Flood Estimation Research Articles

Holocene Thermal Maximum paleofloods improve flood frequency analyses in the lower Tennessee River Basin (USA)

Extreme floods are underrepresented in instrumented flow records. Consequently, flood frequency model estimates of extreme floods contain large uncertainty. The lower Tennessee River (LTR) Basin is located within the southeastern United States and is a large, regulated, and socio-economically significant North American river. The flood-of-record for the LTR occurred in 1867 CE. The 1867 flood-of-record is a design flow for the dams built and managed by the Tennessee Valley Authority. Here we develop and apply an extreme paleoflood chronology and use it to improve flood frequency model estimates of large, rare floods. We created an extreme paleoflood chronology that contained paleodischarge estimates for six extreme paleofloods, dating back to 5900 years before present, for a ∼2 km segment of the LTR. We obtained paleoflood hydrologic data from two sources. These included field sampling and analysis of slackwater deposits found in a rock shelter located on a limestone bluff and published extreme paleoflood data from a terrace located within the study segment.Placed within the context of the extreme paleoflood chronology, the 1867 flood-of-record was the largest flood to occur within the last 2000 years and coincided with the conclusion of the cooler, wetter Little Ice Age. The three highest magnitude extreme floods, however, occurred during a peak in Holocene temperatures 6000 to 5000 years ago, during the mid-Holocene Thermal Maximum. Paleodischarge estimates for the three mid-Holocene extreme floods were three times larger than the estimated discharge of the 1867 CE flood-of-record. Different iterations of a Bayesian flood frequency model showed removing the three most extreme, mid-Holocene paleofloods resulted in a rarer annual exceedance probability for the 1867 CE flood-of-record, changing it from an 0.002 AEP (500-year event) to an 0.0007 AEP (1300-year event) and underestimation of annual exceedance probabilities for design flows commonly used in river management and infrastructure design, including the 100-yr, 500-yr, 1000-yr, and 10,000-yr floods. These results suggest that flood frequency models may underestimate design flows if historic-floods-of-record are assumed to be the most extreme floods within these analyses. This finding has implications for regulated rivers worldwide, which now includes most large rivers. The previously unknown, extreme mid-Holocene paleofloods, which were found to be larger than all known modern or paleofloods, significantly altered the estimates of the final flood frequency model. This finding makes a compelling case for seeking out more extreme flood paleodischarge estimates from the mid-Holocene in rivers of the Northern Hemisphere, where the mid-Holocene Thermal Maximum's occurrence is well substantiated. Flood data from the mid-Holocene Thermal Maximum may help improve flood frequency model-based estimates of extreme floods by minimizing risk associated with flood “unknown unknowns.”

Investigating climate and land use change impacts on design flood estimation over Indian tropical catchments

Investigating climate and land use change impacts on design flood estimation over Indian tropical catchments

Flood Frequency Analysis Using Gumbel Distribution Method: A Case of Robigumero River, Abay Basin, Ethiopia

Hydraulic structures like weirs, dams, spillways, and bridges require precise estimation of flood peaks at the intended return period in order to be planned, built, and maintained. In this paper, the findings of a study conducted on the Robigumoro River and the flow measurements taken are presented. The flood frequency analysis of the Robigumoro River was performed using the Gumbel distribution, which is a probability distribution commonly used for modeling river flows. This analysis is crucial as it aims to safeguard the lives and properties located downstream from the catchment area. The Gumbel distribution was employed to model the highest annual river flow over a span of 20 years (1990-2009). The investigation was carried out by the Ethiopian water and energy office, Abay Basin Development Authority. The Robigumoro River's maximum annual discharge over a 20-year period (1990–2009) was modeled using Gumbel distribution technique. From the trend line equation, R<sup>2</sup> value of 0.935 which shows that Gumbel’s distribution is suitable for predicting expected flow in the river. It can be concluded that the Gumbel distribution can accurately forecast expected river flow. The flood peak values were calculated using the same procedure for various return times. This helps with storm management in the research region. The estimated discharges obtained using the Gumbel's distribution and return periods (T) of 2 years, 10 years, 50 years, 100 years, 150 years, 200 years, 300 years and 400 years are 177.327m<sup>3</sup>/s, 320.784m<sup>3</sup>/s, 446.553m<sup>3</sup>/s, 499.722m<sup>3</sup>/s, 530.727m<sup>3</sup>/s, 552.698m<sup>3</sup>/s, 583.38m<sup>3</sup>/s, and 605.577m<sup>3</sup>/s respectively. The accuracy of flood forecasts in the basin indicates their potential use in various applications such as the design of crucial hydraulic structures, river reach planning, construction of bridges, and conservation efforts for Robigumoro watershed.

Pyraingen: A python package for constrained continuous rainfall generation

Continuous rainfall is often required for flood estimation and water resources assessment. However, stochastically generated continuous rainfall records are typically inconsistent with intensity-frequency-durations (IFDs) used in design, and do not simulate the rainfall behaviour we can expect in a future warmer climate. Here, we present the python package pyraingen to generate ensembles of continuous subdaily rainfalls at a single location that preserve IFD relationships. Rainfall generation can also be conditioned on a climate covariate, in this case temperature, to produce rainfall timeseries reflective of a future climate. We present an implementation at five climatologically unique sites across Australia and demonstrate its ability to produce rainfall timeseries that reflect both present day and future changes in rainfall persistence, temporal patterns, means, and extremes. This package will enable studies using continuous simulation to assess the impacts of climate change using the current guidance on changes to both IFDs and mean annual rainfalls.

Forest fire, thinning, and flood in wildland-urban interface: UAV and lidar-based estimate of natural disaster impacts

ContextWildland-urban interface (WUI) areas are facing increased forest fire risks and extreme precipitation events due to climate change, which can lead to post-fire flood events. The city of Flagstaff in northern Arizona, USA experienced WUI forest thinning, fire, and record rainfall events, which collectively contributed to large floods and damages to the urban neighborhoods and city infrastructure.ObjectivesWe demonstrate multi-temporal, high resolution image applications from an unoccupied aerial vehicle (UAV) and terrestrial lidar in estimating landscape disturbance impacts within the WUI. Changes in forest vegetation and bare ground cover in WUIs are particularly challenging to estimate with coarse-resolution satellite images due to fine-scale landscape processes and changes that often result in mixed pixels.MethodsUsing Sentinel-2 satellite images, we document forest fire impacts and burn severity. Using 2016 and 2021 UAV multispectral images and Structure-from-Motion data, we estimate post-thinning changes in forest canopy cover, patch sizes, canopy height distribution, and bare ground cover. Using repeat lidar data within a smaller area of the watershed, we quantify geomorphic effects in the WUI associated with the fire and subsequent flooding.ResultsWe document that thinning significantly reduced forest canopy cover, patch size, tree density, and mean canopy height resulting in substantially reduced active crown fire risks in the future. However, the thinning equipment ignited a forest fire, which burned the WUI at varying severity at the top of the watershed that drains into the city. Moderate-high severity burns occurred within 3 km of downtown Flagstaff threatening the WUI neighborhoods and the city. The upstream burned area then experienced 100-year and 200–500-year rainfall events, which resulted in large runoff-driven floods and sedimentation in the city.ConclusionWe demonstrate that UAV high resolution images and photogrammetry combined with terrestrial lidar data provide detailed and accurate estimates of forest thinning and post-fire flood impacts, which could not be estimated from coarser-resolution satellite images. Communities around the world may need to prepare their WUIs for catastrophic fires and increase capacity to manage sediment-laden stormwater since both fires and extreme weather events are projected to increase.

Questioning the use of ensembles versus individual climate model generated flows in future peak flood predictions: Plausibility and implications

AbstractAccurate estimation of design floods is necessary for developing effective flood‐management strategies. Climate change (CC) studies on floods generally consider alterations in mean runoff using ensembles compared to a base period. In this study, we examined the plausibility and implications of applying individual climate model‐generated flows versus their ensembles to estimate peak floods (magnitude and timing of occurrence), using Budhigandaki River Basin of Nepal as a case study. Annual maximum one‐day floods were derived for four future climate scenario projections (cold‐dry, cold‐wet, warm‐wet, and warm‐dry) from simulated daily flow series. Future floods of six return periods estimated for the individual climate scenarios were compared with their “Ensemble” (combiner for the ensemble series is the arithmetic mean of daily floods), “Average,” and ‘Baseline.” Results showed that magnitudes of the flood peaks are such that those estimated using “Ensemble” < “Average” < individual series. We conclude that ensemble series should not be used for flood estimation because of the averaging effect. Designers should consider at the least the “Average” instead of the “Ensemble” series while designing climate‐resilient flood structures. Furthermore, the occurrences of flood peaks are likely to be confined within the monsoon season for the “Ensemble” but spread out in the other months for the individual climate scenarios. This could have direct implications on the availability and mobilization of resources as well as the need for a year‐round operational early warning system for flood risk management.

Low-Flow Identification in Flood Frequency Analysis: A Case Study for Eastern Australia

Design flood estimation is an essential step in many water engineering design tasks such as the planning and design of infrastructure to reduce flood damage. Flood frequency analysis (FFA) is widely used in estimating design floods when the at-site flood data length is adequate. One of the problems in FFA with an annual maxima (AM) modeling approach is deciding how to handle smaller discharge values (outliers) in the selected AM flood series at a given station. The objective of this paper is to explore how the practice of censoring (which involves adjusting for smaller discharge values in FFA) affects flood quantile estimates in FFA. In this regard, two commonly used probability distributions, log-Pearson type 3 (LP3) and generalized extreme value distribution (GEV), are used. The multiple Grubbs and Beck (MGB) test is used to identify low-flow outliers in the selected AM flood series at 582 Australian stream gauging stations. It is found that censoring is required for 71% of the selected stations in using the MGB test with the LP3 distribution. The differences in flood quantile estimates between LP3 (with MGB test and censoring) and GEV distribution (without censoring) increase as the return period reduces. A modest correlation is found (for South Australian catchments) between censoring and the selected catchment characteristics (correlation coefficient: 0.43), with statistically significant associations for the mean annual rainfall and catchment shape factor. The findings of this study will be useful to practicing hydrologists in Australia and other countries to estimate design floods using AM flood data by FFA. Moreover, it may assist in updating Australian Rainfall and Runoff (national guide).

Nonstationary Seasonal Design Flood Estimation: Exploring Mixed Copulas for the Nonmonotonic Dependence between Peak Discharge and Timing

Nonstationary Seasonal Design Flood Estimation: Exploring Mixed Copulas for the Nonmonotonic Dependence between Peak Discharge and Timing

Alternative streamflow-based approach to estimate catchment response time in medium to large catchments: case study in Primary Drainage Region X, South Africa

Event-based estimates of the design flood in ungauged catchments are normally based on a single catchment response time parameter expressed as either the time of concentration (TC), lag time (TL) and/or time to peak (TP). In small, gauged catchments, a simplified convolution process between a single observed hyetograph and hydrograph is generally used to estimate these time parameters. In medium to large heterogeneous, gauged catchments, such a simplification is neither practical nor applicable, given that the variable antecedent soil moisture status resulting from previous rainfall events and spatially non-uniform rainfall hyetographs can result in multi-peaked hydrographs. In ungauged catchments, time parameters are estimated using either empirical or hydraulic methods. In South Africa (SA), unfortunately, the majority of the empirical methods recommended for general use were developed and verified in catchments ≤ 0.45 km² without using any local data. This paper presents the further development and verification of the streamflow-based approach developed by Gericke (2016) to estimate observed TP values and to derive a regional empirical TP equation in Primary Drainage Region X, SA. A semi-automated hydrograph analysis tool was developed to extract and analyse complete hydrographs for time parameter estimation using primary streamflow data from 51 flow-gauging sites. The observed TP values were estimated using three methods: (i) duration of total net rise of a multi-peaked hydrograph, (ii) triangular-shaped direct runoff hydrograph approximations, and (iii) linear catchment response functions. The combined use of these methods incorporated the high variability of event-based time parameters, and Method (iii), in conjunction with an ensemble-event approach sampled from the time parameter distributions, should replace the event-based approaches to enable the improved calibration of empirical time parameter equations. The conceptual approach used to derive the regional empirical TP equation should also be adopted when regional equations need to be derived at a national scale in SA.

In Today's World, Is It Worth Performing Flood Frequency Analysis Using Observed Streamflow Data?

Flood Frequency Analysis (FFA) is a process to relate the magnitude of extreme streamflows to their frequency of occurrence through distribution functions, and the results of FFA are useful in design flood estimation for various hydraulic structures. The FFA relies primarily on actual observed streamflow data (Qobts) and certain underlying assumptions that form the basis of FFA's philosophy. In today's world, Qobts time series are available for most river basins; however, it is an indubitable reality that Qobts do not satisfy the necessary requirements. Therefore, an alternative doctrine is needed to create such a dataset. This paper provides an overview of the limitations of utilising Qobts along with its drivers and suggests a surrogate mechanism to perform FFA in today's world.

Improved estimation of extreme floods with data pooling and mixed probability distribution

The accurate estimation of flood probability is crucial for designing water storage and flood retention structures. However, the assumption of identical distribution in flood samples is unrealistic, given the influence of various flood mechanisms. To address this challenge, we proposed a novel framework based on flood clustering and data pooling that encompasses the key steps such as 1) flood event separation based on a peak-detection flood separation algorithm, 2) grouping flood events using the k-prototypes algorithm, 3) application of the UNprecedented Simulated Extreme ENsemble (UNSEEN) approach to pool reforecast ensemble datasets, and 4) statistical mixing approach to derive common quantiles from all the flood groups. We applied the framework to the Dresden gauge in the Elbe River for a detailed case study. Various tests have been performed to assess the applicability of the UNSEEN approach and the reforecast dataset consistently shows the potential for data pooling. The proposed methodology outperformed the classical approach in terms of goodness-of-fit. The relative difference between the classical and the proposed approach ((classical-proposed)/proposed) for the 100-year return level is 0.16, with a reduction in root mean square error (RMSE) value from 163 to 98 m3/s. Further, replication of the approach to the gauges in North Germany exhibited a relative difference ranging from −0.3 to +0.15 and produced better estimates in terms of RMSE compared with the traditional model. In summary, the proposed framework offers a better estimation of flood probability by addressing the inherent sample inhomogeneity along with the inclusion of unprecedented flood samples.

Effects of seasonal changes in vegetation cover on the hydrological responses of the Chemora Wadi catchment to extreme rainfall

The choice of the reference flood for the dimensioning of hydraulic structures is rather delicate, in particular in the not gauged basins. In these basins, the estimation of the project flood requires the use of predetermined methods based on the maximum daily precipitation. In this context, this work consists of evaluating the impact of seasonal changes in vegetation cover on the hydrological responses of the watershed to extreme precipitation events in terms of peaks and water volumes using the HEC-HMS model. The study was based on rainfall and discharge data recorded at rainfall and hydrometric stations in the Wadi Chemora basin (Algeria), in addition to remote sensing data on a monthly scale. The results show that the estimation of the projected flood using methods based only on maxi-mum daily rainfall in semi-arid areas is insufficient, which shows the interest of consider-ing the effects of these changes.

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.

Design of water level detection monitoring system using fusion sensor based on Internet of Things (IoT)

River flooding is a condition when the water in a river overflows and exceeds its normal capacity, thereby flooding the surrounding area. This flood disaster has been a known problem for a long time and causes great damage in the affected areas. Flood events inRivers are influenced by many factors, such as climate change, rapid urbanization, inappropriate land use, ineffective water management patterns, as well as uncontrolled addition of hard soil surfaces. Flood conditions in rivers involve complex processes and are influenced by various factors components, such as rainfall, water flow, topography, vegetation, and many other factors. Therefore, this research is very urgent because it can help reduce the negative impacts of flooding, increase public safety, become a basis for decision making, save costs and resources and make a positive contribution to technological development. This study aims to create a prototype of a flood early warning system. The system is based on a wireless sensor network whose interconnections are connected by a star topology. Every node is a combination of several sensors (sensor fusion) that are related to detecting floods, such as: height sensors, water flow speed sensors and rainfall intensity sensors. Design of hardware (hardware) and software (software) will be done. A classification mechanism based on Fuzzy Logic will be used to estimate flood conditions based on existing data. Flood estimation will determine the time and distance of flood events that will occur. Several experiments in the laboratory will be carried out to determine the performance of the designed system.

Flood Estimation and Control in a Micro-Watershed Using GIS-Based Integrated Approach

Flood analyses when using a GIS-based integrated approach have been successfully applied around the world in large-sized watersheds. This study employed hydrological-hydraulic modeling to analyze flash floods by integrating HEC-HMS, HEC-RAS, and ArcGIS software for flood evaluation and control in a micro-watershed in the Samaru River, Nigeria. The watershed boundaries, its characteristics (soil and land use), the topographical survey, and the intensity duration frequency curve (IDF) of the study area were produced using data-driven techniques. The HEC-HMS model was used to derive the peak discharges for 2-, 5-, 10-, 25-, 50-, 100-, and 200-year return periods with the frequency storm method. Afterward, the water surface profiles for the respective return periods were estimated using the HEC-RAS hydrodynamic model. The simulated design flood for the 2-, 5-, 10-, 25-, 50-, 100-, and 200-year return periods at the reference location (the NUGA gate culvert) were 3.5, 6.8, 9.1, 12.1, 14.3, 16.6, and 19.0 m3/s, respectively, while those at the watershed outlet for the respective return periods were 7.5, 14.9, 20.3, 27.3, 32.6, 38.0, and 43.5 m3/s, respectively (with a water height of 0.9 m, 1.1 m, 1.3 m, 1.33 m, 1.38 m, 1.5 3m, and 1.8 m, respectively), at the NUGA gate culvert cross-section. The maximum water depths of about 0.9 m and 1.0 m were recorded in the right and left overbanks, which were similar to the simulated water depth for the 2- and 5-year return periods. Hence, for the smart control of floods passing through the river and major hydraulic structures, a minimum design height of 1.50 m is recommended. For the most economic trapezoidal channel section, a normal depth of 1.50 m, a bottom width of 1.73 m, a top width of 3.50 m, and a free board of 0.30 m is proposed to curb the overtopping of floods along the channel sub-sections. The findings of this study could help hydraulic engineers minimize flooding in streams and rivers overbanks in a micro-watershed.

PDCA-FORMER: PRIOR-DIAGONAL CROSS ATTENTION-GUIDED TRANSFORMER FOR FLOOD MAPPING FROM SAR IMAGERY: A CASE IN KHARTOUM

Abstract. Floods are considered one of the most serious crises, with severe consequences such as loss of life, destruction of infrastructure, and economic disruption. In recent years, deep learning has gained popularity for fast and accurate flood mapping from synthetic aperture radar (SAR) for damage assessment and proactive mitigation. However, due to the complex characteristics of SAR images, accurate flood mapping remains challenging. In this study, we propose a novel Prior-Diagonal Cross Attention-guided transformer (PDCA-Former) network for flood mapping from SAR images. Specifically, PDCA-Former adopts Prior Siamese Feature Extraction (PSFE) to extract multi-scale deep features from the input SAR images. Additionally, we propose a novel Diagonal Cross-Attention Module (DCAM) to capture relational information of all pixel positions on the entire image. DCAM is integrated into the Transformer to acquire contextual tokens with spatio-temporal information from prior features, resulting in immersed maps. To investigate the potential of SAR images and the proposed PDCA-Former for effective flood detection and estimation of the extent of damaged farmland around the confluence of two rivers, this study chose the Sudanese city of Khartoum as an experimental study area. The experimental results show that PDCA-Former outperforms the latest comparator methods in terms of F1 by 88.9% and IoU by 85.7%. We conclude that PDCA-Former offers a promising solution for accurate and efficient flood mapping from SAR imagery that can be quickly generalized to other regions. Therefore, it can significantly aid disaster management efforts on vulnerable communities.

Derivation of long duration geographically-centred areal reduction factors for catchment design rainfall estimation in South Africa

Study regionSouth Africa. Study focusThe use of Areal Reduction Factors (ARFs) to convert average design point rainfall into areal catchment design rainfall is regarded as fundamental input to event-based deterministic design flood estimation methods. The ARF methods currently used in South Africa are outdated and were neither developed nor verified using local data. This paper presents the development and assessment of a regionalised approach to estimate long duration (≥ 1-day) geographically-centred ARFs using daily rainfall data representative of the 78 homogeneous long duration rainfall clusters associated with the Regional Linear Moment Algorithm and Scale Invariance (RLMA&SI) regionalisation scheme in South Africa. In order to meet the recommended World Meteorological Organisation rainfall station density criteria, the latter clusters were merged into 46 clusters. Subsequently, long duration geographically-centred sample ARFs were estimated using 2 053 circular catchments and 1 779 daily rainfall stations. New hydrological insights for the regionFive ARF regions were deduced from the 46 clusters and a single regional empirical ARF equation, with unique regional calibration coefficients, was derived. The ARF methodology developed and the subsequent findings are not only new to the South African flood hydrology community and practice, but are also probabilistically correct given that the ARF estimates decrease with an increasing catchment area and decrease with a decreasing storm duration and return period, unlike many other methods used internationally.

The Role of Snowmelt Temporal Pattern in Flood Estimation for a Small Snow‐Dominated Basin in the Sierra Nevada

AbstractPrior research confirmed the substantial bias from using precipitation‐based intensity‐duration‐frequency curves (PREC‐IDF) in design flood estimates and proposed next‐generation IDF curves (NG‐IDF) that represent both rainfall and snow processes in runoff generation. This study improves the NG‐IDF technology for a snow‐dominated test basin in the Sierra Nevada. A well‐validated physics‐based hydrologic model, the Distributed Hydrology Soil Vegetation Model (DHSVM), is used to continuously simulate snowmelt and streamflow that are used as benchmark data sets to systematically assess the NG‐IDF technology. We find that, for the studied small snow‐dominated basin, the use of standard rainfall hyetographs in the NG‐IDF technology leads to substantial underestimation of design floods. Thus, we propose probabilistic hyetographs that can represent unique patterns of events with different underlying mechanisms. For the test basin where flooding events are generated entirely by snowmelt, we develop a hyetograph that characterizes snowmelt temporal patterns, which greatly improves the performance of NG‐IDF technology in design flood estimates. In contrast to the standard rainfall hyetographs characterized by a symmetrically peaked, bell‐shaped curve, the snowmelt hyetograph displays a more rapid rise (i.e., greater intensity) and a distinct diurnal pattern influenced by solar energy input. The results also show that the uncertainty of hyetography plays an important role in design flood estimation and can have important implications for future flood projections.

Enhancing megacities' resilience to flood hazard through peri-urban nature-based solutions: Evidence from Mexico City

Floods are one of the most frequent natural hazards in almost every country, with climate change exacerbating their frequency and intensity. Nature-based solutions (NbS) can be a cost-effective way to make human settlements more resilient to flooding. However, decision-makers need reliable information on which to base NbS policy and funding. This research estimates the potential of peri-urban NbS to regulate water flow and the benefits downstream through the development of supply and demand indicators for the context of complex megacities. In our Mexico City case study the supply indicator is the runoff coefficient, which is spatially estimated across peri-urban areas, and the economic value is estimated using replacement cost (grey infrastructure). The demand indicator identifies flood-prone areas based on spatially explicit ponding events and avoided costs of insurance flood claims data and estimates with parametric cost functions. The supply indicator provides straightforward information for decision-makers to spatially target conservation in peri-urban areas where runoff coefficients are high combined with flood-prone areas, while the lowest coefficients reinforce the importance of policies for protected areas. In combination with demand indicator information, we find NbS in peri-urban upstream catchments is cost-effective compared to avoidable flood-related costs and alternative investments in grey infrastructure.

Use of peak over threshold data for flood frequency estimation: An application at the UK national scale

This study investigates choices of statistical distributions to represent the threshold exceedance frequency and magnitude of peaks-over-threshold (POT) series from a national dataset of extreme hydrological events from 842 gauging stations in the UK. From the initial POT series, two new series were created, POT1 and POT3, representing POT series with an average of, respectively, one and three exceedances per year. Using a χ2 goodness-of-fit test, the choice of distributions for both the annual exceedance counts and the magnitude of threshold exceedances were explored for both the POT1 and POT3 datasets. The results show that the negative binomial and geometrical distributions provide a better fit to the annual exceedance count than the Poisson distribution typically assumed. These results are particularly pronounced when considering the POT3 dataset and in the South-East of the UK where many river flow series are dominated by slow responding groundwater dominated catchments. Finally, estimates of design floods with a return period of 2 and 100 years were obtained for each POT series and compared to the equivalent estimates obtained from direct at-site analysis of the annual maximum series. The results show a good alignment between the magnitude of the design floods estimated by the two methods, but a generally lower standard deviation of estimates obtained from the POT data, as quantified using a bootstrap procedure. The results presented here show that the POT series could beneficially replace the current operational guidelines based on annual maximum series for design flood estimation in the UK.