Sensitivity of simulated global-scale freshwater fluxes and storages to input data, hydrological model structure, human water use and calibration

Abstract

Abstract. Global-scale assessments of freshwater fluxes and storages by hydrological models under historic climate conditions are subject to a variety of uncertainties. Using the global hydrological model WaterGAP 2.2, we investigated the sensitivity of simulated freshwater fluxes and water storage variations to five major sources of uncertainty: climate forcing, land cover input, model structure, consideration of human water use and calibration (or no calibration). In a modelling experiment, five variants of the standard version of WaterGAP 2.2 were generated that differed from the standard version only regarding the investigated source of uncertainty. Sensitivity was analyzed by comparing water fluxes and water storage variations computed by the variants to those of the standard version, considering both global averages and grid cell values for the time period 1971–2000. The basin-specific calibration approach for WaterGAP, which forces simulated mean annual river discharge to be equal to observed values at 1319 gauging stations (representing 54% of global land area except Antarctica and Greenland), has the highest effect on modelled water fluxes and leads to the best fit of modelled to observed monthly and seasonal river discharge. Alternative state-of-the-art climate forcings rank second regarding the impact on grid cell specific fluxes and water storage variations, and their impact is ubiquitous and stronger than that of alternative land cover inputs. The diverse model refinements during the last decade lead to an improved fit to observed discharge, and affect globally averaged fluxes and storage values (the latter mainly due to modelling of groundwater depletion) but only affect a relatively small number of grid cells. Considering human water use is important for the global water storage trend (in particular in the groundwater compartment) but impacts on water fluxes are rather local and only important where water use is high. The best fit to observed time series of monthly river discharge (Nash–Sutcliffe criterion) or discharge seasonality is obtained with the standard WaterGAP 2.2 model version which is calibrated and driven by a sequence of two time series of daily observation-based climate forcings, WFD/WFDEI. Discharge computed by a calibrated model version using monthly CRU 3.2 and GPCC v6 climate input reduced the fit to observed discharge for most stations. Taking into account the investigated uncertainties of climate and land cover data, we estimate that the global 1971–2000 discharge into oceans and inland sinks is between 40 000 and 42 000 km3 yr−1. The range is mainly due differences in precipitation data that affect discharge in uncalibrated river basins. Actual evapotranspiration, with approximately 70 000 km3 yr−1, is rather unaffected by climate and land cover in global sum but differs spatially. Human water use is calculated to reduce river discharge by approximately 1000 km3 yr−1. Thus, global renewable water resources are estimated to range between 41 000 and 43 000 km3 yr−1. The climate data sets WFD (available until 2001) and WFDEI (starting in 1979) were found to be inconsistent with respect to short wave radiation data, resulting in strongly different potential evapotranspiration. Global assessments of freshwater fluxes and storages would therefore benefit from the development of a global data set of consistent daily climate forcing from 1900 to current.

Evaluation of Radiation Components in a Global Freshwater Model with Station-Based Observations

“…Even though the model version is not modified, the term model variant is used to distinguish the meteorological forcings with a five-letter abbreviation (WFDEI, PGFv2, ERAID, and ERAIN, see Table 1). The WFDEI variant (see Section 2.1.2), based on ERA-Interim reanalysis, represents the current standard meteorological forcing and thus standard radiation input for WaterGAP (see STANDARD model variant in [27]). The PGFv2 forcing (see Section 2.1.3) is used to include an alternative forcing which is very frequently applied in global scale modeling and is thus evaluated here as well.…”

Reconstruction of global gridded monthly sectoral water withdrawals for 1971–2010 and analysis of their spatiotemporal patterns

“…The four products were generated by 4 GHMs, i.e. WaterGAP (Döll and Siebert, 2002;Alcamo et al, 2003;Döll et al, 2009;Müller Schmied et al, 2014), LPJmL (Rost et al, 2008), H08 (Hanasaki et al, 2008a, b), and PCR-GLOBWB Wada et al, 2011;, and they are all forced by WFDEI climate data. To investigate the uncertainty derived from forcing data, we also use other three simulated irrigation water withdrawal by WaterGAP forced by three datasets (i.e.…”

Water Budget Analysis within the Surrounding of Prominent Lakes and Reservoirs from Multi-Sensor Earth Observation Data and Hydrological Models: Case Studies of the Aral Sea and Lake Mead

“…However, WGHM also does not explicitly consider the variable surface area of lakes/reservoirs for ET calculation. It includes an empirical and global reduction function that reduces evaporation if the lake storage volume decreases [20]. As this function is not specifically adjusted for the Aral Sea, it may not adequately capture the huge reduction of its water surface area which may be the reason for an overestimation of ET for the region.…”