Flood Frequency Curves Research Articles

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

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

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

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

Analysis of Magnitude and Frequency of Floods in the Damanganga Basin: Western India

Flood Frequency Analysis (FFA) method was introduced by Fuller in 1914 to understand the magnitude and frequency of floods. The present study is carried out using the two most widely accepted probability distributions for FFA in the world namely, Gumbel Extreme Value type I (GEVI) and Log Pearson type III (LP-III). The Kolmogorov-Smirnov (KS) and Anderson-Darling (AD) methods were used to select the most suitable probability distribution at sites in the Damanganga Basin. Moreover, discharges were estimated for various return periods using GEVI and LP-III. The recurrence interval of the largest peak flood on record (Qmax) is 107 years (at Nanipalsan) and 146 years (at Ozarkhed) as per LP-III. Flood Frequency Curves (FFC) specifies that LP-III is the best-fitted probability distribution for FFA of the Damanganga Basin. Therefore, estimated discharges and return periods by LP-III probability distribution are more reliable and can be used for designing hydraulic structures.

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.

Regional Flood Frequency Analysis using Dimensionless Index Flood Method

Hydrologic designs require accurate estimation of quartiles of extreme floods. But in many developing regions, records of flood data are seldom available. A model framework using the dimensionless index flood for the transfer of Flood Frequency Curve (FFC) among stream gauging sites in a hydrologically homogeneous region is proposed. Key elements of the model framework include: (1) confirmation of the homogeneity of the region; (2) estimation of index flood-basin area relation; (3) derivation of the regional flood frequency curve (RFFC) and deduction of FFC of an ungauged catchment as a product of index flood and dimensionless RFFC. As an application, 1983 to 2004 annual extreme flood from six selected gauging sites located in Anambra-Imo River basin of southeast Nigeria, were used to demonstrate that the developed index flood model: , overestimated flood quartiles in an ungauged site of the basin. It is recommended that, for wider application, the model results can be improved by the availability and use of over 100 years length of flood data spatially distributed at critical locations of the watershed. Doi: 10.28991/cej-2020-03091627 Full Text: PDF

Flood Frequency Analysis of River Niger, Shintaku Gauging Station, Kogi State, Nigeria

The study outlines a frequency distribution study on the highest annual flood statistics in Niger River located at Shintaku hydrologic Station for period of 58years. In order to determine the optimal model for highest annual flood analysis Generalised extreme value, Log normal, Gumbel maximum, Generalised Pareto and Log Pearson III, were tested. Based on error produced by criteria of goodness of Fit tests, the optimal model was determined. Results obtained indicated that Log Pearson type III was best to model maximum flood magnitude of Niger River at Shintaku station. The flood frequency curve was therefore derived using Log Pearson type III frequency distribution. Flood frequency curve showed that return periods of 50 and 100 years with the probabilities of 2% and1% respectively, yielded discharges of 15300m3/s and 15600m3/s respectively. These results were strongly influenced by their topographical, geographical and climatic factors. The findings of this work will be useful for design engineers in deciding the dimension of hydraulic structures such as spillway, dams, canals, bridges and levees among others. Future studies are required to include flood forecasting in the development of inundation maps for Niger River.Keywords—Return period, Frequency Distribution, Flood, Niger River, Flood Modeling

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.

Quantifying Long‐Term and Event‐Scale Baseflow Effects across the Flood Frequency Curve

AbstractThough metrics that quantify the long‐term water balance of a catchment have been known to influence the statistics of flood frequency data, little has been done to understand exactly what the substantive links are that causes baseflow to impact instantaneous annual maximum flows (IAMF). As abrupt changes to baseflow can occur via climate change and human disturbances, understanding the effects of baseflow on IAMF behavior can provide insight into how groundwater alterations and climate change can impact flood risk assessment, particularly for regional data aggregation. We thus seek to quantify baseflow impacts across the flood frequency curve considering a top down approach enumerating the differential impacts of baseflow (as Baseflow Index) across the IAMF flood frequency curve and then analyzing the impact of baseflow at the event‐scale. Results show that baseflow exerts significant influence over the entire flood frequency curve, showing a slight increase in effect with higher return period flows. At the event‐scale, the permeability of the catchment (proxied by baseflow rate during rising limb), significantly impacted the variability in IAMF and had more of an effect than antecedent moisture of the catchment (proxied by antecedent discharge). Furthermore, the effects of each of the event‐scale variables changed across climate regions and return periods, suggesting that changes to climate and groundwater abstractions would greatly affect the flood frequency curve.

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.

Predicting the impact of climate change on urban drainage systems in northwestern Italy by a copula-based approach

Study regionMilan, northwestern Italy. Study focusThe impact of expected trends in storm temporal structures is analyzed with reference to urban drainage systems, featuring catchment areas spanning from 10 ha to 100 ha. A bivariate stochastic model for the derivation of flood frequency is developed, accounting for the seasonality of storm volumes, durations and their mutual dependence structure. Its reliability is verified by comparing it to continuous hydrodynamic simulations. To do so, a 21-year long series observed at Milan-Monviso raingauge was used. Model comparison evidences a satisfactory agreement between models. New hydrological insights for the regionAlthough the total annual precipitation is not expected to change, relevant increases in flood frequencies are predicted. Such increases vary between 10–20 % and appear to be independent of the return period. Thus, great concerns arise for the existing urban drainage systems located in northwestern Italy, which should basically be unable to face these flood frequency changes. A leading role is played by the intensification of summer and spring storms, both in terms of increase in volumes and decrease in durations. Moreover, changes in the dependence structure have a significant impact when summer storms are considered. Conversely, flood frequency curves are far less sensitive to the storm temporal structures featuring other seasons. These results can be explained by considering the seasonal distribution of storms critical for urban drainage systems.

Development of Regional Flood Frequency Relationship for Narmada Basin using Index Flood Procedure

Water Resources decision making problems such as flood plain zoning, design of hydraulic structures etc. are based on design flood estimate, defined as discharge for a specified probability of exceedance. Flood Frequency Analysis helps to estimate the flood value for a specific return period. This procedure requires sufficient length of observed data of floods on river gauging sites which many a time is not available. In India major rivers have very few gauging sites and their tributaries are mostly ungauged. When quantiles have to be estimated for ungauged sites, Flood Frequency Analysis is neither possible nor reliable. Regional Flood Frequency Analysis is the means to overcome such problems, reasonably quantifying flood estimates at desired frequencies for sites within a more or less hydrological homogeneous region. Narmada Basin located in central India covers an area about 98,976 sq. km, drained by a large number of tributaries, most of which are ungauged, has been considered as the case. Index Flood method utilizing Gumbel’s EV-1 distribution have been used in the present study to develop the Regional flood frequency relationship. The Annual Peak Flood data of 16 gauging sites of Narmada Basin, having record length of 12 to 17 years, is utilized for flood estimation. Flood frequency curves for the considered gauging stations are generated. Development of regional flood frequency relationship leads to the estimation of different return period flood.

Selection of Bias Correction Methods to Assess the Impact of Climate Change on Flood Frequency Curves

Climate projections provided by EURO-CORDEX predict changes in annual maximum series of daily rainfall in the future in some areas of Spain because of climate change. Precipitation and temperature projections supplied by climate models do not usually fit exactly the statistical properties of the observed time series in the control period. Bias correction methods are used to reduce such errors. This paper seeks to find the most adequate bias correction techniques for temperature and precipitation projections that minimizes the errors between observations and climate model simulations in the control period. Errors in flood quantiles are considered to identify the best bias correction techniques, as flood quantiles are used for hydraulic infrastructure design and safety assessment. In addition, this study aims to understand how the expected changes in precipitation extremes and temperature will affect the catchment response in flood events in the future. Hydrological modelling is required to characterize rainfall-runoff processes adequately in a changing climate, in order to estimate flood changes expected in the future. Four catchments located in the central-western part of Spain have been selected as case studies. The HBV hydrological model has been calibrated in the four catchments by using the observed precipitation, temperature and streamflow data available on a daily scale. Rainfall has been identified as the most significant input to the model, in terms of its influence on flood response. The quantile mapping polynomial correction has been found to be the best bias correction method for precipitation. A general reduction in flood quantiles is expected in the future, smoothing the increases identified in precipitation quantiles by the reduction of soil moisture content in catchments, due to the expected increase in temperature and decrease in mean annual precipitations.

Have trends changed over time? A study of UK peak flow data and sensitivity to observation period

Abstract. Classical statistical methods for flood frequency estimation assume stationarity in the gauged data. However, recent focus on climate change and, within UK hydrology, severe floods in 2009 and 2015 has raised the profile of statistical analyses that include trends. This paper considers how parameter estimates for the generalised logistic distribution vary through time in the UK. The UK Benchmark Network (UKBN2) is used to allow focus on climate change separate from the effects of land-use change. We focus on the sensitivity of parameter estimates to adding data, through fixed-width moving window and fixed-start extending window approaches, and on whether parameter trends are more prominent in specific geographical regions. Under stationary assumptions, the addition of new data tends to further the convergence of parameters to some final value. However, addition of a single data point can vastly change non-stationary parameter estimates. Little spatial correlation is seen in the magnitude of trends in peak flow data, potentially due to the spatial clustering of catchments in the UKBN2. In many places, the ratio between the 50-year and 100-year flood is decreasing, whereas the ratio between the 2-year and 30-year flood is increasing, presenting as a flattening of the flood frequency curve.

Modeling the role of reservoirs versus floodplains on large-scale river hydrodynamics

Large-scale hydrologic–hydrodynamic models are powerful tools for integrated water resources evaluation at the basin scale, especially in the context of flood hazard assessment. However, recent model developments have paid little attention to simulate reservoirs’ hydrodynamics within river networks. This study presents an adaptation of the MGB model to simulate reservoirs as an internal boundary condition, enabling the explicit simulation of hydrodynamic processes along reservoirs and their interaction with upstream and downstream floodplains in large basins. A case study is carried out in the Itajai-Acu River Basin in Brazil, which has periodic flood-related disasters and three flood control dams. The model was calibrated for the 1950–2016 period forced with daily observed precipitation. The adjustment was satisfactory, with Nash–Sutcliffe metrics between 0.54 and 0.84 for the 11 gauges analyzed and with flood frequency curves also well represented. Simulation scenarios with and without floodplains and reservoirs were performed to evaluate the relative role of these factors on flood control basin-wide through evaluation of simulated discharges, water levels and flood extent. Itajai do Oeste tributary and Itajai-Acu mainstem present major floodplain attenuation, while in Itajai do Sul and Itajai do Norte tributaries the main flood control occurs due to reservoir attenuation. Downstream from the dams, results indicated that the reservoirs reach their maximum discharge reduction capacity for 5- to 10-year floods, decreasing it for larger floods. The developed model may be very useful for operational uses as flood forecasting and coordinated reservoir operation studies, as well as to enhance the comprehension of flood dynamics at basin scale.

Decreasing uncertainty in flood frequency analyses by including historic flood events in an efficient bootstrap approach

Abstract. Flood frequency curves are usually highly uncertain since they are based on short data sets of measured discharges or weather conditions. To decrease the confidence intervals, an efficient bootstrap method is developed in this study. The Rhine river delta is considered as a case study. We use a hydraulic model to normalize historic flood events for anthropogenic and natural changes in the river system. As a result, the data set of measured discharges could be extended by approximately 600 years. The study shows that historic flood events decrease the confidence interval of the flood frequency curve significantly, specifically in the range of large floods. This even applies if the maximum discharges of these historic flood events are highly uncertain themselves.

Informed attribution of flood changes to decadal variation of atmospheric, catchment and river drivers in Upper Austria

Flood changes may be attributed to drivers of change that belong to three main classes: atmospheric, catchment and river system drivers. In this work, we propose a data-based attribution approach for selecting which driver best relates to variations in time of the flood frequency curve. The flood peaks are assumed to follow a Gumbel distribution, whose location parameter changes in time as a function of the decadal variations of one of the following alternative covariates: annual and extreme precipitation for different durations, an agricultural land-use intensification index, and reservoir construction in the catchment, quantified by an index. The parameters of this attribution model are estimated by Bayesian inference. Prior information on one of these parameters, the elasticity of flood peaks to the respective driver, is taken from the existing literature to increase the robustness of the method to spurious correlations between flood and covariate time series. Therefore, the attribution model is informed in two ways: by the use of covariates, representing the drivers of change, and by the priors, representing the hydrological understanding of how these covariates influence floods. The Watanabe-Akaike information criterion is used to compare models involving alternative covariates. We apply the approach to 96 catchments in Upper Austria, where positive flood peak trends have been observed in the past 50 years. Results show that, in Upper Austria, one or seven day extreme precipitation is usually a better covariate for variations of the flood frequency curve than precipitation at longer time scales. Agricultural land-use intensification rarely is the best covariate, and the reservoir index never is, suggesting that catchment and river drivers are less important than atmospheric ones. Not all the positive flood trends correspond to a significant correlation between floods and the covariates, suggesting that other drivers or other flood-driver relations should be considered to attribute flood trends in Upper Austria.

Non-Stationary Bayesian Modeling of Annual Maximum Floods in a Changing Environment and Implications for Flood Management in the Kabul River Basin, Pakistan

Recent evidence of regional climate change associated with the intensification of human activities has led hydrologists to study a flood regime in a non-stationarity context. This study utilized a Bayesian framework with informed priors on shape parameter for a generalized extreme value (GEV) model for the estimation of design flood quantiles for “at site analysis” in a changing environment, and discussed its implications for flood management in the Kabul River basin (KRB), Pakistan. Initially, 29 study sites in the KRB were used to evaluate the annual maximum flood regime by applying the Mann–Kendall test. Stationary (without trend) and a non-stationary (with trend) Bayesian models for flood frequency estimation were used, and their results were compared using the corresponding flood frequency curves (FFCs), along with their uncertainty bounds. The results of trend analysis revealed significant positive trends for 27.6% of the gauges, and 10% showed significant negative trends at the significance level of 0.05. In addition to these, 6.9% of the gauges also represented significant positive trends at the significance level of 0.1, while the remaining stations displayed insignificant trends. The non-stationary Bayesian model was found to be reliable for study sites possessing a statistically significant trend at the significance level of 0.05, while the stationary Bayesian model overestimated or underestimated the flood hazard for these sites. Therefore, it is vital to consider the presence of non-stationarity for sustainable flood management under a changing environment in the KRB, which has a rich history of flooding. Furthermore, this study also states a regional shape parameter value of 0.26 for the KRB, which can be further used as an informed prior on shape parameter if the study site under consideration possesses the flood type “flash”. The synchronized appearance of a significant increase and decrease of trends within very close gauge stations is worth paying attention to. The present study, which considers non-stationarity in the flood regime, will provide a reference for hydrologists, water resource managers, planners, and decision makers.

On the relationship between flood and contributing area

AbstractAlthough it is well known that the vast majority of the time only a portion of any watershed contributes run‐off to the outlet, this extent is rarely documented. Also, the power law form of the streamflow and contributing area (Q‐Ac) relationship has been known for a half century, but it is uncommon for it to be quantified, and time series of contributing area extensive enough to calculate its frequency distribution are almost non‐existent. Data from the Canadian Prairies, where there are extensive estimates of contributing area during the median annual flood, imply that the power law coefficient for any Q‐Ac curve is a function of flow magnitude and return period. These data also suggest that regional flood frequency curves are a construct of Q‐Ac curves from individual basins. This paper will discuss research that attempted to reproduce the Q‐Ac curves for the La Salle River Watershed with a semidistributed numerical hydrological model, MESH‐PDMROF. The model simulated streamflow reasonably well (Nash Sutcliffe values = 0.62) compared with published examples of comparable models applied in the region. Estimates of the coefficient and exponent of the Q‐Ac power law function ranged from 0.08–0.14 and 0.9–1.12, respectively. These exponent values were lower than those of regional flood frequency curves and support the theory that regional flood frequency curves are a construct of Q‐Ac curves. Simulations of the area contributing to the median annual flood were lower (0.3) than those derived from independent topographic analysis (0.9) described in earlier literature though there is uncertainty in both these estimates. This uncertainty was extended across the flood frequency distribution and may be too large to definitively verify the study hypothesis.

Spatiotemporal evaluation of inundated areas using MODIS imagery at a catchment scale

Accurate spatiotemporal analysis of surface water is important for managing water resources. The main objectives of this study were: 1) to assess different remote sensing algorithms’ ability to accurately determine flood extent using MODIS imagery, and 2) to study the temporal dynamics of inundated areas and corresponding return periods in a large catchment in New South Wales, Australia.The different water detection alternatives used included the Open Water Likelihood (OWL) algorithm, the Brakenridge algorithm for single images (BRAK) and composite images (BRAKC), the Islam methodology (ISL), a combination of Normalized Difference Indices (NDIS), and an ensemble of these. Landsat Thematic Mapper (TM) images were used as a reference to benchmark the performance of the algorithms. Occurrence probability maps derived from inundation images and estimated flood extent frequency curves were compared against the Global Surface Water (GSW) dataset of the Joint Research Centre.The ISL methodology indicated the best performance compared with “ground truth” inundated images derived from Landsat, while the temporal performance of the BRAK algorithm indicated significant noise based on daily imagery. The remaining approaches generally matched stream discharge peaks. Maximum cross-correlation coefficients between daily discharge and inundated areas were calculated for different time lags, indicating high spatial variability across the catchment. Accumulated rainfall accounted for up to 40 and 60% of the variation in inundation extent in daily and composite images, respectively. The ensemble technique outperformed the other algorithms to describe the dynamics of flooding, followed by the NDIS and ISL algorithms. The occurrence probability images and flood frequency curves indicated a higher inundation extent than the GSW product for return periods greater than two years, which can be explained by the different temporal resolutions. The results of this study can be applied in water resources planning and flood mitigation efforts.

Toward a Better Understanding of Recurrence Intervals, Bankfull, and Their Importance

AbstractBankfull is a concept that is intimately tied to the annual flood series through the well‐accepted tenet that bankfull discharge occurs at approximately the 1.5‐year recurrence interval on the annual series. Thus, due to this association the annual series provides a useful diagnostic tool for helping to identify the bankfull elevation in the field. The partial‐duration series does not provide an equivalent tool because paired discharge and recurrence interval values from the flood frequency curve depend upon the minimum threshold selected for developing the partial‐duration series. However, the interpretation that bankfull discharge occurs on average once every 1.5 years, or two out of every three years from that bankfull discharge/recurrence interval relationship on the annual series is incorrect. Frequencies of small floods (those with recurrence intervals ≤10 years) should be obtained using the partial‐duration flood series because it contains a more accurate representation of the size and frequency of small events. We used discharge data from 11 streams in West Virginia watersheds that ranged from about 0.14 to 223 km2 to compare the two series and to illustrate the variability in small flood frequencies through time. Flooding to the bankfull stage was absent some years but occurred as many as four or five times during other years.