Details
| Original language | English |
|---|---|
| Pages (from-to) | 3777-3793 |
| Number of pages | 17 |
| Journal | Modeling Earth Systems and Environment |
| Volume | 9 |
| Issue number | 4 |
| Early online date | 11 Feb 2023 |
| Publication status | Published - Nov 2023 |
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.
Keywords
- Climate projections, Model parameters, Modeling chain, Reservoir sedimentation, Runoff, Uncertainty
ASJC Scopus subject areas
- Environmental Science(all)
- Agricultural and Biological Sciences(all)
- Earth and Planetary Sciences(all)
- Computers in Earth Sciences
- Decision Sciences(all)
- Statistics, Probability and Uncertainty
Sustainable Development Goals
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In: Modeling Earth Systems and Environment, Vol. 9, No. 4, 11.2023, p. 3777-3793.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Assessment of uncertainties in a complex modeling chain for predicting reservoir sedimentation under changing climate
AU - Pesci, María Herminia
AU - Mouris, Kilian
AU - Haun, Stefan
AU - Förster, Kristian
N1 - Funding Information: This study was carried out within the framework of the DIRT-X project, which is part of AXIS, an ERA-NET initiated by JPI Climate, and funded by FFG Austria, BMBF Germany (Grants No. 01LS1902A and 01LS1902B), FOR-MAS Sweden, NWO NL, and RCN Norway with co-funding from the European Union (Grant No. 776608). Stefan Haun is indebted to the Baden-Württemberg Stiftung for the financial support by the Elite program for Postdocs. We particularly thank Thomas Bosshard from the Swedish Meteorological and Hydrological Institute for providing the bias-adjusted climate modeling results that were used as input datasets. We also thank Nils Rüther and the DIRT-X team for providing us with input data and fruitful discussions.
PY - 2023/11
Y1 - 2023/11
N2 - 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.
AB - 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.
KW - Climate projections
KW - Model parameters
KW - Modeling chain
KW - Reservoir sedimentation
KW - Runoff
KW - Uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85147739595&partnerID=8YFLogxK
U2 - 10.1007/s40808-023-01705-6
DO - 10.1007/s40808-023-01705-6
M3 - Article
AN - SCOPUS:85147739595
VL - 9
SP - 3777
EP - 3793
JO - Modeling Earth Systems and Environment
JF - Modeling Earth Systems and Environment
SN - 2363-6203
IS - 4
ER -