Automatic Calibration of the SHETRAN Hydrological Modelling System Using MSCE

Zhang, Rong; Santos, Celso Augusto Guimarães; Moreira, Madalena; Freire, Paula K. M. M.; Corte‐Real, João

doi:10.1007/s11269-013-0395-z

Cited by 30 publications

(14 citation statements)

References 30 publications

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“…Zhang et al [28] performed sensitivity analysis of the parameters of the non-dominated sorting genetic algorithm-II (NSGA-II) [29] on the accuracy of a distributed hydrological model known as Systeme Hydrologique Européen-Transport (SHETRAN) [4,[30][31][32][33][34][35][36]. Simulated binary crossover (SBX) [37] and polynomial mutation (PM) [38] were used for the crossover and mutation, respectively, of three NSGA-II algorithms: the original NSGA-II, the reference point-based-NSGA-II (R-NSGA-II) [39], and the extension ER-NSGA-II [40].…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Automatic Parameter Calibration of a Hydrological Model

Jung

Choi

Kim

2017

Water

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This study proposes variable balancing approaches for the exploration (diversification) and exploitation (intensification) of the non-dominated sorting genetic algorithm-II (NSGA-II) with simulated binary crossover (SBX) and polynomial mutation (PM) in the multiobjective automatic parameter calibration of a lumped hydrological model, the HYMOD model. Two objectives-minimizing the percent bias and minimizing three peak flow differences-are considered in the calibration of the six parameters of the model. The proposed balancing approaches, which migrate the focus between exploration and exploitation over generations by varying the crossover and mutation distribution indices of SBX and PM, respectively, are compared with traditional static balancing approaches (the two dices value is fixed during optimization) in a benchmark hydrological calibration problem for the Leaf River (1950 km 2 ) near Collins, Mississippi. Three performance metrics-solution quality, spacing, and convergence-are used to quantify and compare the quality of the Pareto solutions obtained by the two different balancing approaches. The variable balancing approaches that migrate the focus of exploration and exploitation differently for SBX and PM outperformed other methods.

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Section: Introductionmentioning

confidence: 99%

Multiobjective Automatic Parameter Calibration of a Hydrological Model

Jung

Choi

Kim

2017

Water

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“…Nevertheless, underestimation of discharge peaks and accurate base flow was obtained in both catchments. By contrast, accurate peak discharge estimation and under prediction of phreatic level (base flow) in SHETRAN have previously been reported, and vice versa (Adams, Parkin, Rutherford, Ibbitt, & Elliott, ; Bathurst et al, ; Birkinshaw, ; Ewen & Parkin, ; Janes et al, ; Zhang et al, ). Furthermore, similar behaviour has been observed with other hydrological models (Croke, Merritt, & Jakeman, ; De Roo & Jetten, ; Marsik & Waylen, ).…”

Section: Discussionmentioning

confidence: 94%

“…The reported NSE values for SHETRAN are between 0.6 and 0.8 (Adams, Western, & Seed, ; Janes et al, ; Mourato, Moreira, & Corte‐Real, ). Inaccurate peak discharge prediction has been addressed in previous SHETRAN applications where better estimation of base flow and poorer prediction of discharge peaks have been reported (Zhang, Santos, Moreira, Freire, & Corte‐Real, ). Underestimation of event peak discharge was also found by comparing simulated flow with data from eight gauging stations within a 2,400 km 2 catchment in the United Kingdom (Janes et al, ).…”

Section: Methodsmentioning

confidence: 99%

Assessing the performance of a physically based hydrological model using a proxy‐catchment approach in an agricultural environment

Escobar‐Ruiz

Smith

Blake

et al. 2019

Hydrological Processes

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Physically based models are useful frameworks for testing intervention strategies designed to reduce elevated sediment loads in agricultural catchments. Evaluating the success of these strategies depends on model accuracy, generally established by a calibration and evaluation process. In this contribution, the physically based SHETRAN model was assessed in two similar U.K. agricultural catchments. The model was calibrated on the Blackwater catchment (18 km 2 ) and evaluated in the adjacent Kit Brook catchment (22 km 2 ) using 4 years of 15 min discharge and suspended sediment flux data. Model sensitivity to changes in single and multiple combinations of parameters and sensitivity to changes in digital elevation model resolution were assessed. Model flow performance was reasonably accurate with a Nash-Sutcliffe efficiency coefficient of 0.78 in Blackwater and 0.60 in Kit Brook. In terms of event prediction, the mean of the absolute percentage of difference (μAbs diff ) between measured and simulated flow volume (Qv), peak discharge (Qp), sediment yield (Sy), and peak sediment flux (Sp) showed larger values in Kit Brook (48% [Qv], 66% [Qp], 298% [Sy], and 438% [Sp]) compared with the Blackwater catchment (30% [Qv], 41% [Qp], 106% [Sy], and 86% [Sp]). Results indicate that SHETRAN can produce reasonable flow prediction but performs less well in estimation of sediment flux, despite reasonably similar hydrosedimentary behaviour between catchments. The sensitivity index showed flow volume sensitive to saturated hydraulic conductivity and peak discharge to the Strickler coefficient; sediment yield was sensitive to the overland flow erodibility coefficient and peak sediment flux to raindrop/leaf soil erodibility coefficient. The multiparameter sensitivity analysis showed that different combinations of parameters produced similar model responses. Model sensitivity to grid resolution presented similar flow volumes for different digital elevation model resolutions, whereas event peak and duration (for both flow and sediment flux) were highly sensitive to changes in grid size. K E Y W O R D S catchment modelling, model sensitivity, proxy catchment, sediment yield, SHETRAN

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“…Further studies together with physically-based runoff-erosion models (e.g., Santos et al, 1994Santos et al, , 2003Santos et al, , 2011aSantos et al, b, 2012aSantos et al, b, 2013Zhang et al, 2013) seems to be a promising tool for dealing with erosion issues, as well as the use of wavelet transform (e.g., Santos & Morais, 2013;. [1981][1982][1983][1984].…”

Section: Resultsmentioning

confidence: 99%

Kohonen Neural Networks for Rainfall-Runoff Modeling: Case Study of Piancó River Basin

Farias¹,

Santos²,

Lourenço³

et al. 2013

J. Urb. and Environ. Engng

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Abstract:The existence of long and reliable streamflow data records is essential to establishing strategies for the operation of water resources systems. In areas where streamflow data records are limited or present missing values, rainfall-runoff models are typically used for reconstruction and/or extension of river flow series. The main objective of this paper is to verify the application of Kohonen Neural Networks (KNN) for estimating streamflows in Piancó River. The Piancó River basin is located in the Brazilian semiarid region, an area devoid of hydrometeorological data and characterized by recurrent periods of water scarcity. The KNN are unsupervised neural networks that cluster data into groups according to their similarities. Such models are able to classify data vectors even when there are missing values in some of its components, a very common situation in rainfall-runoff modeling. Twenty two years of rainfall and streamflow monthly data were used in order to calibrate and test the proposed model. Statistical indexes were chose as criteria for evaluating the performance of the KNN model under four different scenarios of input data. The results show that the proposed model was able to provide reliable estimations even when there were missing values in the input data set.

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Automatic Calibration of the SHETRAN Hydrological Modelling System Using MSCE

Cited by 30 publications

References 30 publications

Multiobjective Automatic Parameter Calibration of a Hydrological Model

Multiobjective Automatic Parameter Calibration of a Hydrological Model

Assessing the performance of a physically based hydrological model using a proxy‐catchment approach in an agricultural environment

Kohonen Neural Networks for Rainfall-Runoff Modeling: Case Study of Piancó River Basin

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