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- Title
Identifying Sensitivities in Flood Frequency Analyses using a Stochastic Hydrologic Modeling System.
- Authors
Newman, Andrew J.; Stone, Amanda G.; Saharia, Manabendra; Holman, Kathleen D.; Addor, Nans; Clark, Martyn P.
- Abstract
This study assesses sources of variance in stochastic hydrologic modelling to support flood frequency analyses. The major components of the modelling 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 hydro-climates across the western United States. Ten hydrologic model structures were configured, calibrated and run within the Framework for Understanding Structural Errors (FUSE) modular modelling 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 modelling workflow was developed using the calibrated models; millions of flood event simulations were performed at 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. The attribution of the variance of flood frequencies to each component of a stochastic hydrological modelling framework, including several hydrological model structures, is a novel contribution to the flood modelling literature. Results demonstrate that different components of the modelling chain have different sensitivities for different return periods. Precipitation inputs contribute most to the variance of rare events, while initial conditions are most influential for the 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.
- Subjects
UNITED States; STOCHASTIC analysis; HYDROLOGIC models; STOCHASTIC models; FLOODS; PARAMETER estimation; FLOOD risk
- Publication
Hydrology & Earth System Sciences Discussions, 2021, p1
- ISSN
1812-2108
- Publication type
Abstract
- DOI
10.5194/hess-2021-49