Assessment of uncertainties in a complex modeling chain for predicting reservoir sedimentation under changing climate

authored by

María Herminia Pesci, Kilian Mouris, Stefan Haun, Kristian Förster

Abstract

Long-term predictions of reservoir sedimentation require an objective consideration of the preceding catchment processes. In this study, we apply a complex modeling chain to predict sedimentation processes in the Banja reservoir (Albania). The modeling chain consists of the water balance model WaSiM, the soil erosion and sediment transport model combination RUSLE-SEDD, and the 3d hydro-morphodynamic reservoir model SSIIM2 to accurately represent all relevant physical processes. Furthermore, an ensemble of climate models is used to analyze future scenarios. Although the capabilities of each model enable us to obtain satisfying results, the propagation of uncertainties in the modeling chain cannot be neglected. Hence, approximate model parameter uncertainties are quantified with the First-Order Second-Moment (FOSM) method. Another source of uncertainty for long-term predictions is the spread of climate projections. Thus, we compared both sources of uncertainties and found that the uncertainties generated by climate projections are 408% (for runoff), 539% (for sediment yield), and 272% (for bed elevation in the reservoir) larger than the model parameter uncertainties. We conclude that (i) FOSM is a suitable method for quantifying approximate parameter uncertainties in a complex modeling chain, (ii) the model parameter uncertainties are smaller than the spread of climate projections, and (iii) these uncertainties are of the same order of magnitude as the change signal for the investigated low-emission scenario. Thus, the proposed method might support modelers to communicate different sources of uncertainty in complex modeling chains, including climate impact models.

Organisation(s)

Institute of Hydrology and Water Resources Management

External Organisation(s)

University of Stuttgart

Type

Article

Journal

Modeling Earth Systems and Environment

Volume

9

Pages

3777-3793

No. of pages

17

ISSN

2363-6203

Publication date

11.2023

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

General Environmental Science, General Agricultural and Biological Sciences, Computers in Earth Sciences, Statistics, Probability and Uncertainty

Sustainable Development Goals

SDG 13 - Climate Action

Electronic version(s)

https://doi.org/10.1007/s40808-023-01705-6 (Access: Open)