Flood Frequency Distribution Research Articles

Accounting for hydroclimatic properties in flood frequency analysis procedures

Abstract. Flood hazard is typically evaluated by computing extreme flood probabilities from a flood frequency distribution following nationally defined procedures in which observed peak flow series are fit to a parametric probability distribution. These procedures, also known as flood frequency analysis, typically recommend only one probability distribution family for all watersheds within a country or region. However, large uncertainties associated with extreme flood probability estimates (>50-year flood or Q50) can be further biased when fit to an inappropriate distribution model because of differences in the tails between distribution families. Here, we demonstrate that hydroclimatic parameters can aid in the selection of a parametric flood frequency distribution. We use L-moment diagrams to visually show the fit of gaged annual maxima series across the United States, grouped by their Köppen climate classification and the precipitation intensities of the basin, to a general extreme value (GEV), log normal 3 (LN3), and Pearson 3 (P3) distribution. Our results show that in real space basic hydroclimatic properties of a basin exert a significant influence on the statistical distribution of the annual maxima. The best-fitted family distribution shifts from a GEV towards an LN3 distribution across a gradient from colder and wetter climates (Köppen group D, continental climates) towards more arid climates (Köppen group B, dry climates). Due to the diversity of hydrologic processes and flood-generating mechanisms among watersheds within large countries like the United States, we recommend that the selection of distribution model be guided by the hydroclimatic properties of the basin rather than relying on a single national distribution model.

Characterization of flood and drought hazards on the Gereb-Geba water supply dam in the semi-arid northern Ethiopian highlands

Evaluating the flood and drought hazards provides vital information for sustainable water resources management, particularly in semi-arid, water-deficit environments. Most prior studies were limited in exploring the flood and drought hazards, which are important for early warning systems and preparedness. This study characterized the hydrological extreme hazards on the Gereb-Geba reservoir, namely the Suluh, Genfel, and Agula rivers. Flood frequency analysis was performed using the fitted flood frequency distribution in MATLAB. The 2D hydrodynamic model HEC-RAS was implemented to produce a flood-inundation map. Meteorological, agricultural, and hydrological droughts were analyzed using the Standardized Precipitation Index (SPI), Vegetation Condition Index (VCI), and Streamflow Drought Index (SDI), respectively. Using the Generalized Extreme Value (GEV), the estimated flood magnitude showed an increasing tendency in all the rivers across all the return periods (2-, 5-, 10-, 20-, 50-, and 100-years). The reservoir inundated an area of 12.8 km2 at an elevation of 1830 m.a.s.l. with a water depth of 80 m at the outlet. Suluh experienced more severe to extreme hydrological drought episodes than the Agula and Genfel rivers. Severe to extreme meteorological droughts were also observed in the respective catchments. Moreover, severe agricultural drought prevalence was also detected across all the river catchments. This study provides vital and comprehensive flood and drought information for water resources planning, management, and development.

Nonstationary stochastic paired watershed approach: Investigating forest harvesting effects on floods in two large, nested, and snow-dominated watersheds in British Columbia, Canada

Drawing on advances in nonstationary frequency analysis and the science of causation and attribution, this study employs a newly developed nonstationary stochastic paired watershed approach to determine the effect of forest harvesting on snowmelt-generated floods. Moreover, this study furthers the application of stochastic physics to evaluate the environmental controls and drivers of flood response. Physically-based climate and time-varying harvesting data are used as covariates to drive the nonstationary flood frequency distribution parameters to detect, attribute, and quantify the effect of harvesting on floods in the snow-dominated Deadman River (878 km2) and nested Joe Ross Creek (99 km2) watersheds. Harvesting only 21% of the watershed caused a 38% and 84% increase in the mean but no increase in variability around the mean of the frequency distribution in the Deadman River and Joe Ross Creek, respectively. Consequently, the 7-year, 20-year, 50-year, and 100-year flood events became approximately two, four, six, and ten times more frequent in both watersheds. An increase in the mean is posited to occur from an increase in moisture availability following harvest from suppressed snow interception and increased net radiation reaching the snowpack. Variability was not increased because snowmelt synchronization was inhibited by the buffering capacity of abundant lakes, evenly distributed aspects, and widespread spatial distribution of cutblocks in the watersheds, preventing any potential for harvesting to increase the efficiency of runoff delivery to the outlet. Consistent with similar recent studies, the effect of logging on floods is controlled not only by the harvest rate but most importantly the physiographic characteristics of the watershed and the spatial distribution of the cutblocks. Imposed by the probabilistic framework to understanding and predicting the relation between extremes and their environmental controls, commonly used in the general sciences but not forest hydrology, it is the inherent nature of snowmelt-driven flood regimes which cause even modest increases in magnitude, especially in the upper tail of the distribution, to translate into surprisingly large changes in frequency. Contrary to conventional wisdom, harvesting influenced small, medium, and very large flood events, and the sensitivity to harvest increased with increasing flood event size and watershed area.

Nonstationary Annual Maximum Flood Frequency Analysis Using a Conceptual Hydrologic Model with Time-Varying Parameters

Recent evidence of the impact of watershed underlying conditions on hydrological processes have made the assumption of stationarity widely questioned. In this study, the temporal variations of frequency distributions of the annual maximum flood were investigated by continuous hydrological simulation considering nonstationarity for Weihe River Basin (WRB) in northwestern China. To this end, two nonstationary versions of the GR4J model were introduced, where the production storage capacity parameter was regarded as a function of time and watershed conditions (e.g., reservoir storage and soil-water conservation land area), respectively. Then the models were used to generate long-term runoff series to derive flood frequency distributions, with synthetic rainfall series generated by a stochastic rainfall model as input. The results show a better performance of the nonstationary GR4J model in runoff simulation than the stationary version, especially for the annual maximum flow series, with the corresponding NSE metric increasing from 0.721 to 0.808. The application of the nonstationary flood frequency analysis indicates the presence of significant nonstationarity in the flood quantiles and magnitudes, where the flood quantiles for an annual exceedance probability of 0.01 range from 4187 m3/s to 8335 m3/s for the past decades. This study can serve as a reference for flood risk management in WRB and possibly for other basins undergoing drastic changes caused by intense human activities.

Verification of a Synthetic Rainfall Model Based on the ArRR and Satellite Rainfall

Synthetic rainfall is needed as an input in the analysis of design flood. This research intends to conduct the verification of a synthetic rainfall model based on the ArRR and satellite result. The rainfall data is as the main component in obtaining the design, detail design, operation and maintenance of effective water resource infrastructure. Rainfall data is often used for the analysis of flood balance, flood frequency distribution, estimation of river discharge, and design of water structure. However, the main constraint in designing the water resource infrastructure is less available rainfall data temporally and spatially. The limitation of available rainfall station number causes inaccuracy in analyzing the area mean rainfall. Therefore, sometimes the correlation between recorded data from the rainfall station and water estimation station does not show a positive and strong correlation. This is due to less rainfall data that are spatially representative in the watershed upstream. The verification is carried out in the Ciliwung Hulu watershed. The methodology consists of ArRR analysis of the two extreme rainfall events, then the ArRR is analyzed using the synthetic rainfall model equation that is applied for watershed areas < 750 km2; in the end, we spatially compared the satellite and observed rainfall. The analysis of the ArRR value is based on the formulation as follows: ArRR_i=(0,026FB-0,307*Re_i+1,184 Q_(i+lag)/A+0,693)/(C_ArRR.K_ArRR ). The lag time that is used is 3 h based on the analysis of time to peak by using the Nakaysu hydrograph by assuming that the run-off coefficient is 0.35. The spatial comparison between satellite and observed daily rainfall on February 19, 2021, and October 28, 2021, shows that the rainfall distribution pattern is more similar with adding the ArRR synthetic rainfall station than with only using the ARR station.

Diverse Physical Processes Drive Upper‐Tail Flood Quantiles in the US Mountain West

AbstractFloods in the mountainous western U.S. typically arise from rainfall, snowmelt, or rain‐on‐snow (ROS). This implies that streamflow records comprise “mixtures” of flood regimes with differing physical characteristics. This runs counter to the assumption, made in flood hazard practice, that flood observations are independent and identically distributed. In this study, we examine 308 watersheds and 281 (91%) of which exhibited substantial roles of at least two flood regimes, with rainfall and ROS accounting for the very largest floods. We demonstrate that flood mixtures can have dramatic impacts on upper‐tail flood quantiles—rarer than the 0.01 annual exceedance probability. While the geographic distribution of mixed flood regimes is explained by climate and elevation, such regimes are subject to future change due to climate warming. The complexities brought about by diverse flood mixtures require process‐based approaches for understanding and modeling future flood frequency distributions.

Fitting flood frequency distributions using the annual maximum series and the peak over threshold approaches

Flood frequency plays an important role in the design of hydraulic structures as well as in the management of fisheries and aquatic resources. There are two types of flood frequency analyses, namely the annual maximum series (AMS) analysis and the partial duration series analysis (or peak over threshold, POT). The POT analysis consists of studying discharge data above a specific threshold (or truncation level), whereas the AMS method uses maximum annual discharge data. Both the AMS (generalized extreme value – GEV distribution) and POT (generalized Pareto – GP and exponential – Exp distributions) were used to calculate flood frequencies for four hydrometric stations within the Miramichi River basin of New Brunswick. A simple method was proposed for the selection of truncation levels, that is, values corresponding to 1, 1.5 and 2 flood counts per year. Considering multiple truncation levels in the POT analysis has the advantage of providing more results that are used to identify which level provides a better fit of the flood data. Both the GEV (AMS) and GP (POT) distributions best represented flood data within the Miramichi River whereas the Exp (POT) distribution did not fit well the data, especially for floods with high return periods (>25 years). Results showed the truncation level at a flood count of 1 (highest truncation level) for the POT method, generally provided a better fit of floods with high return periods (>25 years). Moreover, lower truncation levels tended to provide flood estimates with less uncertainties (lower coefficient of variation, as tested using a jackknife technique). Finally, results showed that both the AMS and POT methods are complementary in flood frequency analyses. The AMS is the more classic approach to flood frequency analyses; however, the POT provides a better characterization of floods (e.g. magnitude, duration and flood volume).

Climate change impact on extreme precipitation and peak flood magnitude and frequency: observations from CMIP6 and hydrological models

Changes in climate intensity and frequency, including extreme events, heavy and intense rainfall, have the greatest impact on water resource management and flood risk management. Significant changes in air temperature, precipitation, and humidity are expected in future due to climate change. The influence of climate change on flood hazards is subject to considerable uncertainty that comes from the climate model discrepancies, climate bias correction methods, flood frequency distribution, and hydrological model parameters. These factors play a crucial role in flood risk planning and extreme event management. With the advent of the Coupled Model Inter-comparison Project Phase 6, flood managers and water resource planners are interested to know how changes in catchment flood risk are expected to alter relative to previous assessments. We examine catchment-based projected changes in flood quantiles and extreme high flow events for Awash catchments. Conceptual hydrological models (HBV, SMART, NAM and HYMOD), three downscaling techniques (EQM, DQM, and SQF), and an ensemble of hydrological parameter sets were used to examine changes in peak flood magnitude and frequency under climate change in the mid and end of the century. The result shows that projected annual extreme precipitation and flood quantiles could increase substantially in the next several decades in the selected catchments. The associated uncertainty in future flood hazards was quantified using aggregated variance decomposition and confirms that climate change is the dominant factor in Akaki (C2) and Awash Hombole (C5) catchments, whereas in Awash Bello (C4) and Kela (C3) catchments bias correction types is dominate, and Awash Kuntura (C1) both climate models and bias correction methods are essential factors. For the peak flow quantiles, climate models and hydrologic models are two main sources of uncertainty (31% and 18%, respectively). In contrast, the role of hydrological parameters to the aggregated uncertainty of changes in peak flow hazard variable is relatively small (5%), whereas the flood frequency contribution is much higher than the hydrologic model parameters. These results provide useful knowledge for policy-relevant flood indices, water resources and flood risk control and for studies related to uncertainty associated with peak flood magnitude and frequency.

Revisiting the Application of Halphen Distributions in Flood Frequency Analysis

Flood frequency analysis is the foundation for hydrological and hydraulic design, and fundamental to frequency analysis is the selection of a suitable probability distribution. This study therefore revisits the Halphen frequency distribution family, with parameters estimated by the maximum likelihood estimation (MLE) method and goodness-of-fit evaluated by the Kolmogorov-Smirnov (KS) test. Besides revising the Halphen family, this study (1) proposes the study of kurtosis to assist the selection of a probability density function; (2) applies the kernel density function as the parent distribution function to evaluate flood risk (using 100-year event as an example); and (3) evaluates the mixed Halphen distribution for the heavy-tailed peak flow falling into the Halphen-B region. Using 198 peak flow datasets selected from 18 hydrologic regions in 48 states in the continental US, the study showed that: (1) Halphen-A/Halphen-B distributions were the preferred distributions for 190 datasets; (2) the moment-ratio diagram was found to be a reliable indicator for selecting the appropriate distribution; (3) kurtosis may be a tool to assist with the distribution selection; (4) comparison and risk assessment indicated that the identified Halphen distributions may properly model the flood frequency distribution; and (5) overall, the study validated the applicability of Halphen distributions for flood frequency analysis.

Evaluation of river flood extent simulated with multiple global hydrological models and climate forcings

Global flood models (GFMs) are increasingly being used to estimate global-scale societal and economic risks of river flooding. Recent validation studies have highlighted substantial differences in performance between GFMs and between validation sites. However, it has not been systematically quantified to what extent the choice of the underlying climate forcing and global hydrological model (GHM) influence flood model performance. Here, we investigate this sensitivity by comparing simulated flood extent to satellite imagery of past flood events, for an ensemble of three climate reanalyses and 11 GHMs. We study eight historical flood events spread over four continents and various climate zones. For most regions, the simulated inundation extent is relatively insensitive to the choice of GHM. For some events, however, individual GHMs lead to much lower agreement with observations than the others, mostly resulting from an overestimation of inundated areas. Two of the climate forcings show very similar results, while with the third, differences between GHMs become more pronounced. We further show that when flood protection standards are accounted for, many models underestimate flood extent, pointing to deficiencies in their flood frequency distribution. Our study guides future applications of these models, and highlights regions and models where targeted improvements might yield the largest performance gains.

A River Network‐Based Hierarchical Model for Deriving Flood Frequency Distributions and Its Application to the Upper Yangtze Basin

AbstractIn flood frequency analysis, expanding the additional information with hydrological reasoning beyond local at‐site flood samples can be very useful for improving the accuracy of flood frequency distribution (FFD) estimation as well as reflecting a better understanding of flood characteristics. In this study, a river network‐based hierarchical model is developed to estimate the FFDs in the Upper Yangtze basin by making full use of the hydrological reasoning information of both the flood dependence within the river network and reservoir regulation. Under this hierarchical model, a covariate analysis based on the generalized additive model for location, scale and shape is performed to obtain the conditional distribution of the interested flood variable given both its upstream flood variables and the reservoir index quantifying reservoir regulation; and then, the FFD of the interested flood variable is derived by combining its conditional distribution with the probability distribution of the upstream flood variables. The application to the Upper Yangtze basin indicates that the proposed hierarchical model suggests a satisfactory performance in FFD estimation. It is also found that the reservoir regulation, especially that of the Three Gorges Reservoir, is of great significance in reducing the flood magnitude in the basin. Compared to the conventional FFD estimation method that directly fits the assumed theoretical probability distributions to the at‐site flood samples, the hierarchical model incorporating the flood dependence within the river network exhibits an advantage in capturing the effect of reservoir regulation on the floods as well as in reducing the uncertainty in flood quantile estimation.

Improvements to Flood Frequency Analysis on Alluvial Rivers Using Paleoflood Data

AbstractHydrologists and engineers routinely use flood frequency analyses to compute flood probabilities for mitigation, infrastructure planning, and emergency management. Conventional flood frequency analyses—in which annual discharge maxima from a stream gage are fit to a statistical probability distribution—often encounter large uncertainties when estimating extreme flood levels. Most gage records span relatively short periods of time (<100 years), and thus the most extreme and infrequently occurring flood events tend to be poorly represented in instrumental data sets. Here, we demonstrate how a new generation of paleoflood records derived from floodplain sediments can be used to improve the accuracy and precision of extreme flood probability estimates along alluvial rivers. We use a series of simulation experiments to show that incorporating large numbers of paleoflood events in flood frequency analyses can significantly reduce the uncertainty of extreme flood estimates when the paleoflood data are sufficiently accurate and precise. Our results illustrate that robust paleoflood records can improve the shape parameter of flood frequency distributions, which determine the thickness of distribution tails, when as many as 50 paleoflood events are incorporated. We conclude by demonstrating how an alluvial paleoflood data set reduces uncertainty in a flood frequency analysis for a gage on the lower Mississippi River. Finally, we provide recommendations for how to incorporate paleoflood information into flood frequency analysis to improve the accuracy of extreme flood probabilities.

Stationary vs non-stationary modelling of flood frequency distribution across northwest England

ABSTRACT Extraordinary flood events occurred recently in northwest England, with several severe floods in Cumbria, Lancashire and the Manchester area in 2004, 2009 and 2015. These clustered extraordinary events have raised the question of whether any changes in the magnitude and frequency of river flows in the region can be detected. For this purpose, the annual maximum series of 39 river gauging stations in the study area are analysed. In particular, non-stationary models that include time, annual rainfall and annual temperature as predictors are investigated. Most records demonstrate a marked non-stationary behaviour and an increase of up to 75% in flood quantile estimates during the study period. Annual rainfall explains the largest proportion of variability in the peak flow series relative to other predictors considered in our study, providing practitioners with a useful framework for updating flood quantile estimates based on the dynamics of this highly accessible and informative climate indicator.

Prediction of flood frequency under a changing climate, the case of Hare watershed, Rift Valley Basin of Ethiopia

Climate change is causing unpredictable fluctuation in the rainfall patterns and, frequent heavy rainfall that led to catastrophic flooding and a significant risk on Hare watershed biodiversity in the Rift Valley of Ethiopia. Prediction of flood frequency under changing climate in the Hare watershed has not been well-studied in spite of the risk it poses to the human environment. Thus, this study aimed to predict flood frequency under changing climate in the Hare watershed relevant for effective flood management and early warning systems. The Soil Water Assessment Tool (SWAT) was used to simulate monthly stream flow. The model was calibrated and validated for the period of 1994 to 1998 and 1999 to 2001, respectively. The stationarity of annual peak flow was analyzed using Pettitt’s test. Flood Frequency Distribution (FFD) software package was deployed to determine the flood magnitude and frequency curve for the baseline period (1980–2009) and a predicted period (2021–2050). The result of the SWAT model performance evaluation statistics showed a high potential in the prediction of future streamflow. In addition, the goodness-of-fit test result showed that the Gumbel and Generalized Extreme Value (GEV) were the best-fit probability distribution for baseline and future flood events, respectively. The predicted annual peak flow results show an increasing trend by 60 and 106% under RCP 4.5 and RCP 8.5 emission scenarios, respectively. The future design flood may increase between 6.5 and 119.0% when compared to the baseline period. The study provides valuable information for policy and decision makers during the implementation of different flood events adaptation and mitigation measures for the Hare watershed.

Using Extreme Value Theory to Estimate Available Water in the Upper Awach-Kibuon Catchment in Nyamira County, Kenya

In the management of water resources projects, water managers are often interested in quantifying the frequency and magnitude of floods that will occur at the management areas or units. The frequency analysis of these events is one of the most important aspects that define the relationship between the magnitude and the frequency of an event, for which that event is exceeded. Frequency analysis therefore find its usefulness in the design and implementation of water projects such as in irrigation water requirement where the interest is on how much water can be available from a water resource to support irrigation throughout the year. The objective of this paper therefore was to assess the available water, using the extreme value analyses methods, that can be put to other uses such as irrigation water demand, domestic water requirement after due consideration to the environmental flow. The available water was estimated by deducting the following from the 80% probable flows orQ80: i) Deduction of estimated existing/future abstractions- determined from available information on irrigation activities along the three rivers (upstream and downstream) and ii) Deduction of the environmental flow - taken as 30% of the Q95 probable flow based on monthly mean flows. In particular this research was biased towards supporting the water requirement for a proposed irrigation project in the study area. The area of study was Awach-Kibuon catchment of the Lake Victoria South Catchment Area. This river catchment drains parts of Nyamira County through the Awach-Kasipul sub-catchent (Nyabomite, Charachani and Eaka tributaries). Data used included daily rainfall and temperature data obtained from the Kenya Meteorological Department Headquarters in Nairobi while daily discharge or flow levels data from the three tributaries i.e. Nyabomite, Charachani and Eaka were obtained from the Water Resources Authority (Kisii sub-regional office). The flow levels from Nyabomite, Charachani and Eaka were converted to river discharge using appropriate rating curves. Rainfall data was used with the Curve Number method to estimate river discharge at Eaka where flow levels were hardly available. Flood frequency distributions (GEV, the Gumbel) with different methods of parameter estimations (moments, maximum likelihood, probability weighted moments) were then used to estimate flow magnitudes corresponding to specific return periods (Q50, Q80 and Q95) and generate flow duration curves. Results from the flood frequency analysis from the General Extreme Value and Extreme Value type 1 distribution using the methods of moments (mom), maximum likelihood (ML) and Probability Weighted Moments (PWM) indicated the best distribution to be EV1-PWM since it exibited the lowest standard error estimates. Based on the most suitable distribution (EV1-PWM), the probabilities of exceedance were computed and used to estimate the water available for irrigation purposes at the three target gauging stations in the sub-catchment.From the results, a larger volume of water is available for irrigation at Charachani, for example, lowest being 0.388 cumecs in the month of July compared to 0.147 cumecs for Nyabomite and 0.249 cumecs for Eaka.. The largest amount of water for irrigation is available during the months of May and November, with peaks corresponding to those of the rainy seasons. The months with the least available water for irrigation are December, January and February, which also corresponds with the dry seasons. These results were used to inform planning in setting up of flow control structures for irrigation project in the Charachani-Eaka-Nyabomite cluster in Nyamira County.

Characteristics of hydrological extremes in Kulfo River of Southern Ethiopian Rift Valley Basin

Hydrological extreme events such as floods and drought are common in Ethiopia which eventually causes environmental hazards. Kulfo River is one of Southern Ethiopian Rift Valley Basin that has experienced flooding for years. Therefore, this study aimed characteristics of hydrological extremes (1985–2014) in the Kulfo River, which is important for effective drought and flood monitoring and early warning systems. The hydrological drought was assessed using the streamflow drought index (SDI). Flood frequency distribution (FFD) software package was deployed to determine the flood frequency curve of the Kulfo River. The goodness-of-fit test results showed that the Generalized Extreme Values (GEV) distribution was found the best-fit probability distribution model in the Kulfo River, while the results of SDI values showed that extreme drought events were observed in 1991, 1992, and 2014 with magnitudes ranging from − 2.04 to − 2.7, − 2.0 to − 2.3, and − 2.10 to − 2.24, respectively, which cause reduction of lake level, lowing of groundwater level, and decreased the amount of river flow. SDI value indicated 6-year drought duration has occurred with the relative frequency of 20% in the 3- and 6-month timescales. The flood frequency results show the lowest probability of having flood magnitude has affected the river morphology. The study provides valuable information for policy and decision makers to implement different adaptation and mitigation measures for extreme hydrological events in the Kulfo River.

A new method to restore the impact of land‐use change on flood frequency based on the Hydrologic Engineering Center‐Hydrologic Modelling System model

AbstractTo reveal the impacts of land‐use change on flood frequency distribution, a method to contradiction restore the largest‐gauged annual flood series to current land‐use conditions was proposed, based on the Hydrologic Engineering Center‐Hydrologic Modelling System (HEC‐HMS), with a newly developed iterative asymptotic method to calibrate the model parameters. Using the Xixi Basin on the southeastern coast of China as a case‐study, the HEC‐HMS model was applied to forward restore the largest annual floods between 1956 and 2011 by using the land‐use conditions of 2010. The flood peak flow series derived from forward restoration were used for flood frequency analysis. The results showed that (a) the iterative asymptotic method could calibrate the initial loss ratio and wave velocity relatively well. The physical meaning of the parameter values obtained was clear. The overall model simulation result was satisfactory, with Nash–Sutcliffe efficiency coefficients of 0.827 and 0.843 in the calibration and verification periods, respectively. (b) The calibration method effectively addressed the difficulty in determining the model parameters needed for resolving the restoration of the impacts of land‐use changes on the largest‐gauged annual flood peak flows and provided a newer HEC‐HMS‐based restoration approach for nonstationary flood frequency analysis. (c) Urbanization in the Xixi Basin caused a degradation in forested and arable lands, as well as in grasslands. Its main impact on the flood frequency distribution was that the average flood peak flow increased from 2,633.32 to 2,889.48 m3s−1and the changes in the coefficient of variation and coefficient of skewness were very small.

Regional flood frequency analysis for flood index estimation in hydrologic regions with limited flood data

The objective of this study is to establish a regional relationship between mean annual peak flood and the catchment area based on the annual peak flood frequency analysis. Annual peak flood time series for various gauging sites of six basins in north part of Iraq are used in analysis. The procedure of analysis is approached by scaling a regional flood frequency distribution (index-flood of catchment) which is defined here as the mean annual maximum flood discharge and estimated by linear regression using physiographic and climatic catchment properties. The index-flood method was found to have slightly better prediction accuracies over the direct-regression method. The coefficient of determination R2 between reference and estimated index-floods is relatively high in most cases for all the gauging stations. All regions give very high R 2 correlation and the best model is the model (2), which is also associated with minimum value of RMSE.

Identification of a preferred statistical distribution for at-site flood frequency analysis in Canada

Floods are the most frequent natural disaster in Canada, putting Canadian lives and property at risk. Projected variations in precipitation and temperature are expected to further intensify extreme events, necessitating improved flood planning and water resource management. The Natural Sciences and Engineering Research Council funded FloodNet project is developing a standardized flood estimation manual for Canada; such a nation-wide manual would make flood frequency application more effective, consistent, and reproducible, reducing the need for subjective judgement. This research investigates a preferred at-site flood frequency distribution for Canadian annual peak flow dataset. Four frequently used distributions: Generalized Logistic distribution, Generalized Extreme Value distribution, and Pearson Type III distributions with and without log transformation, are assessed using two robust goodness-of-fit tests (i.e., Modified Anderson-Darling test and L-Moment test) across a wide range of Canadian annual peak flood samples. Goodness-of-fit assessments are analysed using different configurations of flood samples, including annual maximum flow and instantaneous peak flow samples, stationary samples and de-trended non-stationary samples, samples with varied record length, and samples from various geographical regions of Canada. Estimated flood quantiles are compared with estimates of hypothetical true quantiles to assess predictive performance of the considered distribution. Results show the Generalized Extreme Value distribution is better than the other considered distributions because of its acceptance for most station samples and the closeness to the estimates of hypothetical true quantiles. This study provides a basis for recommending the Generalized Extreme Value distribution for at-site flood frequency analysis in Canada.

Identifying the origins of extreme rainfall using storm track classification

Abstract Identifying patterns in data relating to extreme rainfall is important for classifying and estimating rainfall and flood frequency distributions routinely used in civil engineering design and flood management. This study demonstrates the novel use of several self-organising map (SOM) models to extract the key moisture pathways for extreme rainfall events applied to example data in northern Spain. These models are trained using various subsets of a backwards trajectory data set generated for extreme rainfall events between 1967 and 2016. The results of our analysis show 69.2% of summer rainfall extremes rely on recirculatory moisture pathways concentrated on the Iberian Peninsula, whereas 57% of winter extremes rely on deep-Atlantic pathways to bring moisture from the ocean. These moisture pathways have also shown differences in rainfall magnitude, such as in the summer where peninsular pathways are 8% more likely to deliver the higher magnitude extremes than their Atlantic counterparts.