A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset

Abstract. The dataset presented here consists of an ensemble of 10 global hydrological and land surface models for the period 1979–2012 using a reanalysis-based meteorological forcing dataset (0.5° resolution). The current dataset serves as a state of the art in current global hydrological modelling and as a benchmark for further improvements in the coming years. A signal-to-noise ratio analysis revealed low inter-model agreement over (i) snow-dominated regions and (ii) tropical rainforest and monsoon areas. The large uncertainty of precipitation in the tropics is not reflected in the ensemble runoff. Verification of the results against benchmark datasets for evapotranspiration, snow cover, snow water equivalent, soil moisture anomaly and total water storage anomaly using the tools from The International Land Model Benchmarking Project (ILAMB) showed overall useful model performance, while the ensemble mean generally outperformed the single model estimates. The results also show that there is currently no single best model for all variables and that model performance is spatially variable. In our unconstrained model runs the ensemble mean of total runoff into the ocean was 46 268 km3 yr−1 (334 kg m−2 yr−1), while the ensemble mean of total evaporation was 537 kg m−2 yr−1. All data are made available openly through a Water Cycle Integrator portal (WCI, wci.earth2observe.eu), and via a direct http and ftp download. The portal follows the protocols of the open geospatial consortium such as OPeNDAP, WCS and WMS. The DOI for the data is https://doi.org/10.1016/10.5281/zenodo.167070 .

[1]  Wouter Dorigo,et al.  Homogeneity of a global multisatellite soil moisture climate data record , 2016 .

[2]  Martina Flörke,et al.  Multi-model assessment of global hydropower and cooling water discharge potential under climate change , 2016 .

[3]  Dai Yamazaki,et al.  The credibility challenge for global fluvial flood risk analysis , 2016, Environmental Research Letters.

[4]  N. Verhoest,et al.  GLEAM v3: satellite-based land evaporation and root-zone soil moisture , 2016 .

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

[6]  J. Randerson,et al.  International land Model Benchmarking (ILAMB) Package v001.00 , 2016 .

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

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

[9]  A. D. Roo,et al.  Global evaluation of runoff from ten state-of-the-art hydrological models , 2016 .

[10]  Xiaomeng Yu.  Water Scarcity: Fact or Fiction? , 2016 .

[11]  U. Schneider,et al.  GPCC Full Data Reanalysis Version 7.0: Monthly Land-Surface Precipitation from Rain Gauges built on GTS based and Historic Data , 2016 .

[12]  Jakob Zscheischler,et al.  A submonthly database for detecting changes in vegetation‐atmosphere coupling , 2015 .

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

[14]  Jaap Schellekens,et al.  Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations , 2015 .

[15]  Matthew F. McCabe,et al.  The WACMOS-ET project – Part 2: Evaluation of global terrestrial evaporation data sets , 2015 .

[16]  D. Lettenmaier,et al.  Continental runoff into the oceans (1950-2008) , 2015 .

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

[18]  W. Wagner,et al.  Evaluation of the ESA CCI soil moisture product using ground-based observations , 2015 .

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

[20]  Discontinuous Daily Temperatures in the WATCH Forcing Datasets , 2015 .

[21]  F. Pappenberger,et al.  ERA-Interim/Land: a global land surface reanalysis data set , 2015 .

[22]  A. Dijk,et al.  Global high-resolution reference potential evaporation , 2015 .

[23]  C. Kobayashi,et al.  The JRA-55 Reanalysis: General Specifications and Basic Characteristics , 2015 .

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

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

[26]  B. Scanlon,et al.  Uncertainty in evapotranspiration from land surface modeling, remote sensing, and GRACE satellites , 2014 .

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

[28]  S. Seneviratne,et al.  Inferring Soil Moisture Memory from Streamflow Observations Using a Simple Water Balance Model , 2013 .

[29]  A. Tuzet,et al.  Potential evaporation estimation through an unstressed surface-energy balance and its sensitivity to climate change , 2013 .

[30]  S. Seneviratne,et al.  Predictability of soil moisture and streamflow on subseasonal timescales: A case study , 2013 .

[31]  Mark Mulligan,et al.  WaterWorld: a self-parameterising, physically-based model for application in data-poor but problem-rich environments globally , 2013 .

[32]  Wouter Dorigo,et al.  Potential and limitations of multidecadal satellite soil moisture observations for selected climate model evaluation studies , 2013 .

[33]  W. Wagner,et al.  Skill and Global Trend Analysis of Soil Moisture from Reanalyses and Microwave Remote Sensing , 2013 .

[34]  Bertrand Decharme,et al.  Reconciling soil thermal and hydrological lower boundary conditions in land surface models , 2013 .

[35]  Jürgen Vogt,et al.  TOWARD GLOBAL DROUGHT EARLY WARNING CAPABILITY Expanding International Cooperation for the Development of a Framework for Monitoring and Forecasting , 2013 .

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

[37]  Aaron Boone,et al.  Simulation of Northern Eurasian Local Snow Depth, Mass, and Density Using a Detailed Snowpack Model and Meteorological Reanalyses , 2013 .

[38]  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 .

[39]  Bertrand Decharme,et al.  A simple groundwater scheme in the TRIP river routing model: global off-line evaluation against GRACE terrestrial water storage estimates and observed river discharges , 2012 .

[40]  Philippe Ciais,et al.  A framework for benchmarking land models , 2012 .

[41]  Nico Sneeuw,et al.  Analysis of GRACE uncertainties by hydrological and hydro-meteorological observations , 2012 .

[42]  Yi Y. Liu,et al.  Evaluating global trends (1988–2010) in harmonized multi‐satellite surface soil moisture , 2012 .

[43]  J. Hansen,et al.  Perception of climate change , 2012, Proceedings of the National Academy of Sciences.

[44]  Yi Y. Liu,et al.  Trend-preserving blending of passive and active microwave soil moisture retrievals , 2012 .

[45]  F. Landerer,et al.  Accuracy of scaled GRACE terrestrial water storage estimates , 2012 .

[46]  M. Vrac,et al.  Selecting the optimal method to calculate daily global reference potential evaporation from CFSR reanalysis data for application in a hydrological model study , 2012 .

[47]  Chris Derksen,et al.  Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements , 2011 .

[48]  Pavel Kabat,et al.  WATCH: Current Knowledge of the Terrestrial Global Water Cycle , 2011 .

[49]  W. J. Shuttleworth,et al.  Creation of the WATCH Forcing Data and Its Use to Assess Global and Regional Reference Crop Evaporation over Land during the Twentieth Century , 2011 .

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

[51]  P. Cox,et al.  The Joint UK Land Environment Simulator (JULES), model description – Part 2: Carbon fluxes and vegetation dynamics , 2011 .

[52]  P. Cox,et al.  The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes , 2011 .

[53]  Maosheng Zhao,et al.  Improvements to a MODIS global terrestrial evapotranspiration algorithm , 2011 .

[54]  S. Schubert,et al.  MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications , 2011 .

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

[56]  Dennis P. Lettenmaier,et al.  Soil Moisture Drought in China, 1950–2006 , 2011 .

[57]  Robert I. McDonald,et al.  Global Urban Growth and the Geography of Water Availability, Quality, and Delivery , 2011, AMBIO.

[58]  S. Kanae,et al.  A physically based description of floodplain inundation dynamics in a global river routing model , 2011 .

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

[60]  Pavel Kabat,et al.  Global river temperatures and sensitivity to atmospheric warming and changes in river flow , 2011 .

[61]  T. Holmes,et al.  Global land-surface evaporation estimated from satellite-based observations , 2010 .

[62]  Zhuguo Ma,et al.  Comparisons of simulations of soil moisture variations in the Yellow River basin driven by various atmospheric forcing data sets , 2010 .

[63]  Keith Haines,et al.  Global hydrology modelling and uncertainty: running multiple ensembles with a campus grid , 2010, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[64]  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 .

[65]  Anny Cazenave,et al.  Global Evaluation of the ISBA-TRIP Continental Hydrological System. Part II: Uncertainties in River Routing Simulation Related to Flow Velocity and Groundwater Storage , 2010 .

[66]  Faisal Hossain,et al.  Understanding the Scale Relationships of Uncertainty Propagation of Satellite Rainfall through a Distributed Hydrologic Model , 2010 .

[67]  Faisal Hossain,et al.  Benchmarking High-Resolution Global Satellite Rainfall Products to Radar and Rain-Gauge Rainfall Estimates , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[68]  J. M. Van Der Knijff,et al.  LISFLOOD : a GIS-based distributed model for river basin scale water balance and flood simulation , 2008 .

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

[70]  Taikan Oki,et al.  Role of rivers in the seasonal variations of terrestrial water storage over global basins , 2009 .

[71]  B. Hurk,et al.  A Revised Hydrology for the ECMWF Model: Verification from Field Site to Terrestrial Water Storage and Impact in the Integrated Forecast System , 2009 .

[72]  Taotao Qian,et al.  Changes in Continental Freshwater Discharge from 1948 to 2004 , 2009 .

[73]  Jan Polcher,et al.  Sensitivity of the West African hydrological cycle in ORCHIDEE to infiltration processes , 2008 .

[74]  James S. Famiglietti,et al.  GRACE-Based Estimates of Terrestrial Freshwater Discharge from Basin to Continental Scales , 2007 .

[75]  K. Trenberth,et al.  Estimates of the Global Water Budget and Its Annual Cycle Using Observational and Model Data , 2007 .

[76]  B. Ramsay,et al.  Published online in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/hyp.6720 Enhancements to, and forthcoming developments in the Interactive Multisensor Snow and Ice Mapping System (IMS) † , 2022 .

[77]  Guillaume Ramillien,et al.  Validation of the land water storage simulated by Organising Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) with Gravity Recovery and Climate Experiment (GRACE) data , 2007 .

[78]  R. Moore.  The PDM rainfall-runoff model , 2007 .

[79]  S. Swenson,et al.  Remote sensing of groundwater storage changes in Illinois using the Gravity Recovery and Climate Experiment (GRACE) , 2006 .

[80]  James L. Davis,et al.  Land water storage within the Congo Basin inferred from GRACE satellite gravity data , 2006 .

[81]  Naota Hanasaki,et al.  GSWP-2 Multimodel Analysis and Implications for Our Perception of the Land Surface , 2006 .

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

[83]  J. Famiglietti,et al.  Terrestrial water mass load changes from Gravity Recovery and Climate Experiment (GRACE) , 2006 .

[84]  Jouni Pulliainen,et al.  Mapping of snow water equivalent and snow depth in boreal and sub-arctic zones by assimilating space-borne microwave radiometer data and ground-based observations , 2006 .

[85]  Petra Döll,et al.  GRACE observations of changes in continental water storage , 2006 .

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

[87]  I. C. Prentice,et al.  A dynamic global vegetation model for studies of the coupled atmosphere‐biosphere system , 2005 .

[88]  C. Diks,et al.  Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation , 2005 .

[89]  S. Seneviratne,et al.  Basin scale estimates of evapotranspiration using GRACE and other observations , 2004 .

[90]  M. Watkins,et al.  GRACE Measurements of Mass Variability in the Earth System , 2004, Science.

[91]  Victor Zlotnicki,et al.  Time‐variable gravity from GRACE: First results , 2004 .

[92]  Jeffrey P. Walker,et al.  THE GLOBAL LAND DATA ASSIMILATION SYSTEM , 2004 .

[93]  J. Janowiak,et al.  The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present) , 2003 .

[94]  P. de Rosnay,et al.  Integrated parameterization of irrigation in the land surface model ORCHIDEE. Validation over Indian Peninsula , 2003 .

[95]  S. Swenson,et al.  Estimated accuracies of regional water storage variations inferred from the Gravity Recovery and Climate Experiment (GRACE) , 2003 .

[96]  R. Allen,et al.  History and Evaluation of Hargreaves Evapotranspiration Equation , 2003 .

[97]  Ben Domenico,et al.  Thematic Real-time Environmental Distributed Data Services (THREDDS): Incorporating Interactive Analysis Tools into NSDL , 2002, J. Digit. Inf..

[98]  M. Hoerling,et al.  Analysis of a Conceptual Model of Seasonal Climate Variability and Implications for Seasonal Prediction , 2000 .

[99]  B. Ramsay,et al.  The interactive multisensor snow and ice mapping system , 1998 .

[100]  R. Reynolds,et al.  HISTORY AND EVALUATION , 1998 .

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

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

[103]  P. Xie,et al.  Global Precipitation: A 17-Year Monthly Analysis Based on Gauge Observations, Satellite Estimates, and Numerical Model Outputs , 1997 .

[104]  W. P. A. van. Deursen,et al.  Geographical information systems and dynamic models : development and application of a prototype spatial modelling language , 1995 .

[105]  Keith Beven,et al.  The future of distributed models: model calibration and uncertainty prediction. , 1992 .

[106]  C. Priestley,et al.  On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters , 1972 .

[107]  W. R. Hamon.  Estimating Potential Evapotranspiration , 1960 .