This modeling chain permitsĭeviations from AEP neutrality and quantifies the impacts of ICs, model
Scenarios from defined precipitation frequency distributions (Rahman et al., 2002 Paquet et al., 2013 Wright et al., 2020). Parameters, initial conditions (ICs), and precipitation input forcing Simulations using a single hydrologic model with randomly perturbed model In stochastic rainfall–runoff modeling, floodįrequency (FF) estimates are typically produced using stochastic event One way to address this is to perform stochastic The assumption of AEP neutrality is often not verifiable or justified (e.g., Rahman et al., 2002 Kuczera et al., 2006 Small et al., 2006 Pathiraja et al., 2012 Paquet et al., 2013 Ivancic and Shaw, 2015 Sharma et al., 2018 Yu et al., 2019). Methods rely on the assumption of AEP neutrality, i.e., that a rainfallĮvent has a similar AEP to the flood event. Uncertainty of model results (e.g., England et al., 2014). Typically, multiple methods are employed in these analyses to evaluate the Period of the flood is equal to the return period of the precipitation (e.g., Packman and Kidd, 1980 Boughton and Droop, 2003 Swain et al., 2006 Wright et al., 2020), (3) more complex fully stochastic rainfall–runoff modeling toĮxplicitly represent the impacts of hydrological processes on floods (RahmanĮt al., 2002 Schaefer and Barker, 2002 Nathan et al., 2003 Wright et al.,Ģ014), and (4) an analysis of paleo-flood records (England et al., 2010). There are numerous approaches to developing these curves, including (1) statistical stream gauge analysis, e.g., calculating the annual exceedance probability (AEP National Research Council 1988), (2)ĭesign storm rainfall–runoff hydrologic model estimates, where the return Hazard curve is a curve that relates the probability of occurrence to the magnitude of a flood.
Hydrologic hazard curves and flood hydrographs are used toĮvaluate hydrologic risks for a given facility, e.g., a dam. Understanding flood risk is important to support infrastructure design and Processes, and selecting appropriate hydrological models that are consistent with our understanding of flood generation processes. This study highlights the importance of critically assessing model underpinnings, understanding flood generation Important role in specific cases, depending on the basin characteristics and type of flood metric of interest. Structure and structure–parameter interactions together play an equally Most influential for more frequent events. Precipitation inputsĬontribute most to the variance of rare floods, while initial conditions are Results demonstrate that different components of the modeling chain haveĭifferent sensitivities for different return periods. Precipitation inputs to flood magnitudes for different return periods. The analysis of variance method was then used to quantify the relativeĬontributions of model structure, model parameters, initial conditions, and Which millions of flood event simulations were performed for each basin.
Hydrologic modeling workflow was developed using the calibrated models in Model parameters and initialĬonditions were derived from long-term calibrated simulations using aġ00 member historical meteorology ensemble. A total of 10 hydrologic model structures were configured, calibrated, and run within the Framework for Understanding Structural Errors (FUSE) modular modelingįramework for each of the two watersheds. Representing different hydroclimates across the western USA. 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 This study employs a stochastic hydrologic modeling framework to evaluate the sensitivity of flood frequency analyses to different components of the hydrologic modeling chain.
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