Calibrating a large‐extent high‐resolution coupled groundwater‐land surface model using soil moisture and discharge data

We explore the possibility of using remotely sensed soil moisture data and in situ discharge observations to calibrate a large-extent hydrological model. The model used is PCR-GLOBWB-MOD, which is a physically based and fully coupled groundwater-land surface model operating at a daily basis and having a resolution of 30 arc sec (about 1 km at the equator). As a test bed, we use the combined Rhine-Meuse basin (total area: about 200,000 km2), where there are 4250 point-scale observed groundwater head time series that are used to verify the model results. Calibration is performed by simulating 3045 model runs with varying parameter values affecting groundwater head dynamics. The simulation results of all runs are evaluated against the remotely sensed soil moisture time series of SWI (Soil Water Index) and field discharge data. The former is derived from European Remote Sensing scatterometers and provides estimates of the first meter profile soil moisture content at 30 arc min resolution (50 km at the equator). From the evaluation of these runs, we then introduce a stepwise calibration approach that considers stream discharge first, then soil moisture, and finally verify the resulting simulation to groundwater head observations. Our results indicate that the remotely sensed soil moisture data can be used for the calibration of upper soil hydraulic conductivities determining simulated groundwater recharge of the model. However, discharge data should be included to obtain full calibration of the coupled model, specifically to constrain aquifer transmissivities and runoff-infiltration partitioning processes. The stepwise approach introduced in this study, using both discharge and soil moisture data, can calibrate both discharge and soil moisture, as well as predicting groundwater head dynamics with acceptable accuracy. As our approach to parameterize and calibrate the model uses globally available data sets only, it opens up the possibility to set up large-extent coupled groundwater-land surface models in other basins or even globally. Key Points Soil moisture data can be used to calibrate upper soil conductivities Yet, discharge data should be included to fully calibrate the coupled model The combined calibration approach reproduces groundwater head dynamics well ©2013. American Geophysical Union. All Rights Reserved.

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

[2]  Chong-Yu Xu,et al.  Global water-balance modelling with WASMOD-M: Parameter estimation and regionalisation , 2007 .

[3]  Derek Karssenberg,et al.  Linking external components to a spatio-temporal modelling framework: Coupling MODFLOW and PCRaster , 2009, Environ. Model. Softw..

[4]  G. Miguez-Macho,et al.  Potential groundwater contribution to Amazon evapotranspiration , 2010 .

[5]  W. Bastiaanssen,et al.  A remote sensing surface energy balance algorithm for land (SEBAL). , 1998 .

[6]  M. Owe,et al.  Further validation of a new methodology for surface moisture and vegetation optical depth retrieval , 2003 .

[7]  Peter S. Eagleson,et al.  Climate, soil, and vegetation: 3. A simplified model of soil moisture movement in the liquid phase , 1978 .

[8]  Elfatih A. B. Eltahir,et al.  Representation of Water Table Dynamics in a Land Surface Scheme. Part I: Model Development , 2005 .

[9]  T. N. Krishnamurti,et al.  The status of the tropical rainfall measuring mission (TRMM) after two years in orbit , 2000 .

[10]  Soroosh Sorooshian,et al.  Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information , 1998 .

[11]  P.M.M. Warmerdam,et al.  Effect of climate change on the hydrology of the river Meuse , 2001 .

[12]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

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

[14]  A. Holtslag,et al.  A remote sensing surface energy balance algorithm for land (SEBAL)-1. Formulation , 1998 .

[15]  S. M. Jong,et al.  Large-scale monitoring of snow cover and runoff simulation in Himalayan river basins using remote sensing , 2009 .

[16]  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 .

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

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

[19]  G. Tucker,et al.  Effect of limited storm duration on landscape evolution, drainage basin geometry, and hydrograph shapes , 2004 .

[20]  Zhongbo Su,et al.  Reconnoitering the effect of shallow groundwater on land surface temperature and surface energy balance using MODIS and SEBS , 2011 .

[21]  J. Famiglietti,et al.  Improving parameter estimation and water table depth simulation in a land surface model using GRACE water storage and estimated base flow data , 2010 .

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

[23]  D. Lawrence,et al.  Regions of Strong Coupling Between Soil Moisture and Precipitation , 2004, Science.

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

[25]  Hubert H. G. Savenije,et al.  A comparison of alternative multiobjective calibration strategies for hydrological modeling , 2007 .

[26]  Praveen Kumar,et al.  A catchment‐based approach to modeling land surface processes in a general circulation model: 1. Model structure , 2000 .

[27]  Michel Meybeck,et al.  Lithologic composition of the Earth's continental surfaces derived from a new digital map emphasizing riverine material transfer , 2005 .

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

[29]  G Lacey,et al.  STABLE CHANNELS IN ALLUVIUM (INCLUDES APPENDICES). , 1930 .

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

[31]  Z. Su.  The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes , 2002 .

[32]  Arlindo da Silva,et al.  Data assimilation in the presence of forecast bias , 1998 .

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

[34]  Maurice Borgeaud,et al.  A study of vegetation cover effects on ERS scatterometer data , 1999, IEEE Trans. Geosci. Remote. Sens..

[35]  J. Shukla,et al.  Influence of Land-Surface Evapotranspiration on the Earth's Climate , 1982, Science.

[36]  Thomas J. Jackson,et al.  Remote sensing of soil moisture: implications for groundwater recharge , 2002 .

[37]  Ying Fan,et al.  A simple hydrologic framework for simulating wetlands in climate and earth system models , 2011 .

[38]  J. Famiglietti,et al.  Satellite-based estimates of groundwater depletion in India , 2009, Nature.

[39]  Zhao-Liang Li,et al.  A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS/MODIS data , 1997, IEEE Trans. Geosci. Remote. Sens..

[40]  Randal D. Koster,et al.  Bias reduction in short records of satellite soil moisture , 2004 .

[41]  Matthias Drusch,et al.  Observation operators for the direct assimilation of TRMM microwave imager retrieved soil moisture , 2005 .

[42]  Mary C. Hill,et al.  Sensitivity analysis, calibration, and testing of a distributed hydrological model using error‐based weighting and one objective function , 2009 .

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

[44]  Maosheng Zhao,et al.  Development of a global evapotranspiration algorithm based on MODIS and global meteorology data , 2007 .

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

[46]  Thomas J. Jackson,et al.  Soil moisture retrieval from AMSR-E , 2003, IEEE Trans. Geosci. Remote. Sens..

[47]  Rutger Dankers,et al.  Monitoring snow-cover dynamics in Northern Fennoscandia with SPOT VEGETATION images , 2004 .

[48]  Zhongbo Su,et al.  Shallow groundwater effect on land surface temperature and surface energy balance under bare soil conditions: modeling and description , 2011 .

[49]  W. Wagner,et al.  Soil moisture estimation through ASCAT and AMSR-E sensors: An intercomparison and validation study across Europe , 2011 .

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

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

[52]  E. Eltahir,et al.  Representation of Water Table Dynamics in a Land Surface Scheme. Part II: Subgrid Variability , 2005 .

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

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

[55]  V. Singh,et al.  The HBV model. , 1995 .

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

[57]  S. M. Jong,et al.  Observation uncertainty of satellite soil moisture products determined with physically-based modeling , 2012 .

[58]  W. Crow,et al.  Multiobjective calibration of land surface model evapotranspiration predictions using streamflow observations and spaceborne surface radiometric temperature retrievals , 2003 .

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

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

[61]  Yann Kerr,et al.  Soil moisture retrieval from space: the Soil Moisture and Ocean Salinity (SMOS) mission , 2001, IEEE Trans. Geosci. Remote. Sens..

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

[63]  Y. Fan,et al.  Global Patterns of Groundwater Table Depth , 2013, Science.

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

[65]  C. Tiedeman,et al.  Effective Groundwater Model Calibration: With Analysis of Data, Sensitivities, Predictions, and Uncertainty , 2007 .

[66]  Hubert H. G. Savenije,et al.  The width of a bankfull channel; Lacey's formula explained , 2003 .

[67]  Benjamin F. Zaitchik,et al.  Evaluation of the Global Land Data Assimilation System using global river discharge data and a source‐to‐sink routing scheme , 2010 .

[68]  W. R. Gardner.  SOME STEADY‐STATE SOLUTIONS OF THE UNSATURATED MOISTURE FLOW EQUATION WITH APPLICATION TO EVAPORATION FROM A WATER TABLE , 1958 .

[69]  A. V. Loon,et al.  Evaluation of drought propagation in an ensemble mean of large-scale hydrological models , 2012 .

[70]  W. Wagner,et al.  A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data , 1999 .

[71]  Mary C. Hill,et al.  Evaluating model structure adequacy: The case of the Maggia Valley groundwater system, southern Switzerland , 2013 .

[72]  Wolfgang Wagner,et al.  Scatterometer-Derived Soil Moisture Calibrated for Soil Texture With a One-Dimensional Water-Flow Model , 2008, IEEE Transactions on Geoscience and Remote Sensing.

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

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

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

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

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

[78]  Matthew W Becker,et al.  Potential for Satellite Remote Sensing of Ground Water , 2006, Ground water.

[79]  C. Tiedeman,et al.  Effective Groundwater Model Calibration , 2007 .

[80]  P. Jones,et al.  Representing Twentieth-Century Space–Time Climate Variability. Part I: Development of a 1961–90 Mean Monthly Terrestrial Climatology , 1999 .

[81]  M. Bierkens,et al.  Groundwater convergence as a possible mechanism for multi‐year persistence in rainfall , 2007 .

[82]  Yi Y. Liu,et al.  Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals , 2011 .

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

[84]  J. Lenters,et al.  Quantifying the impact of groundwater depth on evapotranspiration in a semi-arid grassland region , 2009 .

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

[86]  J. Hintze,et al.  Violin plots : A box plot-density trace synergism , 1998 .

[87]  T. D. Mitchell,et al.  An improved method of constructing a database of monthly climate observations and associated high‐resolution grids , 2005 .

[88]  Thomas P. Minka,et al.  Gates , 2008, NIPS.