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
Designation and organization
Dr. J C Biswas
Principal Agronomist, BRRI
M Maniruzzaman
Senior Irrigation Engineer, BRRI
F I M Golam Wahed Sarker
Senior Agril. Economist, BRRI
Dr. M Ashiq Iqbal Khan
Senior Pathologist, BRRI
Dr. Nagothu Udaya Sekhar
Director (Asia Projects) , Bioforsk
Dr. Trond Rafoss
Senior Researcher, Bioforsk
Dr. Attila Nemes
Senior Researcher, Bioforsk
Dr. Stefanos Xenarios
Senior Researcher, Bioforsk
Dr. Johannes Deelstra
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: To develop an
integrated adaptation framework in order to
sustain and improve the rice production under
different
climate
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 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
Study Area and Demography
Drought prone area
Saline prone area
Area
Population
Drought prone
798,077
Saline prone
672,560
Total
1,470,637
Overall Study Approach
Downscaling of climate model results
Development of climate variability
scenarios
BRRI
Hydrologic modeling and generation
of water availability scenarios (SWAT)
Climate Change
Scenarios
Water Availability
Scenarios
Crop Model
Field experiments
Dev. of adaptation options
based on the model result
using the field experiments
(DRAS, AQUA Crop etc.)
Crop production/yield reduction under
different CC scenarios through crop modeling
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
Base
Year
(% of Yield
Reduction)
T Aus
% Change of Yield Reduction
2040s
2070s
2100s
A1B
A2
A1B
A2
A1B
A2
Tanore
35
-5
-16
-5
-16
+10
-9
Godagari
34
-4
-13
-5
+3
+11
-7
Gomostapur
38
12
-6
+10
-9
+4
-2
+3
+1
-1
+11
+4
-12
+1
10
+11
+5
+4
+4
+5
-2
15
+6
+5
+2
+1
+4
-6
Tanore
T Aman Godagari
Gomostapur
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
Forboth
bothscenarios
scenariosTTAman
Aus production
productionwill
willbe
beincreased
decreased
from base
from
situation
base situation
except A2
except
(2070s)
A2and
(2100s)
A1B (2100s)
Base Year Yield YR 11%
increase YR 4%
YR 11%
5% Increase
4% decrease
5% increase
decrease YR
Increase
Reduction 10%
from Base
from
from
34%
fromBase
Base
from Base
Base
5%decrease
increase YR
YR 3%
4% Increase
increase YR
YRYR
13%
YR 2%
7% decrease
decrease
fromBase
Base
from Base
Base
from
from
from
from Base
Base
Crop yield reduction of saline prone areas under different
Climate Change Scenarios
Crop
Variety
Upazila
Name
Amtali
T Aus
Patharghata
Kalapara
Amtali
T
Patharghata
Aman
Kalapara
Base
Year
(% of Yield
Reduction)
8
8
7
10
% Change of Yield Reduction
2040s
2070s
2100s
A1B A2 A1B A2 A1B A2
-7
-7
-4
-7
-1
-7
-6
-7
-4
-7
-1
-7
-6
-6
-3
-6
0
-6
+4 +3 +13 +4 +3 +2
11
+7
+10 +19 +12
+2
+8
8
+5
+8
+3
+4
+13
+7
Negative sign: Yield reduction decrease/Crop production increase
Positive sign: Yield reduction increase/Crop production decrease
For both
For both
scenarios
scenarios
T Aus
T Aman
production
production
will bewill
increased
be
decreased
from base
from situation
base situation
Base
Base Year
Year Yield
Yield
Reduction
Reduction11%
8%
YR 6%
7% decrease
increase YR
increase YR
YR
YR19%
4% decrease
YR2%
1%increase
decrease
from
from
from
from Base
Base
from Base
Base
fromBase
Base
YR
increase YR
increase
YR 10%
7% decrease
YR 12%
7% decrease
from Base
from Base
YR 7%
8% decrease
increase
YR
from Base
Irrigation Water Demand at drought prone area different Climate
Change Scenarios
Crop
Variety
Upazila
Name
Base
Year
NIR (mm)
Tanore
T Aus
Godagari
Gomostapur
Tanore
T Aman
Godagari
Gomostapur
Tanore
Boro
Godagari
Gomostapur
319
310
346
156
139
180
1087
1115
1029
Change of NIR (mm)
2040s
2070s
2100s
A1B A2 A1B A2 A1B A2
-67 -65 -87
-91
+50 -41
-64 -75 -57
-32
+51 -57
-65 -64 -77
-17
+73 -80
+72 +42 +37 +12 +66 +1
+70 +39 +42 +50 +68 -32
+61 +31 +40 +43 +76 -23
+38 -64 +58 -70
+66 -33
0 -98 +22 -106 +26 -72
+19 -74 +37 -75
+61 -71
Negative sign: Irrigation water demand will be decreased
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
Aman
Crop
Irrigation Water Demand
scenariosfor T Aus Crop
Irrigation Water Demand under different Climate Change ScenariosSaline Area
Change of NIR (mm)
Base
Upazila
Crop
2040
2070
2100
Year
Name
Name
A2 A1B A2 A1B A2
(NIR (mm) A1B
Amtali
T Aman Patharghata
Kalapara
Amtali
T Aus
Patharghata
Kalapara
Amtali
Boro
Patharghata
Kalapara
97
125
81
117
106
101
881
835
848
+21 +11 +49
+32 +29 +70
+22 +30 +51
-78 -111 -43
-57 -99 -23
-66 -95 -30
+17 -57 +9
+10 -40 +30
+16 -61 +10
+23
+44
+39
-93
-80
-78
-61
-42
-61
+19
+25
+18
-8
+6
-3
+38
+36
+37
Negative sign: Irrigation water demand will be decreased
Positive sign: Irrigation water demand will be increased
+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
Thank You