Global hydrology 2015: State, trends, and directions

By Marc F. P. Bierkens. 2015

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Citations in the corpus

Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2016, background): “Thus, global hydrological models tend to show poor predictive performance in describing subsurface flow processes [38].”
Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data (2018, unknown): “Bierkens MFP (2015) Global hydrology 2015: State, trends, and directions.”
Human impact parameterizations in global hydrological models improve estimates of monthly discharges and hydrological extremes: a multi-model validation study (2018, methodology): “Industrial water demands (supplementary table 1D) are based on historical country-scale estimates from the WWDRII dataset (Shiklomanov 1997, Vörösmarty et al 2005, WRI 1998) and the FAO-AQUASTAT database (www.fao.org/nr/water/aquastat/dbase/index.stm), for PCR-GLOBWB and H08 respectively. WaterGAP2 ”
The food‐energy‐water nexus: Transforming science for society (2017, background): “Global hydrologic and land-surface models also provide important information on water availability for FEW systems [Bierkens, 2015]; however, model intercomparisons reveal large uncertainties in these models [Haddeland et al., 2014; Schewe et al., 2014].”
The evolution of process-based hydrologic models: Historical challenges and the collective quest for physical realism. (2017, unknown): “A major challenge is the parameterization of the deeper subsurface on regional to continental scales in order to support large-domain groundwater modeling (Bierkens, 2015; Clark et al., 2015a).”
Non-renewable groundwater use and groundwater depletion: a review (2019, methodology): “Most water balance methods however rely on global hydrological models (Bierkens 2015).”