Conventional Flood Frequency Analysis Research Articles

Dynamic flood frequency analysis for west flowing rivers of Kerala, India

Conventional Flood Frequency Analysis (FFA) may underestimate flood quantiles and increase hydraulic infrastructure vulnerability in changing climates. This study uses annual maximum streamflow data from 17 hydrologic stations along west-flowing rivers in Kerala, India, for Non-Stationary (NS) FFA. The Generalized Extreme Value model with a linear temporal location parameter worked effectively for five stations. Kidangoor and Pattazhy stations must account for non-stationarity for longer return periods (RPs) (>50 years), whereas Neeleeswaram and Perumannu stations must for shorter RPs (<50 years). An extensive study was conducted for Neeleswaram station (Periyar basin) by simulating NS models incorporating four large-scale climate oscillations as covariates. The stationary assumption underestimated flood return levels of 2-year RP by about 61% which increases the flood risk leading to failure of hydraulic infrastructures. It was observed that the best fitted climate-based NS model achieves stationary return level of 150-years RP at 25-years RP itself. The study proved that the climate-based NS models captured the 2018 August Floods in Periyar basin better than stationary and time-based models. The regional variability in FF curve behaviour concludes that NSFFA for Kerala cannot be generalised and must be done at a local-scale.

Incorporation of Cost-Benefit Analysis Considering Epistemic Uncertainty for Calculating the Optimal Design Flood

Design flood via flood frequency analysis provides basic information for designing hydraulic structures. Quantification of uncertainty in flood frequency analysis has become an important issue during the past three decades. However, few studies have considered practical procedures for selecting a single design flood in the uncertainty range. Cost-benefit analysis can be incorporated to select a single design flood by calculating the optimal value in the total expected cost function. In particular, in this study, the relationship between conventional flood frequency analysis and cost-benefit analysis is addressed. Additionally, the parameter uncertainty is quantified by the Metropolis-Hastings algorithm to find the optimal design floods considering parameter uncertainty. The annual maximum (AM) series and partial duration (PD) series were used to identify the effect of various types of data. The optimal design floods obtained by the cost-benefit analysis considering parameter uncertainty were systematically larger than the design flood obtained by the conventional flood frequency analysis. Regarding the types of data, the generalized Pareto distribution (GPD) had the largest values in all return periods, while the Gumbel distribution had the smallest values in all cases.

Process-based flood frequency analysis in an agricultural watershed exhibiting nonstationary flood seasonality

Abstract. Floods are the product of complex interactions among processes including precipitation, soil moisture, and watershed morphology. Conventional flood frequency analysis (FFA) methods such as design storms and discharge-based statistical methods offer few insights into these process interactions and how they “shape” the probability distributions of floods. Understanding and projecting flood frequency in conditions of nonstationary hydroclimate and land use require deeper understanding of these processes, some or all of which may be changing in ways that will be undersampled in observational records. This study presents an alternative “process-based” FFA approach that uses stochastic storm transposition to generate large numbers of realistic rainstorm “scenarios” based on relatively short rainfall remote sensing records. Long-term continuous hydrologic model simulations are used to derive seasonally varying distributions of watershed antecedent conditions. We couple rainstorm scenarios with seasonally appropriate antecedent conditions to simulate flood frequency. The methodology is applied to the 4002 km2 Turkey River watershed in the Midwestern United States, which is undergoing significant climatic and hydrologic change. We show that, using only 15 years of rainfall records, our methodology can produce accurate estimates of “present-day” flood frequency. We found that shifts in the seasonality of soil moisture, snow, and extreme rainfall in the Turkey River exert important controls on flood frequency. We also demonstrate that process-based techniques may be prone to errors due to inadequate representation of specific seasonal processes within hydrologic models. If such mistakes are avoided, however, process-based approaches can provide a useful pathway toward understanding current and future flood frequency in nonstationary conditions and thus be valuable for supplementing existing FFA practices.

Reducing uncertainty of design floods of two-component mixture distributions by utilizing flood timescale to classify flood types in seasonally snow covered region

The conventional flood frequency analysis typically assumes the annual maximum flood series (AMFS) result from a homogeneous flood population. However, actually AMFS are frequently generated by distinct flood generation mechanisms (FGMs), which are controlled by the interaction between different meteorological triggers (e.g., thunderstorms, typhoon, snowmelt) and properties of underlying surface (e.g., antecedent soil moisture and land-cover types). To consider the possibility of two FGMs in flood frequency analysis, researchers often use the two-component mixture distributions (TCMD) without explicitly linking each component distribution to a particular FGM. To improve the mixture distribution modeling in seasonally snow covered regions, an index called flood timescale (FT), defined as the ratio of the flood volume to peak value and chosen to reflect the relevant FGM, is employed to classify each flood into one of two types, i.e., the snowmelt-induced long-duration floods and the rainfall-induced short-duration floods, thus identifying the weighting coefficient of each component distribution beforehand. In applying the FT-based TCMD to model the AMFS of 34 watersheds in Norway, ten types of mixture distributions are considered. The design floods and associated confidence intervals are calculated using parametric bootstrap method. The results indicate that the FT-based TCMD model reduces the uncertainty in the estimation of design floods for high return periods by up to 40% with respect to the traditional TCMD. The improved predictive ability of the FT-based TCMD model is attributed to its explicit recognition of distinct generation mechanisms of floods, thereby being able to identify the weighting coefficient and FGM of each component distribution without optimization.

On seasonal and semi-annual approach for flood frequency analysis

As a supplementary method to the conventional flood frequency analysis based on annual peak flows, we propose an approach in this paper to infer the flood frequency distribution on quarterly and semi-annual time scale, which are then converted to annual time scale to obtain the floods corresponding to return periods in unit of year. Two criteria for test of data independence, namely, minimum 7 and 15-day interval between two consecutive peak flows, are tested. The proposed approach was applied to Des Moines River at Fort Dodge, Iowa, USA using its 62 years of observation daily flows. The results show that the estimated floods for given return periods from quarter-annual data series are in general higher than the corresponding estimated floods from semi-annual data series, which is further larger than estimated floods from annual peak flows. The floods estimated from semi-annual data series agree well with the results of previous US Geological Survey study.

Applicability of a physically based soil water model (SWMOD) in design flood estimation in eastern Australia

Abstract Event-based rainfall–runoff models are useful tools for hydrologic design. Of the many loss models, the ‘initial loss-continuing loss’ model is widely adopted in practice. Some of the key limitations with these types of loss models include the arbitrary selection of initial moisture (IM) conditions and lack of physically meaningful parameters. This paper investigates the applicability of a physically based soil water balance model (SWMOD) with distributed IM conditions for flood modelling. Four catchments from the east coast of New South Wales, Australia, are modelled. The IM content in SWMOD represents the antecedent moisture condition. A quasi-Monte Carlo simulation framework is adopted, where the IM is stochastically varied according to a lognormal probability distribution. In calibration, it is found that the adopted modelling framework is able to simulate the majority of the observed flood hydrographs with a higher degree of accuracy; however, in a design context, when compared to the results of conventional flood frequency analysis, discrepancies are noted for a range of annual exceedance probabilities. The quasi-Monte Carlo simulation framework proved to be useful in assessing the effect of the IM content on design flood estimates.

Flood frequency analysis using synthetic samples

The design flow is the basis for planning and designing different hydraulic works. The precision in estimated flows is important when analyzing the feasibility of such structures because the value directly influences the evaluation of the failure effects. However, due to flow variability, the precision of the estimate is drastically reduced when small samples are used in a conventional flood frequency analysis (FFA). This paper proposes a new approach based on a combined simulation of the annual peak and mean flows. The method was evaluated by considering 10-, 20-, 30-, 40- and 50-yr subsamples obtained from 13 gauging stations located in the Susquehanna River basin. The results were compared with those obtained by FFA and the regional station-year method. This new approach can reduce the uncertainty in estimating the design flow when few data are available.

Deriving Design Flood Hydrograph Based on Conditional Distribution: A Case Study of Danjiangkou Reservoir in Hanjiang Basin

Design flood hydrograph (DFH) for a dam is the flood of suitable probability and magnitude adopted to ensure safety of the dam in accordance with appropriate design standards. Estimated quantiles of peak discharge and flood volumes are necessary for deriving the DFH, which are mutually correlated and need to be described by multivariate analysis methods. The joint probability distributions of peak discharge and flood volumes were established using copula functions. Then the general formulae of conditional most likely composition (CMLC) and conditional expectation composition (CEC) methods that consider the inherent relationship between flood peak and volumes were derived for estimating DFH. The Danjiangkou reservoir in Hanjiang basin was selected as a case study. The design values of flood volumes and 90% confidence intervals with different peak discharges were estimated by the proposed methods. The performance of CMLC and CEC methods was also compared with conventional flood frequency analysis, and the results show that CMLC method performs best for both bivariate and trivariate distributions which has the smallest relative error and root mean square error. The proposed CMLC method has strong statistical basis with unique design flood composition scheme and provides an alternative way for deriving DFH.

Floodplain Mapping Using HEC-RAS and ArcGIS: A Case Study of Kabul River

This paper describes the application of HEC-RAS model to the development of floodplain maps for the part of Kabul river that lies in Pakistan. The intent is to assist policy makers and planners in the development of flood mitigation measures for the Khyber Pakhtunkhwa Province, which experienced unprecedented floods in July/August 2010 exposing the vulnerability of the province to this natural catastrophe. Owing to its reasonable accuracy and free availability, shuttle radar topography mission digital elevation model was chosen for the extraction of geometrical data for the river. Conventional flood frequency analysis, involving log-normal, Gumbel’s, and log-Pearson type III (LP3) distributions, was used to calculate extreme flows with different return periods. Using Kolmogorov–Smirnov (KS) test, LP3 was found to be the best distribution for the Kabul River. The peak floods from frequency analysis were input into HEC-RAS model to find the corresponding flood levels expected along river reaches extending through Warsak dam to Attock. Results obtained with HEC-RAS model were used in combination with ArcGIS to prepare floodplain maps for different return periods. Through floodplain maps, areas that are vulnerable to flooding hazards have been identified. Analysis of floodplain maps indicated that more than 400 % area is likely to be inundated as compared to the normal flow of the river. Most of the area found to be vulnerable to flooding is currently used for agriculture. Comparison of simulation of 2010 flood with the image of the flood taken by MODIS clearly shows a close agreement between the two.

Design flood recalculation under land surface change

Under the influence of land surface variation, observed flood series are no longer stationary. Therefore, conventional flood frequency analysis methods based on the independent and identically distributed hydrological time series are not authentic, and design flood, which is the core of flood control planning, obtained by conventional methods is inevitably being questioned. In this study, we selected Wangkuai reservoir and Xidayang reservoir catchments in Daqinghe watershed affected by human activities to recalculate design floods. We firstly summarized land-use change and construction of hydraulic structures in the two catchments and found that land-use had changed a lot during 1970–1980, and numerous hydraulic structures had been built which had a significant influence on land surface in both catchments. Then, we used both reconstruction of flood series based on rainfall–runoff relationship and probability distribution methods to reconstruct the annual maximum peak flow series and calculated the design flood peaks under current and past land surface. Finally, we compared the design floods by the two methods with those derived from the direct curve fitting method without taking the land surface change into account, and we found that design flood peak and volume decreased due to land surface change in Wangkuai reservoir catchment and Xidayang reservoir catchment. The results were of great significance for flood control in these two catchments.

Design Flood Hydrograph Based on Multicharacteristic Synthesis Index Method

Flood events consist of flood peaks and flood volumes for various durations that are mutually correlated and need to be described by multiple variables. Conventional flood frequency analysis methods are generally based on the univariate distributions of either flood peaks or flood volumes, and hence only provide a limited assessment of flood events. In this paper, a multicharacteristic synthesis index (MSI) method is developed to describe flood hydrographs in an integrated way by considering the multiple characteristics synthetically, such as flood peak, 1-day maximum flood volume, 3-day maximum flood volume, 7-day maximum flood volume, etc. Annual maximum flood hydrograph series are sampled and then are transformed into corresponding MSI series by the MSI method. The quantile of the MSI series corresponding to a specified design return period is estimated by conventional flood frequency analysis method. A procedure for derivation of design flood hydrograph based on the MSI quantile is proposed. An applic...

Unbiased estimation of flood risk with the GEV distribution

Conventional flood frequency analysis is concerned with providing an unbiased estimate of the magnitude of the design flow exceeded with the probabilityp, but sampling uncertainties imply that such estimates will, on average, be exceeded more frequently. An alternative approach is therefore, to derive an estimator which gives an unbiased estimate of flow risk: the difference between the two magnitudes reflects uncertainties in parameter estimation. An empirical procedure has been developed to estimate the mean true exceedance probabilities of conventional estimates made using a GEV distribution fitted by probability weighted moments, and adjustment factors have been determined to enable the estimation of flood magnitudes exceeded with, on average, the desired probability.