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Agriculture and Water Resources Cynthia Rosenzweig and Max Campos AIACC Trieste Project Development Workshop [email protected] Linking Regional Water Supplies and Water Demands Availability of water for agriculture in the coming decades depends not only on changing climate, but also on population, economic development, and technology Water Availability: Five International Case Studies Rosenzweig et al., 1999, 2001 Linking a suite of models in order to improve projections of water availability, by taking potential changes in both water supply and demand into account. SCENARIOS GCMs variability CLIMATE Precip., Temp. Solar Rad. WATBAL Streamflow PET CERES Crop water demand CROPWAT Regional irrigation SCENARIOS Population, Development, Technology WEAP Evaluation Planning • Runoff, water demands, and water system reliability • Environmental stress due to human use of water resources • Crop yields based on consistent projections of changes in water supply and demand Maize production in 1998 Argentina Brazil China Population (millions) 2020 Rest 1600 Hungary & Romania USA 1400 1200 Soybean production in 1998 Argentina 1000 1995 Low 800 High 600 Rest Brazil 400 200 USA China Hungary & Romania (<0.01%) 0 Brazil China US Cynthia Rosenzweig1, David C. Major1, Kenneth Strzepek2, Ana Iglesias1, David Yates2, Alyssa Holt2, and Daniel Hillel1 SCENARIOS GCMs variability Crop yields and water demands are estimated with process based crop models (calibrated and validated). The ratios (Kc) between simulated and actual crop ET are used to estimate regional water demand with CROPWAT. Irrigation demand is adjusted by a regional irrigation efficiency. Daily climate (34 sites) Process models CERES SOYGRO Monthly climate (27 water regions) REGIONAL DATABASES Crops Soils Yields Management Yields Irrigation Phenology PET, ETc Kc Empirical model CROPWAT CLIMATE CHANGE EFFECTS Phenology CO2 Kc Net irrigation all crops Spatial database Crop areas Irrig. efficiency TOTAL IRRIGATION DEMAND Crop water demand model interactions Water supply calculated using WATBAL PET calculation by Priestley-Taylor (ensuring consistency with the crop models WATBAL is run for 50 yrs of climate change and variability scenarios, using SAMS WG. Modeled vs. observed monthly runoff for the Titsza water region. Schematic of WATBAL processes Evapotranspiration Effective precipitation 1.80 1.60 mm/day 1.40 Surface runoff 1.00 0.80 0.60 0.40 0.20 Baseflow Kaczmarek, 1993; Yates, 1996 Ken Strzepek, Univ. of Colorado, Boulder Modeled Observed Oct-86 Oct-85 Oct-84 Oct-83 Oct-82 Oct-81 0.00 Oct-80 Sub-surface runoff Oct-79 Soil moisture zone Relative depth 1.20 R2= 0.55 Ann. avg mod. = 208 mm Ann. avg obs. = 213 mm Working with Multiple Models: Consistency at different Spatial Scales Harbin (China) 6 ET0 (mm/day) 5 HARA 4 w bHARA 3 Dier Songhua Jian 2 Nen Jian Songhua Jian 1 0 0 50 100 150 200 250 300 350 Day of Year Grand Island (Nebraska) 8 ET0 (mm/day) 7 6 5 GNEA 4 w bGNEA 3 Low er Missouri 2 1 0 0 50 100 150 200 Day of Year 250 300 350 Balance of water supply and demand is undertaken in the Water Evaluation and Planning (WEAP) model. Population and GDP drivers are used to calculate future industrial, municipal, and domestic water use, and forecast increases in irrigation areas. (UN population forecasts and economic forecasts of The Netherlands Central Planning Bureau.) WEAP schematic for the water regions in the US Corn Belt Stockholm Environment Institute, 1997 Boston, MA Annual Runoff (m3x1011) Change in annual runoff and water reliability for the 2020s with change climate scenarios 6 4 2 0 Annual Reliability (%) Danube Argentina Brazil China USA Brazil China US 100 90 80 70 60 50 Danube Argentina Current MPI GFDL GISS Key Water Resource Results 100 Possible decadal surprises 60 40 Demand met Reliability 20 0 1990 2000 2010 2020 2040 2040 2050 Year 400 Change in seasonality Runoff (cfs) Percentage 80 Current GFDL MPI HC 300 200 100 50 O N D J F M A M Months J J A S Strzepek et al., 1999 Projected change in environmental stress for the Danube water regions Reference 1995 Reference 2010 GISS 2010 Reference 2020 GISS 2020 High stress Medium stress Low stress No stress Demand to supply ratio (environmental stress) measures degree of economic development and impacts on ecosystems. If the demand to supply ratio is low, then there is ample water for ecosystem services. Crop Coefficients Corn Adaptation: Optimizing crop varieties P1 P1 Juvenile phase (growing degree days base 8 C from emergence to end of the juvenile phase) P2 P2 P5 P5 Photoperiod sensitivity G2 G2 G5 G5 Potential kernel number Grain filling duration (growing degree days base 8 form silking to physiological maturity) Potential kernel weight (growth rate) Testing adaptation with crop models Irrigation Demand mm/ha Base Climate Effect of Cultivar and Planting Date 350 300 250 day 100 day 110 day 120 day 130 200 150 100 50 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Cultivars Nitrogen Leaching (kg/ha) Base Climate Effect of Cultivar and Planting Date 16 15.9 15.8 15.7 day 130 15.6 15.5 15.4 15.3 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Cultivars Nitrogen Leached: Effect of Precipitation Growing Season Precipitation 493.8 4 sowing to flowering 150 420 440 460 480 500 growing season precipitation Nitrogen Leached 200 floweing to maturity 250 15.7 . 100 469.3 50 434 0 400 47.8 47.8 1 1 48.1 48.1 2 2 47.9 47.9 3 3 50.4 50.4 4 483.1 Corn Growing Season 4 14.1 3 12.6 2 10.9 1 0 5 10 nitrogen leached 15 20