Modelling Freshwater Resources at the Global Scale: Challenges and Prospects

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.

[1]  A. Ducharne,et al.  The impact of global land-cover change on the terrestrial water cycle , 2013 .

[2]  I. Prentice,et al.  Future Global Water Resources with respect to Climate Change and Water Withdrawals , 2010 .

[3]  E. Wood,et al.  Development of a 50-Year High-Resolution Global Dataset of Meteorological Forcings for Land Surface Modeling , 2006 .

[4]  P. J. Smith,et al.  A novel framework for discharge uncertainty quantification applied to 500 UK gauging stations , 2015, Water resources research.

[5]  R. Betts,et al.  Comparing projections of future changes in runoff from hydrological and biome models in ISI-MIP , 2013 .

[6]  C. Simmons,et al.  HydroGeoSphere: A Fully Integrated, Physically Based Hydrological Model , 2012 .

[7]  Petra Döll,et al.  Global‐scale gridded estimates of thermoelectric power and manufacturing water use , 2005 .

[8]  Keith Beven,et al.  Hyperresolution information and hyperresolution ignorance in modelling the hydrology of the land surface , 2014, Science China Earth Sciences.

[9]  Petra Döll,et al.  Quantifying blue and green virtual water contents in global crop production as well as potential production losses without irrigation , 2010 .

[10]  S. Attinger,et al.  Multiscale parameter regionalization of a grid‐based hydrologic model at the mesoscale , 2010 .

[11]  N. Arnell,et al.  The global-scale impacts of climate change on water resources and flooding under new climate and socio-economic scenarios , 2013, Climatic Change.

[12]  O. Geoffroy,et al.  The recent global warming hiatus: What is the role of Pacific variability? , 2015 .

[13]  D. Lettenmaier,et al.  Human‐Induced Changes in the Global Water Cycle , 2016 .

[14]  Marc F. P. Bierkens,et al.  A high-resolution global-scale groundwater model , 2013 .

[15]  Y. Gusev,et al.  Impact of uncertainties in meteorological forcing data and land surface parameters on global estimates of terrestrial water balance components , 2011 .

[16]  Edward A. Sudicky,et al.  Application of a fully‐integrated surface‐subsurface flow model at the watershed‐scale: A case study , 2008 .

[17]  Aditya Sood,et al.  Global hydrological models: a review , 2015 .

[18]  R. Dickinson,et al.  Evidence for decadal variation in global terrestrial evapotranspiration between 1982 and 2002: 1. Model development , 2010 .

[19]  Claudia Ringler,et al.  Calibration and evaluation of a semi-distributed watershed model of Sub-Saharan Africa using GRACE data , 2012 .

[20]  T. Stacke,et al.  Multimodel projections and uncertainties of irrigation water demand under climate change , 2013 .

[21]  A. Güntner,et al.  Calibration analysis for water storage variability of the global hydrological model WGHM , 2009 .

[22]  P. Bauer‐Gottwein,et al.  Combining satellite radar altimetry, SAR surface soil moisture and GRACE total storage changes for hydrological model calibration in a large poorly gauged catchment , 2011 .

[23]  M. Bierkens,et al.  Global modeling of withdrawal, allocation and consumptive use of surface water and groundwater resources , 2013 .

[24]  Huidong Jin,et al.  Continental satellite soil moisture data assimilation improves root-zone moisture analysis for water resources assessment , 2014 .

[25]  B. Jiménez-Cisneros,et al.  Integrating risks of climate change into water management , 2015 .

[26]  A. Arneth,et al.  Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations , 2011 .

[27]  F. Aires,et al.  Surface freshwater storage and variability in the Amazon basin from multi‐satellite observations, 1993–2007 , 2013 .

[28]  B. Scanlon,et al.  Impact of water withdrawals from groundwater and surface water on continental water storage variations , 2012 .

[29]  Ying Fan,et al.  Incorporating water table dynamics in climate modeling: 1. Water table observations and equilibrium water table simulations , 2007 .

[30]  Nigel W. Arnell,et al.  A comparative analysis of projected impacts of climate change on river runoff from global and catchment-scale hydrological models , 2010 .

[31]  Hyungjun Kim,et al.  First look at changes in flood hazard in the Inter-Sectoral Impact Model Intercomparison Project ensemble , 2013, Proceedings of the National Academy of Sciences.

[32]  R. Maxwell,et al.  Capturing the influence of groundwater dynamics on land surface processes using an integrated, distributed watershed model , 2008 .

[33]  J. Kusche,et al.  Calibration/Data Assimilation Approach for Integrating GRACE Data into the WaterGAP Global Hydrology Model (WGHM) Using an Ensemble Kalman Filter: First Results , 2013, Surveys in Geophysics.

[34]  S. Kanae,et al.  Re-evaluation of future water stress due to socio-economic and climate factors under a warming climate , 2012 .

[35]  P. Stott,et al.  Anthropogenic greenhouse gas contribution to flood risk in England and Wales in autumn 2000 , 2011, Nature.

[36]  Dieter Gerten,et al.  Effects of Precipitation Uncertainty on Discharge Calculations for Main River Basins , 2009 .

[37]  Andreas Güntner,et al.  Improvement of Global Hydrological Models Using GRACE Data , 2008 .

[38]  S. Petrovic,et al.  Integration of GRACE mass variations into a global hydrological model , 2009 .

[39]  R. Maxwell,et al.  The groundwater land-surface atmosphere connection: Soil moisture effects on the atmospheric boundary layer in fully-coupled simulations , 2007 .

[40]  W. J. Shuttleworth,et al.  Parameter estimation of a land surface scheme using multicriteria methods , 1999 .

[41]  Keith Beven,et al.  Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology , 2001 .

[42]  T. Blume,et al.  The value of satellite‐derived snow cover images for calibrating a hydrological model in snow‐dominated catchments in Central Asia , 2014 .

[43]  K. Beven,et al.  Comment on “Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water” by Eric F. Wood et al. , 2012 .

[44]  R. Houborg,et al.  Drought indicators based on model‐assimilated Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage observations , 2012 .

[45]  S. Hagemann,et al.  Climate change impact on available water resources obtained using multiple global climate and hydrology models , 2012 .

[46]  C. Vörösmarty,et al.  Anthropogenic Disturbance of the Terrestrial Water Cycle , 2000 .

[47]  Haibin Li,et al.  Groundwater flow across spatial scales: importance for climate modeling , 2014 .

[48]  P. Dirmeyer,et al.  Evaluation of the Second Global Soil Wetness Project soil moisture simulations: 2. Sensitivity to external meteorological forcing , 2006 .

[49]  S. Kanae,et al.  An integrated model for the assessment of global water resources – Part 2: Applications and assessments , 2008 .

[50]  H. Douville,et al.  Anthropogenic influence on multidecadal changes in reconstructed global evapotranspiration , 2012 .

[51]  W. Lucht,et al.  Agricultural green and blue water consumption and its influence on the global water system , 2008 .

[52]  R. Knutti,et al.  Robustness and uncertainties in the new CMIP5 climate model projections , 2013 .

[53]  P. Jones,et al.  Updated high‐resolution grids of monthly climatic observations – the CRU TS3.10 Dataset , 2014 .

[54]  P. Tregoning,et al.  A global water cycle reanalysis (2003-2012) merging satellite gravimetry and altimetry observations with a hydrological multi-model ensemble , 2013 .

[55]  Charon Birkett,et al.  Climatic Effects on Lake Basins. Part I: Modeling Tropical Lake Levels , 2011 .

[56]  G. Balsamo,et al.  The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA‐Interim reanalysis data , 2014 .

[57]  S. Seneviratne,et al.  Recent decline in the global land evapotranspiration trend due to limited moisture supply , 2010, Nature.

[58]  M. Rodell,et al.  Assimilation of GRACE Terrestrial Water Storage Data into a Land Surface Model: Results for the Mississippi River Basin , 2008 .

[59]  U. Schneider,et al.  GPCC's new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle , 2013, Theoretical and Applied Climatology.

[60]  Jens Hartmann,et al.  A glimpse beneath earth's surface: GLobal HYdrogeology MaPS (GLHYMPS) of permeability and porosity , 2014 .

[61]  G. Blöschl,et al.  The value of MODIS snow cover data in validating and calibrating conceptual hydrologic models , 2008 .

[62]  A. Hoekstra,et al.  Global Monthly Water Scarcity: Blue Water Footprints versus Blue Water Availability , 2012, PloS one.

[63]  T. Oki,et al.  Multimodel Estimate of the Global Terrestrial Water Balance: Setup and First Results , 2011 .

[64]  Zong-Liang Yang,et al.  Development of a simple groundwater model for use in climate models and evaluation with Gravity Recovery and Climate Experiment data , 2007 .

[65]  E. Hawkins,et al.  The potential to narrow uncertainty in projections of regional precipitation change , 2011 .

[66]  E. Sudicky,et al.  Hyper‐resolution global hydrological modelling: what is next? , 2015 .

[67]  Arjen Ysbert Hoekstra,et al.  Global water scarcity: the monthly blue water footprint compared to blue water availability for the world's major river basins , 2011 .

[68]  M. Watkins,et al.  The gravity recovery and climate experiment: Mission overview and early results , 2004 .

[69]  P. Döll,et al.  Global-scale modeling of groundwater recharge , 2008 .

[70]  Jean-Christophe Calvet,et al.  Predicting the response of the Amazon rainforest to persistent drought conditions under current and future climates: a major challenge for global land surface models , 2014 .

[71]  F. Ludwig,et al.  Global water resources affected by human interventions and climate change , 2013, Proceedings of the National Academy of Sciences.

[72]  P. Döll,et al.  Global‐scale assessment of groundwater depletion and related groundwater abstractions: Combining hydrological modeling with information from well observations and GRACE satellites , 2014 .

[73]  Dieter Gerten,et al.  Impact of a Statistical Bias Correction on the Projected Hydrological Changes Obtained from Three GCMs and Two Hydrology Models , 2011 .

[74]  M. Lomas,et al.  A multi-model analysis of risk of ecosystem shifts under climate change , 2013 .

[75]  A. Cazenave,et al.  Global Evaluation of the ISBA-TRIP Continental Hydrological System. Part I: Comparison to GRACE Terrestrial Water Storage Estimates and In Situ River Discharges , 2010 .

[76]  William A. Lahoz,et al.  Closing the Gaps in Our Knowledge of the Hydrological Cycle over Land: Conceptual Problems , 2014, Surveys in Geophysics.

[77]  F. Brissette,et al.  Assessing the limits of bias‐correcting climate model outputs for climate change impact studies , 2015 .

[78]  Petra Döll,et al.  Value of river discharge data for global-scale hydrological modeling , 2007 .

[79]  Keith Beven,et al.  Comment on “Pursuing the method of multiple working hypotheses for hydrological modeling” by P. Clark et al. , 2012 .

[80]  Stewart J. Cohen,et al.  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 , 2014 .

[81]  Stephanie Eisner,et al.  Effects of climate model radiation, humidity and wind estimates on hydrological simulations , 2011 .

[82]  M. Ek,et al.  Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water , 2011 .

[83]  P. Cox,et al.  Detection of solar dimming and brightening effects on Northern Hemisphere river flow , 2014 .

[84]  Petra Döll,et al.  Global modeling of irrigation water requirements , 2002 .

[85]  M. Flörke,et al.  Future long-term changes in global water resources driven by socio-economic and climatic changes , 2007 .

[86]  B. Scanlon,et al.  Ground water and climate change , 2013 .

[87]  Felipe J. Colón-González,et al.  Multimodel assessment of water scarcity under climate change , 2013, Proceedings of the National Academy of Sciences.

[88]  P. Döll,et al.  A global hydrological model for deriving water availability indicators: model tuning and validation , 2003 .

[89]  Dennis P. Lettenmaier,et al.  Multi-criteria parameter estimation for the unified land model , 2012 .

[90]  S. Seneviratne,et al.  Global intercomparison of 12 land surface heat flux estimates , 2011 .

[91]  Jens Hartmann,et al.  The new global lithological map database GLiM: A representation of rock properties at the Earth surface , 2012 .

[92]  S. Hagemann,et al.  Comparing large-scale hydrological models to observed runoff percentiles in Europe , 2012 .

[93]  R. Betts,et al.  Detection of a direct carbon dioxide effect in continental river runoff records , 2006, Nature.

[94]  Wolfgang Lucht,et al.  Water savings potentials of irrigation systems:: global simulation of processes and linkages , 2015 .

[95]  P. Döll,et al.  Sensitivity of simulated global-scale freshwater fluxes and storages to input data, hydrological model structure, human water use and calibration , 2014 .

[96]  Marc F. P. Bierkens,et al.  Modelling global water stress of the recent past: on the relative importance of trends in water demand and climate variability , 2011 .

[97]  E. Small,et al.  Use of GPS receivers as a soil moisture network for water cycle studies , 2008 .

[98]  M. Bierkens,et al.  Nonsustainable groundwater sustaining irrigation: A global assessment , 2012 .

[99]  Andreas Schumann,et al.  Global irrigation water demand: Variability and uncertainties arising from agricultural and climate data sets , 2008 .

[100]  I. C. Prentice,et al.  Evaluation of the terrestrial carbon cycle, future plant geography and climate‐carbon cycle feedbacks using five Dynamic Global Vegetation Models (DGVMs) , 2008 .

[101]  Ying Fan,et al.  Incorporating water table dynamics in climate modeling: 2. Formulation, validation, and soil moisture simulation , 2007 .

[102]  A. Hoekstra,et al.  Fresh water goes global , 2015, Science.

[103]  Aurélien Ribes,et al.  Trends in Global and Basin-Scale Runoff over the Late Twentieth Century: Methodological Issues and Sources of Uncertainty , 2011 .

[104]  Rolf H. Reichle,et al.  Connecting Satellite Observations with Water Cycle Variables Through Land Data Assimilation: Examples Using the NASA GEOS-5 LDAS , 2013, Surveys in Geophysics.

[105]  R. Maxwell,et al.  A high-resolution simulation of groundwater and surface water over most of the continental US with the integrated hydrologic model ParFlow v3 , 2015 .

[106]  R. Koster,et al.  Assimilation of GRACE terrestrial water storage into a land surface model: Evaluation and potential value for drought monitoring in western and central Europe , 2012 .

[107]  P. Döll,et al.  MIRCA2000—Global monthly irrigated and rainfed crop areas around the year 2000: A new high‐resolution data set for agricultural and hydrological modeling , 2010 .

[108]  C. Müller,et al.  Constraints and potentials of future irrigation water availability on agricultural production under climate change , 2013, Proceedings of the National Academy of Sciences.

[109]  Petra Döll,et al.  Global-scale analysis of river flow alterations due to water withdrawals and reservoirs , 2009 .

[110]  Florence Habets,et al.  Introduction of groundwater capillary rises using subgrid spatial variability of topography into the ISBA land surface model , 2014 .

[111]  H. Douville,et al.  A Simple Groundwater Scheme for Hydrological and Climate Applications: Description and Offline Evaluation over France , 2012 .

[112]  J. Tailleur,et al.  Global Patterns of Groundwater Table Depth , 2013 .

[113]  Martina Flörke,et al.  Domestic and industrial water uses of the past 60 years as a mirror of socio-economic development: A global simulation study , 2013 .

[114]  Petra Döll,et al.  Seasonal Water Storage Variations as Impacted by Water Abstractions: Comparing the Output of a Global Hydrological Model with GRACE and GPS Observations , 2014, Surveys in Geophysics.

[115]  Murugesu Sivapalan,et al.  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” , 2012 .

[116]  Shunlin Liang,et al.  Evidence for decadal variation in global terrestrial evapotranspiration between 1982 and 2002: 2. Results , 2010 .