Flood Estimation Research Articles

Analisis Debit Banjir Rancangan Sub DAS Air Bengkulu Menggunakan Analisis Frekuensi dan Metode Distribusi

Abstract
 The Design discharge is the important data needed for design of the water infrastructure. The purpose of this study is to estimate the design discharge with a variety of times using a frequency analysis in the Air Bengkulu Sub-watershed. Rain fall data were obtained from the BMKG of Bengkulu province, its starting from 2009 to 2018 years. The design flood estimation method used a statistical method. It is using the distribution method. The initial stage of the research is to process rain data and then conduct frequency analysis. The mathematical equation for frequency analysis includes statistical equations to determine the average value, standard deviation, coefficient of variation, skewness coefficient and kurtosis coefficient. The method used in determining the type of distribution for frequency analysis in this study uses the Gumbel Type I distribution method, Pearson Log Type III, Normal, and Normal Log. Distribution fit test was performed with chi square test and Kalmogorof Smirnov test. The results of estimated design discharges for the 2 years return period, 5 years return period, 10 years return period, 25 years return period, 50 years return period and 100 years returns period in the Air Bengkulu down stream Sub-watershed are respectively 125.16 m3 / sec (2 years return period), 145.24 m3 / sec (5 years return period), 157.59 m3 / sec (10 years return period), 172.43 m3 / sec (25 years return period), 183.03 m3 / sec (50 years return period) and 193.34 m3 / sec (100 years return period). 
 
 Keywords: watershed, Air Bengkulu, Design Discharge, Frequency Analysis, Distribution Analysis

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.

Sensitivity Analysis of Surface Runoff Parameters Towards Peak Discharge and Flood Volume

Inappropriate urban flood management may cause deterioration of urban living quality, known as urban decay. In order to avoid this, the sensitivity of surface runoff parameters towards peak discharge (Qp) and flood volume (Vf) needs to be analysed. By conducting sensitivity analysis, the influence of any runoff parameter to these two output can be assessed quantitatively. This study aims to sort out the sensitivity of seven runoff parameters towards Qp and Vf at part of the suburb of Mawson Lakes, South Australia. Four synthetic rainfall events with the Annual Recurrence Interval (ARI) of 2-year and 50-year; each with the duration of 12 hour and 72-hour; were generated by the AusIFD software. The hourly intensity values of these synthetic rainfall were then fed into the Storm Water Management Model (SWMM) to predict Qp and Vf. Further, the initial values of runoff parameters based on previous studies were used in the first flood estimation. Different sets of parameters which were 25% less than and greater than the initial values were used in the next flood modelling. Results showed that the most sensitive parameters were %-impervious followed by Manning’s n-pervious. Modelling using initial values of parameters produced Qp and Vf of 0.340 m3/s and 2.372 MCM for 2-year ARI respectively, whereas the corresponding values for 50-year ARI were 0.519 m3/s and 3.424 MCM, respectively. Decreasing the parameter initial values by 25% produced. Meanwhile, increasing the parameters’ initial value by 25%.

Correlation Effects? A Major but Often Neglected Component in Sensitivity and Uncertainty Analysis

AbstractGlobal sensitivity analysis (GSA) provides essential insights into the behavior of Earth and environmental systems models and identifies dominant controls of output uncertainty. Previous work on GSA, however, has typically been under the assumption that the controlling factors such as model inputs and parameters are independent, whereas, in many cases, they are correlated and their joint distribution follows a variety of forms. Although this assumption can limit the credibility of GSA and its results, very few studies in the field of water and environmental modeling address this issue. In this paper, we first discuss the significance of correlation effects in GSA and then propose a new GSA framework for properly accounting for correlations in input/parameter spaces. To this end, we extend the “variogram‐based” theory of GSA, called variogram analysis of response surfaces (VARS), and develop a new generalized star sampling technique (called gSTAR) to accommodate correlated multivariate distributions. We test the new gSTAR‐VARS method on two test functions, against a state‐of‐the‐art GSA method that handles correlation effects. We then apply gSTAR‐VARS to the HBV‐SASK model, calibrated via a Bayesian, Markov chain Monte Carlo approach, for design flood estimation in the Oldman River Basin in Canada. Results demonstrate that accounting for correlation effects can be critically important in GSA, especially in the presence of nonlinearity and interaction effects in the underlying response surfaces. The proposed method can efficiently handle correlations and different distribution types and simultaneously generate a range of sensitivity indices, such as total‐variogram effects, variance‐based total‐order effects, and derivative‐based elementary effects.

Projected changes of bivariate flood quantiles and estimation uncertainty based on multi-model ensembles over China

As atmospheric moisture capacity is highly sensitive to rising temperatures, precipitation extremes are widely projected to intensify with a warming climate and thus altering the flooding generation regime. Previous works seldomly focused on bivariate flood quantiles under climate change at a national scale, and fewer flooding projections quantified the estimation uncertainty sourced from sample size limitation. This study systematically investigates the changes in bivariate quantiles of flood peak and volume with incorporation of sampling uncertainty for 151 catchments over China, with climate trajectories projected by a set of multi-model ensemble under representative concentration pathway (RCP) 8.5. After correcting the systematical biases of eight CMIP5 GCM outputs, four state-of-the-art hydrological models are driven and validated for each catchment, and the best-simulation model is selected to project future streamflow scenarios. The copula function is employed to construct the joint distribution of flood peak and volume, and then the most likely realizations of bivariate quantiles are derived under different Joint Return Periods (JRPs), with the uncertainty envelope quantified with the area of 90% confidence ellipse by a copula-based parametric bootstrapping uncertainty (C-PBU) approach. Our results project an overall ascending trend of temperature and precipitation over China, and the bivariate flood quantiles and corresponding estimation uncertainty of most catchments in the future period (2056–2100) are much larger than the baseline (1961–2005), despite accompanied by substantial climate model uncertainty and spatial variability in magnitude. Many basins would be subjected to a dramatic increase of flood magnitude by over 50%, while only few basins are projected to experience a decreasing flood risk, suggesting an urgent need to increase societal resilience to a warming climate over China.

Uncertainty quantification and propagation in bivariate design flood estimation using a Bayesian information-theoretic approach

Copula-based statistical model has been extensively used to characterize the joint probability behaviors of extreme hydro-climatic design events, such as bivariate design floods. However, parameter uncertainty, not desirably but inevitably, accompanies the copula-based design flood estimation (CDFE) model which makes the inference results of design flood ambiguous. In this paper, we develop a Bayesian information-theoretic (BIT) approach to disclose design flood uncertainty arising from parameter uncertainty in CDFE model and furthermore examine how the parameter uncertainty is propagated to design flood estimation. Two catchments located in the Yellow River Basin, China, are selected as study regions. The research we have done indicates that design flood uncertainty is considerably large under parameter uncertainty. Worse still, the uncertainty in design flood increases along with the return period, thus making the design flood inference more ambiguous. Specifically, shape and scale parameters of marginal distributions have the largest contribution to the design flood uncertainty, followed by copula parameter, finally the location parameter of marginal distribution. Furthermore, the interaction between some parameters has dominant effect on design flood uncertainty compared with the individual effect of these parameters. These findings will enable better understanding the design flood uncertainty under parameter uncertainty. Moreover, the proposed BIT approach also offers a promising technical reference for design estimation of other types of extreme hydrological events, not limited to the design flood.

Regionalization of watersheds by finite mixture models

Cluster analysis methods are one of the most widely-used types of methods in regionalization of watersheds for regional flood frequency analysis. Several cluster analysis algorithms have been used in regional frequency analysis studies in the last decades, which most of them have been data-driven algorithms that identify clusters based on distances or dissimilarities between data points. Finite mixture models are a type of statistical models that are able to identify the clusters based on frequency distributions. In this study, the performance of finite mixture models for regionalization of watersheds was evaluated by applying Gaussian mixture models for regionalization of watersheds of Karun-e-bozorg in the southwest of Iran. Gaussian mixture models are the best-known type of finite mixture models. The performance of this method was evaluated according to the homogeneity of regions and the accuracy of flood quantile estimates. In addition, the results of the proposed method were compared with the results provided by using a well-known and efficient data-driven hybrid clustering algorithms. According to the results, the Gaussian mixture models clearly outperformed the hybrid clustering algorithm in identifying homogeneous regions. While one of the Gaussian mixture models assigned all the watersheds to homogeneous regions for five different choices of the number of regions, the hybrid clustering algorithm assigned all the watersheds homogeneous regions only for one choice of the number of regions. Also, the flood estimates provided based on the regions identified by the Gaussian mixture model and the hybrid clustering algorithm were compared with the at-site estimates. As evidenced by the results, the flood estimates related to the regions identified by the Gaussian mixture model are less deviated from at-site estimates in terms of the error measures. In general, results show that finite mixture models can be considered as an efficient option to perform regionalization of watersheds in regional flood frequency analysis studies.

Understanding the dynamic nature of Time-to-Peak in UK streams

In flood forecasting and design for peak flows, understanding and characterizing the hydrologic response to rainfall events is vitally important. One of the key parameters utilized to characterize the catchment response time is the Time-to-Peak (Tp), which represents the net rise time of a storm hydrograph, or the time from when a precipitation event begins to contribute to stream discharge, to the time that peak flow (Qp) is reached. Previously, influencing factors on Tp have been static in nature with no consideration of the variability in Tp due to size of the storm event and the antecedent moisture conditions of the watershed (seasonal effects). Using ~1400 storm event observations and the corresponding catchment characteristics of 153 stream gauges across the United Kingdom (UK), the importance of different factors on estimating Tp are evaluated. These data points span three decades, and this breadth of temporal data allowed meaningful annual trends to be observed, and seasonal variations in soil moisture to be identified and applied. A new “wetness coefficient” is applied herein, to reflect the antecedent conditions within a catchment. The Qp is selected as a dynamic variable, utilized to represent the magnitude of a given storm, due to the demonstrated correlation with Tp. An explicit equation based on gene expression programming is designed, which accounts for the dynamic nature of Tp through Qp and seasonal moisture effects. The results of the proposed model are compared to the results of the existing equation for Tp prediction in the UK, outlined by the Flood Estimation Handbook (FEH). The proposed equation (with Nash-Sutcliffe Coefficient, R2 and RMSE values equal to 0.60, 0.66 and 3.64, respectively), has improved characteristics compared with the traditional FEH equation (Nash = 0.42, R2 = 0.54, and RMSE = 4.37). A forensic analysis of the contributing factors for Tp involves development of an empirical model with improved prediction accuracy, by accounting for the dynamic inputs, improving previous models both statistically, as well as in the hydrologic understanding of the catchment response.

An Integrated Hydrologic and Hydraulic Flood Modeling Study for a Medium-Sized Ungauged Urban Catchment Area: A Case Study of Tiruchirappalli City Using HEC-HMS and HEC-RAS

Floods are a natural calamity that causes loss of human life and destruction of property. They are caused by riverine, coastal and storm surges, cyclones, and tsunami waves. The primary causes of urban floods are an unusual rise in quantum of rainfall, extreme man-made changes in land-use–land-cover patterns, and consequent detrimental hydrological impacts. There are different methods available for flood estimation like rational formulae and empirical methods for approximate estimation of flood peak discharge, but they cannot be applied widely, and their application is confined to a particular region. The current study first discusses the causes of urban floods and then assesses the appropriate flood modeling methods that can be effectively used for urban catchments. Flood modeling methods deployed internationally and nationally in major- and medium-sized cities are discussed, and the research gap is identified. The advantages and demerits of each method are analyzed, and the parameters involved are also identified. An integrated modeling approach is chosen for this case study. In India, only coastal megacities have been considered for flood modeling studies thus far. There have been no research studies conducted on medium-sized urban catchment areas using an integrated modeling approach. Therefore, the present article reviews all the features of a novel integrated flood modeling approach which can be adopted for a medium-sized dendritic pattern ungauged urban catchment area.

Flood frequency analysis

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

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.

Estimation of extreme floods by regional methods in Trois Rivières watershed - North West Algeria

Estimation of extreme floods by regional methods in Trois Rivières watershed - North West Algeria

Investigation of System Design Criteria and the Capital Cost of Varying Design Return Periods for Soil and Water Conservation Structures

Highlights Very few sediment yield events contribute to annual sediment yield. Any rainfall, runoff, and peak discharge event has the potential to generate the most extreme sediment yield event. Twenty year return period recommended for design of conservation structures. Abstract . Design of conservation structures includes both hydrologic and hydraulic designs. Hydrologic design involves estimation of design floods which are required for the sizing of the hydraulic structures. The minimum recommended return period for the design of conservation structures is 10 years but due to the projected levels of risk, and the fact that a few large events are likely to be responsible for the majority of the erosion, the 10-year return period currently recommended may be inadequate. This study investigated system design criteria and the capital cost of varying design return periods for soil and water conservation structures in the sugar industry of South Africa. Observed rainfall data and results of runoff, peak discharge and sediment yield simulated using the Agricultural Catchments Research Unit (ACRU) model were utilized in this study. Relationships between extreme events of sediment yield and the rainfall, runoff and peak discharge events associated with them were analyzed and the capital cost of varying design return periods was also investigated. The results showed that only 0.2% of sediment yield events contributed up to 95% of the annual sediment yield simulated in the sugar production areas in South Africa and that any event of rainfall, runoff and peak discharge had the potential to generate an extreme sediment yield event provided the soil surface was not adequately protected. Based on a sustainable soil loss of 5 t ha-1, the 20-year return period was recommended for the design of soil and water conservation structures. Furthermore, the capital cost implication of varying design return periods from the minimum 10-year return period ranged from an increase of 16% to 35% across the sugar industry. Therefore, given that soil erosion is associated with adverse effects on sustainable crop production and also increases in costs of replanting destroyed crops, the 20-year return period is recommended for the design of soil and water conservation structures in the sugar industry in South Africa. Keywords: Capital cost, Design criteria, Erosion, Return perioW, Risk, Soil and water conservation.

Estimation of extreme floods by regional methods in Trois Rivières watershed - North West Algeria

The objective of this study is to estimate the extreme floods corresponding to different recurrence intervals in Trois Rivieres watershed, a large sub-basin of the Macta basin (NW-Algeria). Two regional models are used flow-duration-frequency (Qdf) modelling and gradex method. A methodology for modelling QdF curves based on the so-called continuous converging model has been applied. The second model is gradex method which is a rainfall-runoff probability approach to computing extreme flood discharges in a river. The QdF model gives a complete description of the flood dynamics of Trois Rivieres basin. Nevertheless, it is recommended to use this method with vigilance for the predetermination of severe floods for important return periods. However, the gradex method overestimates the extreme floods for different recurrence intervals. This study highlights that diverse methods should be employed for the regional analysis of extreme hydrological events.

Rapid urban flood damage assessment using high resolution remote sensing data and an object-based approach

Torrential rainfall can generate landslides, flash floods, and debris flows which might become disasters, causing loss of life and damage to property and infrastructure. To respond opportunely to hydrometeorological hazards, it is necessary to assess, rapidly and accurately, damage to the affected area. This is commonly done through time-consuming reconnaissance visits to obtain detailed field information. This paper proposes a methodology which uses: i) high resolution satellite and RGB images from unmanned aerial vehicles (UAV), ii) digital elevation models (DEM), and iii) object-based image analysis (OBIA) for rapid urban flood damage assessment and estimation of the number of houses washed away, or with a total or partial roof collapse, by comparing pre- and post-event data. The case study was Tropical Storm Earl in 2016 that affected the town of Chicahuaxtla, Puebla, Mexico, due to the overflow of the Zempoloantongo River that cuts through the town causing several loss of life and severe property damage. The results indicate that the three-pronged approach proposed herein is able to discriminate changes before and after the event and improve image classification of washed-away or destroyed houses. The overall accuracy of the proposed automatic classification obtained with UAV data had a value of 97.4%. Structural damage was not assessed in this study.

Estimation of areai reduction factors using daily rainfall data and a geographically centred approach

This paper presents the estimation of geographically centred and probabilistically correct areal reduction factors (ARFs) from daily rainfall data to explain the unique relationship between average design point rainfall and average areal design rainfall estimates at a catchment level in the C5 secondary drainage region in South Africa as a pilot case study. The methodology adopted is based on a modified version of Bell's geographically centred approach. The sample ARF values estimated varied with catchment area, storm duration and return period, hence confirming the probabilistic nature. The derived algorithms also provided improved probabilistic ARF estimates in comparison to the geographically and storm-centred methods currently used in South Africa. At a national level, it is envisaged that the implementation and expansion of the methodology will ultimately contribute towards improved ARF estimations at a catchment level in South Africa. Consequently, the improved ARF estimations will also result in improved design flood estimations.

Comparison of Debris Flow Modeling Results with Empirical Formulas Applied to Russian Mountains Areas

Construction of debris flow protection structures is impossible without studying the processes first. Therefore, the purpose of this research was to calculate the magnitude of debris flows in three study areas. Initial information was provided by JSC Sevkavgiprovodkhoz and the Research Center “Geodinamika”. The first object of this research was the river Ardon and its tributary the Buddon, because of disastrous consequences for Mizur village of passed debris flows and floods. Modeling of unsteady water movement was carried out for estimation of potential flooding. During modeling, 5 cases of flash floods and debris flows of various probabilities from 0.5% to 1% percent were considered. Therefore, maximum floods for the cross-sections above and in the Mizur village itself were obtained. The second study area was the Chat-Bash stream, which is also situated in the north of Caucasus mountains. For this stream, the maximum discharge that could impact the mining complex at Tyrnyauz was determined. The third study area was the Krasnoselskaia river due to frequent floods in Yuzhno-Sakhalinsk. Applying three cases of various probabilities from 10% to 0.1%, the model determined maximum discharge and water level for the last cross-section above confluence into the Susuya river. Numerical experiments for all study areas with different roughness values were conducted to identify optimal ones. Comparing the model results for all study areas with empirical formulas (Golubcov V.V., Herheulidze I.I., Kkhann, Sribnyj and ASFS of EMERCOM of Russia) revealed that formulas contain only average depth slope angle and empirical coefficients and do not allow estimating flood areas and maximum characteristics of the event with a certain degree of accuracy.

Event‐based classification for global study of river flood generating processes

AbstractBetter understanding of which processes generate floods in a catchment can improve flood frequency analysis and potentially climate change impacts assessment. However, current flood classification methods are either not transferable across locations or do not provide event‐based information. We therefore developed a location‐independent, event‐based flood classification methodology that is applicable in different climates and returns a classification of all flood events, including extreme ones. We use precipitation time series and very simply modelled soil moisture and snowmelt as inputs for a decision tree. A total of 113,635 events in 4155 catchments worldwide were classified into one of five hydro‐climatological flood generating processes: short rain, long rain, excess rainfall, snowmelt and a combination of rain and snow. The new classification was tested for its robustness and evaluated with available information; these two tests are often lacking in current flood classification approaches. According to the evaluation, the classification is mostly successful and indicates excess rainfall as the most common dominant process. However, the dominant process is not very informative in most catchments, as there is a high at‐site variability in flood generating processes. This is particularly relevant for the estimation of extreme floods which diverge from their usual flood generation pattern, especially in the United Kingdom, Northern France, Southeastern United States, and India.

Evaluating next‐generation intensity–duration–frequency curves for design flood estimates in the snow‐dominated western United States

AbstractCivil infrastructure such as culverts and bridges are commonly designed using precipitation‐based intensity–duration–frequency (PREC‐IDF) curves, which assume that the occurrence of precipitation is in the form of rainfall and immediately available for the rainfall‐runoff process. In snow‐dominated regions, where most winter precipitation occurs as snow that melts during spring to early summer, the use of standard PREC‐IDF curves may lead to substantial underestimation of design floods and high failure risk of infrastructure. In this context, we developed next‐generation IDF (NG‐IDF) curves that characterize the actual water reaching the land surface (i.e., rainfall plus snowmelt) to enhance standard infrastructure design in snow‐dominated regions. This study evaluates the performance of NG‐IDF curves coupled with U.S. Department of Agriculture Technical Release 55 hydrologic model in estimating design floods for 246 snowy locations in different hydroclimate regimes of the western United States. Design flood estimates from a well‐validated continuous simulation using a physics‐based hydrologic model, the Distributed Hydrology Soil Vegetation Model (DHSVM), were used as the performance benchmark. Compared with the benchmark estimates, the standard PREC‐IDF curves led to substantial errors in design flood estimates, while the NG‐IDF curves significantly reduced these errors. For example, the averaged error in the 50‐year design flood estimates over the 246 locations was reduced from 31% with the use of PREC‐IDF curves to 12% with the use of NG‐IDF curves. Despite the different model structures, the single‐event NG‐IDF approach versus the continuous simulation DHSVM did not exhibit statistically significant differences in 91% of the 246 locations for the 50‐year design flood estimates. This indicates a satisfactory performance of NG‐IDF curves to estimate design flow under the conditions tested in the snow‐dominated western United States. This article also presents technical suggestions and the limitations of infrastructure design using NG‐IDF curves for regulatory agencies and practicing engineers.