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Modelling Freshwater Resources at the Global Scale: Challenges and Prospects

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Remote Sensing and Water Resources

Part of the book series: Space Sciences Series of ISSI ((SSSI,volume 55))

Abstract

Quantification of spatially and temporally resolved water flows and water storage variations for all land areas of the globe is required to assess water resources, water scarcity and flood hazards, and to understand the Earth system. This quantification is done with the help of global hydrological models (GHMs). What are the challenges and prospects in the development and application of GHMs? Seven important challenges are presented. (1) Data scarcity makes quantification of human water use difficult even though significant progress has been achieved in the last decade. (2) Uncertainty of meteorological input data strongly affects model outputs. (3) The reaction of vegetation to changing climate and CO2 concentrations is uncertain and not taken into account in most GHMs that serve to estimate climate change impacts. (4) Reasons for discrepant responses of GHMs to changing climate have yet to be identified. (5) More accurate estimates of monthly time series of water availability and use are needed to provide good indicators of water scarcity. (6) Integration of gradient-based groundwater modelling into GHMs is necessary for a better simulation of groundwater–surface water interactions and capillary rise. (7) Detection and attribution of human interference with freshwater systems by using GHMs are constrained by data of insufficient quality but also GHM uncertainty itself. Regarding prospects for progress, we propose to decrease the uncertainty of GHM output by making better use of in situ and remotely sensed observations of output variables such as river discharge or total water storage variations by multi-criteria validation, calibration or data assimilation. Finally, we present an initiative that works towards the vision of hyperresolution global hydrological modelling where GHM outputs would be provided at a 1-km resolution with reasonable accuracy.

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References

  • Alcamo J, Flörke M, Märker M (2007) Future long-term changes in global water resources driven by socioeconomic and climatic changes. Hydrol Sci J 52(2):247–275. doi:10.1623/hysj.52.2.247

    Google Scholar 

  • Alkama R, Decharme B, Douville H, Becker M, Cazenave A, Sheffield J, Voldoire V, Tyteca S, Le Moigne P (2010) Global evaluation of the ISBA-TRIP continental hydrologic system. Part 1: a twofold constraint using GRACE Terrestrial Water Storage estimates and in situ river discharges. J Hydrometeorol 11:583–600. doi:10.1175/2010JHM1211.1

    Google Scholar 

  • Alkama R, Decharme B, Douville H, Ribes A (2011) Trends in global and basin-scale runoff over the late 20th century? Methodological issues and sources of uncertainty. J Climate 24:2983–2999. doi:10.1175/2010JCLI3921.1

    Google Scholar 

  • Arnell NW, Lloyd-Hughes B (2014) The global-scale impacts of climate change on water resources and flooding under new climate and socio-economic scenarios. Clim Change 122(1–2):127–140. doi:10.1007/s10584-013-0948-4

    Google Scholar 

  • Beven K, Cloke H (2012) Comment on ‘‘Hyperresolution global land surface modeling: meeting a grand challenge for monitoring Earth’s terrestrial water’’ by Eric F. Wood et al. Water Resour Res 48:W01801. doi:10.1029/2011WR010982

  • Beven KJ, Freer J (2001) Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems. J Hydrol 249:11–29. doi:10.1016/S0022-1694(01)00421-8

    Google Scholar 

  • Beven K, Cloke H, Pappenberger F, Lamb R, Hunter N (2015) Hyperresolution information and hyperresolution ignorance in modelling the hydrology of the land surface. Sci China Earth Sci 58:25–35. doi:10.1007/s11430-014-5003-4

    Google Scholar 

  • Biemans H, Hutjes RWA, Kabat P, Strengers BJ, Gerten D, Rost S (2009) Effects of precipitation uncertainty on discharge calculations for main river basins. J Hydrometeorol 10(4):1011–1025. doi:10.1175/2008JHM1067.1

    Google Scholar 

  • Bierkens MFP, Bell VA, Burek P, Chaney N, Condon LE, David CH, de Roo A, Döll P, Drost N, Famiglietti JS, Flörke M, Gochis DJ, Houser P, Hut R, Keune J, Kollet S, Maxwell RM, Reager JT, Samaniego L, Sudicky E, Sutanudjaja EH, van de Giesen N, Winsemius H, Wood EF (2015) Hyper-resolution global hydrological modelling: what is next? Hydrol Process 29(2):310–320. doi:10.1002/hyp.10391

    Google Scholar 

  • Brunner P, Simmons CT (2012) HydroGeoSphere: a fully integrated, physically based hydrological model. Groundwater 50:170–176. doi:10.1111/j.1745-6584.2011.00882.x

    Google Scholar 

  • Chen J, Brissette FP, Lucas-Picher P (2015) Assessing the limits of bias-correcting climate model outputs for climate change impact studies. J Geophys Res Atmos 120:1123–1136. doi:10.1002/2014JD022635

    Google Scholar 

  • Coxon G, Freer J, Westerberg IK, Wagener T, Woods R, Smith PJ (2015) A novel framework for discharge uncertainty quantification applied to 500 UK gauging stations. Water Resour Res 51:5531–5546. doi:10.1002/2014WR016532

    Google Scholar 

  • Dankers R, Arnell NW, Clark DB, Falloon PD, Fekete BM, Gosling SN, Heinke J, Kim H, Masaki Y, Satoh Y, Stacke T, Wada Y, Wisser D (2014) First look at changes in flood hazard in the Inter-Sectoral Impact Model Intercomparison Project ensemble. Proc Natl Acad Sci USA 111(9):3257–3261. doi:10.1073/pnas.1302078110

    Google Scholar 

  • Davie JCS, Falloon PD, Kahana R, Dankers R, Betts R, Portmann FT, Wisser D, Clark DB, Ito A, Masaki Y, Nishina K, Fekete B, Tessler Z, Wada Y, Liu X, Tang Q, Hagemann S, Stacke T, Pavlick R, Schaphoff S, Gosling SN, Franssen W, Arnell NW (2013) Comparing projections of future changes in runoff from hydrological and biome models. Earth Syst Dynam 4:359–374. doi:10.5194/esd-4-359-2013

    Google Scholar 

  • de Graaf IEM, Sutanudjaja EH, van Beek LPH, Bierkens MFP (2015) A high-resolution global-scale groundwater model. Hydrol Earth Syst Sci 19:823–837. doi:10.5194/hess-19-823-2015

    Google Scholar 

  • Döll P, Fiedler K (2008) Global-scale modeling of groundwater recharge. Hydrol Earth Syst Sci 12(3):863–885. doi:10.5194/hess-12-863-2008

    Google Scholar 

  • Döll P, Siebert S (2002) Global modeling of irrigation water requirements. Water Resour Res 38(4):8-1–8- 10. doi:10.1029/2001WR000355

    Google Scholar 

  • Döll P, Kaspar F, Lehner B (2003) A global hydrological model for deriving water availability indicators: model tuning and validation. J Hydrol 270(1–2):105–134. doi:10.1016/S0022-1694(02)00283-4

    Google Scholar 

  • Döll P, Fiedler K, Zhang J (2009) Global-scale analysis of river flow alterations due to water withdrawals and reservoirs. Hydrol Earth Syst Sci 13:2413–2432. doi:10.5194/hess-13-2413-2009

    Google Scholar 

  • Döll P, Hoffmann-Dobrev H, Portmann FT, Siebert S, Eicker A, Rodell M, Strassberg G, Scanlon B (2012) Impact of water withdrawals from groundwater and surface water on continental water storage variations. J Geodyn 59–60:143–156. doi:10.1016/j.jog.2011.05.001

    Google Scholar 

  • Döll P, Fritsche M, Eicker A, Müller Schmied H (2014a) Seasonal water storage variations as impacted by water abstractions: comparing the output of a global hydrological model with GRACE and GPS observations. Surv Geophys 35(6):1311–1331. doi:10.1007/s10712-014-9282-2

    Google Scholar 

  • Döll P, Müller Schmied H, Schuh C, Portmann F, Eicker A (2014b) Global-scale assessment of groundwater depletion and related groundwater abstractions: combining hydrological modeling with information from well observations and GRACE satellites. Water Resour Res 50:5698–5720. doi:10.1002/2014WR015595

    Google Scholar 

  • Döll P, Jiménez Cisneros B, Oki T, Arnell NW, Benito G, Cogley JG, Jiang T, Kundzewicz ZW, Mwakalila S, Nishijima A (2015) Integrating risks of climate change into water management. J Hydrol Sci 60(1):3–14. doi:10.1080/02626667.2014.967250

    Google Scholar 

  • Douville H, Decharme B, Ribes A, Alkama R, Sheffield J (2012) Anthropogenic influence on multi-decadal changes in reconstructed global evapotranspiration. Nat Clim Change. doi:10.1038/NCLIMATE1632

    Google Scholar 

  • Douville H, Voldoire A, Geoffroy O (2015) The recent global warming hiatus: What is the role of Pacific variability? Geophys Res Lett. doi:10.1002/2014GL062775

    Google Scholar 

  • Duethmann D, Peters J, Blume T, Vorogushyn S, Güntner A (2014) The value of satellite-derived snow cover images for calibrating a hydrological model in snow-dominated catchments in Central Asia. Water Resour Res 50(3):2002–2021. doi:10.1002/2013WR014382

    Google Scholar 

  • Eicker A, Schumacher M, Kusche J, Döll P, Müller Schmied H (2014) Calibration/data assimilation approach for integrating GRACE data into the WaterGAP Global Hydrology Model (WGHM) using an ensemble Kalman filter: first results. Surv Geophys 35(6):1285–1309. doi:10.1007/s10712-014-9309-8

    Google Scholar 

  • Elliott J, Deryng D, Müller C, Frieler K, Konzmann M, Gerten D, Glotter M, Flörke M, Wada Y, Eisner S, Folberth C, Foster I, Gosling SN, Haddeland I, Khabarov N, Ludwig F, Masaki Y, Olin S, Rosenzweig C, Ruane AC, Satoh Y, Schmid E, Stacke T, Tang Q, Wisser D (2014) Constraints and potentials of future irrigation water availability on agricultural production under climate change. Proc Natl Acad Sci USA 111(9):3239–3244. doi:10.1073/pnas.1222474110

    Google Scholar 

  • Fan Y, Miguez-Macho G, Weaver CP, Walko R, Robock A (2007) Incorporating water table dynamics in climate modeling: 1. Water table observations and equilibrium water table simulations. J Geophys Res Atmos 112:D10125. doi:10.1029/2006JD008111

  • Fan Y, Li H, Miguez-Macho G (2013) Global patterns of groundwater table depth. Science 339:940–943. doi:10.1126/science.1229881

    Google Scholar 

  • Flörke M, Kynast E, Bärlund I, Eisner S, Wimmer F, Alcamo J (2013) Domestic and industrial water uses of the past 60 years as a mirror of socio-economic development: a global simulation study. Global Environ Change 23:144–156. doi:10.1016/j.gloenvcha.2012.10.018

    Google Scholar 

  • Gedney N, Cox PM, Betts RA, Boucher O, Huntingford C, Stott PA (2006) Detection of a direct carbon dioxide effect in continental river runoff records. Nature 439:835–838. doi:10.1038/nature04504

    Google Scholar 

  • Gedney N, Huntingford C, Weedon GP, Bellouin N, Boucher O, Cox PM (2014) Detection of solar dimming and brightening effects on Northern Hemisphere river flow. Nature Geosci 7:796–800. doi:10.1038/ngeo2263

    Google Scholar 

  • Gerten D, Betts R, Döll P (2014) Cross-chapter box on the active role of vegetation in altering water flows under climate change. In: Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL (eds) Climate Change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. Contribution of working group II to the 5th assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp 157–161

    Google Scholar 

  • Gleeson T, Moosdorf N, Hartmann J, van Beek LPH (2014) A glimpse beneath earth’s surface: gLobal HYdrogeological MaPS (GLHYMPS) of permeability and porosity. Geophys Res Lett 41:3891–3898. doi:10.1002/2014GL059856

    Google Scholar 

  • Gosling SN, Taylor RG, Arnell NW, Todd MC (2011) A comparative analysis of projected impacts of climate change on river runoff from global and catchment-scale hydrological models. Hydrol Earth Syst Sci 15:279–294. doi:10.5194/hess-15-279-2011

    Google Scholar 

  • Gudmundsson L, Tallaksen LM, Stahl K, Clark DB, Dumont E, Hagemann S, Bertrand N, Gerten D, Heinke J, Hanasaki N, Voss F, Koirala S (2012) Comparing large-scale hydrological model simulations to observed runoff percentiles in Europe. J Hydrometeorol 13(2):604–620. doi:10.1175/JHM-D-11-083.1

    Google Scholar 

  • Güntner A (2008) Improvement of global hydrological models using GRACE data. Surv Geophys 29(4):375–397. doi:10.1007/s10712-008-9038-y

    Google Scholar 

  • Guo Z, Dirmeyer PA, Hu Z-Z, Gao X, Zhao M (2006) Evaluation of the Second Global Soil Wetness Project soil moisture simulations: 2. Sensitivity to external meteorological forcing. J Geophys Res 111(D22):D22S03. doi:10.1029/2006JD007845

  • Gupta HV, Bastidas LA, Sorooshian S, Shuttleworth WJ, Yang ZL (1999) Parameter estimation of a land surface scheme using multicriteria methods. J Geophys Res 104(D16):491–503. doi:10.1029/1999JD900154

    Google Scholar 

  • Haddeland I, Clark DB, Franssen W, Ludwig F, Voß F, Arnell NW, Bertrand N, Best M, Folwell S, Gerten D, Gomes S, Gosling SN, Hagemann S, Hanasaki N, Harding R, Heinke J, Kabat P, Koirala S, Oki T, Polcher J, Stacke T, Viterbo P, Weedon GP, Yeh P (2011) Multi-model estimate of the global terrestrial water balance: setup and first results. J Hydrometeorol 12:869–884. doi:10.1175/2011JHM1324.1

    Google Scholar 

  • Haddeland I, Heinke J, Voß F, Eisner S, Chen C, Hagemann S, Ludwig F (2012) Effects of climate model radiation, humidity and wind estimates on hydrological simulations. Hydrol Earth Syst Sci 16:305–318. doi:10.5194/hess-16-305-2012

    Google Scholar 

  • Haddeland I, Heinke J, Biemans H, Eisner S, Flörke M, Hanasaki N, Konzmann M, Ludwig F, Masaki Y, Schewe J, Stacke T, Tessler Z, Wada Y, Wisser D (2014) Global water resources affected by human interventions and climate change. Proc Natl Acad Sci USA 111(9):3251–3256. doi:10.1073/pnas. 1302078110

  • Hagemann S, Chen C, Haerter JO, Heinke J, Gerten D, Piani C (2011) Impact of a statistical bias correction on the projected hydrological changes obtained from three GCMs and two hydrology models. J Hydrometeorol 12(4):556–578. doi:10.1175/2011JHM1336.1

    Google Scholar 

  • Hagemann S, Chen C, Clark DB, Folwell S, Gosling SN, Haddeland I, Hanasaki N, Heinke J, Ludwig F, Voss F, Wiltshire AJ (2013) Climate change impact on available water resources obtained using multiple global climate and hydrology models. Earth Syst Dynam 4:129–144. doi:10.5194/esd-4-129-2013

    Google Scholar 

  • Hanasaki N, Kanae S, Oki T, Masuda K, Motoya K, Shirakawa N, Shen Y, Tanaka K (2008) An integrated model for the assessment of global water resources—part 2: applications and assessments. Hydrol Earth Syst Sci 12(4):1027–1037. doi:10.5194/hess-12-1027-2008

    Google Scholar 

  • Harris I, Jones PD, Osborn TJ, Lister DH (2014) Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 dataset. Int J Climatol 34(3):623–642. doi:10.1002/joc.3711

    Google Scholar 

  • Hartmann J, Moosdorf N (2012) The new global lithological map database GLiM: a representation of rock properties at the Earth surface. Geochem Geophys Geosyst 13:Q12004. doi:10.1029/2012GC004370

  • Hawkins E, Sutton R (2011) The potential to narrow uncertainty in projections of regional precipitation change. Clim Dyn 37:407–418. doi:10.1007/s00382-010-0810-6

    Google Scholar 

  • Hoekstra AY, Mekonnen MM (2011) Global water scarcity: monthly blue water footprint compared to blue water availability for the world’s major river basins. Value of Water Research Report Series No. 53. UNESCO-IHE Institute for Water Education. Delft, The Netherlands

    Google Scholar 

  • Houborg R, Rodell M, Li BL, Reichle R, Zaitchik BF (2012) Drought indicators based on model-assimilated Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage observations. Water Resour Res 48:W07525. doi:10.1029/2011WR011291

  • Hunger M, Döll P (2008) Value of river discharge data for global-scale hydrological modeling. Hydrol Earth Syst Sci 12(3):841–861. doi:10.5194/hess-12-841-2008

    Google Scholar 

  • Jägermeyr J, Gerten D, Heinke J, Schaphoff S, Kummu M, Lucht W (2015) Water savings potentials of irrigation systems: global simulation of processes and linkages. Hydrol Earth Syst Sci 19:3073–3091. doi:10.5194/hess-19-3073-2015

    Google Scholar 

  • Jiménez C, Prigent C, Mueller B, Seneviratne SI, McCabe MF, Wood EF, Rossow WB, Balsamo G, Betts AK, Dirmeyer PA, Fisher JB, Jung M, Kanamitsu M, Reichle RH, Reichstein M, Rodell M, Sheffield J, Tu K, Wang K (2011) Global intercomparison of 12 land surface heat flux estimates. J Geophys Res. doi:10.1029/2010JD014545

  • Jiménez Cisneros BE, Oki T, Arnell NW, Benito G, Cogley JG, Döll P, Jiang T, Mwakalila SS (2014) Freshwater resources. In: Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL (eds). Climate Change 2014: impacts, adaptation, and vulnerability. Part a: global and sectoral aspects. Contribution of working group II to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp 229–269

    Google Scholar 

  • Joetzjer E, Delire C, Douville H, Ciais P, Decharme B, Fisher R, Christoffersen B, Calvet JC, da Costa ACL, Ferreira LV, Meir P (2014) Predicting the response of the Amazon rainforest to persistent drought conditions under current and future climates: a major challenge for global land surface models. Geosci Model Dev 7:2933–2950. doi:10.5194/gmd-7-2933-2014

    Google Scholar 

  • Jones JP, Sudicky EA, McLaren RG (2008) Application of a fully-integrated surface-subsurface flow model at the watershed-scale: a case study. Water Resour Res 44:W03407. doi:10.1029/2006wr005603

  • Jung M, Reichstein M, Ciais P, Seneviratne SI, Sheffield J, Goulden ML, Bonan G, Cescatti A, Chen J, de Jeu R, Dolman AJ, Eugster W, Gerten D, Gianelle D, Gobron N, Heinke J, Kimball J, Law BE, Montagnani L, Mu Q, Mueller B, Oleson K, Papale D, Richardson AD, Roupsard O, Running S, Tomelleri E, Viovy N, Weber U, Williams C, Wood E, Zaehle S, Zhang K (2010) Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature 467:951–954. doi:10.1038/nature09396

    Google Scholar 

  • Jung M, Reichstein M, Margolis HA, Cescatti A, Richardson AD, Arain MA, Arneth A, Bernhofer C, Bonal D, Chen J, Gianelle D, Gobron N, Kiely G, Kutsch W, Lasslop G, Law BE, Lindroth A, Merbold L, Montagnani L, Moors EJ, Papale D, Sottocornola M, Vaccari F, Williams C (2011) Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. J Geophys Res Biogeosci 116(G3):2156–2202. doi:10.1029/2010JG001566

  • Kiguchi M, Shen Y, Kanae S, Oki T (2015) Re-evaluation of future water stress due to socio-economic and climate factors under a warming climate. Hydrol Sci J 60(1–2):14–29. doi:10.1080/02626667.2014.888067

    Google Scholar 

  • Knutti R, Sedlacek J (2013) Robustness and uncertainties in the new CMIP5 climate model projections. Nat Clim Change 3:369–373. doi:10.1038/nclimate1716

    Google Scholar 

  • Kollet SJ, Maxwell RM (2008) Capturing the influence of groundwater dynamics on land surface processes using an integrated, distributed watershed model. Water Resour Res 44:W02402. doi:10.1029/2007WR006004

  • Krakauer NY, Li H, Fan Y (2014) Groundwater flow across spatial scales: importance for climate modeling. Environ Res Lett 9:034003. doi:10.1088/1748-9326/9/3/034003

    Google Scholar 

  • Krogh PE (2011) Large scale hydrological model calibration with remote sensing data from GRACE. PhD thesis, DTU space, National Space Institute, Technical University of Denmark, Copenhagen

    Google Scholar 

  • Lahoz WA, De Lannoy GJM (2014) Closing the gaps in our knowledge of the hydrological cycle over land: conceptual problems. Surv Geophys 35(3):623–660. doi:10.1007/s10712-013-9221-7

    Google Scholar 

  • Larson KM, Small EE, Gutmann ED, Bilich AL, Braun JJ, Zavorotny VU (2008) Use of GPS receivers as a soil moisture network for water cycle studies. Geophys Res Lett 35(24):L24405. doi:10.1029/2008GL036013

  • Li BL, Rodell M, Zaitchik BF, Reichle RH, Koster RD, van Dam TM (2012) Assimilation of GRACE terrestrial water storage into a land surface model: evaluation and potential value for drought monitoring in western and central Europe. J Hydrol 446:103–115. doi:10.1175/2007JHM951.1

    Google Scholar 

  • Livneh B, Lettenmaier DP (2012) Multi-criteria parameter estimation for the Unified Land Model. Hydrol Earth Syst Sci 16(8):3029–3048. doi:10.5194/hess-16-3029-2012

    Google Scholar 

  • Maxwell RM, Chow FK, Kollet SJ (2007) The groundwater-land-surface-atmosphere connection: soil moisture effects on the atmospheric boundary layer in fully-coupled simulations. Adv Water Resour 30:2447–2466. doi:10.1016/j.advwatres.2007.05.018

    Google Scholar 

  • Maxwell RM, Condon LE, Kollet SJ (2015) A high-resolution simulation of groundwater and surface water over most of the continental US with the integrated hydrologic model ParFlow v3. Geosci Model Dev 8:923–937. doi:10.5194/gmd-8-923-2015

    Google Scholar 

  • Miguez-Macho G, Fan Y, Weaver CP, Walko R, Robock A (2007) Incorporating water table dynamics in climate modeling: 2. Formulation, validation, and soil moisture simulation. J Geophys Res Atmos 112:D13108. doi:10.1029/2006JD008112

    Google Scholar 

  • Milzow C, Krogh PE, Bauer-Gottwein P (2011) Combining satellite radar altimetry, SAR surface soil moisture and GRACE total storage changes for hydrological model calibration in a large poorly gauged catchment. Hydrol Earth Syst Sci 15(6):1729–1743. doi:10.5194/hess-15-1729-2011

    Google Scholar 

  • Müller Schmied H, Eisner S, Franz D, Wattenbach M, Portmann FT, Flörke M, Döll P (2014) Sensitivity of simulated global-scale freshwater fluxes and storages to input data, hydrological model structure, human water use and calibration. Hydrol Earth Syst Sci 18:3511–3538. doi:10.5194/hess-18-3511-2014

    Google Scholar 

  • Murray SJ, Foster PN, Prentice IC (2012) Future global water resources with respect to climate change and water withdrawals as estimated by a dynamic global vegetation model. J Hydrol 448–449:14–29. doi:10.1016/j.jhydrol.2012.02.044

    Google Scholar 

  • Nasonova ON, Gusev YM, Kovalev YE (2011) Impact of uncertainties in meteorological forcing data and land surface parameters on global estimates of terrestrial water balance components. Hydrol Process 25(7):1074–1090. doi:10.1002/hyp.7651

    Google Scholar 

  • Niu GY, Yang ZL, Dickinson RE, Gulden LE (2007) Su H (2007) Development of a simple groundwater model for use in climate models and evaluation with Gravity Recovery and Climate Experiment data. J Geophys Res 112:D07103. doi:10.1029/2006JD007522

  • Pall P, Aina T, Stone DA, Stott PA, Nozawa T, Hilberts AGJ, Lohmann D, Allen MR (2011) Anthropogenic greenhouse gas contribution to flood risk in England and Wales in autumn 2000. Nature 470(7334):382–385. doi:10.1038/nature09762

    Google Scholar 

  • Papa F, Frappart F, Güntner A, Prigent C, Aires F, Getirana ACV, Maurer R (2013) Surface freshwater storage and variability in the Amazon basin from multi-satellite observations, 1993–2007. J Geophys Res Atmos 118(21):11951–11965. doi:10.1002/2013JD020500

    Google Scholar 

  • Parajka J, Blöschl G (2008) The value of MODIS snow cover data in validating and calibrating conceptual hydrologic models. J Hydrol 358(3–4):240–258. doi:10.1016/j.jhydrol.2008.06.006

    Google Scholar 

  • Portmann F, Siebert S, Döll P (2010) MIRCA2000—global monthly irrigated and rainfed crop areas around the year 2000: a new high resolution data set for agricultural and hydrological modelling. Global Biogeochem Cy 24:GB1011. doi:10.1029/2008GB003435

    Google Scholar 

  • Reichle RH, De Lannoy GJM, Forman BA, Draper CS, Liu Q (2014) Connecting satellite observations with water cycle variables through land data assimilation: examples using the NASA GEOS-5 LDAS. Surv Geophys 35(3):577–606. doi:10.1007/s10712-013-9220-8

    Google Scholar 

  • Renzullo LJ, van Dijk AIJM, Perraud JM, Collins D, Henderson B, Jin H, Smith AB, McJannet DL (2014) Continental satellite soil moisture data assimilation improves root-zone moisture analysis for water resources assessment. J Hydrol 519:2747–2762. doi:10.1016/j.jhydrol.2014.08.008

    Google Scholar 

  • Rost S, Gerten D, Bondeau A, Lucht W, Rohwer J, Schaphoff S (2008) Agricultural green and blue water consumption and its influence on the global water system. Water Resour Res 44:W09405. doi:10.1029/2007WR006331

  • Samaniego L, Kumar R, Attinger S (2010) Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale. Water Resour Res 46:W05523. doi:10.1029/2008WR007327

  • Schewe J, Heinke J, Gerten D, Haddeland I, Arnell NW, Clark DB, Dankers R, Eisner S, Fekete BM, Colon- Gonzalez FJ, Gosling SN, Kim H, Liu X, Masaki Y, Portmann FT, Satoh Y, Stacke T, Tang Q, Wada Y, Wisser D, Albrecht T, Frieler K, Piontek F, Warszawski L, Kabat P (2014) Multimodel assessment of water scarcity under climate change. Proc Natl Acad Sci USA 111(9):3245–3250. doi:10.1073/pnas. 1222460110

  • Schneider U, Becker A, Finger P, Meyer-Christoffer A, Ziese M, Rudolf B (2014) GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theor Appl Climatol 115:15–40. doi:10.1007/s00704-013-0860-x

    Google Scholar 

  • Sheffield J, Goteti G, Wood EF (2006) Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. J Clim 19:3088–3111. doi:10.1175/JCLI3790.1

    Google Scholar 

  • Siebert S, Döll P (2010) Quantifying blue and green water uses and virtual water contents in global crop production as well as potential production losses without irrigation. J Hydrol 384:198–217. doi:10.1016/j.jhydrol.2009.07.031

    Google Scholar 

  • Sitch S, Huntingford C, Gedney N, Levy PE, Lomas M, Piao SL, Betts R, Ciais P, Cox P, Friedlingstein P, Jones CD, Prentice IC, Woodward FI (2008) Evaluation of the terrestrial carbon cycle, future plant geography and climate-carbon cycle feedbacks using five dynamic global vegetation models (DGVMs). Global Change Biol 14:2015–2039. doi:10.1111/j.1365-2486.2008.01626.x

    Google Scholar 

  • Sood A, Smakhtin V (2015) Global hydrological models: a review. Hydrol Sci J. doi:10.1080/02626667.2014.950580

    Google Scholar 

  • Sterling SM, Ducharne A, Polcher J (2013) The impact of global land-cover change on the terrestrial water cycle. Nat Clim Change 3:385–390. doi:10.1038/nclimate1690

    Google Scholar 

  • Tapley BD, Bettadpur S, Watkins M, Reigber C (2004) The gravity recovery and climate experiment: mission overview and early results. Geophys Res Lett 31:L09607. doi:10.1029/2004GL019920

    Google Scholar 

  • Taylor RG, Scanlon B, Döll P, Rodell M, van Beek R, Wada Y, Longuevergne L, Leblanc M, Famiglietti JS, Edmunds M, Konikow L, Green TR, Chen J, Taniguchi M, Bierkens MFP, MacDonald A, Fan Y, Maxwell RM, Yechieli Y, Gurdak JJ, Allen DM, Shamsudduha M, Hiscock K, Yeh PJF, Holman I, Treidel H (2013) Ground water and climate change. Nat Clim Change 3:322–329. doi:10.1038/nclimate1744

    Google Scholar 

  • van Dijk AIJM, Renzullo LJ, Wada Y, Tregoning P (2014) A global water cycle reanalysis (2003–2012) merging satellite gravimetry and altimetry observations with a hydrological multi-model ensemble. Hydrol Earth Syst Sci 18:2955–2973. doi:10.5194/hess-18-2955-2014

    Google Scholar 

  • Vassolo S, Döll P (2005) Global-scale gridded estimates of thermoelectric power and manufacturing water use. Wat Resour Res 41:W04010. doi:10.1029/2004WR003360

  • Vergnes J-P, Decharme B, Alkama R, Martin E, Habets F, Douville H (2012) A simple groundwater scheme for hydrological and climate applications: description and off-line evaluation over France. J Hydrometeorol 13:1149–1171. doi:10.1175/JHM-D-11-0149

  • Vergnes J-P, Decharme B, Habets F (2014) Introduction of groundwater capillary rises using subgrid spatial variability of topography into the ISBA land surface model. J Geophys Res Atmos 119:11065–11086. doi:10.1002/2014JD021573

    Google Scholar 

  • Vörösmarty CJ, Sahagian D (2000) Anthropogenic disturbance of the terrestrial water cycle. Bioscience 50(9):753–765. doi:10.1641/0006-3568(2000)050[0753:ADOTTW]2.0.CO;2

    Google Scholar 

  • Vörösmarty CJ, Hoekstra AY, Bunn SE, Conway D, Gupta J (2015) Fresh water goes global. Science 349(6247):478–479. doi:10.1126/science.aac6009

    Google Scholar 

  • Wada Y, van Beek LPH, Bierkens MFP (2011) Modelling global water stress of the recent past: on the relative importance of trends in water demand and climate variability. Hydrol Earth Syst Sci 15:3785–3808. doi:10.5194/hess-15-3785-2011

    Google Scholar 

  • Wada Y, van Beek LPH, Bierkens MFP (2012) Nonsustainable groundwater sustaining irrigation: a global assessment. Water Resour Res 48:W00L06. doi:10.1029/2011WR010562

  • Wada Y, Wisser D, Eisner S, Flörke M, Gerten D, Haddeland I, Hanasaki N, Masaki Y, Portmann FT, Stacke T, Tessler Z, Schewe J (2013) Multi-model projections and uncertainties of irrigation water demand under climate change. Geophys Res Lett 40:4626–4632. doi:10.1002/grl.50686

    Google Scholar 

  • Wada Y, Wisser D, Bierkens MFP (2014) Global modeling of withdrawal, allocation and consumptive use of surface water and groundwater resources. Earth Syst Dynam 5:15–40. doi:10.5194/esd-5-15-2014

    Google Scholar 

  • Wang K, Dickinson ED, Wild M, Liang S (2010) Evidence for decadal variation in global terrestrial evapotranspiration between 1982 and 2002: 2. Results. J Geophys Res 115:D20113. doi:10.1029/2009JD013671

  • Warszawski L, Friend A, Ostberg S, Frieler K, Lucht W, Schaphoff S, Beerling D, Cadule P, Ciais P, Clark DB, Kahana R, Ito A, Keribin R, Kleidon A, Lomas M, Nishina K, Pavlick R, Rademacher TT, Buechner M, Piontek F, Schewe J, Serdeczny O, Schellnhuber HJ (2013) A multi-model analysis of risk of ecosystem shifts under climate change. Environ Res Lett 8:044018. doi:10.1088/1748-9326/8/4/044018

    Google Scholar 

  • Weedon GP, Balsamo G, Bellouin N, Gomes S, Best M, Viterbo P (2014) The WFDEI meteorological forcing data set: WATCH forcing data methodology applied to ERA-interim reanalysis data. Water Resour Res 50(9):7505–7514. doi:10.1002/2014WR015638

    Google Scholar 

  • Werth S, Güntner A (2010) Calibration analysis for water storage variability of the global hydrological model WGHM. Hydrol Earth Syst Sci 14(1):59–78. doi:10.5194/hess-14-59-2010

    Google Scholar 

  • Werth S, Güntner A, Schmidt R, Petrovic S (2009) Integration of GRACE mass variations into a global hydrological model. Earth Planet Sci Lett 277:166–173. doi:10.1016/j.epsl.2008.10.021

    Google Scholar 

  • Widén-Nilsson E, Halldin S, Xu C (2007) Global water-balance modelling with WASMOD-M: parameter estimation and regionalization. J Hydrol 340:105–118. doi:10.1016/j.jhydrol.2007.04.002

    Google Scholar 

  • Wisser D, Frolking S, Douglas EM, Fekete BM, Vörösmarty CJ, Schumann AH (2008) Global irrigation water demand: variability and uncertainties arising from agricultural and climate data sets. Geophys Res Lett 35(24):L24408. doi:10.1029/2008GL035296

  • Wood E, Roundy JK, Troy TJ, van Beek R, Bierkens M, Blyth E, de Roo A, Döll P, Ek M, Famiglietti J, Gochis D, van de Giesen N, Houser P, Jaffe P, Kollet S, Lehner B, Lettenmaier DP, Peters-Lidard C, Sivapalan M, Sheffield J, Wade A, Whitehead P (2011) Hyper-resolution global land surface modeling: meeting a grand challenge for monitoring Earth’s terrestrial water. Water Resour Res 47:W05301. doi:10.1029/2010WR010090

  • Wood E, Roundy JK, Troy TJ, van Beek R, Bierkens M, Blyth E, de Roo A, Döll P, Ek M, Famiglietti J, Gochis D, van de Giesen N, Houser P, Jaffe P, Kollet S, Lehner B, Lettenmaier DP, Peters-Lidard C., Sivapalan M, Sheffield J, Wade A, Whitehead P (2012) Reply to comment by Keith J. Beven and Hannah L. Cloke on ‘‘Hyperresolution global land surface modeling: meeting a grand challenge for monitoring Earth’s terrestrial water’’. Water Resour Res 48:W01802. doi:10.1029/2011WR011202

  • Xie H, Longuevergne L, Ringler C, Scanlon BR (2012) Calibration and evaluation of a semi-distributed watershed model of Sub-Saharan Africa using GRACE data. Hydrol Earth Syst Sci 16(9):3083–3099. doi:10.5194/hess-16-3083-2012

    Google Scholar 

  • Zaitchik BF, Rodell M, Reichle RH (2008) Assimilation of GRACE terrestrial water storage data into a land surface model: results for the Mississippi River basin. J Hydrometeorol 9(3):535–548. doi:10.1175/2007JHM951.1

    Google Scholar 

  • Zhou T, Haddeland I, Nijssen B, Lettenmaier DP (2015) Human induced changes in the global water cycle. AGU Geophysical Monograph Series, Submitted

    Google Scholar 

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Authors and Affiliations

  1. Institute of Physical Geography, Goethe University Frankfurt, 60629, Frankfurt, Germany

    Petra Döll & Hannes Müller Schmied

  2. Centre National de Recherches Météorologiques, Météo-France, 42 av. Coriolis, 31057, Toulouse, France

    Hervé Douville

  3. Helmholtz Centre Potsdam, German Research Centre for Geosciences, Telegrafenberg, 14473, Potsdam, Germany

    Andreas Güntner

  4. Center for Climate Systems Research, Columbia University, 2880 Broadway, New York, NY, 10025, USA

    Yoshihide Wada

  5. NASA Goddard Institute for Space Studies, 2880 Broadway, New York, NY, 10025, USA

    Yoshihide Wada

  6. Department of Physical Geography, Faculty of Geosciences, Utrecht University, Heidelberglaan 2, 3584, CS, Utrecht, The Netherlands

    Yoshihide Wada

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  1. Petra Döll
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  2. Hervé Douville
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  3. Andreas Güntner
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  4. Hannes Müller Schmied
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  5. Yoshihide Wada
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Correspondence to Petra Döll .

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Editors and Affiliations

  1. International Space Science Institute, Bern, Switzerland

    A. Cazenave

  2. International Space Science Institute, Bern, Switzerland

    N. Champollion

  3. European Space Research Institute, European Space Agency, Frascati, Italy

    J. Benveniste

  4. Center for Space Research, University of Texas at Austin, Austin, Texas, USA

    J. Chen

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Döll, P., Douville, H., Güntner, A., Schmied, H.M., Wada, Y. (2016). Modelling Freshwater Resources at the Global Scale: Challenges and Prospects. In: Cazenave, A., Champollion, N., Benveniste, J., Chen, J. (eds) Remote Sensing and Water Resources. Space Sciences Series of ISSI, vol 55. Springer, Cham. https://doi.org/10.1007/978-3-319-32449-4_2

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