Effects of Probability-Distributed Losses on Flood Estimates Using Event-Based Rainfall-Runoff Models

Melanie Loveridge,Ataur Rahman

doi:10.3390/w13152049

Abstract

Probability distributions of initial losses are investigated using a large dataset of catchments throughout Australia. The variability in design flood estimates caused by probability-distributed initial losses and associated uncertainties are investigated. Based on historic data sets in Australia, the Gamma and Beta distributions are found to be suitable for describing initial loss data. It has also been found that the central tendency of probability-distributed initial loss is more important in design flood estimation than the form of the probability density function. Findings from this study have notable implications on the regionalization of initial loss data, which is required for the application of Monte Carlo methods for design flood estimation in ungauged catchments.

Highlights

In rainfall-runoff modeling, loss parameter is one of the most important parameters, which refers to amount of rainfall that does not appear at the stream directly, which mainly consists of infiltration, evapotranspiration, interception, depression storage, and transmission losses
Several loss models have been proposed for use with event-based rainfall-runoff models, such as the initial loss-continuing loss (IL-CL) model, the Soil Conservation Service (SCS) Curve Number model and probability-distributed model (PDM) [1]
Several hypothetical probability distributions have been compared with regards to their suitability in representing the true underlying initial loss distribution

Summary

Introduction

In rainfall-runoff modeling, loss parameter is one of the most important parameters, which refers to amount of rainfall that does not appear at the stream directly, which mainly consists of infiltration, evapotranspiration, interception, depression storage, and transmission losses. Several loss models have been proposed for use with event-based rainfall-runoff models, such as the initial loss-continuing loss (IL-CL) model, the Soil Conservation Service (SCS) Curve Number model and probability-distributed model (PDM) [1]. Joint probability approaches [12] are often used to incorporate the natural variability of antecedent moisture conditions in a catchment. This has been considered in several studies based on various loss models including the SCS Curve Number method [8,13,14], PDM [15] and the Green–Ampt infiltration equation [16]. Losses are assumed to be a random variable and is generally specified by a probability distribution

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