@article{Newman-2021-Identifying,
title = "Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system",
author = "Newman, Andrew J. and
Stone, Amanda G. and
Saharia, Manabendra and
Holman, K. D. and
Addor, Nans and
Clark, Martyn P.",
journal = "Hydrology and Earth System Sciences, Volume 25, Issue 10",
volume = "25",
number = "10",
year = "2021",
publisher = "Copernicus GmbH",
url = "https://gwf-uwaterloo.github.io/gwf-publications/G21-67001",
doi = "10.5194/hess-25-5603-2021",
pages = "5603--5621",
abstract = "Abstract. 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.",
}
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<abstract>Abstract. 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.</abstract>
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%0 Journal Article
%T Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system
%A Newman, Andrew J.
%A Stone, Amanda G.
%A Saharia, Manabendra
%A Holman, K. D.
%A Addor, Nans
%A Clark, Martyn P.
%J Hydrology and Earth System Sciences, Volume 25, Issue 10
%D 2021
%V 25
%N 10
%I Copernicus GmbH
%F Newman-2021-Identifying
%X Abstract. 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.
%R 10.5194/hess-25-5603-2021
%U https://gwf-uwaterloo.github.io/gwf-publications/G21-67001
%U https://doi.org/10.5194/hess-25-5603-2021
%P 5603-5621
Markdown (Informal)
[Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system](https://gwf-uwaterloo.github.io/gwf-publications/G21-67001) (Newman et al., GWF 2021)
ACL
- Andrew J. Newman, Amanda G. Stone, Manabendra Saharia, K. D. Holman, Nans Addor, and Martyn P. Clark. 2021. Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system. Hydrology and Earth System Sciences, Volume 25, Issue 10, 25(10):5603–5621.
