Development of the Community Water Model (CWatM v1.04) – a high-resolution hydrological model for global and regional assessment of integrated water resources management

Abstract. We develop a new large-scale hydrological and water resources model, the Community Water Model (CWatM), which can simulate hydrology both globally and regionally at different resolutions from 30 arcmin to 30 arcsec at daily time steps. CWatM is open source in the Python programming environment and has a modular structure. It uses global, freely available data in the netCDF4 file format for reading, storage, and production of data in a compact way. CWatM includes general surface and groundwater hydrological processes but also takes into account human activities, such as water use and reservoir regulation, by calculating water demands, water use, and return flows. Reservoirs and lakes are included in the model scheme. CWatM is used in the framework of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), which compares global model outputs. The flexible model structure allows for dynamic interaction with hydro-economic and water quality models for the assessment and evaluation of water management options. Furthermore, the novelty of CWatM is its combination of state-of-the-art hydrological modeling, modular programming, an online user manual and automatic source code documentation, global and regional assessments at different spatial resolutions, and a potential community to add to, change, and expand the open-source project. CWatM also strives to build a community learning environment which is able to freely use an open-source hydrological model and flexible coupling possibilities to other sectoral models, such as energy and agriculture.

[1]  M. Bierkens,et al.  The Shadow Price of Irrigation Water in Major Groundwater‐Depleting Countries , 2019, Water Resources Research.

[2]  J. Hartmann,et al.  Compiling and Mapping Global Permeability of the Unconsolidated and Consolidated Earth: GLobal HYdrogeology MaPS 2.0 (GLHYMPS 2.0) , 2018 .

[3]  K. Sharp,et al.  Update to the Global Climate Data package: analysis of empirical bias correction methods in the context of producing very high resolution climate projections , 2018 .

[4]  Hoshin Vijai Gupta,et al.  Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling , 2009 .

[5]  Sujan Koirala,et al.  GLOBAL SIMULATION OF GROUNDWATER RECHARGE, WATER TABLE DEPTH, AND LOW FLOW USING A LAND SURFACE MODEL WITH GROUNDWATER REPRESENTATION , 2012 .

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

[7]  G. Fischer,et al.  East African Community Water Vision. Regional Scenarios for Human - Natural Water System Transformations , 2020 .

[8]  Petra Döll,et al.  Validation of a new global 30-min drainage direction map , 2002 .

[9]  Rolf Weingartner,et al.  An introduction to the hydrological modelling system PREVAH and its pre- and post-processing-tools , 2009, Environ. Model. Softw..

[10]  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.

[11]  Marc F. P. Bierkens,et al.  Global hydrology 2015: State, trends, and directions , 2015 .

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

[13]  Keywan Riahi,et al.  Impacts of considering electric sector variability and reliability in the MESSAGE model , 2013 .

[14]  S. M. de Jong,et al.  Calibrating a large‐extent high‐resolution coupled groundwater‐land surface model using soil moisture and discharge data , 2014 .

[15]  E. Stehfest,et al.  Anthropogenic land use estimates for the Holocene – HYDE 3.2 , 2016 .

[16]  B. Lehner,et al.  Estimating the volume and age of water stored in global lakes using a geo-statistical approach , 2016, Nature Communications.

[17]  M Bakker,et al.  Scripting MODFLOW Model Development Using Python and FloPy , 2016, Ground water.

[18]  S. Kanae,et al.  An integrated model for the assessment of global water resources – Part 1: Model description and input meteorological forcing , 2008 .

[19]  Michael Obersteiner,et al.  Crop Productivity and the Global Livestock Sector: Implications for Land Use Change and Greenhouse Gas Emissions , 2013 .

[20]  Luis Samaniego,et al.  Predictions in a data-sparse region using a regionalized grid-based hydrologic model driven by remotely sensed data , 2011 .

[21]  J. Knijff LISFLOOD Distributed Water Balance and Flood Simulation Model , 2008 .

[22]  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 .

[23]  E. Wood,et al.  Solar and wind energy enhances drought resilience and groundwater sustainability , 2019, Nature Communications.

[24]  P. Burek,et al.  Sources and export of nutrients in the Zambezi River basin: status and future trend , 2018 .

[25]  Dmitri Kavetski,et al.  Pursuing the method of multiple working hypotheses for hydrological modeling , 2011 .

[26]  Ulrich Maniak Hydrologie und Wasserwirtschaft , 1988 .

[27]  F. Piontek,et al.  The Inter-Sectoral Impact Model Intercomparison Project (ISI–MIP): Project framework , 2013, Proceedings of the National Academy of Sciences.

[28]  J. Eom,et al.  The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview , 2017 .

[29]  Marc F. P. Bierkens,et al.  Dynamic attribution of global water demand to surface water and groundwater resources: Effects of abstractions and return flows on river discharges , 2013 .

[30]  Bertrand Decharme,et al.  Recent Changes in the ISBA‐CTRIP Land Surface System for Use in the CNRM‐CM6 Climate Model and in Global Off‐Line Hydrological Applications , 2019, Journal of Advances in Modeling Earth Systems.

[31]  Peter Salamon,et al.  A software framework for construction of process-based stochastic spatio-temporal models and data assimilation , 2010, Environ. Model. Softw..

[32]  Naota Hanasaki,et al.  Human–water interface in hydrological modelling : current status and future directions , 2017 .

[33]  N. Djilali,et al.  A Continental‐Scale Hydroeconomic Model for Integrating Water‐Energy‐Land Nexus Solutions , 2018, Water Resources Research.

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

[35]  D. Conway,et al.  Going local: Evaluating and regionalizing a global hydrological model’s simulation of river flows in a medium-sized East African basin , 2018, Journal of Hydrology: Regional Studies.

[36]  A. Western,et al.  Predicting groundwater recharge for varying land cover and climate conditions – a global meta-study , 2017 .

[37]  P. Burek,et al.  Excess nutrient loads to Lake Taihu: Opportunities for nutrient reduction. , 2019, The Science of the total environment.

[38]  D. Yates,et al.  WEAP21—A Demand-, Priority-, and Preference-Driven Water Planning Model , 2005 .

[39]  P. Döll,et al.  Development and testing of the WaterGAP 2 global model of water use and availability , 2003 .

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

[41]  R. Seidl,et al.  Linking scientific disciplines: Hydrology and social sciences , 2017 .

[42]  Andrew Jarvis,et al.  Hole-filled SRTM for the globe Version 4 , 2008 .

[43]  P. Döll,et al.  Groundwater use for irrigation - a global inventory , 2010 .

[44]  Göran Lindström,et al.  A Simple Automatic Calibration Routine for the HBV Model , 1997 .

[45]  R. Reedy,et al.  Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data , 2018, Proceedings of the National Academy of Sciences.

[46]  Stephen E. Fick,et al.  WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas , 2017 .

[47]  L. S. Pereira,et al.  Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .

[48]  Veronika Eyring,et al.  Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization , 2015 .

[49]  I. Supit,et al.  System description of the WOFOST 6.0 crop simulation model implemented in CGMS , 1994 .

[50]  Peter Wallensteen,et al.  Comprehensive Assessment of the Freshwater Resources of the World, International Fresh Water Resources: Conflict or Cooperation , 1997 .

[51]  J. McDonnell,et al.  A decade of Predictions in Ungauged Basins (PUB)—a review , 2013 .

[52]  Naota Hanasaki,et al.  Incorporating anthropogenic water regulation modules into a land surface model , 2012 .

[53]  T. Krisztin,et al.  Increasing nitrogen export to sea: A scenario analysis for the Indus River. , 2019, The Science of the total environment.

[54]  John S. Kimball,et al.  Automated upscaling of river networks for macroscale hydrological modeling , 2008 .

[55]  Y. Wada Modeling Groundwater Depletion at Regional and Global Scales: Present State and Future Prospects , 2016, Surveys in Geophysics.

[56]  Sabine Attinger,et al.  Implications of distributed hydrologic model parameterization on water fluxes at multiple scales and locations , 2013 .

[57]  Van Genuchten,et al.  A closed-form equation for predicting the hydraulic conductivity of unsaturated soils , 1980 .

[58]  Keywan Riahi,et al.  A methodology and implementation of automated emissions harmonization for use in Integrated Assessment Models , 2018, Environ. Model. Softw..

[59]  Arlen W. Harbaugh,et al.  A modular three-dimensional finite-difference ground-water flow model , 1984 .

[60]  R. Nemani,et al.  Global Distribution and Density of Constructed Impervious Surfaces , 2007, Sensors.

[61]  Marc Parizeau,et al.  DEAP: evolutionary algorithms made easy , 2012, J. Mach. Learn. Res..

[62]  Colin J. Gleason,et al.  A Hybrid of Optical Remote Sensing and Hydrological Modeling Improves Water Balance Estimation , 2018 .

[63]  Keywan Riahi,et al.  A new scenario framework for Climate Change Research: scenario matrix architecture , 2014, Climatic Change.

[64]  Naota Hanasaki,et al.  A global hydrological simulation to specify the sources of water used by humans , 2017 .

[65]  Naota Hanasaki,et al.  Modeling global water use for the 21st century : The Water Futures and Solutions (WFaS) initiative and its approaches , 2015 .

[66]  I. Shiklomanov Appraisal and Assessment of World Water Resources , 2000 .

[67]  Tim R. McVicar,et al.  Global‐scale regionalization of hydrologic model parameters , 2016 .

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

[69]  Matti Kummu,et al.  Gridded global datasets for Gross Domestic Product and Human Development Index over 1990–2015 , 2018, Scientific Data.

[70]  H. Steinfeld,et al.  Livestock's long shadow: environmental issues and options. , 2006 .

[71]  H. Hasenauer,et al.  Spatial downscaling of European climate data , 2016 .

[72]  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 .

[73]  Florian Pappenberger,et al.  Technical review of large-scale hydrological models for implementation in operational flood forecasting schemes on continental level , 2016, Environ. Model. Softw..

[74]  P. Döll,et al.  Challenges in developing a global gradient-based groundwater model (G3M v1.0) for the integration into a global hydrological model , 2019, Geoscientific Model Development.

[75]  S. Kanae,et al.  Incorporation of groundwater pumping in a global Land Surface Model with the representation of human impacts , 2015 .

[76]  Taikan Oki,et al.  Projection of future world water resources under SRES scenarios: water withdrawal / Projection des ressources en eau mondiales futures selon les scénarios du RSSE: prélèvement d'eau , 2008 .

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

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

[79]  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 .

[80]  X. R. Liu,et al.  The Xinanjiang model. , 1995 .

[81]  Francesca Pianosi,et al.  A large-scale simulation model to assess karstic groundwater recharge over Europe and the Mediterranean , 2015, Geoscientific Model Development.

[82]  Göran Lindström,et al.  Development and test of the distributed HBV-96 hydrological model , 1997 .

[83]  Jaap Schellekens,et al.  MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data , 2016 .

[84]  P. Döll,et al.  High‐resolution mapping of the world's reservoirs and dams for sustainable river‐flow management , 2011 .

[85]  Fabio A. Diuana,et al.  The Nexus Solutions Tool (NEST): An open platform for optimizing multi-scale energy-water-land system transformations , 2019 .

[86]  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 .

[87]  S. Bergström,et al.  Principles and Confidence in Hydrological Modelling , 1991 .

[88]  Peter H. Gleick,et al.  Comprehensive Assessment of the Freshwater Resources of the World , 1997 .

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

[90]  Jing Gao,et al.  Downscaling Global Spatial Population Projections from 1/8-degree to 1-km Grid Cells , 2017 .

[91]  G. Fischer,et al.  A nexus modeling framework for assessing water scarcity solutions , 2019, Current Opinion in Environmental Sustainability.

[92]  E. Todini The ARNO rainfall-runoff model , 1996 .

[93]  C. Folberth,et al.  Global wheat production potentials and management flexibility under the representative concentration pathways , 2014 .

[94]  C. Justice,et al.  High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.

[95]  Sabine Attinger,et al.  Toward seamless hydrologic predictions across spatial scales , 2017 .

[96]  M. Bierkens,et al.  A global-scale two-layer transient groundwater model: development and application to groundwater depletion , 2016 .

[97]  Zarrar Khan,et al.  The NExus Solutions Tool (NEST) v1.0: an open platform for optimizing multi-scale energy–water–land system transformations , 2020, Geoscientific Model Development.

[98]  H. Velthuizen,et al.  Harmonized World Soil Database (version 1.2) , 2008 .

[99]  Etienne Leblois,et al.  The SAFRAN‐ISBA‐MODCOU hydrometeorological model applied over France , 2008 .

[100]  Peter R. J. North,et al.  The ESA GlobAlbedo Project for mapping the Earth's land surface albedo for 15 Years from European Sensors. , 2012, IGARSS 2012.

[101]  A. Ducharne,et al.  The critical role of the routing scheme in simulating peak river discharge in global hydrological models , 2017 .

[102]  S. Lange Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset , 2017 .

[103]  T. Stacke,et al.  Validation of terrestrial water storage variations as simulated by different global numerical models with GRACE satellite observations. , 2016 .

[104]  Naota Hanasaki,et al.  A reservoir operation scheme for global river routing models , 2006 .

[105]  Marc F. P. Bierkens,et al.  A global-scale two-layer transient groundwater model : Development and application to groundwater depletion , 2017 .

[106]  Peter Burek,et al.  Multi-Criteria Framework to Assess Large Scale Water Resources Policy Measures , 2016 .

[107]  A. Thomson,et al.  The representative concentration pathways: an overview , 2011 .

[108]  Peter Salamon,et al.  Filling the gaps: Calibrating a rainfall-runoff model using satellite-derived surface water extent , 2015 .

[109]  Changsheng Li,et al.  Bi-criteria evaluation of the MIKE SHE model for a forested watershed on the South Carolina coastal plain , 2010 .

[110]  Ian M. Mitchell,et al.  Best Practices for Scientific Computing , 2012, PLoS biology.

[111]  Tyler D. Eddy,et al.  Assessing the impacts of 1.5 °C global warming - simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b) , 2016 .

[112]  S. Bergström,et al.  DEVELOPMENT OF A CONCEPTUAL DETERMINISTIC RAINFALL-RUNOFF MODEL , 1973 .

[113]  G. Blöschl,et al.  Socio‐hydrology: A new science of people and water , 2012 .

[114]  Y. Wada Global Modeling of Withdrawal, Allocation and Consumptive Use of Surface Water and Groundwater Resources , 2014 .

[115]  Brian C. O'Neill,et al.  Spatially explicit global population scenarios consistent with the Shared Socioeconomic Pathways , 2016 .

[116]  A. D. Roo,et al.  Physically-Based River Basin Modelling within a GIS: the LISFLOOD Model. , 2000 .

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

[118]  D. Lettenmaier,et al.  A simple hydrologically based model of land surface water and energy fluxes for general circulation models , 1994 .

[119]  Malin Falkenmark,et al.  Meeting water requirements of an expanding world population , 1997 .

[120]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

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

[122]  Y. Mualem A New Model for Predicting the Hydraulic Conductivity , 1976 .

[123]  Niels Drost,et al.  PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model , 2017, Geoscientific Model Development.

[124]  M. Bierkens,et al.  Global monthly water stress: 1. Water balance and water availability , 2011 .

[125]  Mengru Wang,et al.  The MARINA model (Model to Assess River Inputs of Nutrients to seAs): Model description and results for China. , 2016, The Science of the total environment.

[126]  A. W. Harbaugh MODFLOW-2005 : the U.S. Geological Survey modular ground-water model--the ground-water flow process , 2005 .

[127]  H. Kling,et al.  Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios , 2012 .

[128]  Dmitri Kavetski,et al.  A unified approach for process‐based hydrologic modeling: 1. Modeling concept , 2015 .

[129]  Beck Hylke,et al.  Global-scale regionalization of hydrologic model parameters , 2016 .

[130]  Hyungjun Kim,et al.  Recent progresses in incorporating human land–water management into global land surface models toward their integration into Earth system models , 2016 .

[131]  Petra Döll,et al.  Development and validation of the global map of irrigation areas , 2005 .

[132]  Jens Hartmann,et al.  Mapping permeability over the surface of the Earth , 2011 .

[133]  Gregory V. Wilson,et al.  Four simple recommendations to encourage best practices in research software [version 1; referees: 3 approved] , 2017 .

[134]  M Falkenmark,et al.  Macro-scale water scarcity requires micro-scale approaches. Aspects of vulnerability in semi-arid development. , 1989, Natural resources forum.

[135]  C. Vörösmarty,et al.  Reconstructing 20 th century global hydrography : a contribution to the Global Terrestrial Network-Hydrology ( GTN-H ) , 2010 .

[136]  George H. Hargreaves,et al.  Reference Crop Evapotranspiration from Temperature , 1985 .

[137]  S. Seneviratne,et al.  A two-parameter Budyko function to represent conditions under which evapotranspiration exceeds precipitation , 2016 .

[138]  Mary C. Hill,et al.  MODFLOW-2005, the U.S. Geological Survey modular ground-water model - documentation of shared node local grid refinement (LGR) and the boundary flow and head (BFH) package , 2006 .

[139]  C. Müller,et al.  Modelling the role of agriculture for the 20th century global terrestrial carbon balance , 2007 .

[140]  B. O’Neill,et al.  Global urbanization projections for the Shared Socioeconomic Pathways , 2017 .

[141]  Marcel G. Schaap,et al.  Weighted recalibration of the Rosetta pedotransfer model with improved estimates of hydraulic parameter distributions and summary statistics (Rosetta3) , 2017 .

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