Flood Frequency Estimation Research Articles

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

Regional Flood Frequency Analysis using Soft Computing Techniques

For design of various types of hydraulic structures as well as for taking different flood management measures flood frequency estimates are required. Regional flood frequency analysis is carried out employing L-moments and soft computing techniques viz. artificial neural network (ANN) and fuzzy inference system (FIS) for the lower Godavari subzone 3(f) of India. The study area covers an areal extent of 174,201 km2 and annual maximum peak flood data of 17 catchments ranging in size from 35 to 824 km2 are used. The data screening is carried out employing L-moments based Discordancy measure (Di) and regional homogeneity is examined based on the heterogeneity measure (H). On the basis of the L-moment ratio diagram and Zidist –statistic criteria, Pearson Type III (PE3) distribution is chosen as the suitable frequency distribution for the region. For the region under study, a relationship is developed between mean annual maximum peak flood and area of the catchment using the Levenberg-Marquardt (LM) iteration and the same is coupled with the PE3 based regional flood frequency relationship developed for estimation of floods of various frequencies for the ungauged catchments of the region. The regional flood frequency relationships developed based on L-moments and soft computing techniques are compared.

Reassessing flood frequency for the Sussex Ouse, Lewes: the inclusion of historical flood information since AD 1650

Abstract. The application of historical flood information as a tool for augmenting instrumental flood data is increasingly recognised as a valuable tool. Most previous studies have focused on large catchments with historic settlements, this paper applies the approach to the smaller lowland system of the Sussex Ouse in southeast England. The reassessment of flood risk on the Sussex Ouse is pertinent in light of the severe flooding in October 2000 and heightened concerns of a perceived increase in flooding nationally. Systematic flood level readings from 1960 and accounts detailing past flood events within the catchment are compiled back to ca. 1750. This extended flood record provides an opportunity to reassess estimates of flood frequency over a timescale not normally possible within flood frequency analysis. This paper re-evaluates flood frequency at Lewes on the Sussex Ouse downstream of the confluence of the Sussex Ouse and River Uck. The paper considers the strengths and weaknesses in estimates resulting from contrasting methods of analysis and their corresponding data: (i) single site analysis of gauged annual maxima; (ii) combined analysis of systematic annual maxima augmented with historical peaks of estimated magnitude; (iii) combined analysis of systematic annual maxima augmented with historical peaks of estimated magnitude exceeding a known threshold, and (iv) sensitivity analysis including only the very largest historical flood events. Use of the historical information was found to yield much tighter confidence intervals of risk estimates, with uncertainty reduced by up to 40% for the 100-year return frequency event when historical information was added to the gauged data.

Methodology for flood frequency estimations in small catchments

Abstract. Estimations of flood frequencies in small catchments are difficult due to a lack of measured discharge data. This problem is usually solved in the Czech Republic by hydrologic modelling when there is a reason not to use the data provided by the Czech hydrometeorological institute, which are quite expensive and have a very low level of accuracy. Another way is to use a simple method which provides sufficient estimates of flood frequency based on the available spatial data. A new methodology is being developed considering all important factors affecting flood formation in small catchments. The relationship between catchment descriptors and flood characteristics has been analysed first to get an overview of the importance of each considered descriptor. The results for different descriptors vary from a highly correlated relationship of an expected shape to a relationship which is opposite to that expected, mainly in the case of land use. The parameterisation of the methodology is also presented, including the sensitivity tests on each involved catchment descriptor and cross-validation of achieved results. In its present form, the methodology achieves an Radj2 value of about 0.61 for 10- and 0.60 for 100-year return periods.

Flood frequency under the influence of trends in the Pearl River basin, China: changing patterns, causes and implications

AbstractUsing a nonstationary flood frequency model, this study investigates the impact of trends on the estimation of flood frequencies and flood magnification factors. Analysis of annual peak streamflow data from 28 hydrological stations across the Pearl River basin, China, shows that: (1) northeast parts of the West and the North River basins are dominated by increasing annual peak streamflow, whereas decreasing trends of annual peak streamflow are prevailing in other regions of the Pearl River basin; (2) trends significantly impact the estimation of flood frequencies. The changing frequency of the same flood magnitude is related to the changing magnitude or significance/insignificance of trends, larger increasing frequency can be detected for stations with significant increasing trends of annual peak streamflow and vice versa, and smaller increasing magnitude for stations with not significant increasing annual peak streamflow, pointing to the critical impact of trends on estimation of flood frequencies; (3) larger‐than‐1 flood magnification factors are observed mainly in the northeast parts of the West River basin and in the North River basin, implying magnifying flood processes in these regions and a higher flood risk in comparison with design flood‐control standards; and (4) changes in hydrological extremes result from the integrated influence of human activities and climate change. Generally, magnifying flood regimes in the northeast Pearl River basin and in the North River basin are mainly the result of intensifying precipitation regime; smaller‐than‐1 flood magnification factors along the mainstream of the West River basin and also in the East River basin are the result of hydrological regulations of water reservoirs. Copyright © 2014 John Wiley & Sons, Ltd.

Streamflow simulation methods for ungauged and poorly gauged watersheds

Abstract. Rainfall–runoff modelling procedures for ungauged and poorly gauged watersheds are developed in this study. A well-established hydrological model, the University of British Columbia (UBC) watershed model, is selected and applied in five different river basins located in Canada, Cyprus, and Pakistan. Catchments from cold, temperate, continental, and semiarid climate zones are included to demonstrate the procedures developed. Two methodologies for streamflow modelling are proposed and analysed. The first method uses the UBC watershed model with a universal set of parameters for water allocation and flow routing, and precipitation gradients estimated from the available annual precipitation data as well as from regional information on the distribution of orographic precipitation. This method is proposed for watersheds without streamflow gauge data and limited meteorological station data. The second hybrid method proposes the coupling of UBC watershed model with artificial neural networks (ANNs) and is intended for use in poorly gauged watersheds which have limited streamflow measurements. The two proposed methods have been applied to five mountainous watersheds with largely varying climatic, physiographic, and hydrological characteristics. The evaluation of the applied methods is based on the combination of graphical results, statistical evaluation metrics, and normalized goodness-of-fit statistics. The results show that the first method satisfactorily simulates the observed hydrograph assuming that the basins are ungauged. When limited streamflow measurements are available, the coupling of ANNs with the regional, non-calibrated UBC flow model components is considered a successful alternative method to the conventional calibration of a hydrological model based on the evaluation criteria employed for streamflow modelling and flood frequency estimation.

Regional flood hydrology in a semi-arid catchment using a GLS regression model

Summary The regional flood frequency hydrology of the 86,000 km2 and semi-arid Ebro catchment is investigated using an extended generalised least square model that includes separate descriptions for sampling errors and model errors. The Ebro catchment is characterised by large hydro-climatic heterogeneities among sub-regions. However, differences in flood processes among sites are better explained by a set of new catchment descriptors introduced into hydrological regression models, such as new characteristics derived from the slope of flow duration curves, the ratio of mean annual precipitation to extreme precipitations and the aridity index. These additions enabled a more direct link to be established between the general flow regime and the extreme flood characteristics through-out the entire catchment. The new regression models developed in this study were compared to a set of existing models recommended for flood frequency estimation in Spain. It was found that the generalised least squares model developed in this study improves the existing ordinary least squares models both at regional and trans-regional scales. An adequate description of flood processes is obtained and, as a direct consequence, more reliable flood predictions in ungauged catchments are achieved.

Magnitude Frequency Analysis of Small Floods Using the Annual and Partial Series

Flood frequency analysis using partial series data has been shown to provide better estimates of small to medium magnitude flood events than the annual series, but the annual series is more often employed due to its simplicity. Where partial series average recurrence intervals are required, annual series values are often “converted” to partial series values using the Langbein equation, regardless of whether the statistical assumptions behind the equation are fulfilled. This study uses data from Northern Tasmanian stream-gauging stations to make empirical comparisons between annual series and partial flood frequency estimates and values provided by the Langbein equation. At T = 1.1 years annual series estimates were found to be one third the magnitude of partial series estimates, while Langbein adjusted estimates were three quarters the magnitude of partial series estimates. The three methods converged as average recurrence interval increased until there was no significant difference between the different methods at T = 5 years. These results suggest that while the Langbein equation reduces the differences between the quantile estimates of annual maxima derived from annual maxima series and partial duration series flood frequency estimates, it does not provide a suitable alternative method to using partial series data. These results have significance for the practical estimation of the magnitude-frequency of small floods.

Improving flood frequency analysis with more information

Flood frequency estimates can be improved by combining local flood data with three additional types of information, Viglione et al. show. The authors combined local flood data with temporal information on historic floods, spatial information on floods in nearby catchments, and causal information on the flood process. Previous studies combined flood data with only one additional type of information.

Flood frequency estimation using a joint probability approach within a Monte Carlo framework

Event-based methods are used in flood estimation to obtain the entire flood hydrograph. Previously, such methods adopted in the UK have relied on pre-determined values of the input variables (e.g. rainfall and antecedent conditions) to a rainfall–runoff model, which is expected to result in an output flood of a particular return period. In contrast, this paper presents a method that allows all the input variables to take on values across the full range of their individual distributions. These values are then brought together in all possible combinations as input to an event-based rainfall–runoff model in a Monte Carlo simulation approach. Further, this simulation strategy produces a long string of events (on average 10 per year), where dependencies from one event to the next, as well as between different variables within a single event, are accounted for. Frequency analysis is then applied to the annual maximum peak flows and flow volumes. Citation Svensson, C., Kjeldsen, T.R., and Jones, D.A., 2013. Flood frequency estimation using a joint probability approach within a Monte Carlo framework. Hydrological Sciences Journal, 58 (1), 1–20.

Estimating the flood frequency distribution at seasonal and annual time scales

Abstract. We propose an original approach to infer the flood frequency distribution at seasonal and annual time scale. Our purpose is to estimate the peak flow that is expected for an assigned return period T, independently of the season in which it occurs (i.e. annual flood frequency regime), as well as in different selected sub-yearly periods (i.e. seasonal flood frequency regime). While a huge literature exists on annual flood frequency analysis, few studies have focused on the estimation of seasonal flood frequencies despite the relevance of the issue, for instance when scheduling along the months of the year the construction phases of river engineering works directly interacting with the active river bed, like for instance dams. An approximate method for joint frequency analysis is presented here that guarantees consistency between fitted annual and seasonal distributions, i.e. the annual cumulative distribution is the product of the seasonal cumulative distribution functions, under the assumption of independence among floods in different seasons. In our method the parameters of the seasonal frequency distributions are fitted by maximising an objective function that accounts for the likelihoods of both seasonal and annual peaks. In contrast to previous studies, our procedure is conceived to allow the users to introduce subjective weights to the components of the objective function in order to emphasize the fitting of specific seasons or of the annual peak flow distribution. An application to the time series of the Blue Nile daily flows at the Sudan–Ethiopia border is presented.

An elusive search for regional flood frequency estimates in the River Nile basin

Abstract. Estimation of peak flow quantiles in ungauged catchments is a challenge often faced by water professionals in many parts of the world. Approaches to address such problem exist, but widely used techniques such as flood frequency regionalisation is often not subjected to performance evaluation. In this study, the jack-knifing principle is used to assess the performance of the flood frequency regionalisation in the complex and data-scarce River Nile basin by examining the error (regionalisation error) between locally and regionally estimated peak flow quantiles for different return periods (QT). Agglomerative hierarchical clustering based algorithms were used to search for regions with similar hydrological characteristics. Hydrological data employed were from 180 gauged catchments and several physical characteristics in order to regionalise 365 identified catchments. The Generalised Extreme Value (GEV) distribution, selected using L-moment based approach, was used to construct regional growth curves from which peak flow growth factors could be derived and mapped through interpolation. Inside each region, variations in at-site flood frequency distribution were modelled by regression of the mean annual maximum peak flow (MAF) versus catchment area. The results showed that the performance of the regionalisation is heavily dependent on the historical flow record length and the similarity of the hydrological characteristics inside the regions. The flood frequency regionalisation of the River Nile basin can be improved if sufficient flow data of longer record length of at least 40 yr become available.

A hydrological assessment of the November 2009 floods in Cumbria, UK

In November 2009, record-breaking rainfall resulted in severe, damaging flooding in Cumbria, in the north-west of England. This paper presents an analysis of the river flows and lake levels experienced during the event. Comparison with previous maxima shows the exceptional nature of this event, with new maximum flows being established at 17 river flow gauging stations, particularly on catchments influenced by lakes. The return periods of the flood peaks are estimated using the latest Flood Estimation Handbook statistical procedures. Results demonstrate that the event has had a considerable impact on estimates of flood frequency and associated uncertainty. Analysis of lake levels suggests that their record high levels reduced their attenuating effect, significantly affecting the timing and magnitude of downstream peaks. The peak flow estimate of 700 m3s–1 at Workington, the lowest station on the Derwent, was examined in the context of upstream inputs and was found to be plausible. The results of this study have important implications for the future development of flood frequency estimation methods for the UK. It is recommended that further research is undertaken on the role of abnormally elevated lake levels and that flood frequency estimation procedures in lake-influenced catchments are reviewed.

Discussion on “Flood Frequency and Design Flood Estimation Procedures in the United States: Progress And Challenges” By JF England, Jr

Discussion on “Flood Frequency and Design Flood Estimation Procedures in the United States: Progress And Challenges” By JF England, Jr

Uncertainty evaluation of design rainfall for urban flood risk analysis

A reliable and long dataset describing urban flood locations, volumes and depths would be an ideal prerequisite for assessing flood frequency distributions. However, data are often piecemeal and long-term hydraulic modelling is often adopted to estimate floods from historical rainfall series. Long-term modelling approaches are time- and resource-consuming, and synthetically designed rainfalls are often used to estimate flood frequencies. The present paper aims to assess the uncertainty of such an approach and for suggesting improvements in the definition of synthetic rainfall data for flooding frequency analysis. According to this aim, a multivariate statistical analysis based on a copula method was applied to rainfall features (total depth, duration and maximum intensity) to generate synthetic rainfalls that are more consistent with historical events. The procedure was applied to a real case study, and the results were compared with those obtained by simulating other typical synthetic rainfall events linked to intensity-duration-frequency (IDF) curves. The copula-based multi-variate analysis is more robust and adapts well to experimental flood locations even if it is more complex and time-consuming. This study demonstrates that statistical correlations amongst rainfall frequency, duration, volume and peak intensity can partially explain the weak reliability of flood-frequency analyses based on synthetic rainfall events.

Flood seasonality‐based regionalization methods: a data‐based comparison

AbstractImproving techniques of flood frequency estimation at ungauged catchments is one of the major challenges for hydrologists, especially in arid and semi‐arid regions with insufficient information. Recently, popularity of flood seasonality‐based descriptors has increased among hydrologists for delineating of hydrologically homogenous regions.This study presents a data‐based comparison of three well‐known flood seasonality‐based regionalization methods in Halilrud basin in southeastern Iran. A Jack‐knife procedure is used to assess the performance of the methods for flood quantile estimation at ungauged sites. The results of these three seasonality‐based methods are compared to those results obtained from two alternative methods: a traditional regionalization method based on catchment's hydrogeomorphic characteristics similarity and a scenario that uses all available information without subdividing area.The results indicate that although peak over threshold (POT) approach which uses all flood events during year leads to better performance than other methods, but applying POT data series in a specific season (critical season) based on POT‐CS approach leads to better homogenous region and improves flood quantile estimation at ungauged sites. Copyright © 2011 John Wiley & Sons, Ltd.

On the tails of distributions of annual peak flow

This study discusses an application of heavy-tailed distributions to modelling of annual peak flows in general and of Polish data sets in particular. One- and two-shape parameter heavy-tailed distributions are obtained by transformations of random variables. The correct selection of a flood frequency model with emphasis on heavy-tailed distribution discrimination is then discussed. If a distribution is wrongly assumed, the error, in the upper quantile, arising as a result, depends on the method of parameter estimation and is shown analytically for three methods. Asymptotic and sampling values (got by simulation) were assessed for the pair log-Gumbel (LG) as a false distribution and log-normal (LN) as a true distribution. Comparing the upper quantiles of various distributions with the same values of moments, it is found that heavy-tailed distributions do not consistently provide higher flood frequency estimates than do soft-tailed distributions. Based on L-moment ratio diagrams and the test of linearity on log–log plots, it is concluded that Polish datasets of annual peak flows should be modelled using soft-tailed distributions, such as the three-parameter Inverse Gaussian, rather than heavy-tailed distributions.

Distributed rainfall‐runoff modelling for flood frequency estimation and flood forecasting

AbstractNowadays, in the scientific literature many rainfall‐runoff (RR) models are available ranging from simpler ones, with a limited number of parameters, to highly complex ones, with many parameters. Therefore, the selection of the best structure and parameterisation for a model is not straightforward as it is dependent on a number of factors: climatic conditions, catchment characteristics, temporal and spatial resolution, model objectives, etc. In this study, the structure of a continuous semi‐distributed RR model, named MISDc (‘Modello Idrologico Semi‐Distribuito in continuo’) developed for flood simulation in the Upper Tiber River (central Italy) is presented. Most notably, the methodology employed to detect the more relevant processes involved in the modelling of high floods, and hence, to build the model structure and its parameters, is developed. For this purpose, an intense activity of monitoring soil moisture and runoff in experimental catchments was carried out allowing to derive a parsimonious and reliable continuous RR model operating at an hourly (or smaller) time scale. Specifically, in order to determine the catchment hydrological response, the important role of the antecedent wetness conditions is emphasized. The application of MISDc both for design flood estimation and for flood forecasting is reported here demonstrating its reliability and also its computational efficiency, another important factor in hydrological practice. As far as the flood forecasting applications are concerned, only the accuracy of the model in reproducing discharge hydrographs by assuming rainfall correctly known throughout the event is investigated indepth. In particular, the MISDc has been implemented in the framework of Civil Protection activities for the Upper Tiber River basin. Copyright © 2011 John Wiley & Sons, Ltd.

Flood Frequency and Design Flood Estimation Procedures in the United States: Progress and Challenges

Design flood estimation procedures in the United States have traditionally focused on two primary methods: frequency analysis of peak flows for floodplain management and levee design; and deterministic, probable maximum flood (PMF) estimates for design of dams and nuclear facilities. Federal Agencies in the United States, including the Bureau of Reclamation, US Geological Survey and Army Corps of Engineers, are currently examining potential changes to these standard flood hydrology procedures. This paper presents overviews of some ongoing investigations and data collection studies to support potential changes in design flood estimation. For floodplain management, the current guideline is Bulletin 17B, which specifies the use of an LP3 distribution, method of moments and regional skew information. Potential improvements to Bulletin 17B currently under consideration are: (i) use of historical and paleoflood information; (ii) adjusting for low outliers; (iii) improved plotting positions; and (iv) confidence intervals. Ongoing testing results are presented, highlighting the expected moments algorithm. In contrast to well-established, deterministic (PMF) extreme flood estimates for dam safety, agencies are now moving toward risk-based techniques. The Bureau of Reclamation has developed and applied several methods in order to estimate extreme floods and probabilities for large dams. Techniques used to date are summarised, along with those being considered by other US agencies. Improvements to extreme flood databases that provide inputs, including extreme storms and probable maximum precipitation estimates, precipitation frequency and paleofloods, are ongoing. Some challenges to updating design flood methods and data, including institutional effects, national scale, research to operations and use of new technologies, are described.

Estimation of Major Floods: Applicability of a Simple Probabilistic Model

Estimation of major flood flows is needed in the design and operation of large water infrastructure. This paper presents a simple probabilistic model (PM) that can be used to derive “easy to apply” prediction equations for estimation of major flood flows. The proposed method assumes that the maximum observed flood data over a large number of sites in a region can be pooled together by accounting for the across-site variations in the mean and standard deviation values. The method is developed and tested in this paper using data from 227 catchments across Victoria and NSW. The application to ungauged catchments involves the development of prediction equations using generalised least squares regression for the mean and coefficient of variation of the annual maximum flood series as a function of catchment characteristics. An independent test shows that the PM provides quite reasonable design flood estimation in the study area for average recurrence intervals in the range of 20 to 200 years, with median relative error values (compared to at-site flood frequency estimates) in the range of 10% to 35%. The method is under further development, eg. consideration of the inter-station correlation in pooling the standardised data from the nearby stations and comparison of results with the rainfall-based methods. The method has the potential to derive regional flood prediction equations for major floods by pooling the flood data from all the Australian states.