Time Variant Sensitivity Analysis of Hydrological Model Parameters in a Cold Region Using Flow Signatures

Bajracharya, A. R.; Awoye, Hervé; Stadnyk, Tricia A.; Asadzadeh, Masoud

doi:10.3390/w12040961

Cited by 14 publications

(15 citation statements)

References 64 publications

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“…Other researchers, such as Thorndahl et al [29], performed a sensitivity analysis of a set of parameters, by comparing the conditions of the conceptual model and the general model, finding that the parameter of greater sensitivity is the hydrological reduction factor. Bajracharya et al [30] performed a global sensitivity analysis (using the Variogram Analysis of Response Surfaces technique) of the parameters that govern the behavior of runoffs of the Nelson Churchill River basin, represented in the Hydrological Predictions for the Environment model (HYPE). Other studies that perform sensitivity analysis applied to the SWMM model reveal the behavior of the parameters according to the study area and its characterization [31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of the Rainfall–Runoff Modeling Parameters in Data-Scarce Urban Catchment

et al. 2020

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Rainfall–runoff phenomena are among the main processes within the hydrological cycle. In urban zones, the increases in imperviousness cause increased runoff, originating floods. It is fundamental to know the sensitivity of parameters in the modeling of an urban basin, which makes the calibration process more efficient by allowing one to focus only on the parameters for which the modeling results are sensitive. This research presents a formal sensitivity analysis of hydrological and hydraulic parameters—absolute–relative, relative–absolute, relative–relative sensitivity and R2—applied to an urban basin. The urban basin of Tuxtla Gutiérrez, Chiapas, in Mexico is an area prone to flooding caused by extreme precipitation events. The basin has little information in which the records (with the same time resolution) of precipitation and hydrometry match. The basin model representing an area of 355.07 km2 was characterized in the Stormwater Management Model (SWMM). The sensitivity analysis was performed for eight hydrological parameters and one hydraulic for two precipitation events and their impact on the depths of the Sabinal River. Based on the analysis, the parameters derived from the analysis that stand out as sensitive are the Manning coefficient of impervious surface and the minimum infiltration speed with R2 > 0.60. The results obtained demonstrate the importance of knowing the sensitivity of the parameters and their selection to perform an adequate calibration.

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Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of the Rainfall–Runoff Modeling Parameters in Data-Scarce Urban Catchment

et al. 2020

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“…varying process sensitivities over the course of the year (Sec. 3.3.4) as shown to be of importance previously (Dobler and Pappenberger, 2012;Herman et al, 2013;Günther et al, 2019;Bajracharya et al, 2020).…”

Section: Extended Sobol' Sensitivity Analysis Applied To Hydrologic Mmentioning

confidence: 85%

Simultaneously Determining Global Sensitivities of Model Parameters and Model Structure

Mai¹,

Craig²,

Tolson³

2020

Preprint

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Abstract. Model structure uncertainty is known to be one of the three main sources of hydrologic model uncertainty along with input and parameter uncertainty. Some recent hydrological modeling frameworks address model structure uncertainty by supporting multiple options for representing hydrological processes. It is, however, still unclear how best to analyze structural sensitivity using these frameworks. In this work, we apply an Extended Sobol' Sensitivity Analysis (xSSA) method that operates on grouped parameters rather than individual parameters. The method can estimate not only traditional model parameter sensitivities but is also able to provide measures of the sensitivities of process options (e.g., linear vs. non-linear storage) and sensitivities of model processes (e.g., infiltration vs. baseflow) with respect to a model output. Key to the xSSA method's applicability to process option and process sensitivity is the novel introduction of process option weights in the Raven hydrological modeling framework. The method is applied to both artificial benchmark models and a watershed model built with the Raven framework. The results show that: (1) The xSSA method provides sensitivity estimates consistent with those derived analytically for individual as well as grouped parameters linked to model structure. (2) The xSSA method with process weighting is computationally less expensive than the alternative aggregate sensitivity analysis approach performed for the exhaustive set of structural model configurations, with savings of 81.9 % for the benchmark model and 98.6 % for the watershed case study. (3) The xSSA method applied to the hydrologic case study analyzing simulated streamflow showed that model parameters adjusting forcing functions were responsible for 42.1 % of the overall model variability while surface processes cause 38.5 % of the overall model variability in a mountainous catchment; such information may readily inform model calibration. (4) The analysis of time dependent process sensitivities regarding simulated streamflow is a helpful tool to understand model internal dynamics over the course of the year.

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“…The artificial benchmark models are used to prove that the proposed method of extended Sobol' sensitivity indexes and its implementation is working. They are furthermore employed to demonstrate some limitations of the existing method proposed by Baroni and Tarantola (2014) (discrete value method) to derive sensitivities regarding model structures. The analytically derived values for the traditional approach analyzing the individual 12 models independently (sensitivity metric a) can be found in Appendix B Table B1 for the shared-parameter model setup.…”

Section: Experiments Using the Benchmark Modelsmentioning

confidence: 99%

“…A key purpose of model sensitivity analysis is to inform model calibration or model uncertainty analysis so as to focus either of these analyses on only the model inputs/model structural choices the model outputs are most sensitive to. While the literature on sensitivity analysis for model parameters is rich (Morris, 1991;Sobol', 1993;Demaria et al, 2007;Foglia et al, 2009;Campolongo et al, 2011;Rakovec et al, 2014;Pianosi and Wagener, 2015;Cuntz et al, 2015Cuntz et al, , 2016Razavi and Gupta, 2016a, b;Borgonovo et al, 2017;Haghnegahdar et al, 2017), and there has likewise been a good deal of research into the influence of model input uncertainty (Baroni and Tarantola, 2014;Abily et al, 2016;Schürz et al, 2019), sensitivity to model structural choice has received far less attention (McMillan et al, 2010;Clark et al, 2011). With model structure we refer to various process conceptualizations within a model rather than, for example, model discretization.…”

Section: Introductionmentioning

confidence: 99%

“…The model is then run with the associated pre-sampled parameter set. While the approach may be generally applicable to arbitrary structural differences, in their testing, Baroni and Tarantola (2014) varied only in how the model was internally discretized (i.e., in the number of soil layers). The soil and crop parameters were always used for the same soil and crop process.…”

Section: Introductionmentioning

confidence: 99%

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Simultaneously determining global sensitivities of model parameters and model structure

Mai

Craig

Tolson

2020

Hydrol. Earth Syst. Sci.

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Abstract. Model structure uncertainty is known to be one of the three main sources of hydrologic model uncertainty along with input and parameter uncertainty. Some recent hydrological modeling frameworks address model structure uncertainty by supporting multiple options for representing hydrological processes. It is, however, still unclear how best to analyze structural sensitivity using these frameworks. In this work, we apply the extended Sobol' sensitivity analysis (xSSA) method that operates on grouped parameters rather than individual parameters. The method can estimate not only traditional model parameter sensitivities but is also able to provide measures of the sensitivities of process options (e.g., linear vs. non-linear storage) and sensitivities of model processes (e.g., infiltration vs. baseflow) with respect to a model output. Key to the xSSA method's applicability to process option and process sensitivity is the novel introduction of process option weights in the Raven hydrological modeling framework. The method is applied to both artificial benchmark models and a watershed model built with the Raven framework. The results show that (1) the xSSA method provides sensitivity estimates consistent with those derived analytically for individual as well as grouped parameters linked to model structure. (2) The xSSA method with process weighting is computationally less expensive than the alternative aggregate sensitivity analysis approach performed for the exhaustive set of structural model configurations, with savings of 81.9 % for the benchmark model and 98.6 % for the watershed case study. (3) The xSSA method applied to the hydrologic case study analyzing simulated streamflow showed that model parameters adjusting forcing functions were responsible for 42.1 % of the overall model variability, while surface processes cause 38.5 % of the overall model variability in a mountainous catchment; such information may readily inform model calibration and uncertainty analysis. (4) The analysis of time-dependent process sensitivities regarding simulated streamflow is a helpful tool for understanding model internal dynamics over the course of the year.

show abstract

Time Variant Sensitivity Analysis of Hydrological Model Parameters in a Cold Region Using Flow Signatures

Cited by 14 publications

References 64 publications

Sensitivity Analysis of the Rainfall–Runoff Modeling Parameters in Data-Scarce Urban Catchment

Sensitivity Analysis of the Rainfall–Runoff Modeling Parameters in Data-Scarce Urban Catchment

Simultaneously Determining Global Sensitivities of Model Parameters and Model Structure

Simultaneously determining global sensitivities of model parameters and model structure

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