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The UK is one of several countries around the world that has enacted legislation to reduce its greenhouse gas emissions. Monitoring of emissions has been done through a detailed sectoral level bottom-up inventory (UK National Atmospheric Emissions Inventory, NAEI) from which national totals are submitted yearly to the United Framework Convention on Climate Change. In parallel, the UK government has funded four atmospheric monitoring stations to infer emissions through top-down methods that assimilate atmospheric observations. In this study, we present top-down emissions of methane (CH4) and nitrous oxide (N2O) for the UK and Ireland over the period August 2012 to August 2014. We used a hierarchical Bayesian inverse framework to infer fluxes as well as a set of covariance parameters that describe uncertainties in the system. We inferred average UK emissions of 2.08 (1.72-2.47)Tgyr-1 CH4 and 0.105 (0.087-0.127) Tgyr-1 N2O and found our derived estimates to be generally lower than the inventory. We used sectoral distributions from the NAEI to determine whether these discrepancies can be attributed to specific source sectors. Because of the distinct distributions of the two dominant CH4 emissions sectors in the UK, agriculture and waste, we found that the inventory may be overestimated in agricultural CH4 emissions. We also found that N2O fertilizer emissions from the NAEI may be overestimated and we derived a significant seasonal cycle in emissions. This seasonality is likely due to seasonality in fertilizer application and in environmental drivers such as temperature and rainfall, which are not reflected in the annual resolution inventory. Through the hierarchical Bayesian inverse framework, we quantified uncertainty covariance parameters and emphasized their importance for high-resolution emissions estimation. We inferred average model errors of approximately 20 and 0.4 ppb and correlation timescales of 1.0 (0.72-1.43) and 2.6 (1.9-3.9) days for CH4 and N2O, respectively. These errors are a combination of transport model errors as well as errors due to unresolved emissions processes in the inventory. We found the largest CH4 errors at the Tacolneston station in eastern England, which is possibly to do with sporadic emissions from landfills and offshore gas in the North Sea. [ABSTRACT FROM AUTHOR]

The article focuses on the Future Flows Climate dataset which was developed to offer a set of climate change projections for Great Britain. It says that the Future Flows Climate is derived from the ensemble Projection HadRM3-PPE, part of the basis of UK Climate Projection 2009 (UKCP09) and adds projections on available precipitation and potential evapotranspiration. Furthermore, it mentions the limitations of the dataset, as well as the ways to access the data.

JULES-crop is a parametrisation of crops in the Joint UK Land Environment Simulator (JULES). We investigate the sources of the interannual variability in the modelled maize yield, using global runs driven by reanalysis data, with a view to understanding the impact of various approximations in the driving data and initialisation. The standard forcing dataset for JULES consists of a combination of meteorological variables describing precipitation, radiation, temperature, pressure, specific humidity and wind, at subdaily time resolution. We find that the main characteristics of the modelled yield can be reproduced with a subset of these variables and using daily forcing, with inter nal disaggregation to the model timestep. This has implications in particular for the use of the model with seasonal forcing data, which may not have been provided at subdaily resolution for all required driving variables. We also investigate the effect on annual yield of initialising the model with climatology on the sowing date. This approximation has the potential to considerably simplify the use of the model with seasonal forecasts, since obtaining observations or reanalysis output for all the initialisation variables required by JULES for the start date of the seasonal forecast would present significant practical challenges. [ABSTRACT FROM AUTHOR]

In order to effectively model the potential impacts of future climate change, there is a requirement for climate data inputs which (a) are of high spatial and temporal resolution, (b) explore a range of future climate change scenarios, (c) are consistent with historical observations in the historical period, and (d) provide an exploration of climate model uncertainty. This paper presents a suite of climate projections for the United Kingdom that conform to these requirements: CHESS-SCAPE. CHESS-SCAPE is a 1 km resolution dataset containing 11 near-surface meteorological variables that can be used to as input to many different impact models. The variables are available at several time resolutions, from daily to decadal means, for the years 1980–2080. It was derived from the state-of-the art regional climate projections in the United Kingdom Climate Projections 2018 (UKCP18) regional climate model (RCM) 12 km ensemble, downscaled to 1 km using a combination of physical and empirical methods to account for local topographic effects. CHESS-SCAPE has four ensemble members, which were chosen to span the range of temperature and precipitation change in the UKCP18 ensemble, representing the ensemble climate model uncertainty. CHESS-SCAPE consists of projections for four emissions scenarios, given by the Representative Concentration Pathways 2.6, 4.5, 6.0 and 8.5, which were derived from the UKCP18 RCM RCP8.5 scenarios using time shifting and pattern scaling. These correspond to UK annual warming projections of between 0.9–1.9 K for RCP2.6 up to 2.8–4.3 K for RCP8.5 between 1980–2000 and 2060–2080. Little change in annual precipitation is projected, but larger changes in seasonal precipitation are seen with some scenarios projecting large increases in precipitation in the winter (up to 22 %) and large decreases in the summer (up to - 39 %). All four RCP scenarios and ensemble members are also provided with bias correction, using the CHESS-met historical gridded dataset as a baseline. With high spatial and temporal resolution, an extensive range of warming scenarios and multiple ensemble members, CHESS-SCAPE provides a comprehensive data resource for modellers of climate change impacts in the UK. The CHESS-SCAPE data are available for download from the NERC EDS Centre for Environmental Data Analysis: 10.5285/8194b416cbee482b89e0dfbe17c5786c. [ABSTRACT FROM AUTHOR]

Appropriate adaptation planning is contingent upon information about the potential future impacts of climate change, and hydrological impact assessments are of particular importance. The UKSCAPE-G2G datasets were produced, as part of the Natural Environment Research Council (NERC) UK-SCAPE (UK Status, Change and Projections of the Environment) programme, to contribute to this information requirement. They use the Grid-to-Grid (G2G) national-scale hydrological model configured for both Great Britain and Northern Ireland (and the parts of the Republic of Ireland that drain to rivers in NI). Six separate datasets are provided, for two sets of driving data – one observation-based (1980–2011) and one climate-projection-based (1980–2080) – for both river flows and soil moisture on 1 km × 1 km grids across Great Britain and Northern Ireland. The river flow datasets include grids of monthly mean flow, annual maxima of daily mean flow, and annual minima of 7 d mean flow (m 3 s -1). The soil moisture datasets are grids of monthly mean soil moisture content (m water / m soil), which should be interpreted as depth-integrated values for the whole soil column. The climate-projection-based datasets are produced using data from the 12-member 12 km regional climate model ensemble of the latest UK climate projections (UKCP18), which uses RCP8.5 emissions. The production of the datasets is described, along with details of the file format and how the data should be used. Example maps are provided, as well as simple UK-wide analyses of the various outputs. These suggest potential future decreases in summer flows, annual minimum 7 d flows, and summer/autumn soil moisture, along with possible future increases in winter flows and annual maximum flows. References are given for published papers providing more detailed spatial analyses, and some further potential uses of the data are suggested. The datasets are listed in Table 1. [ABSTRACT FROM AUTHOR]

Recent extreme weather events have had severe impacts on UK crop yields, and so there is concern that a greater frequency of extremes could affect crop production in a changing climate. Here we investigate the impacts of future climate change on wheat, the most widely grown cereal crop globally, in a temperate country with currently favourable wheat-growing conditions. Historically, following the plateau of UK wheat yields since the 1990s, we find there has been a recent significant increase in wheat yield volatility, which is only partially explained by seasonal metrics of temperature and precipitation across key wheat growth stages (foundation, construction and production). We find climate impacts on wheat yields are strongest in years with compound weather extremes across multiple growth stages (e.g. frost and heavy rainfall). To assess how these conditions might evolve in the future, we analyse the latest 2.2 km UK Climate Projections (UKCP Local): on average, the foundation growth stage (broadly 1 October to 9 April) is likely to become warmer and wetter, while the construction (10 April to 10 June) and production (11 June to 26 July) stages are likely to become warmer and slightly drier. Statistical wheat yield projections, obtained by driving the regression model with UKCP Local simulations of precipitation and temperature for the UK's three main wheat-growing regions, indicate continued growth of crop yields in the coming decades. Significantly warmer projected winter night temperatures offset the negative impacts of increasing rainfall during the foundation stage, while warmer day temperatures and drier conditions are generally beneficial to yields in the production stage. This work suggests that on average, at the regional scale, climate change is likely to have more positive impacts on UK wheat yields than previously considered. Against this background of positive change, however, our work illustrates that wheat farming in the UK is likely to move outside of the climatic envelope that it has previously experienced, increasing the risk of unseen weather conditions such as intense local thunderstorms or prolonged droughts, which are beyond the scope of this paper. [ABSTRACT FROM AUTHOR]

In order to effectively model the potential impacts of future climate change, there is a requirement for climate data inputs which are a) high spatial and temporal resolution; b) explore a range of future climate change scenarios; c) are consistent with historical observations in the historical period; and d) provide an exploration of climate model uncertainty. This paper presents a suite of climate projections for the United Kingdom that conform to these requirements: CHESS-SCAPE. CHESS-SCAPE is a 1 km resolution dataset containing eleven near-surface meteorological variables that can be used to as input to many different impacts models. The variables are available at several time resolutions, from daily to decadal means, for the years 1980-2080. It was derived from the state-of the art regional climate projections in the United Kingdom Climate Projections 2018 (UKCP18) Regional Climate Model (RCM) UKCP18 12 km ensemble, downscaled to 1 km using a combination of physical and empirical methods to account for local topographic effects. CHESS-SCAPE has four ensemble members, which were chosen to span the range of temperature and precipitation change in the UKCP18 ensemble, representing the ensemble climate model uncertainty. CHESS-SCAPE consists of projections for four emissions scenarios, given by the Representative Concentration Pathways: RCP2.6, 4.5, 6.0 and 8.5, which were derived from the UKCP18 RCM RCP8.5 scenarios using time shifting and pattern scaling. These correspond to UK annual warming projections of between 0.9-1.9 K for RCP2.6 up to 2.8-4.3 K for RCP8.5 between 1980-2000 and 2060-2080. Little change in annual precipitation is projected, but larger changes in seasonal precipitation are seen with some scenarios projecting large increases in precipitation in the winter (up to 22 %) and large decreases in the summer (up to -39 %). All four RCP scenarios and ensemble members are also provided with bias-correction, using the CHESS-met historical gridded dataset as a baseline. With high spatial and temporal resolution, extensive range of warming scenarios and multiple ensemble members, CHESS-SCAPE provides a comprehensive data resource for modellers of climate change impacts in the UK. The CHESS-SCAPE data are available for download from the NERC EDS Centre for Environmental Data Analysis: http://dx.doi.org/10.5285/8194b416cbee482b89e0dfbe17c5786c (Robinson et al., 2022). [ABSTRACT FROM AUTHOR]

Classical statistical methods for flood frequency estimation assume stationarity in the gauged data. However, recent focus on climate change and, within UK hydrology, severe floods in 2009 and 2015 has raised the profile of statistical analyses that include trends. This paper considers how parameter estimates for the generalised logistic distribution vary through time in the UK. The UK Benchmark Network (UKBN2) is used to allow focus on climate change separate from the effects of land-use change. We focus on the sensitivity of parameter estimates to adding data, through fixed-width moving window and fixed-start extending window approaches, and on whether parameter trends are more prominent in specific geographical regions. Under stationary assumptions, the addition of new data tends to further the convergence of parameters to some final value. However, addition of a single data point can vastly change non-stationary parameter estimates. Little spatial correlation is seen in the magnitude of trends in peak flow data, potentially due to the spatial clustering of catchments in the UKBN2. In many places, the ratio between the 50-year and 100-year flood is decreasing, whereas the ratio between the 2-year and 30-year flood is increasing, presenting as a flattening of the flood frequency curve. [ABSTRACT FROM AUTHOR]

We present a climate-conditioned catastrophe flood model for the UK that simulates pluvial, fluvial and coastal flood risks at 1 arc second spatial resolution (~20–25 m). Hazard layers for ten different return periods are produced over the whole UK for historic, 2020, 2030, 2050 and 2070 conditions using the UKCP18 climate simulations. From these, monetary losses are computed for Great Britain only for five specific global warming levels (0.6, 1.1, 1.8, 2.5 and 3.3 °C). The analysis contains a greater level of detail and nuance compared to previous work and represents our current best understanding of the UK's changing flood risk landscape. Validation against historical national return period flood maps yielded Critical Success Index values of 0.65 and 0.76 for England and Wales respectively, and maximum water levels for the Carlisle 2005 flood were replicated to an RMSE of 0.41 m without calibration. This level of skill is similar to local modelling with site specific data. Expected Annual Damage in 2020 was £730M, which compares favourably to the observed value of £714M reported by the Association of British Insurers. Previous UK flood loss estimates based on government data are ~3x higher and lie well outside our modelled loss distribution, which is plausibly centred on the observations. We estimate that UK 1 % annual probability flood losses were ~6 % greater in the average climate conditions of 2020 than for the period of historical river flow and rainfall observations (centred approximately on 1995) and can be kept to around ~8 % if all countries' COP26 2030 carbon emission reduction pledges and 'net zero' commitments are implemented in full. Implementing only the COP26 pledges increases UK 1 % annual probability flood losses by ~23 % above recent historical values, and potentially ~37 % if climate sensitivity turns out to be higher than currently thought. [ABSTRACT FROM AUTHOR]

A network of three tall tower measurement stations was set up in 2012 across the United Kingdom to expand measurements made at the long-term background northern hemispheric site, Mace Head, Ireland. Reliable and precise in situ greenhouse gas (GHG) analysis systems were developed and deployed at three sites in the UK with automated instrumentation measuring a suite of GHGs. The UK Deriving Emissions linked to Climate Change (UK DECC) network uses tall (165-230m) open-lattice telecommunications towers, which provide a convenient platform for boundary layer trace gas sampling. In this paper we describe the automated measurement system and first results from the UK DECC network for CO2, CH4, N2O, SF6, CO and H2. CO2 and CH4 are measured at all of the UK DECC sites by cavity ring-down spectroscopy (CRDS) with multiple inlet heights at two of the three tall tower sites to assess for boundary layer stratification. The short-term precisions (1s on 1 min means) of CRDS measurements at background mole fractions for January 2012 to September 2015 is < 0.05 µmol mol-1 for CO2 and < 0.3 nmol mol-1 for CH4. Repeatability of standard injections (1s) is < 0.03 µmol mol-1 for CO2 and <0.3nmolmol-1 for CH4 for the same time period. N2O and SF6 are measured at three of the sites, and CO and H2 measurements are made at two of the sites, from a single inlet height using gas chromatography (GC) with an electron capture detector (ECD), flame ionisation detector (FID) or reduction gas analyser (RGA). Repeatability of individual injections (1s) on GC and RGA instruments between January 2012 and September 2015 for CH4, N2O, SF6, CO and H2 measurements were < 2.8 nmol mol-1, < 0.4 nmol mol-1, < 0.07 pmol mol-1, < 2 nmol mol-1 and < 3 nmol mol-1, respectively. Instrumentation in the network is fully automated and includes sensors for measuring a variety of instrumental parameters such as flow, pressures, and sampling temperatures. Automated alerts are generated and emailed to site operators when instrumental parameters are not within defined set ranges. Automated instrument shutdowns occur for critical errors such as carrier gas flow rate deviations. Results from the network give good spatial and temporal coverage of atmospheric mixing ratios within the UK since early 2012. Results also show that all measured GHGs are increasing in mole fraction over the selected reporting period and, except for SF6, exhibit a seasonal trend. CO2 and CH4 also show strong diurnal cycles, with night-time maxima and daytime minima in mole fractions. [ABSTRACT FROM AUTHOR]

An event-based approach has been used to explore the potential effects of climate change on the spatial and temporal coherence of widespread flood events in Great Britain. Time series of daily mean river flow were generated using a gridded national-scale hydrological model (Grid-to-Grid) driven by a 12-member ensemble of regional climate projections from UK Climate Projections 2018 (UKCP18), for 30-year baseline (1980–2010) and future (2050–2080) time slices. From these, sets of widespread extreme events were extracted. The question of what defines a "widespread flood event" is discussed; here it was defined as an event exceeding an at-site 99.5th percentile (equivalent to 2 d per year) simultaneously over an area of at least 20 km 2 , with a maximum duration of 14 d. This resulted in a set of 14 400 widespread events: approximately 20 events per year, per ensemble member, per time slice. Overall, results have shown that events are more temporally concentrated in winter in the future time slice compared to the baseline. Distributions of event area were similar in both time slices, but the distribution of at-site return periods showed some heavier tails in the future time slice. Such information could be useful for adaptation planning and risk management for floods under climate change, but the potential future changes have to be interpreted in the context of some differences in event characteristics between the baseline climate-projection-driven model runs and an observation-driven model run. While the focus here is Great Britain, the methods and analyses described could be applied to other regions with hydrological models and climate projections of appropriate resolution. [ABSTRACT FROM AUTHOR]

Widespread afforestation has been proposed internationally to reduce atmospheric carbon dioxide; however, the specific hydrological consequences and benefits of such large-scale afforestation (e.g. natural flood management) are poorly understood. We use a high-resolution land surface model, the Joint UK Land Environment Simulator (JULES), with realistic potential afforestation scenarios to quantify possible hydrological change across Great Britain in both present and projected climate. We assess whether proposed afforestation produces significantly different regional responses across regions; whether hydrological fluxes, stores and events are significantly altered by afforestation relative to climate; and how future hydrological processes may be altered up to 2050. Additionally, this enables determination of the relative sensitivity of land surface process representation in JULES compared to climate changes. For these three aims we run simulations using (i) past climate with proposed land cover changes and known floods and drought events; (ii) past climate with independent changes in precipitation, temperature, and CO 2 ; and (iii) a potential future climate (2020–2050). We find the proposed scale of afforestation is unlikely to significantly alter regional hydrology; however, it can noticeably decrease low flows whilst not reducing high flows. The afforestation levels minimally impact hydrological processes compared to changes in precipitation, temperature, and CO 2. Warming average temperatures (+3 °C) decreases streamflow, while rising precipitation (130 %) and CO 2 (600 ppm) increase streamflow. Changes in high flow are generated because of evaporative parameterizations, whereas low flows are controlled by runoff model parameterizations. In this study, land surface parameters within a land surface model do not substantially alter hydrological processes when compared to climate. [ABSTRACT FROM AUTHOR]

This paper assesses the relationship between amount of climate forcing - as indexed by global mean temperature change - and hydrological response in a sample of UK catchments. It constructs climate scenarios representing different changes in global mean temperature from an ensemble of 21 climate models assessed in the IPCC AR4. The results show a considerable range in impact between the 21 climate models, with - for example - change in summer runoff at a 2 °C increase in global mean temperature varying between -40% and +20%. There is evidence of clustering in the results, particularly in projected changes in summer runoff and indicators of low flows, implying that the ensemble mean is not an appropriate generalised indicator of impact, and that the standard deviation of responses does not adequately characterise uncertainty. The uncertainty in hydrological impact is therefore best characterised by considering the shape of the distribution of responses across multiple climate scenarios. For some climate model patterns, and some catchments, there is also evidence that linear climate change forcings produce non-linear hydrological impacts. For most variables and catchments, the effects of climate change are apparent above the effects of natural multidecadal variability with an increase in global mean temperature above 1 °C, but there are differences between catchments. Based on the scenarios represented in the ensemble, the effect of climate change in northern upland catchments will be seen soonest in indicators of high flows, but in southern catchments effects will be apparent soonest in measures of summer and low flows. The uncertainty in response between different climate model patterns is considerably greater than the range due to uncertainty in hydrological model parameterisation. [ABSTRACT FROM AUTHOR]

Land surface models are an important tool in the study of climate change and its impacts, but their use can be hampered by uncertainties in input parameter settings and by errors in the models. We apply uncertainty quantification (UQ) techniques to constrain the input parameter space and corresponding historical simulations of JULES-ES-1.0 (Joint UK Land Environment Simulator Earth System), the land surface component of the UK Earth System Model, UKESM1.0. We use an ensemble of historical simulations of the land surface model to rule out ensemble members and corresponding input parameter settings that do not match modern observations of the land surface and carbon cycle. As JULES-ES-1.0 is computationally expensive, we use a cheap statistical proxy termed an emulator, trained on the ensemble of model runs, to rule out parts of the parameter space where the simulator has not yet been run. We use history matching, an iterated approach to constraining JULES-ES-1.0, running an initial ensemble and training the emulator, before choosing a second wave of ensemble members consistent with historical land surface observations. We successfully rule out 88 % of the initial input parameter space as being statistically inconsistent with observed land surface behaviour. The result is a set of historical simulations and a constrained input space that are statistically consistent with observations. Furthermore, we use sensitivity analysis to identify the most (and least) important input parameters for controlling the global output of JULES-ES-1.0 and provide information on how parameters might be varied to improve the performance of the model and eliminate model biases. [ABSTRACT FROM AUTHOR]

Potential evapotranspiration (PET) is the water that would be lost by plants through evaporation and transpiration if water was not limited in the soil, and it is commonly used in conceptual hydrological modelling in the calculation of runoff production and hence river discharge. Future changes of PET are likely to be as important as changes in precipitation patterns in determining changes in river flows. However PET is not calculated routinely by climate models so it must be derived independently when the impact of climate change on river flow is to be assessed. This paper compares PET estimates from 12 equations of different complexity, driven by the Hadley Centre's HadRM3-Q0 model outputs representative of 1961-1990, with MORECS PET, a product used as reference PET in Great Britain. The results show that the FAO56 version of the Penman-Monteith equations reproduces best the spatial and seasonal variability of MORECS PET across GB when driven by HadRM3-Q0 estimates of relative humidity, total cloud, wind speed and linearly bias-corrected mean surface temperature. This suggests that potential biases in HadRM3-Q0 climate do not result in significant biases when the physically based FAO56 equations are used. Percentage changes in PET between the 1961-1990 and 2041-2070 time slices were also calculated for each of the 12 PET equations from HadRM3-Q0. Results show a large variation in the magnitude (and sometimes direction) of changes estimated from different PET equations, with Turc, Jensen-Haise and calibrated Blaney-Criddle methods systematically projecting the largest increases across GB for all months and Priestley- Taylor, Makkink, and Thornthwaite showing the smallest changes. We recommend the use of the FAO56 equation as, when driven by HadRM3-Q0 climate data, this best reproduces the reference MORECS PET across Great Britain for the reference period of 1961-1990. Further, the future changes of PET estimated by FAO56 are within the range of uncertainty defined by the ensemble of 12 PET equations. The changes show a clear northwest-southeast gradient of PET increase with largest (smallest) changes in the northwest in January (July and October) respectively. However, the range in magnitude of PET changes due to the choice of PET method shown in this study for Great Britain suggests that PET uncertainty is a challenge facing the assessment of climate change impact on hydrology mostly ignored up to now. [ABSTRACT FROM AUTHOR]

This paper describes the development of a semi-distributed conceptual rainfall-runoff model, originally formulated to simulate impacts of climate and land-use change on flood frequency. The model has component modules for soil moisture balance, drainage response and channel routing and is grid-based to allow direct incorporation of GIS- and Digital Terrain Model (DTM)-derived data sets into the initialisation of parameter values. Catchment runoff is derived from the aggregation of components of flow from the drainage module within each grid square and from total routed flow from all grid squares. Calibration is performed sequentially for the three modules using different objective functions for each stage. A key principle of the modelling system is the concept of nested calibration, which ensures that all flows simulated for points within a large catchment are spatially consistent. The modelling system is robust and has been applied successfully at different spatial scales to three large catchments in the UK, including comparison of observed and modelled flood frequency and flow duration curves, simulation of flows for uncalibrated catchments and identification of components of flow within a modelled hydrograph. The role of such a model in integrated catchment studies is outlined. [ABSTRACT FROM AUTHOR]

Climate change may significantly increase flood risk globally, but there are large uncertainties in both future climatic changes and how these propagate into changing river flows. Here, the impact of climate change on the magnitude and frequency of high flows is analysed for Great Britain (GB) to provide the first spatially consistent GB projections to include both climate ensembles and hydrological model parameter uncertainties. We use the latest high-resolution (12 km) regional climate model ensemble from the UK Climate Projections (UKCP18). These projections are based on a perturbed-physics ensemble of 12 regional climate model simulations and allow exploration of climate model uncertainty beyond the variability caused by the use of different models. We model 346 larger (>144 km 2) catchments across GB using the DECIPHeR hydrological modelling framework. Generally, results indicated an increase in the magnitude and frequency of high flows (Q10, Q1, and annual maximum) along the western coast of GB in the future (2050–2075), with increases in annual maximum flows of up to 65 % for western Scotland. In contrast, median flows (Q50) were projected to decrease across GB. Even when using an ensemble based on a single regional climate model (RCM) structure, all flow projections contained large uncertainties. While the RCM parameters were the largest source of uncertainty overall, hydrological modelling uncertainties were considerable in eastern and south-eastern England. Regional variations in flow projections were found to relate to (i) differences in climatic change and (ii) catchment conditions during the baseline period as characterised by the runoff coefficient (mean discharge divided by mean precipitation). Importantly, increased heavy-precipitation events (defined by an increase in 99th percentile precipitation) did not always result in increased flood flows for catchments with low runoff coefficients, highlighting the varying factors leading to changes in high flows. These results provide a national overview of climate change impacts on high flows across GB, which will inform climate change adaptation, and highlight the impact of hydrological model parameter uncertainties when modelling climate change impact on high flows. [ABSTRACT FROM AUTHOR]

Spatially extensive multi-year hydrological droughts cause significant environmental stress. The UK is expected to remain vulnerable to future multi-year droughts under climate change. Existing approaches to quantify hydrological impacts of climate change often rely solely on global climate model (GCM) projections following different emission scenarios. This may miss out low-probability events with significant impacts. As a means of exploring such events, physical climate storyline approaches aim to quantify physically coherent articulations of how observed events could hypothetically have unfolded in alternative ways. This study uses the 2010–2012 drought, the most recent period of severe hydrological drought in the UK, as a basis and analyses storylines based on changes to (1) precondition severity, (2) temporal drought sequence, and (3) climate change. Evidence from multiple storylines shows that the maximum intensity, mean deficit, and duration of the 2010–2012 drought were highly influenced by its meteorological preconditions prior to drought inception, particularly for northern catchments at shorter timescales. The influence of progressively drier preconditions reflects both the spatial variation in drought preconditions and the role of physical catchment characteristics, particularly hydrogeology in the propagation of multi-year droughts. There are two plausible storylines of an additional dry year with dry winter conditions repeated either before the observed drought or replacing the observed dramatic drought termination confirm the vulnerability of UK catchments to a "third dry winter" storyline. Applying the UKCP18 climate projections, we find that drought conditions worsen with global warming with a mitigation of drought conditions by wetter winters in northern catchments at high warming levels. Comparison of the storylines with a benchmark drought (1975–1976) and a protracted multi-year drought (1989–1993) shows that, for each storyline (including the climate change storylines), drought conditions could have matched and exceeded those experienced during the past droughts at catchments across the UK, particularly for southern catchments. The construction of storylines based on observed events can complement existing methods to stress test UK catchments against plausible unrealised droughts. [ABSTRACT FROM AUTHOR]

We present a method for estimating fossil fuel methane emissions using observations of methane and ethane, accounting for uncertainty in their emission ratio. The ethane:methane emission ratio is incorporated as a spatially and temporally variable parameter in a Bayesian model, with its own prior distribution and uncertainty. We find that using an emission ratio distribution mitigates bias from using a fixed, potentially incorrect emission ratio and that uncertainty in this ratio is propagated into posterior estimates of emissions. A synthetic data test is used to show the impact of assuming an incorrect ethane:methane emission ratio and demonstrate how our variable parameter model can better quantify overall uncertainty. We also use this method to estimate UK methane emissions from high-frequency observations of methane and ethane from the UK Deriving Emissions linked to Climate Change (DECC) network. Using the joint methane–ethane inverse model, we estimate annual mean UK methane emissions of approximately 0.27 (95 % uncertainty interval 0.26–0.29) Tgyr-1 from fossil fuel sources and 2.06 (1.99–2.15) Tgyr-1 from non-fossil fuel sources, during the period 2015–2019. Uncertainties in UK fossil fuel emissions estimates are reduced on average by 15 % and up to 35 % when incorporating ethane into the inverse model, in comparison to results from the methane-only inversion. [ABSTRACT FROM AUTHOR]

In an increasing hydro-climatic risk context as a result of climate change, this work aims to identify future hydro-hazard hot-spots as a result of climate change across Great Britain. First, flood and drought hazards were defined and selected in a consistent and parallel approach with a threshold method. Then, a nation-wide systematic and robust statistical framework was developed to quantify changes in frequency, magnitude, and duration, and assess time of year for both droughts and floods, and the uncertainty associated with climate model projections. This approach was applied to a spatially coherent statistical database of daily river flows (Future Flows Hydrology) across Great Britain to assess changes between the baseline (1961-1990) and the 2080s (2069-2098). The results showed that hydrohazard hot-spots are likely to develop along the western coast of England and Wales and across north-eastern Scotland, mainly during the winter (floods) and autumn (droughts) seasons, with a higher increase in drought hazard in terms of magnitude and duration. These results suggest a need for adapting water management policies in light of climate change impact, not only on the magnitude, but also on the timing of hydro-hazard events, and future policy should account for both extremes together, alongside their potential future evolution. [ABSTRACT FROM AUTHOR]

Dynamical downscaling of Global Climate Models (GCMs) through regional climate models (RCMs) potentially improves the usability of the output for hydrological impact studies. However, a further downscaling or interpolation of precipitation from RCMs is often needed to match the precipitation characteristics at the local scale. This study analysed three Model Output Statistics (MOS) techniques to adjust RCM precipitation; (1) a simple direct method (DM), (2) quantile-quantile mapping (QM) and (3) a distribution- based scaling (DBS) approach. The modelled precipitation was daily means from 16 RCMs driven by ERA40 reanalysis data over the 1961-2000 provided by the ENSEMBLES (ENSEMBLE-based Predictions of Climate Changes and their Impacts) project over a small catchment located in the Midlands, UK. All methods were conditioned on the entire time series, separate months and using an objective classification of Lamb's weather types. The performance of the MOS techniques were assessed regarding temporal and spatial characteristics of the precipitation fields, as well as modelled runoff using the HBV rainfall-runoff model. The results indicate that the DBS conditioned on classification patterns performed better than the other methods, however an ensemble approach in terms of both climate models and downscaling methods is recommended to account for uncertainties in the MOS methods. [ABSTRACT FROM AUTHOR]

Soil is an important store of carbon (C) and there has been recent concern that accelerated loss of carbon from the soil may be reinforcing climate change. There is therefore a need to both track current trends in soil C storage and to identify how soil can contribute to carbon emission reduction targets. Countryside Survey (CS) is an integrated national monitoring program in which vegetation, topsoil, water and land use measurements are made across Great Britain (GB). The soil component of CS is unique as topsoil C concentrations have been measured at three time points (1978, 1998 and 2007) together with topsoil bulk density (2007 only), vegetation composition (all years), and land use (i.e. Broad Habitat, 1998, 2007). The combined dataset allows estimates of change in topsoil C stock over time and the influence of land use change on topsoil C to be investigated. Results indicate that although there was a small increase (8%) in topsoil C concentration between 1978 and 1998 and small decrease (6.5%) between 1998 and 2007, there was no significant change in GB topsoil C concentration (in g kg-1), density (in t ha-1) or stock (in Tg) between 1978 and 2007. Within individual habitats some consistent trends were observed and by examining plots which had consistent vegetation composition since 1978 we demonstrate that land use change was not responsible for the few significant changes that were found. These results are comparable with the few other estimates of recent topsoil C concentration and stock changes in W. Europe, with the exception of a previous study in England and Wales which reported significant topsoil C losses of up to 50% over a similar period. Possible reasons for the contradictory findings are discussed. An extra 220-730 Tg of C would be stored in topsoil C stocks if all GB soils were optimised at the top 5-25% C densities as recorded for each habitat in 2007. [ABSTRACT FROM AUTHOR]

A grid-based approach to river flow modelling has been developed for regional assessments of the impact of environmental change on hydrologically sensitive systems. The approach also provides a means of assessing, and providing feedback on, the hydrological performance of the land-surface component of a regional climate model (RCM). When combined with information on the evolution of climate, the model can give estimates of the impact of future climate change on river flows and flooding. The high-resolution flow routing and runoff-production model is designed for use with RCM-derived rainfall and potential evaporation (PE), although other sources of gridded rainfall and PE can be employed. Called the "Grid-to-Grid Model", or G2G, it can be configured on grids of different resolution and coverage (a 1 km grid over the UK is used here). The model can simulate flow on an area-wide basis as well as providing estimates of fluvial discharges for input to shelf-sea and ocean models. Configuration of the flow routing model on a relatively high resolution 1 km grid allows modelled river flows to be compared with gauged observations for a variety of catchments across the UK. Modelled flows are also compared with those obtained from a catchment-based model, a parameter-generalised form of the Probability-Distributed Model (PDM) developed for assessing flood frequency. Using RCM re-analysis rainfall and PE as input, the G2G model performs well compared with measured flows at a daily time-step, particularly for high relief catchments. It performs less well for low-relief and groundwater-dominated regions because the dominant model control on runoff production is topography. [ABSTRACT FROM AUTHOR]