Regional Flood Frequency Estimation Research Articles

Comparison of Regionalisation Techniques for Peak Streamflow Estimation in Small Catchments in the Pilbara, Australia

Arid and semi-arid regions typically lack high-resolution river gauging data causing difficulties in understanding rainfall-runoff patterns. A common predictive method for discharge estimation within ungauged catchments is regional flood frequency estimation (RFFE), deriving peak discharge estimates from similar, gauged catchments and applying them to the catchment of interest. The majority of RFFE equations are developed for larger catchments where flow events may be larger and of greater interest. We test a series of RFFE methods derived for the Pilbara region, applying them to new ungauged small catchments under 10 km2. Rainfall values are derived from a guideline Australian design rainfall database, Australian Rainfall and Runoff 2019 (ARR2019) which was recently updated with an additional 30 years of rainfall data. RFFE equations are compared to a direct rainfall model to evaluate their performance within small catchments, identifying key limitations and considerations when modelling small headwater catchments.

Taking Account of Seasonality in a Regional Flood Frequency Estimation Approach Based on Event Simulations

Regional flood estimation is an important issue in hydrology to anticipate and reduce the damages caused by extreme rainfall events. Approaches based on event simulation are particularly suitable to address this. As research has demonstrated the seasonality of rainfall characteristics, many flood frequency estimation approaches take into account rainfall seasonality to include seasonal fluctuations. For an event-based approach, since its hydrological model is initialized for each rainfall event, its performance is very sensitive to the initial states of the model. The seasonality of its hydrological model could thus become a decisive factor. Due to the complexity of the regionalization method, very few flood frequency estimation approaches based on event simulation have been regionalized at a large scale and do not consider the seasonality of hydrological parameters. This is the case for the SHYREG method studied in this article. Using data from HYDRO French database and SAFRAN, we discuss several adapted configurations considering the seasonality of both rainfall and hydrological parameters during its calibration and regionalization phase. Tests were carried out on 1929 catchments throughout France. Rather than calibrating a constant annual parameter for the hydrological model, we calibrated “winter” and “summer” parameters based on different observed flow quantiles (“seasonal”, “annual”, or “both”). Criteria on flood quantiles were calculated for different samplings. We also discuss the representativeness of seasonal parameters for the regionalization procedure and hydrological coherence observed from this seasonal parameterization. It seems that calibrating parameters based on seasonal flow quantiles helps reproduce annual quantiles, while the opposite is not possible. Among all the calibration configurations, calibration performed on both seasonal and annual flow quantiles makes the largest improvement compared to the initial annual parameterization method. It can correctly restitute seasonal flood quantiles for both calibration and validation catchments, with an obvious improvement in terms of estimating flood frequency in ungauged sites. It shows that the seasonality of hydrological parameters is worth considering for a regional flood estimation approach.

Accounting for the Effects of Climate Variability in Regional Flood Frequency Estimates in Western Nigeria

Extreme flood events are becoming more frequent and intense in recent times, owing to climate change and other anthropogenic factors. Nigeria, the case-study for this research experiences recurrent flooding, with the most disastrous being the 2012 flood event that resulted in unprecedented damage to infrastructure, displacement of people, socio-economic disruption, and loss of lives. To mitigate and minimize the impact of such floods now and in the future, effective planning is required, underpinned by analytics based on reliable data and information. Such data are seldom available in many developing regions, owing to financial, technical, and organizational drawbacks that result in short-length and inadequate historical data that are prone to uncertainties if directly applied for flood frequency estimation. This study applies regional Flood Frequency Analysis (FFA) to curtail deficiencies in historical data, by agglomerating data from various sites with similar hydro-geomorphological characteristics and is governed by a similar probability distribution, differing only by an “index-flood”; as well as accounting for climate variability effect. Data from 17 gauging stations within the Ogun-Osun River Basin in Western Nigeria were analysed, resulting in the delineation of 3 sub-regions, of which 2 were homogeneous and 1 heterogeneous. The Generalized Logistic distribution was fitted to the annual maximum flood series for the 2 homogeneous regions to estimate flood magnitudes and the probability of occurrence while accounting for climate variability. The influence of climate variability on flood estimates in the region was linked to the Madden-Julian Oscillation (MJO) climate indices and resulted in increased flood magnitude for regional and direct flood frequency estimates varying from 0% - 35% and demonstrate that multi-decadal changes in atmospheric conditions influence both small and large floods. The results reveal the value of considering climate variability for flood frequency analysis, especially when non-stationarity is established by homogeneity analysis.

Causative classification of river flood events.

A wide variety of processes controls the time of occurrence, duration, extent, and severity of river floods. Classifying flood events by their causative processes may assist in enhancing the accuracy of local and regional flood frequency estimates and support the detection and interpretation of any changes in flood occurrence and magnitudes. This paper provides a critical review of existing causative classifications of instrumental and preinstrumental series of flood events, discusses their validity and applications, and identifies opportunities for moving toward more comprehensive approaches. So far no unified definition of causative mechanisms of flood events exists. Existing frameworks for classification of instrumental and preinstrumental series of flood events adopt different perspectives: hydroclimatic (large‐scale circulation patterns and atmospheric state at the time of the event), hydrological (catchment scale precipitation patterns and antecedent catchment state), and hydrograph‐based (indirectly considering generating mechanisms through their effects on hydrograph characteristics). All of these approaches intend to capture the flood generating mechanisms and are useful for characterizing the flood processes at various spatial and temporal scales. However, uncertainty analyses with respect to indicators, classification methods, and data to assess the robustness of the classification are rarely performed which limits the transferability across different geographic regions. It is argued that more rigorous testing is needed. There are opportunities for extending classification methods to include indicators of space–time dynamics of rainfall, antecedent wetness, and routing effects, which will make the classification schemes even more useful for understanding and estimating floods.This article is categorized under:Science of Water > Water ExtremesScience of Water > Hydrological ProcessesScience of Water > Methods

Hydrodynamic Modeling for Flood Hazard Assessment in a Data Scarce Region: a Case Study of Bharathapuzha River Basin

Flood hazard is assessed for the data scarce lower Bharathapuzha basin in Kerala, India, using a hydrologic-hydraulic approach. L-moment-based regional flood frequency analysis along with a non-dimensional analysis of hydrographs is used to generate scenarios for flood hazard assessment. A fully hydrodynamic 1D river flow model is calibrated for the rivers of lower Bharathapuzha basin for the year 1992 and validated for the year 1994. The widely available SRTM DEM is used to extract river cross-sections and the limited available discharge and water level data are used to carefully calibrate the 1D river flow model. Subsequently, a coupled 1D-2D flood inundation model is used to simulate the flood inundation extent for the year 2002. In the absence of LIDAR data, a cartographic DEM derived from readily available topo maps is used as an input in the coupled 1D-2D model. Further, in the absence of microwave Synthetic Aperture Radar (SAR) data, the flood inundation extent is validated using the readily available optical IRS-1D WiFS sensor data which is mainly intended for vegetation and drought monitoring. A suitable methodology is used to delineate flood inundation extent from the partial cloud-covered WiFS image. The regional flood frequency estimates and the calibrated and validated flood inundation model are then used to assess the flood hazard.

Application of artificial neural network, multiple-regression and index-flood techniques in regional flood frequency estimation

Application of artificial neural network, multiple-regression and index-flood techniques in regional flood frequency estimation

Application of artificial neural network, multiple-regression and index-flood techniques in regional flood frequency estimation

Flood frequency estimation is fundamental in both engineering science and engineering hydrology science. Comparing the efficiencies of artificial neural network (ANN), multiple-regression (MR) and index-flood (IF) techniques based on L-moments in Qazvin Province of Iran was the main objective of this study. Using the main variables affecting flood magnitude, the study area was divided into two regions based on the clustering approach. The homogeneity of these regions was confirmed using the homogeneity test of L-moments approach. Using the L-moment ratios and the Z-statistic criteria, generalised logistic (GLO) and generalised Pareto (GPA) distributions were identified respectively for the first and second homogen regions as the most robust distributions among five candidate distributions. Relative root mean square error (RRMSE) measure was applied for evaluating the performance of three methods in comparison with the curve fitting method. In general, ANN method gives more reliable estimations.

An elusive search for regional flood frequency estimates in the River Nile basin

Abstract. Estimation of peak flow quantiles in ungauged catchments is a challenge often faced by water professionals in many parts of the world. Approaches to address such problem exist, but widely used techniques such as flood frequency regionalisation is often not subjected to performance evaluation. In this study, the jack-knifing principle is used to assess the performance of the flood frequency regionalisation in the complex and data-scarce River Nile basin by examining the error (regionalisation error) between locally and regionally estimated peak flow quantiles for different return periods (QT). Agglomerative hierarchical clustering based algorithms were used to search for regions with similar hydrological characteristics. Hydrological data employed were from 180 gauged catchments and several physical characteristics in order to regionalise 365 identified catchments. The Generalised Extreme Value (GEV) distribution, selected using L-moment based approach, was used to construct regional growth curves from which peak flow growth factors could be derived and mapped through interpolation. Inside each region, variations in at-site flood frequency distribution were modelled by regression of the mean annual maximum peak flow (MAF) versus catchment area. The results showed that the performance of the regionalisation is heavily dependent on the historical flow record length and the similarity of the hydrological characteristics inside the regions. The flood frequency regionalisation of the River Nile basin can be improved if sufficient flow data of longer record length of at least 40 yr become available.

Assessment of regional estimation error by canonical residual kriging

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

Regional Flood Frequency Analysis Using L-Moments for the West Mediterranean Region of Turkey

The aim of this study is to investigate and determine hydrologically homogeneous regions and to derive regional flood frequency estimates for 47 gauged sites in the West Mediterranean River Basins in Turkey, using an index flood method with L-moments parameter estimation. Screening of the data of the gauged site is carried out based on a discordancy measure in terms of the L-moments. Initial candidate regions are established by the cluster analysis of first five L-moment statistics, using k-means method. Homogeneity of the basins is tested using simulation with a four-parameter Kappa distribution and an L-moments based heterogeneity measure. Three subregions are defined, namely the Antalya subregion, the Lower West Mediterranean subregion, and the Upper West Mediterranean subregion. Comparative regional flood frequency estimates are made for each subregion using various distributions, namely the generalized logistic, general extreme value, generalized normal, Pearson type III, generalized Pareto, kappa, and Wakeby distributions. Based on an L-moments goodness-of-fit statistic, the Pearson type III distribution is identified as the best-fit distribution for the Antalya and Lower-West Mediterranean subregions, and the Generalized Logistic for the Upper-West Mediterranean subregion. Monte Carlo simulation is used to evaluate the accuracy of the quantile estimates on the basis of the relative root-mean-square error and relative bias.

Intercomparison of regional flood frequency estimation methods at ungauged sites for a Mexican case study

This paper presents an adaptation of some regional estimation approaches to tropical climates and a comparison of their performance on the basis of their application to data from the Balsas, Lerma and Pánuco River Basins located in Mexico. Four approaches are used in this study for the delineation of homogeneous regions: The first one is the hierarchical cluster analysis approach which leads to fixed hydrologic regions. The second one is the canonical correlation analysis (CCA) which allows the determination of hydrologic neighborhoods that are specific to the site of interest. The third one is a revised version of the canonical correlation analysis approach that is free of parameter optimization and which can be automated easily. Finally, the fourth one is the technique of canonical kriging which consists in interpolating hydrological variables over the canonical physiographical space. The methods based on the canonical correlation analysis approach provide also the basis for identifying the explanatory variables to be used during the step of regional estimation. Regional estimation is carried out based on a multiple regression approach. A data set of 29 stations from several Mexican River Basins in and around the Balsas region is used to show the advantages and weaknesses of each method and to demonstrate their usefulness in the context of regional flood quantile estimation. This study allows also to test the robustness of these methods through their application to a real world case study with a relatively limited number of stations. While all methods performed quite adequately, results indicate clearly the advantages of the neighborhood type of approach and the superiority of the canonical correlation analysis based methods. Results demonstrate that CCA-based methods lead to best performances overall. While hierarchical clustering seems generally to lead to less biased quantile estimates, the lowest root mean square error values are almost consistently obtained for the CCA-based methods. Results seem also to point out that all methods are quite sensitive to the quality and quantity of data available. The method of canonical kriging does not seem to be more sensitive to the database quality than the other two CCA-based methods.

Data-based comparison of seasonality-based regional flood frequency methods

During the last 10 years a growing number of regional flood frequency estimation studies have used flood seasonality descriptors for delineating hydrologically homogeneous regions. Seasonality of floods reflects a complex catchment’s hydrologic response to flood producing processes. Due to the high accuracy and robustness of flood date data, the use of flood seasonality indices became an attractive alternative to traditional regionalization approaches for the delineation of homogeneous regions, a key step in any regionalization study. This paper presents a data-based comparison of three flood seasonality regionalization methods. The methods are applied to a set of catchments in the province of Quebec (Canada). A jack-knife procedure is used to assess the performance of the methods in regional quantile estimation. The results are compared to those obtained from a traditional regionalization approach based on catchment physiographic similarities, and to the results from a scenario where information available in all catchments is used in the analysis. The seasonality method based on the peaks-over-threshold approach was concluded to lead to best results.

A methodology for regional flood frequency estimation based on scaling properties

There is a persisting need for extreme value assessments of discharge for ungauged catchments for design purposes, flood protection and flood risk analysis. A methodology is put forward for estimating flood quantiles in ungauged catchments, taking into account the scaling behaviour of discharge in a homogeneous region. From an assumption that the spatial distribution of specific discharge generation is a shifted exponential distribution, theoretical expressions of the maximum specific discharge values relative to catchment size are developed and a regional scaling equation is presented. The homogeneity constraint refers to climatic conditions, whereas catchment heterogeneity due to physiographic features of catchments is incorporated in the models using multiple regression analysis. The methodology is tested for a region in Southern Norway with homogeneous climatic conditions but with varying physiographic catchment characteristics. It is investigated if the extreme discharge values estimated with traditional methods are adequately reproduced with the proposed methodology. For the tested region, the agreement between flood quantiles estimated with the proposed methodology and flood quantiles estimated from observed data is good. The effective lake percentage and the river gradient proved to be significant descriptors of the deviation from the scaling procedure. Copyright © 2004 John Wiley & Sons, Ltd.

Physiographical space‐based kriging for regional flood frequency estimation at ungauged sites

A physiographical space‐based kriging method is proposed for regional flood frequency estimation. The methodology relies on the construction of a continuous physiographical space using physiographical and meteorological characteristics of gauging stations and the use of multivariate analysis techniques. Two multivariate analysis methods were tested: canonical correlation analysis (CCA) and principal components analysis. Ordinary kriging, a geostatistical technique, was then used to interpolate flow quantiles through the physiographical space. Data from 151 gauging stations across the southern part of the province of Quebec, Canada, were used to illustrate this approach. In order to evaluate the performance of the proposed method, two validation techniques, cross validation and split‐sample validation, were applied to estimate flood quantiles corresponding to the 10, 50, and 100 year return periods. Results of the proposed method were compared to those produced by a traditional regional estimation method using the canonical correlation analysis. The proposed method yielded satisfactory results. It allowed, for instance, for estimating the 10 year return period specific flow with a coefficient of determination of up to 0.78. However, this performance decreases with the increase in the quantile return period. Results also showed that the proposed method works better when the physiographical space is defined using canonical correlation analysis. It is shown that kriging in the CCA physiographical space yields results as precise as the traditional estimation method, with a fraction of the effort and the computation time.

Analysis of the linkage between rain and flood regime and its application to regional flood frequency estimation

Improving techniques of flood frequency estimation at ungauged sites is one of the foremost goals of contemporary hydrology. River flood regime is a resultant reflection of a composite catchment hydrologic response to flood producing processes. In this sense the process of identifying homogeneous pooling groups can be plausibly based on catchment similarity in flood regime. Unfortunately the application of any pooling approach that is based on flood regime is restricted to gauged sites. Because flood regime can be markedly determined by rainfall regime, catchment similarity in rainfall regime can be an alternative option for identifying flood frequency pooling groups. An advantage of such a pooling approach is that rainfall data are usually spatially and temporary more abundant than flood data and the approach can also be applied at ungauged sites. Therefore in this study we have quantified the linkage between rainfall and flood regime and explored the appropriateness of substituting rainfall regime for flood regime in regional pooling schemes. Two different approaches to describing rainfall regime similarity using tools of directional statistics have been tested and used for evaluation of the potential of rainfall regime for identification of hydrologically homogeneous pooling groups. The outputs were compared to an existing pooling framework adopted in the Flood Estimation Handbook. The results demonstrate that regional pooling based on rainfall regime information leads to a high number of initially homogeneous groups and seems to be a sound pooling alternative for catchments with a close linkage between rain and flood regimes.

Regional flood frequency estimation with canonical correlation analysis

Despite its potential advantages, canonical correlation analysis (CCA) has been little used in the fields of hydrology and water resources. In a regional flood frequency analysis, canonical correlations can be used to investigate the correlation structure between the two sets of variables represented by watershed characteristics and flood peaks. This paper presents a clear theoretical framework for the use of canonical correlations in regional flood frequency analysis. Some additional results are also presented for the case of gauged target-basins. The approach described in this paper allows one to carry out the determination of homogeneous hydrologic neighborhoods and identifies the variables to use during the step of regional estimation. A data set of 106 stations from the province of Ontario (Canada) is used to demonstrate the advantages of this method and investigate various aspects in relation with its robustness. Results indicate that the method is robust to such factors as the number of stations and the type of parametric distribution being used. Step-by-step algorithms for the delineation of hydrologic neighborhoods in the cases of gauged and ungauged basins are also presented.

A worldwide comparison of regional flood estimation methods and climate

A summary of the results of regional flood frequency analyses that have been carried out in many tropical and sub-tropical countries is presented. The results provide a set of flood estimation methods which should be of immediate usefulness in practical applications, bearing in mind the errors of generalization. By defining the representative values of the mean annual flood and of the flood frequency curves for each region, an attempt has been made to develop relationships between the methods and climatic characteristics. These show that adequate relationships can be obtained based solely on the median annual average rainfall and the median catchment area for the regions. In particular, the steepness of the regional flood frequency curve can be estimated reasonably well from these two parameters. The relationships confirm that, broadly speaking, for humid regions the “average” flood—the flood that occurs regularly every one or two years—is relatively large, but the flood frequency curve is not very steep; rare floods (occurring once in every 100 to 1000 years) are not very much larger than the average flood. Conversely, in arid regions, the average flood is small, but rare floods can be extremely large multiples of the average. These relationships provide a useful initial indication of the type of regional flood frequency estimation method likely to be found in tropical and sub-tropical regions throughout the world.

An analysis of the influence of river channel properties on flood frequency

A one-dimensional flood routing model (DAMBRK) is used to explore the influence of some channel and floodplain hydraulic and geometric properties on the frequency distribution of peak flood flow rates as flood waves propagate downstream. Peak flood inflows for selected return periods are derived from a known flood frequency distribution and the effects of channel and floodplain properties on the associated hydrographs and peak flow rates are examined as the floods propagate down a single river reach; geometric and hydraulic properties of the reach are varied over a wide range. With no lateral inflow to the reach, a one-to-one mapping of computed peak flows and return periods of the routed hydrographs is used to determine the flood frequency distribution at downstream locations. For an upstream peak flood flow regime defined by an extreme value type I distribution (EV I) with coefficient of variation 0.6, the form of the downstream peak flow distribution appears to change from EV I to either EV II or EV III whenever inundation of the floodplain occurs, and the peak flow distribution becomes less variable with the amount of reduction in variance depending on the effective storage of the floodplain-channel reach. The findings are important when selecting data for use in regional flood frequency estimation.

Towards a systematic approach to comparing distributions used in flood frequency analysis

The estimation of flood quantiles from available streamflow records has been a topic of extensive research in this century. However, the large number of distributions and estimation methods proposed in the scientific literature has led to a state of confusion, and a gap prevails between theory and practice. This concerns both at-site and regional flood frequency estimation. To facilitate the work of ȁhydrologists, designers of hydraulic structures, irrigation engineers and planners of water resourcesȁ, the World Meteorological Organization recently published a report which surveys and compares current methodologies, and recommends a number of statistical distributions and estimation procedures. This report is an important step towards the clarification of this difficult topic, but we think that it does not effectively satisfy the needs of practitioners as intended, because it contains some statements which are not statistically justified and which require further discussion. In the present paper we review commonly used procedures for flood frequency estimation, point out some of the reasons for the present state of confusion concerning the advantages and disadvantages of the various methods, and propose the broad lines of a possible comparison strategy. We recommend that the results of such comparisons be discussed in an international forum of experts, with the purpose of attaining a more coherent and broadly accepted strategy for estimating floods.

A hierarchical approach to regional flood frequency analysis

A hierarchical approach to regional flood frequency estimation is described, where higher‐order parameters controlling the skewness of annual maximum floods are determined from larger regions than are used for determining parameters controlling the coefficient of variation. Procedures based on two‐component extreme value and generalized extreme value distributions are developed, and it is shown that dividing a region into subregions to estimate some parameters can lead to more precise quantile estimates. The practical problem of defining the optimum number of subregions is discussed.