Flood Frequency Analysis in Seonath and Hasdeo River Basins

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.

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.

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.

<p>Flood frequency analysis (FFA) has long been the standard procedure for obtaining design floods for all kinds of purposes. Ideally, the data at the basis of the statistical operations have a high temporal resolution, in order to facilitate a full account of the observed flood peaks and hence a precise model fitting and flood quantile estimation.</p><p>Unfortunately, high-resolution flows are rarely disposable. Often, average daily flows pose the only available/sufficiently long base for flood frequency analysis. This averaging naturally causes a significant smoothing of the flood wave, such that the “instantaneous” peak can no longer be observed. As a possible consequence, design floods derived from these data may be severely underrated.</p><p>How strongly the original peaks are flattened and how this influences the design flood estimation depends on a variety of factors and varies from gauge to gauge. In this study we are looking at a range of errors arising from the use of daily instead of instantaneous flow data. These include differences in the observed individual flood peaks and mean annual maximum floods, as well as the estimated distribution parameters and flood quantiles. The aim is to identify catchment specific factors that influence the magnitude of these errors, and ultimately to provide a means for error assessment on the mere basis of local hydrological conditions, specifically where no high-resolution data is available.</p><p>The analyses are carried out on an all-German dataset of discharge gauges, for which high-resolution data is available for at least 30 years. The classical FFA approach of fitting distributions to annual maximum series is utilized for error assessment. For identification of influencing factors, both the discharge series themselves and a catalogue of climatic and physiographic catchment descriptors are screened.</p>

The recurring flooding causes loss of life and damage to buildings and other structures, including bridges, sewerage systems, roadways and canals. It also frequently damages power transmission and sometimes power generation, which then has knock-on effects caused by the loss of power. The present study compares the relative performance of flood frequency methods to estimate design flood, using available data of 18 small catchments in the Mahanadi River basin (India). The primary objective of the referred approach was for design flood estimation at gauge sites; however, the main focus is referred to ungauged catchments by an interpolation method. In this regard, three interpolation methods are used: (1) inverse distance weighing method, (2) ordinary kriging and (3) area weighted method. As per the recent trends, flood frequency analysis methods are used specifically for two data types, i.e., at-site analysis and for regionalizing the available data within the homogeneous region. In this study, an attempt has been made to categorize the interpolation properties, where the first two approaches belong to site analysis and the third one uses the regional analysis. In the first approach, the output results in terms of flood quantiles are interpolated for the intermediate results, which is generally termed as direct interpolation of flood quantile, and the second approach uses the linear interpolating or L-moments in flood estimation. The above one refers to the interpolation of L-moments, while flood index is interpolated in the third approach, which is named as ‘flood index procedure.’ In the study, it was observed that the designed flood quantile results were better by using the flood index approach at lower return periods at 2 and 5 years, and the direct interpolation method gave a better estimation for higher return periods. Further, it was found that the difference in prediction error of direct interpolation of flood quantiles and the flood index procedure is negligible.

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.

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.

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.

Regional flood frequency analysis for the Gan-Ming River basin in China

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.

The paper illustrates the results of regional flood frequency analysis of eleven sites of the four major rivers of Punjab, Pakistan. The discordancy measure (D i ) shows that none of the site, in the cluster of eleven sites, is discordant. Timing of the flood occurrences has been used, initially, for the formation of homogeneous region, but the heterogeneity measure (H) has shown a small amount of heterogeneity. To reduce the amount of heterogeneity, a subjective adjustment has been made considering the fact that a couple of site is located at the confluence of rivers. As a result, two homogeneous regions have been identified, i.e., Region 1 having nine sites and Region 2 containing couple of site located at the confluence of the rivers. In order to find out suitable candidate(s) for regional distribution L-moment ration diagram, average weighted distance and Z DIST statistics have been used. To obtain the flood quantiles at ungauged sites, multiple linear regression relationship has been developed, between mean of annual maximum peak flows (AMPF) at gauged sites (l 1) and best subset of site characteristics, i.e., average rainfall during monsoon and catchment area of the rivers to which the sites belong. A cross-validation with the observed AMPF from 2010 to 2014 and historical data (the highest of the AMPF attained so far at various sites of Region 1) showed that the estimates based on this model/procedure are accurate and useful, especially for the longer return periods.

A common problem in flood frequency analysis is estimation, from a short gauged record, of the flood corresponding to a return period of 50–1000 years. One would expect that an estimate of the historical maximum event; i.e., the maximum flood in a period of perhaps 100–200 years, would, if properly included in the estimation procedure, improve the resulting estimate of the flood magnitude at the given return period. We attempt to assess the value of historical information using computer simulation. An annual flood sequence and a historical maximum event are generated from ah extreme‐value distribution (of type I or II) and quantitiles of the distribution are estimated by maximum likelihood, both including the excluding the historical event. After many such simulations have been performed the accuracy of the quantile estimates is assessed. We also consider the effect of errors in the magnitude of historical discharge estimates and the use of historical data in regional flood frequency analysis.

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.

Traditional flood frequency analysis assumes that the probability distribution is stationary over time. However, this assumption has been challenged, given widespread changes in catchments and climate. One of the inherent handicaps of the stationarity assumption is its non-inclusion of changes in extremes associated with future climatic conditions. To overcome this handicap, climate covariates can be incorporated into the estimation of flood probability through the non-stationary Climate-Informed Flood Frequency Analysis (CIFFA). The CIFFA methodology comprises 1) selection of predictands (usually seasonal maxima), 2) identification of suitable predictors (large-scale climate indices), and 3) derivation of a statistical link between predictands and predictors. Since CIFFA typically considers the flood peaks in the dominant season, its applicability to gauges, where flood extremes occur in several seasons, is limited. Here, we develop and test a novel non-stationary Climate-Informed-Seasonal-Mixing approach across various European basins. In the proposed Climate-Informed-Seasonal-Mixing approach, we fit the seasonal peak distribution (boreal seasons) with the location parameter conditioned on the selected covariate using the Bayesian Inference. The best climate covariates for each season among a set of predictors are identified based on widely applicable information criterion (WAIC), which computes log posterior predictive density and adjusts the overfitting using the effective number of parameters. Even the traditional stationary model could be a preferred model for any season if it has a minimum WAIC value. Following the estimation of seasonal distribution parameters, the annual flood quantiles are derived by multiplicatively mixing all the seasonal distributions. In order to demonstrate the performance of the proposed approach, we split the entire period into calibration and validation sets, fitting the model based only on calibration samples. The projected quantiles during the validation period are then compared with a benchmark model (traditional model fitted solely with validation samples). Our results suggest that for many gauges, the flood quantiles estimated by the proposed Climate-Informed-Seasonal-Mixing approach align with the baseline estimates where the traditional approaches fall short.