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

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2049
Author(s):  
Melanie Loveridge ◽  
Ataur Rahman
Keyword(s):  
Probability Distributions ◽  
Data Sets ◽  
Rainfall Runoff ◽  
Design Flood ◽  
Ungauged Catchments ◽  
Large Dataset ◽  
Loss Data ◽  
Flood Estimation ◽  
Historic Data ◽  
Event Based

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.

The influence of soil moisture conditions on the spatio-temporal variability of event rainfall-runoff coefficients in UK catchments

2021 ◽  
Author(s):  
Yanchen Zheng ◽  
Ross Woods ◽  
Jianzhu Li ◽  
Ping Feng
Keyword(s):  
Soil Moisture ◽  
Temporal Variability ◽  
Estimation Methods ◽  
Runoff Coefficient ◽  
Rainfall Runoff ◽  
Design Flood ◽  
Ungauged Catchments ◽  
Flood Estimation ◽  
Spatio Temporal ◽  
Moisture Conditions

<p>Since the bias and uncertainties of the current design flood estimation methods for ungauged catchments are inevitable, estimation of the design flood in ungauged catchments still remains an unsolved problem. The derived distribution approach appears to be the one of the promising design flood estimation methods, as this method can improve the understanding on which processes contribute most to flood in ungauged catchments. Generally, the distribution of rainfall characteristics and lumped rainfall-runoff modelling was incorporated to estimate the flood magnitude in this method. However, we should note that rainfall is not the only driving factor of flood events. Soil moisture conditions are also an important driving factor affecting the rainfall-runoff transformation, and may even control rainfall-runoff coefficients to a higher degree than does rainfall. Hence, here we perform soil moisture analysis at national scale by employing GLDAS-Noah datasets, and link this to observed event runoff coefficients from a large sample of UK catchments. The relationship between soil moisture conditions and rainfall-runoff coefficient was explored to analyse the spatio-temporal variability of runoff coefficient. This study laid the foundation for further development of a practical derived distribution method, by considering the statistical distribution of rainfall-runoff coefficients and the influence of soil moisture conditions.</p>

scholarly journals Probability distributions for explaining hydrological losses in South Australian catchments

2013 ◽  
Vol 10 (4) ◽  
pp. 4597-4626
Author(s):  
S. H. P. W. Gamage ◽  
G. A. Hewa ◽  
S. Beecham
Keyword(s):  
Goodness Of Fit ◽  
Probability Distributions ◽  
Joint Probability ◽  
Accurate Estimation ◽  
Rainfall Runoff ◽  
Design Flood ◽  
Wide Variability ◽  
Multiple Variables ◽  
Two Parameter ◽  
Non Parametric

Abstract. The wide variability of hydrological losses in catchments is due to multiple variables that affect the rainfall-runoff process. Accurate estimation of hydrological losses is required for making vital decisions in design applications that are based on design rainfall models and rainfall-runoff models. Using representative single values of losses, despite their wide variability, is common practice, especially in Australian studies. This practice leads to issues such as over or under estimation of design floods. Probability distributions can be used as a better representation of losses. In particular, using joint probability approaches (JPA), probability distributions can be incorporated into hydrological loss parameters in design models. However, lack of understanding of loss distributions limits the benefit of using JPA. The aim of this paper is to identify a probability distribution function that can successfully describe hydrological losses in South Australian (SA) catchments. This paper describes suitable parametric and non-parametric distributions that can successfully describe observed loss data. The goodness-of-fit of the fitted distributions and quantification of the errors associated with quantile estimation are also discussed a two-parameter Gamma distribution was identified as one that successfully described initial loss (IL) data of the selected catchments. Also, a non-parametric standardised distribution of losses that describes both IL and continuing loss (CL) data were identified. The results obtained for the non-parametric methods were compared with similar studies carried out in other parts of Australia and a remarkable degree of consistency was observed. The results will be helpful in improving design flood applications.

scholarly journals Regional analysis using the Geomorphologic Instantaneous Unit Hydrograph (GIUH) method

2014 ◽  
Vol 9 (No. 1) ◽  
pp. 25-30 ◽  
Author(s):  
M.R. Khaleghi ◽  
J. Ghodusi ◽  
H. Ahmadi
Keyword(s):  
High Efficiency ◽  
Linear Regression Analysis ◽  
Regional Analysis ◽  
Suitable Model ◽  
Rainfall Runoff ◽  
Unit Hydrograph ◽  
Design Flood ◽  
Ungauged Catchments ◽  
Northern Iran ◽  
Simulation Accuracy

The construction of design flood hydrographs for ungauged drainage areas has traditionally been approached by regionalization, i.e. the transfer of information from the gauged to the ungauged catchments in a region. Such approaches invariably depend upon the use of multiple linear regression analysis to relate unit hydrograph parameters to catchment characteristics and generalized rainfall statistics. In the present study, Geomorphologic Instaneous Unit Hydrograph (GIUH) was applied to simulate the rainfall-runoff process and also to determine the shape and dimensions of outlet runoff hydrographs in a 37.1 km<sup>2</sup> area in the Ammameh catchment, located at northern Iran. The first twenty-one equivalent rainfall-runoff events were selected, and a hydrograph of outlet runoff was calculated for each event. An intercomparison was made for the three applied approaches in order to propose a suitable model approach that is the overall objective of this study. Hence, the time to peak and peak flow of outlet runoff in the models were then compared, and the model that most efficiently estimated hydrograph of outlet flow for similar regions was determined. Statistical analyses of the models demonstrated that the GIUH model had the smallest main relative and square error. The results obtained from the study confirmed the high efficiency of the GIUH and its ability to increase simulation accuracy for runoff and hydrographs. The modified GIUH approach as described is therefore recommended for further investigation and intercomparison with regression-based regionalization methods.

scholarly journals A Method for Estimating the Regional Initial and Constant Loss for Design Flood Estimation in West Peninsular Malaysia

2013 ◽  
Vol 74 (2) ◽  
Author(s):  
Sazali Osman ◽  
Ismail Abustan ◽  
Rozi Abdullah
Keyword(s):  
Peninsular Malaysia ◽  
Flood Frequency ◽  
Percentage Error ◽  
Rainfall Runoff ◽  
Design Flood ◽  
Frequency Method ◽  
Flood Estimation ◽  
Common Technique ◽  
Loss Method ◽  
Regional Design

Floods are known as one of the world’s most frequent and devastating events. Techniques to predict and estimate the size of floods is depend on the availability of hydrological data. Using the conceptual of lump model, rainfall-runoff method is widely used in design flood estimation, which represents the input of rainfall and catchment characteristics such as rainfall depth, rainfall intensity, baseflow and losses. 7o calculate the catchments runoff, amount of losses shall be determine accurately by considering various source of the rainfall losses such as evaporation, inßltration, interception, depression storage and loss in groundwater recharge. In Malaysia, the common technique to estimate the hydrological losses is using initial and constant loss method. Furthermore, the value has been used in Urban Stormwater Manual for Malaysia (USMA) are adopted from the other literatures which is not represented the value from local catchment.7he objective of this study is to derive the initial and constant loss values using the data from selected local catchments in west Peninsular Malaysia. 7he calculated initial and constant loss will be further used to derive design flood discharge based on the design rainfall. An initial loss and constant loss model was examined in this study to observe the loss rate parameters in heterogeneous catchments and evaluate their signißcance as well as their potential influence on design peak floods. 7he study has been utilised the rainfall and runoff data from 113 storms over 15 catchments. 7he loss parameters were obtained from model optimi3ation using the HEC-HMS Modeling program. From the analyses, the median initial loss is 21.54mm with the standard deviation 7.85mm. 7he value shows higher than the value adopted in USMA. Meanwhile, the value for constant loss is 8.07mm which between the range of USMA. Based on the ßndings of design initial loss analyses, the values of initial loss were 49.3, 57.6, 64.1, 69.4, 73.3 and 76.6 for ARI 2,5,10,20,50 and 100, respectively. 7he percentage error between design initial loss and constant loss method and flood frequency method shows good results which are most of the percentage error less than 35%. It shows that the design initial loss and constant loss method produce reasonable accurate results when compared to the rainfall-runoff method and flood frequency method. Based on the ßndings, it can be suggested that the regional design initial loss and the constant loss rates would be able to serve reasonably well in determining catchment loss for the design purposes.

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