PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model

Abstract. We present PCR-GLOBWB 2, a global hydrology and water resources model. Compared to previous versions of PCR-GLOBWB, this version fully integrates water use. Sector-specific water demand, groundwater and surface water withdrawal, water consumption, and return flows are dynamically calculated at every time step and interact directly with the simulated hydrology. PCR-GLOBWB 2 has been fully rewritten in Python and PCRaster Python and has a modular structure, allowing easier replacement, maintenance, and development of model components. PCR-GLOBWB 2 has been implemented at 5 arcmin resolution, but a version parameterized at 30 arcmin resolution is also available. Both versions are available as open-source codes on https://github.com/UU-Hydro/PCR-GLOBWB_model (Sutanudjaja et al., 2017a). PCR-GLOBWB 2 has its own routines for groundwater dynamics and surface water routing. These relatively simple routines can alternatively be replaced by dynamically coupling PCR-GLOBWB 2 to a global two-layer groundwater model and 1-D–2-D hydrodynamic models. Here, we describe the main components of the model, compare results of the 30 and 5 arcmin versions, and evaluate their model performance using Global Runoff Data Centre discharge data. Results show that model performance of the 5 arcmin version is notably better than that of the 30 arcmin version. Furthermore, we compare simulated time series of total water storage (TWS) of the 5 arcmin model with those observed with GRACE, showing similar negative trends in areas of prevalent groundwater depletion. Also, we find that simulated total water withdrawal matches reasonably well with reported water withdrawal from AQUASTAT, while water withdrawal by source and sector provide mixed results.

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

[2]  Tessa Eikelboom,et al.  A physically based model of global freshwater surface temperature , 2012 .

[3]  A. Sterl,et al.  The ERA‐40 re‐analysis , 2005 .

[4]  Eric F. Wood,et al.  Predicting the Discharge of Global Rivers , 2001, Journal of Climate.

[5]  F. Ludwig,et al.  Vulnerability of US and European electricity supply to climate change , 2012 .

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

[7]  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 .

[8]  Yoshihide Wada,et al.  High‐resolution modeling of human and climate impacts on global water resources , 2013 .

[9]  Stefan Hagemann,et al.  An improved land surface parameter dataset for global and regional climate models , 2002 .

[10]  Dean B. Gesch,et al.  New land surface digital elevation model covers the Earth , 1999 .

[11]  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.

[12]  Albert J. Kettner,et al.  WBMsed, a distributed global-scale riverine sediment flux model: Model description and validation , 2013, Comput. Geosci..

[13]  M. Watkins,et al.  Quantifying and reducing leakage errors in the JPL RL05M GRACE mascon solution , 2016 .

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

[15]  B. Lehner,et al.  Water on an Urban Planet: Urbanization and the Reach of Urban Water Infrastructure , 2014 .

[16]  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 .

[17]  P. Döll,et al.  Development and validation of a global database of lakes, reservoirs and wetlands , 2004 .

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

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

[20]  F. W. Murray,et al.  On the Computation of Saturation Vapor Pressure , 1967 .

[21]  S. Hagemann,et al.  Hydrological droughts in the 21st century, hotspots and uncertainties from a global multimodel ensemble experiment , 2013, Proceedings of the National Academy of Sciences.

[22]  Anthony M. Castronova,et al.  A generic approach for developing process-level hydrologic modeling components , 2010, Environ. Model. Softw..

[23]  Wolfgang Grabs,et al.  High‐resolution fields of global runoff combining observed river discharge and simulated water balances , 2002 .

[24]  L. Konikow Contribution of global groundwater depletion since 1900 to sea‐level rise , 2011 .

[25]  M. Watkins,et al.  Improved methods for observing Earth's time variable mass distribution with GRACE using spherical cap mascons , 2015 .

[26]  K. Verdin,et al.  New Global Hydrography Derived From Spaceborne Elevation Data , 2008 .

[27]  Jannis M. Hoch,et al.  Assessing the impact of hydrodynamics on large-scale flood wave propagation – a case study for the Amazon Basin , 2016 .

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

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

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

[31]  H. A. Mooney,et al.  Maximum rooting depth of vegetation types at the global scale , 1996, Oecologia.

[32]  Stefan Hagemann,et al.  Improving a subgrid runoff parameterization scheme for climate models by the use of high resolution data derived from satellite observations , 2003 .

[33]  Naota Hanasaki,et al.  Virtual water trade flows and savings under climate change , 2013 .

[34]  Fedor Baart,et al.  GLOFRIM v1.0 – A globally applicable computational framework for integrated hydrological–hydrodynamic modelling , 2017 .

[35]  Nigel W. Arnell,et al.  Simulating current global river runoff with a global hydrological model: model revisions, validation, and sensitivity analysis , 2011 .

[36]  Marc F. P. Bierkens,et al.  A virtual water network of the Roman world , 2014 .

[37]  Dieter Gerten,et al.  Human alterations of the terrestrial water cycle through land management , 2008 .

[38]  A. Weerts,et al.  Skill of a global seasonal streamflow forecasting system, relative roles of initial conditions and meteorological forcing , 2013 .

[39]  M. Hulme,et al.  A high-resolution data set of surface climate over global land areas , 2002 .

[40]  Brenden Jongman,et al.  Assessing flood risk at the global scale: model setup, results, and sensitivity , 2013 .

[41]  M. Puma,et al.  Groundwater depletion embedded in international food trade , 2017, Nature.

[42]  M. Bierkens,et al.  Global depletion of groundwater resources , 2010 .

[43]  L. V. Beek,et al.  Global patterns of change in discharge regimes for 2100 , 2011 .

[44]  Robert M. Argent,et al.  An overview of model integration for environmental applications--components, frameworks and semantics , 2004, Environ. Model. Softw..

[45]  Gaylon S. Campbell,et al.  A SIMPLE METHOD FOR DETERMINING UNSATURATED CONDUCTIVITY FROM MOISTURE RETENTION DATA , 1974 .

[46]  Arlen W. Harbaugh,et al.  MODFLOW-2000, The U.S. Geological Survey Modular Ground-Water Model - User Guide to Modularization Concepts and the Ground-Water Flow Process , 2000 .

[47]  F. Pappenberger,et al.  Deriving global flood hazard maps of fluvial floods through a physical model cascade , 2012 .

[48]  S. M. de Jong,et al.  Large-scale groundwater modeling using global datasets: a test case for the Rhine-Meuse basin , 2011 .

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

[50]  D. Vuuren,et al.  Global drivers of future river flood risk , 2016 .

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

[52]  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.

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

[54]  H.A.J. van Lanen,et al.  Global hydrological droughts in the 21st century under a changing hydrological regime , 2014 .

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

[56]  S. M. de Jong,et al.  Large-scale groundwater modeling using global datasets: a test case for the Rhine-Meuse basin , 2011 .

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

[58]  L. V. Beek,et al.  Human water consumption intensifies hydrological drought worldwide , 2012 .

[59]  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 .

[60]  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 .

[61]  Ian D. Moore,et al.  Modeling subsurface stormflow on steeply sloping forested watersheds , 1984 .

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

[63]  L. Alfieri,et al.  GloFAS – global ensemble streamflow forecasting and flood early warning , 2012 .

[64]  Rolf Weingartner,et al.  Global monthly water stress: 2. Water demand and severity of water stress , 2011 .

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

[66]  B. Chao,et al.  Past and future contribution of global groundwater depletion to sea‐level rise , 2012 .

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

[68]  Yoshihide Wada,et al.  Decadal predictability of river discharge with climate oscillations over the 20th and early 21st century , 2015 .

[69]  Marc F. P. Bierkens,et al.  Hydrological impacts of global land cover change and human water use , 2016 .

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

[71]  Edwin Sutanudjaja,et al.  Towards a global land subsidence map , 2015 .

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

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

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

[75]  N. Wanders,et al.  Human and climate impacts on the 21st century hydrological drought , 2015 .

[76]  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 .

[77]  S. Kanae,et al.  Global Hydrological Cycles and World Water Resources , 2006, Science.

[78]  D. A. Kraijenhoff van de Leur,et al.  A study of non-steady groundwater flow with special reference to a reservoir coefficient , 1958 .

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

[80]  Michael Botzet,et al.  Derivation of global GCM boundary conditions from 1 km land use satellite data , 1999 .

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

[82]  E. Sutanudjaja The use of soil moisture - remote sensing products for large-scale groundwater modeling and assessment , 2012 .

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

[84]  Derek Karssenberg,et al.  Modelling landscape dynamics with Python , 2007, Int. J. Geogr. Inf. Sci..

[85]  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..

[86]  Limin Yang,et al.  Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data , 2000 .

[87]  H. Winsemius,et al.  A framework for global river flood risk assessments , 2012 .

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

[89]  Marc F. P. Bierkens,et al.  Sustainability of global water use: past reconstruction and future projections , 2014 .

[90]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[91]  Derek Karssenberg,et al.  Integrating dynamic environmental models in GIS: The development of a Dynamic Modelling language , 1996, Trans. GIS.

[92]  Eric F. Wood,et al.  Long-Term Regional Estimates of Evapotranspiration for Mexico Based on Downscaled ISCCP Data , 2010 .

[93]  P. Bates,et al.  A simple inertial formulation of the shallow water equations for efficient two-dimensional flood inundation modelling. , 2010 .

[94]  G. Hornberger,et al.  Empirical equations for some soil hydraulic properties , 1978 .

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

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

[97]  Arthur van Dam,et al.  Efficient scheme for the shallow water equations on unstructured grids with application to the Continental Shelf , 2011 .

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

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

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

[101]  Hubert H. G. Savenije,et al.  The importance of interception and why we should delete the term evapotranspiration from our vocabulary , 2004 .

[102]  Marc F. P. Bierkens,et al.  Seasonal Predictability of European Discharge: NAO and Hydrological Response Time , 2009 .

[103]  E. Fetzer,et al.  The Observed State of the Water Cycle in the Early Twenty-First Century , 2015 .

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

[105]  S. Kanae,et al.  Model estimates of sea-level change due to anthropogenic impacts on terrestrial water storage , 2012 .

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

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

[108]  S. Kanae,et al.  Global flood risk under climate change , 2013 .

[109]  M. G. Bos Discharge measurement structures , 1976 .

[110]  L. V. Beek,et al.  Water balance of global aquifers revealed by groundwater footprint , 2012, Nature.