Spatial and temporal variability of flood seasonality in Wales

High‐magnitude floods across Europe within the last decade have resulted in the widespread reassessment of flood risk; this coupled with the introduction of the Water Framework Directive (2000) has increased the need for a detailed understanding of seasonal variability in flood magnitude and frequency. Mean day of flood (MDF) and flood seasonality were calculated for Wales using 30 years of gauged river‐flow records (1973–2002). Noticeable regional variations in timing and length of flood season are evident, with flooding occurring earlier in small catchments draining higher elevations in north and mid‐west Wales. Low‐altitude regions in West Wales exposed to westerly winds experience flooding during October–January, while large eastern draining catchments experience later flooding (January–February). In the northeast and mid‐east regions December–January months experience the greatest number of floods, while the southeast has a slightly longer flood season (December–February), with a noticeable increase in January floods. Patterns obtained from MDF data demonstrate their effectiveness and use in analysing regional patterns in flood seasonality, but catchment‐specific determinants, e.g. catchment wetness, size and precipitation regime are important factors in flood seasonality. Relatively strong correlations between precipitation and flood activity are evident in Wales, with a poorer relationship between flooding and weather types and the North Atlantic Oscillation (NAO). Copyright © 2010 John Wiley & Sons, Ltd.     

Performance of flood frequency pooling analysis in a low CV context

Abstract

Pooling of flood data is widely used to provide a framework to estimate design floods by the Index Flood method. Design flood estimation with this approach involves derivation of a growth curve which shows the relationship between X_T and the return period T, where X_T ?=?Q_T /Q_I and Q_I is the index flood at the site of interest. An implicit assumption with the Index Flood procedure of pooling analysis is that the X_T –T relationship is the same at all sites in a homogeneous pooling group, although this assumption would generally be violated to some extent in practical cases, i.e. some degree of heterogeneity exists. In fact, in only some cases is the homogeneity criterion effectively satisfied for Irish conditions. In this paper, the performance of the index-flood pooling analysis is assessed in the Irish low CV (coefficient of variation) hydrology context considering that heterogeneity is taken into account. It is found that the performance of the pooling method is satisfactory provided there are at least 350 station years of data included. Also it is found that, in a highly heterogeneous group, it is more desirable to have many sites with short record lengths than a smaller number of sites with long record lengths. Increased heterogeneity decreases the advantage of pooling group-based estimation over at-site estimation. Only a heterogeneity measure (H1) less than 4.0 can render the pooled estimation preferable to that obtained for at-site estimation for the estimation of 100-year flood. In moderately to highly heterogeneous regions it is preferable to conduct at-site analysis for the estimation of 100-year flood if the record length at the site concerned exceeds 50.

Editor Z.W. Kundzewicz; Associate editor A. Carsteanu

Citation Das, S. and Cunnane, C., 2012. Performance of flood frequency pooling analysis in a low CV context. Hydrological Sciences Journal, 57 (3), 433–444.     

Pooled flood frequency analysis: a comparison based on peaks-over-threshold and annual maximum series

Despite some theoretical advantages of peaks-over-threshold (POT) series over annual maximum (AMAX) series, some practical aspects of flood frequency analysis using AMAX or POT series are still subject to debate. Only minor attention has been given to the POT method in the context of pooled frequency analysis. The objective of this research is to develop a framework to promote the implementation of pooled frequency modelling based on POT series. The framework benefits from a semi-automated threshold selection method. This study introduces a formalized and effective approach to construct homogeneous pooling groups. The proposed framework also offers means to compare the performance of pooled flood estimation based on AMAX or POT series. An application of the framework is presented for a large collection of Canadian catchments. The proposed POT pooling technique generally improved flood quantile estimation in comparison to the AMAX pooling scheme, and achieved smaller uncertainty associated with the quantile estimates.     

Comparison of scenario‐neutral approaches for estimation of climate change impacts on flood characteristics

Scenario‐neutral assessments of climate change impact on floods analyse the sensitivity of a catchment to a range of changes in selected meteorological variables such as temperature and precipitation. The key challenges of the approach are the choice of the meteorological variables and statistics thereof and how to generate time series representing altered climatologies of the selected variables. Different methods have been proposed to achieve this, and it remains unclear if and to which extent they result in comparable flood change projections. Here, we compare projections of annual maximum floods (AMFs) derived from three different scenario‐neutral methods for a prealpine study catchment. The methods chosen use different types of meteorological data, namely, observations, regional climate model output, and weather generator data. The different time series account for projected changes in the seasonality of temperature and precipitation, in the occurrence statistics of precipitation, and of daily precipitation extremes. Resulting change in mean AMF peak magnitudes and volumes differs in sign between the methods (range of ?6% to +7% for flood peak magnitudes and ?11% to +14% for flood volumes). Moreover, variability of projected peak magnitudes and flood volumes depends on method with one approach leading to a generally larger spread. The differences between the methods vary depending on whether peak magnitudes or flood volumes are considered and different relationships between peak magnitude and volume change result. These findings can be linked to differing flood regime changes among the three approaches. The study highlights that considering selected aspects of climate change only when performing scenario‐neutral studies may lead to differing representations of flood generating processes by the approaches and thus different quantifications of flood change. As each method comes with its own strengths and weaknesses, it is recommended to combine several scenario‐neutral approaches to obtain more robust results.     

Regional flood frequency analysis using residual kriging in physiographical space

In this article, an approach using residual kriging (RK) in physiographical space is proposed for regional flood frequency analysis. The physiographical space is constructed using physiographical/climatic characteristics of gauging basins by means of canonical correlation analysis (CCA). This approach is a modified version of the original method, based on ordinary kriging (OK). It is intended to handle effectively any possible spatial trends within the hydrological variables over the physiographical space. In this approach, the trend is first quantified and removed from the hydrological variable by a quadratic spatial regression. OK is therefore applied to the regression residual values. The final estimated value of a specific quantile at an ungauged station is the sum of the spatial regression estimate and the kriged residual. To evaluate the performance of the proposed method, a cross‐validation procedure is applied. Results of the proposed method indicate that RK in CCA physiographical space leads to more efficient estimates of regional flood quantiles when compared to the original approach and to a straightforward regression‐based estimator. Copyright © 2010 John Wiley & Sons, Ltd.     

Regional flood frequency analysis using Bayesian generalized least squares: a comparison between quantile and parameter regression techniques

Regression‐based regional flood frequency analysis (RFFA) methods are widely adopted in hydrology. This paper compares two regression‐based RFFA methods using a Bayesian generalized least squares (GLS) modelling framework; the two are quantile regression technique (QRT) and parameter regression technique (PRT). In this study, the QRT focuses on the development of prediction equations for a flood quantile in the range of 2 to 100 years average recurrence intervals (ARI), while the PRT develops prediction equations for the first three moments of the log Pearson Type 3 (LP3) distribution, which are the mean, standard deviation and skew of the logarithms of the annual maximum flows; these regional parameters are then used to fit the LP3 distribution to estimate the desired flood quantiles at a given site. It has been shown that using a method similar to stepwise regression and by employing a number of statistics such as the model error variance, average variance of prediction, Bayesian information criterion and Akaike information criterion, the best set of explanatory variables in the GLS regression can be identified. In this study, a range of statistics and diagnostic plots have been adopted to evaluate the regression models. The method has been applied to 53 catchments in Tasmania, Australia. It has been found that catchment area and design rainfall intensity are the most important explanatory variables in predicting flood quantiles using the QRT. For the PRT, a total of four explanatory variables were adopted for predicting the mean, standard deviation and skew. The developed regression models satisfy the underlying model assumptions quite well; of importance, no outlier sites are detected in the plots of the regression diagnostics of the adopted regression equations. Based on ‘one‐at‐a‐time cross validation’ and a number of evaluation statistics, it has been found that for Tasmania the QRT provides more accurate flood quantile estimates for the higher ARIs while the PRT provides relatively better estimates for the smaller ARIs. The RFFA techniques presented here can easily be adapted to other Australian states and countries to derive more accurate regional flood predictions. Copyright © 2011 John Wiley & Sons, Ltd.     

A regional Bayesian POT model for flood frequency analysis

Flood frequency analysis is usually based on the fitting of an extreme value distribution to the local streamflow series. However, when the local data series is short, frequency analysis results become unreliable. Regional frequency analysis is a convenient way to reduce the estimation uncertainty. In this work, we propose a regional Bayesian model for short record length sites. This model is less restrictive than the index flood model while preserving the formalism of “homogeneous regions”. The performance of the proposed model is assessed on a set of gauging stations in France. The accuracy of quantile estimates as a function of the degree of homogeneity of the pooling group is also analysed. The results indicate that the regional Bayesian model outperforms the index flood model and local estimators. Furthermore, it seems that working with relatively large and homogeneous regions may lead to more accurate results than working with smaller and highly homogeneous regions.     

Seasonality and magnitude of floods in Switzerland under future climate change

The flood seasonality of catchments in Switzerland is likely to change under climate change because of anticipated alterations of precipitation as well as snow accumulation and melt. Information on this change is crucial for flood protection policies, for example, or regional flood frequency analysis. We analysed projected changes in mean annual and maximum floods of a 22‐year period for 189 catchments in Switzerland and two scenario periods in the 21st century based on an ensemble of climate scenarios. The flood seasonality was analysed with directional statistics that allow assessing both changes in the mean date a flood occurs as well as changes in the strength of the seasonality. We found that the simulated change in flood seasonality is a function of the change in flow regime type. If snow accumulation and melt is important in a catchment during the control period, then the anticipated change in flood seasonality is most pronounced. Decreasing summer precipitation in the scenarios additionally affects the flood seasonality (mean date of flood occurrence) and leads to a decreasing strength of seasonality, that is a higher temporal variability in most cases. The magnitudes of mean annual floods and more clearly of maximum floods (in a 22‐year period) are expected to increase in the future because of changes in flood‐generating processes and scaled extreme precipitation. Southern alpine catchments show a different signal, though: the simulated mean annual floods decrease in the far future, that is at the end of the 21st century. Copyright © 2013 John Wiley & Sons, Ltd.     

Timescale-based flood typing to estimate temporal changes in flood frequencies

ABSTRACT

The flood peak is the dominating characteristic in nearly all flood-statistical analyses. Contrary to the general assumptions of design flood estimation, the peak is not closely related to other flood characteristics. Differentiation of floods into types provides a more realistic view. Often different parts of the probability distribution function of annual flood peaks are dominated by different flood types, which raises the question how shifts in flood regimes would modify the statistics of annual maxima. To answer this, a distinction into five flood types is proposed; then, temporal changes in flood-type frequencies are investigated. We show that the frequency of floods caused by heavy rain has increased significantly in recent years. A statistical model is developed that simulates peaks for each event type by type-specific peak–volume relationships. In a simulation study, we show how changes in frequency of flood event type lead to changes in the quantiles of annual maximum series.     

Hydrological Sciences Journal Contents 2002

Abstract

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     

Determination of flood seasonality from hydrological records / Détermination de la saisonnalité des crues à partir de séries hydrologiques

Abstract

Abstract The identification of flood seasonality is a procedure with many practical applications in hydrology and water resources management. Several statistical methods for capturing flood seasonality have emerged during the last decade. So far, however, little attention has been paid to the uncertainty involved in the use of these methods, as well as to the reliability of their estimates. This paper compares the performance of annual maximum (AM) and peaks-over-threshold (POT) sampling models in flood seasonality estimation. Flood seasonality is determined by two most frequently used methods, one based on directional statistics (DS) and the other on the distribution of monthly relative frequencies of flood occurrence (RF). The performance is evaluated for the AM and three common POT sampling models depending on the estimation method, flood seasonality type and sample record length. The results demonstrate that the POT models outperform the AM model in most analysed scenarios. The POT sampling provides significantly more information on flood seasonality than the AM sampling. For certain flood seasonality types, POT samples can lead to estimation uncertainty that is found in up to ten-times longer AM samples. The performance of the RF method does not depend on the flood seasonality type as much as that of the DS method, which performs poorly on samples generated from complex seasonality distributions.     

Dependence between flood peaks and volumes: a case study on climate and hydrological controls

Abstract

The aim of this paper is to understand the causal factors controlling the relationship between flood peaks and volumes in a regional context. A case study is performed based on 330 catchments in Austria ranging from 6 to 500 km² in size. Maximum annual flood discharges are compared with the associated flood volumes, and the consistency of the peak–volume relationship is quantified by the Spearman rank correlation coefficient. The results indicate that climate-related factors are more important than catchment-related factors in controlling the consistency. Spearman rank correlation coefficients typically range from about 0.2 in the high alpine catchments to about 0.8 in the lowlands. The weak dependence in the high alpine catchments is due to the mix of flood types, including long-duration snowmelt, synoptic floods and flash floods. In the lowlands, the flood durations vary less in a given catchment which is related to the filtering of the distribution of all storms by the catchment response time to produce the distribution of flood producing storms.

Editor Z.W. Kundzewicz     

Preliminary flood frequency estimates for Lebanon

Abstract

This paper describes a first attempt at developing a regional flood estimation methodology for Lebanon. The analyses are based on instantaneous flood peak data for the whole country, and cover the period from the start of observations in the 1930s to the start of the civil war in the mid-1970s. Three main flood-generating zones are identified, and regional flood growth curves are derived for each zone using the Generalized Extreme Value distribution fitted by probability-weighted moments. Typical parameter values are presented, together with regression coefficients for estimating the mean annual flood. Based on this work, several recommendations are made on the future data collection and analysis requirements to develop a national flood estimation methodology for Lebanon.     

Dependence of model-based extreme flood estimation on the calibration period: case study of the Kamp River (Austria)

Abstract

The Kamp River is a particularly interesting case study for testing flood frequency estimation methods, since it experienced a major flood in August 2002. Here, the Kamp catchment is studied in order to quantify the influence of such a remarkable flood event on the calibration of a rainfall–runoff model, in particular when it is used in a stochastic simulation method for flood estimation, by performing numerous rainfall–runoff model calibrations (based on split-sample and bootstrap tests). The results confirmed the usefulness of the multi-period and bootstrap testing schemes for identifying the dependence of model performance and flood estimates on the information contained in the calibration period. The August 2002 event appears to play a dominating role for the Kamp River, since the presence or absence of the event within the calibration sub-periods strongly influences the rainfall–runoff model calibration and the extreme flood estimations that are based on the calibrated model.     

Derivation of a curve number and kinematic-wave based flood frequency distribution

Abstract

The physically-based flood frequency models use readily available rainfall data and catchment characteristics to derive the flood frequency distribution. In the present study, a new physically-based flood frequency distribution has been developed. This model uses bivariate exponential distribution for rainfall intensity and duration, and the Soil Conservation Service-Curve Number (SCS-CN) method for deriving the probability density function (pdf) of effective rainfall. The effective rainfall-runoff model is based on kinematic-wave theory. The results of application of this derived model to three Indian basins indicate that the model is a useful alternative for estimating flood flow quantiles at ungauged sites.     

Assessment of regional estimation error by canonical residual kriging

Various regional flood frequency analysis procedures are used in hydrology to estimate hydrological variables at ungauged or partially gauged sites. Relatively few studies have been conducted to evaluate the accuracy of these procedures and estimate the error induced in regional flood frequency estimation models. The objective of this paper is to assess the overall error induced in the residual kriging (RK) regional flood frequency estimation model. The two main error sources in specific flood quantile estimation using RK are the error induced in the quantiles local estimation procedure and the error resulting from the regional quantile estimation process. Therefore, for an overall error assessment, the corresponding errors associated with these two steps must be quantified. Results show that the main source of error in RK is the error induced into the regional quantile estimation method. Results also indicate that the accuracy of the regional estimates increases with decreasing return periods. Copyright © 2010 John Wiley & Sons, Ltd.     

INTERNATIONAL HYDROLOGICAL DECADE / WORKING GROUP ON “THE INFLUENCE OF MAN ON THE HYDROLOGICAL CYCLE”

Abstract

Floods, as extreme hydrological phenomena, can be described by more than one correlated characteristic, such as peak, volume and duration. These characteristics should be jointly considered since they are generally not independent. For an ungauged site, univariate regional flood frequency analysis (FA) provides a limited assessment of flood events. A recent study proposed a procedure for regional FA in a multivariate framework. This procedure represents a multivariate version of the index-flood model and is based on copulas and multivariate quantiles. The performance of the proposed procedure was evaluated by simulation. However, the model was not tested on a real-world case study data. In the present paper, practical aspects are investigated jointly for flood peak (Q) and volume (V) of a dataset from the Côte-Nord region in the province of Quebec, Canada. The application of the proposed procedure requires the identification of the appropriate marginal distribution, the estimation of the index flood and the selection of an appropriate copula. The results of the case study show that the regional bivariate FA procedure performed well. This performance depends strongly on the performance of the two univariate models and, more specifically, the univariate model of Q. The results show also the impact of the homogeneity of the region on the performance of the univariate and bivariate models.

Editor D. Koutsoyiannis