Abstract
An assessment of uncertainty in flood hydrograph features, e.g., peak discharge and flood volume due to variability in the rainfall‐runoff model (HEC‐HMS) parameters and rainfall characteristics, e.g., depth and duration, is conducted. Flood hydrographs are generated using a rain pattern generator (RPG) and HEC‐HMS models through Monte Carlo simulation considering uncertainty in stochastic variables. The uncertainties in HEC‐HMS parameters (e.g., loss, base flow, and unit hydrograph) are estimated using their probability distribution functions. The flood events are obtained by simulating runoff for rainfall events using the generated model parameters. The uncertainties due to rainfall and model parameters on generated flood hydrographs are evaluated using the relative coefficient of variation (RCV). The results reveal a higher RCV index for flood volume (RCV = 153) than peak discharge (RCV = 116) for a 12‐hr rainfall duration. The average relative RCV (ARRCV) index computed for hydrological component (e.g., base flow, loss, or unit hydrograph) indicates the highest impact of rainfall depth on flood volume and peak. The results indicate that rainfall depth is the main source of uncertainty of flood peak and volume.
Highlights
Reliable estimation of flood characteristics is essential for flood mitigation planning and designing of urban hydraulic structures [1,2,3,4,5]
E prediction of floods using rainfall-runoff models are associated with uncertainty due to the uncertainty in input variables, model parameters, and model structure [3, 9,10,11,12,13,14]. e frequency analysis approach is generally used for the estimation of the probable maximum flood (PMF) from the time series of observed peak discharge [15]. e inherent uncertainty of floods is considered as the primary source of uncertainty in such procedure [16,17,18]
E validity of flood hydrograph generation methodology is investigated by comparing observed flood volume and peak discharge within the significant band (90% confidence interval) of generated hydrographs (Figure 3). e results reveal that flood volumes and peak discharges of all observed flood hydrographs are within the significant band of generated flood hydrographs
Summary
Reliable estimation of flood characteristics is essential for flood mitigation planning and designing of urban hydraulic structures [1,2,3,4,5]. E frequency analysis approach is generally used for the estimation of the probable maximum flood (PMF) from the time series of observed peak discharge [15]. A flood occurs for a probable maximum precipitation (PMP) event in a catchment having a favourable hydraulic condition such as saturated soil moisture condition [19]. E PMP is estimated by fitting probability distribution function (PDF) to annual maximum precipitation (AMP) series. E extreme value distributions such as log Pearson, log normal, GEV, and Gumbel are commonly used for fitting AMP time series [21]. Various methods are used for the estimation of fitted PDF parameters such as L-moments, maximum likelihood estimator, generalized maximum likelihood, Bayesian methods, probability weighted moments, least square, and many others [23,24,25]. Estimated PDF parameter values vary significantly when different methods are used for the estimation of PDF parameters [26]
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