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

The UK solar energy resource and the impact of climate change

AbstractSolar energy use in the UK is increasing dramatically, providing both heat energy and generation of electricity. This trend is expected to continue due to solar technologies becoming cheaper and more readily available along with low carbon government legislation such as the Renewable Heat Incentive (RHI) and Feed in Tariffs (FiTs) supporting solar energy deployment. However, the effects of climate change on the solar resource remain largely unstudied. Climate change affects cloud cover characteristics and consequently directly affects the performance of solar energy technologies.This paper investigates the UK solar irradiation resource for both the present and future climates.The present solar irradiation level was assessed through the conversion of 30 years of observed historical monthly average sunshine duration data. The method and results are validated by comparing the converted solar irradiation levels to actual solar irradiance measurements at weather stations with significant historical records of solar irradiance data.The impact of climate change is investigated across different regions of the UK by using the UKCP09 probabilistic climate change projections.We find that the current average UK annual solar resource is 101.2 Wm−2, ranging from 128.4 Wm−2 in the south of England to 71.8 Wm−2 in the northwest of Scotland. It seems likely that climate change will increase the average resource in the south of the UK, while marginally decreasing it in the Northwest. The overall effect is a mean increase of the UK solar resource, however it will have greater seasonal variability and discrepancies between geographical regions will be reinforced

Local Flood Risk Management Strategies in England: Patterns of Application

In England, the Flood and Water Management Act 2010 provides specific roles for Lead Local Flood Authorities in flood and coastal erosion risk management. Under Section 9 of the Act, authorities are responsible for preparing, applying and monitoring a local flood risk management strategy that balances community input into flood management with national policy objectives. Authorities are legally obliged to consider specified requirements in strategy production, including consultation with the public. Using an evaluative framework based on legal requirements and local government guidelines, this article assesses the extent to which these requirements have been met in a sample of 43 strategies. Our findings suggest that strategies generally meet minimal legal requirements, although variance exists in approaches adopted, particularly in respect of consultation and links to other environmental management aspects. Recommendations for enhancing future practice are provided

Long-term wind resource assessment for small and medium-scale turbines using operational forecast data and measure-correlate-predict

Output from a state-of-the-art, 4 km resolution, operational forecast model (UK4) was investigated as a source of long-term historical reference data for wind resource assessment. The data were used to implement measure-correlate-predict (MCP) approaches at 37 sites throughout the United Kingdom (UK). The monthly and hourly linear correlation between the UK4-predicted and observed wind speeds indicates that UK4 is capable of representing the wind climate better than the nearby meteorological stations considered. Linear MCP algorithms were implemented at the same sites using reference data from UK4 and nearby meteorological stations to predict the long-term (10-year) wind resource. To obtain robust error statistics, MCP algorithms were applied using onsite measurement periods of 1-12 months initiated at 120 different starting months throughout an 11 year data record. Using linear regression MCP over 12 months, the average percentage errors in the long-term predicted mean wind speed and power density were 3.0% and 7.6% respectively, using UK4, and 2.8% and 7.9% respectively, using nearby meteorological stations. The results indicate that UK4 is highly competitive with nearby meteorological observations as an MCP reference data source. UK4 was also shown to systematically improve MCP predictions at coastal sites due to better representation of local diurnal effects

Incorporating the effect of weather in construction scheduling and management with sine wave curves: application in the United Kingdom

The impact of (adverse) weather is a common cause of delays, legal claims and economic losses in construction projects. Research has recently been carried out aimed at incorporating the effect of weather in project planning; but these studies have focussed on either a narrow set of weather variables, or a very limited range of construction activities or projects. A method for processing a country’s historical weather data into a set of weather delay maps for some representative standard construction activities is proposed. Namely, sine curves are used to associate daily combinations of weather variables to delay and provide coefficients for expected productivity losses. A complete case study comprising the construction of these maps and the associated sine waves for the UK is presented along with an example of their use in building construction planning. Findings of this study indicate that UK weather extends project durations by an average of 21%. However, using climatological data derived from weather observations when planning could lead to average reductions in project durations of 16%, with proportional reductions in indirect and overhead costs

Regional trends in soil acidification and exchangeable metal concentrations in relation to acid deposition rates

The deposition of high levels of reactive nitrogen (N) and sulphur (S), or the legacy of that deposition, remain among the world's most important environmental problems. Although regional impacts of acid deposition in aquatic ecosystems have been well documented, quantitative evidence of wide-scale impacts on terrestrial ecosystems is not common. In this study we analysed surface and subsoil chemistry of 68 acid grassland sites across the UK along a gradient of acid deposition, and statistically related the concentrations of exchangeable soil metals (1 M KCl extraction) to a range of potential drivers. The deposition of N, S or acid deposition was the primary correlate for 8 of 13 exchangeable metals measured in the topsoil and 5 of 14 exchangeable metals in the subsoil. In particular, exchangeable aluminium and lead both show increased levels above a soil pH threshold of about 4.5, strongly related to the deposition flux of acid compound

Modelling the impacts of agricultural management practices on river water quality in Eastern England

Agricultural diffuse water pollution remains a notable global pressure on water quality, posing risks to aquatic ecosystems, human health and water resources and as a result legislation has been introduced in many parts of the world to protect water bodies. Due to their efficiency and cost-effectiveness, water quality models have been increasingly applied to catchments as Decision Support Tools (DSTs) to identify mitigation options that can be introduced to reduce agricultural diffuse water pollution and improve water quality. In this study, the Soil and Water Assessment Tool (SWAT) was applied to the River Wensum catchment in eastern England with the aim of quantifying the long-term impacts of potential changes to agricultural management practices on river water quality. Calibration and validation were successfully performed at a daily time-step against observations of discharge, nitrate and total phosphorus obtained from high-frequency water quality monitoring within the Blackwater sub-catchment, covering an area of 19.6 km2. A variety of mitigation options were identified and modelled, both singly and in combination, and their long-term effects on nitrate and total phosphorus losses were quantified together with the 95% uncertainty range of model predictions. Results showed that introducing a red clover cover crop to the crop rotation scheme applied within the catchment reduced nitrate losses by 19.6%. Buffer strips of 2 m and 6 m width represented the most effective options to reduce total phosphorus losses, achieving reductions of 12.2% and 16.9%, respectively. This is one of the first studies to quantify the impacts of agricultural mitigation options on long-term water quality for nitrate and total phosphorus at a daily resolution, in addition to providing an estimate of the uncertainties of those impacts. The results highlighted the need to consider multiple pollutants, the degree of uncertainty associated with model predictions and the risk of unintended pollutant impacts when evaluating the effectiveness of mitigation options, and showed that high-frequency water quality datasets can be applied to robustly calibrate water quality models, creating DSTs that are more effective and reliable