Transcript here. - RICECLIMA
Presentation on WP1-climate change scenarios, water availability and crop modeling Under the study project of
Climate Change Impacts, Vulnerability and Adaptation: Sustaining Rice Production in Bangladesh
Motaleb Hossain Sarker Director, Ecology Division, CEGIS (On behalf of CEGIS Team)
Acknowledgement
Norwegian Embassy : Specially Mr. Arne Haug, Counselor/Deputy Head of Mission We also acknowledge BRRI and Bioforsk: Specially following Experts Name Dr. J C Biswas M Maniruzzaman F I M Golam Wahed Sarker Dr. M Ashiq Iqbal Khan Dr. Nagothu Udaya Sekhar Dr. Trond Rafoss Dr. Attila Nemes Dr. Stefanos Xenarios Dr. Johannes Deelstra Designation and organization Principal Agronomist, BRRI Senior Irrigation Engineer, BRRI Senior Agril. Economist, BRRI Senior Pathologist, BRRI Director (Asia Projects) , Bioforsk Senior Researcher, Bioforsk Senior Researcher, Bioforsk Senior Researcher, Bioforsk Senior Researcher, Bioforsk
Presentation Outline
• Project goal and objectives • Study area • Brief methodology • Outputs and results of WP1 • Conclusions and recommendations • CEGIS Capacity in future works (Phase-II)
Goal of the Study
Goal of the overall study different climate : To develop an integrated adaptation framework in order to sustain and improve the rice production under change scenarios in Bangladesh Goal of the modeling exercise (WP1):
- 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 models results water using the model outputs scenario generation availability scenarios climate change scenarios through crop modeling using hydrological model based on the downscaled climate - To assess the yield reduction of rice crop under different - To develop the different GIS maps through GIS analysis - To prepare document on modeling activities and - To assist BRRI for developing adaptation options using climate model result results through field experiments
Study Area and Demography
Drought prone area Saline prone area Area Drought prone Saline prone
Total
Population
798,077 672,560 1,470,637
Overall Study Approach
Downscaling of climate model results Development of climate variability scenarios Hydrologic modeling and generation of water availability scenarios (SWAT) Climate Change Scenarios Water Availability Scenarios Crop Model (DRAS, AQUA Crop etc.) Crop production/yield reduction under different CC scenarios through crop modeling BRRI Field experiments Dev. of adaptation options based on the model result using the field experiments Dissemination of results to the end users (Planner, Decision Makers and Farmers)
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)
A 1 B A 2 High temperature in dry season • More evaporation • Increase water demand Less rainfall in dry season • Less water availability • More irrigation water need
Temperature and Rainfall: Amtali (Saline Prone Area)
A 1 B A 2 High temperature in dry season • More evaporation • Increase water demand Less rainfall in dry season • Less water availability • More salinity
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
Scenario A1B A2 Change in water availability (%) in drought prone area Dry Season -13 -20 Wet Season 9 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
Water availability assessment results in saline prone area - under different climate change scenarios Change in water availability (%) in saline prone area Scenario Dry Season Wet Season A1B A2 -15 -23 10 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
Crop Modeling (DRAS) Results : Assessment of yield reduction and water demand of crops under different climate change scenarios
Crop yield reduction of drought prone areas under different Climate Change Scenarios Crop Variety Upazila Name
T Aus Tanore Godagari Gomostapur Tanore T Aman Godagari Gomostapur
Base Year (% of Yield Reduction) 35 34 38 12 10 15 % Change of Yield Reduction 2040s A1B A2 -5 -4 -6 +10 -16 -13 -9 +4 +11 +6 +5 +5 A1B -5 -5 -2 +3 +4 +2 2070s A2 -16 +3 +1 -1 +4 +1 +4 +5 +4 2100s A1B +10 +11 +11 A2 -9 -7 -12 +1 -2 -6 Negative sign: Yield reduction decrease/Crop production increase Positive sign: Yield reduction increase/Crop production decrease 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 :
For both scenarios T Aus production will be increased from base situation except A2 (2070s) and A1B (2100s) Base Year Yield Reduction 34% YR 4% decrease from Base YR 5% decrease from Base YR 11% Increase from Base YR 13% decrease from Base YR 3% Increase from Base YR 7% decrease from Base
Crop yield reduction of saline prone areas under different Climate Change Scenarios Crop Variety
T Aus T Aman
Upazila Name
Amtali Patharghata Kalapara Amtali Patharghata Kalapara
Base Year (% of Yield Reduction) 8 8 7 10 % Change of Yield Reduction 2040s A1B A2 -7 -7 -6 -7 -6 +4 -6 +3 2070s A1B -4 -4 -3 +13 A2 -7 -7 -6 +4 A1B -1 -1 0 2100s +3 A2 -7 -7 -6 +2 11 8 +7 +5 +10 +8 +19 +13 +12 +7 +2 +3 +8 +4 Negative sign: Yield reduction decrease/Crop production increase Positive sign: Yield reduction increase/Crop production decrease
For both scenarios T Aus production will be increased from base situation 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
Irrigation Water Demand at drought prone area different Climate Change Scenarios Crop Variety
T Aus T Aman Boro
Upazila Name
Tanore Godagari Gomostapur Tanore Godagari Gomostapur Tanore Godagari Gomostapur
Base Year NIR (mm) 319 310 346 156 139 180 1087 1115 1029 2040s Change of NIR (mm) 2070s 2100s A1B A2 -67 -65 A1B -87 A2 -91 A1B +50 A2 -41 -64 -65 -75 -64 +72 +42 +70 +39 +61 +31 +38 0 +19 -64 -98 -74 -57 -77 +37 +42 +40 +58 +22 +37 -32 -17 +12 +50 +43 -70 -106 -75 Negative sign: Irrigation water demand will be decreased +51 +73 +66 +68 +76 +66 +26 +61 -57 -80 +1 -32 -23 -33 -72 -71 Positive sign: Irrigation water demand will be increased
Irrigation water demand maps for winter rice (Boro) crop under different CC
Irrigation Water Demand for T Aus Crop
Irrigation Water Demand under different Climate Change Scenarios Saline Area Crop Name Upazila Name Base Year (NIR (mm) Change of NIR (mm) 2040 2070 2100 A1B A2 A1B A2 A1B A2
T Aman T Aus Boro Amtali Patharghata Kalapara Amtali Patharghata Kalapara Amtali Patharghata Kalapara
97 125 81 117 106 101 881 835 848 +21 +32 +22 -78 -57 -66 +17 +10 +16 +11 +29 +30 -111 -99 -95 -57 -40 -61 +49 +70 +51 -43 -23 -30 +9 +30 +10 +23 +44 +39 -93 -80 -78 -61 -42 -61 Negative sign: Irrigation water demand will be decreased Positive sign: Irrigation water demand will be increased +19 +25 +18 -8 +6 -3 +38 +36 +37 +7 +27 +22 -86 -73 -67 -22 -10 -34
Conclusions
Incase of 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 Incase of 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 For both scenarios T Aman production will be decreased from base situation except A2 (2100s) in drought prone area 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
• • • • • • • •
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