Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models

[1] The problems of identifying the most appropriate model structure for a given problem and quantifying the uncertainty in model structure remain outstanding research challenges for the discipline of hydrology. Progress on these problems requires understanding of the nature of differences between models. This paper presents a methodology to diagnose differences in hydrological model structures: the Framework for Understanding Structural Errors (FUSE). FUSE was used to construct 79 unique model structures by combining components of 4 existing hydrological models. These new models were used to simulate streamflow in two of the basins used in the Model Parameter Estimation Experiment (MOPEX): the Guadalupe River (Texas) and the French Broad River (North Carolina). Results show that the new models produced simulations of streamflow that were at least as good as the simulations produced by the models that participated in the MOPEX experiment. Our initial application of the FUSE method for the Guadalupe River exposed relationships between model structure and model performance, suggesting that the choice of model structure is just as important as the choice of model parameters. However, further work is needed to evaluate model simulations using multiple criteria to diagnose the relative importance of model structural differences in various climate regimes and to assess the amount of independent information in each of the models. This work will be crucial to both identifying the most appropriate model structure for a given problem and quantifying the uncertainty in model structure. To facilitate research on these problems, the FORTRAN-90 source code for FUSE is available upon request from the lead author.

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

[2]  Randal D. Koster,et al.  The Interplay between Transpiration and Runoff Formulations in Land Surface Schemes Used with Atmospheric Models , 1997 .

[3]  P. Milly.  Climate, soil water storage, and the average annual water balance , 1994 .

[4]  George H. Leavesley,et al.  The model parameter estimation experiment (MOPEX) , 2006 .

[5]  Keith Beven,et al.  TOPMODEL : a critique. , 1997 .

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

[7]  Dong-Jun Seo,et al.  Towards the characterization of streamflow simulation uncertainty through multimodel ensembles , 2004 .

[8]  Soroosh Sorooshian,et al.  Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops , 2006 .

[9]  Hoshin Vijai Gupta,et al.  A process‐based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model , 2008 .

[10]  Eric A. Anderson,et al.  National Weather Service river forecast system: snow accumulation and ablation model , 1973 .

[11]  Vijay P. Singh,et al.  The NWS River Forecast System - catchment modeling. , 1995 .

[12]  George H. Leavesley,et al.  The Modular Modeling System (MMS): User's Manual , 1996 .

[13]  M. Sivapalan,et al.  Climate and landscape controls on water balance model complexity over changing timescales , 2002 .

[14]  Hoshin Vijai Gupta,et al.  Do Nash values have value? , 2007 .

[15]  Keith Beven,et al.  On hydrologic similarity: 2. A scaled model of storm runoff production , 1987 .

[16]  Eric F. Wood,et al.  A land-surface hydrology parameterization with subgrid variability for general circulation models , 1992 .

[17]  K. Beven,et al.  A physically based, variable contributing area model of basin hydrology , 1979 .

[18]  George Kuczera,et al.  Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory , 2006 .

[19]  V. Singh,et al.  Mathematical Modeling of Watershed Hydrology , 2002 .

[20]  Q. Duana,et al.  Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops , 2006 .

[21]  George Kuczera,et al.  Bayesian analysis of input uncertainty in hydrological modeling: 2. Application , 2006 .

[22]  George H. Leavesley,et al.  A modular approach to addressing model design, scale, and parameter estimation issues in distributed hydrological modelling , 2002 .

[23]  R. Dickinson,et al.  Effects of frozen soil on soil temperature, spring infiltration, and runoff: Results from the PILPS 2(d) experiment at Valdai, Russia , 2003 .

[24]  Michael Smith,et al.  Hydrology laboratory research modeling system (HL-RMS) of the US national weather service , 2004 .

[25]  C. E. Desborough,et al.  The Impact of Root Weighting on the Response of Transpiration to Moisture Stress in Land Surface Schemes , 1997 .

[26]  George Kuczera,et al.  Semidistributed hydrological modeling: A “saturation path” perspective on TOPMODEL and VIC , 2003 .

[27]  Ross Woods,et al.  Increased flexibility in base flow modelling using a power law transmissivity profile , 2008 .

[28]  Henrik Madsen,et al.  An evaluation of the impact of model structure on hydrological modelling uncertainty for streamflow simulation , 2004 .

[29]  Keith Beven,et al.  Towards an alternative blueprint for a physically based digitally simulated hydrologic response modelling system , 2002 .

[30]  Soroosh Sorooshian,et al.  Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods , 2000 .

[31]  P. Reed,et al.  Characterization of watershed model behavior across a hydroclimatic gradient , 2008 .

[32]  Paul C.D. Milly,et al.  Macroscale water fluxes 2. Water and energy supply control of their interannual variability , 2002 .

[33]  Aaron Boone,et al.  Issues related to low resolution modeling of soil moisture: experience with the PLACE model , 1996 .

[34]  Zong-Liang Yang,et al.  Modeling vadose zone liquid water fluxes: Infiltration, runoff, drainage, interflow , 1996 .

[35]  R. Dickinson,et al.  The Representation of Snow in Land Surface Schemes: Results from PILPS 2(d) , 2001 .

[36]  D. Seo,et al.  Overall distributed model intercomparison project results , 2004 .

[37]  R. Dickinson,et al.  The Project for Intercomparison of Land Surface Parameterization Schemes (PILPS): Phases 2 and 3 , 1993 .

[38]  G. H. Leavesley,et al.  Precipitation-runoff modeling system; user's manual , 1983 .

[39]  Stephen J. Burges,et al.  A framework for classifying and comparing distributed hillslope and catchment hydrologic models , 2007 .

[40]  M. Clark,et al.  Probabilistic Quantitative Precipitation Estimation in Complex Terrain , 2005 .

[41]  George Kuczera,et al.  Model smoothing strategies to remove microscale discontinuities and spurious secondary optima in objective functions in hydrological calibration , 2007 .

[42]  Norman L. Miller,et al.  A generalized power function for the subsurface transmissivity profile in TOPMODEL , 1997 .

[43]  André Musy,et al.  Generalization of TOPMODEL for a power law transmissivity profile , 1997 .

[44]  Bruce A. Robinson,et al.  Treatment of uncertainty using ensemble methods: Comparison of sequential data assimilation and Bayesian model averaging , 2007 .

[45]  S. Sorooshian,et al.  Effective and efficient global optimization for conceptual rainfall‐runoff models , 1992 .

[46]  Hoshin V. Gupta,et al.  Toward a model space and model independence metric , 2008 .

[47]  C. E. Desborough,et al.  Surface energy balance complexity in GCM land surface models , 1999 .

[48]  Jan Seibert,et al.  Multi‐criterial validation of TOPMODEL in a mountainous catchment , 1999 .

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

[50]  K. Beven,et al.  Toward a generalization of the TOPMODEL concepts:Topographic indices of hydrological similarity , 1996 .