1. Global Irrigation Water Demand: Variability and Uncertainties Arising from Agricultural and Climate Data Sets Dominik Wisser 1, Steve Frolking 1, Ellen Douglas 2, Balazs Fekete 1, Charlie Vörösmarty 1, Andreas Schumann 3 1 – University of New Hampshire; 2 – University of Massachusetts, Boston, 3 - Ruhr-University Bochum, Germany Methodology Irrigation water demand: Daily time step water balance simulation (see figure to right). Crop ET calculated as potential ET (Hamon function) times crop water demand coefficient from FAO. If/when crop root zone soil water drops below crop-specific threshold, irrigation water is added to fill root zone to field capacity. Additions accumulate through the cropping season(s). Paddy rice: kept flooded, so water loss is by both ET and percolation (soil texture dependent). Irrigation water added as needed to keep flooded. Total irrigation water requirement equals irrigation water added (as above) divided by efficiency factor (national values from FAO; global mean ~ 0.4). Variability: Assume constant irrigation area (FAO/GMIA) and crop distribution (c. 2000 values) and simulate 1963-2002 weather (CRU only). Variability in irrigation water requirement reflects actual variability in demand (and maybe water use). Uncertainties: Run set of simulations with different combinations of weather and irrigated area data; different assumptions about crop season, crop type, rice percolation. Variability reflects uncertainty in magnitude of global irrigation water demand. Methodology Irrigation water demand: Daily time step water balance simulation (see figure to right). Crop ET calculated as potential ET (Hamon function) times crop water demand coefficient from FAO. If/when crop root zone soil water drops below crop-specific threshold, irrigation water is added to fill root zone to field capacity. Additions accumulate through the cropping season(s). Paddy rice: kept flooded, so water loss is by both ET and percolation (soil texture dependent). Irrigation water added as needed to keep flooded. Total irrigation water requirement equals irrigation water added (as above) divided by efficiency factor (national values from FAO; global mean ~ 0.4). Variability: Assume constant irrigation area (FAO/GMIA) and crop distribution (c. 2000 values) and simulate 1963-2002 weather (CRU only). Variability in irrigation water requirement reflects actual variability in demand (and maybe water use). Uncertainties: Run set of simulations with different combinations of weather and irrigated area data; different assumptions about crop season, crop type, rice percolation. Variability reflects uncertainty in magnitude of global irrigation water demand. ABSTRACT: Water withdrawals for agriculture account for ~80% of total surface- and ground-water withdrawals. FAO projects that irrigated areas in developing countries will grow at 0.6%/yr, representing a decline in per-capita irrigated area. We use a new irrigation water balance model (WBMPlus) to estimate variability and uncertainties in irrigation water requirements arising from agricultural and climate data sets. The variability we assess arises from interannual variability in weather, using two ~50 year reconstructions of global daily weather. We do not account for variability or trends in irrigation area or cropping, but use a contemporary map (so we do not generate an irrigation water use history). The uncertainties arose from the location and extent of irrigation (two different global maps), the actual weather (two reconstructions), soil properties, irrigated crop types, and growing/irrigation season timing. Simulated global irrigation water use varied by about 30%, depending on choice of irrigation map or choice of weather data. The combined effect of irrigation map and weather data generated a global irrigation water use range of 2200 to 3900 km 3 y -1. Weather driven variability was generally less than ±100 km 3 y -1, globally, but could be as large as ±30% at the national scale. Acknowledgements: Support from NASA IDS program (NNX07AH32G ) and NASA Terrestrial Hydrology Program (NNX07AW08G ). We thank Chrandrashekar Biradar for help with the IWMI/GIAM data. WBMPlus Model Vertical structure of WBMPlus. Model also includes horizontal water flows between grid cells in river channels, following a 30-min global river routing network. ChinaIndiaUSA Global total NCEP & IWMI NCEP & FAO CRU & FAO CRU & IWMI 0.5° latitude bins (°N) Precipitation (mm/y) CRU NCE Irrigation (10 3 km 2 ) FAO IWMI Input data: Soils FAO soil drainage class map: paddy percolation rates; root zone water capacity. Crops G lobal 5-min crop data (Monfredo et al. 2008; Ramankutty et al. 2008), aggregated to 4 classes (rice, perennial, vegetable, other annual), and to 30- min resolution; crop water requirements from FAO. Input data: Soils FAO soil drainage class map: paddy percolation rates; root zone water capacity. Crops G lobal 5-min crop data (Monfredo et al. 2008; Ramankutty et al. 2008), aggregated to 4 classes (rice, perennial, vegetable, other annual), and to 30- min resolution; crop water requirements from FAO. Weather (see figure below; data binned by half-degree latitude) 1. CRU T2.1 gauge-based monthly gridded; stochastically downscaled to daily. 2. NCEP/NCAR reanalysis product. Irrigation area (see figure below; data binned by half-degree latitude) 1. FAO/GMIA V.4.0.1 (Siebert et al. 2005) –320 Mha. 2. IWMI/GIAM (Thenkabail et al. 2006) –450 Mha. Weather (see figure below; data binned by half-degree latitude) 1. CRU T2.1 gauge-based monthly gridded; stochastically downscaled to daily. 2. NCEP/NCAR reanalysis product. Irrigation area (see figure below; data binned by half-degree latitude) 1. FAO/GMIA V.4.0.1 (Siebert et al. 2005) –320 Mha. 2. IWMI/GIAM (Thenkabail et al. 2006) –450 Mha. Conclusions Total : Based on 2 irrigation area maps and 2 weather data sets, global total irrigation water demand (including inefficiency losses) is 3000 ± 600 km 3 yr -1. National totals were generally consistent with reported data from FAO AQUASTAT (see figure below-left), though some combinations of weather and irrigation area data were biased low for most countries, but high for India and China. Variability : Global, weather-driven variability is generally <5% of global demand (see figure below-center). Regionally or nationally, this can be substantially higher (see figure below-right), particularly for regions where some crop water demand is typically met by precipitation. For China, high minus low weather-driven demand is up to 250 km 3 yr -1 (~3x the annual discharge of the Yellow River). Uncertainty : Uncertainty in irrigation total with respect to weather data and irrigation area is much larger than weather-driven variability (see figure below-center & right); similar potential uncertainty results for rice area and rice percolation loss rates (not shown).