The DOI matches its Crossref record; 99 of the 100 references it cites were found in a registry or an index.
Sensitivity of simulated global-scale freshwater fluxes and storages to input data, hydrological model structure, human water use and calibration
Open accessPublished version of a preprint
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 (Water – Global Assessment and Prognosis) 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/refinements, consideration of human water use and calibration (or no calibration) against observed mean river discharge. In a modeling 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. The basin-specific calibration approach for WaterGAP was found to have the largest effect on grid cell fluxes as well as on global AET (actual evapotranspiration) and discharge into oceans for the period 1971–2000. Regarding grid cell fluxes, climate forcing ranks second before land cover input. Global water storage trends are most sensitive to model refinements (mainly modeling of groundwater depletion) and consideration of human water use. The best fit to observed time series of monthly river discharge or discharge seasonality is obtained with the standard WaterGAP 2.2 model version which is calibrated and driven by daily reanalysis-based WFD/WFDEI (combination of Watch Forcing Data based on ERA40 and Watch Forcing Data based on ERA-Interim) climate data. Discharge computed by a calibrated model version using monthly CRU TS (Climate Research Unit time-series) 3.2 and GPCC (Global Precipitation Climatology Center) v6 climate input reduced the fit to observed discharge for most stations. Taking into account uncertainties of climate and land cover data, global 1971–2000 discharge into oceans and inland sinks ranges between 40 000 and 42 000 km3 yr−1. Global actual evapotranspiration, with 70 000 km3 yr−1, is rather unaffected by climate and land cover uncertainties. Human water use reduced river discharge by 1000 km3 yr−1, such that 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 shortwave radiation data, resulting in strongly different actual 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 present.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Furthermore, WaterGAP is calibrated to estimate discharge from major river systems (Alcamo et al., 2003;Müller Schmied et al., 2014).This means that while the renewable part of groundwater is considered, fossil groundwater is omitted.”
From Life cycle assessment and water use impacts of lithium production from salar deposits: Challenges and opportunities (2024) · cites this paper as Müller Schmied et al., 2014
In the citing paper
“AWARE, based on the WaterGap 2.2 global hydrological model (Müller Schmied et al., 2014), quantifies the relative amount of fresh water remaining after the demands of aquatic ecosystems and anthropogenic activities are met.”
From Life cycle assessment and water use impacts of lithium production from salar deposits: Challenges and opportunities (2024) · cites this paper as Müller Schmied et al., 2014
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
River flow forecasting through conceptual models part I — A discussion of principles
Nash, J. E., & Sutcliffe, J. V. (1970) · Journal of Hydrology · doi:10.1016/0022-1694(70)90255-6 · Cited by 24,401
Updated world map of the Köppen-Geiger climate classification
(2007) · doi:10.5194/hess-11-1633-2007 · Cited by 13,640
World map of the Köppen-Geiger climate classification updated
Kottek, M., Grieser, J., Beck, C., Rudolf, B., & Rubel, F. (2006) · Meteorologische Zeitschrift · doi:10.1127/0941-2948/2006/0130 · Cited by 11,456
On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters
Priestley, C. H. B., & Taylor, R. J. (1972) · Monthly Weather Review · doi:10.1175/1520-0493(1972)100 · Cited by 6,545
Updated high‐resolution grids of monthly climatic observations – the CRU TS3 .10 Dataset
Harris, I. C., Jones, P. D., Osborn, T. J., & Lister, D. (2013) · International Journal of Climatology · doi:10.1002/joc.3711 · Cited by 6,326
Global hydrological cycles and world water resources
Oki, T., & Kanae, S. (2006) · Science · doi:10.1126/science.1128845 · Cited by 4,392
Improvements to a MODIS global terrestrial evapotranspiration algorithm
Mu, Q., Zhao, M., & Running, S. W. (2011) · Remote Sensing of Environment · doi:10.1016/j.rse.2011.02.019 · Cited by 2,915
High‐resolution mapping of the world's reservoirs and dams for sustainable river‐flow management
Lehner, B., Liermann, C. R., Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., et al. (2011) · Frontiers in Ecology and the Environment · doi:10.1890/100125 · Cited by 2,515
Self-citationNew Global Hydrography Derived from Spaceborne Elevation Data
Lehner, B., Verdin, K., & Jarvis, A. (2008) · Eos, Transactions American Geophysical Union · doi:10.1029/2008eo100001 · Cited by 2,411
Development and validation of a global database of lakes, reservoirs and wetlands
Lehner, B., & Döll, P. (2004) · Journal of Hydrology · doi:10.1016/j.jhydrol.2004.03.028 · Cited by 2,380
Self-citation
Research works printed but not linked: 4
- Alcamo, J., Leemans, R., and Kreileman, E. (Eds.): Global Change Scenarios of the 21st Century – Results from the IMAGE 2.1 Model, Pergamon, Oxford, 1998.
- Kaspar, F.: Entwicklung und Unsicherheitsanalyse eines globalen hydrologischen Modells, Ph.D. thesis, University of Kassel, Kassel, Germany, 2003.
- Schulze, K. and Döll, P.: Neue Ansätze zur Modellierung von Schneeakkumulation und -schmelze im globalen Wassermodell WaterGAP, in: Tagungsband zum 7. Workshop zur großskaligen Modellierung in der Hydrologie, edited by: Ludwig, R., Reichert, D., and Mauser, W., Kassel University Press, Kassel, 2004.
- Shuttleworth, W. J.: Evaporation, in: Handbook of Hydrology, edited by: Maidment, D. R., McGraw-Hill, New York, 4.1–4.53, 1993.
Cited by
Sorted by citations
Items 101–200 of 415
Anabalón, A., & Sharma, A. (2017) · Earth s Future · doi:10.1002/2016ef000499 · Cited by 73
Khaki, M., Forootan, E., Kühn, M., Awange, J. L., van Dijk, A. I. J. M., Schumacher, M., & Sharifi, M. A. (2018) · Advances in Water Resources · doi:10.1016/j.advwatres.2018.02.008 · Cited by 73
Alkon, M., He, X., Paris, A. R., Liao, W., Hodson, T., Wanders, N., & Wang, Y. (2019) · Energy Policy · doi:10.1016/j.enpol.2019.06.044 · Cited by 73
Grogan, D., Zuidema, S., Prusevich, A., Wollheim, W. M., Glidden, S., & Lammers, R. B. (2022) · Geoscientific model development · doi:10.5194/gmd-15-7287-2022 · Cited by 72
Ferreira, V. G., Yong, B., Tourian, M. J., Ndehedehe, C. E., Shen, Z., Seitz, K., & Dannouf, R. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.137354 · Cited by 71
Assessing global water mass transfers from continents to oceans over the period 1948–2016
Cáceres, D., Marzeion, B., Malles, J., Gutknecht, B. D., Schmied, H. M., & Döll, P. (2020) · Hydrology and earth system sciences · doi:10.5194/hess-24-4831-2020 · Cited by 71
Self-citationMultimodel assessment of water budget in Indian sub-continental river basins
Kushwaha, A., Tiwari, A., Dangar, S., Shah, H. L., Mahto, S., & Mishra, V. (2021) · Journal of Hydrology · doi:10.1016/j.jhydrol.2021.126977 · Cited by 65
Schmied, H. M., Trautmann, T., Ackermann, S., Cáceres, D., Flörke, M., Gerdener, H., Kynast, E., Peiris, T. A., Schiebener, L., Schumacher, M., et al. (2024) · Geoscientific model development · doi:10.5194/gmd-17-8817-2024 · Cited by 89
Self-citationAWARE-US: Quantifying water stress impacts of energy systems in the United States
Lee, U., Hui, X., Daystar, J., Elgowainy, A., & Wang, M. (2018) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2018.08.250 · Cited by 70
Using the Budyko Framework for Calibrating a Global Hydrological Model
Greve, P., Burek, P., & Wada, Y. (2020) · Water Resources Research · doi:10.1029/2019wr026280 · Cited by 70
Sarrazin, F., Hartmann, A., Pianosi, F., Rosolem, R., & Wagener, T. (2018) · Geoscientific model development · doi:10.5194/gmd-11-4933-2018 · Cited by 75
Zhang, L., Dobslaw, H., Stacke, T., Güntner, A., Dill, R., & Thomas, M. (2017) · Hydrology and earth system sciences · doi:10.5194/hess-21-821-2017 · Cited by 68
Importance of Spatial Resolution in Global Groundwater Modeling
Reinecke, R., Wachholz, A., Mehl, S. W., Foglia, L., Niemann, C., & Döll, P. (2020) · Ground Water · doi:10.1111/gwat.12996 · Cited by 68
Self-citationWater Debt Indicator Reveals Where Agricultural Water Use Exceeds Sustainable Levels
Tuninetti, M., Tamea, S., & Dalin, C. (2019) · Water Resources Research · doi:10.1029/2018wr023146 · Cited by 71
Kabir, T., POKHREL, Y. N., & Felfelani, F. (2022) · Water Resources Research · doi:10.1029/2021wr030828 · Cited by 67
Filling the gaps: Calibrating a rainfall-runoff model using satellite-derived surface water extent
Revilla-Romero, B., Beck, H. E., Burek, P., Salamon, P., de Roo, A. P. J., & Thielen, J. (2015) · Remote Sensing of Environment · doi:10.1016/j.rse.2015.10.022 · Cited by 66
Quinteiro, P. S. G. N., Ridoutt, B. G., Arroja, L., & Dias, A. C. (2017) · The International Journal of Life Cycle Assessment · doi:10.1007/s11367-017-1304-0 · Cited by 65
On the evaluation of climate change impact models
Wagener, T., Reinecke, R., & Pianosi, F. (2022) · Wiley Interdisciplinary Reviews Climate Change · doi:10.1002/wcc.772 · Cited by 64
Planetary boundaries for water – A review
Bunsen, J., Berger, M., & Finkbeiner, M. (2020) · Ecological Indicators · doi:10.1016/j.ecolind.2020.107022 · Cited by 72
Krysanova, V., Zaherpour, J., Didovets, I., Gosling, S. N., Gerten, D., Hanasaki, N., Schmied, H. M., POKHREL, Y. N., Satoh, Y., Tang, Q., et al. (2020) · Climatic Change · doi:10.1007/s10584-020-02840-0 · Cited by 62
Deepening water scarcity in breadbasket nations
Deng, Q., Sharretts, T., Ali, T., Ao, Y. Z., Chiarelli, D. D., Demeke, B. W., Marston, L. T., Mehta, P., Mekonnen, M. M., Rulli, M. C., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-56022-6 · Cited by 64
Schumacher, M., Kusche, J., & Döll, P. (2016) · Journal of Geodesy · doi:10.1007/s00190-016-0892-y · Cited by 68
Self-citationYoshida, T., Hanasaki, N., Nishina, K., Boulangé, J., Okada, M., & Troch, P. A. (2022) · Water Resources Research · doi:10.1029/2021wr030660 · Cited by 61
Halkes, R. T., Hughes, A., Wall, F., Petavratzi, E., Pell, R., & Lindsay, J. J. (2024) · Resources, Conservation and Recycling · doi:10.1016/j.resconrec.2024.107554 · Cited by 61
Chao, N., Jin, T., Cai, Z., Chen, G., Liu, X., Wang, Z., & Yeh, P. J. ‐. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.125661 · Cited by 60
A Spatially Explicit Assessment of Growing Water Stress in China From the Past to the Future
Liu, X., Tang, Q., Liu, W., Veldkamp, T., Boulangé, J., Liu, J., Wada, Y., Huang, Z., & Yang, H. (2019) · Earth s Future · doi:10.1029/2019ef001181 · Cited by 57
Feng, D., Beck, H. E., de Bruijn, J., Sahu, R. K., Satoh, Y., Wada, Y., Liu, J., Pan, M., Lawson, K., & Shen, C. (2024) · Geoscientific model development · doi:10.5194/gmd-17-7181-2024 · Cited by 75
Zaherpour, J., Mount, N. J., Gosling, S. N., Dankers, R., Eisner, S., Gerten, D., Liu, X., Masaki, Y., Schmied, H. M., Tang, Q., et al. (2019) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2019.01.003 · Cited by 57
Self-citationGirotto, M., Reichle, R. H., Rodell, M., Liu, Q., Mahanama, S., & Lannoy, G. J. M. D. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.04.001 · Cited by 57
Shi, H., Luo, G., Zheng, H., Chen, C., Hellwich, O., Bai, J., Liu, T., Liu, S., Xue, J., Cai, P., et al. (2021) · Hydrology and earth system sciences · doi:10.5194/hess-25-901-2021 · Cited by 57
Khaki, M., Forootan, E., Kühn, M., Awange, J. L., Papa, F., & Shum, C. K. (2018) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2017.12.289 · Cited by 56
Khorrami, B., Arık, F., & Gündüz, O. (2021) · GIScience & Remote Sensing · doi:10.1080/15481603.2021.2000349 · Cited by 53
Sensitivity of water scarcity events to ENSO-driven climate variability at the global scale
Veldkamp, T., Eisner, S., Wada, Y., Aerts, J. C. J. H., & Ward, P. J. (2015) · Hydrology and earth system sciences · doi:10.5194/hess-19-4081-2015 · Cited by 52
Self-citationXu, Y., Jin, L., Wang, J., Chen, J., Liu, Y., Ni, S., Zhang, Z., & Ke, C. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.125229 · Cited by 51
Domisch, S., Portmann, F. T., Kuemmerlen, M., O’Hara, R. B., Johnson, R. K., Davy‐Bowker, J., Bækken, T., Zamora‐Muñoz, C., Sáinz‐Bariain, M., Bonada, N., et al. (2017) · Ecohydrology · doi:10.1002/eco.1895 · Cited by 50
Self-citationMehboob, M. S., & Kim, Y. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.144820 · Cited by 52
Evaluating future water security in the upper Yangtze River Basin under a changing environment
Liang, H., Zhang, D., Wang, W., Yu, S., & Nimai, S. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.164101 · Cited by 48
Multimodel assessments of human and climate impacts on mean annual streamflow in China
Liu, X., Liu, W., Yang, H., Tang, Q., Flörke, M., Masaki, Y., Schmied, H. M., Ostberg, S., POKHREL, Y. N., Satoh, Y., et al. (2019) · Hydrology and earth system sciences · doi:10.5194/hess-23-1245-2019 · Cited by 49
Self-citationWAYS v1: a hydrological model for root zone water storage simulation on a global scale
Mao, G., & Liu, J. (2019) · Geoscientific model development · doi:10.5194/gmd-12-5267-2019 · Cited by 49
Khaki, M., & Awange, J. L. (2021) · Sensors · doi:10.3390/s21134304 · Cited by 48
Guillaume, J. H. A., Kummu, M., Eisner, S., & Varis, O. (2015) · Water · doi:10.3390/w7084200 · Cited by 47
Self-citationGuo, X., Zhao, Q., Ditmar, P., Sun, Y., & Liu, J. (2018) · Journal of Geophysical Research Solid Earth · doi:10.1029/2018jb015601 · Cited by 46
Sub-national regionalisation of the AWARE indicator for water scarcity footprint calculations
Boulay, A., & Lenoir, L. (2020) · Ecological Indicators · doi:10.1016/j.ecolind.2019.106017 · Cited by 46
GRACE Detected Rise of Groundwater in the Sahelian Niger River Basin
Werth, S., White, D. D., & Bliss, D. W. (2017) · Journal of Geophysical Research Solid Earth · doi:10.1002/2017jb014845 · Cited by 45
Understanding terrestrial water storage variations in northern latitudes across scales
Trautmann, T., Koirala, S., Carvalhais, N., Eicker, A., Fink, M., Niemann, C., & Jung, M. (2018) · Hydrology and earth system sciences · doi:10.5194/hess-22-4061-2018 · Cited by 44
Climate change impact on water availability of main river basins in Ukraine
Didovets, I., Krysanova, V., Hattermann, F. F., López, M. D. R. R., Snizhko, S., & Schmied, H. M. (2020) · Journal of Hydrology Regional Studies · doi:10.1016/j.ejrh.2020.100761 · Cited by 62
Environmental flow requirements largely reshape global surface water scarcity assessment
Liu, X., Liu, W., Liu, L., Tang, Q., Liu, J., & Yang, H. (2021) · Environmental Research Letters · doi:10.1088/1748-9326/ac27cb · Cited by 44
Multimodel uncertainty changes in simulated river flows induced by human impact parameterizations
Liu, X., Tang, Q., Cui, H., Mu, M., Gerten, D., Gosling, S. N., Masaki, Y., Satoh, Y., & Wada, Y. (2017) · Environmental Research Letters · doi:10.1088/1748-9326/aa5a3a · Cited by 43
Integrated Solutions for the Water-Energy-Land Nexus: Are Global Models Rising to the Challenge?
Johnson, N., Burek, P., Byers, E. A., Falchetta, G., Flörke, M., Fujimori, S., Havlík, P., Hejazi, M. I., Hunt, J. D., Krey, V., et al. (2019) · Water · doi:10.3390/w11112223 · Cited by 49
Self-citationBjørn, A., Sim, S., Boulay, A., King, H., Clavreul, J., Lam, W. Y., Barbarossa, V., Bulle, C., & Margni, M. D. (2019) · Ecological Indicators · doi:10.1016/j.ecolind.2019.105865 · Cited by 43
Sepúlveda, U. M., Mendoza, P. A., Mizukami, N., & Newman, A. J. (2022) · Hydrology and earth system sciences · doi:10.5194/hess-26-3419-2022 · Cited by 46
Huang, Z., Yeh, P. J. ‐., Jiao, J. J., Luo, X., Pan, Y., Long, Y., Zhang, C., & Zheng, L. (2023) · Water Resources Research · doi:10.1029/2022wr032091 · Cited by 42
Hasan, E., & Tarhule, A. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.124870 · Cited by 42
Stewardson, M. J., & Guarino, F. (2018) · Freshwater Biology · doi:10.1111/fwb.13102 · Cited by 42
Climate change exposure of waterbird species in the African-Eurasian flyways
Nagy, S., Breiner, F. T., Anand, M., Butchart, S. H. M., Flörke, M., Fluet‐Chouinard, E., Guisan, A., Hilarides, L., Jones, V. R., Kalyakin, M., et al. (2021) · Bird Conservation International · doi:10.1017/s0959270921000150 · Cited by 41
Self-citationYin, W., Li, T., Zheng, W., Hu, L., Han, S., Tangdamrongsub, N., Šprlák, M., & Huang, Z. (2020) · Hydrogeology Journal · doi:10.1007/s10040-020-02157-3 · Cited by 41
Dettmering, D., Ellenbeck, L., Scherer, D., Schwatke, C., & Niemann, C. (2020) · Remote Sensing · doi:10.3390/rs12203320 · Cited by 40
Malakar, P., Mukherjee, A., Bhanja, S. N., Sarkar, S., Saha, D., & Ray, R. K. (2021) · ACS ES&T Engineering · doi:10.1021/acsestengg.0c00238 · Cited by 42
The Global Surface Area Variations of Lakes and Reservoirs as Seen From Satellite Remote Sensing
Bonnema, M., David, C. H., Frasson, R. P. D. M., Oaida, C. M., & Yun, S. (2022) · Geophysical Research Letters · doi:10.1029/2022gl098987 · Cited by 42
Rickards, N. J., Thomas, T., Kaelin, A., Houghton-Carr, H. A., Jain, S. K., Mishra, P. K., Nema, M. K., Dixon, H., Rahman, M. M., Horan, R., et al. (2020) · Water · doi:10.3390/w12061762 · Cited by 40
Yang, X., Tian, S., Feng, W., Ran, J., You, W., Jiang, Z., & Gong, X. (2020) · Remote Sensing · doi:10.3390/rs12213578 · Cited by 40
Data-driven appraisal of renewable energy potentials for sustainable freshwater production in Africa
Angelis, P. D., Tuninetti, M., Bergamasco, L., Calianno, L., Asinari, P., Laio, F., & Fasano, M. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111414 · Cited by 41
Weiskel, P. K., Wolock, D. M., Zarriello, P. J., Vogel, R. M., Levin, S. B., & Lent, R. M. (2014) · Hydrology and earth system sciences · doi:10.5194/hess-18-3855-2014 · Cited by 39
Malsy, M., Flörke, M., & Borchardt, D. (2016) · Regional Environmental Change · doi:10.1007/s10113-016-1005-4 · Cited by 39
Self-citationA new approach for generating optimal GLDAS hydrological products and uncertainties
Fatolazadeh, F., Eshagh, M., & Goı̈ta, K. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.138932 · Cited by 38
Analysis of variations and controls of evapotranspiration over major Indian River Basins (1982–2014)
Soni, A., & Syed, T. H. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.141892 · Cited by 40
Unpacking the modelling process via sensitivity auditing
Piano, S. L., Sheikholeslami, R., Puy, A., & Saltelli, A. (2022) · Futures · doi:10.1016/j.futures.2022.103041 · Cited by 40
Wiedemann, S. G., Clarke, S. J., Nguyen, Q. V., Cheah, Z. X., & Simmons, A. T. (2023) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2023.107119 · Cited by 37
LCA WATER FOOTPRINT AWARE CHARACTERIZATION FACTOR BASED ON LOCAL SPECIFIC CONDITIONS
Ansorge, L., & Beránková, T. (2017) · European Journal of Sustainable Development · doi:10.14207/ejsd.2017.v6n4p13 · Cited by 37
The Impact of Meteorological and Hydrological Memory on Compound Peak Flows in the Rhine River Basin
Khanal, S., Lutz, A., Immerzeel, W. W., de Vries, H., Wanders, N., & van den Hurk, B. (2019) · Atmosphere · doi:10.3390/atmos10040171 · Cited by 38
Regional Carrying Capacities of Freshwater Consumption—Current Pressure and Its Sources
Motoshita, M., Pfister, S., & Finkbeiner, M. (2020) · Environmental Science & Technology · doi:10.1021/acs.est.0c01544 · Cited by 37
Divergent Changes in Terrestrial Water Storage Across Global Arid and Humid Basins
Hu, B., Wang, L., Li, X., Zhou, J., & Pan, Y. (2020) · Geophysical Research Letters · doi:10.1029/2020gl091069 · Cited by 37
Satoh, Y., Shiogama, H., Hanasaki, N., POKHREL, Y. N., Boulangé, J., Burek, P., Gosling, S. N., Grillakis, M. G., Koutroulis, A., Schmied, H. M., et al. (2021) · Environmental Research Letters · doi:10.1088/1748-9326/ac2348 · Cited by 37
Frost, K., & Hua, I. (2019) · Water Resources and Industry · doi:10.1016/j.wri.2019.100115 · Cited by 39
Liang, W., Zhang, W., Jin, Z., Yan, J., Lü, Y., Li, S., & Yu, Q. (2020) · Journal of Geophysical Research Atmospheres · doi:10.1029/2020jd033380 · Cited by 36
Uncertainty in the extreme flood magnitude estimates of large-scale flood hazard models
Devitt, L., Neal, J. C., Wagener, T., & Coxon, G. (2021) · Environmental Research Letters · doi:10.1088/1748-9326/abfac4 · Cited by 36
Water Allocation Management Under Scarcity: a Bankruptcy Approach
Zheng, Y., Sang, X., Liu, Z., Zhang, S., & Liu, P. (2022) · Water Resources Management · doi:10.1007/s11269-022-03098-0 · Cited by 36
Water Demand Scenarios for Electricity Generation at the Global and Regional Levels
Terrapon-Pfaff, J., Ortíz, W., Viebahn, P., Kynast, E., & Flörke, M. (2020) · Water · doi:10.3390/w12092482 · Cited by 35
Self-citationThe effect of GCM biases on global runoff simulations of a land surface model
Papadimitriou, L. V., Koutroulis, A., Grillakis, M. G., & Tsanis, I. K. (2017) · Hydrology and earth system sciences · doi:10.5194/hess-21-4379-2017 · Cited by 35
Occurrence and distribution of estrogenic substances in the northern South China Sea
Deich, C., Frazão, H. C., Appelt, J.-S., Li, W., Pohlmann, T., & Waniek, J. J. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.145239 · Cited by 34
Rateb, A., Scanlon, B. R., & Kuo, C. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.144337 · Cited by 33
Xue, D., Dongwei, G. U. I., Ci, M., Liu, Q., Wei, G., & Liu, Y. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.167908 · Cited by 34
Understanding Hydrological Alteration
Stewardson, M. J., Acreman, M., Costelloe, J. F., Fletcher, T. D., Fowler, K., Horne, A., Liu, G., McClain, M. E., & Peel, M. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-803907-6.00003-6 · Cited by 33
Jia, B., Cai, X., Zhao, F., Liu, J., Chen, S., Luo, X., Xie, Z., & Xu, J. (2020) · Advances in Water Resources · doi:10.1016/j.advwatres.2020.103635 · Cited by 32
Growing control of climate change on water scarcity alleviation over northern part of China
Huang, Z., Yuan, X., Liu, X., & Tang, Q. (2023) · Journal of Hydrology Regional Studies · doi:10.1016/j.ejrh.2023.101332 · Cited by 32
The updated and improved method for water scarcity impact assessment in LCA, AWARE2.0
Seitfudem, G., Berger, M., Schmied, H. M., & Boulay, A. (2025) · Journal of Industrial Ecology · doi:10.1111/jiec.70023 · Cited by 31
Döll, P., Trautmann, T., Göllner, M., & Schmied, H. M. (2019) · Ecohydrology · doi:10.1002/eco.2175 · Cited by 33
Self-citationKaewmai, R., Grant, T., Eady, S., Mungkalasiri, J., & Musikavong, C. (2019) · Science of The Total Environment · doi:10.1016/j.scitotenv.2019.05.013 · Cited by 30
Responsibility for sustainable water consumption in the global supply chains
Motoshita, M., Pfister, S., Sasaki, T., Nansai, K., Hashimoto, S., Yokoi, R., Islam, K., & Finkbeiner, M. (2023) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2023.107055 · Cited by 30
Dutta, D., Vaze, J., Kim, S. S. H., Hughes, J. D., Yang, A., Teng, J., & Lerat, J. (2017) · Journal of Hydrology · doi:10.1016/j.jhydrol.2017.01.040 · Cited by 30
Deggim, S., Eicker, A., Schawohl, L., Gerdener, H., Schulze, K., Engels, O., Kusche, J., Saraswati, A. T., van Dam, T. M., Ellenbeck, L., et al. (2021) · Earth system science data · doi:10.5194/essd-13-2227-2021 · Cited by 34
Zeng, Y., Liu, D., Guo, S., Xiong, L., Liu, P., Chen, J., Yin, J., Wu, Z., & Zhou, W. (2023) · Agricultural Water Management · doi:10.1016/j.agwat.2023.108279 · Cited by 30
Quantifying uncertainty for AWARE characterization factors
Boulay, A., Lesage, P., Amor, B., & Pfister, S. (2021) · Journal of Industrial Ecology · doi:10.1111/jiec.13173 · Cited by 29
Global overlooked multidimensional water scarcity
Liu, W., Fu, Z., van Vliet, M. T. H., Davis, K. F., Ciais, P., Bao, Y., Bai, Y., Du, T., Kang, S., Yin, Z., et al. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2413541122 · Cited by 29
Production weighted water use impact characterisation factors for the global mining industry
Northey, S. A., Madrid López, C., Haque, N., Mudd, G. M., & Yellishetty, M. (2018) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2018.02.307 · Cited by 28
Changes in groundwater levels across China from 2005 to 2016
Zhang, D., Liu, X., Simmons, C. T., Zhang, L., & Zhang, Q. (2023) · Journal of Hydrology · doi:10.1016/j.jhydrol.2023.129781 · Cited by 28
Analysing the contribution of snow water equivalent to the terrestrial water storage over Canada
Bahrami, A., Goı̈ta, K., & Magagi, R. (2019) · Hydrological Processes · doi:10.1002/hyp.13625 · Cited by 28
Kaewmai, R., Grant, T., Mungkalasiri, J., & Musikavong, C. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.143000 · Cited by 27
Breiner, F. T., Anand, M., Butchart, S. H. M., Flörke, M., Fluet‐Chouinard, E., Guisan, A., Hilarides, L., Jones, V. R., Kalyakin, M., Lehner, B., et al. (2021) · Global Change Biology · doi:10.1111/gcb.15961 · Cited by 27
Self-citationBlume, T., Schneider, L., & Güntner, A. (2021) · Hydrological Processes · doi:10.1002/hyp.14461 · Cited by 27
Metadata
- Authors
- Müller Schmied, H., Eisner, S., Franz, D., Wattenbach, M., Portmann, F. T., Flörke, M., & Döll, P.
- Affiliations
- University of Kassel (DE)
- GFZ Helmholtz Centre for Geosciences (DE)
- Goethe University Frankfurt (DE)
- Corresponding author
- Eisner, S.
- Franz, D.
- Wattenbach, M.
- Venue
- Hydrology and Earth System Sciences
- Publisher
- Copernicus GmbH
- Published
- 2014-09-10
- Volume
- 18
- Issue
- 9
- Pages
- 3511-3538
- ISSN
- 1027-5606 (ISSN-L)
- 1607-7938
- OpenAlex
- W2114721961
- Cited by
- 415
- References
- 100
- Citations by year
- 2026: 13 · 2025: 36 · 2024: 26 · 2023: 38 · 2022: 32 · 2021: 66 · 2020: 51 · 2019: 37 · 2018: 36 · 2017: 33 · 2016: 25 · 2015: 16 · 2014: 6
- FWCI
- 20.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Hydrology and earth system sciences (publishedVersion, cc-by)
- Publication Database GFZ (GFZ German Research Centre for Geosciences) (submittedVersion, cc-by)
- Publication Database GFZ (GFZ German Research Centre for Geosciences) (submittedVersion, other-oa)
- Publication Server of Goethe University Frankfurt am Main (Goethe University Frankfurt) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Environmental Science › Water Science and Technology › Hydrology and Watershed Management Studies
- Physical Sciences › Earth and Planetary Sciences › Geophysics › Geophysics and Gravity Measurements
- Physical Sciences › Environmental Science › Environmental Engineering › Soil Moisture and Remote Sensing
- Keywords
- freshwater flux, model uncertainty, human water use, river discharge, actual evapotranspiration, radiative forcing, land cover, model calibration, groundwater depletion, renewable water resources, climate reanalysis data, CRU TS, shortwave radiation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review