Details zur Publikation

Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.1002/2017WR020767
Titel (primär) Making the most out of a hydrological model data set: Sensitivity analyses to open the model black-box
Autor Borgonovo, E.; Lu, X.; Plischke, E.; Rakovec, O. ORCID logo ; Hill, M.C.
Quelle Water Resources Research
Erscheinungsjahr 2017
Department CHS
Band/Volume 53
Heft 9
Seite von 7933
Seite bis 7950
Sprache englisch
Keywords sensitivity analysis; model parameters; hydrological model; uncertainty
UFZ Querschnittsthemen 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.
dauerhafte UFZ-Verlinkung
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