Modelling hydrological losses for varying rainfall and moisture conditions in South Australian catchments

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• 作者：S.H.P.W. Gamage^a ; ^{sithara.walpitagamage@unisa.edu.au" class="auth_mail" title="E-mail the corresponding author} ; G.A. Hewa^a ; S. Beecham^a ; ^b
• 关键词：ARR ; Australian Rainfall and Runoff ; APIk ; antecedent precipitation index ; AW ; antecedent wetness ; CL ; continuing loss ; D ; storm duration ; DfW ; Department for Water ; IL ; initial loss ; JPA ; joint probability approach ; PL ; proportional loss ; QF ; quickflow ; RORB ; Run Off Routing Burroughs ; SA ; South Australia ; TR ; total rainfall ; TRV ; total rainfall volume ; TSV ; total surface-runoff volume ; WBNM ; Water Bound Network Model
• 刊名：Journal of Hydrology: Regional Studies
• 出版年：2015
• 出版时间：September 2015
• 年：2015
• 卷：4
• 期：part_PB
• 页码：1-21
• 全文大小：2971 K

文摘

The study is based on unregulated catchments located in Mt. Lofty, Northern and Yorke regions of South Australia (SA).

Study focus

Hydrological losses, which are frequently used in design flood estimation, have a wide range of spatial and temporal variability. However, the current practice for many design applications is to use a single loss value. Adopting a single representative value for loss is likely to introduce a high degree of uncertainty and potential bias. This paper identifies the relationships between losses and other parameters that can be incorporated in hydrological models to make reasonably accurate estimates of the losses. This paper assesses the variability of losses and identifies a method that can model initial loss (IL) using the parameters total rainfall (TR), rainfall duration (D) and antecedent wetness (AW). This study is based on 1162 rainfall events from the selected catchments.

New hydrological insights for the region

This paper introduces two nomographs, TR–D and TR–AW, which are implemented using k-colour maps and a central tendency method. The developed methods are then validated using the rainfall runoff model, Water Bound Network Model (WBNM). This study will yield improvements to existing loss models by utilising rainfall and antecedent data, instead of using representative values to generalise real situations.