Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models. Issue 1 (17th January 2014)

• Record Type: Journal Article
• Authors: Rakovec, O.; Hill, M. C.; Clark, M. P.; Weerts, A. H.; Teuling, A. J.; Uijlenhoet, R.
• Is Part Of: Water resources research. Volume 50:Issue 1(2014:Jan.)
• Page Start: 409
• Page End: 426
• Publication Date: 2014-01-17
• Subjects: Hydrology -- Periodicals; 333.91
• DOI: 10.1002/2013WR014063
• Languages: English
• ISSNs: 0043-1397

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>[1] This paper presents a hybrid local‐global sensitivity analysis method termed the Distributed Evaluation of Local Sensitivity Analysis (DELSA), which is used here to identify important and unimportant parameters and evaluate how model parameter importance changes as parameter values change. DELSA uses derivative‐based "local" methods to obtain the distribution of parameter sensitivity across the parameter space, which promotes consideration of sensitivity analysis results in the context of simulated dynamics. This work presents DELSA, discusses how it relates to existing methods, and uses two hydrologic test cases to compare its performance with the popular global, variance‐based Sobol' method. The first test case is a simple nonlinear reservoir model with two parameters. The second test case involves five alternative "bucket‐style" hydrologic models with up to 14 parameters applied to a medium‐sized catchment (200 km<sup>2</sup>) in the Belgian Ardennes. Results show that in both examples, Sobol' and DELSA identify similar important and unimportant parameters, with DELSA enabling more detailed insight at much lower computational cost. For example, in the real‐world problem the time delay in runoff is the most important parameter in all models, but DELSA shows that for about 20% of parameter sets it is not important at all and alternative mechanisms and parameters dominate. Moreover, the time delay was identified as important in regions producing poor model fits, whereas other parameters were identified as more important in regions of the parameter space producing better model fits. The ability to understand how parameter importance varies through parameter space is critical to inform decisions about, for example, additional data collection and model development. The ability to perform such analyses with modest computational requirements provides exciting opportunities to evaluate complicated models as well as many alternative models.</p> </abstract>