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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 NERC UK-SCAPE 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 x 1 km grids across GB and NI. The river flow datasets include grids of monthly mean flow, annual maxima of daily mean flow, and annual minima of 7-day mean flow (m3s-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 12km 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-day 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.

National or regional grid-based hydrological models are usually run at relatively fine spatial resolutions. But the meteorological data necessary to drive such models are often coarser resolution, so some form of spatial downscaling is generally required. A 1km hydrological model for Great Britain is used to test the performance of a simple method of downscaling precipitation based on 1km patterns of long-term mean annual rainfall (Standard Average Annual Rainfall; SAAR). For a range of coarser resolutions (5, 10, 25 and 50km), a 1km grid of multiplicative scaling factors is derived as the ratio of the 1km grid box SAAR divided by the mean SAAR of the coarser resolution grid box that contains it. A dataset of 1km daily observation-based precipitation is then degraded to the coarser resolutions, and application of SAAR scaling factors is compared to no downscaling and direct use of 1km data, for simulating river flows for a large set of catchments. SAAR-based downscaling provides a clear improvement over no downscaling. Using monthly rather than annual long-term mean rainfall patterns provides minimal further improvement. There are no strong relationships between performance and catchment properties, but performance using 50km precipitation without downscaling tends to be worse for smaller, steeper catchments and those with a more south-westerly aspect; these benefit more from SAAR-based downscaling. An assessment using high resolution convection-permitting model data shows relatively small changes in derived SAAR scaling factors between a baseline and far-future period, suggesting that use of historical scaling factors for future periods is reasonable. Applicability of this simple downscaling method for other parts of the world should be similarly assessed, for both historical and future periods. While use of annual patterns seems to be sufficient in Britain, areas where spatial rainfall patterns are more variable through the year may require use of sub-annual patte

An increase in extreme weather events is leading to increased flood occurrence and risk in many areas. Although climate mitigation strategies are being implemented, it is widely accepted that societies must adapt to climate variability and climate change. Traditional climate change impact studies have used projections for future time-slices, often for a range of possible emissions scenarios. Recently however, there has been a move to instead consider climate change impacts relative to global mean surface temperature (GMST) change, to try to encourage action to avoid the more severe impacts from higher GMST changes. To support adaptation planning, more localised information on impacts is required. Here, data on the potential range of changes in flood peaks is generated by combining flood response surfaces and the new UK Climate Projections 2018, for every river cell on a 1 km grid across Britain, for GMST changes of 1–4.5°C. The results show significant spatial variation, with impacts typically higher in the west than the east, and generally increasing with GMST change. Some southern regions show flood peak changes accelerating with GMST change. The changes in flood peaks can be translated into changes in flood inundation and associated flood risk under alternative adaptation assumptions.

Across the UK, water abstracted from ground, surface, and tidal stores is regulated through a system of licenses to protect both the sources and the environment. Similar permits are required for discharging wastewater to rivers or onto the ground. These abstractions and discharges can have a significant impact on UK Rivers, but measurements are not readily available, which discourages their use in hydrological models of river flows. However, these very unique data sets provide a means to improve the performance of spatially distributed hydrological models, particularly during periods when abstraction regulations change and at ungauged river locations. To demonstrate this, point source abstraction and discharge measurements across England have been transformed into 1 × 1 km resolution gridded data and used with an enhanced formulation of the Grid-to-Grid (G2G) hydrological model where these processes are mathematically represented. A comparison of G2G-simulated and gauged river flows for 605 catchments across England between 1999 and 2014 indicates that model simulation of river flows is generally improved at gauged locations downstream of abstraction/discharge sites. The main improvement is in the simulation of low flows, for which the median performance is improved by 10.7%, however, the impact on simulation of high river flows is more modest (1.5% improvement). These results demonstrate the potential gains available to the international hydrological and land-surface modeling community from using records of actual water use (where available) in models, in place of more widely used national statistics.

Assessments of the impacts of climate change are typically made using climate scenarios based on assumptions about future emissions of greenhouse gases, but policymakers and climate risk communicators are increasingly asking for information on impacts at different levels of warming. This paper provides this information for a set of indicators of climate risks in the UK for levels of warming up to 4 °C above pre-industrial levels. The results show substantial increases in climate risks at 2 °C, which is often inferred in the media to be a 'safe' level of climate change. In a 2 °C world, the chance of a heatwave is doubled, and the frequency of heat stress affecting people, crops and animals can be increased by a factor of five. Cooling degree days more than double, wildfire danger can increase by 40%–70%, the frequency of agricultural and water resources droughts doubles in England, and flood frequency in Wales increases by 50%. At 4 °C the increases in risk are considerably greater: heatwaves occur in virtually every year. The frequency of cold weather extremes reduces, but is not eliminated, with increasing warming. The rate of change in an indicator with warming varies across the UK. For temperature-based indicators this reflects variability in current climate, but for rainfall-based indicators reflects variations in the change in climate. Most indicators show a generally linear increase in risk with level of warming (although the change in risk from now is around 2.4 times higher in a 4 °C world than a 2 °C world because of warming experienced so far). However, some indicators—particularly relating to heat extremes—show a highly non-linear increase with level of warming. The range in change in indicator at a given level of warming is primarily caused by uncertainty in the estimated regional response of to increasing forcing.

Surface water flooding (SWF) is a recurrent hazard that affects lives and livelihoods. Climate change is projected to change the frequency of extreme rainfall events that can lead to SWF. Increasingly, data from Regional Climate Models (RCMs) are being used to investigate the potential water‐related impacts of climate change; such assessments often focus on broad‐scale fluvial flooding and the use of coarse resolution (>12 km) RCMs. However, high‐resolution (<4 km) convection‐permitting RCMs are now becoming available that allow impact assessments of more localised SWF to be made. At the same time, there has been an increasing demand for more robust and timely real‐time forecast and alert information on SWF. In the UK, a real‐time SWF Hazard Impact Model framework has been developed. The system uses 1‐km gridded surface runoff estimates from a hydrological model to simulate the SWF hazard. These are linked to detailed inundation model outputs through an Impact Library to assess impacts on property, people, transport, and infrastructure for four severity levels. Here, a set of high‐resolution (1.5 km and 12 km) RCM data has been used as input to a grid‐based hydrological model over southern Britain to simulate Current (1996–2009) and Future (~2100s; RCP8.5) surface runoff. Counts of threshold‐exceedance for surface runoff and precipitation (at 1‐, 3‐ and 6‐hr durations) are analysed. Results show that the percentage increases in surface runoff extremes, are less than those of precipitation extremes. The higher‐resolution RCM simulates the largest percentage increases, which occur in winter, and the winter exceedance counts are greater than summer exceedance counts. For property impacts, the largest percentage increases are also in winter; however, it is the 12‐km RCM output that leads to the largest percentage increase in impacts. The added‐value of high‐resolution climate model data for hydrological modelling is from capturing the more intense convective storms in s

The MaRIUS‐G2G datasets were produced for the MaRIUS (Managing the Risks, Impacts and Uncertainties of drought and water Scarcity) project, using the Grid‐to‐Grid (G2G) national‐scale hydrological model for Great Britain. There are six separate datasets, with each of three combinations of meteorological driving data (two observation‐based and one from climate model ensembles) used to produce two types of outputs (daily time‐series of natural river flow for 260 sites, and monthly 1 × 1 km grids of natural flow and soil moisture in the unsaturated zone). The driving data required by G2G are rainfall and potential evaporation (PE). Two of the datasets from observation‐based driving data use rainfall from CEH‐GEAR (CEH‐Gridded Estimates of Areal Rainfall) with PE from MORECS (Met Office Rainfall and Evaporation Calculation System), and cover the period 1960–2015, while the other two use CEH‐GEAR rainfall with PE derived from 5 km temperature data using the Oudin method, and cover 1891–2015. The two datasets based on driving data (rainfall and PE) from the climate model ensembles cover three periods: Historical Baseline (1900–2006), Near‐Future (2020–2049), and Far‐Future (2070–2099). Data for a 30‐year Baseline period (1975–2004), against which the Near‐Future and Far‐Future periods should be compared, are also available directly. There are 100 members in each ensemble, and the future periods use the RCP8.5 emissions scenario. This paper provides details of the G2G model and the different sets of meteorological driving data, as well as the availability and formatting of the output datasets. It also describes some recent and potential applications of the datasets, which have already been used to support historical and future analyses of low flow and drought characteristics across Britain, and provides some guidance on how the climate model‐driven datasets should (and should not) be used.

Observations of climate are often available on very different spatial scales from observations of the natural environments and resources that are affected by climate change. In order to help bridge the gap between these scales using modelling, a new dataset of daily meteorological variables was created at 1 km resolution over Great Britain for the years 1961–2012, by interpolating coarser resolution climate data and including the effects of local topography. These variables were used to calculate atmospheric evaporative demand (AED) at the same spatial and temporal resolution. Two functions that represent AED were chosen: one is a standard form of potential evapotranspiration (PET) and the other is a derived PET measure used by hydrologists that includes the effect of water intercepted by the canopy (PETI). Temporal trends in these functions were calculated, with PET found to be increasing in all regions, and at an overall rate of 0.021 ± 0.021 mm day−1 decade−1 in Great Britain. PETI was found to be increasing at a rate of 0.019 ± 0.020 mm day−1 decade−1 in Great Britain, but this was not statistically significant. However, there was a trend in PETI in England of 0.023 ± 0.023 mm day−1 decade−1. The trends were found to vary by season, with spring PET increasing by 0.043 ± 0.019 mm day−1 decade−1 (0.038 ± 0.018 mm day−1 decade−1 when the interception correction is included) in Great Britain, while there is no statistically significant trend in other seasons. The trends were attributed analytically to trends in the climate variables; the overall positive trend was predominantly driven by rising air temperature, although rising specific humidity had a negative effect on the trend. Recasting the analysis in terms of relative humidity revealed that the overall effect is that falling relative humidity causes the PET to rise. Increasing downward short- and longwave radiation made an overall positive contribution to the PET trend, while decreasing wind speed made a negati

Climate model data are increasingly used to drive hydrological models, to assess the possible impacts of climate change on river flows. Hydrological models often require potential evaporation (PE) from vegetation, alongside precipitation, but PE is not usually output by climate models so has to be estimated from other meteorological variables. Here, the Penman-Monteith formula is applied to estimate PE using data from a 12 km Regional Climate Model (RCM) and a nested very high resolution (1.5 km) RCM covering southern Britain. PE estimates from RCM runs driven by reanalysis boundary conditions are compared to observation-based PE data, to assess performance. The comparison shows that both the 1.5 and 12 km RCMs reproduce observation-based PE well, on daily and monthly time-steps, and enables choices to be made about application of the formula using the available data. Data from Current and Future RCM runs driven by boundary conditions from a Global Climate Model are then used to investigate potential future changes in PE, and how certain factors affect those changes. In particular, the importance of including changes in canopy resistance is demonstrated. PE projections are also shown to vary to some extent according to how aerosols are modelled in the RCMs.