A two-stage storage routing model for green roof runoff detention

Green roofs have been adopted in urban drainage systems to control the total quantity and volumetric flow rate of runoff. Modern green roof designs are multi-layered, their main components being vegetation, substrate and, in almost all cases, a separate drainage layer. Most current hydrological models of green roofs combine the modelling of the separate layers into a single process; these models have limited predictive capability for roofs not sharing the same design. An adaptable, generic, two-stage model for a system consisting of a granular substrate over a hard plastic “egg box”-style drainage layer and fibrous protection mat is presented. The substrate and drainage layer/protection mat are modelled separately by previously verified sub-models. Controlled storm events are applied to a green roof system in a rainfall simulator. The time-series modelled runoff is compared to the monitored runoff for each storm event. The modelled runoff profiles are accurate (mean Rt 2 = 0.971), but further characterization of the substrate component is required for the model to be generically applicable to other roof configurations with different substrate

Design flood estimation and utility of high-resolution calibration data in small, heavily urbanised catchments

Design flood estimates are often required for small, heavily urbanised catchments, which respond quickly to storm events. However, hydrological models are most frequently calibrated using daily or hourly data on larger, more rural catchments, which respond on much longer timescales. Here, we calibrate a lumped, conceptual rainfall‐runoff model (ReFH2) in three small (2–6 km2), heavily urbanised catchments in Swindon, UK, assessing the benefits of using high‐resolution temporal and spatial data. Modelling shows that heavy urbanisation does not by itself invalidate the applicability of a lumped, conceptual model. However, we find great dissimilarities between runoff behaviour in different heavily urbanised catchments, with some types behaving similarly to rural catchments. In other cases, response and contributing catchment area can depend more on underground topology than catchment topography. Calibrated runoff response is insensitive to the temporal resolution of the calibration events in all study catchments. Future research should aim to differentiate between different types of heavily urbanised catchment, potentially through landscape metrics to measure the connectivity and isolation of different land surface types

An updated national-scale assessment of trends in UK peak river flow data: how robust are observed increases in flooding?

A cluster of recent floods in the UK has prompted significant interest in the question of whether floods are becoming more frequent or severe over time. Many trend assessments have addressed this in recent decades, typically concluding that there is evidence for positive trends in flood magnitude at the national scale. However, trend testing is a contentious area, and the resilience of such conclusions must be tested rigorously. Here, we provide a comprehensive assessment of flood magnitude trends using the UK national flood dataset (NRFA Peak Flows). Importantly, we assess trends using this full dataset as well as a subset of near-natural catchments with high-quality flood data to determine how climate-driven trends compare with those from the wider dataset that are subject to a wide range of human disturbances and data limitations. We also examine the sensitivity of reported trends to changes in study time window using a ‘multitemporal’ analysis. We find that the headline claim of increased flooding generally holds up regionally to nationally, although we show a much more complicated picture of spatio-temporal variability. While some reported trends, such as increasing flooding in northern and western Britain, appear to be robust, trends in other regions are more mixed spatially and temporally – for example, trends in recent decades are not necessarily representative of longer-term change, and within regions (e.g. in southeast England) increasing and decreasing trends can be found in close proximity. While headline conclusions are useful for advancing national flood-risk policy, for flood-risk estimation, it is important to unpack these local changes, and the results and methodological toolkit provided here could provide such supporting information to practitioners

CCRA3 flooding projections, task 2a: high resolution climate change projections — fluvial. Technical note

As part of the CCRA3 flooding projections project, this task provides: 1. estimates of percentage changes in flood peaks for locations across the UK, using UKCP18 probabilistic projections applied for a set of global mean surface temperature (GMST) changes (ranging from 1.0°C to 4.5°C in increments of 0.5°C); and 2. estimates of change in return period corresponding to a range of peak flow uplifts, as look-up tables, for locations across the UK. The data are provided for use within the Future Flood Explorer (FFE) to investigate potential future flood risks under climate change, under a range of adaptation options. This technical report details the methodology used to produce the flood peak and return period data, including differences in the methods used for Great Britain and Nothern Ireland

Making better use of local data in flood frequency estimation

Flood frequency estimates are an essential part of flood risk management. They are an important ingredient of many important decisions, informing the cost-effectiveness, design and operation of flood defences, flood mapping and planning decisions in flood risk areas. They also inform the National Flood Risk Assessment, the setting of insurance premiums and long-term investment planning. Methods described in the Flood Estimation Handbook (FEH) published in 1999, and many subsequent updates, are considered the industry standard for flood estimation in the UK. They are used extensively by hydrologists from both the public and private sectors. Flood frequency estimates – also known as design flood estimates – are associated with many sources of uncertainty. These hydrological uncertainties often constitute the most uncertain component in any flood study. Uncertainty can lead to difficulty in having confidence in the outputs of studies, whether these are for investment planning, insurance, asset design, development planning or other purposes. As a result, there is considerable benefit to be gained from any reduction in the uncertainty of flood frequency estimation. There are many supplementary sources of information that can help to refine estimates of design floods and potentially reduce uncertainty. Examples include long-term flood history, river level records, photographs of floods and information obtained from field visits. These and similar types of information are defined as ‘local data’. The FEH Local research project aimed to: quantify the uncertainty of design floods estimated from FEH methods develop procedures and guidance for incorporating local and historical data into flood estimation to reduce uncertainties The primary objective of this report is to describe the reviews and research carried out during the FEH Local project. Another output from the project was a document giving guidance to practitioners on how to estimate uncertainty in flood frequency and how to find and incorporate local data. The practitioner guidance, ‘Using Local Data to Reduce Uncertainty in Flood Frequency Estimation’, will be disseminated early in 2017. This report aims to avoid duplication with the practitioner guidance and so is intended mainly for those with an interest in the background to the methods presented in the guidance

Independent Validation of the SWMM Green Roof Module

Green roofs are a popular Sustainable Drainage Systems (SuDS) technology. They provide multiple benefits, amongst which the retention of rainfall and detention of runoff are of particular interest to stormwater engineers. The hydrological performance of green roofs has been represented in various models, including the Storm Water Management Model (SWMM). The latest version of SWMM includes a new LID green roof module, which makes it possible to model the hydrological performance of a green roof by directly defining the physical parameters of a green roof’s three layers. However, to date, no study has validated the capability of this module for representing the hydrological performance of an extensive green roof in response to actual rainfall events. In this study, data from a previously-monitored extensive green roof test bed has been utilised to validate the SWMM green roof module for both long-term (173 events over a year) and short-term (per-event) simulations. With only 0.357% difference between measured and modelled annual retention, the uncalibrated model provided good estimates of total annual retention, but the modelled runoff depths deviated significantly from the measured data at certain times (particularly during summer) in the year. Retention results improved (with the difference between modelled and measured annual retention decreasing to 0.169% and the Nash-Sutcliffe Model Efficiency (NSME) coefficient for per-event rainfall depth reaching 0.948) when reductions in actual evapotranspiration due to reduced substrate moisture availability during prolonged dry conditions were used to provide revised estimates of monthly ET. However, this aspect of the model’s performance is ultimately limited by the failure to account for the influence of substrate moisture on actual ET rates. With significant differences existing between measured and simulated runoff and NSME coefficients of below 0.5, the uncalibrated model failed to provide reasonable predictions of the green roof’s detention performance, although this was significantly improved through calibration. To precisely model the hydrological behaviour of an extensive green roof with a plastic board drainage layer, some of the modelling structures in SWMM green roof module require further refinement

The effect of depth‐duration‐frequency model recalibration on rainfall return period estimates

In November 2009 and December 2015, two record‐breaking 24‐hr rainfalls occurred in Cumbria, UK, significantly changing the perception of flood risk for local communities. FEH13, the current UK rainfall depth‐duration‐frequency (DDF) model, estimated return periods of around 1,000 years for both events. The previous model, FEH99, received criticism from panel engineers responsible for making technical safety decisions relating to reservoirs for appearing to estimate relatively short return periods for extreme events. Although FEH13 is more consistent with current probable maximum precipitation (PMP) estimates, there is high uncertainty in both models due to the limited number of extremes captured by UK rain gauges. Furthermore, neither model included the 2009 or 2015 event in its calibration. Here, we re‐calibrate FEH13 using additional gauged rainfall data collected in Cumbria during 2006–2016, including the record‐breaking 2009 and 2015 storms. Using the updated calibration data set reduces the estimated return periods of the 2009 and 2015 events to approximately 140 years each. This case study illustrates the considerable uncertainty in short‐sample records, demonstrates the importance of maximising the quantity of relevant calibration data, shows that perception of risk depends upon the method and data used, and illustrates the difficulty of separating trends and natural variability

A generic hydrological model for a green roof drainage layer

A rainfall simulator of length 5 m and width 1 m was used to supply constant intensity and largely spatially uniform water inflow events to 100 different configurations of commercially available green roof drainage layer and protection mat. The runoff from each inflow event was collected and sampled at one-second intervals. Time-series runoff responses were subsequently produced for each of the tested configurations, using the average response of three repeat tests. Runoff models, based on storage routing (dS/dt = I–Q) and a power-law relationship between storage and runoff (Q = kSn), and incorporating a delay parameter, were created. The parameters k, n and delay were optimized to best fit each of the runoff responses individually. The range and pattern of optimized parameter values was analysed with respect to roof and event configuration. An analysis was performed to determine the sensitivity of the shape of the runoff profile to changes in parameter values. There appears to be potential to consolidate values of n by roof slope and drainage component material

A Depth-duration-frequency analysis for short-duration rainfall events in Engalnd and Wales

This study presents a depth–duration–frequency (DDF) model, which is applied to the annual maxima of sub-hourly rainfall totals of selected stations in England and Wales. The proposed DDF model follows from the standard assumption that the block maxima are GEV distributed. The model structure is based on empirical features of the observed data and the assumption that, for each site, the distribution of the rainfall maxima of all durations can be characterised by common lower bound and skewness parameters. Some basic relationships between the location and scale parameters of the GEV distributions are enforced to ensure that frequency estimates for different durations are consistent. The derived DDF curves give a good fit to the observed data. The rainfall depths estimated by the proposed model are then compared with the standard DDF models used in the United Kingdom. The proposed model performs well for the shorter return periods for which reliable estimates of the rainfall frequency can be obtained from the observed data, while the standard methods show more variable results. Although the standard methods used no or little sub-hourly data in their calibration, they give fairly reliable estimates for the estimated rainfall depths overall