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Climate Change Impacts, Vulnerability and Adaptation: Sustaining Rice
Production in Bangladesh
Motaleb Hossain Sarker
Director, Ecology Division, CEGIS
(On behalf of CEGIS Team)
Presentation on
WP1-climate change scenarios, water availability and crop modeling
Under the study project of
Name Designation and organizationDr. J C Biswas Principal Agronomist, BRRIM Maniruzzaman Senior Irrigation Engineer, BRRIF I M Golam Wahed Sarker Senior Agril. Economist, BRRIDr. M Ashiq Iqbal Khan Senior Pathologist, BRRIDr. Nagothu Udaya Sekhar Director (Asia Projects) , BioforskDr. Trond Rafoss Senior Researcher, BioforskDr. Attila Nemes Senior Researcher, BioforskDr. Stefanos Xenarios Senior Researcher, BioforskDr. Johannes Deelstra Senior Researcher, Bioforsk
Acknowledgement
Norwegian Embassy: Specially Mr. Arne Haug, Counselor/Deputy Head of Mission
We also acknowledge BRRI and Bioforsk: Specially following Experts
Presentation Outline•Project goal and objectives
•Study area
•Outputs and results of WP1
•Conclusions and recommendations
•CEGIS Capacity in future works (Phase-II)
•Brief methodology
Goal of the Study
Goal of the modeling exercise (WP1):
Goal of the overall study: To develop an integrated adaptation framework in order to sustain and improve the rice production under different climate change scenarios in Bangladesh
- To generate the climate change scenarios- To assess the water availability using
hydrological model (SWAT)- To asses the yield reduction of rice crops
under different CC scenarios in Bangladesh
Objectives of the work package 1(WP1)- To downscale the climate model result for
generating climate variability scenarios- To generate water availability scenarios using
hydrological model based on the downscaled climate models results
- To assess the yield reduction of rice crop under different climate change scenarios through crop modeling
- To develop the different GIS maps through GIS analysis using the model outputs
- To prepare document on modeling activities and scenario generation
- To assist BRRI for developing adaptation options using climate model result results through field experiments
Area Population
Drought prone 798,077
Saline prone 672,560
Total 1,470,637
Study Area and Demography
Drought prone area
Saline prone area
Overall Study Approach
Downscaling of climate model results
Development of climate variability scenarios
Hydrologic modeling and generation of water availability scenarios (SWAT)
Crop production/yield reduction under different CC scenarios through crop modeling
Field experiments
Dev. of adaptation options based on the model result
using the field experiments
Climate Change Scenarios
Water AvailabilityScenarios
Crop Model(DRAS, AQUA Crop
etc.) Dissemination of results to the end users (Planner,
Decision Makers and Farmers)
BRRI
Study Methodology-Downscaling of climate model results - using PRECIS
Climate Change scenario: A1B : Average Emission Scenario (Rapid economic growth) A2 : High Emission Scenario (Moderate economic growth)
Scenarios have been developed for the time frame: 2011-2040 (40s) 2041-2070 (70s) 2071-2100 (2100s)
Results and Analysis – Downscaling of Climate Model Results
Temperature and Rainfall: Gomastapur (Drought Prone Area)
Less rainfall in dry season• Less water availability• More irrigation water need
High temperature in dry season• More evaporation• Increase water demand
A1B
A2
Temperature and Rainfall: Amtali (Saline Prone Area)
High temperature in dry season• More evaporation• Increase water demand
Less rainfall in dry season• Less water availability• More salinity
A1B
A2
Results and Analysis – Water availability assessment using SWAT
Model
Water availability assessment using SWAT
• SWAT an water balanced model which has been used for water availability assessment under different climate change scenarios for the study upazilas
Major inputs of SWAT model:• Digital Elevation Model (DEM)• Soil Classification• Land Cover and Use• Slope• Weather Data: Rainfall,
Temperature, Humidity, Solar Radiation, Wind Speed, Evaporation
• Hydrological data: Discharge
Water availability assessment results in drought prone area - under different climate change scenarios
Change in water availability (%) in drought prone areaScenario Dry Season Wet Season
A1B -13 9A2 -20 38
- Dry season water availability will be reduced 13% in A1B and 20% in A2 scenario
- Wet season water availability will increased 9% in A1B and 38% in A2 scenarios
- Wet season water availability increasing rate in A2 is high due to rainfall will be more under A2 CC scenarios condition
- Increase of monsoon flow is higher for drought prone area than saline prone area
Change in water availability (%) in saline prone area
Scenario Dry Season Wet Season
A1B -15 10A2 -23 16
- Dry season water availability will be reduced 15% in A1B and 23% in A2 scenario
- Wet season water availability will increased 10% in A1B and 16% in A2 scenarios
- Dry season water availability decreasing rate in A2 is higher than A1B may be due to less dry season rainfall under A2 CC scenarios condition
- Reduction of dry season flow is higher for saline prone area than drought prone area
Water availability assessment results in saline prone area - under different climate change scenarios
Crop Modeling (DRAS) Results : Assessment of yield reduction and
water demand of crops under different climate change scenarios
Crop Variety
Upazila Name
Base Year
(% of Yield Reduction)
% Change of Yield Reduction
2040s 2070s 2100s
A1B A2 A1B A2 A1B A2
T Aus
Tanore 35 -5 -16 -5 -16 +10 -9
Godagari 34 -4 -13 -5 +3 +11 -7
Gomostapur 38 -6 -9 -2 +1 +11 -12
T Aman
Tanore 12 +10 +4 +3 -1 +4 +1
Godagari 10 +11 +5 +4 +4 +5 -2
Gomostapur 15 +6 +5 +2 +1 +4 -6Negative sign: Yield reduction decrease/Crop production increase
Positive sign: Yield reduction increase/Crop production decrease
Crop yield reduction of drought prone areas under different Climate Change Scenarios
T.Aus (monsoon crop): For A1B scenarios- during 2040 and 2070 yield reduction will decreased and during 2100 yield reduction will increase. Further yield reduction will decrease for all the period (40s, 70s and 2100) except Godagari and Gomastapur under A2 Scenarios :
YR 4% decrease from
Base
YR 5% decrease from
Base
YR 11% Increase from
Base
Base Year Yield Reduction 34%
YR 13% decrease from
Base
YR 3% Increase from Base
YR 7% decrease from
Base
For both scenarios T Aus production will be increased from base situation except A2 (2070s)
and A1B (2100s)
YR 11% increase from
Base
YR 4% increase from
Base
YR 5% Increase from
Base
Base Year Yield Reduction 10%
YR 5% increase from Base
YR 4% increase from Base
YR 2% decrease from
Base
For both scenarios T Aman production will be decreased from base situation except A2
(2100s)
Crop Variety
Upazila Name
Base Year
(% of Yield Reduction)
% Change of Yield Reduction
2040s 2070s 2100s
A1B A2 A1B A2 A1B A2
T Aus
Amtali 8 -7 -7 -4 -7 -1 -7Patharghata 8 -6 -7 -4 -7 -1 -7
Kalapara 7 -6 -6 -3 -6 0 -6
T Aman
Amtali 10 +4 +3 +13 +4 +3 +2
Patharghata 11 +7 +10 +19 +12 +2 +8
Kalapara 8 +5 +8 +13 +7 +3 +4Negative sign: Yield reduction decrease/Crop production increase
Positive sign: Yield reduction increase/Crop production decrease
Crop yield reduction of saline prone areas under different Climate Change Scenarios
Base Year Yield
Reduction 8%
YR 6% decrease from
Base
YR 4% decrease from
Base
YR 1% decrease from
Base
YR 7% decrease from
Base
YR 7% decrease from
Base
YR 7% decrease from
Base
For both scenarios T Aus production will be increased from base situation
Base Year Yield
Reduction 11%
YR 7% increase from
Base
YR 19% increase from
Base
YR 2% increase from
Base
YR 10% increase from
Base
YR 12% increase from
Base
YR 8% increase from
Base
For both scenarios T Aman production will be decreased from base situation
Crop Variety
Upazila
Name
Base Year
NIR (mm)
Change of NIR (mm)2040s 2070s 2100s
A1B A2 A1B A2 A1B A2
T AusTanore 319 -67 -65 -87 -91 +50 -41
Godagari 310 -64 -75 -57 -32 +51 -57Gomostapur 346 -65 -64 -77 -17 +73 -80
T AmanTanore 156 +72 +42 +37 +12 +66 +1
Godagari 139 +70 +39 +42 +50 +68 -32Gomostapur 180 +61 +31 +40 +43 +76 -23
BoroTanore 1087 +38 -64 +58 -70 +66 -33
Godagari 1115 0 -98 +22 -106 +26 -72Gomostapur 1029 +19 -74 +37 -75 +61 -71
Irrigation Water Demand at drought prone area different Climate Change Scenarios
Negative sign: Irrigation water demand will be decreased
Positive sign: Irrigation water demand will be increased
Irrigation Water Demand for T Aus CropIrrigation Water Demand for T Aman CropIrrigation water demand maps for winter rice (Boro) crop under different CC scenarios
Crop Name
Upazila
Name
Base Year
(NIR (mm)
Change of NIR (mm)2040 2070 2100
A1B A2 A1B A2 A1B A2
T AmanAmtali 97 +21 +11 +49 +23 +19 +7Patharghata 125 +32 +29 +70 +44 +25 +27Kalapara 81 +22 +30 +51 +39 +18 +22
T AusAmtali 117 -78 -111 -43 -93 -8 -86Patharghata 106 -57 -99 -23 -80 +6 -73Kalapara 101 -66 -95 -30 -78 -3 -67
BoroAmtali 881 +17 -57 +9 -61 +38 -22Patharghata 835 +10 -40 +30 -42 +36 -10Kalapara 848 +16 -61 +10 -61 +37 -34
Irrigation Water Demand under different Climate Change Scenarios- Saline Area
Negative sign: Irrigation water demand will be decreased
Positive sign: Irrigation water demand will be increased
Conclusions
For both scenarios T Aman (monsoon) crop production will be decreased from base situation in saline prone area. But T.Aus (pre-monsoon) crop production will increase
For both scenarios T Aman production will be decreased from base situation except A2 (2100s) in drought prone area
• Dry season water availability will be reduced 15% in A1B and 23% in A2 scenario. Wet season water availability will increased 10% in A1B and 16% in A2 scenarios
• Dry season water availability decreasing rate in A2 is higher than A1B may be due to less dry season rainfall in under A2 CC scenarios condition
• Increase of monsoon flow is higher for drought prone area than saline prone area
• Dry season water availability will be reduced 15% in A1B and 23% in A2 scenario. Wet season water availability will increased 10% in A1B and 16% in A2 scenarios
• Dry season water availability decreasing rate in A2 is higher than A1B may be due to less dry season rainfall under A2 CC scenarios condition
• Reduction of dry season flow is higher for saline prone area than drought prone area
Incase of Drought prone area
Incase of saline prone area
Recommendations • Higher resolution climate model downscaled results very
essential. Research fellowship can be introduced in the second phase of the project to get high resolution CC result can be obtained from ICTP Italy.
• Sensor based climate and other field data collection is highly essentials for the local level adaptation strategy formulation
• Model performance can be improved based on secondary and primary information (sensor based data) of water availability
• Not only water controls the yield, nutrient with water is also essential. Thus influence of nutrient is essential to adapt yield reduction
• Water availability estimation should be based on quality and quantity
• Couple of salinity intrusion and water availability model can use in coastal area
• Field level implementation of DRAS and AquaCrop model should be enhanced for scheduling of real time irrigation
• For better crop production Project Stakeholder Advisory Committee will demonstrate new technology to the farmers
Future activities • Union wise water scarcity can be studied through assessing water
availability using GIS/RS based model
• Development of Local level Adaptation Plan for Action (LAPA) is very essential. Union wise LAPA can developed considering climate induced disasters and agro-ecological zones
• Assessment of climate change impact on livelihood for the local level adaptation strategy formulation
• Agricultural Water Management Committee or Group formation under Triple (PPP) system providing technology based irrigation scheduling and fertilizer recommendations
• Study on sensor based field data collection by DAE field officials and farmers
• Field level implementation of DRAS and AquaCrop model at DAE. Training for Union Agriculture Officers for growing more crop using less irrigation water
Thank You
