2008
DOI: 10.1029/2007wr006271
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Characterization of watershed model behavior across a hydroclimatic gradient

Abstract: [1] A fundamental tradeoff exists in watershed modeling between a model's flexibility for representing watersheds with different characteristics versus its potential for overparameterization. This study uses global sensitivity analysis to investigate how a commonly used intermediate-complexity model, the Sacramento Soil Moisture Accounting Model (SAC-SMA), represents a wide range of watersheds with diverse physical and hydroclimatic characteristics. The analysis aims to establish a detailed understanding of mo… Show more

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Cited by 189 publications
(208 citation statements)
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References 53 publications
(76 reference statements)
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“…There is an interesting difference between the sensitivity to RMSE for event scales versus longer time scales. The latter was analyzed for the lumped SAC-SMA model by van Werkhoven et al [2008b], who found that the controlling parameters were located in both the upper and the lower zone. This is not the case for the shorter time scale.…”
Section: Resultsmentioning
confidence: 99%
See 2 more Smart Citations
“…There is an interesting difference between the sensitivity to RMSE for event scales versus longer time scales. The latter was analyzed for the lumped SAC-SMA model by van Werkhoven et al [2008b], who found that the controlling parameters were located in both the upper and the lower zone. This is not the case for the shorter time scale.…”
Section: Resultsmentioning
confidence: 99%
“…The ''total'' Sobol' sensitivity index used in this study reflects the combined effect of the parameter alone (i.e., individual sensitivity) plus its interactions with all other parameters in the analysis. For more details on Sobol''s method see Sobol' [1993Sobol' [ , 2001, Saltelli et al [1999], or van Werkhoven et al [2008b.…”
Section: Methods Model and Datamentioning
confidence: 99%
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“…However, we also argue that SA is a useful perspective for conceptualizing and understanding hydrological models for several reasons. As indicated by Rakovec et al (2014), SA can be used to: (a) detect when increasing model complexity can no longer be supported by observations and whether it is likely to affect model predictions (e.g., Saltelli et al, 1999;van Werkhoven et al, 2008a;Doherty and Welter, 2010;Rosolem et al, 2012;Gupta et al, 2012;Foglia et al, 2013); (b) reduce the time required for model calibration by focusing estimation efforts on parameters that are important for calibration metrics and predictions (e.g., Anderman et al, 1996;Hamm et al, 2006;Zambrano-Bigiarini and Rojas, 2013); (c) determine the priorities for theoretical and site-specific model development (e.g., Hill and Tiedeman, 2007;Saltelli et al, 2008;Kavetski and Clark, 2010); and (d) identify the advantageous placement and timing of new measurements (e.g., Tiedeman et al, 2003Tiedeman et al, , 2004.…”
Section: Implications and Roles Of Sa In Hydrological Modelingmentioning
confidence: 99%
“…from 30 to 70 percentile flows) and either construct objective measures (i.e. volumetric bias, error in the slope) for each segment (van Werkhoven et al, 2008) or select evaluation points along the FDC and thereby evaluate the deviation between simulated and observed discharge. Therefore, current FDC calibration methods have a limited ability to extract the distributional information contained in the probability density function (pdf) of the flow signal.…”
Section: Introductionmentioning
confidence: 99%