An advanced regularization methodology for use in watershed model calibration

Doherty, John; Skahill, Brian E.

doi:10.1016/j.jhydrol.2005.11.058

Cited by 66 publications

(40 citation statements)

References 26 publications

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“…There are two different executable programs, which are used to call methods in this dll. HBV-light-GUI provides the user with a graphical user interface to interact with the model, and HBV-light-CLI is a command line interface allowing the user to run HBV-light from the command line or other applications such as PEST, which is a computer program for modelindependent parameter estimation and uncertainty analysis (Doherty and Johnston, 2003;Doherty and Skahill, 2006). With HBV-light-CLI it is also possible for the user to easily program tools for repetitive simulation tasks.…”

Section: Technical Implementationmentioning

confidence: 99%

Teaching hydrological modeling with a user-friendly catchment-runoff-model software package

Seibert

Vis

2012

Hydrol. Earth Syst. Sci.

465

327

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Abstract. Computer models, especially conceptual models, are frequently used for catchment hydrology studies. Teaching hydrological modeling, however, is challenging, since students have to both understand general model concepts and be able to use particular computer programs when learning to apply computer models. Here we present a new version of the HBV (Hydrologiska Byråns Vattenavdelning) model. This software provides a user-friendly version that is especially useful for education. Different functionalities, such as an automatic calibration using a genetic algorithm or a Monte Carlo approach, as well as the possibility to perform batch runs with predefined model parameters make the software interesting especially for teaching in more advanced classes and research projects. Different teaching goals related to hydrological modeling are discussed and a series of exercises is suggested to reach these goals.

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Section: Technical Implementationmentioning

confidence: 99%

Teaching hydrological modeling with a user-friendly catchment-runoff-model software package

Seibert

Vis

2012

Hydrol. Earth Syst. Sci.

465

327

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“…Several researchers have tried to calibrate HSPF using the Parameter Estimation (PEST) software tool [5,[14][15][16]. However, the Gauss-Levenberg-Marquardt (GLM) search algorithm employed in PEST is not necessarily capable of locating a global optimum solution, and its performance is dependent upon an initial parameter set specified by the user [16,17].…”

Section: Introductionmentioning

confidence: 99%

Automatic Calibration Tool for Hydrologic Simulation Program-FORTRAN Using a Shuffled Complex Evolution Algorithm

Seong

Her

Benham

2015

Water

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Hydrologic Simulation Program-Fortran (HSPF) model calibration is typically done manually due to the lack of an automated calibration tool as well as the difficulty of balancing objective functions to be considered. This paper discusses the development and demonstration of an automated calibration tool for HSPF (HSPF-SCE). HSPF-SCE was developed using the open source software "R". The tool employs the Shuffled Complex Evolution optimization algorithm (SCE-UA) to produce a pool of qualified calibration parameter sets from which the modeler chooses a single set of calibrated parameters. Six calibration criteria specified in the Expert System for the Calibration of HSPF (HSPEXP) decision support tool were combined to develop a single, composite objective function for HSPF-SCE. The HSPF-SCE tool was demonstrated, and automated and manually calibrated model performance were compared using three Virginia watersheds, where HSPF models had been previously prepared for bacteria total daily maximum load (TMDL) development. The example applications demonstrate that HSPF-SCE can be an effective tool for calibrating HSPF.

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“…When this dimension becomes so large that the unknowns cannot be uniquely constrained by the data, the problem is said to be ill posed or poorly conditioned. ''Regularization'' is a mathematical strategy that helps to stabilize ill-posed problems by the enabling the inclusion of additional information [e.g., Tikhonov and Arsenin, 1977;Lawson and Hanson, 1995;Weiss and Smith, 1998;Doherty and Skahill, 2006;Linden et al, 2005;Tonkin and Doherty, 2005;Isaaks and Srivastava, 1989]. By using prior information (either direct or indirect) related to the parameters, regularization is able to ''better condition'' the objective function response surface, either via some kind of penalty function [Carrera and Neuman, 1986;Doherty and Skahill, 2006] or by imposing constraints that reduce the dimensionality of the parameter search space [see Pokhrel et al, 2008Pokhrel et al, , 2009].…”

Section: Introductionmentioning

confidence: 99%

On the use of spatial regularization strategies to improve calibration of distributed watershed models

Pokhrel

Gupta

2010

Water Resources Research

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[1] Hydrologic models require the specification of unknown model parameters via calibration to historical input-output data. For spatially distributed models, the large number of unknowns makes the calibration problem poorly conditioned. Spatial regularization can help to stabilize the problem by facilitating inclusion of additional information. While a common regularization approach is to apply a scalar multiplier to the prior estimate of each parameter field, this can cause problems by simultaneously changing both the mean and the variance of the distribution. This paper explores a multiple-criteria regularization approach that facilitates adjustment of the mean, variance, and shape of the parameter distribution, using prior information to constrain the problem while providing sufficient degrees of freedom to enable model performance improvements. We also test simple squashing functions to help in maintaining conceptually reasonable parameter values throughout the spatial domain. We apply the method to three basins in the context of the Distributed Model Intercomparison Project (DMIP2), obtaining considerable performance improvements at the basin outlet. However, the prior parameter estimates are found to give much better performance at the interior points (treated as ungauged), suggesting that the spatial information has not been properly exploited. The results also suggest that basin outlet hydrographs may not be particularly sensitive to spatial parameter variability and that an overall basin mean value may be sufficient for flow forecasting at the outlet, although not at the interior points. We discuss weaknesses in our study approach and suggest diagnostically more powerful strategies to be pursued.Citation: Pokhrel, P., and H. V. Gupta (2010), On the use of spatial regularization strategies to improve calibration of distributed watershed models, Water Resour. Res., 46, W01505,

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An advanced regularization methodology for use in watershed model calibration

Cited by 66 publications

References 26 publications

Teaching hydrological modeling with a user-friendly catchment-runoff-model software package

Teaching hydrological modeling with a user-friendly catchment-runoff-model software package

Automatic Calibration Tool for Hydrologic Simulation Program-FORTRAN Using a Shuffled Complex Evolution Algorithm

On the use of spatial regularization strategies to improve calibration of distributed watershed models

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