Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system
<p>This study employs a stochastic hydrologic modeling framework to evaluate the sensitivity of flood frequency analyses to different components of the hydrologic modeling chain. The major components of the stochastic hydrologic modeling chain, including model structure, model parameter estima...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2021-10-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://hess.copernicus.org/articles/25/5603/2021/hess-25-5603-2021.pdf |
| _version_ | 1818358141925982208 |
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| author | A. J. Newman A. G. Stone M. Saharia M. Saharia K. D. Holman N. Addor M. P. Clark M. P. Clark |
| author_facet | A. J. Newman A. G. Stone M. Saharia M. Saharia K. D. Holman N. Addor M. P. Clark M. P. Clark |
| author_sort | A. J. Newman |
| collection | DOAJ |
| description | <p>This study employs a stochastic hydrologic modeling framework to evaluate the sensitivity of flood frequency analyses to different components of the hydrologic modeling chain. The major components of the stochastic hydrologic modeling chain, including model structure, model parameter estimation, initial conditions, and precipitation inputs were examined across return periods from 2 to 100 000 years at two watersheds
representing different hydroclimates across the western USA. A total of 10 hydrologic model structures were configured, calibrated, and run within the Framework for Understanding Structural Errors (FUSE) modular modeling
framework for each of the two watersheds. Model parameters and initial
conditions were derived from long-term calibrated simulations using a
100 member historical meteorology ensemble. A stochastic event-based
hydrologic modeling workflow was developed using the calibrated models in
which millions of flood event simulations were performed for each basin. The analysis of variance method was then used to quantify the relative
contributions of model structure, model parameters, initial conditions, and
precipitation inputs to flood magnitudes for different return periods.
Results demonstrate that different components of the modeling chain have
different sensitivities for different return periods. Precipitation inputs
contribute most to the variance of rare floods, while initial conditions are
most influential for more frequent events. However, the hydrological model
structure and structure–parameter interactions together play an equally
important role in specific cases, depending on the basin characteristics and type of flood metric of interest. This study highlights the importance of critically assessing model underpinnings, understanding flood generation
processes, and selecting appropriate hydrological models that are consistent with our understanding of flood generation processes.</p> |
| first_indexed | 2024-12-13T20:24:17Z |
| format | Article |
| id | doaj.art-e06e807c9d954537908af164eede2315 |
| institution | Directory Open Access Journal |
| issn | 1027-5606 1607-7938 |
| language | English |
| last_indexed | 2024-12-13T20:24:17Z |
| publishDate | 2021-10-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Hydrology and Earth System Sciences |
| spelling | doaj.art-e06e807c9d954537908af164eede23152022-12-21T23:32:36ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382021-10-01255603562110.5194/hess-25-5603-2021Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling systemA. J. Newman0A. G. Stone1M. Saharia2M. Saharia3K. D. Holman4N. Addor5M. P. Clark6M. P. Clark7Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, USATechnical Service Center, Bureau of Reclamation, Lakewood, CO, USAResearch Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, USAnow at: Department of Civil Engineering, Indian Institute of Technology, New Delhi, IndiaTechnical Service Center, Bureau of Reclamation, Lakewood, CO, USAGeography, College of Life and Environmental Sciences, University of Exeter, Exeter, UKResearch Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, USAnow at: Centre for Hydrology, University of Saskatchewan, Canmore, Alberta, Canada<p>This study employs a stochastic hydrologic modeling framework to evaluate the sensitivity of flood frequency analyses to different components of the hydrologic modeling chain. The major components of the stochastic hydrologic modeling chain, including model structure, model parameter estimation, initial conditions, and precipitation inputs were examined across return periods from 2 to 100 000 years at two watersheds representing different hydroclimates across the western USA. A total of 10 hydrologic model structures were configured, calibrated, and run within the Framework for Understanding Structural Errors (FUSE) modular modeling framework for each of the two watersheds. Model parameters and initial conditions were derived from long-term calibrated simulations using a 100 member historical meteorology ensemble. A stochastic event-based hydrologic modeling workflow was developed using the calibrated models in which millions of flood event simulations were performed for each basin. The analysis of variance method was then used to quantify the relative contributions of model structure, model parameters, initial conditions, and precipitation inputs to flood magnitudes for different return periods. Results demonstrate that different components of the modeling chain have different sensitivities for different return periods. Precipitation inputs contribute most to the variance of rare floods, while initial conditions are most influential for more frequent events. However, the hydrological model structure and structure–parameter interactions together play an equally important role in specific cases, depending on the basin characteristics and type of flood metric of interest. This study highlights the importance of critically assessing model underpinnings, understanding flood generation processes, and selecting appropriate hydrological models that are consistent with our understanding of flood generation processes.</p>https://hess.copernicus.org/articles/25/5603/2021/hess-25-5603-2021.pdf |
| spellingShingle | A. J. Newman A. G. Stone M. Saharia M. Saharia K. D. Holman N. Addor M. P. Clark M. P. Clark Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system Hydrology and Earth System Sciences |
| title | Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system |
| title_full | Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system |
| title_fullStr | Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system |
| title_full_unstemmed | Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system |
| title_short | Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system |
| title_sort | identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system |
| url | https://hess.copernicus.org/articles/25/5603/2021/hess-25-5603-2021.pdf |
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