Diagnostic Evaluation of Large‐Domain Hydrologic Models Calibrated Across the Contiguous United States

[1]  Ramakrishna R. Nemani,et al.  MTCLIM: a mountain microclimate simulation model , 1989 .

[2]  R. Koster,et al.  Land Surface Controls on Hydroclimatic Means and Variability , 2012 .

[3]  Yuqiong Liu,et al.  Reconciling theory with observations: elements of a diagnostic approach to model evaluation , 2008 .

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

[5]  Sabine Attinger,et al.  The impact of standard and hard‐coded parameters on the hydrologic fluxes in the Noah‐MP land surface model , 2016 .

[6]  Martyn P. Clark,et al.  HESS Opinions: The need for process-based evaluation of large-domain hyper-resolution models , 2015 .

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

[8]  Joseph Hamman,et al.  The Variable Infiltration Capacity model version 5 (VIC-5): infrastructure improvements for new applications and reproducibility , 2018, Geoscientific Model Development.

[9]  Ranga B. Myneni,et al.  Estimation of global leaf area index and absorbed par using radiative transfer models , 1997, IEEE Trans. Geosci. Remote. Sens..

[10]  Joseph R. Kasprzyk,et al.  Exploring snow model parameter sensitivity using Sobol' variance decomposition , 2017, Environ. Model. Softw..

[11]  Zhenghui Xie,et al.  A new parameterization for surface and groundwater interactions and its impact on water budgets with the variable infiltration capacity (VIC) land surface model , 2003 .

[12]  Shih-Chieh Kao,et al.  A large-scale, high-resolution hydrological model parameter data set for climate change impact assessment for the conterminous US , 2013 .

[13]  Zong-Liang Yang,et al.  Effects of soil‐type datasets on regional terrestrial water cycle simulations under different climatic regimes , 2016 .

[14]  K. Mo,et al.  Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products , 2012 .

[15]  C. Buontempo,et al.  Hydrological Forecasts and Projections for Improved Decision-Making in the Water Sector in Europe , 2019, Bulletin of the American Meteorological Society.

[16]  Bart Nijssen,et al.  Global Retrospective Estimation of Soil Moisture Using the Variable Infiltration Capacity Land Surface Model, 1980–93 , 2001 .

[17]  S. Attinger,et al.  Improving the realism of hydrologic model functioning through multivariate parameter estimation , 2016 .

[18]  Y. Hundecha,et al.  Evaluation of an ensemble of regional hydrological models in 12 large-scale river basins worldwide , 2017, Climatic Change.

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

[20]  Martyn P. Clark,et al.  Representation of spatial and temporal variability in large-domain hydrological models: case study for a mesoscale pre-Alpine basin , 2016 .

[21]  Lifeng Luo,et al.  Basin‐scale assessment of the land surface water budget in the National Centers for Environmental Prediction operational and research NLDAS‐2 systems , 2016 .

[22]  Martyn P. Clark,et al.  On the choice of calibration metrics for “high-flow” estimation using hydrologic models , 2019, Hydrology and Earth System Sciences.

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

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

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

[26]  Luis Samaniego,et al.  Toward computationally efficient large‐scale hydrologic predictions with a multiscale regionalization scheme , 2013 .

[27]  Eric F. Wood,et al.  An efficient calibration method for continental‐scale land surface modeling , 2008 .

[28]  J. Townshend,et al.  NDVI-derived land cover classifications at a global scale , 1994 .

[29]  Lifeng Luo,et al.  Basin‐Scale Assessment of the Land Surface Energy Budget in the NCEP Operational and Research NLDAS‐2 Systems , 2015 .

[30]  Sabine Attinger,et al.  Accelerating advances in continental domain hydrologic modeling , 2015 .

[31]  Eric A. Rosenberg,et al.  A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United States: Update and Extensions* , 2002 .

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

[33]  Bryan A. Tolson,et al.  Dynamically dimensioned search algorithm for computationally efficient watershed model calibration , 2007 .

[34]  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 .

[35]  Thomas J. Jackson,et al.  Estimating soil water‐holding capacities by linking the Food and Agriculture Organization Soil map of the world with global pedon databases and continuous pedotransfer functions , 2000 .

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

[37]  Luis Samaniego,et al.  Influence of soil textural properties on hydrologic fluxes in the Mississippi river basin , 2015 .

[38]  Martyn P. Clark,et al.  The CAMELS data set: catchment attributes and meteorology for large-sample studies , 2017 .

[39]  M. Ek,et al.  Continental‐scale water and energy flux analysis and validation for North American Land Data Assimilation System project phase 2 (NLDAS‐2): 2. Validation of model‐simulated streamflow , 2012 .

[40]  E. Wood,et al.  In Quest of Calibration Density and Consistency in Hydrologic Modeling: Distributed Parameter Calibration against Streamflow Characteristics , 2018, Water Resources Research.

[41]  S. Sorooshian,et al.  Shuffled complex evolution approach for effective and efficient global minimization , 1993 .

[42]  J. D. Tarpley,et al.  The multi‐institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system , 2004 .

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

[44]  Hoshin Vijai Gupta,et al.  Large-sample hydrology: a need to balance depth with breadth , 2013 .

[45]  Martyn P. Clark,et al.  Benchmarking of a Physically Based Hydrologic Model , 2017 .

[46]  Zong-Liang Yang,et al.  Assessment of simulated water balance from Noah, Noah‐MP, CLM, and VIC over CONUS using the NLDAS test bed , 2014 .

[47]  Sabine Attinger,et al.  Multiscale and Multivariate Evaluation of Water Fluxes and States over European River Basins , 2016 .

[48]  Martyn P. Clark,et al.  Improving the theoretical underpinnings of process-based hydrologic models , 2016 .

[49]  Mario Putti,et al.  Physically based modeling in catchment hydrology at 50: Survey and outlook , 2015 .

[50]  H. Gupta,et al.  A constraint-based search algorithm for parameter identification of environmental models , 2014 .

[51]  D. Lettenmaier,et al.  A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United States* , 2002 .

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

[53]  Douglas A. Miller,et al.  A Conterminous United States Multilayer Soil Characteristics Dataset for Regional Climate and Hydrology Modeling , 1998 .

[54]  V. Klemeš,et al.  Operational Testing of Hydrological Simulation Models , 2022 .

[55]  Bart Nijssen,et al.  Monte Carlo sensitivity analysis of land surface parameters using the Variable Infiltration Capacity model , 2007 .

[56]  Martyn P. Clark,et al.  Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance , 2014 .

[57]  Luis Samaniego,et al.  Towards seamless large‐domain parameter estimation for hydrologic models , 2017 .

[58]  Balaji Rajagopalan,et al.  Are we unnecessarily constraining the agility of complex process‐based models? , 2015 .