Index Flood Estimation Research Articles

Estimating the UK Index Flood: an Improved Spatial Flooding Analysis

Flooding is one of the major natural hazards in the UK. Accurate flood estimation at ungauged catchment is an important component to understand and mitigate flood hazards, but still a difficult issue. This study therefore attempts to explore and improve an index flood estimation model, known as the FEH-QMED model, popular in the UK. It was developed under the assumption that the index flood of QMED, i.e., the median of the set of annual maximum (AMAX) flood data, standing for a flooding level of 2-year return period, can be explained by catchment descriptors. In this study, two fundamentals are empirically explored, including assessing reliability of the nonlinear functional impacts of the catchment descriptors on the logarithmic transformation of QMED, specified by the FEH-QMED model, and the potential to improve the model for more accurate index flood estimation, based on the flooding data of 586 gauged stations across the UK. Through a spatial additive regression analysis, we empirically find that the nonlinear impacts of the catchment descriptors in an updated FEH-QMED model appear reliable. However, spatial correlation tests including Moran’s I and Lagrange multiplier tests show that strong spatial dependence exists in the residuals of, but was not fully taken into account by, the QMED type models. We have therefore empirically established new spatial index flood estimation models by proposing spatial autoregressive models to model the impacts of the neighboring sites. Cross-validation assessments demonstrate that the suggested spatial error-based index flood model outperforms the updated FEH-QMED model with a significant improvement, which is robust in the sense of different error measures, say by a reduction of 13.8% of the mean squared error of prediction, for the UK index flood estimation.

Continuous streamflow simulation for index flood estimation in an Alpine basin of northern Italy

Flood frequency estimation is crucial in both engineering practice and hydrological research. Regional analysis of flood peak discharges is used for more accurate estimates of flood quantiles in ungauged or poorly gauged catchments. This is based on the identification of homogeneous zones, where the probability distribution of annual maximum peak flows is invariant, except for a scale factor represented by an index flood. The numerous applications of this method have highlighted obtaining accurate estimates of index flood as a critical step, especially in ungauged or poorly gauged sections, where direct estimation by sample mean of annual flood series (AFS) is not possible, or inaccurate. Therein indirect methods have to be used. Most indirect methods are based upon empirical relationships that link index flood to hydrological, climatological and morphological catchment characteristics, developed by means of multi-regression analysis, or simplified lumped representation of rainfall–runoff processes. The limits of these approaches are increasingly evident as the size and spatial variability of the catchment increases. In these cases, the use of a spatially-distributed, physically-based hydrological model, and time continuous simulation of discharge can improve estimation of the index flood. This work presents an application of the FEST-WB model for the reconstruction of 29 years of hourly streamflows for an Alpine snow-fed catchment in northern Italy, to be used for index flood estimation. To extend the length of the simulated discharge time series, meteorological forcings given by daily precipitation and temperature at ground automatic weather stations are disaggregated hourly, and then fed to FEST-WB. The accuracy of the method in estimating index flood depending upon length of the simulated series is discussed, and suggestions for use of the methodology provided.Editor D. Koutsoyiannis

Clustering Hydrological Homogeneous Regions and Neural Network Based Index Flood Estimation for Ungauged Catchments: an Example of the Chindwin River in Myanmar

A neural network-based regionalization approach using catchment descriptors was proposed for flood management of ungauged catchments in a developing country with low density of the hydrometric network. Through the example of the Chindwin River basin in Myanmar, the study presents the application of principal components and clustering techniques for detecting hydrological homogeneous regions, and the artificial neural network (ANN) approach for regional index flood estimation. Based on catchment physiographic and climatic attributes, the principal component analysis yields three component solutions with 79.2 % cumulative variance. The Ward’s method was used to search initial cluster numbers prior to k-means clustering, which then objectively classifies the entire catchment into four homogeneous groups. For each homogeneous region clustered by the leading principal components, the regional index flood models are developed via the ANN and regression methods based on the longest flow path, basin elevation, basin slope, soil conservation curve number and mean annual rainfall. The ANN approach captures the nonlinear relationships between the index floods and the catchment descriptors for each cluster, showing its superiority towards the conventional regression method. The results would contribute to national water resources planning and management in Myanmar as well as in other similar regions.

Spatial prediction of the runoff coefficient in Southern Peninsular Italy for the index flood estimation

The runoff coefficient φ is a crucial parameter for the estimation of the mean value of annual maximum flood peak discharges in ungauged watersheds, where no direct measures are available. If the rational method is applied as a rainfall–runoff transformation model, the runoff coefficient accounts for all the hydrological losses, and it can be conceptually defined as the fraction of the total rainfall contributing to the flood peak response. In the present paper, focusing on Southern Peninsular Italy, a regression model is proposed to improve the prediction of the above defined runoff coefficient as a function of several parameters describing both morphological and mean annual climatic watershed characteristics. Morphological features are described by using the Soil Conservation Service permeability classification and the related variable S, referred to an average antecedent moisture condition. Different climatic indices enable a subsequent enhancement of S in order to account for the specific mean annual moisture condition of each watershed.

Bankfull discharge and recurrence intervals in Irish rivers

Different definitions of the bankfull condition in rivers are based on morphological characteristics, boundary conditions and geometrical properties. Consequently, the magnitude and associated return period of the bankfull discharge can be ambiguous. Knowledge of this discharge is important in index flood estimation and subsequent regional flood frequency analysis. This study investigates bankfull discharges and recurrence intervals at 88 locations in the Irish river network using a combination of surveyed bankfull levels, rating curves and equations and photographic records at the sites in question. Catchments ranged in area from approximately 23 km2 to 2778 km2. Recurrence intervals were determined by fitting generalised extreme value (GEV) distributions to the annual maximum flow series at the sites investigated. These intervals were found to be less than 2 years (the median annual flood) at 42 stations (48%) and less than 2·33 years (the mean annual flood assuming a GEV type 1 distribution) at 47 stations (53%). Higher return periods of between 2·33 and 10 years and 10 and 25 years were observed at a further 20% and 6% of locations respectively. Using multivariate regression analysis, the computed bankfull discharges are correlated with catchment descriptors and three expressions are presented for estimating bankfull flows.

Indirect Estimation of Design Flood in Urbanized River Basins Using a Distributed Hydrological Model

AbstractA method for indirect estimation of design flood in poorly gauged urbanized river basins by using distributed hydrological modelling is presented. It is based on the critical flood design criterion that maximizes peak flow for a given return period by transforming precipitation of depth duration frequency curve into runoff. The indirect method is compared against the direct method based on regional approach and index flood estimation. This paper shows that the direct method significantly underestimates design flood in case discharge measurements are strongly affected by artificial alteration of water courses with possible catastrophic consequences in terms of loss of life and damage to property when the cause of alteration would be removed. Moreover, the use of a spatially distributed model allows taking into account the heterogeneity that generally characterizes river basins with an high degree of urbanization. The presented indirect method provides a design hydrograph that is useful for those ca...

Uncertainty in index flood modelling due to calibration data sizes

AbstractRegional frequency analysis is an important tool in estimating design flood for ungauged catchments. Index flood is an important component in regionalized flood formulas. In the past, many formulas have been developed based on various numbers of calibration catchments (e.g. from less than 20 to several hundred). However, there is a lack of systematic research on the model uncertainties caused by the number of calibration catchments (i.e. what is the minimum number of calibration catchment? and how should we choose the calibration catchments?). This study uses the statistical resampling technique to explore the impact of calibration catchment numbers on the index flood estimation. The study is based on 182 catchments in England and an index flood formula has been developed using the input variable selection technique in the data mining field. The formula has been used to explore the model uncertainty due to a range of calibration catchment numbers (from 15 to 130). It is found that (1) as expected, the more catchments are used in the calibration, the more reliable of the models developed are (i.e. with a narrower band of uncertainty); (2) however, poor models are still possible with a large number of calibration catchments (e.g. 130). In contrast, good models with a small number of calibration catchments are also achievable (with as low as 15 calibration catchments). This indicates that the number of calibration catchments is only one of the factors influencing the model performance. The hydrological community should explore why a smaller calibration data set could produce a better model than a large calibration data set. It is clear from this study that the information content in the calibration data set is equally if not more important than the number of calibration data. Copyright © 2011 John Wiley & Sons, Ltd.

Review of recent advances in index flood estimation

Abstract. Index flood estimation for regional flood frequency analysis needs to be based on the information available. The most appropriate method depends on the specific application and its choice requires a problem-oriented analysis. This paper presents a simple theoretical framework to deal with index flood estimation for a specific river site. The methodological approaches available for the purpose are reviewed. For each, the information required is specified and the reliability of the estimate, particularly desirable in risk analysis and management, is discussed. Where flood observations are lacking, indirect estimation must be undertaken using scenarios including those commonly met in hydrological practice; generally, these depend on the amount and type of information available. For each scenario, the methodologies are outlined, in order of the expected degree of complexity. After a guided analysis, an investigator can adopt the method providing the best tradeoff between effort in collecting and handling data and the resultant reliability which can be expected. Keywords: direct and indirect methods, index flood estimation, reliability, scenarios.

Regional flood estimation for ungauged basins in Sarawak, Malaysia

Design flood estimation is an important task that is required in the planning and design of many civil engineering projects. In this study, the flood records of more than 23 gauged river basins in Sarawak, Malaysia, are examined using an index-flood estimation procedure based on L-moments. Two homogeneous regions were identified and the Generalized Extreme Value and the Generalized Logistic distributions are found to describe the distribution of extreme flood events appropriately within the respective regions. A regional growth curve is subsequently developed for each of the regions. These curves can be used for the estimation of design floods in ungauged basins in Sarawak within the limitations identified for the method. The results presented herein are useful for practicing engineers in Sarawak while the general methodology may be used in any other regions, provided flood records are available.

A comparison of index flood estimation procedures for ungauged catchments

Flood frequency analysis is used by water resources professionals to estimate the probability of exceedence associated with a flood of a given magnitude. The estimation of flood frequencies is important because they are used in the planning and design of hydraulic structures, in flood-plain management, and in reservoir operation. The index flood method is commonly used to develop a flood frequency curve that relates flood magnitude to flood rarity. This method involves scaling a dimensionless flood frequency curve by the index flood. The index flood is a middle-sized flood for which the mean or median of the flood data series is typically used. When the catchment of interest is ungauged, statistical models, such as multiple regression, are often used to relate the index flood to catchment descriptors. In this study six different parameter estimation techniques and three regionalization techniques are compared in terms of ability to predict the index flood for an ungauged catchment. A case study employing a split-sample experiment with data from catchments in Ontario, Canada, was used to evaluate the approaches. The models were assessed using three performance indices to evaluate the capability to predict the index flood for 20 stations. The dimensionless nonlinear model outperformed all of the other parameter estimation techniques for each of the three indices selected. The performance was improved through the use of geostatistical residual mapping, however, the improvement was small. The residual mapping was found to greatly improve the estimates obtained using ordinary least-squares regression.Key words: index flood, flood frequency analysis, regression, residual mapping, geostatistics.

Comparison of regional index flood estimation procedures based on the extreme value type I distribution

A comparison of different methods for estimating T-year events is presented, all based on the Extreme Value Type I distribution. Series of annual maximum flood from ten gauging stations at the New Zealand South Island have been used. Different methods of predicting the 100-year event and the connected uncertainty have been applied: At-site estimation and regional index-flood estimation with and without accounting for intersite correlation using either the method of moments or the method of probability weighted moments for parameter estimation. Furthermore, estimation at ungauged sites were considered applying either a log-linear relationship between at-site mean annual flood and catchment characteristics or a direct log-linear relationship between 100-year events and catchment characteristics. Comparison of the results shows that the existence of at-site measurements significantly diminishes the prediction uncertainty and that the presence of intersite correlation tends to increase the uncertainty. A simulation study revealed that in regional index-flood estimation the method of probability weighted moments is preferable to method of moment estimation with regard to bias and RMSE.

Estimating the index flood using indirect methods

Three indirect techniques for index flood estimation are analysed in order to evaluate their applicability and effectiveness. These indirect techniques, based on both statistical and conceptual approaches, are applied to a set of 33 hydrometric stations, located in a large area in northern-central Italy. The results show that the statistical model, due to its flexible structure, has a better descriptive ability than the physically-based models, which are rigidly structured as they conceptualize the rainfall-runoff transformation. However, the rigid structure of the conceptual approaches reduces their dependence on the specific information of the single stations and therefore increases their robustness. Finally, the results highlight that direct estimation techniques could be advisable for catchments with peculiar geomorphoclimatic properties; that is to say properties which differ substantively from those of the majority of the basins considered in the identification of the indirect models. This conclusion seems to hold even when a very limited amount of hydrometric information is available.