A new framework for comprehensive, robust, and efficient global sensitivity analysis: 2. Application

Razavi, Saman; Gupta, Hoshin

doi:10.1002/2015wr017559

Cited by 113 publications

(146 citation statements)

References 16 publications

(30 reference statements)

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“…For comprehensive reviews, please refer to Song et al (2015), and Pianosi and Wagener (2015), and for a relatively recent interesting insight into the SA problem to Razavi and Gupta (2015, 2016a, 2016b.…”

Section: Global Sensitivity Analysis Methodsmentioning

confidence: 99%

Practical experience and framework for sensitivity analysis of hydrological models: six methods, three models, three criteria

Wang

Solomatine

2018

Preprint

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Abstract. Sensitivity Analysis (SA) and Uncertainty Analysis (UA) are important steps for better understanding and evaluation of hydrological models. The aim of this paper is to briefly review main classes of SA methods, and to presents the results of 10 the practical comparative analysis of applying them. Six different global SA methods: Sobol, eFAST, Morris, LH-OAT, RSA and PAWN are tested on three conceptual rainfall-runoff models with varying complexity: (GR4J, Hymod and HBV) applied to the case study of Bagmati basin (Nepal), and also initially tested on the case of Dapoling-Wangjiaba catchment in China.The methods are compared with respect to effectiveness, efficiency and convergence. A practical framework of selecting and using the SA methods is presented. The result shows that, first of all, all the six SA methods are effective. Morris and LH-15 OAT methods are the most efficient methods in computing SI and ranking. eFAST performs better than Sobol, thus can be seen as its viable alternative for Sobol. PAWN and RSA methods have issues of instability which we think are due to the ways CDFs are built, and using Kolmogorov-Smirnov statistics to compute Sensitivity Indices. All the methods require sufficient number of runs to reach convergence. Difference in efficiency of different methods is an inevitable consequence of the differences in the underlying principles. For SA of hydrological models, it is recommended to apply the presented practical 20 framework assuming the use of several methods, and to explicitly take into account the constraints of effectiveness, efficiency (including convergence), ease of use, as well as availability of software.

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Section: Global Sensitivity Analysis Methodsmentioning

confidence: 99%

Practical experience and framework for sensitivity analysis of hydrological models: six methods, three models, three criteria

Wang

Solomatine

2018

Preprint

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“…Various approaches to sensitivity analysis have been used in the literature, including simple one-factor-at-a-time strategies [36], Monte-Carlo filtering [37], variance-based approaches [38], and a variogram-based framework [39,40]. In this study, a Monte-Carlo Analysis (MOCA) framework was used with many model simulations, each using a different set of parameter values sampled randomly from a certain range of model parameters.…”

Section: Parameter Sensitivitymentioning

confidence: 99%

Quantifying Spatial Changes in the Structure of Water Quality Constituents in a Large Prairie River within Two Frameworks of a Water Quality Model

Hosseini

Chun

2016

Water

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Abstract:A global sensitivity analysis was carried out on a water quality model to quantify the spatial changes in parameter sensitivity of a model of a large prairie river, the South Saskatchewan River (SSR). The method is used to assess the relative impacts of major nutrient loading sources and a reservoir on the river's water quality. The river completely freezes over during winter; hence, the sensitivity analysis was carried out seasonally, for winter and summer, to account for the influence of ice-covered conditions on nutrient transformations. Furthermore, the integrity of the river's aquatic ecosystem was examined through the inter-relationship between variables and comparing hierarchy index values and water quality indices at four locations along the river. Sensitivities of model parameters varied slightly at different locations along the river, with the phytoplankton growth rate being the most influential parameter. Nitrogen and phosphorus transformation processes were more sensitive in winter, while chlorophyll-a and dissolved oxygen parameters showed higher sensitivity in summer. A more complicated correlation between variables was observed downstream of the junction of the Red Deer River. Our results reveal that the lower correlation between variables may suggest a more balanced and healthier system, although further analysis is needed to support this statement.

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“…The spatially lumped HYMOD Version 1 (HYMOD1) conceptual rainfall-runoff model with five/six parameters has previously been used in several studies (Boyle et al, 2000;Vrugt et al, 2003Vrugt et al, , 2009Moradkhani et al, 2005;Duan et al, 2007;Wang et al, 2009;Razavi and Gupta, 2016). The model is driven using daily precipitation and PET data to generate daily estimates of AET (HAET: HYMODgenerated AET) and streamflow.…”

Section: The Hymod2 Hydrologic Modelmentioning

confidence: 99%

Using satellite-based evapotranspiration estimates to improve the structure of a simple conceptual rainfall–runoff model

Roy

Gupta

Serrat-Capdevila

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Daily, quasi-global (50 • N-S and 180 • W-E), satellite-based estimates of actual evapotranspiration at 0.25 • spatial resolution have recently become available, generated by the Global Land Evaporation Amsterdam Model (GLEAM). We investigate the use of these data to improve the performance of a simple lumped catchmentscale hydrologic model driven by satellite-based precipitation estimates to generate streamflow simulations for a poorly gauged basin in Africa. In one approach, we use GLEAM to constrain the evapotranspiration estimates generated by the model, thereby modifying daily water balance and improving model performance. In an alternative approach, we instead change the structure of the model to improve its ability to simulate actual evapotranspiration (as estimated by GLEAM). Finally, we test whether the GLEAM product is able to further improve the performance of the structurally modified model. Results indicate that while both approaches can provide improved simulations of streamflow, the second approach also improves the simulation of actual evapotranspiration significantly, which substantiates the importance of making "diagnostic structural improvements" to hydrologic models whenever possible.

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A new framework for comprehensive, robust, and efficient global sensitivity analysis: 2. Application

Cited by 113 publications

References 16 publications

Practical experience and framework for sensitivity analysis of hydrological models: six methods, three models, three criteria

Practical experience and framework for sensitivity analysis of hydrological models: six methods, three models, three criteria

Quantifying Spatial Changes in the Structure of Water Quality Constituents in a Large Prairie River within Two Frameworks of a Water Quality Model

Using satellite-based evapotranspiration estimates to improve the structure of a simple conceptual rainfall–runoff model

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