Flood Frequency Estimation Research Articles

Flood risk mapping and analysis: A case study of Andheri Khola catchment, Sindhuli district, Nepal

Nepal is one of the world's most vulnerable countries to a variety of risks, including severe floods. This could result in the loss of lives and property, the relocation of people, the damage of physical infrastructures, homes, and the disruption of people's socioeconomic functions and the country's economy in a variety of ways. River flooding is caused by heavy monsoon rainfall, weak geology, unplanned infrastructure construction along the embankments, and mining in upstream riverbeds. The Andheri Khola (river) is a tributary of the Sunkoshi River that frequently floods, affecting the inhabitants along the way. In Nepal, little effort has been made to comprehend the flood risk in tiny catchment regions such as Andheri Khola, despite the fact that this sort of small catchment is affecting Nepal's numerous new rising towns and urban areas in many ways. To analyse the one-dimensional flood plain, HEC-RAS, Ras Mapper, and ArcGIS were used. The WECS/DHM approach was used to estimate flood frequency in different return periods in order to determine the flood risk in the research area. The study finds that the floods of 2, 50, 100, and 1000 years return periods cover a maximum of 35, 41.9, 42.7, and 49.72 hectare area, respectively. The majority of the flooded sites had water depths of more than 3 meters. More than 70% of the sandy area in the study region is prone to flooding. Furthermore, the cultivated areas are located in a low to moderate risk area.

Application of Regional Climatic Models to Assess the Performance Evaluation of Changes on Flood Frequency in Woybo Catchment, Ethiopia

Climate change increases the possibility in varying rainfall and temperature that needs a detail study to estimate flood frequency under changing climate for the Woybo catchment in the Omo River basin of Ethiopia. The impact on flood frequency was evaluated using multiple climate models under RCP4.5 and RCP8.5 emission scenarios for 2020s, 2050s, and 2080s compared to 1976–2005. Hydrologic Engineering Center of Hydrologic Modeling System (HEC‐HMS) was used to simulate streamflow after sensitivity analysis, calibration, and validation of the model for the catchment. Flood frequency analysis was initiated after discharge through a longer section followed by frequency analysis by partial duration series approach which provides a better result even though threshold selection is cumbersome. The results from the nonparametric Mann–Kendall test illustrate a slight increase in annual rainfall. The projected flow is expected to increase in autumn, summer, and annually by 8.34, 13.54, and 5.02% in the 2050s and 12.20, 18.06, and 11.87% in 2080s, respectively, under RCP4.5, while it is likely to increase in summer and annually by 15.66 and 5.82% in 2050s and 10.55 and 29.51% in 2080s, respectively, under RCP8.5. Flood frequency was analyzed by using Gumbel’s method. The flood magnitude predicts a positive change for 10, 50, 100, 200, 500, and 1000 years recurrent periods under both scenarios. This research paves way to reduce the negative impacts of flooding and plays a vital role in providing estimates of recurrence floods which are used in designing roads, dams, and bridges for sustainable water resources management.

Nonstationary Bayesian Modeling of Extreme Flood Risk and Return Period Affected by Climate Variables for Xiangjiang River Basin, in South-Central China

The accurate design flood of hydraulic engineering is an important precondition to ensure the safety of residents, and the high precision estimation of flood frequency is a vital perquisite. The Xiangjiang River basin, which is the largest river in Hunan Province of China, is highly inclined to floods. This paper aims to investigate the annual maximum flood peak (AMFP) risk of Xiangjiang River basin under the climate context employing the Bayesian nonstationary time-varying moment models. Two climate covariates, i.e., the average June-July-August Artic Oscillation and sea level pressure in the Northwest Pacific Ocean, are selected and found to exhibit significant positive correlation with AMFP through a rigorous statistical analysis. The proposed models are tested with three cases, namely, stationary, linear-temporal and climate-based conditions. The results both indicate that the climate-informed model demonstrates the best performance as well as sufficiently explain the variability of extreme flood risk. The nonstationary return periods estimated by the expected number of exceedances method are larger than traditional ones built on the stationary assumption. In addition, the design flood could vary with the climate drivers which has great implication when applied in the context of climate change. This study suggests that nonstationary Bayesian modelling with climatic covariates could provide useful information for flood risk management.

A process-based flood frequency analysis within a trivariate statistical framework. Application to a semi-arid Mediterranean case study

This paper proposes a trivariate methodology for flood frequency estimation. It combines the flood peak, storm magnitude, and initial soil moisture condition (ISMC) as the main flood-related statistical variables to be considered. The semi-arid Mediterranean “Rambla del Poyo” catchment has been used as a representative case study where the influence of the spatio-temporal variability of the storms and the ISMC on floods can lead to differences of up to two orders of magnitude in quantiles when the most commonly used methods are applied. In order to incorporate the main flood-generating mechanisms, the integrated use of a multidimensional storm generator with distributed hydrological modelling is proposed. Flood quantiles are then estimated by combining the maximum flows with the storm magnitude and ISMC in a trivariate probability distribution function through the application of Bayes’ theorem and Lagrange’s Mean Value theorem. Although the methodology proposed in this paper has been applied and tested in only one case study, it can be extended to other case studies due to its process-based orientation.

Sampling uncertainty of UK design flood estimation

ABSTRACT The UK standard for estimating flood frequencies is outlined by the flood estimation handbook (FEH) and associated updates. Estimates inevitably come with uncertainty due to sampling error as well as model and measurement error. Using resampling approaches adapted to the FEH methods, this paper quantifies the sampling uncertainty for single site, pooled (ungauged), enhanced single site (gauged pooling) and across catchment types. This study builds upon previous progress regarding easily applicable quantifications of FEH-based uncertainty estimation. Where these previous studies have provided simple analytical expressions for quantifying uncertainty for single site and ungauged design flow estimates, this study provides an easy-to-use method for quantifying uncertainty for enhanced single site estimates.

Can we estimate flood frequency with point-process spatial-temporal rainfall models?

Stochastic rainfall models are commonly used in practice for long-term flood risk management. One of the most widely used model types is based on point processes. Despite the widespread use of such models, whether their known simplifications in describing the space–time structure of rainfall will affect the accuracy of flood estimation has not been quantified. In this study, we quantify the biases introduced by the rainfall model limitations to flood estimates in two medium-sized river catchments (717 km2 and 844 km2) in the South East of the UK. To achieve this, we used nine years of hourly radar rainfall data, a dense network of hourly rain gauges, a spatial–temporal rainfall stochastic model based on point processes, and a fully distributed hydrological model. We modelled the corresponding catchment water dynamics using observed and simulated hourly rainfall and then assessed whether the errors introduced by the stochastic model will propagate in the river flow dynamics. Our results show that the stochastic rainfall model properly captures the point-scale rainfall statistics, including point extremes and the cross-site spatial correlations. However, the model results in a bias on extremes of areal statistics, including an overestimation of the areal reduction factor, extreme areal mean precipitation, and the areal fraction of rain (wet area ratio). Using this as input for continuous hydrological simulations, we find that the flow duration curves are well preserved, particularly in the high flow seasons (relative bias is less than 7%). The model also reproduces well the flood frequency curves at a daily scale with an averaged relative bias of 0.36–16.9% at 10-year return levels, confirming its ability to infer the long-term flood risk for medium-sized catchments. However, the summer-season hourly peak discharge is highly overestimated with a relative bias of over 163.5% at the same return level. The overestimation in summer hourly peak discharge is explained by the dominating convective systems in summer and the misrepresented spatial structure in the model.

Issues in generating stochastic observables for hydrological models

AbstractThis paper provides a historical review and critique of stochastic generating models for hydrological observables, from early generation of monthly discharge series, through flood frequency estimation by continuous simulation, to current weather generators. There are a number of issues that arise in such models, from uncertainties in the observational data on which such models must be based, to the potential persistence effects in hydroclimatic systems, the proper representation of tail behaviour in the underlying distributions, and the interpretation of future scenarios.

Comparative analysis of routed flood frequency for reservoirs in parallel incorporating bivariate flood frequency and reservoir operation

AbstractFlood frequency is commonly defined by natural flood characteristics such as flood peak, volume and duration. However, for floods at reservoir site, reservoir operation will significantly alter the natural flood process and flood frequency. This article presents a framework of routed flood frequency which incorporates multi‐reservoir flood correlation and considers the effect of reservoir operation. Aggregated flood volume during reservoir operation has been adopted as indicator for the routed flood frequency curve (RFFC). A comparison has been made with a 62‐year series of historic flood inflows observed at two parallel reservoirs in Eastern China. By comparing the derived RFFC with traditional univariate and bivariate flood frequency results, it can be seen that the RFFC results provide a reasonable estimation of flood frequency. A sensitivity analysis indicated that the reservoir maximum release allocation has significant impact on RFFC, and a balanced allocation of flood peak can greatly decrease the overtopping risk. The “peak–peak” contour lines of the RFFC show that there are different flood combination zones which need different operation strategies in real‐time operation. Our findings improve the understanding of flood frequency concepts when considering the impact of reservoir operation and provide reference to manage real‐time reservoir operation.

New flood frequency estimates for the largest river in Norway based on the combination of short and long time series

Abstract. The Glomma River is the largest in Norway, with a catchment area of 154 450 km2. People living near the shores of this river are frequently exposed to destructive floods that impair local cities and communities. Unfortunately, design flood predictions are hampered by uncertainty since the standard flood records are much shorter than the requested return period and the climate is also expected to change in the coming decades. Here we combine systematic historical and paleo information in an effort to improve flood frequency analysis and better understand potential linkages to both climate and non-climatic forcing. Specifically, we (i) compile historical flood data from the existing literature, (ii) produce high-resolution X-ray fluorescence (XRF), magnetic susceptibility (MS), and computed tomography (CT) scanning data from a sediment core covering the last 10 300 years, and (iii) integrate these data sets in order to better estimate design floods and assess non-stationarities. Based on observations from Lake Flyginnsjøen, receiving sediments from Glomma only when it reaches a certain threshold, we can estimate flood frequency in a moving window of 50 years across millennia revealing that past flood frequency is non-stationary on different timescales. We observe that periods with increased flood activity (4000–2000 years ago and <1000 years ago) correspond broadly to intervals with lower than average summer temperatures and glacier growth, whereas intervals with higher than average summer temperatures and receding glaciers overlap with periods of reduced numbers of floods (10 000 to 4000 years ago and 2200 to 1000 years ago). The flood frequency shows significant non-stationarities within periods with increased flood activity, as was the case for the 18th century, including the 1789 CE (“Stor-Ofsen”) flood, the largest on record for the last 10 300 years at this site. Using the identified non-stationarities in the paleoflood record allowed us to estimate non-stationary design floods. In particular, we found that the design flood was 23 % higher during the 18th century than today and that long-term trends in flood variability are intrinsically linked to the availability of snow in late spring linking climate change to adjustments in flood frequency.

Flood frequency estimation and uncertainty in arid/semi-arid regions

At site flood frequency analysis (FFA) in arid/semi-arid watersheds poses unique challenges to researchers and practitioners due to the generally limited data records. This study presents a comprehensive evaluation of FFA in arid/semi-arid watersheds in relation to the unique characteristics of these regions, such as the limited number of floods occurring each year and the large variability of the flood peak discharges. Study cases in Israel and the US are examined and compared with non-arid watersheds, characterized by Mediterranean climate, and with synthetic flood records.Results show that the tail of extreme value distributions describing arid/semi-arid watersheds is found to be heavier than the one describing Mediterranean watersheds. The number of yearly floods and the variability of flood peak discharge are shown to have a crucial impact on the accuracy of the quantile estimates with smaller number of events per year and larger coefficient of variation of flood peak discharge being related to larger errors in the estimated quantiles. Partial duration series approach provides a slightly reduced bias in the estimates, but should not be blindly preferred over annual maxima series as it presents comparable estimation uncertainty. In general, the generalized extreme value and the generalized Pareto distribution are found to be non-optimal choices for the examined arid/semi-arid watersheds.

Regional analysis of PROQ transforms for flood frequency estimation based on GRADEX principles

ABSTRACT The PROQ transform which converts rainfall P to runoff RO, then factored to produce peak discharge Q, can be used for regional design flood estimation. SPEED, a French method based on GRADEX principles is a PROQ transform. 1 in 100 annual exceedance probability discharges was estimated using SPEED for 40 Australian non-urban catchments. Inputs are catchment area and areal design rainfall depth. The SPEED transform involves three constant parameters: scaling factor a, non-linearity b and retention L. SPEED was found to be simple and effective (Nash-Sutcliffe E = 0.82). A local version of SPEED produced a similar performance (E = 0.74). A general form of PROQ was developed to explain similarities with other models such as the Probabilistic Rational Method and to develop the local SPEED transform. Gauge sites can be grouped based on a and L ‘bins’, using site-based mean values of these parameters. A PROQ model using two a bins and two L bins significantly enhanced predictive accuracy (E = 0.92).

An evaluation of a multi-day rainfall – runoff volume – peak discharge transform for flood frequency estimation

ABSTRACT A new method to estimate design discharge quantiles is described based on converting multi-day rainfall P to flood event runoff RO, factored to generate discharge Q. The so-called PROQ transfer function is founded on simple flood volume-peak and GRADEX rainfall-runoff tanh relationships. Performance testing of PROQ, in both at-site and regional design flood contexts up to 1 in 100 annual exceedance probability, was made using south east Queensland streamgauge data. A statistical comparison against proven methods showed that the PROQ transform has significant potential as an alternative for design flood estimation. An example of how PROQ can be used within a design flood framework and recommendations for further enhancement are provided. Abbreviations: AEP: Annual exceedance probability; AMS: Annual maximum series, extracted from the flood record at a gauge site; ANOVA: Analysis of variance; ARR: Australian Rainfall and Runoff guidelines; A-S: At-site. Describes a set of methods to estimate design flood quantiles by statistical analysis of the flood record at an individual gauge site; E: Nash–Sutcliffe efficiency; FFA: Flood frequency analysis; G-B: Multiple Grubbs-Beck test recommended by ARR 2019 for low flow censoring. Used for at-site flood frequency analysis; GEV: General extreme value probability distribution; GRADEX: Gradient of extreme values. Design flood probability concept originating in France based on parallelism of rainfall and runoff quantile curves; L: Retention of rainfall within the catchment during flood event, expressed as a depth; LP3: Log Pearson 3 probability distribution; P: Rainfall depth; PRM: Probabilistic Rational Method. An ARR method for ungauged, undeveloped Australian catchments superseded in 2016; PROQ: Transfer function based on converting P to RO and then factoring RO to estimate Q; PW: Palmen and Weeks. Regional method for ungauged, undeveloped Queensland catchments developed by Palmen and Weeks (2011); R: Retention curve number. Used in probabilistic charting of design floods based on PROQ; RE: Absolute relative error; REG: Regional. Describes a set of methods to estimate design flood quantiles using information obtained from at-site analyses of several representative catchments within a region; RO: Flood event runoff depth; SR30: Strike rate of estimates within ±30% tolerance.

Flood frequency estimation in New Zealand using a region of influence approach and statistical depth functions

A region of influence approach in which statistical depth functions are used to select gauged catchments having similar flood generation behaviour to a given ungauged basin coupled with nonparametric regression on catchment characteristics is employed to estimate mean annual and a quantile flood of 100 year return period in New Zealand basins. This approach may be applied internationally. Relative and root mean square errors assessed by jack-knife resampling are too large to be useful for detailed design in ungauged catchments but are much smaller than those resulting from a conventional multiple nonlinear regression approach. The distribution of errors for samples of sites indicates that large error values result from contributions from a relatively small number of statistical outliers Although these are all natural catchments, the outlier basins have particular physical characteristics such as moraines, karst features and pumice sediments which affect their flood generation processes. Relative errors in estimates for an ungauged basin may be predicted using a relationship involving differences in area, rainfall and statistical depth between the ungauged catchment and gauged similar catchments. Improved analytical techniques and selection and measurement of catchment characteristics should reduce error magnitude but not perhaps sufficiently for use in detailed design. Parallel development based on rainfall-runoff models is desirable but the present lack of requisite information largely concerning rainfalls means that attainment of desired accuracy is a distant prospect.

Revised Dicken’s method for flood frequency estimation of Upper Ganga basin

Flood discharge and its frequency is an important input to many infrastructure development, management and decision making models. However, for a number of potential sites, the data used for statistical analysis of flood discharge and its frequency, are either insufficient or unavailable. In such a situation, empirical formulae are the only alternative method to provide an estimate of design flood. Dicken’s formula is one of such empirical formulae used by practicing field engineers for flood estimation. In spite of its simplicity and applicability, Dicken’s formula is not capable of yielding flood volume at different desired frequency. In the present study an attempt has been made to modify the Dicken’s coefficient so as to accommodate the frequency component in flood estimation. For this purpose, observed flood discharge data sixteen gauging points of Ganga basin have been used to develop regional flood frequency curve, using probability weighted method, Index flood method, General extreme value and Wakeby distribution. Modified Dicken’s coefficient CT shows a sharp gradient for sub-catchments draining up to 5000 Km2. Further, CT value, ranges from 0.834 to 6.924. The CT –values are lower when calculated by Index Flood method while these are higher by Wakeby method. In all of these methods, CT –values do not approach the values of 11 or 14 as suggested for northern Indian catchments. An analysis of flood volume corresponding to CT –values of 11 or 14, reveals that these values yield high flood volume that correspond to return period of more than 1000 years and 3000 years respectively. These values are certainly very high for any rational design. Best fit distribution is decided, based on annual maximum discharge peak (ADF), Efficiency (EFF) and Standard Error (SE) parameters. Prediction accuracy of flood frequency analysis using revised Dicken’s formula is found to be above 85% for the best-fit distribution. General Extreme Value is found to be the best fit distribution for the Upper Ganga basin.

Uncertainty introduced by flood frequency analysis in projections for changes in flood magnitudes under a future climate in Norway

Study regionThe study considers 115 unregulated catchments in Norway, with areas from 6 to 15,449 km2 and flood generation regimes ranging from snowmelt-dominated to ‘mixed’ (snowmelt and rainfall) to fully rainfall-driven. Study focusBias-corrected EURO-CORDEX RCM output for RCP 8.5 is used to generate an ensemble of 500 hydrological simulations for assessing changes in flood magnitudes under a future climate. Flood estimates are based on three extreme value distributions (EVDs), Gumbel, Generalised Extreme Value and Generalised Pareto, with confidence intervals calculated using parametric bootstrapping, and uncertainty introduced into the ensemble by the flood estimation is evaluated using variance decomposition. Changes in EVD parameters under future conditions are also assessed. New hydrologic insight for the regionThere are large differences in projected changes between catchments, with median estimates ranging from −48 % to +99 % for the 200 year flood. Flood magnitudes in all catchments with rainfall-dominant or mixed flood regimes are expected to increase. EVDs with a shape parameter (GEV and GPD) indicate larger increases in higher flood quantiles than the Gumbel distribution (e.g. by 5–8 percentage points for the 200 year flood). Flood frequency estimation contributes 30–52 % of the total ensemble range in individual catchments. Location and scale parameters generally increase in catchments with increasing flood magnitudes, and some catchments with mixed flood regimes also exhibit increases in the shape parameter under future conditions.

The bias of the maximum likelihood estimates of flood quantiles based solely on the largest historical records

The common practice to improve the assessments of design (upper) quantiles of annual flow maxima distribution is incorporating historical data into flood frequency estimates, which in general are greater than the systematic flood maxima records. A big body of literature was dedicated to this issue showing the advantages and constraints of this approach, but the research was not concentrated on the largest floods which for many reasons cannot be treated in the same way as less important ones. Assuming that only few (k) largest flood peaks in the specified time period (M) are known we applied maximum likelihood censoring type II method to estimate a hundred-year flow quantile. The relative bias supported by the relative root mean square error of the estimates were evaluated in numerical simulation experiments for different values of k and M performed under true and false distribution assumptions. The main and surprising result of the experiments is that in case of a false model assumption the estimation of the upper tail quantiles based on few largest floods in a given period can provide comparable or even better estimates in the sense of the relative bias and relative root mean square error than the estimation for the whole sample.Encouraged by the results of numerical experiments in the aspect of historical heavily censored samples we found that it might be reasonable to use this approach in a situation where all elements of the series of observation are known, but the upper tail of the distribution (in our case, F = 0.99 quantile) will be assessed using only the largest records. The results are presented in the case study research.

Analytical derivation of urban flood frequency models accounting saturation-excess runoff generation

Derived flood frequency models developed specifically for urban stormwater management purposes are referred to as analytical probabilistic stormwater models. These models use closed-form analytical equations to estimate flood frequencies under various land use conditions which facilitate quick analysis of different design alternatives. In this paper, in order to further improve the capability of the analytical probabilistic models, additional analytical expressions relating a catchment’s peak discharge rate to its physical characteristics and the local rainfall statistics are derived considering saturation-excess in addition to infiltration-excess runoff generation and various possible hydrograph shapes. These analytical expressions along with the frequency distributions of the input rainfall event volume and duration are used to develop a new derived flood frequency model suitable for catchments where saturation-excess runoff generation is possible. An example application of the new model to a test catchment in Hamilton, Ontario, Canada is illustrated to verify the accuracy of the new model. Assuming hypothetically that the same catchment is instead located at several other places in the United States, the reliability of the analytical probabilistic models under different climatic conditions is verified as well.

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.

Sensitivity of flood frequency analysis to data record, statistical model, and parameter estimation methods: An evaluation over the contiguous United States

ABSTRACTThe current statistical methods applied in flood frequency analysis require long data records to obtain reliable estimates, particularly for long return periods. Moreover, the choice of the statistical model and the parameter estimation procedure may introduce uncertainty in the estimates. In this work, we investigate the sensitivity of flood frequency analysis to various sample sizes, statistical models, and parameter estimation methods over six major hydrological regions in the contiguous United States. Results show that flood frequency estimates based on annual maximum series approach convergence to the reference values (estimates derived from 70 years record) in terms of median for 35‐year or longer records. However, the uncertainty remains significant and a record of 35 years (20 years) is associated with ~50% (100%) larger uncertainty on the estimated 100‐year flood. The generalised extreme value distribution combined with maximum likelihood estimation method is associated with the largest uncertainty, while the log‐Pearson type III exhibits comparable bias and smaller uncertainty. Application of the partial duration series approach to 20‐year records shows no significant advantage. Our findings suggest that the hydroclimatic characteristics of the catchments exhibit limited impact on the uncertainty.

Selecting the best probability distribution for at-site flood frequency analysis; a study of Torne River

At-site flood frequency analysis is a direct method of estimation of flood frequency at a particular site. The appropriate selection of probability distribution and a parameter estimation method are important for at-site flood frequency analysis. Generalized extreme value, three-parameter log-normal, generalized logistic, Pearson type-III and Gumbel distributions have been considered to describe the annual maximum steam flow at five gauging sites of Torne River in Sweden. To estimate the parameters of distributions, maximum likelihood estimation and L-moments methods are used. The performance of these distributions is assessed based on goodness-of-fit tests and accuracy measures. At most sites, the best-fitted distributions are with LM estimation method. Finally, the most suitable distribution at each site is used to predict the maximum flood magnitude for different return periods.