Understanding the time-varying importance of different uncertainty sources in hydrological modelling using global sensitivity analysis

Francesca Pianosi*, Thorsten Wagener

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

41 Citations (Scopus)
288 Downloads (Pure)

Abstract

Simulations from hydrological models are affected by potentially large uncertainties stemming from various sources, including model parameters and observational uncertainty in the input/output data. Understanding the relative importance of such sources of uncertainty is essential to support model calibration, validation and diagnostic evaluation, and to prioritise efforts for uncertainty reduction. It can also support the identification of ‘disinformative data’ whose values are the consequence of measurement errors or inadequate observations. Sensitivity Analysis (SA) provides the theoretical framework and the numerical tools to quantify the relative contribution of different sources of uncertainty to the variability of the model outputs. In traditional applications of GSA, model outputs are aggregations of the full set of a simulated variable. For example, many GSA applications use a performance metric (e.g. the root mean squared error) as model output that aggregates the distances of a simulated time series to available observations. This aggregation of propagated uncertainties prior to GSA may lead to a significant loss of information and may cover up local behaviour that could be of great interest. Time-varying sensitivity analysis (TVSA), where the aggregation and SA are repeated at different time-steps, is a viable option to reduce this loss of information. In this work, we use TVSA to address two questions: [1] Can we distinguish between the relative importance of parameter uncertainty versus data uncertainty in time? [2] Do these influences change in catchments with different characteristics? To our knowledge, the results present one of the first quantitative investigation on the relative importance of parameter and data uncertainty across time. We find that the approach is capable of separating influential periods across data and parameter uncertainties, while also highlighting significant differences between the catchments analysed.
Original languageEnglish
Pages (from-to)3991–4003
Number of pages13
JournalHydrological Processes
Volume30
Issue number22
Early online date30 Aug 2016
DOIs
Publication statusPublished - 30 Oct 2016

Keywords

  • Conceptual models
  • parameter uncertainty
  • data uncertainty
  • sensitivity analysis

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