Regional Flood Frequency Analysis Methods Research Articles

Alternate pathway for regional flood frequency analysis in data-sparse region

Accurately analyzing flood frequency is crucial for developing effective flood management strategies and designing flood protection infrastructure, but the complex and nonlinear nature of the hydrological system poses significant challenges. The use of advanced statistical techniques, hydrological modeling, and computational tools has facilitated the implementation of several regional flood frequency analysis (RFFA) methods, including machine learning (ML) approaches. However, such regional methods, being statistical and data-intensive with limited hydrologic inferences, can be challenging for regions with limited data. In this study, we evaluated an alternate approach or path for RFFA in two contrasting regions: a data-sparse region in India and a data-dense region in the USA. The alternate approach involves using deep learning (DL)-based model for streamflow prediction, followed by at-site flood frequency analysis to estimate flood quantiles. The proposed alternate approach was compared with the classical RFFA method, which utilized the Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) algorithms to establish the intricate relationship between different flood quantiles and predictor variables related to physio-meteorological conditions. The findings showed that the alternate approach outperformed the classical RFFA method in the data-sparse region of India, reducing the mean absolute error (MAE) and root mean squared error (RMSE) by approximately 50 %, and achieving a higher coefficient of determination (R2 = 0.85 to 0.96). In the data-dense region, the classical and alternate approaches produced comparable results. However, the alternate approach has the benefit of flexibility and offers a complete time series of daily flow at the ungauged watersheds, enabling the estimation of other flow attributes or magnitudes for any return period without needing a separate classical RFFA model. The study demonstrates that the alternate approach can produce reliable flood quantile estimates in regions with limited data, offering a promising solution for managing floods in these areas.

Comparison between Quantile Regression Technique and Generalised Additive Model for Regional Flood Frequency Analysis: A Case Study for Victoria, Australia

For design flood estimation in ungauged catchments, Regional Flood Frequency Analysis (RFFA) is commonly used. Most of the RFFA methods are primarily based on linear modelling approaches, which do not account for the inherent nonlinearity of rainfall-runoff processes. Using data from 114 catchments in Victoria, Australia, this study employs the Generalised Additive Model (GAM) in RFFA and compares the results with linear method known as Quantile Regression Technique (QRT). The GAM model performance is found to be better for smaller return periods (i.e., 2, 5 and 10 years) with a median relative error ranging 16–41%. For higher return periods (i.e., 20, 50 and 100 years), log-log linear regression model (QRT) outperforms the GAM model with a median relative error ranging 31–59%.

Comparing Performance of ANN and SVM Methods for Regional Flood Frequency Analysis in South-East Australia

Design flood estimations at ungauged catchments are a challenging task in hydrology. Regional flood frequency analysis (RFFA) is widely used for this purpose. This paper develops artificial intelligence (AI)-based RFFA models (artificial neural networks (ANN) and support vector machine (SVM)) using data from 181 gauged catchments in South-East Australia. Based on an independent testing, it is found that the ANN method outperforms the SVM (the relative error values for the ANN model range 33–54% as compared to 37–64% for the SVM). The ANN and SVM models generate more accurate flood quantiles for smaller return periods; however, for higher return periods, both the methods present a higher estimation error. The results of this study will help to recommend new AI-based RFFA methods in Australia.

Artificial Intelligence-Based Regional Flood Frequency Analysis Methods: A Scoping Review

Flood is one of the most destructive natural disasters, causing significant economic damage and loss of lives. Numerous methods have been introduced to estimate design floods, which include linear and non-linear techniques. Since flood generation is a non-linear process, the use of linear techniques has inherent weaknesses. To overcome these, artificial intelligence (AI)-based non-linear regional flood frequency analysis (RFFA) techniques have been introduced over the last two decades. There are limited articles available in the literature discussing the relative merits/demerits of these AI-based RFFA techniques. To fill this knowledge gap, a scoping review on the AI-based RFFA techniques is presented. Based on the Scopus database, more than 1000 articles were initially selected, which were then screened manually to select the most relevant articles. The accuracy and efficiency of the selected RFFA techniques based on a set of evaluation statistics were compared. Furthermore, the relationships among countries and researchers focusing on AI-based RFFA techniques are illustrated. In terms of performance, artificial neural networks (ANN) are found to be the best performing techniques among all the selected AI-based RFFA techniques. It is also found that Australia, Canada, and Iran have published the highest number of articles in this research field, followed by Turkey, the United Arab Emirates (UAE), India, and China. Future research should be directed towards identification of the impacts of data quantity and quality, model uncertainty and climate change on the AI-based RFFA techniques.

Regional Flood Frequency Analysis Using the FCM-ANFIS Algorithm: A Case Study in South-Eastern Australia

Regional flood frequency analysis (RFFA) is widely used to estimate design floods in ungauged catchments. Both linear and non-linear methods are adopted in RFFA. The development of the non-linear RFFA method Adaptive Neuro-fuzzy Inference System (ANFIS) using data from 181 gauged catchments in south-eastern Australia is presented in this study. Three different types of ANFIS models, Fuzzy C-mean (FCM), Subtractive Clustering (SC), and Grid Partitioning (GP) were adopted, and the results were compared with the Quantile Regression Technique (QRT). It was found that FCM performs better (with relative error (RE) values in the range of 38–60%) than the SC (RE of 44–69%) and GP (RE of 42–78%) models. The FCM performs better for smaller to medium ARIs (2 to 20 years) (ARI of five years having the best performance), and in New South Wales, over Victoria. In many aspects, the QRT and FCM models perform very similarly. These developed RFFA models can be used in south-eastern Australia to derive more accurate flood quantiles. The developed method can easily be adapted to other parts of Australia and other countries. The results of this study will assist in updating the Australian Rainfall Runoff (national guide)-recommended RFFA technique.

Using Physically Based Synthetic Peak Flows to Assess Local and Regional Flood Frequency Analysis Methods

AbstractAlthough prior studies have evaluated the role of sampling errors associated with local and regional methods to estimate peak flow quantiles, the investigation of epistemic errors is more difficult because the underlying properties of the random variable have been prescribed using ad‐hoc characterizations of the regional distributions of peak flows. This study addresses this challenge using representations of regional peak flow distributions derived from a combined framework of stochastic storm transposition, radar rainfall observations, and distributed hydrologic modeling. The authors evaluated four commonly used peak flow quantile estimation methods using synthetic peak flows at 5,000 sites in the Turkey River watershed in Iowa, USA. They first used at‐site flood frequency analysis using the Pearson Type III distribution with L‐moments. The authors then pooled regional information using (1) the index flood method, (2) the quantile regression technique, and (3) the parameter regression. This approach allowed quantification of error components stemming from epistemic assumptions, parameter estimation method, sample size, and, in the regional approaches, the number of pooled sites. The results demonstrate that the inability to capture the spatial variability of the skewness of the peak flows dominates epistemic error for regional methods. We concluded that, in the study basin, this variability could be partially explained by river network structure and the predominant orientation of the watershed. The general approach used in this study is promising in that it brings new tools and sources of data to the study of the old hydrologic problem of flood frequency analysis.

Assessing the performance of regional flood frequency analysis methods in South Africa

In engineering and flood hydrology, the estimation of a design flood associates the magnitude of a flood with a level of exceedance, or return period, for a given site. The use of a regional flood frequency analysis (RFFA) approach improves the accuracy and reliability of estimates of design floods. However, no RFFA method is currently widely used in South Africa, despite a number of RFFA studies having been undertaken in Africa and which include South Africa in their study areas. Hence, the performance of the current RFFA approaches needs to be assessed in order to determine the best approaches to use and to determine if a new RFFA approach needs to be developed for use in South Africa. Through a review of the relevant literature it was found that the Meigh et al. (1997) method, the Mkhandi et al. (2000) method, the Görgens (2007) Joint Peak-Volume (JPV) method and the Haile (2011) method are available for application in a nationwide study. The results of the study show that the Haile method generally performs better than the other RFFA methods; however, it also consistently underestimates design floods. Due to the poor overall performance of the RFFA methods assessed, it is recommended that a new RFFA method be developed for application in design flood practice in South Africa.

Development of regional flood frequency analysis techniques using generalized additive models for Australia

Estimation of flood quantiles in ungauged catchments is a common problem in hydrology. For this, the log-linear regression model is widely adopted. However, in many cases, a simple log transformation may not be able to capture the complexity and nonlinearity in flood generation processes. This paper develops generalized additive model (GAM) to deal with nonlinearity between the dependent and predictor variables in regional flood frequency analysis (RFFA) problems. The data from 85 gauged catchments from New South Wales State in Australia is used to compare the performances of a number of alternative RFFA methods with respect to variable selection, variable transformation and delineation of regions. Four RFFA methods are compared in this study: GAM with fixed region, log-linear model, canonical correlation analysis (to form neighbourhood in the space catchment attributes) and region-of-influence approach. Based on the outcome from a leave-one-out validation approach, it has been found that the GAM method generally outperforms the other methods even without linking GAM with a neighbourhood/region-of-influence approach. The main strength of GAM is that it captures the non-linearity between the dependent and predictor variables without any restrictive assumption. The findings of this study will encourage other researchers worldwide to apply GAM in RFFA studies, allowing development of more flexible and realistic RFFA models and their wider adoption in practice.

Flood estimation in ungauged catchments: application of artificial intelligence based methods for Eastern Australia

Regional flood frequency analysis (RFFA) is used to estimate design floods in ungauged and data poor gauged catchments, which involves the transfer of flood characteristics from gauged to ungauged catchments. In Australia, RFFA methods have focused on the application of empirical methods based on linear forms of traditional models such as the probabilistic rational method, the index flood method and the quantile regression technique (QRT). In contrast to these traditional linear-models, non-linear methods such as artificial neural networks (ANNs) and gene expression programming (GEP) can be applied to RFFA problems. The particular advantage of these techniques is that they do not impose a model structure on the data, and they can better deal with non-linearity of the input and output relationship in regional flood modelling. These non-linear techniques have been applied successfully in a wide range of hydrological problems; however, there have been only limited applications of these techniques in RFFA problems, particularly in Australia. This paper focuses on the development and testing of the ANNs and GEP based RFFA models for eastern parts of Australia. This involves relating flood quantiles to catchment characteristics so that the developed prediction models can be used to estimate design floods in ungauged site. A data set comprising of 452 stations from eastern Australia was used to develop the new RFFA models. An independent testing shows that the non-linear methods are quite successful in RFFA and can be used as an alternative method to the more traditional approaches currently used in eastern Australia. The results based on ANN and GEP-based RFFA techniques have been found to outperform the ordinary least squares based QRT (linear technique).

Regional Flood Frequency Analysis using Support Vector Regression under historical and future climate

Summary Regional Flood Frequency Analysis (RFFA) is a statistical method widely used to estimate flood quantiles of catchments with limited streamflow data. In addition, to estimate the flood quantile of ungauged sites, there could be only a limited number of stations with complete dataset are available from hydrologically similar, surrounding catchments. Besides traditional regression based RFFA methods, recent applications of machine learning algorithms such as the artificial neural network (ANN) have shown encouraging results in regional flood quantile estimations. Another novel machine learning technique that is becoming widely applicable in the hydrologic community is the Support Vector Regression (SVR). In this study, an RFFA model based on SVR was developed to estimate regional flood quantiles for two study areas, one with 26 catchments located in southeastern British Columbia (BC) and another with 23 catchments located in southern Ontario (ON), Canada. The SVR-RFFA model for both study sites was developed from 13 sets of physiographic and climatic predictors for the historical period. The Ef (Nash Sutcliffe coefficient) and R2 of the SVR-RFFA model was about 0.7 when estimating flood quantiles of 10, 25, 50 and 100 year return periods which indicate satisfactory model performance in both study areas. In addition, the SVR-RFFA model also performed well based on other goodness-of-fit statistics such as BIAS (mean bias) and BIASr (relative BIAS). If the amount of data available for training RFFA models is limited, the SVR-RFFA model was found to perform better than an ANN based RFFA model, and with significantly lower median CV (coefficient of variation) of the estimated flood quantiles. The SVR-RFFA model was then used to project changes in flood quantiles over the two study areas under the impact of climate change using the RCP4.5 and RCP8.5 climate projections of five Coupled Model Intercomparison Project (CMIP5) GCMs (Global Climate Models) for the 2041–2100 period. The results suggest that due to a projected increase in the mean annual precipitation, and rainfall of a given return period, the flood quantile is projected to increase by about 7% for the southeastern BC and 29% for southern ON region in the mid- and late 21st century.

Regional flood frequency analysis method for Tasmania, Australia: a case study on the comparison of fixed region and region-of-influence approaches

A new regional flood frequency analysis method for Tasmania is developed using data from 53 gauged catchments. The Bayesian generalized least squares regression (BGLSR) approach is used to developing prediction equations for selected flood quantiles using a quantile regression technique (QRT) and the first three moments of the log Pearson Type 3 distribution by means of a parameter regression technique (PRT). Regions are formed in three ways: (a) a fixed-region approach is examined in which all the sites in Tasmania are assumed to form one region; (b) a region of influence (ROI) approach is investigated in which a region is formed around each of the sites based on the criterion of minimum model error variance; and (c) the state of Tasmania is divided into two fixed regions (east Tasmania and west Tasmania) based on their distinct rainfall regimes. Independent testing showed that the BGLSR-ROI approach can deal quite well with the uncertainty in the regional estimation, as shown by the various regression diagnostics. Furthermore, the PRT-ROI approach was found to provide quantile estimates that are generally more accurate and consistent than the QRT. Generally, the ROI approach outperformed the fixed-region approach for Tasmania.

Performance of regional flood frequency analysis methods in KwaZulu-Natal, South Africa

Estimates of design floods are required for the design of hydraulic structures and to quantify the risk of failure of the structures. Many international studies have shown that design floods estimated using a regionalised method result in more reliable estimates of design floods than values computed from a single site or from other methods. A number of regional flood frequency analysis (RFFA) methods have been developed, which cover all or parts of South Africa. These include methods developed by Van Bladeren (1993), Mkhandi et al. (2000), Görgens (2007) and Haile (2011). The performance of these methods has been assessed at selected flow-gauging sites in the province of KwaZulu-Natal (KZN), South Africa. It is recommended that the limitations of available flow records to estimate extreme flow events need to be urgently addressed. From the results for KZN the JPV method, with a regionalised GEV distribution with the veld zone regionalisation, generally gave the best performance when compared to design floods estimated from the annual maximum series extracted from the observed data. It is recommended that the performance of the various RFFA methods needs to be assessed at a national scale and that a more detailed regionalisation be used in the development of an updated RFFA method for South Africa.Keywords: regional flood frequency analysis, KwaZulu-Natal

Regionalisation of the parameters of the log-Pearson 3 distribution: a case study for New South Wales, Australia

The index flood method is widely used in regional flood frequency analysis (RFFA) but explicitly relies on the identification of ‘acceptable homogeneous regions’. This paper presents an alternative RFFA method, which is particularly useful when ‘acceptably homogeneous regions’ cannot be identified. The new RFFA method is based on the region of influence (ROI) approach where a ‘local region’ can be formed to estimate statistics at the site of interest. The new method is applied here to regionalize the parameters of the log-Pearson 3 (LP3) flood probability model using Bayesian generalized least squares (GLS) regression. The ROI approach is used to reduce model error arising from the heterogeneity unaccounted for by the predictor variables in the traditional fixed-region GLS analysis. A case study was undertaken for 55 catchments located in eastern New South Wales, Australia. The selection of predictor variables was guided by minimizing model error. Using an approach similar to stepwise regression, the best model for the LP3 mean was found to use catchment area and 50-year, 12-h rainfall intensity as explanatory variables, whereas the models for the LP3 standard deviation and skewness only had a constant term for the derived ROIs. Diagnostics based on leave-one-out cross validation show that the regression model assumptions were not inconsistent with the data and, importantly, no genuine outlier sites were identified. Significantly, the ROI GLS approach produced more accurate and consistent results than a fixed-region GLS model, highlighting the superior ability of the ROI approach to deal with heterogeneity. This method is particularly applicable to regions that show a high degree of regional heterogeneity. Copyright © 2014 John Wiley & Sons, Ltd.

Application of artificial neural networks in regional flood frequency analysis: a case study for Australia

Regional flood frequency analysis (RFFA) is widely used in practice to estimate flood quantiles in ungauged catchments. Most commonly adopted RFFA methods such as quantile regression technique (QRT) assume a log-linear relationship between the dependent and a set of predictor variables. As non-linear models and universal approximators, artificial neural networks (ANN) have been widely adopted in rainfall runoff modeling and hydrologic forecasting, but there have been relatively few studies involving the application of ANN to RFFA for estimating flood quantiles in ungauged catchments. This paper thus focuses on the development and testing of an ANN-based RFFA model using an extensive Australian database consisting of 452 gauged catchments. Based on an independent testing, it has been found that ANN-based RFFA model with only two predictor variables can provide flood quantile estimates that are more accurate than the traditional QRT. Seven different regions have been compared with the ANN-based RFFA model and it has been shown that when the data from all the eastern Australian states are combined together to form a single region, the ANN presents the best performing RFFA model. This indicates that a relatively larger dataset is better suited for successful training and testing of the ANN-based RFFA models.

Regional flood frequency analysis in eastern Australia: Bayesian GLS regression-based methods within fixed region and ROI framework – Quantile Regression vs. Parameter Regression Technique

Summary In this article, an approach using Bayesian Generalised Least Squares (BGLS) regression in a region-of-influence (ROI) framework is proposed for regional flood frequency analysis (RFFA) for ungauged catchments. Using the data from 399 catchments in eastern Australia, the BGLS-ROI is constructed to regionalise the flood quantiles (Quantile Regression Technique (QRT)) and the first three moments of the log-Pearson type 3 (LP3) distribution (Parameter Regression Technique (PRT)). This scheme firstly develops a fixed region model to select the best set of predictor variables for use in the subsequent regression analyses using an approach that minimises the model error variance while also satisfying a number of statistical selection criteria. The identified optimal regression equation is then used in the ROI experiment where the ROI is chosen for a site in question as the region that minimises the predictive uncertainty. To evaluate the overall performances of the quantiles estimated by the QRT and PRT, a one-at-a-time cross-validation procedure is applied. Results of the proposed method indicate that both the QRT and PRT in a BGLS-ROI framework lead to more accurate and reliable estimates of flood quantiles and moments of the LP3 distribution when compared to a fixed region approach. Also the BGLS-ROI can deal reasonably well with the heterogeneity in Australian catchments as evidenced by the regression diagnostics. Based on the evaluation statistics it was found that both BGLS-QRT and PRT-ROI perform similarly well, which suggests that the PRT is a viable alternative to QRT in RFFA. The RFFA methods developed in this paper is based on the database available in eastern Australia. It is expected that availability of a more comprehensive database (in terms of both quality and quantity) will further improve the predictive performance of both the fixed and ROI based RFFA methods presented in this study, which however needs to be investigated in future when such a database is available.

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

AbstractRegression‐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.

Scaling property of regional floods in New South Wales Australia

Regional flood frequency analysis (RFFA) is often used in hydrology to estimate flood quantiles when there is a limitation of at-site recorded flood data. One of the commonly used RFFA methods is the index flood method, which is based on the assumptions that a region satisfies criterion of simple scaling and it can be treated homogeneous. Another RFFA method is quantile regression technique where prediction equations are developed for flood quantiles of interest as function of catchment characteristics. In this paper, the scaling property of regional floods in New South Wales (NSW) State in Australia is investigated. The results indicate that the annual maximum floods in NSW satisfy a simple scaling assumption. The application of a heterogeneity test, however, reveals that NSW flood data set does not satisfy the criteria for a homogeneous region. Finally, a set of prediction equations are developed for NSW using quantile regression technique; an independent test shows that these equations can provide reasonably accurate design flood estimates with a median relative error of about 27%.

Design Flood Estimation in Ungauged Catchments: A Comparison Between the Probabilistic Rational Method and Quantile Regression Technique for NSW

Design flood estimation for ungauged catchments is often required in hydrologie design. The most commonly adopted regional flood frequency analysis methods used for this purpose include the index flood method, regression based techniques and various forms of the rational method. This paper first examines the similarities and differences between the probabilistic rational method (PRM) (the currently recommended method for Victoria and eastern NSW in Australian Rainfall and Runoff) and the generalised least squares (GLS) based quantile regression technique (QRT). It then uses data from 107 catchments in NSW to compare the performance of these two methods. To make a valid comparison, the same predictor variables and data set have been used for both methods. Based on one-at-a-time and split sample validation tests and a range of evaluation statistics, it has been found that the GLS-based QRT performs better than the PRM. The particular advantage of the QRT is that it does not require a contour map of the runoff coefficient as with the PRM. The QRT also offers potential for improvement through inclusion of additional predictor variables. The QRT can also be integrated with the region of influence approach, where a region can be formed around an ungauged catchment by selecting an “appropriate number” of neighbouring gauged catchments. Overall, the QRT offers much greater flexibility and potential in terms of error analysis and further development as compared to the PRM.

Comparison of Ordinary and Generalised Least Squares Regression Models in Regional Flood Frequency Analysis: A Case Study for New South Wales

Regional flood frequency analysis (RFFA) techniques are commonly used to estimate design floods for ungauged catchments. In Australian Rainfall and Runoff (ARR), the probabilistic rational method (PRM) was recommended for eastern New South Wales (NSW). Recent studies in Australia have shown that regression-based RFFA methods can provide more accurate design flood estimates than the PRM. This paper compares ordinary least squares (OLS) and generalised least squares (GLS) based quantile regression techniques using data from 96 small-to medium-sized catchments across NSW for average recurrence intervals of 2 to 100 years. The advantages of the GLS regression are that this accounts for the inter-station correlation and varying record lengths from site to site. An independent test based on both the split-sample and one-at-a-time validation approaches employing a wide range of statistical diagnostics indicates that the GLS regression provides more accurate flood quantile estimates than the OLS one. The developed regression equations are relatively easy to apply, which require data for only two to three predictors, catchment area, design rainfall intensity and stream density. The findings from this study together with those from other RFFA studies being examined as a part of ARR upgrade projects will inform the development of RFFA techniques for inclusion in the revised edition of ARR.

A reliability‐based selective index for regional flood frequency analysis methods

AbstractIn this paper, a new index is proposed for the selection of the best regional frequency analysis method. First, based on the theory of reliability, the new selective index is developed. The variances of three regional T‐year event estimators are then derived. The proposed methodology is applied to an actual watershed. For each regional method, the reliability of various T‐year regional estimates is computed. Finally, the reliability‐based selective index graph is constructed from which the best regional method can be determined. In addition, the selection result is compared with that based on the traditional index, root mean square error. The proposed new index is recommended as an alternative to the existing indices such as root mean square error, because the influence of uncertainty and the accuracy of estimates are considered. Copyright © 2003 John Wiley & Sons, Ltd.