Publication Details

Category Text Publication
Reference Category Journals
DOI 10.1002/2017WR020767
Title (Primary) Making the most out of a hydrological model data set: Sensitivity analyses to open the model black-box
Author Borgonovo, E.; Lu, X.; Plischke, E.; Rakovec, O. ORCID logo ; Hill, M.C.
Source Titel Water Resources Research
Year 2017
Department CHS
Volume 53
Issue 9
Page From 7933
Page To 7950
Language englisch
Data and Software links
Keywords sensitivity analysis; model parameters; hydrological model; uncertainty
UFZ wide themes RU5;
Abstract 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 L1-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.
Persistent UFZ Identifier
Borgonovo, E., Lu, X., Plischke, E., Rakovec, O., Hill, M.C. (2017):
Making the most out of a hydrological model data set: Sensitivity analyses to open the model black-box
Water Resour. Res. 53 (9), 7933 - 7950 10.1002/2017WR020767