Regional flood frequency analysis for hydrologically diverse regions in New Zealand

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.

Flood is one of the worst natural disasters, which causes the damage of billions of dollars each year globally. To reduce the flood damage, we need to estimate design floods accurately, which are used in the design and operation of water infrastructure. For gauged catchments, flood frequency analysis can be used to estimate design floods; however, for ungauged catchments, regional flood frequency analysis (RFFA) is used. This paper compares two popular RFFA techniques, namely the quantile regression technique (QRT) and the index flood method (IFM). A total of 181 catchments are selected for this study from south-east Australia. Eight predictor variables are used to develop prediction equations. It has been found that IFM outperforms QRT in general. For higher annual exceedance probabilities (AEPs), IFM generally demonstrates a smaller estimation error than QRT; however, for smaller AEPs (e.g. 1 in 100), QRT provides more accurate quantile estimates. The IFM provides comparable design flood estimates with the Australian Rainfall and Runoff—the national guide for design flood estimation in Australia.

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Regional flood frequency analysis (RFFA) is often used in hydrological studies to estimate design floods for ungauged catchments.The RFFA techniques in arid and semi-arid regions are more difficult to develop, largely because of paucity of the recorded streamflow data, extreme temporal and spatial variability of floods and long periods in the streams without any significant flow.The current national design guideline, Australian Rainfall and Runoff (ARR), contains little information and guidance on RFFA in arid and semi-arid regions of Australia.This study develops a RFFA method based on the best available data set for the arid and semi-arid regions of Australia.Here, an index type RFFA procedure is adopted.Dimensionless regional flood frequency curves are developed in the form of regional growth factors.The study uses streamflow and catchment characteristics data from 45 catchments in the arid and semi-arid regions of New South Wales (NSW), Victoria (VIC), Queensland (QLD), South Australia (SA) and Northern Territory (NT).The mean annual rainfall values for these selected catchments are smaller than 450 mm.The partial duration series flood data are used in this study to estimate the flood quantiles for average recurrence intervals (ARI) of 2 to 100 years using the generalised Pareto distribution fitted with the method of Lmoments.The areas and streamflow record lengths of the selected catchments range from 3.8 to 5,975 km 2 and 10 to 46 years, respectively.The index-flood, which is taken as the mean annual flood in this study, is related to catchment area and design rainfall intensity by ordinary least squares regression to provide a means of estimating the mean annual flood for an ungauged catchment.This estimated mean annual flood and the regional growth factors permit flood quantile estimation for different ARIs in the study regions.Separate sets of prediction equations for mean annual flood are developed for VIC & NSW (combined), QLD, SA and NT.For the testing of the developed regional flood estimation methods, a one-at-a-time validation procedure is adopted.A number of evaluation statistics have been employed to assess the performances of the developed RFFA models.It has been found that the developed prediction equations provide relatively accurate design flood quantile estimates in the study regions with median relative error in the range of 20% to 110%.The steepness of the developed growth curves reflects the variability of the flood discharge with ARIs and it has been found that SA has the steepest growth curve and QLD the least one.This implies that SA design floods in arid and semi-arid regions show greater variability across different ARIs as compared to those of other Australian states.The developed RFFA methods in the arid and semi-arid regions are based on relatively shorter streamflow records and a smaller number of stations as compared to the coastal regions of Australia and hence have relatively lower degree of accuracy.

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).

Flood is one of the worst natural disasters, which causes significant damage to economy and society. Flood risk assessment helps to reduce flood damage by managing flood risk in flood affected areas. For ungauged catchments, regional flood frequency analysis (RFFA) is generally used for design flood estimation. This study develops a Convolutional Neural Network (CNN) based RFFA technique using data from 201 catchments in south-east Australia. The CNN based RFFA technique is compared with multiple linear regression (MLR), support vector machine (SVM), and decision tree (DT) based RFFA models. Based on a split-sample validation using several statistical indices such as relative error, bias and root mean squared error, it is found that the CNN model performs best for annual exceedance probabilities (AEPs) in the range of 1 in 5 to 1 in 100, with median relative error values in the range of 29–44%. The DT model shows the best performance for 1 in 2 AEP, with a median relative error of 24%. The CNN model outperforms the currently recommended RFFA technique in Australian Rainfall and Runoff (ARR) guideline. The findings of this study will assist to upgrade RFFA techniques in ARR guideline in near future.

The regional flood frequency analysis (RFFA) technique is widely used to estimate design floods in ungauged catchments. This study presents development and testing of an ordinary kriging based RFFA technique using peaks-over-threshold (POT) model. Ordinary kriging offers significant advantages in RFFA by providing unbiased, optimal interpolation of spatial data, leading to more accurate flood quantile estimation. This study uses flood and catchment characteristics data from 419 stream gauging stations in eastern Australia. It has been found that the median relative error values are found to be in the range of 29 and 33%, which is more accurate than regression based RFFA models recommended in Australian Rainfall and Runoff (national guideline). The POT based kriging model is found to provide more accurate design flood estimates in coastal areas than the inner part of Australia. Further study should focus on developing a bias correction method to minimize tendency of over-estimation by the new kriging based RFFA model.

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.

Regional flood frequency analysis is still an important area of hydrology research as there are many ungauged catchments. The majority of hydrological methods in regional flood frequency analysis involve complex non‐linear relationships between predictor variables and flood characteristics. In the past, dimensionality reduction techniques based on linear methods such as canonical correlation analysis (CCA) were used in regional flood frequency analysis to delineate hydrological clusters. Non‐linear dimensionality reduction techniques, such as KCCA and multidimensional scaling (MDS), have been used in several fields of science, but not explicitly in regional flood frequency analysis. To determine hydrologically similar clusters, the approaches considered in this article use CCA, KCCA, and MDS as dimensionality reduction techniques in conjunction with Gaussian mixture models (GMM). Log‐linear regression and generalized additive models are then applied to the hydrological clusters to evaluate regional flood frequency analysis. A comparison of linear and non‐linear (NL) methods is performed using data from Victoria, Australia, to demonstrate the benefit of these methods. It has been found that the non‐linear frameworks of multi‐dimensional scaling with Gaussian mixture model‐non‐linear (MDSGMM‐NL) and KCCA with Gaussian mixture model‐non‐linear (KCCAGMM‐NL), as well as the mixed frameworks (i.e. KCCA and Gaussian mixture model‐non‐linear [CCAGMM‐NL]), can be used to represent the non‐linear complexities of hydrological processes in regional flood frequency analysis.

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.

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.

In water resources management, environmental and ecological studies, estimation of design streamflow is often needed. For gauged catchments, at-site flood frequency analysis is used for this purpose; however, for ungauged catchments, regional flood frequency analysis (RFFA) is the preferred method. RFFA attempts to transfer flood characteristics from gauged to ungauged catchments based on the assumption of regional homogeneity. A bibliometric analysis on RFFA is presented here using Web of Science (WoS) and Scopus databases. A total of 626 articles were selected from these databases. From the bibliometric analysis, it has been found that Journal of Hydrology and Water Resources Research are the two leading journals reporting RFFA research. In RFFA research, leading countries include Canada, USA, UK, Italy and Australia. In terms of citations, the top performing researchers are Ouarda T, Burn D, Rahman A, Haddad K and Chebana F. Future research should be directed towards the identification of homogeneous regions, application of efficient artificial intelligence (AI)-based RFFA models, incorporation of climate change impacts and uncertainty analysis.

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.

The use of rainfall–runoff models constitutes an alternative to statistical approaches (such as at-site or regional flood frequency analysis) for design flood estimation and represents an answer to the increasing need for synthetic design hydrographs associated with a specific return period. Nevertheless, the lack of streamflow observations and the consequent high uncertainty associated with parameters estimation usually pose serious limitations to the use of process-based approaches in ungauged catchments, which in contrast represent the majority in practical applications. This work presents a Bayesian procedure that, for a predefined rainfall–runoff model, allows for the assessment of posterior parameters distribution, using limited and uncertain information available about the response of ungauged catchments, i.e. the regionalized first three L-moments of annual streamflow maxima. The methodology is tested for a catchment located in southern Italy and used within a Monte Carlo scheme to obtain design flood values and simulation uncertainty bands through both event-based and continuous simulation approaches. The obtained results highlight the relevant reduction in uncertainty bands associated with simulated peak discharges compared to those obtained considering a prior uniform distribution for model parameters. A direct impact of uncertainty in regional estimates of hydrological signatures on posterior parameters distribution is also evident. For the selected case study, continuous simulation, generally, better matches the estimates of the statistical flood frequency analysis.

Effect of Homogeneity on Flood Estimation at the West Mediterranean Region In regional flood frequency analysis, identification of homogeneous sub-regions is a fundamental factor for reliable flood quantile estimation in hydrologic modeling, engineering practice for water structures design and management. In this study, regional flood frequency analysis is carried out for annual maximum flood series of stream gauging stations with Dalrymple and L-moments homogeneity approaches for the West Mediterranean River basins in Turkey. The studied region is divided into three homogeneous sub-regions namely Antalya, Lower West Mediterranean and Upper West Mediterranean based on Dalrymple and L-moment homogeneity tests. Design floods with various recurrence intervals are calculated for stream gauging stations in each homogeneous sub-region. The results showed that the difference between design floods