Flood Estimation Handbook Research Articles

Hydrological models and datasets to support flood flow estimation at river-bridge intersections

A working group on the "Hydraulic Compatibility of Bridges" (sites.google.com/view/gii-ponti) has been recently established in the hydraulic engineering academic community aiming at reviewing methodologies, studying good practices, and suggesting guidelines for assessing the bridge hydraulic compatibility. In this initiative, a subgroup is focusing on the hydrological aspects as in Italy there are not standard procedures to estimate design flood values requested by the Italian technical standards of constructions (NTC 2018) over the whole country as, for example, in the UK where the Flood Estimation Handbook provides standard data and methods. Some models are available at the regional scale but are often based on not up-to-date data; moreover, while in large rivers flood discharge values, defined according to the European Flood Directive have been computed and mapped, in small river catchments they are not available.In this context, the unit of Politecnico di Torino working on hydrology is going to release some country-wide hydrological datasets, including all the available official information on discharge and rainfall extremes updated to recent years, that can be used as a common reference for bridge analyses. These include high-resolution maps of extreme precipitation statistics, a database of flood flow extremes recorded at more than 600 river sections, and a catalogue of river catchments characteristics (morphology, climate, land use, etc).Nation-wide datasets can support the application of standardized hydrological analyses, needed for flood hazard assessment, especially in small basins. Operational products will be made available within the PNRR RETURN project in order to be easily available and referable for any hydrological analysis in Italy.

A region of influence approach for attributing fluvial climate change allowances

ABSTRACT For practical purposes the current guidance for attributing fluvial climate change allowances (recommended peak-discharge adjustments) in England is based on percentage changes to the 50-year return level attributed homogeneously to geographical regions. This proof-of-concept study introduces an approach enabling the practitioner to derive allowances for the full distribution of extremes (as opposed to single return periods) and is based on catchment characteristics. A region of influence approach, adopted by the Flood Estimation Handbook (FEH), is applied to estimate changes to the parameters of statistical models of extreme flows, as opposed to flow peaks. The approach is distribution neutral and can be applied to any catchment for which an FEH analysis has been undertaken. Results for an example scenario (Representative Concentration Pathway (RCP)8.5 2080s) are compared to a geographically regional method and an example is provided for a single catchment estimating extremes with the FEH method and adjusting them for the RCP8.5 2080s scenario.

The Performance of Natural Flood Management at the Large Catchment-Scale: A Case Study in the Warwickshire Stour Valley

The limited understanding of Natural Flood Management (NFM) performance, especially at large hydrological scales, is considered a critical barrier for the further funding and implementation of these nature-based solutions to the increasing international problem of flooding. The publications of the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report and Environment Agency’s National Flood and Coastal Erosion Risk Management Strategy (NFCERMS) for England have shown that extreme weather, including increased likelihood of high magnitude flood events, will occur and will require more novel management methods. This study focused on the ability of co-designed NFM measures to ameliorate downstream fluvial flooding by attenuating catchment response through a highly spatially distributed network of attenuating and roughening measures. Performance was characterised by the ability of NFM to attenuate flood peaks at different spatial scales across a large (187 km2) dendritic catchment, including the lowering of flood peaks and delaying the time-to-peak. Using a coupled modelling methodology and applying it to the upper Stour Valley, Warwickshire-Avon, UK, a rural response to the application of a set of NFM interventions was developed using the hydrodynamic model Flood Modeller Pro and XPSWMM ©. The method demonstrated a means of incorporating local knowledge in a realistic set of NFM schemes, tested to multiple flood risk scenarios (including climate change). Under frequent, smaller design storm events (e.g., Index Flood (QMED) and 3.3% AEP), flood peaks were lowered across all hydrological scales tested (5.8 km2 to 187 km2). As the design flood event severity increases, impact from upstream NFM attenuation on downstream peak response diminished significantly, especially at the largest hydrological scales. However, even at the largest hydrological scale, delays in time-to-peak were noted, increasing the ability of downstream communities to respond and enact flood preparation activities, thus increasing resilience to potential flooding events. While the benefits were limited to large flood events, the modelling indicated that NFM has the potential to reduce downstream flood risk. However, greater integration of observed data to improve model confidence and reduce uncertainty in modelled events is needed, especially the uncertainty associated with using single peaked design storm events from the Flood Estimation Handbook (FEH). This paper proposes a future Before–After Control–Impact (BACI) monitoring programme that could be integrated with models and applied across non-tidally influenced catchments seeking to empirically test the hydrological performance of in-situ NFM.

Sampling uncertainty of UK design flood estimation

ABSTRACT The UK standard for estimating flood frequencies is outlined by the flood estimation handbook (FEH) and associated updates. Estimates inevitably come with uncertainty due to sampling error as well as model and measurement error. Using resampling approaches adapted to the FEH methods, this paper quantifies the sampling uncertainty for single site, pooled (ungauged), enhanced single site (gauged pooling) and across catchment types. This study builds upon previous progress regarding easily applicable quantifications of FEH-based uncertainty estimation. Where these previous studies have provided simple analytical expressions for quantifying uncertainty for single site and ungauged design flow estimates, this study provides an easy-to-use method for quantifying uncertainty for enhanced single site estimates.

Estimating single-site design flood variance using a generalised logistic distribution

An easy-to-use equation is presented for calculating the variance of a design flood estimated using a generalised logistic distribution with model parameters estimated using single-site analysis, as described in the UK Flood Estimation Handbook. The equation is applicable for return periods in the range 2–1000 years and for annual maximum flood series with L-skewness of −0.45 to 0.45, which is considered representative of most UK flood data and practical uses.

The 2019/2020 floods in the UK: a hydrological appraisal

The 2019/2020 floods in the <scp>UK</scp>: a hydrological appraisal

Assessment of Peak Flow Scaling and Its Effect on Flood Quantile Estimation in the United Kingdom

AbstractRegional flood frequency analysis (RFFA) methods are essential tools to assess flood hazard and plan interventions for its mitigation. They are used to estimate flood quantiles when the at‐site record of streamflow data is not available or limited. One commonly used RFFA method is the index flood method (IFM), which assumes that peak floods satisfy the simple scaling hypothesis.In this work we present an integrated approach to assess the spatial scaling behavior of floods in the United Kingdom (UK) for 540 catchments, where the IFM is currently used operationally. This assessment employs product moments, probability weighted moments, and quantile analysis, and is applied to two different types of “hydrologically homogeneous” UK regions: geographical regions as defined in the Flood Studies Report (NERC, 1975) and pooling‐groups as defined in the updated Flood Estimation Handbook (FEH; Institute of Hydrology, 1999). To understand which variables play a significant role in the flood‐peak generating mechanism, the assessment approach considers scaling not only of drainage area alone but also of other hydro‐geomorphological variables. Results provided by the different methodologies consistently showed that only part (ranging from 30% to 70%) of the peak flow variability is explained by drainage area alone; this fraction increases (up to 80%–95%) when multiple regression is used. Supported by the peak flow spatial scaling assessment, we compared the proposed approach for peak flow quantile estimation with the current FEH method in ungauged catchments. The quantile regression method based on the pooling‐group outperforms the current FEH‐ungauged method, providing a 14% relative improvement in root mean square error over the entire country.

Urban flooding in Britain: an approach to comparing ancient and contemporary flood exposure

Using modified UK Environment Agency Flood Estimation Handbook techniques, inundation extent and likely flood hydrographs for 0.1% probability annual return periods are compared for twelve Roman town sites in the UK, both at the present day and for simulated Roman catchment conditions. Eight of the study sites appear to have suffered minimal urban flood liability as occupied in the Roman period. The exceptions were Canterbury, York, Leicester, and Chichester. It is reasonable to expect flood characteristics to have changed subsequently in response to transformations in catchment land use, urban expansion, wetland reclamation, and floodway engineering. However, modelling results suggest limited differences in flood flows attributable to such factors. Greater present-day urban damage liability essentially results from floodplain urban extension. There are also contrasts between sites: those Roman towns lying on floodplains themselves, rather than on slightly elevated terraces (Canterbury, Chichester), are dominated by groundwater regimes with attenuated flood peaks. Taken together, these results suggest some Roman awareness of the actualities of urban flood liability at the time. Site sensitivity has not been carried forward as urban expansion has flourished, especially from the nineteenth century with suburban and industrial expansion. The straightforward mapping approach here suggested should in future take account of multiple century-scale hydroclimatic changes, morphological river channel and floodplain transformations over similar time periods, and on-going improvements to inundation modelling.

New hourly extreme precipitation regions and regional annual probability estimates for the UK

AbstractRecent flooding related to extreme precipitation in the UK has highlighted the importance of better understanding these events. Many studies have quantified annual exceedance probabilities (or return periods) for UK extreme daily precipitation using fixed regions (e.g., HadUKP) and region of interest (ROI) (e.g., Flood Estimation Handbook) approaches, although fewer have evaluated short‐duration events, which are important for flash flooding. Existing UK extreme precipitation regions are based on daily datasets which have different characteristics compared to sub‐daily extremes, and their application to quantify short‐duration extremes may therefore be inappropriate. We use a recently available, quality‐controlled hourly precipitation dataset for the UK from 1992 to 2014 to derive various extreme precipitation indices (e.g., annual maxima, 0.99 quantile) which are combined with additional climatological variables (e.g., temperature), geographical characteristics (e.g., latitude), and weather patterns (WPs) to characterize the UK hourly extreme precipitation climatology and to define five new hourly extreme regions. These regions fulfil regional homogeneity and discordancy statistical measures, and reflect the dynamical processes associated with the weather pattern categorisation defined over the UK and surrounding European area. Thereafter, we use regional frequency analysis (RFA) to fit generalized extreme value (GEV) and generalized Pareto (GP) distributions to 1 hr annual maxima (AMAX) and 0.99 quantile (Q99) precipitation, respectively, to calculate regional annual probability estimates (AEP) for 20%, 10%, and 2% (i.e., 5‐, 10‐, and 50‐year return periods). The new regions capture the spatial variation of hourly precipitation across the UK. Furthermore, the AEP estimates using both distributions are similar for each region. Finally, the WPs associated with the frequency and intensity of the most extreme hourly precipitation accumulations are not identical to results reported by others for daily precipitation.

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

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

Proposal of the ‘extreme rank plot’ for extreme value analysis: with an emphasis on flood frequency studies

AbstractTo assess model ‘fit’ for flood frequency studies, the annual maximum flow sample is often plotted alongside the frequency curve in an extreme value plot. The comparison is ill advised because a value is speculatively assigned for each rank, in the form of a frequency (the plotting positions); in which case a model of the frequencies is being used to assess a frequency model. The modelled and observed, with attributed plotting positions, are incommensurable. The proposed method in this paper does not attempt to provide a frequency attributed to the ranks and instead attributes ranks to the model under investigation. If the model is used to simulate thousands of samples of the same size as the observed, a distribution of possible peak flow values for each rank can be obtained. For each simulated sample, descriptive statistics can be derived. In this way, statistical testing can be undertaken to compare the observed with the modelled. Using such methods, two or more models can also be compared. Examples are provided for Flood Estimation Handbook pooling and continuous simulation models along with a comparison of the two.

Revising the BFIHOST catchment descriptor to improve UK flood frequency estimates

Abstract The estimate of the base flow index (BFI) based on the Hydrology of Soil Types (HOST) classification, BFIHOST, provides a measure of catchment responsiveness. BFIHOST is used with other variables to estimate the median annual maximum flood (QMED) in the UK standard Flood Estimation Handbook (FEH) statistical method and is also an explanatory variable in ReFH2, the FEH design hydrograph package. The current estimates of BFIHOST are derived from a restricted linear model, and a number of issues in the catchment dataset have been identified since the original work in 1995. The BFI calculated through base flow separation tends to be underestimated in clay-dominated catchments, and the calculation technique performs poorly in ephemeral catchments or those with missing data. The pragmatic bounding of BFI coefficients for permeable soils overlying aquifer outcrops is also problematic for small catchments. This paper investigates alternative regression methods to improve base flow estimates using the HOST class data for 991 stations (compared to 575 in the original); beta regression was found to give the best performance. Combining multiple rare classes into single classes is also shown to improve performance. The new version of BFIHOST was applied to the QMED equation, showing improved performance.

The challenges of including historical events using Bayesian methods to improve flood flow estimates in the United Kingdom: A practitioner's point of view

Estimates of design flood flows; are important for the design of a wide variety of civil engineering structures. In the United Kingdom, the Flood Estimation Handbook (FEH) methodologies are used by practitioners to estimate flood flows. Until recently it was challenging for practitioners to include additional non‐continuous flood information, such as historical flood descriptions from historical archives, in their analyses, to reduce the uncertainty within the FEH approaches. This paper shows how Bayesian statistical methods can be applied to historical data which have degrees of uncertainty associated with then to improve design flood flows. The paper uses the River Avon at Evesham in the United Kingdom as a case study to illustrate the advantages of the method. The inclusion of historical information at this site improves the estimates of the one in 100 year flood flow compared to the values generated by the FEH pooling group method. The paper makes recommendations as to how practitioners could be encouraged to use historical flood water levels in their analysis of floods more regularly.

Use of Flood Seasonality in Pooling‐Group Formation and Quantile Estimation: An Application in Great Britain

AbstractRegional flood frequency analysis is one of the most commonly applied methods for estimating extreme flood events at ungauged sites or locations with short measurement records. It is based on: (i) the definition of a homogeneous group (pooling‐group) of catchments, and on (ii) the use of the pooling‐group data to estimate flood quantiles. Although many methods to define a pooling‐group (pooling schemes, PS) are based on catchment physiographic similarity measures, in the last decade methods based on flood seasonality similarity have been contemplated. In this paper, two seasonality‐based PS are proposed and tested both in terms of the homogeneity of the pooling‐groups they generate and in terms of the accuracy in estimating extreme flood events. The method has been applied in 420 catchments in Great Britain (considered as both gauged and ungauged) and compared against the current Flood Estimation Handbook (FEH) PS. Results for gauged sites show that, compared to the current PS, the seasonality‐based PS performs better both in terms of homogeneity of the pooling‐group and in terms of the accuracy of flood quantile estimates. For ungauged locations, a national‐scale hydrological model has been used for the first time to quantify flood seasonality. Results show that in 75% of the tested locations the seasonality‐based PS provides an improvement in the accuracy of the flood quantile estimates. The remaining 25% were located in highly urbanized, groundwater‐dependent catchments. The promising results support the aspiration that large‐scale hydrological models complement traditional methods for estimating design floods.

Estimating the index flood with continuous hydrological models: an application in Great Britain

Abstract Estimating peak river discharge, a critical issue in engineering hydrology, is essential for designing and managing hydraulic infrastructure such as dams and bridges. In the UK, practitioners typically apply the Flood Estimation Handbook (FEH) statistical method which estimates the design flood as the product of a relatively frequent flow estimate (the index flood, IF) and a regional growth factor. For gauged catchments the IF is estimated from observations. For ungauged catchments it is computed through a multiple regression model. While the FEH IF method provides peak flow estimates that are statistically robust, it does not readily take into account catchment heterogeneity or effect of environmental change on river flows. This study presents a new methodology to estimate the IF at national scale using continuous simulation from a physically based hydrological model (Grid-to-Grid). The methodology is tested across Great Britain and compares well with IF estimates at 550 gauging stations (R2 = 0.91). The promising results for Great Britain support the aspiration that continuous simulation from large-scale hydrological models coupled with increasing availability of global weather and climate products, could be used to estimate design floods in regions with limited gauge data or affected by environmental change.

Defining the hundred year flood: A Bayesian approach for using historic data to reduce uncertainty in flood frequency estimates

This paper describes a Bayesian statistical model for estimating flood frequency by combining uncertain annual maximum (AMAX) data from a river gauge with estimates of flood peak discharge from various historic sources that predate the period of instrument records. Such historic flood records promise to expand the time series data needed for reducing the uncertainty in return period estimates for extreme events, but the heterogeneity and uncertainty of historic records make them difficult to use alongside Flood Estimation Handbook and other standard methods for generating flood frequency curves from gauge data. Using the flow of the River Eden in Carlisle, Cumbria, UK as a case study, this paper develops a Bayesian model for combining historic flood estimates since 1800 with gauge data since 1967 to estimate the probability of low frequency flood events for the area taking account of uncertainty in the discharge estimates. Results show a reduction in 95% confidence intervals of roughly 50% for annual exceedance probabilities of less than 0.0133 (return periods over 75 years) compared to standard flood frequency estimation methods using solely systematic data. Sensitivity analysis shows the model is sensitive to 2 model parameters both of which are concerned with the historic (pre-systematic) period of the time series. This highlights the importance of adequate consideration of historic channel and floodplain changes or possible bias in estimates of historic flood discharges. The next steps required to roll out this Bayesian approach for operational flood frequency estimation at other sites is also discussed.

Reducing uncertainty in small-catchment flood peak estimation

Every year in the UK, many flood risk assessments are carried out on small catchments, typically draining areas of less than 25 km2 . Standard hydrological practice in all UK catchments is to apply the methods presented in the Flood Estimation Handbook (FEH) and its subsequent updates. FEH methods are practical, relatively easy to apply and based on extensive statistical analyses. However, uncertainties can be large, especially in atypical catchments, and small catchments can present unique challenges in terms of heavy urbanisation and rapid flood responses. Compared to larger catchments, small catchment flood data are limited. In this study, we use a dataset of annual maxima and digital catchment descriptors at 205 small catchments to benchmark the QMED and Q100 estimation performance of current UK flood estimation methods: the FEH statistical method, ReFH2 and MacDonald and Fraser’s method, in rural and urbanised catchments separately. All methods perform similarly in rural catchments overall, although MacDonald and Fraser’s method underestimates QMED in urbanised catchments. The methods show a larger factorial standard error against this small catchment dataset than they do against typical datasets of mixed-size catchments. Further work will evaluate the performance of ReFH2 in combination with the latest FEH13 rainfall model.

How reliable are design flood estimates in the UK?

AbstractDesign flood estimates in the UK are routinely obtained by using the improved Flood Estimation Handbook (FEH) statistical procedure. This paper presents a practical framework for assessing the uncertainty associated with estimates of the index flood (QMED) obtained for a range of commonly encountered problems: the gauged, the ungauged and the sparsely gauged catchment. An assessment is presented of the uncertainty of design flood estimates when estimated at ungauged catchments for a range of return periods. The results show that the inclusion of data from nearby gauged catchments increases the reliability of the estimates when compared to an automated application of the improved FEH methods relying on catchment descriptors only.

Application of a 2D Hydrodynamic Model for Assessing Flood Risk from Extreme Storm Events

In the wake of increasing flood disasters, there is an increasing use of flood inundation models to assess risks and impacts at different temporal and spatial scales. Assessing the impacts of extreme climatic rainfall events will require developing design rainfall profiles to represent rainfall under different conditions. Rainfall profiles of different return periods were developed using the Flood Estimation Handbook (FEH) methodology for a small rural catchment of Scotland, to assess flood risks at a catchment scale. Rainfall induced runoff flows were estimated based on a set of catchment characteristics. The channel and floodplain flows were modelled using a two-dimensional hydrodynamic model-TUFLOW. The main channel was represented by a one-dimensional linear channel based on surveyed data and the floodplain topography, was represented by a digital terrain model based on Light Detection and Ranging (LiDAR). A range of hydrological events with different return periods are simulated. Results show that many residential houses and an extensive area of agricultural land are at risk of flooding from extreme events such as a 1 in 100 year flood.

기후변화를 고려한 일강우량의 지역빈도해석

Global warming and climate change have influence on abnormal weather pattern and the rainstorm has a localized and intensive tendency in Korea. IPCC(2007) also reported the rainstorm and typhoon will be more and more stronger due to temperature increase during the 21st century. Flood Estimation Handbook(Institute of Hydrology, 1999) published in United Kingdom, in the case that the data period is shorter than return period, recommends the regional frequency analysis rather than point frequency analysis. This study uses Regional Climate Model(RCM) of Korea Meteorological Administration(KMA) for obtaining the rainfall and for performing the regional frequency analysis. We used the rainfall data from 58 stations managed by KMA and used L-moment algorithm suggested by Hosking and wallis(1993) for the regional frequency analysis considering the climate change. As the results, in most stations, the rainfall amounts in frequencies have an increasing tendency except for some stations. According to the A1B scenario, design rainfall is increased by 7~10% compared with the reference period(1970-2010).