The Value of Historical Data in Flood Frequency Analysis

A realistic estimation of flood magnitude for a given return period is essential for successful operation and economical design of pivotal hydraulic structures such as dams, reservoirs and spillways, bridges, small culverts, urban drainage systems, flood plain zoning, economic evaluation of flood protection projects, etc. The flood frequency analysis is generally used for flood estimation, entirely based on the assumption that the floods are random and the floods in future are supposed to bear similar statistical properties that have occurred in the past. If adequate data are available at-site flood frequency analysis is used, otherwise regional flood frequency analysis (RFFA) is preferred for flood estimation. The RFFA for various small and medium catchments of India with different approaches have been carried out and reported in the literature. The focus of the present study is to carry out the RFFA using different approaches i.e. conventional method and L-moment and comparing the results for the Krishna and Penner subzone 3(h) of India. It was found that the values of growth factors obtained by the conventional method are higher than those of L-moment methods, thus leading to conservative estimate of the peak floods.

Advances in space–time tools and techniques offer new possibilities to improve methods for exploratory data analysis and parameter estimation in regional flood frequency analysis (FFA). A general framework and methodological approach are proposed which integrate concepts and techniques of regional FFA, geostatistical theory and analytical geographical information systems (GIS) using data on climate, vegetation, geography and flood timing and magnitude statistics. Non-parametric methods are used to screen data and to delineate homogeneous regions. Simulations are used to identify discordant sites, diagnose each region using the signal-to-noise ratio and test regions for homogeneity based on L-moment ratios. Geostatistical measures of spatial autocorrelation are used to diagnose hierarchical spatial models for each L-moment ratio and to obtain map estimates of parameters using spatially explicit kriging techniques (analogous to regression). In addition to storing and displaying the spatio-temporal information accurately, the GIS is used to quantify spatial associations between dependent and independent variables and to diagnose homogeneous regions for further refinement using a simple spatial contrast measure. Analysis of data from central and eastern Canada (except the eastern parts of Newfoundland), encompassing a large area with significant random and systematic variability, demonstrates that: (i) map sets of L-mean and L-CV (coefficient of variation) for flood timing and magnitude can serve as indicators of climatic influences on the flood-generating mechanisms; (ii) models of spatial autocorrelation can be used to map point variables and their geostatistical spatial variance, which indicates whether maps are significant; and (iii) associations between L-CV and snow or vegetation could support improved mapping using co-kriging or geostatistical simulations. The map-based method provides parameter values at ungauged sites and maps of spatial variance that could support decisions to add or remove gauges from a hydrometric network. Copyright © 2000 John Wiley & Sons, Ltd.

Flood frequency analysis estimates reliable design flood, which is critical for the safety of any hydraulic, hydrologic construction and management. The overall objective of this study is to perform flood frequency analysis for Seonath and Hasdeo river basins of Chhattisgarh state, India. In this study, homogeneous regions are delineated by Ward cluster analysis. Cluster variables are L-moment ratios of annual maximum events and catchment characters. Index flood is performed for parametric quantile estimation of flood events. L-moments are employed in regionalization, frequency distribution selection, and distribution parameter estimation. Locally weighted polynomial model (Locfit) is performed for at-site frequency analysis. Flood quantiles are estimated for regions and at sites. In regional flood frequency analysis, regionalisation based on annual maximum flood series shows better flood quantiles than regionalisation based on site characteristics. It reveals that geophysical homogeneity does not mean hydrologic homogeneity. Regional formulae for annual mean discharge are formulated based on catchment character for estimation of flood quantiles for ungauged catchments.KeywordsFlood frequency analysisCluster analysisL-momentsIndex floodLocfit

Hurricane Harvey was an unprecedented event that resulted in immense damage to life and property. As a result, it is important to determine how this event, as well as past and future events like it, will impact engineering design equations that are based upon historical data, such as flood frequency analysis equations. This study seeks to contribute to this discussion by evaluating the extent to which Harvey influenced estimations of instantaneous peak discharges in rural ungauged basins in southeast Texas. Results indicate that Harvey significantly increased the computations of design floods using Log‐Pearson Type III analysis (e.g., 3–55% for 2‐year flood and 3–80% for the 100‐year flood). This subsequently impacted the estimation of instantaneous peak discharges through regional flood frequency analysis by up to 28%. These results highlight the influence that recent and future hurricanes can have on engineering design equations that are used for managing floodplains, assessing flood risk, and designing infrastructure such as levees, bridges, and culverts.

A basin -wide flood frequency analysis using Index flood and L-moment s approach offers an attractive solution to provide flood quant ile estimations at hundreds of ungauged sites within the Red River Basin of the North . L-moments diagrams and hierarchical clustering techniques were used initially to delineate hydrologic regions within the basin . Although the northern plain is relatively flat and almost monotonous, the analyses have show n that four homogeneous regions could be established for the basin . The whole basin as a region is also established for the purpose of flood estimations along the main stem . Appropriate probability distrib utions are fitted to t he pooled regional flood peak s in each region . Monte Carlo simulations are performed to decide the best distribution for each region , and the dominant distributions found are the Log -Pearson Type III and the generalized Pareto distrib utions . The regression of index flood with the physical factors of drainage area and basin slopes for two of the five regions are not very satisfactory . However, t he flood quantile estimates are sufficient for preliminary planning purposes. Basis for Reg ional Flood Frequency Analysis and L -moments The Red River of the North Basin has 91 gauged sites but that number was not sufficient for economic planning purposes as flood quantile estimations at hundreds of ungauged sites within the basin were required . Regional flood frequency analysis is a good alternative approach to flood quantile estimation using a single station approach. It is well accepted that using a regional approach in flood frequency analysis is effective in extending the flood information at a site to sites within a homogeneous region. Recent papers by Burn and Goel (2000), Cunderlik and Burn (2002), Pfister et al. (2002), and Lim and Lye (2003) are examples of such undertakings. The extension enables flood quantile estimates for any site i n a region to be expressed or inferred in terms of flood data recorded at all gauging sites in that region. Design flood estimations using a regional approach can often be carried out using methods such as the index -flood method and the direct regression o n quantiles method.

Extremely great floods are among environmental events with the most disastrous consequences for the entire world. Estimates of their return periods and design values are of great importance in hydrologic modeling, engineering practice for water resources and reservoirs design and management, planning for weather-related emergencies, etc. Regional flood frequency analysis resolves the problem of estimating the extreme flood events for catchments having short data records or ungauged catchments. This paper analyzes annual maximum peak flood discharge data recorded from more than 50 stream flow gauging sites in Sicily, Italy, in order to derive regional flood frequency curves. First these data are analyzed to point out some problems concerning the homogeneity of the single time series. On the basis of the L-moments and using cluster analysis techniques, the entire region is subdivided in five subregions whose homogeneity is tested using the L-moments based heterogeneity measure. Comparative regional flood frequency analysis studies are carried out employing the L-moments based commonly used frequency distributions. Based on the L-moment ratio diagram and other statistic criteria, generalized extreme value (GEV) distribution is identified as the robust distribution for the study area. Regional flood frequency relationships are developed to estimate floods at various return periods for gauged and ungauged catchments in different subregions of the Sicily. These relationships have been implemented using the L-moment based GEV distribution and a regional relation between mean annual peak flood and some geomorphologic and climatic parameters of catchments.

A novel approach to regional flood frequency analysis is presented and evaluated. The technique is referred to as the region of influence approach in that every site can have a potentially unique set of gauging stations for use in the estimation of at‐site extremes. The rationale for the methodology is discussed, and several options for incorporating the approach into regional flood frequency analysis are developed and compared with traditional regional estimation procedures. Through a Monte Carlo experiment, the region of influence approach is demonstrated to provide improved at‐site estimates of extreme flow quantlies in terms of network average root mean squared error and comparable results for bias. The method is further shown to have attractive features for estimating extremes for unusual sites in a network of gauging stations.

Regional flood frequency estimation with canonical correlation analysis

In semi-arid catchments, the contribution of floods to annual runoff is important. The High Atlas Mountain catchments (N’Fis, Rheraya, Ourika, Zat and R’dat) located in the south of Morocco, upstream of the city of Marrakech, are an example of those basins where floods provide the main contribution to surface water resources. The goal of this study is to evaluate whether a regional flood frequency analysis could improve the estimation of the magnitude and the occurrence of floods in these mountainous catchments. The database considered is long-term measurement of daily discharge at the outlets with record length varying from 35 to 45 years. The index flood method is considered to build a regional model based on the generalized extreme values distribution. The results showed a contrasted seasonal behavior, with floods caused by either rainfall during the autumn season or a mix of rainfall and snowmelt for spring events. As a consequence, two distinct regional models have been computed, one for autumn and one for spring events. No significant trends have been found for seasonal maximum discharge in all the catchments. The results of the regional frequency analysis show that the regional model provides better flood quantiles estimates than a standard at-site model. However, there is a much greater uncertainty for both local and regional estimates of floods occurring during the autumn than during spring events, which are estimated with a good level of accuracy. This research provides insights into how to improve the estimation of flood return levels useful for water resources management in these semi-arid mountainous catchments.

Local and regional flood frequency analysis based on hierarchical Bayesian model in Dongting Lake Basin, China

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.

This paper presents the development and validation of an artificial intelligence based regional flood frequency analysis (RFFA) model for application in ungauged catchments of eastern Australia. The artificial intelligence based techniques with a flexible model structure and non-linear approach can overcome the limitations of the conventional RFFA models, which are generally based on linear relationship between flood statistics and catchment characteristics. Till now, there have been limited applications of artificial intelligence based techniques to RFFA problems in Australia. This study has developed four artificial intelligence based RFFA models for eastern Australia, using a comprehensive flood database available as a part of Australian Rainfall and Runoff (ARR) revision 'Project 5 Regional flood methods'. These four RFFA models are based on artificial neural network (ANN), genetic algorithm based artificial neural network (GAANN), gene-expression programing (GEP) and co-active neuron fuzzy inference system (CANFIS). A total of 452 catchments from the states of New South Wales, Victoria, Queensland and Tasmania have been considered by this study. These dataset is divided into training and validation sets. Data of 362 catchments (training data set) have been used to train the model and the data from the remaining 90 catchments (validation data set) used to validate the model. The models have been trained/calibrated using the training data set that involved minimisation of the mean squared error between the observed and predicted flood quantiles by the model (being trained) for a given ARI for the training data set. Six average recurrence intervals (ARI) flood quantiles (2, 5, 10, 20, 50 and 100 years) were considered in this study. Four evaluation statistics are adopted to assess the model accuracy: median ratio of the predicted flood quantile (Qpred) and observed flood quantile (Qobs), denoted by Qpred/Qobs ratio, plots of Qobs and Qpred, median relative error and coefficient of efficiency. This is initially done for the training data set and then repeated for the validation data set. The artificial intelligence based RFFA models have been ranked based on their relative performances in relation to the above criteria to identify the best trained/calibrated model. It has been found that none of the four models is superior across all the six ARIs against the adopted criteria. The ANN based RFFA model has a better ranking score in terms of training/calibration; therefore, it is suggested that the ANN-based RFFA model is the best calibrated model among the four artificial intelligence based RFFA models for eastern Australia.

This paper examines the applicability of principal component analysis (PCA) and cluster analysis in regional flood frequency analysis. A total of 88 sites in New South Wales, Australia are adopted. Quantile regression technique (QRT) is integrated with the PCA to estimate the flood quantiles. A total of eight catchment characteristics are selected as predictor variables. A leave-one-out validation is applied to determine the efficiency of the developed statistical models using an ensemble of evaluation diagnostics. It is found that the PCA with QRT model does not perform well, whereas cluster/group formed with smaller sized catchments performs better (with a median relative error values ranging from 22% to 37%) than other clusters/groups. No linkage is found between the degree of heterogeneity in the clusters/groups and precision of flood quantile prediction by the multiple linear regression technique.

Regional flood frequency analysis is a convenient way to reduce estimation uncertainty when few data are available at the gauging site. In this work, a model that allows a non‐null probability to a regional fixed shape parameter is presented. This methodology is integrated within a Bayesian framework and uses reversible jump techniques. The performance on stochastic data of this new estimator is compared to two other models: a conventional Bayesian analysis and the index flood approach. Results show that the proposed estimator is absolutely suited to regional estimation when only a few data are available at the target site. Moreover, unlike the index flood estimator, target site index flood error estimation seems to have less impact on Bayesian estimators. Some suggestions about configurations of the pooling groups are also presented to increase the performance of each estimator.

Regional flood frequency analysis by combining self-organizing feature map and fuzzy clustering