Climate change impact
assessment and agricultural
land use decision making in the
Vietnamese Mekong Delta
Nguyen Hieu Trung1, Van Pham Dang Tri1, Truong Chi Quang1,
Huynh Xuan Hiep 2, Alexis Drogoul3
1College of Environment and Natural Resources (CENRes), Can Tho University;
Campus 2, 3/2 street, Ninh Kieu district, Can Tho city, Vietnam. e-mail:
[email protected]
2College of Information and Tele Communication (CITC), Can Tho University.
3 IRD, UMI 209, UMMISCO, IRD France;
Contents
• The Mekong Delta’s agriculture land use
change driving factors.
• CC impact assessment
• Agricultural land use decision making
• the IRD-CTU research team: Decisionsupport Research for Environmental
Applications and Models (DREAM)
Fast land use change in MRD
Department of Land Resources, Can Tho University, 2013
Agriculture land use change driving factors
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Long river from very high elevation (~4000m) to the sea level (0.5 - 1m).
70-80% of the precipitation concentrated into four months  annual flood
in rainy season.
Tides: East sea is semi-diurnal (amplitude: 2.5 – 3.0 m), and West Sea
tide is diurnal (amplitude: 0.4 – 1.2 m)  annual saline intrusion in dry
season.
Autonomous adaptation (farming techniques, new short duration rice,
new crop, aquaculture, integrated farming techniques)
Plan adaptation (flood and salinity control system)
Dry season
Rainy season
Saline intrusion 1998 (dry year)
West sea
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Agriculture land use change driving factors
 Over exploitation of ground water (for
urban, industry and rural)  Saline
intrusion in ground water and
 Land subsidence.
(Source: Erban et al., 2014)
Agriculture land use change driving factors
Cross boundary water competition
Existing irrigation projects
Planned irrigation projects
Existing, under-construction
and proposed hydro-power
projects
Agriculture land use change driving factors
Future climate change and sea level rise
Sea level rise: East Sea: Average 4.7
mm/year (1993-2009)  Projected to
2050: + 30 cm; 2100: + 70 cm
Agriculture land use change driving factors
• Regional development
• Increase accessibility need:
both physically (e.g. road)
and non-physically (data,
information, knowledge)
• Increase resource demands
(both natural and socioeconomic)
Complex land use and resource
planning  an interactive land
use planning approach
Contents
• The Mekong Delta’s agriculture land use
change driving factors.
• CC impact assessment
• Agricultural land use decision making
• the IRD-CTU research team: Decisionsupport Research for Environmental
Applications and Models (DREAM)
CC impact assessment
Basin/regional scale:
• Flood modeling
• Saline intrusion modeling
Sub-ecological scale:
• Flood zone modeling
• Coastal zone modeling
Field scale:
• Crop modeling
Rainfall – Runoff model
SWAT
Socioeconomic
scenarios
Impact to
saline
intrusion
Integrated Quantity and Quality Model
(IQQM)
Water
demand
Agriculture
dev.
Hydropower dev.
Upstream scenarios
Hydraulic model
Water quality model
(from Kratie, Mike 11)
Upstream
boundary:
flow at
Kratie
Mekong delta
development scenarios
Sea level rise scenarios
Saptial analsys
- Land use
- Inundation
- Salinity intrusion
Temporal
analysis
- Probability
- max
- min
- Averahe
Water management
scenarios
Downstream
boundary
Results
- Changes of inundation area
- Changes of saline inundation area
(SIWRR, 2012)
Results
- Changes of discharge
- Change of salinity level
Impacts to saline intrusion (dry season)
Scenario 1 - SLR 30 cm
Scenario 2 (worse case) =
Scenario 1 + upstream
projects, dry year)
Scenario 3 = Scenario 2 +
structural intervention
(larger river mouse sluice
gates
Source: Mekong Future project (Collaboration with CSIRO)
Saline iso-line (4 g/l)
Impact to flood (rainy season)
• Longer flood duration (2000
vs 2050)
• Two groups of flooding:
– Upstream of Mekong
Delta by Mekong river
flow.
– Coastal of Mekong Delta
by tide, especially the
west coast (more than 4
months).
(Van Pham Dang Tri, et.al. 2012)
Contents
• The Mekong Delta’s agriculture land use
change driving factors.
• CC impact assessment
• Agricultural land use decision making
• the IRD-CTU research team: Decisionsupport Research for Environmental
Applications and Models (DREAM)
Agricultural land use decision making
(Mekong future project, 2010-2012)
Agricultural land use decision making
Decision support information
(graphs, maps, tables, reports)
Suitability and
yield/LMU
Soils, water (inundation, salinity)
Hydraulic models
Biophysical
Land
Evaluation
LMU
Available capital
Available labor
• Data management tools
• Land use analysis tools
(Optimization)
• Visualization tools
Available land area
LU allocation of scenario 1
LUTs’ cost/LMU
Require labor/LUT/LMU
Socio-economic
analysis
Current land use
Production price
(Trung, et. al. 2014)
Research theme 5. CLUES project
(Trung et. al., 2014)
Contents
• The Mekong Delta’s agriculture land use
driving factors.
• CC impact assessment
• Agricultural land use decision making
• the IRD-CTU research team: Decisionsupport Research for Environmental
Applications and Models (DREAM)
the Decision-support Research for
Environmental Applications and
Models (DREAM) research team
– To enhance the cooperation among relevant
colleges (CENRes, CIT, CNS ) and IRD in applying
modern information technology and mathematical
solutions for sustainable development of the VMD.
– Tools for interactive LUP approach could be
applied in the context of the VMD.
Activities of DREAM’s LUP team
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•
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Project: Adaptation to Climate Change: Land-use Innovative Models Applied to
Environmental management (ACCLIMATE)
Organization of training on ABM (GAMA) + GIS + Hydrological models.
Insertion of the three Master modules in existing curricula in CTU
Supervised PhD and MSc subjects from College of Information Technology and
College of Environment and Natural Resources
Activities of DREAM’s LUP team
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A multi-disciplinary research team on LUP in CTU.
Good facilities for training and research.
An ABM model (GAMA) on land use decision making.
A server ready for WebGIS and DBMS.
Join training and workshop with PEERS project.
Join publications.
Future development: Interactive and real-time
land use management DSS
Decision support information
(graphs, maps, tables, reports)
Sensor network
Suitability and
yield/LMU
Soils, water (inundation, salinity)
Hydraulic models
Biophysical
Land
Evaluation
LMU
Available capital
Available labor
• Data management tools
• Land use analysis tools
(Optimization and ABM)
• Visualization tools
Participatory
monitoring
Available land area
LU allocation of scenario 1
LUTs’ cost/LMU
Require labor/LUT/LMU
Socio-economic
analysis
Production price
Participatory planning
Current land use
precision farming
Contact: Nguyen Hieu Trung, Assoc. Prof. Dr.
email: [email protected]