Flood Frequency Curves Research Articles

Evaluating changes in flood frequency due to climate change in the Western Cape, South Africa

This study assesses the impact of climate change on flood frequency across seven sites in the Western Cape province of South Africa. The calibrated Water Resources Simulation Model (WRSM)/Pitman hydrological model was run using precipitation inputs from two representative concentration pathways (RCP) scenarios (RCP 4.5 and 8.5) using a combination of eight global circulatory models (GCM) for the two periods (2030–2060 and 2070–2100). GCMs were statistically downscaled using the delta change (DC), linear scaling (LS) and quantile delta mapping (QDM) approaches. Average daily discharge was estimated from each downscaled daily precipitation dataset using the Pitman/WRSM model with the Fuller and Sangal estimation methods used to calculate daily instantaneous peak flows. Flood frequency curves (FFC) were generated using the annual maximum series (AMS) for the GCM ensemble mean and individual GCMs for the return periods between 2 and 100 years. FFCs generated based on LS and QDM downscaling methods were aligned for the GCM ensemble mean in terms of the direction of FFCs. Further analysis was conducted using outputs based on the QDM approach, given its suitability in projecting peak flows. Under this method, both Fuller and Sangal FFCs exhibited a decreasing trend across the Jonkershoek and Little Berg River sites; however, estimated quantiles for low-probability events were higher under the Fuller method. This study noted the variation in FFCs from individual GCMs compared to the FFC representing the GCM ensemble mean. Further research on climate change flood frequency analysis (FFA) in South Africa should incorporate other advanced downscaling and instantaneous peak flow estimation (IPF) methods.

Developing Non-Stationary Frequency Relationships for Greater Pamba River basin, Kerala India incorporating dominant climatic precursors

Abstract. Global climate changes significantly contribute to increased frequency of hydrologic extremes. This significantly underestimates the hydrologic design parameters, bringing of hydro systems to increased failure risk. In order to address this concern, the current practice of development of hydrologic frequency tools need to be updated accounting for non-stationarity. This study first considered a diverse set of statistical tests to examine the trend, change points, non-stationarity and randomness of streamflow, rainfall and temperature time series of scales ranging from daily to annual. The annual maxima time series indicated non stationarity against the stationary behaviour of daily series of hydro-meteorological datasets of the basin. Subsequently, this study developed the Temperature Duration Frequency (TDF), Rainfall Intensity Duration Frequency (IDF) and Flood Frequency (FF) curves of Greater Pamba river basin in Kerala India, the part of which was most severely affected by the near century return period flood event of 2018. The analysis was performed for a multitude of combinations of variations in distribution parameters with time and climatic drivers as physical covariates in the extreme value formulations. The study proposed a novel wavelet coherence (WC) based driver selection of most dominant combination of climatic precursors in developing FF and IDF relations of three locations of Kalloopara, Malakkara and Thumpamon and TDF curve of Kuttanad region in the basin, considering data of 1985–2015 period. The proposed WC framework considers bi-multi-and partial effects of climatic oscillations (COs) like El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Pacific Decadal Oscillation (PDO) and North Atlantic Oscillation (NAO) in identifying potential drivers. The different WC formulations captured in-phase relationships of streamflows and rainfall with COs at intra-annual, annual and inter annual scales up to 4 years. The methods showed that addition of climatic precursors improved the NS estimates of flood and rainfall quantiles by more accurately capturing the magnitudes of extreme streamflows and rainfalls of 2018, 2021 than the time covariate formulations. However, the role of COs on extreme temperature is not found to be influential in developing TDF relationships, which needs further investigation.

Influence of Hydrological Variation Pointers and Incorporated Muskingum Flow Scrutiny on Flooding Frequency in the Upper and Lower Ikpoba Stream, Southern Nigeria

Flood frequency curve using River Ikpoba as Case study was estimated for a return period of 100 years, the flood magnitude is 461 m3/s as against the 380m3/s which was observed to be the highest discharge value throughout the simulation period. This value was found to have occurred in September 2012. The flood frequency curve therefore can aid in flood hazard preparedness since it can be employed to forecast the flood magnitude at different recurrent intervals. There is an increase in the number of wetted with corresponding increase in recurrence interval. For example, for 50yrs and 100 years return period, the number of wetted cells was observed to be 26171with a runoff volume of value of 0.14 x 108m3/s. Using the computed volume of runoff and the corresponding number of wetted cells, the spatial extent of flooding for each return period was generated. Similarly, red spots which are seen in areas such as Lucky Igbinedion Way, Temboga, Second Lagos Road, Mission Road, Ewah Road, Federal Road, Ikpoba, Oregbeni and MM Way shows that these areas are flood prone areas and requires adequate attention and effective early warnings systems in order to alleviate the magnitude of destruction that is caused by flood. Likewise, there is an indicate that as the day’s progress the flow rate was increasing for consecutive 30 days but at low flow rate, but at 90 days it declines. For maximum value as day progress the values decreases.

To what extent does river routing matter in hydrological modeling?

Abstract. Spatially distributed hydrology and land surface models are typically applied in combination with river routing schemes that convert instantaneous runoff into streamflow. Nevertheless, the development of such schemes has been somehow disconnected from hydrologic model calibration research, although both seek to achieve more realistic streamflow simulations. In this paper, we seek to bridge this gap to understand the extent to which the configuration of routing schemes affects hydrologic model parameter searches in water resources applications. To this end, we configure the Variable Infiltration Capacity (VIC) model coupled with the mizuRoute routing model in the Cautín River basin (2770 km2), Chile. We use the Latin hypercube sampling (LHS) method to generate 3500 different model parameters sets, for which basin-averaged runoff estimates are obtained directly (no routing or instantaneous runoff case) and are subsequently compared against outputs from four routing schemes (unit hydrograph, Lagrangian kinematic wave, Muskingum–Cunge, and diffusive wave) applied with five different routing time steps (1, 2, 3, 4, and 6 h). The results show that incorporating routing schemes may alter streamflow simulations at sub-daily, daily, and even monthly timescales. The maximum Kling–Gupta efficiency (KGE) obtained for daily streamflow increases from 0.64 (instantaneous runoff) to 0.81 (for the best routing scheme), and such improvements do not depend on the routing time step. Moreover, the optimal parameter sets may differ depending on the routing scheme configuration, affecting the baseflow contribution to total runoff. Including routing models decreases streamflow values in flood frequency curves and may alter the probabilistic distribution of the medium- and low-flow segments of the flow duration curve considerably (compared to the case without routing). More generally, the results presented here highlight the potential impacts of river routing implementations on water resources applications that involve hydrologic models and, in particular, parameter calibration.

Frequency and trend analyses of annual peak discharges in the Lower Mekong Basin

The effectiveness of annual peak discharges under the anthropogenic impact and climate change has significance for disaster management and planning. Therefore, an attempt has been made to study the trend of annual maximum series (AMS) discharges and flood frequency in the Lower Mekong Basin (LMB). The AMS data of five stations in the LMB were procured from the Mekong River Commission for analyses of trends of the AMS and flood frequency. The Mann-Kendall test showed a significant decrease in the magnitude of annual peak floods for all the discharge gauging sites in the LMB. Likewise, the analysis of the annual discharge departure from the mean reveals noteworthy variations and departure (positive and negative) in the annual peak discharges. The goodness-of-fit (GoF) tests showed that Log-Pearson Type-III (LP-III) is the best distribution for AMS of the Mekong River than Gumbel Extreme Value Type-I (GEVI). Therefore, predicted discharges for different return periods and predicted recurrence intervals for average annual discharges (Qm), large floods (Qlf), and maximum annual peak discharge during the recording period (Qmax) by LP-III are trustworthy. The flood frequency curve specified that all the observed discharges were fairly on the best-fitted line and falls between upper and lower confidence limits. Inclusively, the results of the trend in annual peak discharges and flood frequency are consistent and can be used for water management, controlling flood disasters, and flood planning in the LMB.

Changes in flood-associated rainfall losses under climate change

Climate change is expected to impact the severity and frequency of floods, which has implications for flood risk management. Design floods and derived flood frequency curves obtained using event-based rainfall-runoff models are widely used in industry to assess flood risks for planning and design purposes. For these approaches it is necessary to have (a) rainfall inputs, and (b) rainfall losses specified, the latter representing the amount of rainfall that is either intercepted, stored on the surface, or infiltrated into the soil and does not contribute to the flood hydrograph. There is extensive research on changes in flooding under climate change that focus on projections of rainfall. However, there is little research into projections of rainfall losses under climate change, despite the knowledge that their changes will modulate the flood response. Here, we present one of the first studies seeking to quantify how rainfall losses, as represented by estimates of initial and continuing losses used in event-based models, are projected to change under climate change.We identify dependencies between rainfall losses and antecedent soil moisture in around half (over 200) of the largely unregulated catchments (i.e. watersheds) in Australia analysed in this study and use these relationships to project rainfall losses under climate change. Near universal increases in both the mean and variance of both initial losses and continuing losses are projected in these catchments, suggesting that increased rainfall losses could offset the impact of increased rainfalls for frequently occurring floods and result in an increased variance in flood responses.

Development of a Parametric Flood Frequency Curves of Rainfall Magnitude Associated with Return Periods

Development of a Parametric Flood Frequency Curves of Rainfall Magnitude Associated with Return Periods

Diverging projections for flood and rainfall frequency curves

Flood damages are projected to increase with climate change due to intensification of extreme rainfalls, with water scarcity also projected to increase due to longer periods of drought leading to less runoff from flood events. In order to adapt to climate change, precise and robust projections of flood events are required of a magnitude (or rarity) relevant to engineering design and water resources planning. However, due to the complexity of catchment-specific processes which influence flood response, many studies disagree on the direction and magnitude of the projected change: some studies project increases in flood magnitude, even above projected increases in rainfall maxima, while other studies project decreases. Using a combination of continentally downscaled rainfall projections for Australia and rainfall-runoff models locally calibrated to rare floods using a novel objective function, we show that projected changes in flooding do not follow projected changes in extreme rainfall, with changes in flooding depending on region, runoff mechanism, and event severity. Decreases in frequent flooding up to the 1 in 5 annual exceedance probability are projected across the continent, with increases only projected for frequent flooding in some tropical regions, diverging from the projected increases in rainfall maxima across a majority of the continent. For rare events of annual exceedance probability 1 in 50, the flood magnitude is projected to increase across the northern and eastern coasts by the end of the century – commensurate with the median projected increase of 20% in rainfall maxima. However, decreases in both rainfall maxima and flooding are still projected for south-western Australia, even for the rarest events. The regional coherence of our results suggests the patterns in the projected changes may be applicable to other tropical, temperate, and arid regions around the world.

Identifying discontinuities of flood frequency curves

Discontinuities in flood frequency curves, here referred to as flood divides, hinder the estimation of rare floods. In this paper we develop an automated methodology for the detection of flood divides from observations and models, and apply it to a large set of case studies in the USA and Germany. We then assess the reliability of the PHysically-based Extreme Value (PHEV) distribution of river flows to identify catchments that might experience a flood divide, validating its results against observations. This tool is suitable for the identification of flood divides, with a high correct detection rate especially in the autumn and summer seasons. It instead tends to indicate the emergence of flood divides not visible in the observations in spring and winter. We examine possible reasons of this behavior, finding them in the typical streamflow dynamics of the concerned case studies. By means of a controlled experiment we also re-evaluate detection capabilities of observations and PHEV after discarding the highest maxima for all cases where both empirical and theoretical estimates display flood divides. PHEV mostly confirms its capability to detect a flood divide as observed in the original flood frequency curve, even if the shortened one does not show it. These findings prove its reliability for the identification of flood divides and set the premises for a deeper investigation of physiographic and hydroclimatic attributes controlling the emergence of discontinuities in flood frequency curves.

Flood Frequency Estimation in Data-Sparse Wainganga Basin, India, Using Continuous Simulation

Monsoon-related extreme flood events are experienced regularly across India, bringing costly damage, disruption and death to local communities. This study provides a route towards estimating the likely magnitude of extreme floods (e.g., the 1-in-100-year flood) at locations without gauged data, helping engineers to design resilient structures. Gridded rainfall and evapotranspiration estimates were used with a continuous simulation hydrological model to estimate annual maximum flow rates at nine locations corresponding with river flow gauging stations in the Wainganga river basin, a data-sparse region of India. Hosking–Wallis distribution tests were performed to identify the most appropriate distribution to model the annual maxima series, selecting the Generalized Pareto and Pearson Type III distributions. The L-moments and flood frequency curves of the modeled annual maxima were compared to gauged values. The Probability Distributed Model (PDM), properly calibrated to capture the dynamics of peak flows, was shown to be effective in approximating the Generalized Pareto distribution for annual maxima, and may be useful in modeling peak flows in areas with sparse data. Confidence in the model structure, parameterization, input data and catchment representation build confidence in the modeled flood estimates; this is particularly relevant if the method is applied in a location where no gauged flows exist for verification.

Projecting Flood Frequency Curves Under Near‐Term Climate Change

AbstractFlood‐frequency curves, critical for water infrastructure design, are typically developed based on a stationary climate assumption. However, climate changes are expected to violate this assumption. Here, we propose a new, climate‐informed methodology for estimating flood‐frequency curves under non‐stationary future climate conditions. The methodology develops an asynchronous, semiparametric local‐likelihood regression (ASLLR) model that relates moments of annual maximum flood to climate variables using the generalized linear model. We estimate the first two marginal moments (MM) – the mean and variance – of the underlying log‐Pearson Type‐3 distribution from the ASLLR with the monthly rainfall and temperature as predictors. The proposed methodology, ASLLR‐MM, is applied to 40 U.S. Geological Survey streamgages covering 18 water resources regions across the conterminous United States. A correction based on the aridity index was applied on the estimated variance, after which the ASLLR‐MM approach was evaluated with both historical (1951–2005) and projected (2006–2035, under RCP4.5 and RCP8.5) monthly precipitation and temperature from eight Global Circulation Models (GCMs) consisting of 39 ensemble members. The estimated flood‐frequency quantiles resulting from the ASLLR‐MM and GCM members compare well with the flood‐frequency quantiles estimated using the historical period of observed climate and flood information for humid basins, whereas the uncertainty in model estimates is higher in arid basins. Considering additional atmospheric and land‐surface conditions and a multi‐level model structure that includes other basins in a region could further improve the model performance in arid basins.

Flood risk analysis of reservoirs based on full-series ARIMA model under climate change

• A novel full-series ARIMA stochastic model has been built. • This model can reflect the statistical characteristics of past and future floods. • The BMA method was utilized to analyze the uncertainty of flood frequency curves. • More comprehensive and reliable flood risks considering the weight of each GCM have been obtained. To assess the flood risks of reservoirs in a changing environment, this study considers the uncertainty of future floods and argues that the future flood projected by Global Climate Models (GCMs) is only one of the possibilities. This possibility contains some main statistical characteristics of future floods. The autoregressive integrated moving average (ARIMA) model was used to extract such statistical characteristics of the future flood series. Moreover, the initial values of the model were assumed to obey the probability distribution derived from historical floods. Hence, a novel full-series ARIMA stochastic model which can simultaneously reflect the statistical characteristics of past and future floods has been built to simulate the possible future extreme floods. Synthetic flood series under different future climate scenarios were randomly generated by the proposed model. Then the flood risks exceeding the design flood and check flood can be obtained. The Bayesian model averaging (BMA) method was used to weight the flood risks projected by each GCM to better incorporate the uncertainty of GCMs. The Yangfanggou Reservoir in Yalong River basin of China was selected as a case study. The future flood risks under three Representative Concentration Pathways (RCPs) were assessed. The results highlight that future flood risks have an increasing trend. Under future RCP2.6, RCP4.5 and RCP8.5 scenarios, the risk rates of exceeding design flood are in the range of 0.63%–0.74%, 1.31%–4.58% and 2.63%–5.23%, respectively. The risk rates of exceeding check flood are in the range of 0.20%–0.25%, 0.41%–2.14% and 0.97%–2.48%, respectively. This study provides a comprehensive evaluation of flood risk and could be useful when planning to fortify existing structures and update design standards.

Partial afforestation has uncertain effect on flood frequency and peak discharge at large catchment scales (100–1000 km2), south‐central Chile

AbstractAn extensive programme of commercial afforestation in south‐central Chile provides a unique opportunity to extend the field‐based understanding of forest impact on floods to the large catchment scale (100–1000 km2). Until now, much of that understanding has been limited to small catchments (<10 km2). Data for an approximately 50‐year period are analysed systematically for four catchments (434–1545 km2) with 18%–54% increases in forest cover. For each catchment, the forest impact is quantified by pairing annual maximum flood frequency curves and peak discharge/storm rainfall relationships for the pre‐ and post‐afforestation periods. These periods are themselves defined using land use surveys and cumulative double mass curves for runoff and rainfall. Scaling the frequency curves with a discharge corresponding to mean annual rainfall normalizes for rainfall differences and thus isolates the effect of forest cover. Partial afforestation rates of up to 50%, while markedly affecting annual evapotranspiration and runoff, have less certain effects on peak discharges. Downward shifts in the flood frequency curves occur following afforestation but appear more readily explained by changes in rainfall regime. These changes include a small decline in annual rainfall and a downward shift in some of the frequency curves for the storm rainfall totals corresponding to the annual maximum discharges. Nevertheless, some forest impact cannot be ruled out and the effect of a 100% change in forest cover on flood response remains unknown. The results suggest complexity in large catchment flood response and provide only weak support for afforestation with the sole purpose of reducing flood peaks at the large catchment scale. This is a significant conclusion, given that it challenges the largely uncritical view among the public, governments and development agencies worldwide that forests prevent floods.

Regional Flood Frequency Analysis for Sustainable Water Resources Management of Genale–Dawa River Basin, Ethiopia

Regional information on stream discharge is needed in order to improve flood estimates based on the limited data availability. Regional flood estimation is fundamental for designing hydraulic structures and managing flood plains and water resource projects. It is essential for estimating flood risks during recurrent periods due to suitable distributions. Regional flood frequency analysis is crucial for evaluating design flows in ungauged basins, and can complement existing time series in gauged sites and transfer them to ungauged catchments. Hence, this study aims to perform a regional flood frequency analysis of the Genale–Dawa River Basin of Ethiopia using the index flood and L-moments approach for sustainable water resource management. Three homogeneous hydrological regions were defined and delineated based on homogeneity tests from data of 16 stream-gauged sites, named Region-A, Region-B, and Region-C. The discordancy index of regional data for L-moment statistics was identified using MATLAB. All regions showed promising results of L-moment statistics with discordance measures (discordance index less than 3) and homogeneity tests (combined coefficient of variation (CC) less than 0.3). L-moment ratio diagrams were used to select best fit probability distributions for areas. Generalized extreme value, log-Pearson type III, and generalized Pareto distributions were identified as suitable distributions for Region-A, Region-B, and Region-C, respectively, for accurately modeling flood flow in the basin. Regional flood frequency curves were constructed, and peak flood was predicted for different return periods. Statistical analysis of the gauged sites revealed an acceptable method of regionalization of the basin. This study confirms that the robustness of the regional L-moments algorithm depends on particular criteria used to measure the performance of estimators. The identified regions should be tested with other physical catchment features to enhance flood quantile estimates at gauged and ungauged sites. Henceforth, this study’s findings can be further extended into flood hazard, risk, and inundation mapping of identified regions of the study area. Furthermore, this study’s approach can be used as a reference for similar investigations of other river basins.

Climate change allowances, non‐stationarity and flood frequency analyses

AbstractWhen considering future adaptation to climate change in UK fluvial flood alleviation schemes, the current recommendation by the Environment Agency (England) is to increase peak design flood flows by a preselected percentage. This allowance varies depending on the period for which the estimate is being made, the vulnerability of the development being considered and its location. Recently, questions have been raised as to whether these percentage uplifts should be kept the same, or whether change has already happened within the baseline period and so uplifts should be reduced. A complicating factor is that changes in flood frequency can occur for reasons in addition to climate change, such as land‐use change or natural variability. This article describes current approaches taken by different stakeholders for catchments in England and Wales to account for climate change, and discusses these allowances where there is already an observed presence of trend in flood regimes. Theil–Sen estimators of trend were used in comparing non‐stationary and stationary flood frequency curves with allowances applied, leading to a recommendation of evaluating non‐stationary models at 1990, the end of the reference period. Examples were explored such as the Eden catchment, which was heavily affected by Storm Desmond in December 2015.

The need for paleoflood investigations on the American reach of the Red River of the North

Over the past century, the Red River of the North has been the least stationary river in the continental United States. In Canada, historical and paleoenvironmental evidence indicates severe floods were common during the early 1800s, with the record ce 1826 flood having an estimated peak discharge 50% higher than the second-most severe flood ever observed. Unfortunately, the recorded history of flooding upstream in the United States does not begin until seven decades after this event. If 1826 was an equally exceptional flood on American reach of the river, then current flood-frequency curves for the river underestimate significantly the risks posed by future flooding. Alternatively, if the American stretch did not produce a major flood in 1826, then the recent spate of flooding that has occurred over the past two decades is exceptional within the context of the past 200 years. Communities in the Fargo-Moorhead metropolitan area are building a 58-km long, $2.75 billion (USD) diversion channel that would redirect floodwaters westward around the two cities before returning it to the main channel. Because this and other infrastructure in North Dakota and Minnesota is intended to provide protection against low-probability, high-magnitude floods, new paleoflood investigations in the region would help local, state, and federal policy-makers better understand the true flood threats posed by the Red River of the North.

Exploring the Application of Flood Scaling Property in Hydrological Model Calibration

AbstractModel calibration has always been one major challenge in the hydrological community. Flood scaling properties (FS) are often used to estimate the flood quantiles for data-scarce catchments based on the statistical relationship between flood peak and contributing areas. This paper investigates the potential of applying FS and multivariate flood scaling properties [multiple linear regression (MLR)] as constraints in model calibration. Based on the assumption that the scaling property of flood exists in four study catchments in northern China, eight calibration scenarios are designed with adopting different combinations of traditional indicators and FS or MLR as objective functions. The performance of the proposed method is verified by employing a distributed hydrological model, namely, the Soil and Water Assessment Tool (SWAT) model. The results indicate that reasonable performance could be obtained in FS with fewer requirements of observed streamflow data, exhibiting better simulation of flood peaks than the Nash–Sutcliffe efficiency coefficient calibration scenario. The observed streamflow data or regional flood information are required in the MLR calibration scenario to identify the dominant catchment descriptors, and MLR achieves better performance on catchment interior points, especially for the events with uneven distribution of rainfall. On account of the improved performance on hydrographs and flood frequency curve at the watershed outlet, adopting the statistical indicators and flood scaling property simultaneously as model constraints is suggested. The proposed methodology enhances the physical connection of flood peak among subbasins and considers watershed actual conditions and climatic characteristics for each flood event, facilitating a new calibration approach for both gauged catchments and data-scarce catchments.Significance StatementThis paper proposes a new hydrological model calibration strategy that explores the potential of applying flood scaling properties as constraints. The proposed method effectively captures flood peaks with fewer requirements of observed streamflow time series data, providing a new alternative method in hydrological model calibration for ungauged watersheds. For gauged watersheds, adopting flood scaling properties as model constraints could make the hydrological model calibration more physically based and improve the performance at catchment interior points. We encourage this novel method to be adopted in model calibration for both gauged and data-scarce watersheds.

Design flood estimation for global river networks based on machine learning models

Abstract. Design flood estimation is a fundamental task in hydrology. In this research, we propose a machine-learning-based approach to estimate design floods globally. This approach involves three stages: (i) estimating at-site flood frequency curves for global gauging stations using the Anderson–Darling test and a Bayesian Markov chain Monte Carlo (MCMC) method; (ii) clustering these stations into subgroups using a K-means model based on 12 globally available catchment descriptors; and (iii) developing a regression model in each subgroup for regional design flood estimation using the same descriptors. A total of 11 793 stations globally were selected for model development, and three widely used regression models were compared for design flood estimation. The results showed that (1) the proposed approach achieved the highest accuracy for design flood estimation when using all 12 descriptors for clustering; and the performance of the regression was improved by considering more descriptors during training and validation; (2) a support vector machine regression provided the highest prediction performance amongst all regression models tested, with a root mean square normalised error of 0.708 for 100-year return period flood estimation; (3) 100-year design floods in tropical, arid, temperate, cold and polar climate zones could be reliably estimated (i.e. <±25 % error), with relative mean bias (RBIAS) values of −0.199, −0.233, −0.169, 0.179 and −0.091 respectively; (4) the machine-learning-based approach developed in this paper showed considerable improvement over the index-flood-based method introduced by Smith et al. (2015, https://doi.org/10.1002/2014WR015814) for design flood estimation at global scales; and (5) the average RBIAS in estimation is less than 18 % for 10-, 20-, 50- and 100-year design floods. We conclude that the proposed approach is a valid method to estimate design floods anywhere on the global river network, improving our prediction of the flood hazard, especially in ungauged areas.

Understanding the relationship between rainfall and flood probabilities through combined intensity-duration-frequency analysis

The aim of this paper is to explore how rainfall mechanisms and catchment characteristics shape the relationship between rainfall and flood probabilities. We propose a new approach of comparing intensity-duration-frequency statistics of maximum annual rainfall with those of maximum annual streamflow in order to infer the catchment behavior for runoff extremes. We calibrate parsimonious intensity-duration-frequency scaling models to data from 314 rain gauges and 428 stream gauges in Austria, and analyze the spatial patterns of the resulting distributions and model parameters. Results indicate that rainfall extremes tend to be more variable in the dry lowland catchments dominated by convective rainfall than in the mountainous catchments where annual rainfall is higher and rainfall mechanisms are mainly orographic. Flood frequency curves are always steeper than the corresponding rainfall frequency curves with the exception of glaciated catchments. Based on the proposed approach of combined intensity-duration-frequency statistics we analyze elasticities as the percent change of flood discharge for a 1% change in extreme rainfall through comparing rainfall and flood quantiles. In wet catchments, the elasticities tend to unity, i.e. rainfall and flood frequency curves have similar steepness, due to persistently high soil moisture levels. In dry catchments, elasticities are much higher, implying steeper frequency curves of floods than those of rainfall, which is interpreted in terms of more skewed distributions of event runoff coefficients. While regional differences in the elasticities can be attributed to both dominating regional rainfall mechanisms and regional catchment characteristics, our results suggest that catchment characteristics are the dominating controls. With increasing return period, elasticities tend towards unity, which is consistent with various runoff generation concepts. Our findings may be useful for process-based flood frequency extrapolation and climate impact studies, and further studies are encouraged to explore the tail behavior of elasticities.

Stochastic Hybrid Event Based and Continuous Approach to Derive Flood Frequency Curve

This study proposes a methodology that combines the advantages of the event-based and continuous models, for the derivation of the maximum flow and maximum hydrograph volume frequency curves, by combining a stochastic continuous weather generator (the advanced weather generator, abbreviated as AWE-GEN) with a fully distributed physically based hydrological model (the TIN-based real-time integrated basin simulator, abbreviated as tRIBS) that runs both event-based and continuous simulation. The methodology is applied to Peacheater Creek, a 64 km2 basin located in Oklahoma, United States. First, a continuous set of 5000 years’ hourly weather forcing series is generated using the stochastic weather generator AWE-GEN. Second, a hydrological continuous simulation of 50 years of the climate series is generated with the hydrological model tRIBS. Simultaneously, the separation of storm events is performed by applying the exponential method to the 5000- and 50-years climate series. From the continuous simulation of 50 years, the mean soil moisture in the top 10 cm (MSM10) of the soil layer of the basin at an hourly time step is extracted. Afterwards, from the times series of hourly MSM10, the values associated to all the storm events within the 50 years of hourly weather series are extracted. Therefore, each storm event has an initial soil moisture value associated (MSM10Event). Thus, the probability distribution of MSM10Event for each month of the year is obtained. Third, the five major events of each of the 5000 years in terms of total depth are simulated in an event-based framework in tRIBS, assigning an initial moisture state value for the basin using a Monte Carlo framework. Finally, the maximum annual hydrographs are obtained in terms of maximum peak-flow and volume, and the associated frequency curves are derived. To validate the method, the results obtained by the hybrid method are compared to those obtained by deriving the flood frequency curves from the continuous simulation of 5000 years, analyzing the maximum annual peak-flow and maximum annual volume, and the dependence between the peak-flow and volume. Independence between rainfall events and prior hydrological soil moisture conditions has been proved. The proposed hybrid method can reproduce the univariate flood frequency curves with a good agreement to those obtained by the continuous simulation. The maximum annual peak-flow frequency curve is obtained with a Nash–Sutcliffe coefficient of 0.98, whereas the maximum annual volume frequency curve is obtained with a Nash–Sutcliffe value of 0.97. The proposed hybrid method permits to generate hydrological forcing by using a fully distributed physically based model but reducing the computation times on the order from months to hours.