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When assessing global water resources with hydrological models, it is essential to know about methodological uncertainties. The values of simulated water balance components may vary due to different spatial and temporal aggregations, reference periods, and applied climate forcings, as well as due to the consideration of human water use, or the lack thereof. We analyzed these variations over the period 1901–2010 by forcing the global hydrological model WaterGAP 2.2 (ISIMIP2a) with five state-of-the-art climate data sets, including a homogenized version of the concatenated WFD/WFDEI data set. Absolute values and temporal variations of global water balance components are strongly affected by the uncertainty in the climate forcing, and no temporal trends of the global water balance components are detected for the four homogeneous climate forcings considered (except for human water abstractions). The calibration of WaterGAP against observed long-term average river discharge Q significantly reduces the impact of climate forcing uncertainty on estimated Q and renewable water resources. For the homogeneous forcings, Q of the calibrated and non-calibrated regions of the globe varies by 1.6 and 18.5 %, respectively, for 1971–2000. On the continental scale, most differences for long-term average precipitation P and Q estimates occur in Africa and, due to snow undercatch of rain gauges, also in the data-rich continents Europe and North America. Variations of Q at the grid-cell scale are large, except in a few grid cells upstream and downstream of calibration stations, with an average variation of 37 and 74 % among the four homogeneous forcings in calibrated and non-calibrated regions, respectively. Considering only the forcings GSWP3 and WFDEI_hom, i.e., excluding the forcing without undercatch correction (PGFv2.1) and the one with a much lower shortwave downward radiation SWD than the others (WFD), Q variations are reduced to 16 and 31 % in calibrated and non-calibrated regions, respectively. These simulation results support the need for extended Q measurements and data sharing for better constraining global water balance assessments. Over the 20th century, the human footprint on natural water resources has become larger. For 11–18% of the global land area, the change of Q between 1941–1970 and 1971–2000 was driven more strongly by change of human water use including dam construction than by change in precipitation, while this was true for only 9–13 % of the land area from 1911–1940 to 1941–1970.
When assessing global water resources with hydrological models, it is essential to know the methodological uncertainties in the water resources estimates. The study presented here quantifies effects of the uncertainty in the spatial and temporal patterns of meteorological variables on water balance components at the global, continental and grid cell scale by forcing the global hydrological model WaterGAP 2.2 (ISI-MIP 2.1) with five state-of-the-art climate forcing input data-sets. While global precipitation over land during 1971–2000 varies between 103 500 and 111 000 km3 yr−1, global river discharge varies between 39 200 and 42 200 km3 yr−1. Temporal trends of global wa- ter balance components are strongly affected by the uncertainty in the climate forcing (except human water abstractions), and there is a need for temporal homogenization of climate forcings (in particular WFD/WFDEI). On about 10–20 % of the global land area, change of river discharge between two consecutive 30 year periods was driven more strongly by changes of human water use including dam construction than by changes in precipitation. This number increases towards the end of the 20th century due to intensified human water use and dam construction. The calibration approach of WaterGAP against observed long-term average river discharge reduces the impact of climate forcing uncertainty on estimated river discharge significantly. Different homgeneous climate forcings lead to a variation of Q of only 1.6 % for the 54 % of global land area that are constrained by discharge observations, while estimated renewable water resources in the remaining uncalibrated regions vary by 18.5 %. Uncertainties are especially high in Southeast Asia where Global Runoff Data Centre (GRDC) data availability is very sparse. By sharing already available discharge data, or installing new streamflow gauging stations in such regions, water balance uncertainties could be reduced which would lead to an improved assessment of the world’s water resources.
The assessment of water balance components using global hydrological models is subject to climate forcing uncertainty as well as to an increasing intensity of human water use within the 20th century. The uncertainty of five state-of-the-art climate forcings and the resulting range of cell runoff that is simulated by the global hydrological model WaterGAP is presented. On the global land surface, about 62 % of precipitation evapotranspires, whereas 38 % discharges into oceans and inland sinks. During 1971–2000, evapotranspiration due to human water use amounted to almost 1 % of precipitation, while this anthropogenic water flow increased by a factor of approximately 5 between 1901 and 2010. Deviation of estimated global discharge from the ensemble mean due to climate forcing uncertainty is approximately 4 %. Precipitation uncertainty is the most important reason for the uncertainty of discharge and evapotranspiration, followed by shortwave downward radiation. At continental levels, deviations of water balance components due to uncertain climate forcing are higher, with the highest discharge deviations occurring for river discharge in Africa (−6 to 11 % from the ensemble mean). Uncertain climate forcings also affect the estimation of irrigation water use and thus the estimated human impact of river discharge. The uncertainty range of global irrigation water consumption amounts to approximately 50 % of the global sum of water consumption in the other water use sector.
A data set of monthly growing areas of 26 irrigated crops (MGAG-I) and related crop calendars (CC-I) was compiled for 402 spatial entities. The selection of the crops consisted of all major food crops including regionally important ones (wheat, rice, maize, barley, rye, millet, sorghum, soybeans, sunflower, potatoes, cassava, sugar cane, sugar beets, oil palm, rapeseed/canola, groundnuts/peanuts, pulses, citrus, date palm, grapes/vine, cocoa, coffee), major water-consuming crops (cotton), and unspecified other crops (other perennial crops, other annual crops, managed grassland). The data set refers to the time period 1998-2002 and has a spatial resolution of 5 arc minutes by 5 arc minutes which is 8 km by 8 km at the equator. This is the first time that a data set of cell-specific irrigated growing areas of irrigated crops with this spatial resolution was created. The data set is consistent to the irrigated area and water use statistics of the AQUASTAT programme of the Food and Agriculture Organization of the United Nations (FAO) (http://www.fao.org/ag/agl/aglw/aquastat/main/index.stm) and the Global Map of Irrigation Areas (GMIA) (http://www.fao.org/ag/agl/aglw/aquastat/irrigationmap/index.stm). At the cell-level it was tried to maximise consistency to the cropland extent and cropland harvested area from the Department of Geography and Earth System Science Program of the McGill University at Montreal, Quebec, Canada and the Center for Sustainability and the Global Environment (SAGE) of the University of Wisconsin at Madison, USA (http://www.geog.mcgill.ca/~nramankutty/ Datasets/Datasets.html and http://geomatics.geog.mcgill.ca/~navin/pub/Data/175crops2000/). The consistency between the grid product and the input data was quantified. MGAG-I and CC-I are fully consistent to each other on entity level. For input data other than CC-I, the consistency of MGAG-I on cell level was calculated. The consistency of MGAG-I with respect to the area equipped for irrigation (AEI) of GMIA and to the cropland extent of SAGE was characterised by the sum of the cell-specific maximum difference between the MGAG-I monthly total irrigated area and the reference area when the latter was exceeded in the grid cell. The consistency of the harvested area contained in MGAG-I with respect to SAGE harvested area was characterised by the crop-specific sum of the cell-specific difference between MGAG-I harvested area and the SAGE harvested area when the latter was exceeded in the grid cell. In all three cases, the sums are the excess areas that should not have been distributed under the assumption that the input data were correct. Globally, this cell-level excess of MGAG-I as compared to AEI is 331,304 ha or only about 0.12 % of the global AEI of 278.9 Mha found in the original grid. The respective cell-level excess of MGAG-I as compared to the SAGE cropland extent is 32.2 Mha, corresponding to about 2.2 % of the total cropland area. The respective cell-level excess of MGAG-I as compared to the SAGE harvested area is 27 % of the irrigated harvested area, or 11.5 % of the AEI. In a further step that will be published later also rainfed areas were compiled in order to form the Global data set of monthly irrigated and rainfed crop areas around the year 2000 (MIRCA2000). The data set can be used for global and continental-scale studies on food security and water use. In the future, it will be improved, e.g. with a better spatial resolution of crop calendars and an improved crop distribution algorithm. The MIRCA2000 data set, its full documentation together with future updates will be freely available through the following long-term internet site: http://www.geo.uni-frankfurt.de/ipg/ag/dl/forschung/MIRCA/index.html. The research presented here was funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) within the framework of the research project entitled "Consistent assessment of global green, blue and virtual water fluxes in the context of food production: regional stresses and worldwide teleconnections". The authors thank Navin Ramankutty and Chad Monfreda for making available the current SAGE datasets on cropland extent (Ramankutty et al., 2008) and harvested area (Monfreda et al., 2008) prior to their publication.