Review: Sources of Hydrological Model Uncertainties and Advances in Their Analysis
Despite progresses in representing different processes, hydrological models remain uncertain. Their uncertainty stems from input and calibration data, model structure, and parameters. In characterizing these sources, their causes, interactions and different uncertainty analysis (UA) methods are revi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/13/1/28 |
| _version_ | 1797543534400634880 |
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| author | Edom Moges Yonas Demissie Laurel Larsen Fuad Yassin |
| author_facet | Edom Moges Yonas Demissie Laurel Larsen Fuad Yassin |
| author_sort | Edom Moges |
| collection | DOAJ |
| description | Despite progresses in representing different processes, hydrological models remain uncertain. Their uncertainty stems from input and calibration data, model structure, and parameters. In characterizing these sources, their causes, interactions and different uncertainty analysis (UA) methods are reviewed. The commonly used UA methods are categorized into six broad classes: (i) Monte Carlo analysis, (ii) Bayesian statistics, (iii) multi-objective analysis, (iv) least-squares-based inverse modeling, (v) response-surface-based techniques, and (vi) multi-modeling analysis. For each source of uncertainty, the status-quo and applications of these methods are critiqued in gauged catchments where UA is common and in ungauged catchments where both UA and its review are lacking. Compared to parameter uncertainty, UA application for structural uncertainty is limited while input and calibration data uncertainties are mostly unaccounted. Further research is needed to improve the computational efficiency of UA, disentangle and propagate the different sources of uncertainty, improve UA applications to environmental changes and coupled human–natural-hydrologic systems, and ease UA’s applications for practitioners. |
| first_indexed | 2024-03-10T13:46:01Z |
| format | Article |
| id | doaj.art-eec0ba4e6cdf46a5948765bb9f04c72e |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T13:46:01Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-eec0ba4e6cdf46a5948765bb9f04c72e2023-11-21T02:36:41ZengMDPI AGWater2073-44412020-12-011312810.3390/w13010028Review: Sources of Hydrological Model Uncertainties and Advances in Their AnalysisEdom Moges0Yonas Demissie1Laurel Larsen2Fuad Yassin3Department of Geography, University of California Berkeley, Berkeley, CA 94709, USADepartment of Civil and Environmental Engineering, Washington State University, Richland, WA 99354, USADepartment of Geography, University of California Berkeley, Berkeley, CA 94709, USAGlobal Institute for Water Security, University of Saskatchewan, Saskatoon, SK S7N 5A2, CanadaDespite progresses in representing different processes, hydrological models remain uncertain. Their uncertainty stems from input and calibration data, model structure, and parameters. In characterizing these sources, their causes, interactions and different uncertainty analysis (UA) methods are reviewed. The commonly used UA methods are categorized into six broad classes: (i) Monte Carlo analysis, (ii) Bayesian statistics, (iii) multi-objective analysis, (iv) least-squares-based inverse modeling, (v) response-surface-based techniques, and (vi) multi-modeling analysis. For each source of uncertainty, the status-quo and applications of these methods are critiqued in gauged catchments where UA is common and in ungauged catchments where both UA and its review are lacking. Compared to parameter uncertainty, UA application for structural uncertainty is limited while input and calibration data uncertainties are mostly unaccounted. Further research is needed to improve the computational efficiency of UA, disentangle and propagate the different sources of uncertainty, improve UA applications to environmental changes and coupled human–natural-hydrologic systems, and ease UA’s applications for practitioners.https://www.mdpi.com/2073-4441/13/1/28hydrological model uncertaintysources of model uncertaintyreview of uncertainty analysismethods of uncertainty analysisuncertainty analysis in ungauged basins |
| spellingShingle | Edom Moges Yonas Demissie Laurel Larsen Fuad Yassin Review: Sources of Hydrological Model Uncertainties and Advances in Their Analysis Water hydrological model uncertainty sources of model uncertainty review of uncertainty analysis methods of uncertainty analysis uncertainty analysis in ungauged basins |
| title | Review: Sources of Hydrological Model Uncertainties and Advances in Their Analysis |
| title_full | Review: Sources of Hydrological Model Uncertainties and Advances in Their Analysis |
| title_fullStr | Review: Sources of Hydrological Model Uncertainties and Advances in Their Analysis |
| title_full_unstemmed | Review: Sources of Hydrological Model Uncertainties and Advances in Their Analysis |
| title_short | Review: Sources of Hydrological Model Uncertainties and Advances in Their Analysis |
| title_sort | review sources of hydrological model uncertainties and advances in their analysis |
| topic | hydrological model uncertainty sources of model uncertainty review of uncertainty analysis methods of uncertainty analysis uncertainty analysis in ungauged basins |
| url | https://www.mdpi.com/2073-4441/13/1/28 |
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