideal methodology is the combination of the subjective and objective methods regarding the significance and
the efficiency of the homogenization. Moreover, a myriad of methods have
been developed to adjust station data for discontinuities (e.g. Easterling and Pe38
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
terson, 1995), and Karl et al. (1986) developed a method to adjust data from a
meteorological network for changes in observing time. Karl et al. (1988) developed a population-based method to adjust temperature data for accelerating
urbanization effects. The following four steps are commonly followed by a
homogeneity test (Aguilar et al., 2003): (i) metadata analysis and basic quality
control, (ii) construction of reference time series, (iii) non-homogeneity detection, and (iv) data adjustment.
The standard normal homogeneity test (SNHT) developed by Alexandersson (1986) is one of the most widely-used subjective homogeneity tests.
Alexande-rsson and Moberg (1997) modified the original SNHT to account
for more th-an one discontinuity, testing for inhomogeneous series rather than
just breaks, and inclusion of change in variance. Since then, this test has been
applied in a number of studies either for the homogenization of climate data or
for studying discontinuities in climate data both in the form of trends and
jumps. The SNHT is a likelihood ratio test. The test is performed on a ratio or
difference series between the candidate station and a reference series. The null
hypothesis is that the data have independent and identical normal distribution.
The alternative is that a step-wise shift in the mean level (a break) is present.
The SNHT for a single break has the capability of capturing the location of the
period (month or year) where a break is likely to appear, and it identifies
breaks relatively easily near the beginning and the end of a series (DucréRobitaille et al., 2003; Wijngaard et al., 2003). The following are examples to
confirm the robustness of SNHT method in homogeneity test.
Tuomenvirta and Drebs (1994) have tested annual mean temperatures
and precipitation totals in Finland in subsections with SNHT. Hanssen-Bauer
and Førland (1994) also used the standard normal homogeneity test to investigate the 165 Norwegian precipitation series of 75 years by repeating
homogeneity tests several times on the entire lengths of records and the data
have been improved by corrections for significant non-homogeneities. At the
Norwegian Meteorological Institute (DNMI), the standard normal homogeneity
test has been applied to test for homogeneity of series of precipitation, temperature and pressure (Hanssen-Bauer et al., 1991). Gonza´lez-Rouco et al.
(2000) tested the homogeneity of precipitation data in the southwest of Europe
39
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
based on SNHT. Klingbjer and Moberg (2003) use the standard normal homogeneity test (SNHT) for single break points of monthly temperature data in
northern Sweden. Precipitation series in the Iberian Peninsula was examined
using SNHT for the analysis of the evolution of droughts from 1910 to 2000
(Vicente-Serrano, 2006). Breaks in radiosonde time series were estimated by
applying a variant of the Standard Normal Homogeneity Test (SNHT) with apriori information about changes of observation practice by Haimberger (2005).
However, several studies also have provided the weakness of SNHT in
break detection that SNHT has poor performance for identifying the breaks located at the beginning and the end of series. For instance, Alexandersson and
Moberg (1997a) demonstrated because the exact distribution of the test statistic
under the null hypothesis is unknown, reported on significant levels of the test
statistic for examined series with a number of values from 10 to 250. Khaliq
and Ouarda (2007) extended these significant levels from 10 to 50 000. DucréRo-bitaille et al. (2003) have found that SNHT detected false break when a set
of homogeneous series was tested. This increases at the beginning and the end
of the series even if the ratio of the total number of false breaks is quite low
(8.6%). DeGaetano (2006) have also indicated the number of identified discontinuities declined sharply (a single abrupt discontinuity was added to their
simulated series) if the tested series is shorter than 21 years. One way has been
proposed by Toreti et al. (2011) to overcome this situation is to apply the series from different meteorological networks. This increases the probability to
identify breaks affecting the entire network. Unfortunately, the National Center
for Atmospheric Research (NCAR) from which we obtained the raw climate
data does not provide us with a density meteorological network in Jiangxi to
estimate the influence of this SNHT barrier on trend analysis.
Not all changes are recorded in the station history reports and observing
manuals. This is why many approaches and statistical techniques have been
developed for detecting non-homogeneities and adjusting climate datasets.
Methods for homogenization are designed and applied to fulfill different requirements concerning the data characteristics, regional topographic features and
weighted aspects of users. None of these approaches can be regarded perfect.
Even adjusted datasets are imperfect and should be used with caution (Karl et
40
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
al., 2006; Randel and Wu, 2006; Sherwood et al., 2005). The advantages and
limitations of different homogenization approaches are discussed in the studies
of Costa and Soares (2009) and Peterson et al. (1998). Each methodology of
homogeneity test has its own advantages and disadvantages. It therefore depends on the available data ready for homogenization and the objectives of
researchers. Nevertheless, the standard normal homogeneity test has been
shown to have superior accuracy in identifying the position of a discontinuity
or trend non-homogeneity under all circumstances without precise priori
knowledge of the date of non-homogeneity. SNHT is thus used in our study.
The China Homogenized Historical Temperature Datasets (CHHT1.0)
was officially released in December 2006 by the China Meteorological Administration, which offers valuable basic datasets for climate change research in
China. The datasets are available at http://cdc.cma.gov.cn. Unfortunately, these
datasets are paid and only confined to open to specific users in China with
auth-orized candidate and other homogenized climatic elements in addition to
surface air temperatures are not included in the datasets. Currently, no extensive dataset of homogeneous long-term climate series in our study site, Jiangxi,
is available, also in other parts of China. The Bureau of Meteorology in Jiangxi
has maintained a climate measurement and observation record since 1954.
Temperature and precipitation series of this network are included in different
local and regional data sets and the raw observational records without homogenization have been widely used in the studies of regional climate variability
(Gu et al., 2009; Guo et al., 2006; Guo et al., 2007; Wang and Li, 2008a; Ye et
al., 2009). In order to provide the public with more reliable outcomes, the climate trends need to be sufficiently analyzed. To avoid misinterpretation of
climate trends, long-term data homogenization requires to be applied to the
long-term instrumental time series prior to the analysis of climatic trends.
In this study, the adjustment method (Gonza´lez-Rouco et al., 2000) and the
homogeneity test method (Moberg and Alexandersson, 1997) along with partial station history files are used for the homogenization of monthly mean
temperature and precipitation totals across Jiangxi China for the period 19512000. The non-homogeneity types, causes, amplitudes and timings are discussed. Finally, temporal and spatial patterns of seasonal and annual mean
41
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
temperature and precipitation in Jiangxi province are investigated. Regional
differences among the climatic variations from different analyses are large
(Hansen et al., 2001a). The results of Jiangxi are therefore in comparison with
the findings of Global Historical Climatology Network (GHCN). This study
concerns only climate anomalies, not absolute temperature or precipitation.
Climate anomalies are computed relative to the base period 1961-1990. The
reason to work with anomalies, rather than absolute temperature or precipitation is that absolute temperature or precipitation varies markedly in short
distances, while monthly or annual temperature anomalies are representative of
a much larger region. Anomalies are computed for 30-year periods beginning
with each decade (e.g., 1961-1990). If data are not available for thirty years,
then it is permissible to compute averages for shorter periods of at least 10
years of record if the data reflect both current observation practices and the
climate at the station. Our study presents two novelties: (1) the spatiotemporal
trends of precipitation over Jiangxi from 1951 to 2000 are obtained based on
the homogenized series of monthly precipitation totals, and (2) the climate
trends over Jiangxi are assessed with different homogenization algorithms.
There are four questions require to be addressed:
1. Is there a non-climatic non-homogeneity in a time series?
2. What is the amplitude of a non-homogeneity that has been detected?
3. Are there differences between the climate trends calculated from GHCN and
from homogenized data in this study for Jiangxi?
4. How is the climate pattern of Jiangxi described?
2.2 Materials and Methods
2.2.1
Study site
Jiangxi is situated in Basin of Poyang Lake along the middle range of Yangtze
River and has a total area about 170 458 km2. Mountains and hills account for
78% of Jiangxi province (Jiangxi Meteorological Bureau, 2010). Jiangxi is dominated by humid subtropical climate, Cfa (Trewartha, 1968). Annual mean
temperature is 16 to 19 ºC and annual mean precipitation is 1,400 to 2,400 mm.
Rainy season concentrates on April to July. Lake Poyang, the largest freshwa42
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
ter lake in China, is situated in the north of Jiangxi province. It has a total area
of 162 200 km2. Lake Poyang flows into the Yangtze River, which forms part
of the northern border of Jiangxi. Poyang Lake is the largest habitat for migrating birds in the world and a renowned protection zone of rare and endangered
migrating birds as well (Jiangxi Meteorological Bureau, 2010).
2.2.2
Data
Temperature and precipitation anomalies, rather than absolute values were used.
Most meteorological stations in Jiangxi were established in the early 1950s and
reliable continuous records have been generated since the 1960s. Therefore, the
first 30-yr period (1961-90) had been used as the baseline for comparison for
many different purposes in climatic studies. On the other hand, the World Meteorological Organization (1996) used the period 1961-1990 as climatological
anomalies for some 4000 stations in 130 countries at the present time. Monthly
mean temperature and precipitation totals of 36 available stations in Jiangxi
and contiguous 6 provinces for the period of 1951 to 2000 were obtained from
the Computational and Information Systems Laboratory (CISL) at the National
Center for Atmospheric Research (NCAR). All stations had continuous monthly data for the entire 50-year period except for the missing data for November
and December in 2000. The data set numbered ds578.1, is available from the
RDA (http://dss.ucar.edu). Data format is ASCII. GHCN data was used for
comparison analysis, which is available online at (http://lwf.ncdc.noaa.gov/oa/climate/research/ghcn/ghcngrid.html) from the National Oceanic and Atmospheric Administration, National Climatic Data Center, Asheville, North Carolina, USA. Both NCAR and GHCN datasets have continuous data for stations
in Jiangxi and its contiguous provinces for the period 1951-1990. The data obtained from NCAR had not been homogenized prior to this study. The data
from GHCN was homogenized based on pairwise comparison (Menne and
Williams, 2009). Seasons are defined as the three-month averages for temperature and three-month sum for precipitation: DJF, MAM, JJA, and SON. Shuttle
Radar Topography Mission (SRTM) 90m digital elevation data were supplied
by Consortium for Spatial Information (CGIAR-CSI). The location of Jiangxi
43
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
in China and spatial distribution of the stations used in our study are shown in
Figure 2.1.
2.2.3
Homogenization
i. Construction of reference series
The reference series should ideally consist of high-quality data from stations
where climatic variations resemble those at the candidate station. The number
of reference stations should be large enough to mask any eventual non-homogeneity in the reference data. The appropriate number of reference stations depends on the objective of the test, the station network density, and the data
quality. For example, Hansen-Bauer and Førland (1994) used five to nine reference stations and Peterson and Easterling (1994) used five reference stations
and suggested using successive differences, dT/dt (where T is the climatological variable and t is time), instead of the raw time series to calculate the correlation coefficients between the candidate and reference stations. This would reduce the risk of poor estimates of correlations if one or both of them with nonhomogeneities within the common time period was used for the calculation of
correlation coefficients. Reference series generally had been constructed as a
weighted average of the data from reference stations. The average was applied
to the dT/dt-series by Alexandersson (1986) with the modification introduced
by Peterson and Easterling (1994). Moberg and Alexandersson (1997) selected
twenty years as the common period. In our work five reference stations highly
correlated with the candidate and the common period of thirty years were used.
Furthermore, station records were not significantly correlated at separation distances larger than 450 km (Dai et al., 1997). The reference stations were thus
excluded when departing the candidate station over 450km despite in high correlation. The main steps were outlined:
(1) Calculate the correlation coefficients between the candidate station‟s dT/dt
time series and the potential reference stations‟ dT/dt series.
(2) Combine the five most highly positively correlated stations into a reference
value using a weighted mean.
(3) Convert the reference values into a reference series．
44
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
A more detailed description of the method can be seen in (Peterson and Easterling, 1994).
ii. The Standard Normal Homogeneity Test (SNHT)
The homogeneity test is based on the Standard Normal Homogeneity Test
(SNHT) developed by Alexandersson (1986) with the modification introduced
by Moberg and Alexandersson (1997) . The SNHT is a parametric test using
neighboring station(s) as a reference to identify non-homogeneities in the time
series of the station being tested (candidate station). This test is based on the
assumption that the ratio/difference Q between the candidate station and a neighboring reference station remains constant in time. This relationship is expressed in terms of the ratio/difference Q between the candidate stations normalized values and those of a time series defined as a weighted average of several reference stations. Thus, the ratio/difference Q in a specific year can be
denoted as
Qri =
Fi
,
Gi
i=1,…, n,
(1)
and the difference termed as
Qdi = Fi Gi i=1,…, n,
(2)
n being the number of time steps, and Fi and Gi denote a specific value at the
candidate and reference stations at year (time step) i that are formed as
Fi 
ki Qij
 vj
Qj
Gi  j  1
ki
 vj
Pi
p
(3)
j 1
Pi denotes the candidate series, Qij a reference series at station j, and vj a weight
factor for reference station. In this work vj is the squared correlation coefficient
between candidate and reference series. Overbars denote a time mean. Here ki
is the number of reference stations used in time step i that varies with different
length of time series and the existence of missing data.
45
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
Normalized series of the ratios/differences, Zi, used in SNHT, is defined
as
Zi =
(Qi  Q)
sQ
(4)
Where Q is the sample mean and SQ the standard deviation of Qi at year (time
step) i. Normalized series causes the ratios to fluctuate around one and differences around zero. All values in the normalized series of ratios/differences are
normally distributed with a mean value zero and unity standard deviation, N (0,
1). Therefore, the homogeneity test is based on the following hypothesis,
Null hypothesis: The candidate series is homogeneous, that is the Q-series
has a constant mean level.
H0 : Zi  N 0, 1  i {1,..., n}
Alternative hypothesis: The candidate series is non-homogeneous. At certain
time the mean level of the Q-series changes abruptly, while the standard deviation remains unchanged.
Z  N( 1,  ) i {1,...,a}

 i
Z  N( 2,  ) i {a+1,...,n}

 i
H1: 
Where 1 is the mean value during the first a years,  2 is the mean value during the last (n-a) years, and  is the sample standard deviation.
The SNHT can be used to detect and adjust multiple non-homogeneities
in the candidate series. However, it is necessary to examine a series with multiple non-homogeneities piece-wise when the tests that are designed for one ininhomo-geneity only. Alexandersson (1986) and Alexandersson and Moberg
(1997) indicated how the test parameter, T, which separates H1 from H0, is
computed. A high T value at year a suggests that 1 and  2 depart significantly
s
from zero, making H1 likely. The maximum T value, denoted as Tmax
, is
2
2
s
Tmax
 Max{Tas }  Max{az1  (n  a)z2 }
1a n 1
1a  n 1
(5)
Where z1 and z2 are the mean values before and after the non-homogeneity. In
some studies on change-point analysis (e.g. Elsner et al., 2003), the correspo46
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
nding a is the most possible change point. The null hypothesis can be rejected,
s
if Tmax
exceeds the given significance level, which only depends on the length
of the series. The critical values corresponding to 90, 92, 94, 95, 97.5 and 99%
levels are given for sample sizes ranging from 10 to 50 000 (see Khaliq and
s
Ouarda (2007)). A series can be classified as non-homogeneous when Tmax
ex-
ceeds a significant level departing five years or more from either end of the
series (Gonza´lez-Rouco et al., 2000; Hanssen-Bauer et al., 1991). The adjustment value should be applied (i) if the value of standard normal homogeneity
test statistic T lies between the 95% and 90% significance levels and there is
metadata documentation to support that change, or (ii) if the value of test statistic T exceeds the commonly used 95% level (without support in metadata)
(Hansen-Bauer and Førland, 1994).
The non-homogeneities are restricted further than five years apart from
the start and the end of the series. The reason for not accepting nonhomogeneities unexplained by the station history close to the ends of the series
is an increased probability for high T values near the ends (Hawkins, 1977).
Also correction factors in short parts of series are less reliable. Thus the nonhomogeneities occurred at the first and last five years of the test period can be
omitted (Hansen-Bauer and Førland, 1994).
Once a series is identified as non-homogeneous, for the series with only
one discontinuity, the adjustment for years from 1 to a is Qb / Qa for ratios, and
Qb  Qa for differences, where Qa and Qb are the mean values of Qi before and
after the discontinuity. Double shift adjustments are calculated in a similar
manner as the single shift. Trend adjustments must be calculated with  1 and
 2. See detailed description in Tuomenvirta (2002) and Gonza'lez-Rouco et al.
(2000).
iii. Outlier removal
The temporal check for outliers for a specific station is based on the premise
that an individual monthly value should be “similar” (in a statistical sampling
sense) to values for the same month of other years (Eischeid et al., 1995). Outliers often occur in single monthly records. A Q-series was created for each
month and its mean and standard deviation were calculated. If a Q-value de47
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
parted more than four standard deviations from the mean, the value for that
month at the candidate station was replaced by its reference value.
iv. Homogeneity test
A procedure for homogenization of monthly temperature and precipitation totals series was applied for obtaining homogeneous series. The dataset should
be primarily considered as a whole rather than as individual records. The process consisted of a preliminary and a final test round. Both included a series of
test sessions. The procedure was developed by Moberg and Alexandersson
(1997).
A test session: A test session started with one of the stations as candidate
and all the others as potential reference stations. The candidate should pass
through three steps; (i) selection of reference stations, (ii) detection and removal of outliers, and (iii) non-homogeneity detection. After all series from one
candidate station were tested for homogeneity, the test continued with the next
candidate station. A test session was over when all stations had been used as
candidates. Corrections were made after one complete test session.
Preliminary test round: The preliminary test round started with a session
where all stations were used both as candidates and as potential reference stations. After the first session, all values that were not homogeneous were adjusted. In the second session, all stations were used as potential reference stations,
but stations with homogeneous data were excluded from the candidates. All
remaining candidates passed again through the three steps described in above a
test session. New corrections were applied when the second session was completed. The preliminary test round then continued in further sessions, including
new corrections and exclusions of homogeneous series from the candidates in
later sessions. The homogeneity of the potential reference series was successively improved from one session to the following. Adjustments were made for
all non-homogeneities identified after one complete test session. The preliminary test round was over when no more non-homogeneities were identified in
any series.
Final test round: The set of reference data was corrected for all detected
non-homogeneities before the final test round. In the final test round, outliers
were removed. All outliers identified during the first session of the final round
48
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
were substituted with the corresponding reference values after the first session
was completed. This procedure was repeated after each session. (In the preliminary test round, outliers were only removed in the candidate series temporarily
before the homogeneity tests). In the first session of the final test round, all raw
unadjusted series were used as candidate series and again passed through the
three steps. Stations with homogeneous data were excluded from the candidates in the second session. The remaining stations passed the three steps of
session two and those with homogeneous data after corrections were excluded
from the candidates in session three and so on, until all candidates were homogeneous after adjustments (Moberg and Alexandersson, 1997).
2.2.4
Spatial interpolation
To correct the climate data by elevation, multiple linear regression has been
deemed a satisfactory method for identifying variables used to model climate
surfaces (Cornford, 1997; Goodale et al., 1998; Ishida and Kawashima, 1993;
Lennon and Turner, 1995; Matthews et al., 1994). The elevation data were extracted from DEM raster. Multiple linear regression was performed with temperature as the dependent, location and elevation as the independent. The equations were developed for temperatures of each month. Temperature fields for
each month were predicted using the developed regression models. The residuals from these models were interpolated using ordinary kriging (Aspinall and
Matthews, 1994; Cornford, 1997; Oliver et al., 1989) and subtracted from the
predicted „observation‟ surfaces performing ordinary kriging. Raster maps of
corrected temperature by elevation can be obtained by the predicted minus residual surfaces. The spatialization of precipitation was performed by Ordinary
Kriging. We interpolated linearly the instrumental data of temperature and precipitation to each observational station on 1°×1°grid.
2.3 Results
All seasonal precipitation series were classified as homogeneous. For seasonal
temperature series, 3697 series of total 7200 series were classified as non-homogeneous, 51% of the series were homogeneous in all seasonal series. Most of
the non-homogeneities, in total 2967 (80%), appeared as abrupt discontinuities
49
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
and the remaining 730 (20%) as trend non-homogeneities. Some key information from the homogeneity tests is presented in Table 2.1. To evaluate the test
results, station history for these stations was studied thoroughly. The metadata
were coded and are presented together with the test findings in Table 2.2 and
Figure 2.2.
2.3.1
Abrupt discontinuities
Since 1960s, meteorological observation underwent frequent change in China.
Table 2.1 indicates the change of observation specification. Figure 2.2 shows
the seasonal frequency for discontinuities detected in homogeneity tests in the
final test round on decadal level for the period of 1951-2000. 1951-1960 contributed most to the abrupt discontinuities (42.0%). The second was 1970-1980
(26.5%) and the least was 1990-2000 (5.4%). Discontinuities occurred most
frequently in winter (41.0%) and least in spring (16.4%). The abrupt shift corresponded to an absolute change of temperature mean before and after the shift.
Adjustment values, Qb  Qa , ranged from 0.01 to 0.5℃ with the average of
0.05℃. Significant shifts departing larger than 0.5℃ and smaller than 0.01℃
were relatively few, 1% and 4% respectively. Some such small non-homogeneities were not detectable in statistical tests because they were small compared to the natural temperature variability. Abrupt shifts may be actually even
more significant than our results revealed, because certain notable trends caused by successive shifts were undetectable without station histories. Also no
such detailed station history records can support our analysis. Actually, even
the most complete station history files probably do not contain information on
all changes at a station (Easterling et al., 2000).
Averaged correction was slightly positive (-0.05℃), and the number of
negative shifts was 20% more than that of positive shifts. This indicates that
most changes in station conditions cause observation temperatures to a lower
level. The similar findings are obtained by Wang and Huang (2008) and Karl et
al. (1986). The change of observation time from local mean time in 1951 to
120°E standard time in 1953, and to Beijing time in 1960 may account for this.
Cooler temperatures may be also due to the improvements of instrumentation.
That a switch from a liquid-in-glass thermometer to a more delicate platinum
50
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
resist-ance temperature transducer often caused observed temperatures to be
slightly lower in China, was documented in several studies (Gu and Wang,
2003; Quayle et al., 1991; Wang and Liu, 2002; Yu et al., 2007). But the
change of screen had little effect on measured temperatures (Xiong et al., 2006).
Also Nordli et al. (1997) showed that the historical improvements of the screen
had significant impact on the series during spring and summer but no obvious
effect on winter and autumn. However, Meulen and Brandsma (2008) exhibited
different results from the parallel measurements with a well-documented station history. The effect of the screen change (in combination with a small
relocation) caused warmer temperatures (0.48°C).
Shifts are most common in summer and little average change was in winter because differences between various thermometers were often reported to
be small during cold months and large during warm months (Moberg and Alexandersson, 1997). Due to the frequently changed observation specification
during the study period, the type and height of instruments also changed.
Measured and statistical results derived from different periods and instruments
were not corrected at stations, although the observation records by newly
changed instrument were compared and analyzed in history (Huang, 2006; Liu
and Hu, 2005; Wang and Huang, 2008). The thermometer was placed 1.5 meters above ground before 1953, 2.0 meters during 1954 and 1960 and 1.5
meters after 1961 (Table 2.1). The temperature monitoring conditions therefore
changed when the thermometer at a new height was introduced. The abrupt
shifts occurred in 1960 at Jiujiang (60) and in 1980 at Anqing (58) station were
likely to be explained by changed observation specification.
Furthermore, abrupt shifts were possibly caused by station relocations
and successive urban warming, which were difficult to determine without detailed metadata. Station relocation, in particular changes in instrument
exposure, can often trigger non-homogeneities. Vincent and Gullett (1999)
found that station relocation and changes in observing time were the most
common causes of non-homogeneities in Canadian surface temperature. Li et
al (2004a) also noted that station relocation was the main cause of nonhomogeneities in Chinese surface air temperature series during the last 50
years. The positive and negative effects of station relocations are most likely to
51
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
distribute randomly. 81% of national benchmark stations in China were moved
prior to 1980 and 70% of national observing stations were relocated more than
once. The change in surrounding environment contributed to most of the relocations, rather than systematic relocations (Wu, 2005). Only seven stations in
Jiangxi were not moved and even some of them were relocated several times,
e.g. Ganzhou (76) station had been relocated five times since 1950 (Wang and
Huang, 2008). Relocation can thus account for the shift of Ganzhou station.
2.3.2
Trend non-homogeneities
The non-homogeneities that occurred as trends spanned 5-27 years. Table 2.2
indicates the population data for the cities or counties where the meteorological
stations are located and the changes of mean temperature level gradually as a
trend homogeneity starting and ending at arbitrary points of time, a and b respectively. The change of mean temperature level is Qb  Qa . Qb  Qa , for
these trends were on average 0.10℃. Station history is important to determine
conclusively the causes for these trends. They may be caused by gradual
changes of the radiative properties of the thermometer screens (leading to
warming); or gradual changes of the surroundings, e.g. urban heating island effect or growing vegetation (leading to cooling if the thermometer screen becomes more shaded or leading to warming if the observation site becomes less
ventilated). Trend non-homogeneities may also be caused by successive relatively small abrupt shifts with the same sign (leading to either cooling or warming), but on average, these effects are probably outweighed by urban warming (Moberg and Alexandersson, 1997; Oke, 1989). A number of studies examined the impact of urbanization on the climate record and arrived at similar
conclusions: approximately 0.1℃ of the observed 0.6℃ warming since the late
1800s in the global temperature time series is due to urban warming (Easterling
et al., 1997; Jones et al., 1990).
Positive trends (totally 21) appeared to be 68 per cent more than negative
ones (4). Positive trends were identified in data from 19 urban stations. Trend
was the only form of non-homogeneities in all the urban stations. Southeast
China is the most significant area influenced by urban heating in China (Li et
al., 2005). Difference between urban and rural areas was based on the popula52
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
tion of the city associated with the station. All the 19 stations located in cities
have population more than 2 000 000 and the trend sections for urban stations
started at the year 1970. Some stations, used to be in rural areas or open fields
before 1970s, became cities or counties with the process of urbanization, although they were not moved. The surrounding environment changed (Jiang et
al., 2008). The cause for the trends can thus be contributed to urban warming.
Seasonal temperatures at stations situated in cities with populations more
than 100 000 (threshold by Moberg and Alexandersson, 1997) are affected by
urban heating. Details of the estimated trends for urban stations are listed together with population in Table 2.2. Nearly half of trend sections were found in
winter data and the same occurrence frequency as single shifts. The seasonal
temperature series at Guangzhou station with population of 6 854 000 (0.05℃
winter) had larger urban biases than Xiamen station (0.03℃ winter) with population of 2 053 051. Portman (1993) found that the urban bias at stations in
China‟s northern plains generally was greater during spring and summer and
smaller during winter and autumn. However, our results indicated larger urban
biases occurred in winter and autumn and smaller in summer and spring. The
stations of our site are located in the South China. Further study with larger
number of samples has to be made for the conclusion of the seasonal variability.
On the whole, the causes of these non-homogeneities vary from station to station.
2.3.3
Outliers
The total number of outliers in monthly temperature and precipitation data at
36 stations was 32 and 68, respectively, 0.14 per cent out of all 7800 monthly
temperature values and 0.87 per cent out of all 7800 monthly precipitation values. Outliers may be caused by natural variability or artificial errors. Applying
the 4-level standard deviation for the detection of outliers, 0.006 per cent (in
our results, only one) is expected to result from natural variability. The other
outliers are likely to be caused by transmission and typographical errors.
2.3.4
Examples of non-homogeneities
Example of temperature
53
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
SNHT was applied to monthly mean temperature series at stations in Jiangxi
and its contiguous provinces for the period 1951-2000. Two examples of nonhomogeneities are described. One example was the abrupt discontinuity occurred in the mean temperature series in February at Guangchang (74) station,
and the other example was the trend non-homogeneity detected in the temperature series in December at Jiujiang (60) station, Jiangxi. The station history
for both Guangchang and Jiujiang documented the changes in thermometer
type and in observation hours and times in 1954, 1960 and 1980 as well as the
changes of the height of instrument in 1954 and 1960 (Table 2.1). The reference series was constructed with five most highly correlated reference stations
and the homogeneity was tested based on the procedure described in 2.2.3.
The Q-series before and after adjustment on temperature series in December from Jiujiang is indicated in Figure 2.3 and the corresponding T-series
(test statistics) obtained by applying the shift SNHT to the Q-series is in Figure
2.4. The significance level, T95 (10.431), T90 (8.923) are also marked. The Qseries before 1960 departed strongly from zero value and was mostly less than
s
zero. The T-series indicated a noticeable peak with Tmax
=16.73 at the year of
s
s
1960 ( Tmax
>T95). By the trend SNHT Tmax
=13.5 for a=1961, b=1963 was ob-
tained. The period of trend section was shorter than five years; the trend nonhomogeneity was thus not accepted. The abrupt discontinuity in 1960 was acs
cepted. Tmax
is well above the significance level, so the null hypothesis of a
homogeneous series is rejected. Further, this could be explained by the changes
of observation practices and instrument in 1960. The subsections before and after the abrupt discontinuity in 1960 were retested separately, and both were
found to be homogeneous by visual inspection and test statistics.
The difference of the mean levels before and after the abrupt discontinuity was Qb  Qa =0.01℃. The adjustments were made on the time series to set
the section before 1960 in agreement with 1960-2000, the homogeneous part of
the series. The adjustments for the period of 1951-1955 were excluded because
the non-homogeneities for the five years at the beginning and the end of the entire series may be false identified (described in 2.2.3). The shift test statistic of
the new adjusted series was then recalculated. The adjusted Q-series and final
54
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
T-series indicated that the adjusted series was homogeneous with respect to the
reference series.
Non-homogeneity detection for Guangchang was performed in a similar
way to Jiujiang. The Q-series before and after adjustment on temperature series
in February from Jiujiang is shown in Figure 2.5 and the corresponding Tseries in Figure 2.6. The shift test statistic was applied to the series at Jiujiang.
s
No predominant peak Tmax
was given but a section above the significance level.
Q-values were low in the early years but increased afterwards (Figure 2.5).The
Q-series and the T-series indicated that Jiujiang station was affected by gradual
s
warming. The trend SNHT was then applied and gave Tmax
= 16.4, well above
the 95% significance level for a=1977, b=1982 and Qb  Qa =0.01℃. Unfortunately, no station historical records can be referenced with respect to the suitable period for the estimated trend section. However, China has undergone great
urbanization since 1980s. A myriad of suburb stations also became city stations
quickly. So the trend non-homogeneity for the section period of 1977-1982
was accepted and temperature records were adjusted. The adjusted Q-series
were then created (Figure 2.5) and the corresponding final T-series did not
reach 95% significance level. A homogenized temperature series in February at
Jiujiang was obtained.
Example of precipitation
The Q-series on precipitation series in June from Jiujiang station (1951-2000)
is indicated in Figure 2.7 and the corresponding T-series (test statistics) obtained by applying the shift SNHT to the Q-series is in Figure 2.8. The significance level, T95 (10.431), T90 (8.923) are also marked. Q-values do not have
apparent displacements from unity and fluctuate around unity (Figure 2.7). T
statistic is far below the significance level (Figure 2.8). The results from testing
the series show no significant non-homogeneities, and the mean value is fairly
constant throughout the series. Despite the documented changes in the observation practices and instrument in 1954, 1960 and 1980, the precipitation series
in June from Jiujiang can be classified as homogeneous.
55
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
2.3.5
Temperature trend
Temporal and spatial patterns based on adjusted temperature
Temporal changes of temperature averaged over Jiangxi during 1951-2000 are
shown at annual and seasonal scale (Figure 2.9). In terms of annual mean temperature, Jiangxi warmed 0.76℃/100a (Figure 2.9E). Winter and spring warmed 1.78℃/100a and 0.83℃/100a respectively, summer cooled 0.48℃/100a and
autumn warmed 0.82℃/100a (Figure 2.9A-D). None of these changes were statistically significant (P > 0.05). Winter contributed most to the annual warming
while summer exhibited an opposite cooling trend. A feature of warming winter versus cooling summer is revealed. The temperature variability for spring
and autumn showed similar warming amplitude as the annual trend. The extreme cold winter occurred in 1984, the temperature anomaly was -2.53℃ and
the extreme hot summer occurred in 1971 with 0.95℃ of the temperature anomaly. 1984, the extreme cold year, had the lowest temperature anomaly 0.68℃. Since 1986, winters were colder than the mean level of 1961-1990. The
annual temperature anomaly for 1998 was 1.20℃, which was well above zero.
It was clear that 1998 was an extreme warm year and it was probably affected
by the strong ENSO (El Niño and Southern Oscillation), which occurred at the
beginning of 1998, faded during that year by August 2000 but enhanced somewhat by November 2000. Summer climate in China was significantly affected
by ENSO, as reported in many studies (Fu and Teng, 1988; Lau and Wu, 2001;
Wang et al., 2000).
Spatial pattern of temperature changes based on local linear trends in
Jiangxi (1951-2000) is shown in Figure 2.10, from the top to the bottom: annual mean, winter, spring, summer and autumn mean temperature respectively.
For annual mean temperature, strong warming in the south and weak in the
north was seen and the difference of warming was not large. Warming in the
west and east was in a similar amplitude. The largest warming was in the
southwest of Wuyi Mo-untain and the smallest in the north near Poyang Lake
watershed. Winter and autumn indicated warming and partial areas in Jiangxi
showed summer and spring cooling. Winter indicated a heterogeneity warming
pattern. A strong warming (peak at 2.26℃) in winter and relatively weak
warming in autumn can be found. Summer and spring showed the opposite
56
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
south-north pattern to the annual temperature change. Slight spring cooling was
shown in the southwest of Wuyi Mountain. Summer cooling was centralized in
Poyang Lake watershed and distributed along Gan River. Interestingly, south
cooling versus north war-ming in spring was revealed. No significant difference from the south to the north for autumn warming but the difference of east
and west. On the whole, a pattern of warming winter versus cooling summer
was presented and Poyang Lake watershed was the center of summer cooling.
Comparison between Jiangxi, Northern hemisphere and global temperature
Jiangxi, Northern hemisphere and global temperature anomalies for the period
of 1951-2000, relative to 1961-1990, are compared based on adjusted data in
Figure 2.12 (A-C). Temperature indicated a similar warming trend at different
spatial scales but different warming effects. The warming trends were not statistically significant, P > 0.05. Surface air temperature anomalies from 19512000 increased by 1.00℃/100a for the globe, by 0.85℃/100a for the northern
hemisphere and by 0.76℃/100a for Jiangxi. The warming effect in Jiangxi was
weaker than global and northern hemispheric level. That ocean responds more
slowly than the land due to the ocean‟s large thermal inertia may partially account for this. For Jiangxi, Northern hemisphere and the globe, temperature rose
1950 to 1960, declined between 1960 and 1975, and increased again 1975 to
2000. Warming in recent decades was noticeably larger than the former decades (Intergovernmental Panel on Climate Change., 2007), especially since
1980, by 0.15℃/100a for the globe, 0.20℃/100a for the northern hemisphere
and 0.37℃/100a for Jiangxi. Thus the warming effects of different spatial
scales can vary from the temporal scales.
Comparison with unadjusted data
Comparison of the linear trends and spatial patterns between annual mean temperature changes over Jiangxi on unadjusted and adjusted data (1951-2000) are
shown in Figure 2.13, Figure 2.10 and Figure 2.11 respectively. Figure 2.14
indicates the seasonal temperature changes for unadjusted data. The trend was
calculated before and after adjustment: 0.82℃/100a and 0.76℃/100a respectively. The adjusted trend corresponds to the regional average temperature
changes in Jiangxi. The difference between unadjusted and adjusted data was
0.06℃. The seasonal temperature differences were much larger, for winter
57
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
0.35℃/100a (before and after adjustment: 2.13℃/100a and 1.78℃/100a respectively), for spring 0.06℃/100a (before and after adjustment: 0.89℃/100a
and 0.83℃/100a respectively), for summer -0.11℃/100a (before and after adjustment: -0.59℃/100a and -0.48℃/100a respectively) and for autumn
0.05℃/100a (before and after adjustment: 0.82℃/100a and 0.87℃/100a respectively). Temperature differences in winter and summer were the largest and
far from the annual mean level whereas in autumn it was minus value and the
smallest. The amplitude of temperature difference in spring and autumn was
similar to annual mean level. Inversely, The findings of Tuomenvirta et al.
(2000) and Nordli (1997) indicated that the inhomogeneous effect was largest
in spring and autumn and small in winter as well as in summer. Generally the
effect of the change is largest for continental stations and for maritime stations
the difference is relatively smaller. Spatially, stronger summer cooling was revealed based on unadjusted data and no obvious difference was observed in the
other seasons between unadjusted and adjusted data (Figure 2.10 and Figure
2.11).
Comparison between the temperature trends from GHCN and from our analysis on
adjusted data
Figure 2.15 compares the temperature trends obtained from GHCN and from
our analysis on homogenized data for the period 1951-1990. Temperature
changes show a similar increasing trend and both are not statistically significant, P > 0.05. The mean temperature in GHCN analysis (0.48℃/100a) was
0.3℃/100a warmer than that in our analysis (0.18℃/100a). GHCN analysis for
a few years, 1951-1954, 1986 and 1989, were slightly cooler than our analysis.
In GHCN analysis, 1951 was the coldest year while 1998 in our analysis. This
can be explained by the use of two different stations for Jiangxi or different
methods for homogeneity test. Jingdezhen and Yichun stations were included
in the GHCN analysis and our analysis contained Guixi station. Nevertheless,
the overall linear trend of temperature was almost identical by both datasets but
a slight discrepancy was found in the increasing amplitude.
Spatial pattern of surface air temperature changes based on local linear
tre-nds in Jiangxi (1951-1990) is shown in Figure 2.16 for our analysis and in
58
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
Figure 2.17 for GHCN analysis, from the top to the bottom: annual mean, winter, spring, summer and autumn mean temperature respectively. For annual
mean temperature, no noticeable warming centre was observed from both analyses due to the heterogeneity. Our analysis indicated more heterogeneity
warming over Jiangxi than GHCN analysis. GHCN analysis revealed a pattern
of high warming in the south and low warming in the north. But the difference
of war-ming was not large. Warming in the west and east for both analyses was
in a similar amplitude. In terms of seasons, both analyses revealed a spatially
coherent distribution across Jiangxi but different amplitude of warming, particularly in spring and autumn. Winter and autumn indicated warming and partial
areas indicated summer and spring cooling. Winter showed strong and heterogeneity warming and summer cooling concentrated on the two sides of Poyang
Lake watershed for both analyses. Spring cooling was obvious in the southwest
of Wuyi Mountain for both analyses but GHCN indicated a larger cooling amplitude.
Two predominant differences can be obtained from the two analyses.
Autu-mn from our analysis showed the pattern of high warming in the west and
low warming in the east but inversely from GHCN analysis. On the other hand,
the amplitude of spring cooling from GHCN analysis was considerably greater
than that from our analysis. The temperature pattern calculated from different
datasets can thus have different conclusions. For both analyses, predominant
spring cooling in the southwest of Wuyi Mountain and the centre of summer
cooling in Poyang Lake watershed were found.
2.3.6
Precipitation trend
Temporal and spatial patterns of precipitation based on homogeneous data
Figure 2.18 shows annual and seasonal precipitation changes averaged over
Jiangxi for the period 1951-2000. Annual precipitation indicated a slight upward trend with the rate of 230.5 mm /100a. Annual precipitation 1951-2000
averaged 1604 mm. Precipitation rose 4.5% /100a. The precipitation variability
for winter and summer showed similar wetting trend as the annual trend. Summer contributed most to the annual precipitation increase at the rates of 264.9
mm/100a and winter 72.7 mm/100a while spring and autumn showed an oppo59
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
site arid trend at the decrease rates of 57.7 mm/100a and 49.3 mm/100a, respectively. In comparison with annual precipitation mean based on 384 stations
across China 1951-1990, annual precipitation mean was 850mm and the rate of
decline was 46.5mm/100a, 5.5%/100a (Wang, 1996). Our analysis over Jiangxi
(1951-1990) showed a similar trend. Annual precipitation mean was 1571.6mm
and the rate of decline was 17.0mm/100a, 1.1%/100a. Figure 2.18 showed remarkable inter-decadal variation but no evident inter-annual change. Particularly for the period of 1991-2000, annual precipitation averaged 1720.0mm, the
rate of increase was 238.0mm/100a, 13.8%/100a. None of these changes were
statistically significant (P > 0.05). The flood year 1998 corresponded to the extreme warm year. The PDO-related atmospheric circulation anomaly may explain the summertime precipitation anomalies (Huang Ronghui et al., 1999;
Zhongwei, 1999).
Spatial precipitation change based on local linear trends over Jiangxi
(1951-2000) at annual and seasonal scales is shown in Figure 2.19. For annual
precipitation, a pattern of high wetting in the south and north and low wetting
in the middle was revealed. The wetting effects in the south and north were
identical. The strongest wetting was in the southwest of Wuyi Mountain. Winter and su-mmer indicated wetting and most areas in Jiangxi showed autumn
and spring drying. Winter wetting effect in the west is more obvious than that
in the east and inversely in autumn. In terms of seasons, the differences of wetting effects over Jiangxi were small. A remarkable feature of strong spring
drying versus summer wetting over Jiangxi was found. The spatial patterns of
spring and summer wetting were in good agreement with that of spring and
summer cooling. Generally global warming can result in more global precipitation. Our results are in agreement with the findings of Malhi and Wright
(2004), Portmanna et al. (2009) and Rodriguez-Puebla et al. (1998).
2.4 Discussions
This study aims to obtain homogenized climatological datasets over Jiangxi for
the period 1951-2000 for the analysis of climate change with confidence and
compare the climate trends computed from GHCN datasets and from our analysis. Our findings confirm the robustness of SNHT method for detecting the
60
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
non-homogeneities of in situ observed temperatures and precipitations influenced by local microclimate and non-climatic changes at regional scale despite
some uncertainties. Results also indicate that the adjusted monthly temperatures have smoothed the warming effect, particularly in winter. The spatial cooling effect in summer is exaggerated by unadjusted data. Further, the comparison between GHCN and our analysis on adjusted monthly temperatures indicates that the resulting climate trend varies slightly from datasets to datasets. In
terms of monthly precipitation totals, the series are found to be homogeneous
and a wetting trend is revealed. The spatial patterns of spring and summer wetting are in good agreement with the trends of spring and summer cooling.
The introduction of more sensitive measurement instrument and the
change of observation time are the possible explanations for the reduced warming effect by adjusted monthly temperatures. The change of observation time
from local mean time in 1951 to 120°E standard time in 1954, to Beijing time
observation in 1960 caused cooler observation temperatures. Wang and Huang
(2008) found that the temperature at Guixi (72) by local mean time, 01:00,
07:00, 13:00 and 19:00, four times observation appeared to be slightly lower
than by 120°E standard time and Beijing time observation owe to the lower
mean level . Karl et al. (1986) also reported a change in the observing time
from late afternoon to morning in the COOP network over the past 20 years
has resulted in an artificial cooling in the time series due to an abrupt discontinuity to cooler monthly averaged temperatures. Errors in temperatures associated with different observing times are also identified at a number of U.S. stations (DeGaetano, 1999; Janis, 2000). In some cases this observation time shift
introduces a non-homogeneity that is dealt within the same fashion as an instrumental change (Lanzante et al., 2003). The historic development of the
instrument has gone from dry bulb thermometer, through wet and dry bulb
ther-mometer, bimetal thermometer, and to the present day platinum resistance
temperature transducer. Progressive improvements of instrumentation introduce artificial systematic bias. Quayle et al. (1991) reported the mean monthly
temperature range became smaller due to a change from liquid-in-glass to electronic thermometers in the U.S. Cooperative Observing Network without major
relocations at the same time. The findings of decreasing temperatures by the
61
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
changes in instrument are also obtained by Gu and Wang (2003), Keckhut et al.
(1999), Lanzante et al. (2003), Wang and Liu (2002) and Yu et al. (2007).
We find station relocation is also the probable cause for cooler observation temperature. Station relocations are a common feature for the most of
stations in long-term Chinese temperature records (Li et al., 2004a). Stations
may be moved from one place to another within the same city/town (i.e. from
the city center to outskirts in the distant past and, more recently, from outskirts
to airfields and airports far away from urban influence) and from one setting
(roofs) to another (courtyards). That stations are relocated from vegetation
canopy to a more ventilated environment has a cooling effect. Allen and
DeGaetano (2000), Li et al (2004a), Roger et al. (2007), Vincent and Gullett
(1999), Wijngaard et al. (2003) report the identical findings that the warming
effect is smoothed due to the introduction of a ventilated observation. Moreover, the tendency to chan-ge thermometer exposures from courtyard-level to
roof-level and vice versa could also be another cause of this warm-bias (Brunet
et al., 2006). But we have no metadata to support this. Gradual warming
induced by urbanization is another explanation. City stations is prone to urban
heating. Each reference series generally consists of some urban staions. The
effect of urban warming is finally removed after correction when all the
stations are used both as candi-date and reference stations. The examples of the
effect of urban heating have been reported by a myriad of studies (e.g. Hansen
et al., 2001a; Jones et al., 1990; Moberg Anders and Hans, 1997; Portman,
1993; Slonosky et al., 1999; Tuomenvirta and Drebs, 1994; Zhou et al., 2004a).
The resulting difference between GHCN and our analysis is attributable to
different homogenization algorithm. Pairwise comparison algorithm (GHCNbased) is an automated test procedure without requiring manual analysis and is
shown to be robust and efficient at detecting undocumented abrupt discontinuities under a variety of simulated scenarios with step- and trend-type non-homogeneities. GHCN also carried out an evaluation to distinguish trend non-homogeneities from abrupt shifts. However, its application on regional scale (e.g.
Jiangxi in our study) has limitation in the number of stations. The pairwise algorithm starts by finding the 100 nearest neighbors for each candidate station
as reference stations within a network of stations, otherwise scarce reference
62
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
stations can lead to biased estimation. The density of station network in China
is inadequate for the application of this method; however SNHT can be more
appropriate for our situation.
Our results show the series of precipitation are homogeneous. Due to its
higher variability, fewer breaks can be detected and the possibilities for trend
detection in precipitation series are also reduced (Michael et al., 2005;
Wijngaard et al., 2003).
The novel findings of this work are:
(1) A remarkable feature of strong spring drying versus summer wetting and
warming winter versus cooling summer in Jiangxi (1951-2000) is presented.
(2) Poyang Lake watershed is the center of summer cooling in Jiangxi (19512000).
One limitation of our work is that only six available stations over Jiangxi is
used for climate estimation. Vose and Menne (2004) found that the same basic
relationship exists between station density and the error in calculating the mean
U.S. temperature trend, whether unadjusted or adjusted data are used. The fewer number of reference stations, the more non-homogeneities are detected. The
scarcity of detailed metadata is another limitation. In reality, even the most complete station history files probably do not contain information on all changes
at a station.
To summarize, homogenization of climatic series is important, thus must
be performed with caution. The process of homogenization is to remove nonclim-atic factors and to preserve the real climate signal. An incorrect application of homogenization procedures to climate data could subsequently lead to
unreliable climate analysis. The comparison between several tests on detected
non-homogeneities is suggested a good strategy, especially in the absence of
meta-data. Overestimation and correction of false non-homogeneities are thus
to some extent avoided. Promising techniques would be required involving information from parallel measurements, other weather variables and reanalysis
comparison, such as solar radiation or cloud cover amounts. In spite of a difficult task, daily values are generally too noisy for a successful detection. Increased concern has been devoted to the need to homogenize daily data during the
recent years.
63
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
2.5 Conclusions
Adjusted temperatures have smoothed the warming in Jiangxi particularly in
winter. The spatial cooling in summer is exaggerated by unadjusted data. The
comparison between GHCN and our analysis on adjusted temperatures indicates that the resulting climate trends vary slightly from datasets to datasets. Precipitation series are found to be homogeneous. A feature of warming winter
versus cooling summer and spring drying versus summer wetting is revealed.
Poyang Lake watershed is the center of summer cooling.
64
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
Tables
Table 2.1 Changes of observation specification in China since 1960s
Observation specification
(1951)
Observation specification
(1954)
Observation specification
(1960)
Observation specification
(1980)
Implementation
time
Observation time
1951-01-01 to 1953-12-31
1954-01-01 to 1960-06-30
1960-07-01 to 1979-12-31
1980-01-01 to now
120°E Standard Time
Local Mean Time
Beijing Time
Beijing Time
Observation times
National benchmark station:
24 hours
National benchmark station:
01:00, 07:00, 13:00, 19:00
four times
National observing station:
01:00, 07:00, 13:00, 19:00
four times
National general station:
07:00, 13:00, 19:00
three times
Temperature:
wet and dry bulb hygrometer
National benchmark station:
02:00, 08:00, 14:00, 20:00
four times
National observing station:
02:00, 08:00, 14:00, 20:00
four times
National general station:
08:00, 14:00, 20:00
three times
Temperature:
bimetal thermometer
National benchmark station:
24 hours
Precipitation:
rain gauge without shield
2 m above ground
Precipitation:
siphon rain gauge
1.5 m above ground
Precipitation:
tipping bucket rain gauge
1.5 m above ground
National observing station:
at least 8 times
Instrument type
Instrument height
National general station:
06:00, 14:00, 21:00
three times
Temperature:
dry bulb thermometer
Precipitation:
rain gauge with shield
1.5 m above ground
65
National observing station:
02:00, 08:00, 14:00, 20:00
four times
National general station:
08:00, 14:00, 20:00
three times
Temperature:
platinum resistance temperature transducer
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
Table 2.2 Changes of mean temperature level have taken place gradually as a linear
trend starting and ending at arbitrary points of time, a and b respectively. Population
data are given for the cities or counties where the stations are located. The change of
mean temperature level is Qb  Qa .
Station
Population
1982a
Population
2000b
Anqing
423 600
5 177 000
Guangchang
Hangzhou
167 543
1 191 582
209 274
6 878 000
Lingling
745 447
5 367 106
Nanchang
Shaoguan
2 484 293
676 014
4 329 600
2 735 433
Xiamen
965 985
2 053 051
Hefei
821 812
4 283 900
Wuhan
3 251 591
7 491 900
Guangzhou
3 148 281
6 854 000
Ningbo
468 230
5 963 362
Shantou
7 228 050
4 671 100
Fuzhou
1 129 251
6 386 015
508 611
7 558 000
Wenzhou
a
b
Season
Winter
Summer
Winter
Summer
Spring
Spring
Winter
Winter
Summer
Winter
Autumn
Winter
Summer
Autumn
Spring
Winter
Winter
Summer
Summer
Winter
Autumn
Spring
Winter
Spring
Spring
Trend-test results
a
b
(year)
(year)
1969
1983
1967
1983
1980
1990
1970
1990
1950
1970
1970
1980
1983
1989
1977
1983
1980
1990
1972
1978
1980
1990
1957
1964
1981
1989
1963
1973
1978
1985
1991
1996
1984
1999
1973
1986
1991
1998
1981
1996
1952
1967
1977
1982
1977
1992
1953
1980
1975
1982
Population census of China in 1982. Beijing, 1985
Population census of China in 2000. Beijing, 2001
66
b-a
(years)
14
16
10
20
20
10
6
6
10
6
10
7
8
10
7
5
15
13
7
15
15
5
15
27
7
Qb  Qa (℃)
0.39
-0.17
0.08
0.2
-0.15
0.08
0.33
0.11
0.02
0.14
0.35
0.03
-0.02
0.09
0.12
0.13
0.05
0.15
0.08
0.16
0.24
0.01
0.12
-0.14
0.04
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
Figures
Figure 2.1 Location of the study area in China (above) and locations of 36 meteorological stations in Jiangxi and its contiguous 6 provinces used in this study (below) are
shown. (Overlaid on a 90m DEM, station numbers see Table 1 in Appendix)
67
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
Figure 2.2 Plotting for the discontinuities detected in homogeneity tests in the final
test round. The histogram shows the seasonal frequency for discontinuities on decadal
level for the period of 1951-2000. The frequency of winter, spring, summer and autumn are denoted by blue, red, green and violet columns respectively.
68
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
Figure 2.3 Q-values on temperature series in December from Jiujiang station (19512000). Blue solid line denotes the results for the original data and pink diamondshaped point for the adjusted data.
Figure 2.4 T-values from performing the single shift SNHT on temperature seres in
December from Jiujiang station (1951-2000). Blue solid line denotes the results for the
original data and pink diamond-shaped point for the adjusted data. Significant levels
of 90% and 95% are also marked.
69
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
Figure 2.5 Q-values on temperature series in February from Guangchang station
(1951-2000). Blue column denotes the results for the original data and pink diamondshaped point for the adjusted data.
Figure 2.6 T-values from performing the single shift SNHT on temperature series in
February from Guangchang station (1951-2000).Blue solid line denotes the results for
the original data. Significant levels of 90% and 95% are also marked.
70
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
Figure 2.7 Q-values on precipitation series in June from Jiujiang station (1951-2000).
Blue solid line denotes the results for the original.
Figure 2.8 T-values from performing the single shift SNHT on precipitation series in
June from Jiujiang station (1951-2000). Blue solid line denotes the results for the original data. Significant levels of 90% and 95% are also marked.
71
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
Figure 2.9 Anomalies of the mean temperature over Jiangxi province for adjusted data
(1951-2000) relative to 1961-1990 (A) adjusted winter, (B) adjusted spring, (C) adjusted summer, (D) adjusted autumn, and (E) adjusted annual mean. Annual mean
(dashed line) 5-year running average (solid line). P > 0.05.
72
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
Figure 2.10 Surface temperature change based on local linear trends over Jiangxi for
our analysis (1951-2000), from the top to the bottom: annual mean, winter, spring,
summer and autumn mean temperature respectively.
73
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
Figure 2.11 The same as Figure 2.10, but for unadjusted data.
74
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
Figure 2.12 Surface air temperature anomalies relative to 1961-1990 mean for (A)
Jiangxi (B) Northern hemisphere (C) Globe is denoted by dotted line for annual mean,
solid line for 5-year running average. P > 0.05
75
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
Figure 2.13 Surface air temperature anomaly relative to 1961-1990 averaged over
Jiangxi province based on unadjusted raw and adjusted data (1951-2000). Dashed line
denotes unadjusted annual mean, dotted line (adjusted annual mean), crossed-solid
line (adjusted 5-year running average), solid line (unadjusted 5-year running average)
and dashed-dotted line (trend line). P > 0.05.
76
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
Figure 2.14 Anomalies of the mean temperature over Jiangxi province for unadjusted
data (1951-2000) relative to 1961-1990 (A) unadjusted winter, (B) unadjusted spring,
(C) unadjusted summer, and (D) unadjusted autumn. Annual mean (dashed line) 5year running average (solid line). P > 0.05.
77
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
Figure 2.15 Surface air temperature anomaly relative to 1961-1990 averaged over
Jiangxi (1951-1990). Triangle-dashed line denotes annual mean for GHCN homogenized data, dotted line (annual mean for adjusted data), crossed-solid line (5-year
running average for GHCN homogenized data) and solid line (5-year running average
for adjusted data). P > 0.05.
78
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
Figure 2.16 Surface air temperature change based on local linear trends over Jiangxi
for our analysis (1951-1990), from the top to the bottom: annual mean, winter, spring,
summer and autumn mean temperature respectively.
79
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
Figure 2.17 Surface air temperature change based on local linear trends over Jiangxi
for GHCN analysis (1951-1990), from the top to the bottom: annual mean, winter,
spring, summer and autumn mean temperature respectively.
80
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
Figure 2.18 Anomalies of the mean precipitation over Jiangxi province for homogenized data (1951-2000) relative to 1961-1990. Homogeneous (A) winter, (B) spring,
(C) summer, (D) autumn, and (E) annual mean. Annual mean (dashed line) 5-year
running average (solid line). P > 0.05.
81
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
Figure 2.19 Precipitation change based on local linear trends over Jiangxi for our
analysis (1951-2000), from the top to the bottom: annual mean, winter, spring, summer and autumn mean respectively.
82
2. Regional temperature and precipitation trends based on homogenized time series in
Jiangxi Province, China
2.6 Appendix
Appendix Table1. Thirty-six meteorological stations in Jiangxi and its contiguous
provinces
Elevation based on DEM
Station NO.
ID
LAT
LON
NAME
58
58424
30°32' N
117°03' E
ANQING
25
48
58221
32°57' N
117°23' E
BENGBU
22
65
57662
29°03' N
111°41' E
CHANGDE
69
77
57679
28°12' N
113°05' E
CHANGSHA
74
79
57972
25°48' N
113°02' E
CHENGZHOU
309
102
57447
30°17' N
109°28' E
ENSHI
1291
49
58203
32°56' N
115°49' E
FUYANG
34
70
58847
26°05' N
119°17' E
FUZHOU
359
76
57993
25°51' N
114°57' E
GANZHOU
149
74
58813
26°51' N
116°20' E
GUANGCHANG
808
87
59288
23°08' N
113°19' E
GUANGZHOU
7
72
58626
28°18' N
117°13' E
GUIXI
56
57
58457
30°14' N
120°10' E
HANGZHOU
13
55
58321
31°52' N
117°14' E
HEFEI
9
78
57872
26°54' N
112°36' E
HENGYANG
122
86
59293
23°44' N
114°41' E
HEYUAN
42
75
57799
27°07' N
114°58' E
JIAN
334
60
58502
29°44' N
116°00' E
JIUJIANG
56
80
57865
26°14' N
111°37' E
LINGLING
131
83
59117
24°16' N
116°06' E
MEIXIAN
160
73
58606
28°36' N
115°55' E
NANCHANG
41
66
58562
29°52' N
121°34' E
NINGBO
441
69
58731
27°55' N
118°32' E
PUCHENG
797
67
58633
28°58' N
118°52' E
QUZHOU
138
84
59316
23°24' N
116°41' E
SHANTOU
4
85
59082
24°48' N
113°35' E
SHAOGUAN
382
59
58531
29°43' N
118°17' E
TUNXI
481
68
58659
28°00' N
120°40' E
WENZHOU
410
61
57494
30°37' N
114°08' E
WUHAN
20
82
59134
24°29' N
118°04' E
XIAMEN
291
88
59663
21°52' N
111°58' E
YANGJIANG
22
64
57461
30°42' N
111°18' E
YIYANG
186
71
58921
25°58' N
117°21' E
YONGAN
875
63
57584
29°23' N
113°05' E
YUEYANG
26
89
59658
21°13' N
110°24' E
ZHANJIANG
4
81
57745
27°27' N
109°41' E
ZHIJIANG
638
62
57378
31°10' N
112°34' E
ZHONGXIANG
100
83
(Meters above sea level)
3. Regional scale impacts of land use and land cover changes on climate change in Jiangxi
Province, China
3 Regional scale impacts of land use and land cover
changes on climate change in Jiangxi Province, China
3.1 Introduction
Humans have modified the Earth‟s climate through emissions of greenhouse
gases and through land use/land cover (LULC) change (Christensen et al., 2007;
Kalnay and Cai, 2003; Pitman et al., 2011). In some cases, LULC change response to climate, in the form of urbanization, agricultural activity and
deforestation, may even exceed the contribution from greenhouse gases
(Dirmeyer et al., 2010; Roger A. Pielke 2005). Increasing concentrations of
greenhouse gases in the atmosphere warm the mid-latitudes more than the tropics, in part owing to a reduced snow-albedo feedback as snow cover decreases.
Higher concentration of carbon dioxide also increases precipitation in many regions, as a result of an intensification of the hydrological cycle (Christensen et
al., 2007). The biophysical effects of LULC change since pre-industrial times
have probably cooled temperate and boreal regions and warmed some tropical
regions (Lawrence and Chase, 2010). LULC will suppress the impacts of, for
example, increasing CO2 in some regions that cool due to land cover change,
and amplify the impacts of increasing CO2 in regions that warm due to land
cover change (Pielke et al., 2011b). Changes in rainfall and snow caused by increases in CO2 dominate how LULC change affects climate, therefore requiring
an accurate location of changes in snow cover and rainfall geographically coincident with regions of LULC change (Pitman et al., 2011).
Regional scale LULC change over the last several centuries using observed
and modelled data have altered biogeophysical and biogeochemical surface
fluxes (Pielke et al., 2011a). The National Research Council (2005) has stated
the following:
Regional variations in radiative forcing may have important regional and
global climatic implications that are not resolved by the concept of global
mean radiative forcing. Tropospheric aerosols and landscape changes have
particularly heterogeneous forcings. To date, there have been only limited stud84
3. Regional scale impacts of land use and land cover changes on climate change in Jiangxi
Province, China
ies of regional radiative forcing and response. Indeed, it is not clear how best
to diagnose a regional forcing and response in the observational record; regional forcings can lead to global climate responses, while global forcings can
be associated with regional climate responses. Regional diabatic heating can
also cause atmospheric teleconnections that influence regional climate thousands of kilometres away from the point of forcing. Improving societally
relevant projections of regional climate impacts will require a better understanding of the amplitudes of regional forcings and the associated climate
responses.
It is the regional responses, not a global average, that produce drought, floods,
and other societally important climate impacts.
Prior work has well documented, both globally and regionally, the impacts of LULC change on climate change (Anantharaj et al., 2010; Costa and
Pires, 2010; Fall et al., 2010a; Fall et al., 2010a; Kishtawal et al., 2010; Lim et
al., 2008; Strengers et al., 2010; Xiao et al., 2010). The role of LULC change in
altering convective precipitation has been sufficiently analysed (e.g. Pielke,
2001; Pitman, 2003). Cotton and Pielke (2007) report how regional weather patterns are affected by LULC change. Warm season precipitation should be
expected to change whenever deep cumulus convection is common in a region,
since the surface fluxes of moisture, sensible, and latent heat change. This is the
fuel for thunderstorms both in terms of moisture and in altering the convective
available potential energy (Stull, 1988). Simulated dry season orographic cloudiness, rainfall, and orographic flow patterns using the Regional Atmospheric
Modelling over Kilimanjaro are in good response to deforested and reforested
land cover (Fairman et al., 2011). It has been also reported that land use changes
due to agriculture lead to decreased surface temperatures (Lobell and Bonfils,
2008; Mahmood et al., 2006; Roy et al., 2007). The estimated urbanization impact differs significantly, which strongly depends on the criteria in classifying
urban and rural areas (Easterling et al., 1996; Hansen et al., 2001b). In China,
simulation and observation results reveal land-cover change significantly influences hydrologic processes at the watershed scale (Ma et al., 2009; Zhang et al.,
2011). Yang et al.(2009a) found that LULC change has a stable and systemic
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impact on surface air temperature. The surface warming of each land type reacted differently to global warming.
Owe to the difficulty in separating the influence merely resulted from
LULC, the Observation Minus Reanalysis (OMR) method developed by Kalnay and Cai (2003) is recently used to assess the impact of land use change by
taking the difference between the observations by surface stations and reanalysis data. It takes the advantage of the insensitivity of the reanalysis to land
surface type, and removes the natural variability due to changes in circulation
(since they are also included in the reanalysis), thus isolating surface effects
from greenhouse warming by the surface observations subtracting the reanalysis (Kalnay and Cai, 2008). The advantage for the OMR method is to provide
us a substantial surface climate change signal arising from different types of
regional surface cover through separating near-surface warming patterns from
the global warming signal.
Reanalysis, particularly the National Centers for Environmental Prediction-National Center for Atmospheric Research Reanalysis (NCEP-NCAR
Reanalysis-NNR) , can be regarded as an independent estimate of the surface
temperature change, including trends associated with both the large-scale circulation variability and anthropogenic radiative forcings (Fall et al., 2010b;
Frauenfeld et al., 2005; Kalnay and Cai, 2003; Kalnay et al., 2006; Lim et al.,
2005; Lim et al., 2008; Zhou et al., 2004a). Reanalyses are data products based
on a combination of various observations by assimilating them into a global
weather forecasting model to obtain meteorological fields that are consistent
both with the observations and with the model physics (Kalnay et al., 1996;
Kistler et al., 2001; Uppala et al., 2005). These fields are potentially appealing
for climate research because they are spatially and temporally complete and
encompass a full suite of variables. Moreover, reanalyses are done with a fixed
numerical weather model, thus avoiding non-homogeneities associated with
changes in forecast models and analysis methods over time (Fall et al., 2010b).
The reanalysis, combined with its model parameterizations of surface processes, creates its own estimate of surface fields from the upper air observations.
The surface parameters in a reanalysis have less dependence on local characteristics than the actual surface observations. As a result, the reanalysis excludes
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3. Regional scale impacts of land use and land cover changes on climate change in Jiangxi
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surface urbanization or land use effects even though it should show climate
change to the extent that they influence the observations above the surface (Fall
et al., 2010b; Kistler et al., 2001). It follows that it would possibly attribute the
differences between monthly or annually averaged observation and reanalysis
surface temperatures primarily to urbanization/land use changes (Kalnay et al.,
2006), and other local land cover effects, although a portion of differences
might also be due to errors in interpolating reanalysis data to instrument height,
particularly in the stable nocturnal boundary layer.
Several studies use OMR method to assess the surface temperature trend
caused by different land types. Kalnay and Cai (2003) initially assessed the decadal surface warming associated with local land uses over the eastern U.S.
region by subtracting reanalysis trend from observation. Regional surface
warming identified by OMR has good agreement with the trends obtained by
Hansen et al. (2001) using nightlights to classify urban and rural stations (Kalnay et al., 2006). Fall et al. (2010b) applied the OMR method to investigate the
impacts of sensitivity of surface temperature trends to LULC change over the
conterminous United States. The impacts of different land cover types on temperature in China, Argentina as well as globe and the consequences from
temperature associated with land uses in Tibetan Plateau have been assessed by
OMR method (Frauenfeld et al., 2005; Hu et al., 2010; Lim et al., 2005;
Nun˜ez et al., 2008; Yang et al., 2009b; Zhou et al., 2004b). Results confirm
the robustness of the OMR method for capturing patterns of LULC changes at
regional scales. Fall et al. (2010c) applied the OMR method in precipitation in
India. Results do not find well defined rainfall pattern as a function of OMR.
Currently, no studies have examined the influence of LULC on climate in
greater detail especially in relation with regional characteristics, which play an
important role in local and regional climate.
The objective of this study is to examine if the LULC change might be responsible for the changes in temperature and precipitation in combination with
regional topographic factors over Jiangxi province China. By analyzing OMR
trends and incorporating regional topographic factors into regression assessments of temperature and precipitation, we assess the relationship between
OMR trends and the regional land vegetation types (derived from satellite87
3. Regional scale impacts of land use and land cover changes on climate change in Jiangxi
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Normalized Difference Vegetation Index (NDVI)). OMR trends and the sensitivity of surface temperatures and precipitations to land cover types over JiangJiangxi are presented. Our study indicates the significance of regional LULC
change on climate. The teleconnection effect, where regions remote from the
landscape conversion have altered surface meteorology (e.g., as discussed in
Avissar and Werth, 2005 and; Chase et al., 2000) is not the focus of this study.
Section 3.2 reviews the data and methods. Section 3.3 presents OMR trends
and the sensitivity of surface temperatures and precipitations to land cover
types over Jiangxi. The discussions and conclusions are shown in section 3.4
and 3.5 respectively.
3.2 Data and methodology
3.2.1
Data
For the surface observations, homogenized continuous monthly surface instrumental temperature and precipitation dataset of 36 available stations over
Jiangxi and contiguous 6 provinces for the period of 1951 to 1999 were obtained from section 2. All stations have continuous monthly data for the entire
49-year period. Monthly surface temperature mean and monthly mean of precipitation rate time series from NCEP-NCAR reanalysis (NNR) are used, both
for the period 1951-1999. Advanced Very High Resolution Radiometer
(AVHRR) NDVI, 15-day composite product dataset at 1°resolution for the period 1981-2000, derived from Global Land Cover Facility, Global Inventory
Modelling and Mapping Studies (GIMMS) (GIMMS;
http://glcf.umiacs.umd.edu/data/gimms/). We used this to correlate the distribution of the surface vegetation and its seasonal change to the decadal OMR and
observation trends. This NDVI dataset has been corrected for calibration, view
geometry, volcanic aerosols, and other effects not related to vegetation change.
Seasons are defined as the three-month averages for temperature and NDVI,
three-month sum for precipitation: DJF, MAM, JJA, and SON. Shuttle Radar
Topography Mission (SRTM) 90m digital elevation data supplied by Consorti-
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um for Spatial Information (CGIAR-CSI) is used as elevation factor for regression assessment and correction of spatialization.
3.2.2
Data analysis
Maximum value composite of NDVI
Atmospheric calibration of original 15-day composite AVHRR-NDVI data was
done to reduce cloud and other noise. The two calibrated 15-day NDVI images
for each month were composited first by taking the maximum value within two
15-day time periods for each pixel, eliminating pixels with cloud cover. Then
we averaged the monthly NDVI values for each pixel into annual and seasonal
mean NDVI values. The result was an almost cloud free data set of peak NDVI
value from 1982 to 2000 covering Jiangxi. Maximum-value composite technique has the advantage of minimizing cloud contamination and off-nadir
viewing effects since these factors tend to reduce the NDVI values over green
surfaces (Holben, 1986; Holben and Fraser, 1984). Applying in the maximum
value compositing process, the highest value record in the time range is retained for each pixel location (Holben, 1986). Due to the growing circle of
vegetation in Jiangxi, the NDVI mean of growing season (April to October)
was used to reflect the general vegetation state for the entire study period.
Land use analysis is done through the classes of NDVI. It requires the
assignment of each pixel on an image to a class. The NDVI value is defined as
the ratio of the difference to the total reflectance: (near-infrared - red) / (nearinfrared + red). Green leaves commonly have larger reflectance in the nearinfrared than in the visible range. Clouds, water, and snow have larger reflectance in the visible than in the near-infrared, so that negative values of the
vegetation index may correspond to snow or ice cover, whereas the NDVI value is almost zero for bare soils such as deserts. As a result, NDVI values can
range from -1.0 to 1.0, with higher values associated with greater density and
greenness of vegetation covers (Lim et al., 2008). Based on Wang et al. (2006)
and Jiangxi‟s vegetation distribution, NDVI images after maximum value
composite were assigned as six categories, NDVI value (1) -1.0 0; (2) 0 0.2;
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(3) 0.2 0.45; (4) 0.45 0.6; (5) 0.6 0.75; (6) 0.75 1.0. Different land cover
types in response to climate changes were further analyzed.
OMR method
Surface climate change trends are analysed using the OMR method suggested
by Kalnay and Cai (2003). No surface data was included in the data assimilation of NNR. Due to the insensitivity of the reanalysis to surface processes,
surface effects caused by regional vegetation type changes could be separated
from greenhouse warming. The OMR decadal trend is obtained by taking the
average of two decadal mean differences (the 1990s minus the 1980s and the
1970s minus the 1960s).
The trends were computed as changes in decadal averages in order to reduce random errors. Only two decadal trends, the decade 1990-1999 minus
1980-1989 and 1970-1979 minus 1960-1969 are considered. The trend changes
between the decade 1960-1969 minus 1950-1959 and 1980-1989 minus 19701979 were excluded due to the scheduling changes during 1958 and changes in
the observing systems starting in 1979 (Kistler et al., 2000). These two major
changes affect the trends in the NNR. The two decadal changes of the 1990s
minus 1980s (20 years with satellite data) and the 1970s minus 1960s (20 years
essentially without satellite data) were used. Thus, we obtain decadal trends
from two independent and largely homogeneous 20-year periods.
3.3 Results
3.3.1
Temperature trends of observation and reanalysis
Monthly surface air temperature anomalies for five decades (1950s-1990s) relative to the normal period 1961-1990 from observation and NNR reanalysis
averaged over Jiangxi are shown in Figure 3.1. The correlation between observations and NNR is also presented. There was a good agreement in the interannual variability and the long-term trends between observations and the NNR
in the 1960s, 1970s, 1980s and 1990s, with correlation coefficients r=+0.80,
+0.78, +0.79 and +0.76 respectively. Relatively low correlation is exhibited in
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1950s (r=+0.53), because the observing system in China during the 1950s especially the initial several years was considerably less reliable than in later
decades, and it underwent significant scheduling changes during the 1950s
(See Section 2). The difference (TOMR) between the surface observations and
NNR is the largest, increasing to 0.05℃ in the 1970s. As revealed in Figure 3.1
and many other studies (e.g. Kalnay and Cai, 2003; Lim et al., 2008), the NNR
has strong ability to capture surface temperature variations caused by atmospheric storms, advection of warm/cold air, and variations in the frequency or
track of major storms. In contrast to the actual surface observations, no statistically significant difference is found in the NNR estimation of station trends.
The NNR trend is not affected by the change of surface properties. These arguments suggest that we could attribute the differences between monthly or
annually averaged surface-temperature trends derived from observations and
from the NNR primarily to changes in land use. Decadal trends can be locally
dominated by inter-annual and decadal variability of temperature due to anomalies in the circulation rather than to LULC change effects that are excluded by
taking the differences between observation and NNR temperatures.
Figure 3.2 a-c show the decadal trends of temperature for the
observations, the NNR and the difference between these two trends (OMR)
respectively averaged at six stations over Jiangxi. The reanalyses exhibit a
weaker warming trend than observations, as reported in Kalnay and Cai (2003)
and Lim et al. (2005). This feature is found over most of Jiangxi. Due to this,
the OMR pattern obtained by subtracting reanalyses from observations shows a
positive trend. This is at least partially attributable to changes in land surface
use. The average warming amplitude of the observations, NNR and OMR is
+0.078℃ , +0.051℃ and +0.027℃ per decade respectively. therefore about
35% of the obervation warming could be associated with urbanization and
other land use change. The average observation, NNR and OMR trends present
a overall consistent warming effect. The strong warming concentrates on
northern Jiangxi, Poyang Lake watershed, both for OMR and observations. In
comparison to NNR, the OMR trend indicates a weaker and inverse warming
effect. For assessing the surface temperature trend associated with land surface
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types, we may conclude that the OMR has the advantage of demonstrating the
effect resulted from LULC change on climate.
3.3.2
Precipitation trends of observation and reanalysis
Daily mean precipitation anomalies for five decades (1950s-1990s) relative to
the normal period 1961-1990 from observation and NNR reanalysis over
Jiangxi are shown in Figure 3.3. The correlation between observations and
NNR is also presented. There was a good agreement in the inter-annual variability and the long-term trends between observations and the NNR in the
1970s, 1980s and 1990s, with correlation coefficients r=+0.66, +0.64 and
+0.67 respectively. Poor correlation is exhibited in 1950s and 1960s (r=+0.49
and r=+0.20 respectively). The results may stem from the less reliable observing system during the 1950s as pointed out in 3.3.1. As revealed in Figure 3.3,
the NNR has good ability to capture precipitation variations during the 1970s,
1980s and 1990s. No statistically significant difference is found between the
NNR and actual surface observations. The NNR trend is not influenced by the
change of surface information. Therefore the differences between daily mean
precipitation trends derived from observations and from the NNR could be
primarily attributed to land use change except in the 1950s.
The decadal trends of precipitation for observations, the NNR and the
OMR averaged at six stations over Jiangxi are shown in Figure 3.4a , b and c
respectively. The observations exhibit a stronger wetting trend than NNR over
Jiangxi. Due to this feature, the OMR pattern obtained by subtracting
reanalyses from observations reveals an overall positive trend. This can be
partially attributable to changes in land use. The average wetting amplitude of
the observations, NNR and OMR is 0.320mm , 0.024mm and 0.296mm per
decade respectively. In comparison with the observation trend of precipitation,
NNR exihibits an inverse pattern especially in northern Jiangxi, where a drying
trend is shown. The spatial pattern of OMR precipitation is similar with that of
observation precipitation, but in a weaker wetting effect. The feature of more
wetting in the north and less wetting in the south for both OMR and
observation precipitation is revealed. Decadal trend of observation
precipitation has good agreement with that of observation temperature. To as92
3. Regional scale impacts of land use and land cover changes on climate change in Jiangxi
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sess the precipitation trend response to land surface types, the OMR trend of
precipitation has the ability to represent the effect of LULC change.
3.3.3
NDVI trend
Figure 3.5 indicates the geographical variations of the annual and seasonal
mean NDVI in Jiangxi (1982-2000). The pattern of surface vegetation with
their greenness ranges is shown. Different vegetation greenness of NDVI is a
good indicator to reflect the discrimination between different land cover types.
The range is from the least sparsely vegetation cover, 0 to the highest density
vegetation canopy, 1.0. Water bodies with negative NDVI values had nearly no
seasonal change. The very sparse vegetation areas with vegetation index
(0~0.2) show a slight seasonal change. The areas characterized by large vegetation index (0.2~0.75) show a noticeable seasonal change (Figure 3.5b-e).
Evergreen tree cover areas (NDVI (0.75~1.0)) exhibited a relatively weak seasonal change. An overall strong seasonal NDVI change is shown. The findings
are similar to Wang and Li (2008b).
3.3.4
Surface temperature and precipitation trends with respect to
LULC changes
Relationship between temperature and land vegetation types
To examine the surface temperature with respect to LULC change in Jiangxi,
we relate the temperature change estimated by observation, NNR and OMR to
surface vegetation types. The decadal observation, NNR and OMR trends at
each grid point was scatter plotted with mean NDVI of growing season. The
outliers in the scatterplots were removed based on four-fold standard deviations. Results show the decadal observation, NNR and OMR trends were all
inversely proportional to the surface vegetation index. Decadal trend in NNR
shows no significant relationship with NDVI (r=-0.02). Poor correlations between NDVI and decadal trends of temperature in OMR and observations are
indicated (r=-0.31 and r=-0.30 respectively). The surface warming response to
the surface vegetation cover was poorly represented by OMR. In contrast, Lim
et al. (2005; 2008) and Fall et al. (2010b) pointed to the good representative of
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3. Regional scale impacts of land use and land cover changes on climate change in Jiangxi
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OMR. However, the large positive correlations in the scatterplots were all in
the areas with low NDVI values (0~0.4) for OMR and observations, whereas
no such findings for NNR. Consistent results from Kalnay and Cai (2003)
pointed out that the NNR should not be sensitive to land use effects, although it
will show climate changes to the extent that they affect the observations above
the surface. However, NNR from modeling experiments reveals that a strong
surface warming correlated well with low vegetation index areas (Dai et al.,
2004; Hales et al., 2004; Xue and Shukla, 1993), which contradicted our findings.
Relationship between precipitation and land vegetation types
Decadal observation, NNR and OMR trends of precipitation were associated
with mean NDVI of growing season to discuss the relationship between precipitation and surface vegetation types in Jiangxi. The decadal observation, NNR
and OMR trends at each grid point was scatter plotted with NDVI and the outliers in the scatterplots were also removed. Results show that decadal
observation, NNR and OMR trends are not significantly correlated with NDVI
(r=0.20, r=-0.02 and r=0.25 respectively). The OMR trend has low correlation
with surface greenness. Thus, surface vegetation type response to precipitation
was poorly represented by OMR trend. The findings are consistent with Fall et
al. (2010), who did not indicate good correlation between OMR trend of precipitation and surface vegetation type in India.
The trends of temperature and precipitation associated with different land
vegetation types
The observation and OMR trends of temperature and precipitation were related
to different surface vegetation types over Jiangxi. The results of correlation coefficients are presented in Table 3.1. Both observation and OMR trends of
temperature show a significant negative correlation with the vegetation type
(NDVI (0~0.2)). The correlation coefficients are -0.61 and -0.62, respectively.
For both temperatures, the correlation decreases with the increase of vegetation
greenness. No significant correlation between other vegetation type and corresponding observation and OMR trends of temperature is exhibited. The finding
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is consistent with Lim et al. (2008), who indicated the OMR trend decreased
with surface vegetation greenness.
No significant correlation is indicated between both observation and
OMR trends of precipitation and different land vegetation types over Jiangxi
(Table 3.1). For both trends of precipitation, the correlation does not reveal
regular change with the vegetation greenness.
3.3.5
Temperature/precipitation change as a function of different land
cover types incorporating regional characteristics
Using ordinary least squares, temperature and precipitation (both for OMR and
observation) were regressed on each land cover type and corresponding regional topographic factors including elevation, latitude, longitude and slope
according to the form:
Temperature (or precipitation) = a*(land cover) +b *(elevation) +c
*(latitude) +d* (longitude) +e *(slope) +f
Where: a, b, c, d, e are corresponding regression coefficients of land cover,
elevation, latitude, longitude and slope, and f is constant.
The regression results are indicated in Table 3.2. They are statistically significant at 99% level. Both the trends of the observations and OMR of
temperature show a dependence on the vegetation greenness. The dependence
in combination with regional topographic factors on each land cover type performed better than that removing regional topographic impact. The promising
finding is that 81.1% of surface warming (OMR temperature: 0.108°C/decade)
over low vegetation cover areas (NDVI (0~0.2)) is explained by regional land
surface cover change and elevation, latitude, longitude and slope. 49.1% of surface warming is attributable to regional land cover change over these areas.
Over the areas with the highest vegetation index (0.75~1.0), land cover change
can explain 19.6% of the surface warming integrating regional elevation, latitude, longitude and slope. The explanation ability is even poorer when
removing regional topographic factors. As land cover greenness increased, the
worse dependence of decadal OMR trends of both temperature and precipitation on vegetation cover greenness is found (Table 3.2). The response of land
cover change to OMR trend of temperature is consistent with Lim et al. (2008)
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and modelling analysis by Dai et al. (2004), Hales et al. (2004) and Xue and
Shukla (1993).
Overall poor response of the changes of various land cover type to OMR
and observation precipitation, both integrating and removing regional topographic characteristics, is revealed (Table 3.2). The results were similar to Fall
et al. (2010c), who obtained a poor LULC functional response to OMR precipitation in India. As indicated in Table 3.2, 38.8% of the surface drying (OMR
precipitation: -0.063mm/decade) over the areas with vegetation index (0~0.2)
is attributable to regional land cover change and topographic factors. 28.2% of
the surface drying can be explained without the regional topographic factors into regression assessment. The explanation level of the dependence of OMR
precipitation on the change of other land cover types is worse. The worst performance is the areas with vegetation index (0.75~1.0).
Table 3.3 shows the decadal trends of temperature and precipitation in
response to land surface types and the number of 1°×1°calculated grids for
each land type. Results show that the OMR trend of temperature decreases and
the OMR trend of precipitation increases with the increase of surface greenness. OMR warming over the areas with low vegetation cover (NDVI
(0~0.20)) is the strongest, and that areas with NDVI (0.20~0.45) indicate a second strongest warming. OMR cooling is found over the areas with vegetation
index (0.45~1.0). Very density vegetation cover signified by evergreen tree
cover with less anthropogenic activities shows a larger cooling than the areas
with intermediate vegetation cover (NDVI (0.45~0.60)). The assessment reveals that surface warming is larger for the areas with low vegetation cover
(NDVI (0~0.20)), anthropogenically developed, but smaller for the areas covered with very density vegetation (NDVI (0.75~1.0)) (see also Table 3.3).
OMR drying is shown over the areas with low vegetation cover (NDVI
(0~0.20)) and water bodies (NDVI (-1.0~0.2)). The other land types indicate
OMR wetting and the areas with vegetation index (0.60~0.75) show the largest
wetting.
The areas with vegetation index (0.6-1) generally are composed of tree
cover, which leads to the suppression of surface warming and the increment of
surface moisture by the strong transpiration wetting and evaporation cooling
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from leaves with 0.009°C/decade and 0.126°C/decade surface cooling and
0.145mm/decade and 0.005mm/decade surface wetting respectively. This is in
agreement with Giambelluca et al. (1997), Lim et al. (2008), Shukla et al.
(1990) and Xue and Shukla (1993).The areas with moderate vegetation index
(0.45~0.6) characterized by herbaceous and shrub cover show 0.008°C/decade
surface cooling and 0.114mm/decade surface wetting (Table 3.3). The moderate contribution is because the cooling and wetting feedbacks from leaves are
weaker than those in trees. Owe to little evaporation negative feedback in low
vegetation cover areas (NDVI (0~0.2)), a strong surface warming and drying
effect separating from the observations could be explained by regional LULC
change anthropogenically driven under the same amount of radiative forcings.
The findings are in agreement with Diffenbaugh (2005), Lim et al. (2005,
2008) and Fall et al. (2010b).
3.4 Discussions
We aim to investigate the impact of regional LULC change on the variations of
temperature and precipitation. OMR method (observation minus reanalysis) is
used to estimate the impact of changes in land use by computing the difference
between the trends of the adjusted surface observations (which reflect all the
sources of climate forcing, including surface effects) and the NCEP/NCAR reanalysis (which only contains the forcing influencing the assimilated
atmospheric trends). We analyzed the observation and reanalysis trends of decadal temperature and precipitation over Jiangxi and examined the sensitivity
of surface temperature/precipitation to LULC by using OMR trends as a function of land surface types and regional topographic factors. Key findings are
presented as follows:
(1) OMR approach is effective to examine the trends of temperature and
precipitation driven by the impact of regional land-cover types because land
surface observations are not used in the data assimilation into a physically consistent atmospheric model, reanalysis data is thus insensitive to local surface
properties. The characteristic of the reanalysis provides us with the possibility
of detecting surface climate change associated with regional land cover types
by taking the difference between observed and reanalysis climate time series.
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(2) Our results show a good temporal and spatial consistency between the
observed and reanalysed temperature/precipitation trends and exhibit the ability
of the reanalysis to capture regional inter-annual variability. Similar findings
are obtained from Fall et al. (2010b), Frauenfeld et al. (2005), Kalnay et al.
(2006), Lim et al. (2008) and Zhou et al. (2004a).
(3) Our analysis of OMR trends associated with land types using AVHRRNDVI dataset in Jiangxi show that land surface type with vegetation index (00.2) exhibits the largest surface warming and the vegetation index (0.75-1.0)
area indicates the strongest cooling trend. We found varied positive OMR
trends, 0.108 °C per decade in contrast to the findings of Fall et al. (2010b)
(0.034°C) on regional scale and Lim et al. (2005) 0.3 °C on global scale for
bare soils. We find the strong surface warming response to land barrenness and
weak warming response to land greenness. The results are consistent with these
of Dai et al. (2004), Diffenbaugh (2005), Hales et al. (2004) and Lim et al.
(2008).
(4) OMR trend of precipitation is also assessed as a function of land surface
types and regional topographic factors. It shows that the OMR trend is insensitive to different surface greenness. This may be due to the length of the dry
season and the seasonality of precipitation. Fall et al. (2010c) revealed the similar finding of precipitation trends as a poor function of LULC change over
India. However, NDVI has been shown to be a sensitive indicator of the interannual variability of precipitation (Prasad et al., 2005). A positive correlation
between the annual mean NDVI and precipitation is indicated in arid regions of
Central Asia (Ichii et al., 2002). Moreover, the sensitivity of NDVI to precipitation variability in drier regions is found to be higher than in other wet forest
types (Wang et al., 2003a). In agreement with our results, NDVI has relatively
better response to the variation of precipitation over more bare areas than density vegetation cover. Prasad et al. (2005), Schultz and Halpert (1993) and
Gómez-Mendoza et al. (2008) pointed to the significance of lag in the correlation of precipitation and vegetation types. The lag response of LULC change to
precipitation requires to be conducted. Despite our results indicate limited relationship, but provide guidance for an attribution of regional precipitation.
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Our novel findings indicate 81.1% of the surface warming over the areas
with vegetation index (0~0.2) attributes to regional LULC change and topographic factors, whereas 49.1% explanation level is shown without regional
topographic factors into regression assessment. The contribution capability of
LULC change decreases as land cover greenness increases. Regional climate
change is a complex process involving the interactions and feedbacks with
LULC change. It is well known that regional climate change is influenced
heavily by regional topographic features. It is worth mentioning that the integral of the entire topography has negligible effect on LULC change, thus
indirectly affects regional climate. The topography of hilly landscapes modifies
land environment by changing the fluxes of water and energy, increasing the
vulnerability of land systems, which could become more accentuated under
climate change (drought, increased variability of precipitation). Model simulations show cropland change is significantly related to a slope×elevation index
in the southern Mediterranean climate even under the high emission scenario
(Ferrara et al., 2010). Daly et al. (2009) found a complex temperature landscape composed of steep gradients in temporal variation is controlled largely
by gradients in elevation and topographic position. The combination of regional topographic features in the regression estimation allows a more accurate
determination of which components forming processes have adapted to ongoing climate change, and to what extent.
It should be noted that the methods used to derive NDVI need further
refinement and calibrations. On the other hand, the estimate of land-use
influence on temperature and precipitation is limited by station density (six
stations over Jiangxi are available). Kalnay and Cai (2003) have found that the
correlation between the observation and reanalysis temperature is lower to low
density of the meteorological stations, thus decreasing the ability of OMR to
represent the LULC change. Notwithstanding its limitation, this study does
suggest that LULC change integrating regional topographic factors contributes
to regional surface temperature change. To what extent this effect has is
strongly land-type dependent. Further, we further confirm the ability of the
OMR method for quantitative estimation of the surface climate variability with
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3. Regional scale impacts of land use and land cover changes on climate change in Jiangxi
Province, China
respect to LULC change on regional scale by removing the effect of
greenhouse gases.
This study is likely to be an important consideration in understanding the
impacts of LULC change on climate change on regional scale especially in
hilly region. Our results also have important implications in the monitoring and
modeling processes of regional climate. LULC change should be considered
along with greenhouse gas as a forcing factor in regional climate modeling.
Further, the application of OMR method on regional scale needs to consider
greater details. Also the combination and correlation of different sensors and
satellites will provide better tools to understand the relationship between
LULC change and climate change. People and ecosystems experience the
effects of environmental change regionally and not as globally averaged.
Besides the greenhouse gases and aerosol-driven radiative forcings, the impact
of local and regional LULC on climate merits more attention in future
regional-scale study of global change.
3.5 Conclusions
OMR trends associated with different land surface types show that strong surface warming response to land barrenness and weak warming response to land
greenness. 81.1% of the surface warming over vegetation index areas (0~0.2)
attributes to regional LULC change and topographic factors. The contribution
capability of LULC change decreases as land cover greenness increases. OMR
trends of precipitation have a weak dependence on regional land surface types
and topographic factors.
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Tables
Table 3.1 Correlation coefficients (r) between temperature/precipitation and corresponding land cover types
Land cover types
Temperature-observation
Temperature-OMR
Precipitation-observation
Precipitation-OMR
NDVI (-1.0~0)
-0.47
-0.29
0.41
0.37
NDVI (0~0.2)
NDVI (0.2~0.45)
NDVI (0.45~0.6)
-0.61
0.35
-0.34
-0.62
0.41
-0.43
0.34
0.40
-0.39
0.32
0.25
-0.24
NDVI (0.6~0.75)
-0.36
0.38
0.32
0.21
NDVI (0.75~1.0)
-0.26
-0.31
-0.30
-0.23
Temperature-OMR: Temperature-Observation Minus Reanalysis (OMR);
Precipitation-OMR: Precipitation-Observation Minus Reanalysis (OMR).
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Table 3.2 Summary for regression models of different land cover types
Vegetation cover types
NDVI (1)
NDVI (2)
NDVI (3)
NDVI (4)
NDVI (5)
NDVI (6)
Model
AR2
Sig.
AR2
Sig.
AR2
Sig.
AR2
Sig.
AR2
Sig.
AR2
Sig.
T-OMR1
0.832
0.000
0.811
0.000
0.292
0.000
0.310
0.000
0.273
0.000
0.196
0.000
T-OMR2
0.344
0.000
0.491
0.000
0.274
0.000
0.291
0.000
0.247
0.000
0.135
0.000
T-OBR1
0.469
0.000
0.666
0.000
0.435
0.000
0.474
0.000
0.434
0.000
0.333
0.000
T-OBR2
0.171
0.000
0.484
0.000
0.306
0.000
0.321
0.000
0.292
0.000
0.207
0.000
P-OMR1
0.321
0.001
0.388
0.000
0.321
0.000
0.194
0.000
0.179
0.000
0.132
0.000
P-OMR2
0.135
0.000
0.282
0.000
0.293
0.000
0.164
0.000
0.149
0.000
0.122
0.000
P-OBR1
0.433
0.000
0.233
0.002
0.256
0.000
0.161
0.001
0.142
0.001
0.158
0.000
P-OBR2
0.156
0.009
0.204
0.002
0.116
0.000
0.111
0.007
0.092
0.001
0.058
0.000
Predictors of T-OMR1: (Constant), NDVI, elevation, latitude, longitude and slope. Dependent
variable of T-OMR1: Observation Minus Reanalysis (OMR) of temperature.
Predictors of T-OMR2: (Constant), NDVI. Dependent variable of T-OMR2: Observation Minus
Reanalysis (OMR) of temperature.
Predictors of T-OBR1: (Constant), NDVI, elevation, latitude, longitude and slope. Dependent
variable of T-OBR1: observation temperature.
Predictors of T-OBR2: (Constant), NDVI. Dependent variable of T-OBR2: observation
temperature.
Predictors of P-OMR1: (Constant), NDVI, elevation, latitude, longitude and slope. Dependent
variable of P-OMR1: Observation Minus Reanalysis (OMR) of precipitation.
Predictors of P-OMR2: (Constant), NDVI. Dependent variable of P-OMR2: Observation Minus
Reanalysis (OMR) of precipitation.
Predictors of P-OBR1: (Constant), NDVI, elevation, latitude, longitude and slope. Dependent
variable of P-OBR1: observation precipitation.
Predictors of P-OBR2: (Constant), NDVI. Dependent variable of P-OBR2: observation
precipitation.
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Table 3.3 Decadal trends of temperature (°C/decade) and precipitation (mm/decade)
response to surface vegetation types and the number of 1°×1°grids for the calculation
of correlation between vegetation types and temperature/precipitation trend
Number of
Temperature-
Temperature-OMR
Precipitation-observation
Precipitation-OMR
grids
observation (℃)
(℃)
(mm)
(mm)
NDVI (-1.0~0)
56
0.855
0.153
0.544
-0.241
NDVI (0~0.20)
295
0.818
0.108
0.670
-0.063
NDVI (0.20~0.45)
440
0.725
0.029
0.715
0.096
NDVI (0.45~0.60)
762
0.708
-0.008
0.706
0.114
NDVI (0.60~0.75)
942
0.708
-0.009
0.716
0.145
NDVI (0.75~1.0)
395
0.595
-0.126
0.673
0.005
Vegetation cover types
Temperature-OMR: Temperature-Observation Minus Reanalysis (OMR);
Precipitation-OMR: Precipitation-Observation Minus Reanalysis (OMR).
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3. Regional scale impacts of land use and land cover changes on climate change in Jiangxi
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Figures
Figure 3.1 Monthly mean surface air temperature anomalies (observation [dashed red]
and NCEP-NCAR reanalysis (NNR) [solid blue]) (in ℃) over Jiangxi relative to 19611990. Five decades (1950s-1990s) are compared. TOMR is observation minus reanalysis temperature. r denotes correlation coefficient between NNR and surface
observations.
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3. Regional scale impacts of land use and land cover changes on climate change in Jiangxi
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Figure 3.2 Decadal trends of monthly mean temperature. The Observation Minus Reanalysis (OMR) trend per decade (in ℃) at each grid point was obtained by the average
of the‘90s minus 80s’and ‘80s minus 70s’ temperatures. The mean value of the
decadal trend is denoted in each panel on the left. a observation; b NNR and c OMR.
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3. Regional scale impacts of land use and land cover changes on climate change in Jiangxi
Province, China
Figure 3.3 Daily mean precipitation anomalies (observation [dashed red] and NCEPNCAR reanalysis (NNR) [solid blue]) (in mm) over Jiangxi relative to 1961-1990.
Five decades (1950s-1990s) are compared. TOMR is observation minus reanalysis
precipitation. r denotes correlation coefficient between NNR and surface observations.
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Province, China
Figure 3.4 Decadal trend of daily precipitation averaged at stations over Jiangxi. The
Observation Minus Reanalysis (OMR) trend per decade (in mm) at each grid point
was obtained by the average of the „90s minus 80s‟ and „80s minus 70s‟. The mean
value of the decadal trend is denoted in each panel on the left. a observation; b NNR
and c OMR.
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3. Regional scale impacts of land use and land cover changes on climate change in Jiangxi
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Figure 3.5 Vegetation index map derived from NDVI. a Annual mean; b Winter (DJF);
c Spring (MAM); d Summer (JJA) and e Autumn (SON).
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4
Driving forces of regional cropland and built-up land
transition in Jiangxi Province, China
4.1 Introduction
Human societies constantly coevolve with their environment through change,
instability and mutual adaptation (Lambin and Meyfroidt, 2010). The impact of
human being on environment has gained in importance as rapid population
growth and economic development intensify the stresses human beings place
on the biosphere and ecosystems (Omenn, 2006). As a result, land use change
is non-linear and associated with other societal and biophysical changes
through a series of transitions. Deforestation and irrigation were the largest
sources of human-released greenhouse gasses to the atmosphere until the advent of industrial era fossil-fuel burning, and as much as 35% of the humaninduced CO2 equivalents in the atmosphere today can be traced to the totality
of land use and land cover changes (Foley et al., 2005; Williams, 2005). These
land-based changes currently support over 6 billion people with food, fiber,
water, and other benefits, and they support the highest global average per capita consumption ever known. This unprecedented level of land production,
however, has been matched by unparalleled impacts on the earth system.
Change in land use and its implication for global environmental change and
sustainability are therefore a huge research challenge. Furthermore, no facet of
land use change research has been more contested than that of cause (Turner II
et al., 2007b). Land use change is driven by multiple, interacting factors that
originate from the local to the global scales and they form a complex system of
dependencies, interactions, and feedback loops. Land-use changes are now increasingly analysed as part of the system interactions triggering coevolution of
natural and social systems. The coupled human-environment systems should
therefore be considered as a whole when we assess sustainability and vulnerability. Analyses of the causes of land use change have thus moved from
simplistic single cause explanations to an integration of multiple causes and
their complex interactions (Lambin et al., 2003b). For a deep and robust under109
4. Driving forces of regional cropland and built-up land transition in Jiangxi Province,
China
standing of the forces and processes at any scale, appropriate methods, procedures, and instructions are required (Dammers and Keiner, 2006); Moreover,
the proper choice of indicators is essential to investigate the progress of land
use change (Bossel, 1999; Prescott-Allen, 1997).
LULC changes have a myriad of forms and their driving forces are numerous and complex. Several typologies of drivers were considered for the
Millennium Ecosystem Assessment conceptual framework-primary versus
proximate, anthropogenic versus biophysical, dependent versus independent,
primary versus secondary. The proximate and primary driver terminology, for
example, is widely used in the land use change and climate change literature
(e.g. Folland et al., 2001; Turner II et al., 1995). Proximate and primary drivers
are conceptually similar to direct and indirect drivers respectively, but tend to
be used when analysing specific spatial processes in which the human intent
(primary) is associated with actual physical actions (proximate) (Millennium
Ecosystem Assessment, 2005). Driving forces are categorized as demographic,
economic, socio-political, cultural and religious, scientific and technological,
and biophysical. According to Lambin and Geist (2007), economy, technology,
policies, institutions and culture work on a general level - as “underlying” factors. On the other hand, each locality has its own specific characteristics that
work as “proximate” factors - they determine what impacts the general underlying factors will have on land use in each respective locality. Proximate causes
are human activities or immediate actions at the local level, such as agricultural
expansion, that originate from intended land use and directly affect land cover.
Underlying driving forces are fundamental social processes, such as human
population dynamics or agricultural policies that underpin the proximate factors and either operate at the local level or have an indirect impact from the
national or global level (Geist and Lambin, 2002). Globally and historically,
land use dynamics appear to track well with the population, affluence, technology and policy variables of the environmental impact because they capture the
demand for land and resources and the means by which they are supplied
(Lambin et al., 2001a; Turner II et al., 2007a; Waggoner and Ausubel, 2002).
Biophysical forces define the natural capacity or predisposing environmental conditions for land use change, with the set of abiotic and biotic factors
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- climate, soils, lithology, topography, relief, hydrology, and vegetation - varying among localities and regions and across time (Lambin et al., 2001b). Sociopolitical drivers encompass the forces that influence decision making in the
large conceptual space between economics and culture. The boundaries among
economic, socio-political, and cultural categories of drivers are fluid, and they
change with time, level of analysis, and observer (Young, 2002). Economic
drivers are determined by ecosystem services (natural capital), the number and
skills of humans (labour and human capital), the stock of built resources (manufactured capital), and the nature of human institutions, both formal and
informal (social capital) (Millennium Ecosystem Assessment, 2005). These
drivers can work over time, e.g., population and income growth interacting
with technological advances that lead to climate change, or over levels of organization. Reviews of case studies of deforestation and desertification reveal
that synergetic factor combinations are the most common (Geist and Lambin,
2002, 2004), i.e., the combined effects of multiple drivers that are amplified by
reciprocal action and feedbacks (Nelson et al., 2006). Any specific LULC
change is the result of a number of interactions among drivers. Case studies indicate that not all driving forces of land use change and all levels of
organization are equally important. For any given human-land system, a limited number of causes are essential to predict the general trend in LULC
change (Stafford and Reynolds, 2002)
Despite the rich diversity of causes and situations triggering land use
change, there exist some generalizable patterns of change that result from recurrent interactions between driving forces, following specific sequences of
events. Even though, at the detailed level, these sequences may work differently in specific situations, their identification may confer some predictive power
by analogy with similar pathways in comparable regional and historical contexts (Lambin et al., 2003b)
In view of the complexity of land-use change with respect to its process,
dynamic, and driving forces (Burgi and Turner, 2002; Lambin et al., 2003b;
Theobald, 2001), Verburg and Veldkamp (2001) have stated that any method
alone is not sufficient to analyse the complex interactions between LULC processes and socio-economic and political transformations. Instead, a
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combination of multiple approaches is necessary for land-use change research
(Cai, 2001; Lambin et al., 2000; Vela´zquez et al., 2003). Policy makers are
now encouraging scientists to improve models, and develop new techniques for
integration of quantitative and qualitative analysis for local and regional LULC
(Grosskurth, 2007). This analysis is undertaken by various communities, including remote sensing, political ecology, resource economics, institution
governance, landscape ecology, and integrated assessment, among others. Its
most comprehensive form joins the human, environmental, and geographical
information-remote sensing sciences in an interdisciplinary effort increasingly
referred to as LULC change research (Turner II et al., 2007b). Remote sensing
(RS) and geographic information systems (GIS) have been commonly recognized as powerful and effective techniques for detecting the spatiotemporal
dynamics of LULC (Fazal, 2000; Gao et al., 2006; Herold, 2006; Long et al.,
2009; Nagendra et al., 2004; Weng, 2002). Various approaches of change detection and statistical analysis, which have increased our ability to monitor and
explore the structural modification among different land use types and to find
the causes of land use change based on time-series biophysical and socioeconomic indicators (Aspinall, 2004; Kaufmann and Seto, 2001; Krausmann et
al., 2003; Liu et al., 2003; Long et al., 2009; Xie et al., 2005). These integrative
analyses of LULC change can provide decision-makers with important information for sustainable land management and regional development.
The current integrity of the planet is being stressed beyond its biological
capacity, and understanding human created landscapes is more important now
than ever. Changes in land cover, through the appropriation of natural landscapes
for human use, has been found to be one of the most pervasive forms to alter
ecosystems resulting from human activities (Foley et al., 2005; Liu et al., 2007).
In extent, the most important alteration to land surface is a transition of crop and
pastoral land in natural ecosystems (Lambin and Meyfroidt, 2011). Land change
for construction and for the demands of populations itself drives other types of
environmental change. It is also at the regional scale that LULC changes driven
by and resulting from human activities are most apparent (Nancy et al., 2008).
Regional imbalanced development in economy and policy has led to the migrations and degraded landscapes since the second half of the 20th century
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4. Driving forces of regional cropland and built-up land transition in Jiangxi Province,
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(Meyerson et al., 2007). China has been experiencing an unprecedented and accelerated transition of cropland and built-up land since the 1980s (Deng et al.,
2008). Currently, both rural and urban development in China are in a transition
period from a traditional agricultural society to a modern industrial and urban
society (Long et al., 2007c). Meanwhile, a series of development strategies, for
instance “Western Development”, “Revitalization of Northeast”, and “Rising of
Central China” have been implemented across the nation (Liu et al., 2010). More
attention has been devoted to cropland transformation and built-up sprawl in
China. Causes and processes of these changes have been discussed by numerous
studies at different scales. (e.g. Lichtenberg and Ding, 2008; Liu et al., 2005;
Long et al., 2009; Long et al., 2007b; Tan et al., 2005b; Verburg and Veldkamp,
2001; Xu, 2004). The built-up land in Huang-Huai-Hai Plain (traditional agricultural zone) and Yangtze River Delta zone has expanded significantly, accounting
for 50% of the total increased built-up land in China, which partly attributes to
the dense population and facilitated infrastructure. Sichuan Basin has the highest
increasing rate of built-up area deriving from rapid agricultural development.
Except for the process of urbanization, rural enterprises have also acted as one
of the driving forces of the built-up land expansion. The fragmentation of
cropland can also be observed due to the construction on rural land (Lin and Ho,
2003; Sargeson, 2002). Moreover, the adoption of market principles has resulted
in the internal restructuring of agricultural land use from traditional paddy production to more diversified agricultural activities such as growing cash crops,
fruits and aquaculture (Heilig, 1999; Li and Yeh, 2004). The most conspicuous
urban expansion in China in the 1990s is in Zhu-jiang Delta and Fujian coastal
areas. However, transition from arable land to built-up land from 1995 to 2000 is
impeded due to the implemented legislations of land management (Liu et al.,
2003).
Jiangxi province is an important region for the production of commodity
grain and conservation of the wetland and ecology in China. In the rapid economic development and urbanization, the intensity of land use and the transition
frequency among land use types are progressively increasing. There exist predominant conflicts between cultivated land, built-up area, forest, pastoral area
and water body (Jiangxi Province Statistical Bureau, 1996; Yu and Lu, 2004).
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Jiangxi has become a typical region of cultivated land transition (Zhan et al.,
2010a). Driving forces of land use change in Jiangxi has been examined by several authors with a focus on either agriculture, forests or urban (Deng et al., 2006;
Wang et al., 2003b; Yu and Lu, 2004; Zhan et al., 2010a; Zhan et al., 2010b;
Zhan et al., 2011). However, the ambiguities still exist due to a lack of consensus
on the driving forces. Moreover, much less attention have been devoted to landuse change at its policy dimension in China (Long et al., 2009). Or policy analysis of political influences on land use change is often implied or embedded in the
selection of socio-economic factors or statistical analysis interpretations without
quantitative assessment (Long et al., 2007a; Long et al., 2009; Xie and Fan,
2003; Xie et al., 2005; Zhan et al., 2010a). Appropriate policies can lead to national-scale land use transitions, spare land for forests, deal with the impacts of
globalization and, therefore, prevent a transition of all available land (Lambin
and Meyfroidt, 2011; Robinson, 2004). Further, to date, there exist no consistent
criteria for assessing the driving forces of land use change neither on the global
nor the regional level.
The objective of this study is to investigate how the biophysical, socioeconomic and policy driving forces affects land use transition, mainly focusing
on cropland and built-up land in Jiangxi Province China 1995-2005. We examine the dynamic patterns of land use and identify the key drivers of these
changes based on an integrated approach with quantitative policy effect involving remote sensing, geographic information systems (GIS) and statistical
techniques. We have two novel facets: (1) Engel‟s coefficient is considered as
one of the socio-economic indicators affecting Jiangxi‟ land use change. (2)
The integrated method in combination with policy effect is introduced in the
analysis of driving forces of land use change in Jiangxi. An accurate analysis of
the driving forces of land use change can provide decision-makers with important information for sustainable land management and regional
development. The integrated method in combination with policy analysis for
the study of cropland and built-up land transition is introduced in Section 4.2.2
focusing on how to quantify the policy drivers. The cropland returning policy,
representative indicators of biophysical and socio-economic driving forces (including Engel‟s coefficient) of LULC processing are explained in Section
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4. Driving forces of regional cropland and built-up land transition in Jiangxi Province,
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4.2.3. The detection of cropland and built-up land dynamics stemmed from
high-resolution remote sensing data is described in Section 4.2.4. Section 4.3
reports the results and analyses the interacting mechanisms of policy, biophysical and socio-economic forces on cropland and built-up land use transition. The
significances, limitations and future improvements of the method and direction
for investigating the forces driving LULC changes are discussed in section 4.4.
Section 4.5 concludes the study with main findings.
4.2 Methodologies
4.2.1
Overview of interdisciplinary methodology on LULC change research
Many useful techniques have been developed and applied to quantify land use
and land cover changes (LULC) and to investigate biophysical, social, political
and economic drivers forcing the changes. We used an integrated method developed by Xie et al.(2005) to analyse biophysical and socio-economic impacts
on cropland and built-up land and to understand the processes how these biophysical and socio-economic factors shape cropland and built-up patterns
under varied policies. This integrated approach involved remote sensing, GIS
and regression analysis. Remote sensing is commonly used to obtain reliable
measurements of LULC at different spatial and temporal scales (US Climate
Change Science Program, 2003). Reliable measurements of LULC can be related to a variety of biophysical and socio-economic statistical analyses. These
observations of spatial trends and rates of LULC can also develop empirical diagnostic models and short-term prognostic models (Lambin, 1994). GIS is
often applied to visualize spatial patterns of LULC and its driving factors as
well as the relationships among them (Fischer et al., 1998; Vela´zquez et al.,
2003; Xie et al., 2005). Regression analysis is used for quantitative assessment
of the relationships between the cropland and built-up land transition and the
corresponding natural, socio-economic and policy factors.
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4.2.2
The integrated method to examine cropland and built-up land
transition
There are two general rules that guide how to quantify policy impacts applying
regression analysis. Firstly, better data alone are insufficient for improved models and projections of LULC without enhanced understanding of causes of
changes (Committee on Global Change Research, 1999; Lambin et al., 2001b).
Secondly, policy analysis often relies on an assumption of continuity (Berkhout
and Hertin, 2000). Future trend is often predicted by analysing the present from
the past. What has occurred at present is deduced from the past current policy
impacts. However, this assumption does not meet the frequent political orientation and policy adjustments in Jiangxi since 1949, such as, Land reform Policy
in the early 1950s, Land reclamation from the lake policy in the late 1970s, and
Grain for lake, pasture and forest policy in the mid and late 1990s (Ministry of
Land and Resources of PRC, 2011). These considerations introduce two complementary methods of applying regression in policy studies: (1) the proxy
approach, and (2) the comparison approach.
The proxy approach in policy analysis is to use measurable variables that
have direct responses to a policy change as policy proxy variables or indicators. For instance, the values of budget surplus, the level of inflation, and the
measure of trade openness are used as the proxies to the fiscal policy, monetary
policy, and trade policy, respectively when examining the relationship between
national economic policy, economic growth and foreign aid (Burnside and Dollar, 2000; Easterly et al., 2003; Xie et al., 2005). It is important to detect
quantitative variables that have causal relationship with the policies in study.
However, the proxy or causal relationship is often contaminated by measurement errors due to many reasons (Brumm, 2003). Techniques have been developed to reduce regression measurement errors, such as the adoption of instrumental variables, a popular technique for identifying more exogenous variables
to count for unmeasured errors (Angrist and Imbens, 1995; Stock and Watson,
2003). Serious limitations with these methods still exist (Xie et al., 2005).
The comparison approach of examining policy impacts is to apply regression analysis to longitudinal datasets during different time periods that
correspond to significant policy changes (Diggle et al., 1994). A categorical
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4. Driving forces of regional cropland and built-up land transition in Jiangxi Province,
China
policy variable is constructed to record different status of a policy (Jaeger and
Page, 1996). This categorical policy variable is often recoded as dummy variables to be included in a regression model, and each dummy variable indicates
the existence or non-existence of a specific policy impact (Miles and Shevlin,
2001; Xie et al., 2005). Two categorical dummy variables indicating the absence and presence of a land policy in Jiangxi are constructed in this study.
The workflow of integrated policy analysis to examine cropland and built-up
land transition is shown in Figure 4.1. The analysis starts with a policy review
to introduce political information and to detect significant policy changes in
Jiangxi regarding cropland transition. Accurate account of cropland and builtup land transition is obtained by the interpretation of land use category derived
from remote sensing images. Then we conduct data mining to identify causal
relationship between cropland and built-up land transition and possible biophysical and socio-economic driving factors (1995-2005) under identified
policy changes. Stepwise multiple regression analysis is applied and relative
importance of each independent variable is automatically assigned. The results
of the multiple regression analyses indicate the socio-economic factors that
drive cropland and built-up land transition under varied policy impacts. Afterwards, a dummy variable, POLICY, is introduced to represent the absence (in
some counties) and presence (in the other counties) of the cropland transition
legislation and is included in the regression analyses of cropland-dependent
and built-up land-dependent with the identified variables through the data mining (regression analyses) with distinct policies. The output of the two new
regression models indicating the correlation between the cropland and built-up
land transition and the changes of POLICY and socio-economic factors is further examined. The analyses of spatial correlations between cropland, built-up
land transition and their corresponding POLICY and socio-economic impacting
factors are visually presented with GIS mapping techniques.
4.2.3
The cropland returning policy, biophysical and socio-economic data
Since the visit of Mr. Deng Xiaoping to the southern parts of China in 1992,
economic booming in China has resulted in rapid development of agribusiness
enterprise, characterized by a great increase in built-up area (Fischer et al.,
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4. Driving forces of regional cropland and built-up land transition in Jiangxi Province,
China
1998; Long et al., 2009). As the population increases, a large number of areas
of forest, lake and pasture are occupied by cultivated land in Jiangxi, a province with short arable land. However, unprecedented flood disaster occurred in
1998 brought huge economic and environmental loss. For keeping away from
flood threatens, the central government issued administrative decree: “Grain
for Lake” “and Grain for pasture” in December 1998 as well as “Grain for
Green” in January 2003. The decree orders local governments to reinvestigate
all the cultivated areas and to recover the areas of forest, lake and pasture that
were formerly occupied by agricultural uses (Department of land and resources
of Jiangxi Province, 2011). Projects of land use transformations started in 2000
and ended before 2005 (Jiang, 2009). Afterwards, no intimately related land
use policy was proposed. The year of 1995 and 2005 can thus be deemed as an
important turning point regarding Jiangxi‟s policies of economic development
and cultivated land management. This date is used in our study to examine the
impacts of land management policy changes on land use and land cover.
The biophysical and socio-economic data are from the published Jiangxi
statistical yearbooks (Jiangxi Province Statistical Bureau, 1996, 2006). The selected indicators of biophysical and socio-economic data utilized the literatures
whose research field focuses on China (Deng et al., 2006; Liu et al., 2010;
Long et al., 2009; Xie et al., 2005; Zhan et al., 2010b). Most importantly, Engel‟s coefficient was, for the first time, related to the driving forces of land use
change in Jiangxi. The selected indicators are listed in Table 4.1. Data were
compiled for county level (88 in total). The yearly change rates were computed
from the observed values of the selected variables between 1995 and 2005,
based on the equation (Xie et al., 2005),
1
 V2  n
Ri =    1 ,
 V1 
(1)
where Ri is the change rate of the observed value of a socio-economic indicator; V1 the value of an indicator i at the date t1; V2 the value of the indicator i at
the date t2; n the difference of years between the two dates (9 years between
1995 and 2005 in this case study).
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4. Driving forces of regional cropland and built-up land transition in Jiangxi Province,
China
4.2.4
Detecting the cropland and built-up land dynamics from remote
sensing images
There are two approaches to extract land-use change information from remote
sensing data: (i) by comparison between two temporal classified maps to detect
the dynamics, and (ii) by the information extracted directly by temporal variation characteristics of surface radiation. The former requires much higher
classification standards and accuracy as well as more labour efforts, whereas
the latter has the rigorous need for the selections of remote sensing data
sources and accurate data handling (Liu et al., 2003). In this paper, two-phase
remotely sensed images in 1995 and 2005 were interpreted and compared to
extract land use change information in computer-aided interactive way.
LULC data were obtained from Landsat TM (Thematic Mapper) satellite
images (12 scenes in 1995/1996 and 12 scenes in 2005/2006) with a spatial
resolution of 30m×30m. Pre-process of the images was done though georeferencing, mosaicking, calibration and clipping. The land cover classification
maps were created by interpreting Landsat TM images in 1995 and 2005 and
comparison was made between the two. Maps of Jiangxi‟s vegetation distribution, Jiangxi administrative district (2005) and land resource investigation data
were collected as the ancillary data. In agreement with other studies in China,
land cover classification system of Chinese resource and environment database
was applied. We classified Landsat TM images as six land use categories: water body, forest, cropland, grassland, barren land and built-up land. The overall
classification accuracy was 88.5%, among which the accuracy of cropland can
reach 90.7% and 95.4% for built-up land. The wrong identifications were all
corrected. The transition rate for each land use type was calculated according
the following formula (FAO, 1996a):

Rc  1 

1
S

S

 1 2  n 1,

S1 
119
(2)
4. Driving forces of regional cropland and built-up land transition in Jiangxi Province,
China
where Rc is the transition rate, S1 the area of land use category c at the date t1;
S2 the area of land use category c at the date t2; and n the difference of years
between the two dates (9 years between 1995 and 2005 in this case study).
4.3 Results
4.3.1
Land use change and spatial pattern 1995-2005
Change of each land use and land cover category for the period 1995-2005 in
Jiangxi is presented in Table 4.2 and Figure 4.2, Figure 4.3 and Figure 4.4
shows spatial dynamic of land use and land cover. Water body, forest and
cropland had negative transition rate (-1.3%, -1.2% and -0.3%, respectively)
from 1995 to 2005. In contrast, grassland, barren and built-up land increased at
the transition rate of 6.9%, 17.6% and 2.7%, respectively (Table 4.2). There are
obvious variations among different land use types between 1995 and 2005.The
most conspicuous transition occurred in barren land and water body. The area
of barren land in 2005 was 311025 hectares more than that in 1995, whereas
water body lost 69326 hectares in terms of absolute volume of land area over
the same period. Cropland and barren land are the largest beneficiaries of water
body loss, which is especially significant in Poyang Lake watershed (Figure
4.4). During the same time, the cropland lost 168,340 hectares, 15.0% of the
total losses of natural land covers. Large area of cropland loss was in the upper
reach of Gan River, South Jiangxi. And built-up area expanded by 117 034 hectares occupying 10.3% of the total increase of land area. Most of built-up area
expansion was converted from cropland and forest. Our results indicated builtup land expansion accounted for 13.6% cropland decrease (Table 4.2). Fragmentation is the obvious characteristics of cropland and built-up area change.
Low patch size and degree of aggregation is also indicated (Figure 4.4). The
accelerated process of industrialization and urbanization may enhance the degree of fragmentation and structural complexity of LULC.
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4. Driving forces of regional cropland and built-up land transition in Jiangxi Province,
China
4.3.2
Regression analysis of the driving forces and interactions of
cropland 1995-2005
The regression analysis revealed the relationships between the cropland transition and the biophysical and socio-economic factors with POLICY explanatory
variable in Jiangxi during the period of 1995-2005 (Table 4.4 and Table 4.5).
Table 4.3 exhibits annual change rates of biophysical and socio-economic indicators on the average of 88 counties in Jiangxi. The spatial patterns of cropland
transition and its driving forces are shown in Figure 4.5. POLICY is a dummy
variable that has a value 0 for the counties without the implement of cropland
returning policy (indicating the absence of the cropland legislation) and a value
1 for the counties in the presence of cropland returning policy. County scale
(88 in total) study indicated two contrasting effects of implemented policies on
cropland transition. The designed policies affected the labour intensity, migration and cropland area. The regression analysis, with the explanatory variable
POLICY, reveals how policy change associates with the cropland loss and how
other socio-economic factors affect cropland transition when experiencing policy change. TIRI (Total income of rural inhabitants), GDPP (Gross product
value-added of primary industry), R-POP (Rural population) and policy are
suggested the major driving forces of cropland change (Table 4.4 and Table
4.5). These socio-economic and political parameters together explain 84.0% of
cropland transition. The regression analysis confirmed that socio-economic
growth and political intervention over the ten years period of 1995-2005 had a
direct impact on cropland change. 76.8% of cropland transition can be accounted by the changes of TIRI, GDPP, and R-POP. The policy intervention
(the explanatory variable, POLICY) is the largest contributor and itself had
significant statistical correlation with the cropland decrease. This was indicated
by the improvement of contribution level from 76.8% to 84.0% in comparison
with other impacting factors when POLICY variable was introduced. Further,
the Beta value of policy impact shows reversed direction from the cropland
transition. GDPP and TIRI affected the cropland transition at moderate level
and the influence of R-POP was relatively small. TIRI and GDPP showed a
yearly increase of 9.24% and 20.51% respectively. R-POP kept a slight slower
rise at the rate of 1.24% per year (Table 4.3). The change rate of these driving
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4. Driving forces of regional cropland and built-up land transition in Jiangxi Province,
China
factors seems conflict with the cropland loss, because they were spatially uneven distributed for varied counties. The GIS thematic maps reveal matched
spatial patterns between cropland transition and the change of TIRI, GDPP, and
R-POP (Figure 4.5).
It is obvious that the policy intervention over the period 1995-2005 has
successfully controlled cropland transition due to economic development.
Moreover, biophysical factors had no significant impact on cropland change.
Biophysical factors driving land use pattern are relatively stable during the period of several decades, except for some extreme events (e.g. flood and
drought). They are thus not direct driving forces, but the socio-economic and
political factors are the primary causes affecting land use change dynamics in
Jiangxi (1995-2005). However, the institutionalized policies “Grain for Green”,
“Grain for Lake” “and “Grain for pasture” were proposed rooted from the catastrophic flood in 1998. If TIRI, GDPP, R-POP and policy are the proximate
factors influencing cropland change, flood is the underlying cause.
The process of cropland transition in Jiangxi (1995-2005) can be concluded as three processes and their interacting mechanisms: Land use zoning,
intensification and remittance. Land use zoning and agricultural intensification
are commonly proposed strategies to control cropland expansion and therefore
promote nature conservation. Various land use zoning schemes allocate land to
restricted uses to ensure that valuable natural ecosystems are not converted
(Lambin and Meyfroidt, 2011). Land use policies aimed at reducing environmental pressure in a place, like Grain for green, lake and pasture, caused a
migration to another place, therefore bring land change in the other locality.
Out-immigration of rural population from the zone of cropland returning in
Jiangxi due to policy influence curbed the cropland expansion. Outmigration
alone rarely leads to land abandonment, but rather to an extensification of land
use (Radel and Schmook, 2008). Intensifying agriculture is thought to spare
cropland for nature by higher yields. The introduction of new agricultural technology and innovations effectively reduces the land resource use. Land use
efficiency is increased by allowing for regional specialization in land use and
productivity elevates as a response to an increasing level of food stresses. The
shortened period for cultivation and higher quantity and quality of crop output
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4. Driving forces of regional cropland and built-up land transition in Jiangxi Province,
China
due to new technology brought more profit, which can explain the increased
GDPP drives cropland shrinking. GDPP is the gross domestic product value
obtained from farming, forestry, animal husbandry and fishery industries. TIRI
is the income obtained from various channels including agricultural and nonagricultural economic activities (Jiangxi Province Statistical Bureau, 2006).
Massive upsurge of rural industrialization is at the core of China‟s recent economic miracle (Sun et al., 1999). Rural non-agricultural employment
constituted over one-third of the employment of rural households in the mid1990s, and contributed above 40% of their total income (Xie et al., 2005). Inflow of remittances of migrant labours and rural industries can increase total
rural income rather free land from agricultural activities. This massive transfer
of funds may facilitate the re-transition of family members at home to the rural
nonfarm economy, thus decreasing pressure on land. In summary, TIRI, GDPP,
R-POP and policy have a strong influence on cropland transition.
4.3.3
Regression analysis of the driving forces and interactions of builtup land 1995-2005
The relationships between built-up land transition and the socio-economic statistics (1995-2005) are presented in Table 4.6 and Table 4.7. The spatial
patterns of built-up land transition and its driving forces are shown in Figure
4.6. The expansion of built-up area was mainly attributed to the changes of
four factors: GDP (Gross domestic product value), EC (Engel‟s coefficient),
GDPT (Gross product value-added of tertiary industry) and GDPS (Gross
product value-added of secondary industry) (Table 4.6 and Table 4.7). The four
factors together can explain 76.7% of the built-up land transition. Our novelty
was the introduction of EC (Engel‟s coefficient), which was a statistically significant factor driving built-up land transition. Based on the same approach as
the study of cropland transition, regression analysis with POLICY explanatory
variable indicated that the policy change had no noticeable influence on builtup land change and other socio-economic forces correlated with built-up land
expansion. The regression analysis reveals several findings. (1) GDP is the
largest contributor affecting built-up transition. (2) The changes of both EC and
GDPT have significant correlation with built-up transition. (3) The force of
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4. Driving forces of regional cropland and built-up land transition in Jiangxi Province,
China
GDPS driving built-up land transition is relatively weak. The annual change
rates of GDP, EC, GDPT and GDPS are 15.26%, 0.35%, 29.39% and 40.41%
respectively (Table 4.3). Spatial patterns of the change rates of these factors
matched well with built-up land transition in the GIS thematic maps. Further,
the spatial pattern explained why the Beta value of EC factor shows reversed
direction from the built-up area transition (Figure 4.6). Regression analysis also found biophysical factors had no direct and significant influence on built-up
land transition. However, out-immigration, to a small extent, may cause the
relative decrease of built-up area due to policy intervention. The socioeconomic factors are thus the major driving forces of built-up land transition in
Jiangxi 1995 to 2005.
Built-up area in Jiangxi increased annually by 2.7 % from 1995 to 2005
(Table 4.2). The China‟s economic reforms played an important role in the
transformation of built-up area. The major land-use change is caused by the increasing demand for non-agricultural land because of urban and manufacturing
development (Verburg et al., 1999). Built-up land transition is characterized by
the changes of urban and rural settlements and construction in China (Long et
al., 2007a). The expansion of construction land was largely a result of rural industrialization in China (Lin and Ho, 2003; Xie et al., 2005). Numerous factory
buildings can be easily seen in the residential backyards of the villagers in
coastal China (Shen and Ma, 2005). The development of rural enterprises
(household-based small-scale township- and village-run) is the primary driving
force converting built-up land in rural areas. The direct result of urbanization
was the reduction of cropland by increasing urban settlements. Urban-related
industrialization is well known to be one of the most important causes of landuse changes in China (Wu et al., 2004). The strong positive relationship was
found between industrial output value and construction land (Long et al.,
2008). GDPS is the gross domestic product value obtained from the industries
of producing, manufacturing and construction and so on. The gross domestic
product value from the industries outside GDPP and GDPS, such as transportation, information, commerce, catering, culture, recreation and service, is
attributed to GDPT (Jiangxi Province Statistical Bureau, 1996, 2006). The increase of GDPS and GDPT intimately associated with the expanded
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4. Driving forces of regional cropland and built-up land transition in Jiangxi Province,
China
construction of facilities and infrastructure for manufacturing and living. GDP
is the largest contributor to the built-up land transition. It encompasses a large
portion of agriculturally associated output value except for GDPS and GDPT,
though its share was diminishing (Jiangxi Province Statistical Bureau, 1996,
2006). This may lead to more contribution to the built-up transition than GDPS
and GDPT. Urban and rural construction can thus be interpreted by the changes
of GDP, GDPS and GDPT in Jiangxi. Generally, urban and rural inhabitants in
China prefer new multifunctional or spacious housing if they become affluent,
although present housing can fundamentally meet their needs. Roads and other
available infrastructure close to their houses are also developed. The houses
usually have small-scale household (in which normally three persons or so
live) or larger courtyards. A cycle of replacing old house with a new one is
short. As a result, more urban and rural housing occupied land. That can explain the expansion of urban and rural settlements.
In addition, the decrease of Engel‟s coefficient signifies the demand for
non-agricultural goods and services increases faster than demand for agricultural products. When the demand for more diverse diets is satisfied, further
income growth is spent almost entirely on non-agricultural goods and services.
This leads to a dramatic change in the structure of economic activity and a true
structural transformation (Nelson et al., 2006). Producers responded by devoting relatively more invest to industry and service activities than to agriculture.
Therefore, Engel‟s coefficient has indirect impact on the non-agricultural industries associated with GDPS and GDPT. The process driving built-up land
expans-ion is caused by the mutual interaction of the socio-economic factors,
GDP, GDPS, GDPT and EC.
4.4 Discussions
Our purpose is to explore the forces and their interactions affecting cropland
and built-up land change in Jiangxi for the period 1995-2005. Our major findings are (1) the area of cropland decreased at the rate of 0.3% and built-up land
expanded by 2.7% annually. (2) socio-economic forces (TIRI, GDPP, and RPOP) and policy are the proximate factors influencing cropland transition and
biophysical factor (flood) is the underlying cause. (3) The built-up land transi125
4. Driving forces of regional cropland and built-up land transition in Jiangxi Province,
China
tion is caused by the mutual interactions of the socio-economic factors, GDP,
GDPS, GDPT and EC. (4) Integrative method with quantitative policy effect
has the robustness for the investigation of driving forces of land use change.
The government is able to control the cropland transition (supporting by
the fact that cropland displayed a negative increase), however, the demands of
more lands for built-up are not diminishing. In similarity with our findings, a
myriad of studies have reported the interacting socio-economic factors drove
built-up expansion (e.g. Huang et al., 2007; Long et al., 2008; Nancy et al.,
2008; Qu, 2007; Xie et al., 2005). Yin et al. (2011) pointed out that urban
growth have advanced at an unprecedented pace over the past few decades following the rapid economic development. However, determinative socioeconomic indicators hinges on the specific regional characteristics. Our results
have shown that Jiangxi is following the trends of built-up growth witnessed in
coastal and west region of China from 1995 to 2005, although the significance
of various land-use drivers differs in distinct areas (for instance, GDP is the
dominant factor in Jiangxi). Inconsistent results of Zhan et al (2010b) show
that population is the largest contributor driving land-use transition from
cropland to built-up area in Jiangxi 1988-2005. Arable land protection policy,
to certain extent, has limited the built-up growth.
Moreover, our results have indicated that policy and socio-economic
forces have shaped the decreasing cropland pattern in Jiangxi (1995-2005).
Among all drivers, policy plays the most important role. The findings from
modelling analysis in Jiangxi‟ land use also present the cropland loss (Wang et
al., 2003b). Different results of Zhan et al (2010b) show that population is a
predominant factor driving the cropland transition at counties level in Jiangxi
over the period 1988 to 2005, while social and economic factors are determinant factors in the short term. Specifically, the size of agricultural population
and the amplitude of agricultural input determine the agricultural production to
a large extent; population size, plain area proportion at counties and land
manag-ement policies together affect the direction and amplitude of transition
between cropland land and built-up area. The downward trend of cropland use
is consistent with the other areas in China, such as Beijing, Shanghai, Tianjin,
Zhejiang, Fujian, Hubei and Guangdong (Ding et al., 2011; Liu et al., 2003;
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4. Driving forces of regional cropland and built-up land transition in Jiangxi Province,
China
Liu et al., 2008; Qiao et al., 2009; Tan et al., 2005a; Xu et al., 2010; Zhu et al.,
2007). However, the drivers are in regional disparity. Zhu et al (2007) have
exhibited that the reduction of intensification contributed most to the cropland
loss. The importance of grain production to farmers is decreasing. Farmer‟s
pursuance has shifted from maximizing the output of land to maximizing the
income of labor force with the development of market economy. The input of
cultivated land, expense and labor in grain production is reduced, thus decreasing cropland. Liu et al. (2003) assumed that built-up expansion was the key
factor for cropland transition. Even though there are relative items to protect
cropland in the land management law of China, the conspicuous conflicts also
exist between cropland protection and urban expansion.
There are two novel facets of this study. Firstly, an integrative approach
with quantitative policy impact involving remote sensing, GIS and regression
analysis is applied to analyse the causes of land use change in Jiangxi. Secondly, EC, the share of income spent on food, is used as one of the socio-economic
indicators affecting land use change. To my main research question, we connect patterns of economic growth (GDP, GDPS and GDPT) and EC with land,
which are explored in the context of built-up land initiatives. The promising
finding is EC is a strong factor driving built-up land transition.
EC has never been used for the estimation of driving forces of land use
change in Jiangxi, also seldom in China. The expenditure on food consumption
was the major part of urban and rural inhabitant income in the former. However, the expenditure structure has been altered accompanying as per capita
income grows. The demand for non-agricultural goods and services increases
faster than demand for agricultural products; this leads to a dramatic change in
the structure of economic activity and a true structural transformation. Higher
non-food expenditures usually mean higher purchasing power. This increase in
purchasing power is usually reflected in increasing demands of non-food items
such as cars, houses, household goods, recreation, and travel. To satisfy the
higher life demands, the production of all these products and services exert
stresses on built-up land in the form of housing, sprawl and public infrastructure and facilities and so on. The change of EC is intimately associated with the
shift in economic structure from agricultural production to industry and, to a
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4. Driving forces of regional cropland and built-up land transition in Jiangxi Province,
China
greater extent, services (Nelson et al., 2006). Therefore, EC should be included
in the analysis of drivers of land use change, particularly in built-up land.
Integrative method has shown that the interactions of policy, regional
economy and rural population have shaped cropland use patterns in Jiangxi
1995-2005. Contrasting results have indicated different driving mechanism. By
the calculation of correlation coefficient and principal component analysis,
Deng et al. (2006) found population and economy contributed most to the
cropland transition rather than policy impact for the period 1996-2004. But
multicollinearity of driving forces may raise question to this conclusion. Similarly, Zhan et al. (2010a) indicated population was the strongest forces in
Jiangxi‟ cropland use change 1988-2005. Socio-economic data for land use
change stem from surveying statistical data on county scale. The uncertain facet exists in the process of spatialization of socio-economic data from county
scale to 1KM grid. In agreement with our finding, Wu et al. (2008) also reported the policy was the largest driver of cropland change in Jiangxi 1985-2005
but policy was used as statistical analysis interpretation without objective
quantitative assessment.
Biophysical, socio-economic, and political systems are interacting in
complex ways to drive land use dynamics at various spatiotemporal scales.
Due to the great uncertainties of the dynamics, multi-methods have to be combined to understand the temporal trajectory and spatial imprint of land use
change. Simple quantitative (statistical) analysis, qualitative (policy) assessment or remote sensing detection is not enough. The integrative analysis begins
with a policy review that is conducted to guide policy research design. Secondly, the acquisition of land use data is realized through remote sensing analysis.
Remote sensing-based data on land use may have higher accuracy than statistical data on land use changes. Thirdly, we relate the land use change data with
biophysical and socio-economic data at county scale and the multiple and categorical regression analyses are made to examine how the policy affects
cropland and built-up land transition and how biophysical and socio-economic
factors interact with each other to drive land use changes with the status of policy implementation at different county. Spatial interactions between land use
change and its drivers can be discernible supported by GIS visualization in the
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4. Driving forces of regional cropland and built-up land transition in Jiangxi Province,
China
form of interactive and synthetic data analyses. The thematic maps of spatial
relationships between cropland (built-up land) transition and its corresponding
driving forces (Fig. 4.6 and 4.7) help to understand and clarify the extent and
direction of their interactions. By the application of this integrative approach,
explicit and key findings are obtained for the better understanding of the interactions and feedback of human-land systems.
Further, our results are lack of the analysis of fine temporal resolution of
land use dynamics due to limited schedule. The LULC needs more accurate
ground truth verification. In addition, attention has to be paid to the policy
study using informatics and statistical methods. Mathematical modelling is often developed to obtain a preliminary examination of complicated social issues
or policy impacts (Batty, 1997). However, society is a complexity and policies
can be seldom explained simply by modelling (Odum, 1997). A full appreciation of what is going on must be matched by enhanced understanding of
physical and anthropogenic causes (Xie et al., 2005). Notwithstanding its limitation, this study does suggest the driving mechanism of cropland and built-up
land in Jiangxi 1995-2005. Our study helps to navigate a transition toward
more efficient land use, through varying combinations of strategies in Jiangxi.
Appropriate land use policy can reconcile the demand for land with environmental protection. The information provided by the land-use analysis
ultimately helps to tailor policies and plans for better land management.
Our study gives the following suggestions:
(1) The land use research on microscopic level, especially household level,
is seldom carried out in China. Farmers are the decision-maker, and their strategies have a direct impact on land use change. The studies on this level are
therefore indispensable.
(2) Globalization brings new opportunity and challenge for regional development. Thus, the impact of global trade should not be neglected in future
study of regional land use change.
(3) Systematical land-use change study generally encompasses the following facets: change detect, driving forces analysis, global change impact on
land-use change, modelling and simulation on the land-use change, and biogeochemical process affected by land-use change, etc. To date, we have analysed
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4. Driving forces of regional cropland and built-up land transition in Jiangxi Province,
China
the impact of biophysical factors and anthropogenic social and economic activities on land-use change at regional scales. Also we have examined the influinfluence of land use/land cover change on climate change as a case study of
Jiangxi. In future, we need to study deeply the effect of land-use change on
life material‟s physical, chemical and biological processes, and finally to model
and prognosticate regional land use change.
4.5 Conclusions
Cropland transition in Jiangxi has been achieved through multiple interacting
mechanisms including the socio-economic forces (TIRI, GDPP, and R-POP)
and policy as the proximate factors and biophysical factor (flood) as the underlying cause. The pathways leading to built-up land transition rely to various
degrees and combinations on the socio-economic factors, GDP, GDPS, GDPT
and EC.
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4. Driving forces of regional cropland and built-up land transition in Jiangxi Province,
China
Tables
Table 4.1 List of the selected biophysical and socio-economic factors affecting land
use
Abbreviation
Description
Unit
T-POP
R-POP
Total population
Rural population
10 thousand individual
10 thousand individual
GDP
GDPP
GDPS
GDPT
TRSCG
MP
Gross domestic product value
Gross product value-added of primary industry
Gross product value-added of secondary industry
Gross product value-added of tertiary industry
Total retail sales of consumer goods
Total output of meat production
10 thousand Chinese Yuan
10 thousand Chinese Yuan
10 thousand Chinese Yuan
10 thousand Chinese Yuan
10 thousand Chinese Yuan
Ton
TIRI
EC
SAT
ELE
Total income of rural inhabitants
Engel‟s coefficient
Surface air temperature
Elevation
Chinese Yuan
Degree
Meter
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4. Driving forces of regional cropland and built-up land transition in Jiangxi Province,
China
Table 4.2 Change matrix of each land use type and transition rates in 1995 and 2005
2005 land
use
(hectare)
WB
FR
CR
GR
BL
BUL
1995 Total
1995 land use (hectare)
WB
FR
CR
GR
BL
BUL
2005 Total
345911.7
9200.4
142374.6
2649.4
72121.2
45748.0
618005.5
64948.9
6059949.1
1323486.9
835334.6
32811.0
111817.2
8428347.9
68415.2
1040025.3
4223791.5
383512.4
253628.4
273861.0
6243233.9
2640.2
376558.6
137226.2
307646.0
9815.6
5752.3
839639.1
2910.4
4473.2
55169.1
4029.4
24789.6
3113.1
94485.1
63889.1
49377.7
192933.2
4684.4
12329.4
111227.4
434441.5
548715.6
7539584.5
6074981.7
1537856.3
405495.4
551519.3
16658153.1
1995-2005
Transition
rate (%)
-1.3
-1.2
-0.3
6.9
17.6
2.7
WB = Water Body, FR = Forest, CR = Cropland, GR = Grassland, BL = Barren Land, BUL =
Built-up Land.
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4. Driving forces of regional cropland and built-up land transition in Jiangxi Province,
China
Table 4.3 Annual change rates of biophysical and socio-economic indicators
Factors
Change rate 1995-2005
T-POP
R-POP
GDP
GDPP
GDPS
0.0113
0.0124
0.1526
0.2051
0.4041
GDPT
TRSCG
MP
TIRI
EC
SAT
ELE
0.2939
0.1303
0.0064
0.0924
0.0035
0.2210
0.0000
T-POP = Total population
R-POP = Rural population
GDP = Gross domestic product value
GDPP = Gross product value-added of primary industry
GDPS = Gross product value-added of secondary industry
GDPT = Gross product value-added of tertiary industry
TRSCG = Total retail sales of consumer goods
MP = Total output of meat production
TIRI = Total income of rural inhabitants
EC = Engel‟s Coefficient
SAT = Surface air temperature
ELE = Elevation
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4. Driving forces of regional cropland and built-up land transition in Jiangxi Province,
China
Table 4.4 Summary for regression models of cropland with explanatory variable,
POLICY (1995-2005)
R2
Adjusted R2
Std. error of the estimate
1
2
3
a
0.744
0.846b
0.881c
0.553
0.716
0.776
0.548
0.710
0.768
0.02572
0.02061
0.01842
4
0.920d
0.847
0.840
0.01532
Model
R
a
Predictors: (Constant), TIRI
Predictors: (Constant), TIRI, GDPP
c
Predictors: (Constant), TIRI, GDPP, R-POP
d
Predictors: (Constant), TIRI, GDPP, R-POP, POLICY
b
Table 4.5 Coefficients for the final regression model of cropland (1995-2005)b
b
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
(Constant)
-0.061
0.010
-5.865
0
POLICY
-0.031
0.005
-0.401
-6.206
0
GDPP
0.291
0.045
0.350
6.519
0
TIRI
0.177
0.053
0.203
3.320
0.001
R-POP
0.331
0.103
0.169
3.199
0.002
Dependent Variable: Cropland
134
4. Driving forces of regional cropland and built-up land transition in Jiangxi Province,
China
Table 4.6 Summary for regression models of built-up land (1995-2005)
R2
Adjusted R2
Std. error of the estimate
1
2
a
0.751
0.843b
0.564
0.711
0.559
0.704
0.02245
0.01839
3
4
0.871c
0.882d
0.759
0.778
0.750
0.767
0.01689
0.01633
Model
R
a
Predictors: (Constant), GDP
Predictors: (Constant), GDP, GDPT
c
Predictors: (Constant), GDP, GDPT, EC
d
Predictors: (Constant), GDP, GDPT, EC, GDPS
b
Table 4.7 Coefficients for the final regression model of built-up land (1995-2005)a
(Constant)
GDP
EC
GDPT
GDPS
a
Unstandardized Coefficients
Standardized Coefficients
B
Std. Error
Beta
-0.009
0.431
-0.032
0.302
0.181
0.013
0.064
0.012
0.052
0.043
0.460
-0.144
0.372
0.270
Dependent Variable: Built-up land
135
t
Sig.
-0.694
6.734
-2.625
5.871
4.249
0.489
0
0.010
0
0
4. Driving forces of regional cropland and built-up land transition in Jiangxi Province,
China
Figures
Figure 4.1 Integrated method for studying driving forces of LULC changes
136
4. Driving forces of regional cropland and built-up land transition in Jiangxi Province,
China
Figure 4.2 Change pattern of cropland in Jiangxi in 1995 and 2005
Figure 4.3 Change pattern of built-up land in Jiangxi in 1995 and 2005
137
4. Driving forces of regional cropland and built-up land transition in Jiangxi Province,
China
Figure 4.4 Land use and land cover map of Jiangxi in 1995 and 2005
138
4. Driving forces of regional cropland and built-up land transition in Jiangxi Province,
China
Figure 4.5 Spatial patterns of cropland transition and driving factors (1995-2005).
(The left upper is the thematic map of cropland transition rate and the right upper is
the thematic map of rural population (R-POP) change rate. The left bottom is the thematic map of gross product value-added of primary industry (GDPP) change rate and
the right bottom is the thematic map of rural income (TIRI) change rate.
139
4. Driving forces of regional cropland and built-up land transition in Jiangxi Province,
China
Figure 4.6 Spatial patterns of built-up land transition and driving factors (1995-2005).
(The upper is the thematic map of built-up land transition rate. The left middle is the
thematic map of gross domestic product value (GDP) change rate and the right middle
is the thematic map of gross product value-added of tertiary industry (GDPT) change
rate. The left bottom is the thematic map of gross product value-added of secondary
industry (GDPS) change rate and the right bottom is the thematic map of Engel‟s coefficient (EC) change rate.
140
5. Conclusions
5 Conclusions
The objective of our study is to assess the impacts of LULC change on climate
change and to investigate the driving forces of cropland and built-up land transition on regional scale. Jiangxi province China is used as the study site. First,
to obtain reliable climate data, homogenization is applied in monthly mean
temperature and precipitation totals over Jiangxi for the period of 1951-2000.
The non-homogeneity types, causes, amplitudes and timings are discussed.
Further, spatiotemporal patterns of seasonal and annual mean temperature and
precipitation are described. The climate trends computed from GHCN datasets
and from our analysis are also compared. Second, to examine the impact of LULC change on the variations of regional temperature and precipitation, we analysed the observation and reanalysis trends of decadal and monthly temperature and precipitation anomaly over Jiangxi and examine the sensitivity of surface temperature/precipitation to land surface type by using OMR trends as a
function of land surface types (derived from AVHRR NDVI) integrating regional topographic characteristics. Third, to identify the key drivers of cropland and built-up land transition in Jiangxi for the period 1995-2005, an integrative process with quantitative policy effect involving remote sensing, GIS
and statistical techniques is used. How the biophysical, socio-economic and
policy driving forces affect land use transition is also analysed.
Results indicate that the adjusted monthly temperatures have smoothed
the warming effect, particularly in winter. The spatial cooling effect in summer
is exaggerated by unadjusted data. Further, the comparison between GHCN
and our analysis on adjusted monthly temperatures indicates that the resulting
climate trend varies slightly from datasets to datasets. The series of precipitation are detected to be homogeneous and a wetting trend is revealed. The
spatial patterns of spring and summer wetting are in good agreement with the
trends of spring and summer cooling. A feature of warming winter versus cooling summer and spring drying versus summer wetting is revealed. Poyang
Lake watershed is the centre of summer cooling.
Our findings present that OMR approach is effective to examine the
trends of temperature and precipitation driven by the impact of regional land141
5. Conclusions
cover types. OMR trends associated with land types show that strong surface
warming response to land barrenness and weak warming response to land
greenness. OMR trends of precipitation are insensitive to different land-surface
types both incorporating and removing regional topographic characteristics.
Moreover, our findings indicate 81.1% of the surface warming over vegetation
index areas (0~0.2) attributes to the LULC change incorporating regional topographic characteristics, whereas 49.1% explanation level over the same land
cover type is shown without regional topographic characteristics into regression assessment. The contribution capability of LULC change decreases as
land cover greenness increases, both under the condition of incorporating and
removing regional topographic characteristics.
We find that the socio-economic forces (TIRI, GDPP, and R-POP) and
policy are the proximate factors influencing cropland transition and biophysical
factor (flood) is the underlying cause. The built-up land transition is caused by
mutual interactions of the socio-economic factors, GDP, GDPS, GDPT and EC.
Integrative approach with quantitative policy effect has the robustness for the
investigation of driving forces of land use change.
142
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