European precipitation connections with large-scale mean sea-level pressure (MSLP) fields

To advance understanding of hydroclimatological processes, this paper links spatiotemporal variability in gridded European precipitation and large-scale mean sea-level pressure (MSLP) time series (1957–2002) using monthly concurrent correlation. Strong negative (positive) correlation near Iceland and (the Azores) is apparent for precipitation in northwest Europe, confirming a positive North Atlantic Oscillation (NAO) association. An opposing pattern is found for southwest Europe, and the Mediterranean in winter. In the lee of mountains, MSLP correlation is lower reflecting reduced influence of westerlies on precipitation generation. Importantly, European precipitation is shown to be controlled by physically interpretable climate patterns that change in extent and position from month to month. In spring, MSLP–precipitation correlation patterns move and shrink, reaching a minimum in summer, before expanding in the autumn, and forming an NAO-like dipole in winter. These space–time shifts in correlation regions explain why fixed-point NAO indices have limited ability to resolve precipitation for some European locations and seasons

Assessing the impact of climate change and extreme value uncertainty to extreme flows across Great Britain

Floods are the most common and widely distributed natural risk, causing over £1 billion of damage per year in the UK as a result of recent events. Climatic projections predict an increase in flood risk; it becomes urgent to assess climate change impact on extreme flows, and evaluate uncertainties related to these projections. This paper aims to assess the changes in extreme runoff for the 1:100 year return period across Great Britain as a result of climate change using the Future Flows Hydrology database. The Generalised Extreme Value (GEV) and Generalised Pareto (GP) models are automatically fitted for 11‐member ensemble flow series available for the baseline and the 2080s. The analysis evaluates the uncertainty related to the Extreme Value (EV) and climate model parameters. Results suggest that GP and GEV give similar runoff estimates and uncertainties. From the baseline to the 2080s, increasing estimate and uncertainties is evident in east England. With the GEV the uncertainty attributed to the climate model parameters is greater than for the GP (around 60% and 40% of the total uncertainty, respectively). This shows that when fitting both EV models, the uncertainty related to their parameters has to be accounted for to assess extreme runoffs

Low flow response surfaces for drought decision support: a case study from the UK

Droughts are complex natural hazards, and planning future management is complicated by the difficulty of projecting future drought and low flow conditions. This paper demonstrates the use of a response surface approach to explore the hydrological behavior of catchments under a range of possible future conditions. Choosing appropriate hydrological metrics ensures that the response surfaces are relevant to decision-making. Examples from two contrasting English catchments show how low flows in different catchments respond to changes in rainfall and temperature. In an upland western catchment, the Mint, low flows respond most to rainfall and temperature changes in summer, but in the groundwater dominated catchment of the Thet, changes in spring rainfall have the biggest impact on summer flows. Response surfaces are useful for understanding long-term changes, such as those projected in climate projections, but they may also prove useful in drought event management, where possible future conditions can be plotted onto the surface to understand the range of conditions the manager faces. Developing effective response surfaces requires considerable involvement and learning from catchment decision-makers at an early stage, and this should be considered in any planned application

Probabilistic estimates of climate change impacts on UK water resources

Climate change will increase temperatures and change rainfall across the UK. In turn, this will modify patterns of river flow and groundwater recharge, affecting the availability of water. There have been many studies of the impact of climate change on river flows in the UK, but coverage has been uneven and methods have varied. Consequently, it has been very difficult to compare different locations and hard to identify appropriate adaptation responses

Bias correction of daily precipitation simulated by a regional climate model: a comparison of methods

Quantifying the effects of future changes in the frequency of precipitation extremes is a key challenge in assessing the vulnerability of hydrological systems to climate change but is difficult as climate models do not always accurately simulate daily precipitation. This article compares the performance of four published techniques used to reduce the bias in a regional climate model precipitation output: (1) linear, (2) nonlinear, (3) γ -based quantile mapping and (4) empirical quantile mapping. Overall performance and sensitivity to the choice of calibration period were tested by calculating the errors in the first four statistical moments of generated daily precipitation time series and using a cross-validation technique. The study compared the 1961–2005 precipitation time series from the regional climate model HadRM3.0-PPE-UK (unperturbed version) with gridded daily precipitation time series derived from rain gauges for seven catchments spread throughout Great Britain. We found that while the first and second moments of the precipitation frequency distribution can be corrected robustly, correction of the third and fourth moments of the distribution is much more sensitive to the choice of bias correction procedure and to the selection of a particular calibration period. Overall, our results demonstrate that, if both precipitation data sets can be approximated by a γ -distribution, the γ -based quantilemapping technique offers the best combination of accuracy and robustness. In circumstances where precipitation data sets cannot adequately be approximated using a γ -distribution, the nonlinear method is more effective at reducing the bias, but the linear method is least sensitive to the choice of calibration period. The empirical quantile mapping method can be highly accurate, but results were very sensitive to the choice of calibration time period. However, it should be borne in mind that bias correction introduces additional uncertainties, which are greater for higher order moments

Scoping study for precipitation downscaling and bias-correction

Various methods exist for correcting biases in climate model precipitation data. This study has investigated four of these bias-correction methods, here called linear, non-linear, gamma and empirical, and extensively tested their performance and suitability for biascorrecting daily precipitation outputs from a Regional Climate Model (RCM) for use as inputs to hydrological models over six test regions spanning the Great Britain. The RCM daily precipitation data were taken from the unperturbed variant of the Met Office Hadley Centre Regional Model Perturbed Physics Ensemble (HadRM3-PPE-UK), and observed daily precipitation data were taken from the Continuous Estimation of River Flows gridded precipitation dataset. Spatial downscaling (re-gridding) and correction of the fraction of rain-days were undertaken as pre-processing steps before the bias-correction procedure, which translated the RCM data from a 0.22° grid sca le to the 1 km grid scale of the observed dataset. Re-sampling tests were used to assess the performance of the bias-correction methods in terms of the first four statistical moments, and cumulative distribution functions (cdfs) were produced to compare the distribution of the bias-corrected precipitation with respect to the observed and pre-processed RCM precipitation. We found that whilst the first and second moments of the precipitation frequency distribution can be corrected robustly, correction of the third and fourth moments of the distribution is much more sensitive to the choice of biascorrection procedure and to the selection of a particular calibration period. Overall, our results demonstrate that, if both precipitation datasets can be approximated by a gamma distribution, the gamma-based quantile-mapping technique offers the best combination of accuracy and robustness. In circumstances where precipitation datasets cannot adequately be approximated using a gamma distribution, the non-linear method is more effective at reducing the bias but the linear method is least sensitive to the choice of calibration period. The empirical quantile mapping method can be highly accurate, but results were very sensitive to the choice of calibration time period. Examination of the seasonal variation of the non-linear bias-correction factors showed that the bias-correction applied to the HadRM3 daily precipitation varied with season, location, topography and precipitation intensity, suggesting that the method is capable of reproducing many features of the complex spatial and temporal patterns of UK daily precipitation. Taking the known limitations into account this study concluded that the gamma-based quantile-mapping technique is the most suitable for bias-correcting daily HadRM3 precipitation for use in hydrological modelling in the UK

Historical gridded reconstruction of potential evapotranspiration for the UK

Potential evapotranspiration (PET) is a necessary input data for most hydrological models and is often needed at a daily time step. An accurate estimation of PET requires many input climate variables which are, in most cases, not available prior to the 1960s for the UK, nor indeed most parts of the world. Therefore, when applying hydrological models to earlier periods, modellers have to rely on PET estimations derived from simplified methods. Given that only monthly observed temperature data is readily available for the late 19th and early 20th century at a national scale for the UK, the objective of this work was to derive the best possible UK-wide gridded PET dataset from the limited data available. To that end, firstly, a combination of (i) seven temperature-based PET equations, (ii) four different calibration approaches and (iii) seven input temperature data were evaluated. For this evaluation, a gridded daily PET product based on the physically based Penman–Monteith equation (the CHESS PET dataset) was used, the rationale being that this provides a reliable “ground truth” PET dataset for evaluation purposes, given that no directly observed, distributed PET datasets exist. The performance of the models was also compared to a “naïve method”, which is defined as the simplest possible estimation of PET in the absence of any available climate data. The “naïve method” used in this study is the CHESS PET daily long-term average (the period from 1961 to 1990 was chosen), or CHESS-PET daily climatology. The analysis revealed that the type of calibration and the input temperature dataset had only a minor effect on the accuracy of the PET estimations at catchment scale. From the seven equations tested, only the calibrated version of the McGuinness–Bordne equation was able to outperform the “naïve method” and was therefore used to derive the gridded, reconstructed dataset. The equation was calibrated using 43 catchments across Great Britain. The dataset produced is a 5 km gridded PET dataset for the period 1891 to 2015, using the Met Office 5 km monthly gridded temperature data available for that time period as input data for the PET equation. The dataset includes daily and monthly PET grids and is complemented with a suite of mapped performance metrics to help users assess the quality of the data spatially. This dataset is expected to be particularly valuable as input to hydrological models for any catchment in the UK. The data can be accessed at https://doi.org/10.5285/17b9c4f7-1c30-4b6f-b2fe-f7780159939c

Modelling the future impacts of climate and land-use change on suspended sediment transport in the River Thames (UK)

The effects of climate change and variability on river flows have been widely studied. However the impacts of such changes on sediment transport have received comparatively little attention. In part this is because modelling sediment production and transport processes introduces additional uncertainty, but it also results from the fact that, alongside the climate change signal, there have been and are projected to be significant changes in land cover which strongly affect sediment-related processes. Here we assess the impact of a range of climatic variations and land covers on the River Thames catchment (UK). We first calculate a response of the system to climatic stressors (average precipitation, average temperature and increase in extreme precipitation) and land-cover stressors (change in the extent of arable land). To do this we use an ensemble of INCA hydrological and sediment behavioural models. The resulting system response, which reveals the nature of interactions between the driving factors, is then compared with climate projections originating from the UKCP09 assessment (UK Climate Projections 2009) to evaluate the likelihood of the range of projected outcomes. The results show that climate and land cover each exert an individual control on sediment transport. Their effects vary depending on the land use and on the level of projected climate change. The suspended sediment yield of the River Thames in its lowermost reach is expected to change by −4% (−16% to +13%, confidence interval, p = 0.95) under the A1FI emission scenario for the 2030s, although these figures could be substantially altered by an increase in extreme precipitation, which could raise the suspended sediment yield up to an additional +10%. A 70% increase in the extension of the arable land is projected to increase sediment yield by around 12% in the lowland reaches. A 50% reduction is projected to decrease sediment yield by around 13%

Demonstrating the utility of a drought termination framework: prospects for groundwater level recovery in England and Wales in 2018 or beyond

During prolonged droughts, information is needed about when and how the extreme event is likely to terminate. A drought termination framework based on historical data comprising current rate and historical ensemble approaches is presented here for assessing the prospects of groundwater level recovery. The current rate approach is evaluated across all initialisation months in the historical record and provides reasonable estimates for the duration of recovery from relatively severe groundwater level deficiencies in some slowly responding boreholes. The utility of the framework is demonstrated through a near-real-time application to 30 groundwater boreholes in England and Wales from October 2017 onwards. Recovery during winter 2017/18 was considered unlikely, as some aquifers required increases in groundwater levels that have occurred seldom, if ever before, in long historical records. Data to February 2018 confirmed the success of these pre-winter outlooks. Recovery by mid- to late-2018 or beyond was more likely; slow rates of recovery by October 2017 and increasing return periods of effective rainfall required for recovery over timeframes in the summer half-year underlined the importance of winter rainfall and suggested that the historical ensemble may underestimate the duration of recovery. There was moderate confidence for a delay in recovery beyond the end of 2018 in some slowly responding Chalk boreholes in south-central and eastern England. There is considerable potential for the transferability of the drought termination framework beyond the UK wherever there are sufficient historical data. The two approaches provide limited information in distinctly different circumstances and their relevance and value may differ in space and time, suggesting their complimentary use as the most robust way to incorporate information on the prospects for groundwater level recovery into existing seasonal forecasting services, supporting decision-making by water managers during prolonged droughts

Benchmarking ensemble streamflow prediction skill in the UK

Skilful hydrological forecasts at sub-seasonal to seasonal lead times would be extremely beneficial for decision-making in water resources management, hydropower operations, and agriculture, especially during drought conditions. Ensemble streamflow prediction (ESP) is a well-established method for generating an ensemble of streamflow forecasts in the absence of skilful future meteorological predictions, instead using initial hydrologic conditions (IHCs), such as soil moisture, groundwater, and snow, as the source of skill. We benchmark when and where the ESP method is skilful across a diverse sample of 314 catchments in the UK and explore the relationship between catchment storage and ESP skill. The GR4J hydrological model was forced with historic climate sequences to produce a 51-member ensemble of streamflow hindcasts. We evaluated forecast skill seamlessly from lead times of 1 day to 12 months initialized at the first of each month over a 50-year hindcast period from 1965 to 2015. Results showed ESP was skilful against a climatology benchmark forecast in the majority of catchments across all lead times up to a year ahead, but the degree of skill was strongly conditional on lead time, forecast initialization month, and individual catchment location and storage properties. UK-wide mean ESP skill decayed exponentially as a function of lead time with continuous ranked probability skill scores across the year of 0.75, 0.20, and 0.11 for 1-day, 1-month, and 3-month lead times, respectively. However, skill was not uniform across all initialization months. For lead times up to 1 month, ESP skill was higher than average when initialized in summer and lower in winter months, whereas for longer seasonal and annual lead times skill was higher when initialized in autumn and winter months and lowest in spring. ESP was most skilful in the south and east of the UK, where slower responding catchments with higher soil moisture and groundwater storage are mainly located; correlation between catchment base flow index (BFI) and ESP skill was very strong (Spearman's rank correlation coefficient = 0.90 at 1-month lead time). This was in contrast to the more highly responsive catchments in the north and west which were generally not skilful at seasonal lead times. Overall, this work provides scientific justification for when and where use of such a relatively simple forecasting approach is appropriate in the UK. This study, furthermore, creates a low cost benchmark against which potential skill improvements from more sophisticated hydro-meteorological ensemble prediction systems can be judged