Combined analysis of time-varying sensitivity and identifiability indices to diagnose the response of a complex environmental model
- Sensitivity and identifiability analyses are common diagnostic tools to address over-parametrization in complex environmental models, but a combined application of the two analyses is rarely conducted. In this study, we performed a temporal global sensitivity analysis using the variance-based method of Sobol’ and a temporal identifiability analysis of model parameters using the dynamic identifiability method (DYNIA). We discuss the relationship between the two analyses with a focus on parameter identification and output uncertainty reduction. The hydrological model HydroGeoSphere was used to simulate daily evapotranspiration, water content, and seepage at the lysimeter scale. We found that identifiability of a parameter does not necessarily reduce output uncertainty. It was also found that the information from the main and total effects (main Sobol' sensitivity indices) is required to allow uncertainty reduction in the model output. Overall, the study highlights the role of combined temporal diagnostic tools for improving ourSensitivity and identifiability analyses are common diagnostic tools to address over-parametrization in complex environmental models, but a combined application of the two analyses is rarely conducted. In this study, we performed a temporal global sensitivity analysis using the variance-based method of Sobol’ and a temporal identifiability analysis of model parameters using the dynamic identifiability method (DYNIA). We discuss the relationship between the two analyses with a focus on parameter identification and output uncertainty reduction. The hydrological model HydroGeoSphere was used to simulate daily evapotranspiration, water content, and seepage at the lysimeter scale. We found that identifiability of a parameter does not necessarily reduce output uncertainty. It was also found that the information from the main and total effects (main Sobol' sensitivity indices) is required to allow uncertainty reduction in the model output. Overall, the study highlights the role of combined temporal diagnostic tools for improving our understanding of model behavior.…
| Author details: | Mehdi GhasemizadeORCiD, Gabriele BaroniORCiDGND, Karim Abbaspour, Mario Schirmer |
|---|---|
| DOI: | https://doi.org/10.1016/j.envsoft.2016.10.011 |
| ISSN: | 1364-8152 |
| ISSN: | 1873-6726 |
| Title of parent work (English): | Environmental modelling & software with environment data news |
| Publisher: | Elsevier |
| Place of publishing: | Oxford |
| Publication type: | Article |
| Language: | English |
| Date of first publication: | 2016/11/16 |
| Publication year: | 2017 |
| Release date: | 2022/07/04 |
| Tag: | HydroGeoSphere; Identifiability; Output uncertainty; Preferential flow; Temporal sensitivity |
| Volume: | 88 |
| Number of pages: | 13 |
| First page: | 22 |
| Last Page: | 34 |
| Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Geowissenschaften |
| DDC classification: | 5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften |
