Tingju Zhu, Claudia Ringler, Mark W. Rosegrant
Development of a Global Hydrological Model for
Integrated Assessment Modeli...
2
Global Hydrologic Modeling in the Context of
“Water4Food”
 Irrigation is the largest water user, and key for securing
f...
Source: Shiklomanov (2000)
Global Water Consumption
0
500
1000
1500
2000
2500
1900 1920 1940 1960 1980 2000
Volume(km
3
/y...
4
Global Hydrologic Modeling in the Context of
“Water4Food”
 Irrigation is the largest water user, and key for securing
f...
“Linking” Models
 Global Hydrologic Model (IGHM) simulates natural
hydrological cycle, providing a consistent estimation
...
IMPACT – Partial Equilibrium Agricultural Sector Model
Source: Rosegrant et al. (2012)
Spatial Units of IMPACT Model Simulations
River Basins
Food Producing Unit
8
Linking Global Hydrology Model to Water Management Simulation
Source: Zhu and Ringler (2012)
9
Scope vs. Complexity – How detailed is detailed enough for
global water modeling?
Determinants of model complexity
• Res...
10
IGHM Main Structure and Major Assumption
 GRPET n 





 Spatial Resolution: 0.5˚ latitude x 0.5˚ longitude g...
11
0
100
200
300
400
500
600
700
8000
1000
2000
3000
4000
5000
6000
7000
Jan-71
Jan-72
Jan-73
Jan-74
Jan-75
Jan-76
Jan-77
...
Source: GPCC v5
Mean Annual Precipitation 1971-2000
Source: IGHM simulation (2013)
Mean Annual Potential ET 1971-1990
Source: IGHM simulation (2013)
Open water evaporation (lakes and rivers)
Runoff Simulation - Annual
Source: IGHM simulation (2013)
Runoff Simulation - January
Source: IGHM simulation (2013)
Runoff Simulation - July
Source: IGHM simulation (2013)
Runoff Simulation Jan-Dec
179
1570
1749
3762
4700
4916
8684
13232
0 2000 4000 6000 8000 10000 12000 14000
Middle East & North Africa
Europe Develope...
Mean Annual Runoff Changes under CSIRO-A1b Scenario in 2050
Source: IGHM simulation (2013)
Mean Annual Runoff Changes under CSIRO-b1 Scenario in 2050
Source: IGHM simulation (2013)
Mean Annual Runoff Changes under MIROC-a1b Scenario in 2050
Source: IGHM simulation (2013)
Mean Annual Runoff Changes under MIROC-b1 Scenario in 2050
Source: IGHM simulation (2013)
Conclusions
 Global hydrological modeling is needed for global
water and food system modeling, and other IAM
efforts
 Ex...
Development of a Global Hydrological Model for Integrated Assessment Modeling
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Development of a Global Hydrological Model for Integrated Assessment Modeling

  1. 1. Tingju Zhu, Claudia Ringler, Mark W. Rosegrant Development of a Global Hydrological Model for Integrated Assessment Modeling of Global Climate Change International Food Policy Research Institute Washington, DC World Environmental & Water Resources Congress 2013, Cincinnati, OH
  2. 2. 2 Global Hydrologic Modeling in the Context of “Water4Food”  Irrigation is the largest water user, and key for securing future food supply • Accounting for 70% global water withdraw, and 90% global water consumption • Accounting for less than 20% of global cropland, but contributing ~40% of global cereals production  Integrated modeling of global water and food systems requires spatially explicit simulations of water availability  Climate change impacts and adaptations modeling (for water management and agriculture) require quantifying hydrological responses to climate change
  3. 3. Source: Shiklomanov (2000) Global Water Consumption 0 500 1000 1500 2000 2500 1900 1920 1940 1960 1980 2000 Volume(km 3 /yr) Global Water Consumption Irrigation Water Consumption
  4. 4. 4 Global Hydrologic Modeling in the Context of “Water4Food”  Irrigation is the largest water user, and key for securing future food supply • Accounting for 70% global water withdraw, and 90% global water consumption • Accounting for less than 20% of global cropland, but contributing ~40% of global cereals production  Integrated modeling of global water and food systems requires spatially explicit simulations of water availability  Climate change impacts and adaptations modeling (for water management and agriculture) require quantifying hydrological responses to climate change
  5. 5. “Linking” Models  Global Hydrologic Model (IGHM) simulates natural hydrological cycle, providing a consistent estimation of water availability over space and time  Water Management Model simulates human interventions to water resources systems, enabling tests of policy and investment scenarios  Together, the “water models” estimate the effects of water stress on agricultural production, which affect trade, consumption, and malnutrition
  6. 6. IMPACT – Partial Equilibrium Agricultural Sector Model Source: Rosegrant et al. (2012)
  7. 7. Spatial Units of IMPACT Model Simulations River Basins Food Producing Unit
  8. 8. 8 Linking Global Hydrology Model to Water Management Simulation Source: Zhu and Ringler (2012)
  9. 9. 9 Scope vs. Complexity – How detailed is detailed enough for global water modeling? Determinants of model complexity • Research questions • Data availability and quality • Understanding of processes and settings • Applicability to a wide range of climatic conditions Scale-related issues • Processes take place on all scales. Analysis of the smallest scale only does not provide information on processes that take place on larger scales. • Sub-grid variability of model parameters -- spatial heterogeneity in a large grid cell
  10. 10. 10 IGHM Main Structure and Major Assumption  GRPET n        Spatial Resolution: 0.5˚ latitude x 0.5˚ longitude grid cells covering the entire global land surface except the Antarctic  Temporal Resolution: Monthly simulation over multi-decadal period Potential Evapotranspiration - Priestley-Taylor equation Runoff Generation Variable soil moisture holding capacity within a grid cell Linear reservoir representing groundwater modulation of base flow Source: Zhu and Ringler (2012)
  11. 11. 11 0 100 200 300 400 500 600 700 8000 1000 2000 3000 4000 5000 6000 7000 Jan-71 Jan-72 Jan-73 Jan-74 Jan-75 Jan-76 Jan-77 Jan-78 Jan-79 Jan-80 Jan-81 Jan-82 Jan-83 Jan-84 Jan-85 Jan-86 Jan-87 Jan-88 Jan-89 Jan-90 Jan-91 Jan-92 Jan-93 Jan-94 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Precipitation Runoff (a) Botswana Precip Qsim Qobs Nash-Sutcliffe model efficiency coefficient is 0.913 in the calibration period (1971-85) and is 0.906 in the validation period (1986-2000). IGHM Model Runoff Calibration and Validation for Botswana Catchment of the Limpopo River Basin Source: Zhu and Ringler (2012)
  12. 12. Source: GPCC v5 Mean Annual Precipitation 1971-2000
  13. 13. Source: IGHM simulation (2013) Mean Annual Potential ET 1971-1990
  14. 14. Source: IGHM simulation (2013) Open water evaporation (lakes and rivers) Runoff Simulation - Annual
  15. 15. Source: IGHM simulation (2013) Runoff Simulation - January
  16. 16. Source: IGHM simulation (2013) Runoff Simulation - July
  17. 17. Source: IGHM simulation (2013) Runoff Simulation Jan-Dec
  18. 18. 179 1570 1749 3762 4700 4916 8684 13232 0 2000 4000 6000 8000 10000 12000 14000 Middle East & North Africa Europe Developed South Asia Sub-Saharan Africa North America Europe & Central Asia East Asia & Pacific Latin America & Caribbean Source: IGHM simulation using the 1971-2000 climatology. Unit: km3/yr. Water Resource Distribution
  19. 19. Mean Annual Runoff Changes under CSIRO-A1b Scenario in 2050 Source: IGHM simulation (2013)
  20. 20. Mean Annual Runoff Changes under CSIRO-b1 Scenario in 2050 Source: IGHM simulation (2013)
  21. 21. Mean Annual Runoff Changes under MIROC-a1b Scenario in 2050 Source: IGHM simulation (2013)
  22. 22. Mean Annual Runoff Changes under MIROC-b1 Scenario in 2050 Source: IGHM simulation (2013)
  23. 23. Conclusions  Global hydrological modeling is needed for global water and food system modeling, and other IAM efforts  Existing global database (e.g. climate, soil, LCLU, typology) make possible hydrological modeling at global scale  Tradeoff between model complexity and spatial scope  Inter-model comparisons (e.g. Water-MIP) can potentially improve model performance

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