Assessment of Land Use Change and Climate
Assessment of Land Use Change and Climate Variability on Hydrological Processes in the Upper Ma River Basin, Northwest Vietnam for future land use planning and water resources management Ngo Thanh Son1,2, Nguyen Duy Binh1, Sangam Shrestha2, Vo Trong Hoang3, Nguyen Duc Loc1, Nguyen Anh Tuan1, Nguyen Dinh Cong1,4, Rajendra Prasad Shrestha2 Vietnam National University of Agriculture Asian Institute of Technology Vietnam Academy of Science and Technology Mekong River Commission, Cambodia Outline 1. Background and Objectives 2. Study area 3. Methodology 4. Results 5. Conclusions Background Land use change and climate variability are two main factors directly affecting regional hydrological conditions. Changes in land use/cover have affected the water balance and transformed the water flow pathways in the hydrological processes. (Chhabra et al., 2006); Changes in climate are predicted to have severe, direct impacts of hydrology, ecosystem, and livelihoods, and are the wide issue (IPCC, 2007) Understanding the responses of hydrological processes to land use and climate variability is improtant for land use planning and water resources management (Dao NK et al., 2013) Background (cont.) In the Northwest of Viet Nam (Upper Ma River basin) has been significantly changing in hydrology and sediment, mainly due to land use and climate change; GIS, RS, and hydrological modeling are extremely useful tools to analyze land use data , to give valuable insights into processes of land use change and their underlying causes; To assess the impacts of land use and climate change on water quantity and quality Soil and Water Assessment Tool (SWAT) is frequently used to assess hydrology and water quality in the basin because of user-friendly in handling input data. Objectives Calibrate and validate the SWAT model in term of stream flow; Assess the individual impacts of land use change and climate variability on hydrological processes; Assess the combination impacts of land use change and climate variability on hydrological processes; Provide decision makers with a comprehensive understanding of the interaction among land use change, climate change and hydrological processes; Study area Area 3501 km2, Mountain, Poverty, Hydrological variations Soil degradation - Erosion Methodology Observed Hydrological Data Meteorological Data GIS Data Observed daily flow (Precipitation, temperature, humidity, wind speed, solar radiation) DEM, Soil types, Land use/Land cover, Slope SWAT Model Sensitive Analysis Calibration and Validation Assessment of Land Use Change and Climate Variability Impacts on Hydrological Process Framework of the study Input data preparation Data type Source Resolution Description Topography Map MONRE 30m x 30m Digital Elevation Map (DEM) Soil types (FAO) MONRE and MRC 1km Soil classification and physical properties Land Use USGS 30m Land use classification Climate data National Climatic Center of Vietnam Daily Daily precipitation, min and ma temperature at 3 stations Stream flow National Climatic Center of Vietnam Daily Xa La station Figure 2 Soil map of the upper Ma River Basin, Northwest of Vietnam Fig 3 Land use map in 1993 (left) and 2009 (right) of the upper Ma river basin Modelling procedure calibration: using monthly flow monitoring data from hydrological stations during 1993-2000; validation: using monthly flow monitoring data from hydrological stations 20012009; applications: S1: Land use in 1993 and climate data 1993–2000 (Baseline). S2: Land use in 2009 and climate data 1993–2000 (Land use change). S3: Land use in 1993 and climate data 2001–2009 (Climate change). S4: Land use in 2009 and climate data 2001–2009 (Land use and climate change). Indicators: Nash-Sutcliffe efficiency (NSE), determination (R2), and percent bias (PBIAS) coefficient 2 n i 2 n i i i NSE 1 Qobs Qsim / Qobs Q sim i 1 i 1 i i Qobs Qobs Qsim Qsim R 2 n i 1 n 2 2 i i Qobs Qobs Qsim Qsim n i 1 2 i 1 n PBIAS i 1 i Qobs i Qsim 100 / Q n i 1 i obs where, n is the number of events, Qiobs, and Qisim are the observed and simulated data on the ith time events, 𝑄𝑜𝑏𝑠 is the mean of observed data across the n evaluation time steps of Results Calibration period (1993-2000) and validation period (2001-2009) Indicator SWAT model can simulate well for stream flow for the upper Ma river basin using calibrated parameters Runoff Calibration (1993-2000) Validation (2001-2009) NSE 0.94 0.74 R2 0.96 0.75 PBIAS (%) 12.27 -6.04 Land Use Change (1993-2009) Land use types 1993 Area (km2) 2363.44 Forest Perennial crop 101.21 19.64 Paddy field 1.02 Miscellaneous 38.85 Water body Field crop 978.63 Total 3502.79 2009 (%) 67.47 2.89 0.56 0.03 1.11 27.94 100 Area (km2) 1615.01 343.39 49.55 5.68 67.25 1421.91 3502.79 Change (%) 46.11 9.80 1.41 0.16 1.92 40.59 100 Area (km2) -748.43 242.18 29.92 4.66 28.39 443.28 (%) -21.36 6.91 0.85 0.13 0.81 12.65 Impact of land use change on hydrological processes Surface runoff (mm) Items Lateral Flow (mm) Base Flow Percolation ET (mm) (mm) (mm) Water yield (mm) S1 164.9 74.5 520.8 568.4 482.2 758.4 S2 182.2 68.0 447.3 489.9 556.3 695.6 Change (mm) 17.2 -6.5 -73.5 -78.5 74.1 -62.9 Percent (%) 10.4 -8.7 -14.1 -13.8 15.4 -8.3 % 20 15 10 5 0 -5 -10 -15 -20 S2/S1 S2/S1 Surface runoff 10.4 Lateral Flow Base Flow Percolation ET Water Yield -8.7 -14.1 -13.8 15.4 -8.3 Impact of Climate Variability on hydrological processes Surface runoff (mm) Items Lateral Flow (mm) Base Flow Percolation ET (mm) (mm) (mm) Water yield (mm) S1 164.9 74.5 520.8 568.4 482.2 758.4 S3 Change (mm) Percent (%) 231.3 66.4 40.2 84.3 9.9 13.3 620.5 99.7 19.1 675.3 106.8 18.8 413.0 -69.2 -14.4 934.4 176.0 23.2 50 40 30 20 % 10 S3/S1 0 -10 -20 S3/S1 Surface runoff 40.2 Lateral Flow Base Flow Percolation ET Water Yield 13.3 19.1 18.8 -14.4 23.2 Assessment of combined impacts of Land use change and climate variability on hydrological processes Items Surface runoff (mm) S1 164.9 241.2 76.3 46.3 S4 Change (mm) Percent (%) % 50 45 40 35 30 25 20 15 10 5 0 S4/S1 Lateral Flow (mm) 74.5 76.1 1.6 2.2 Base Flow (mm) Percolation (mm) 520.8 522.0 1.2 0.2 568.4 570.0 1.5 0.3 ET (mm) 482.2 521.9 39.7 8.2 Water yield (mm) 758.4 837.5 79.1 10.4 S4/S1 Surface runoff 46.3 Lateral Flow Base Flow Percolation ET Water Yield 2.2 0.2 0.3 8.2 10.4 Simulated average-annual stream flow under the impacts of land use and climate change Scenarios Land use Climate S1 S2 S3 S4 1993 2009 1993 2009 1993-2000 1993-2000 2001-2009 2001-2009 Measured (mm) 164.1 164.1 224.7 224.7 Simulation (mm) 164.9 182.2 231.3 241.2 Simulated changes (mm) 17.3 66.4 76.3 Percent (%) 10.5 40.3 46.3 50.0 40.0 30.0 S2 20.0 S3 S4 10.0 0.0 -10.0 Wet Dry Annual Fig 4 Changes in annual and seasonal stream flow under the impact of land use and climate changes Conclusions • SWAT model was applied successfully to simulate stream flow in the upper Ma River Basin • Rapid land use change occurred from 1993 to 2009, especially dramatically decreased forest from 67.47% to 46.11% and increased field crop from 27.94% to 40.59%. • individual land use change in the study increased surface runoff and ET considerably while decreased lateral flow, base flows, percolation, and water. • individual climate variability led to significantly increased all hydrological processes (percent contribution were surface runoff (40.2%), lateral flow (13.3%), base flow (19.1%) percolation (18.8%), and water yield (23.2%) • combined impacts of land use and climate variability caused all hydrological components, especially surface water. • climate variability influenced hydrological processes more significantly and strongly than the land-use change Limitations Model data requirements proved to be the main issue for the study huge amount of data requires skills in GIS ( ArcGIS, DEM, dbf, etc.), in programming, in hydrology and soil science (to assess reliability of data), etc. dealing with changes in time of land use and infrastructures have impacts on monitored flow data. Future research • Developing website to disseminate study results to the public • allowing users to investigate land use and climate change impacts by modification of climate data, running models and displaying modeling results, • expecting to raise land use and climate change awareness among the public. Acknowledgements The authors would like to thank IDRC-CRDI for funding this project. The author is also grateful to SEARCA for funding the travel grants and giving me a chance to be here to present my paper. Thank you for your attention
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