A framework for development and application of hydrological models

By Thorsten Wagener, D. Boyle, M. Lees, Howard S. Wheater, Hoshin V. Gupta, Soroosh Sorooshian. Hydrology and Earth System Sciences, 2001

619 citations recorded by the source corpus.

Abstract. Many existing hydrological modelling procedures do not make best use of available information, resulting in non-minimal uncertainties in model structure and parameters, and a lack of detailed information regarding model behaviour. A framework is required that balances the level of model complexity supported by the available data with the level of performance suitable for the desired application. Tools are needed that make optimal use of the information available in the data to identify model structure and parameters, and that allow a detailed analysis of model behaviour. This should result in appropriate levels of model complexity as a function of available data, hydrological system characteristics and modelling purpose. This paper introduces an analytical framework to achieve this, and tools to use within it, based on a multi-objective approach to model calibration and analysis. The utility of the framework is demonstrated with an example from the field of rainfall-runoff modelling. Keywords: hydrological modelling, multi-objective calibration, model complexity, parameter identifiability

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/920382338e33174739e661145b32a2d9ce3b08c8

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Citations in the corpus

An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction (2006, methodology): “The results reveal that the distributions for all HYMOD parameters are approximately normal.”
Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter (2005, methodology): “[35] The simple conceptual hydrologic model (HyMOD) has its origins in the probability distributed moisture model (PDM) [Moore, 1985] and has been used by several authors for testing batch and recursive calibration strategies [Boyle et al., 2000; Wagener et al., 2001; Vrugt et al., 2003; Misirli et ”
Simultaneous heat and water (SHAW) model: model use, calibration, and validation. (2012, background): “RSA, an extension of global or generalized sensitivity analysis (Spear and Hornberger, 1980; Hornberger and Spear, 1981), decomposes model parameters into a cumulative distribution function for a range (10 bins) of model performances (Freer et al., 1996; Wagener et al., 2001).”
Characterising performance of environmental models (2013, background): “For instance, in hydrology, previous work has been completed on general modelling frameworks that consider performance criteria as part of the iterative modelling process (Jakeman et al., 2006; Refsgaard et al., 2005; Wagener et al., 2001).”
Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling? (2009, methodology): “The HYMOD model has been discussed extensively in many previous papers that study streamflow forecasting and automatic model calibration (Boyle 2000; Wagener et al. 2001; Vrugt et al. 2003).”
The role of hydrograph indices in parameter estimation of rainfall–runoff models (2005, methodology): “The objective of HYMOD is to provide a research tool for scientific evaluation purposes (e.g., Wagener et al., 2001; Vrugt et al., 2003).”
A process‐based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model (2008, neutral): “... problem of improving the behavioral performance of distributed watershed models, by assimilating information from a variety of different data sources [Madsen, 2003; Leavesley et al., 2003; P. Pokhrel et al., A spatial regularization approach to parameter estimation for a distributed watershed mo”
Towards reduced uncertainty in conceptual rainfall‐runoff modelling: dynamic identifiability analysis (2003, methodology): “It also leads to problems with the identification of those parameters associated with response modes that do not significantly influence the selected objective function (Wagener et al., 2001a).”
Sensitivity-guided reduction of parametric dimensionality for multi-objective calibration of watershed models (2009, background): “gineering, Pennsylvania State given the amount of information contained in hydrological data [3,35,43].”
Flow Prediction in Ungauged Catchments Using Probabilistic Random Forests Regionalization and New Statistical Adequacy Tests (2019, methodology): “Such modeling assumptions require careful testing, as demonstrated in previous studies that explored model structure adequacy (Bulygina & Gupta, 2010; Clark et al., 2008; Clark et al., 2011; Fenicia et al., 2008; Fenicia et al., 2011; Wagener et al., 2001), the quality of hydrological observations u”
Model identification for hydrological forecasting under uncertainty (2005, methodology): “…function is based on the erroneous assumption that all the available information can be summarized (in a recoverable form) using a single aggregate measure of model performance, leading unavoidably to the loss of information (see e.g. Wagener et al. 2001) and therefore poor discriminative power.”
Estimating epistemic and aleatory uncertainties during hydrologic modeling: An information theoretic approach (2013, methodology): “The basin has been widely used in many previous investigations of catchment modeling [Bulygina and Gupta, 2009, 2010, 2011; Sorooshian et al., 1993; Thiemann et al., 2001; Vrugt et al., 2003; Wagener et al., 2001].”
Reconciling theory with observations: elements of a diagnostic approach to model evaluation (2008, background): “These ideas have been further codified in the argument that all model identification problems are inherently multicriteria in nature (Gupta et al., 1998; Boyle et al., 2001; Wagener et al., 2001, 2003a, b; Vrugt et al., 2003), and that the challenge lies in (a) proper selection of measures of mode”
Time‐varying sensitivity analysis clarifies the effects of watershed model formulation on model behavior (2013, methodology): “…gradient [van Werkhoven et al., 2008a]; and second, that parameter sensitivities will change in time as real-world dominant processes change [Wagener et al., 2001, 2003; Sieber and Uhlenbrook, 2005; Cloke et al., 2008; Wagener et al., 2009; Reusser et al., 2011; Reusser and Zehe, 2011;…”
Transferability of hydrological models and ensemble averaging methods between contrasting climatic periods (2016, background): “HyMod (Hydrologic Model) [Wagener et al., 2001] has five reservoirs including a nonlinear soil moisture store, three ‘‘quick’’ flow linear reservoirs (in series) and a parallel groundwater reservoir.”