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Making the most out of a hydrological model data set: Sensitivity analyses to open the model black‐box
Author(s) -
Borgonovo E.,
Lu X.,
Plischke E.,
Rakovec O.,
Hill M. C.
Publication year - 2017
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2017wr020767
Subject(s) - sensitivity (control systems) , set (abstract data type) , prioritization , computer science , metric (unit) , variance (accounting) , identification (biology) , variance based sensitivity analysis , data mining , data set , mathematics , machine learning , artificial intelligence , analysis of variance , one way analysis of variance , engineering , operations management , botany , accounting , management science , electronic engineering , business , biology , programming language
In this work, we investigate methods for gaining greater insight from hydrological model runs conducted for uncertainty quantification and model differentiation. We frame the sensitivity analysis questions in terms of the main purposes of sensitivity analysis: parameter prioritization, trend identification, and interaction quantification. For parameter prioritization, we consider variance‐based sensitivity measures, sensitivity indices based on the L 1 ‐norm, the Kuiper metric, and the sensitivity indices of the DELSA methods. For trend identification, we investigate insights derived from graphing the one‐way ANOVA sensitivity functions, the recently introduced CUSUNORO plots, and derivative scatterplots. For interaction quantification, we consider information delivered by variance‐based sensitivity indices. We rely on the so‐called given‐data principle, in which results from a set of model runs are used to perform a defined set of analyses. One avoids using specific designs for each insight, thus controlling the computational burden. The methodology is applied to a hydrological model of a river in Belgium simulated using the well‐established Framework for Understanding Structural Errors (FUSE) on five alternative configurations. The findings show that the integration of the chosen methods provides insights unavailable in most other analyses.