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A primary objective of hydrological modelling (HM) is to monitor the water balance in catchments and provide forecasts of key variables and fluxes (i.e., soil moisture and streamflow) at the seasonal scale. Within the Copernicus Climate Change Service, a global seasonal forecasting framework using four state-of-the-art HMs (i.e., HTESSEL, Jules, mHM and PCR-GLOBWB) is developed. The system is required to provide skillful forecasts to provide an added-value to society. In this study, we evaluate the skill of streamflow forecasts using established skill metrics such as Continuous Rank Probability Score and Brier Score.As a first step, we evaluated the performance of the reference run of the four models that is based on ERA5-Land forcing. We employed more than 3100 river flow measurements obtained from the Global Runoff Data Centre (GRDC). These data allow us to classify basins according to aridity and evaporation indices and to evaluate their performance according to geographical regions. In a Budyko analysis, all models adhere to the expected theoretical functions that respect both the conservation of energy and water. However, applying the Budyko analysis to the observational records and reanalysis data only, some basins do not adhere to the energy limit by, for example, having more actual evaporation than potential evaporation. These findings suggest that water may have been transported across basins or that groundwater wells may have been overdrafted. For the development of the global forecasting system, an evaluation of model performance at these basins should be taken with care, and hydrologic models should not be calibrated here. By comparing the regional differentiated Budyko analysis and evaluating the skill metrics of the four HMs, we aim to discriminate among structural and forcing errors. Eventually, this analysis would allow improving the skills of the global seasonal forecasts system.

Land surface models such as JULES (Joint UK Land Environment Simulator) are usually run on a rectilinear grid, yielding gridded outputs for variables such as soil moisture and evapotranspiration. JULES also models surface and subsurface water fluxes, and these can be used as inputs to a river routing model to predict river flows. Here we investigate the effect of clustering groups of grid cells into ‘Land Response Units’ (LRUs) in JULES, using a hierarchical multivariate clustering technique to group underlying grid cells together based on characteristics including soil type, elevation and land cover. Using LRUs rather than grid cells has the potential to reduce computational expense as well as providing an alternative to tiling approaches for capturing sub-grid heterogeneity. Here, LRUs are used exclusively in the land surface part of modelling, i.e., separate from river routing.We investigate the effect of the LRU approach on JULES soil moisture in part of the Thames catchment in the UK, and compare LRU and gridded soil moisture predictions with measurements from the UKCEH COSMOS-UK soil moisture observation network. We find that use of LRUs leads to good soil moisture prediction while reducing computational expense compared to a gridded approach, but that this is strongly dependent on the characteristics used to create the LRUs. We also consider how the LRU approach impacts predicted river flows, and compare routed JULES outputs with observed river flow from a number of NRFA gauges in the catchment. We show that less computationally expensive LRU JULES outputs give similar river flow results to standard 1 km gridded JULES outputs when routed at 1km resolution, and that the LRU approach can outperform gridded river flow predictions when routed at higher resolution.

There is an immediate need to develop accurate and reliable global hydrological forecasts in light of the future vulnerability to hydrological hazards and water scarcity under a changing climate. As a part of the World Meteorological Organization’s (WMO) Global Hydrological Status and Outlook System (HydroSOS) initiative, we investigated different approaches to blending multi-model simulations for developing holistic operational global forecasts. The ULYSSES (mULti-model hYdrological SeaSonal prEdictionS system) dataset, to be published as ‘‘Global seasonal forecasts and reforecasts of river discharge and related hydrological variables ensemble from four state-of-the-art land surface and hydrological models’’ is used in this study. The first step for improving these forecasts is to investigate ways to improve the model simulations, as global models are not calibrated for local conditions. The analysis was performed over 119 different catchments worldwide for the baseline period of 1981–2019 for three variables: evapotranspiration, surface soil moisture and streamflow. This study evaluated blending approaches with a performance metric based (weighted) averaging of the multi-model simulations, using the catchment’s Kling-Gupta Efficiency (KGE) for the variable to define the weight. Hydrological model simulations were also bias-corrected to improve the multi-model blending output. Weighted blending in conjunction with bias-correction provided the best improvement in performance for the catchments investigated. Applying modelled weights during blending original simulations improved performance over ungauged catchments. The results indicate that there is potential to successfully and easily implement the bias-corrected weighted blending approach to improve operational forecasts globally. This work can be used to improve water resources management and hydrological hazard mitigation, especially in data-sparse regions. This research was funded by the Natural Environment Research Council, UK through UKCEH’s SUNRISE (award number NE/R000131/1) and NC-International (award number NE/X006247/1) programmes delivering National Capability, as well as the Net Zero Capacity Building Project and UKRI-NERC STF Global Water Tools Project.

Land surface models have an increasing scope. Initially designed to capture the feedbacks between the land and the atmosphere as part of weather and climate prediction, they are now used as a critical tool in the urgent need to inform policy about land-use and water-use management in a world that is changing physically and economically. This paper outlines the way that models have evolved through this change of purpose and what might the future hold. It highlights the importance of distinguishing between advances in the science within the modelling components, with the advances of how to represent their interaction. This latter aspect of modelling is often overlooked but will increasingly manifest as an issue as the complexity of the system, the time and space scales of the system being modelled increase. These increases are due to technology, data availability and the urgency and range of the problems being studied.

Improving our understanding of soil moisture and hydraulics is crucial for flood prediction, smart agriculture, modelling nutrient and pollutant spread and evaluating the role of land as a sink or source of carbon and other greenhouse gases. State of the art land surface models rely on poorly-resolved soil textural information to parametrise arbitrarily layered soil models; soils rich in organic matter – key to understanding the role of the land in achieving net zero carbon – are not well modelled. Here, we build a predictive data-driven model of soil moisture using a neural network composed of transformer layers to process time series data from point-sensors (precipitation gauges and sensor-derived estimates of potential evaporation) and convolutional layers to process spatial atmospheric driving data and contextual information (topography, land cover and use, location and catchment behaviour of water bodies). We train the model using data from the COSMOS-UK sensor network and soil moisture satellite products and compare the outputs with JULES to investigate where and why the models diverge. Finally, we predict regions of high peat content and propose a way to combine theory with our data-driven approach to move beyond the sand-silt-clay modelling framework.

It is a well-known fact that multi-model forecast systems provide greater reliability over single-model systems, as hydrological models have a solid contribution to forecast uncertainty [1]. Yet many prevalent skill scores for verification of forecasting systems are calculated relative to a benchmark skill. Benchmark skills across hydrological models could vary largely because the benchmark simulation is biased. This bias is not accounted for in the benchmark skill. For example, hydrological models with a high auto-correlation in their state variables tend to have increased skill but do not necessarily have the lowest error when compared against observations [2]. Thus, the correct interpretation of skill can only be conducted if the models are compared against the ground truth, i.e. observations.With this outlook, we assess the performance of ULYSSES [3] - the first seamless global multi-model hydrological seasonal prediction system. Using four state-of-the-art hydrological models (Jules, HTESSEL, mHM, and PCR-GLOBWB), the production chain utilizes identical land surface datasets (e.g. DEM, soil properties) and forecast inputs for all HMs, and the same river routing scheme (i.e., the multi-scale Routing Model; mRM). The system initializes based on the ERA5-Land dataset, and the seasonal forecasts are driven by a 51-member ensemble generated by the ECMWF seasonal forecasting system 5.The skill assessment includes the verification of seasonal streamflow forecast at 2400+ GRDC gauges distributed globally during the period from 1993 to 2019, at a monthly time scale. The set of skill scores considered includes metrics concerning monthly observations (Kling-Gupta efficiency skill score, i.e., KGESS, KGE components, Equitable Threat Score for droughts, relative bias), skills with reference to benchmark run (CRPSS) and skills on forecast characteristic (forecast extremity, spread). On average, the system was found to have the skill (monthly KGESS) at most for two months. At the lead of 1 month, mHM exhibits KGESS of 0.56, HTESSEL has KGESS of 0.5, Jules 0.48, and PCR-GLOBWB 0.46. A KGESS value of one corresponds to a perfect forecast. Evaluating the median KGE r component (or Pearson's correlation), mHM (0.68), PCR-GLOBWB (0.59), HTESSEL (0.59) and Jules (0.57). The percentage of gauges with positive KGESS is distributed evenly with PCR-GLOBWB (84.9 %), HTESSEL (84.2 %), Jules (82.4 %) and mHM (80.5 %). Model performances over median skill and gauges with positive skill indicates models to have contrasting performance at high and low skill gauges. Besides, the spatial distribution of KGESS shows marked seasonal changes in the skill of the hydrological models. All of this provides insights on the strengths and weaknesses of the models for further improvement of the system.In the future, the skill assessment would be expanded to additionally compare forecasted fluxes and state variables (e.g., terrestrial water storage anomalies, soil moisture) against other observations such as GRACE, SMOS, etc. All ULYSSES outputs will be made available in the Copernicus Climate Data Store [4] and will be open access. We aim to engage institutions and researchers around the world that are willing to evaluate the forecasts model performance to improve the system in the future.[1] https://doi.org/10.1175/BAMS-D-17-0274.1[2] https://doi.org/10.1175/JHM-D-18-0040.1[3] https://www.ufz.de/ulysses[4] https://cds.climate.copernicus.eu

Global hydrological models (GHMs) are a fundamental component of the Earth System Modeling initiative that aims to realize a Digital Twin in the next five to ten years [1]. Recent model evaluations of the state-of-the-art global hydrological models [2,3], however, indicate that existing models have several deficiencies that lead to poor model efficiencies of key terrestrial environmental variables such as runoff, evapotranspiration, and soil moisture, among others. In this study, we evaluate four hydrological models: JULES, HTESSEL, mHM, and PCR-GLOBWB. These models are part of the modelling chain of the Copernicus Climate Change Service project ULYSSES [4], which aims to deliver global operational hydrological forecasts at a spatial resolution of 0.1$^\circ$. The operational service started in July 2020 and the data will be provided every month through the Copernicus Data Store.The initial conditions of the GHMs for the hindcast skill assessment are obtained with the ERA5-land reanalysis [5]. This global dataset provides meteorological forcings (e.g., precipitation and temperature) since 1950 with daily time steps. For this reason, historical simulations of streamflow, obtained with these GHMs from 1981 until 2020 will be cross-evaluated against observed streamflow provided by 2850 GRDC gauging stations. Simulations of evapotranspiration and terrestrial water storage anomalies were evaluated against GRACE and FLUXNET datasets, respectively.During the model calibration phase, models were evaluated in a stratified sample of size 120 basins (i.e., considering hydroclimatic regions and locations around the world). The results of the evaluation indicate that the median value of the Nash-Sutcliffe efficiency obtained with daily streamflow for these models varies from 0.20 to 0.50. The mean Kling-Gupta efficiency (KGE) metric ranges from 0.45 to 0.63. The maximum KGE value corresponds to the mHM model, while the other models are clustered around 0.45.This result alone is quite promising considering the results presented in Beck et al. [2]. One reason for these good results is the relation between the standardization of the input data sets and the common routing model (mRM [6]) with a very detailed river network [7]. The considerable difference in performance between mHM and the other GHMs can be attributed to the parameterization of the models and model structure. mHM is the only GHM that employs the MPR technique [8] and includes fast and slow interflow components. Evaluation metrics obtained with the ILAMB [8] tool indicate that all models have exhibited satisfactory efficiencies (> 0.5 variable score) for monthly climatologies of latent heat, evapotranspiration and runoff. mHM, JULES, and PCR-GLOBWB, perform relatively well, representing the terrestrial water storage anomaly, although any of these models have explicit a detailed representation of the groundwater aquifers.In this presentation, specific results of the model cross-validation, per geographic region will be presented. Finally, recommendations for further GHM model improvement will be discussed.References:[1] Bauer, P. et al. https://doi.org/10.1038/s43588-021-00023-0 2021[2] Beck, H.E., et al. https://doi.org/10.1002/2015WR018247, 2016.[3] Harrigan, S et al. https://doi.org/doi:10.5194/essd-12-2043-2020 2020. [4] https://www.ufz.de/ulysses, ECMWF/COPERNICUS/2019/C3S\_432\_Lot3\_UFZ[5] https://www.ecmwf.int/en/era5-land[6] Thober et al. https://doi.org/10.5194/gmd-12-2501-2019, 2019[7] http://hydro.iis.u-tokyo.ac.jp/~yamadai/cama-flood/index.html[8] Samaniego et al. https://doi.org/10.1029/2008WR007327, 2010[9] https://www.ilamb.org.

Knowledge of how uncertainty propagates through a hydrological land surface modelling sequence is of crucial importance in the identification and characterisation of system weaknesses in the prediction of droughts and floods at global scale. We evaluated the performance of five state-of-the-art global hydrological and land surface models in the context of modelling extreme conditions (drought and flood). Uncertainty was apportioned between the model used (model skill) and also the satellite-based precipitation products used to drive the simulations (forcing data variability) for extreme values of precipitation, surface runoff and evaporation. We found in general that model simulations acted to augment uncertainty rather than reduce it. In percentage terms, the increase in uncertainty was most often less than the magnitude of the input data uncertainty, but of comparable magnitude in many environments. Uncertainty in predictions of evapotranspiration lows (drought) in dry environments was especially high, indicating that these circumstances are a weak point in current modelling system approaches. We also found that high data and model uncertainty points for both ET lows and runoff lows were disproportionately concentrated in the equatorial and southern tropics. Our results are important for highlighting the relative robustness of satellite products in the context of land surface simulations of extreme events and identifying areas where improvements may be made in the consistency of simulation models.

Soil moisture information is essential to monitoring of the intensity of droughts, the start of the rainy season, planting dates and early warnings of yield losses. We assess spatial and temporal trends of drought over the Brazilian semiarid region by combining soil moisture observations from 360 stations, root zone soil moisture from a leading land surface model, and a vegetation health index from remote sensing. The soil moisture dataset was obtained from the network of stations maintained by the National Center of Monitoring and Early Warning of Natural Disasters (Cemaden), in Brazil. Soil water content at 10 to 35 cm depth, for the period 1979–2018, was obtained from running the JULES land surface model (the Joint UK Land Environment Simulator). The modelled soil moisture was correlated with measurements in the common period of 2015–2018, resulting in an average correlation coefficient of 0.48 across the domain. The standardized soil moisture anomaly (SMA) was calculated for the long-term modelled soil moisture and revealed strong negative values during well-known drought periods in the region, especially during El-Nino years. The performance of SMA in identifying droughts during the first 2 months of the raining and cropping season was similar to the Standardized Precipitation Index (SPI), commonly used for drought assessment: 12–14 events were identified by both indices. Finally, the temporal relationship between both SMA and SPI with the Vegetation Health Index (VHI) was assessed using the cross-wavelet transform. The results indicated lagged correlations of 1 to 1.5 months in the annual scale, suggesting that negative trends in SMA and SPI can be an early warning to yield losses during the growing season. Public policies on drought assessment should consider the combination of multiple drought indices, including soil moisture anomaly.

Wetlands play a key role in hydrological and biogeochemical cycles and provide multiple ecosystem services to society. However, reliable data on the extent of global inundated areas and the magnitude of their contribution to local hydrological dynamics remain surprisingly uncertain. Global hydrological models and land surface models (LSMs) include only the most major inundation sources and mechanisms; therefore, quantifying the uncertainties in available data sources remains a challenge. We address these problems by taking a leading global data product on inundation extents (Global Inundation Extent from Multi-Satellites, GIEMS) and matching against predictions from a global hydrodynamic model (Catchment-based Macro-scale Floodplain – CaMa-Flood) driven by runoff data generated by a land surface model (Joint UK Land and Environment Simulator, JULES). The ability of the model to reproduce patterns and dynamics shown by the observational product is assessed in a number of case studies across the tropics, which show that it performs well in large wetland regions, with a good match between corresponding seasonal cycles. At a finer spatial scale, we found that water inputs (e.g. groundwater inflow to wetland) became underestimated in comparison to water outputs (e.g. infiltration and evaporation from wetland) in some wetlands (e.g. Sudd, Tonlé Sap), and the opposite occurred in others (e.g. Okavango) in our model predictions. We also found evidence for an underestimation of low levels of inundation in our satellite-based inundation data (approx. 10 % of total inundation may not be recorded). Additionally, some wetlands display a clear spatial displacement between observed and simulated inundation as a result of overestimation or underestimation of overbank flooding upstream. This study provides timely information on inherent biases in inundation prediction and observation that can contribute to our current ability to make critical predictions of inundation events at both regional and global levels. This research has been supported by the Natural Environment Research Council (grant no. NE/S017380/1).

The newly developed WRF-Hydro model is a fully coupled atmospheric and hydrological processes model suitable for studying the intertwined atmospheric hydrological processes. This study utilizes the WRF-Hydro system on the Three-River source region. The Nash-Sutcliffe efficiency for the runoff simulation is 0.55 compared against the observed daily discharge amount of three stations. The coupled WRF-Hydro simulations are better than WRF in terms of six ground meteorological elements and turbulent heat flux, compared to the data from 14 meteorological stations located in the plateau residential area and two flux stations located around the lake. Although WRF-Hydro overestimates soil moisture, higher anomaly correlation coefficient scores (0.955 versus 0.941) were achieved. The time series of the basin average demonstrates that the hydrological module of WRF-hydro functions during the unfrozen period. The rainfall intensity and frequency simulated by WRF-Hydro are closer to global precipitation mission (GPM) data, attributed to higher convective available potential energy (CAPE) simulated by WRF-Hydro. The results emphasized the necessity of a fully coupled atmospheric-hydrological model when investigating land-atmosphere interactions on a complex topography and hydrology region.

In this editorial, we present general principles as well as specific notions that guide the strategy of JAMES′ editors in realizing the journal's mission.

Accurate soil moisture predictions from land surface models are important in hydrological, ecological and agricultural applications. Despite increasing availability of wide area soil moisture measurements, few studies have combined soil moisture predictions from models with in-situ observations beyond the point scale. This work uses the LAVENDAR data assimilation framework to markedly improve soil moisture estimates from the JULES land surface model using field scale Cosmic Ray Neutron sensor observations from the UKCEH COSMOS-UK network. Rather than directly updating modelled soil moisture estimates towards measured values, we optimize constants in the underlying pedotransfer functions (PTF) which relate soil texture to soil hydraulics parameters. In this way we generate a single set of newly calibrated PTFs based on field scale observations from a number of UK sites with different soil types. We demonstrate that calibrating PTFs in this way can improve the performance of JULES. Further, we suggest that calibrating PTFs for the soils on which they are to be used and at the scales at which land surface models are applied (rather than on small-scale soil samples) will ultimately improve the performance of land surface models, potentially leading to improvements in flood, drought and climate projections.

International audience; Up to now, relatively little effort has been dedicated to the quantitative assessment of the differences in spatial patterns of model outputs. In this paper, we employed a variogram-based methodology to quantify the differences in the spatial patterns of root-zone soil moisture, net radiation, and latent and sensible heat fluxes simulated by three land surface models (SURFEX/ISBA, JULES and CHTESSEL) over three European geographic domains – namely, UK, France and Spain. The model output spatial patterns were quantified through two metrics derived from the variogram: i) the variogram sill, which quantifies the degree of spatial variability of the data; and ii) the variogram integral range, which represents the spatial length scale of the data. The higher seasonal variation of the spatial variability of sensible and latent heat fluxes over France and Spain, compared to the UK, is related to a more frequent occurrence of a soil-moisture-limited evapotranspiration regime during summer dry spells in the south of France and Spain. The small differences in spatial variability of net radiation between models indicate that the spatial patterns of net radiation are mostly driven by the climate forcing data set. However, the models exhibit larger differences in latent and sensible heat flux spatial variabilities, which are related to their differences in i) soil and vegetation ancillary datasets and ii) physical process representation. The highest discrepancies in spatial patterns between models are observed for soil moisture, which is mainly related to the type of soil hydraulic function implemented in the models. This work demonstrates the capability of the variogram to enhance our understanding of the spatiotemporal structure of the uncertainties in land surface model outputs. Therefore, we strongly encourage the implementation of the variogram metrics in model intercomparison exercises.

Timely predictions of fluvial flooding are important for national and regional planning and real-time flood response. Several new computational techniques have emerged in the past decade for making rapid fluvial flood inundation predictions at time and space scales relevant to early warning, although their efficient use is often constrained by the trade-off between model complexity, topographic fidelity and scale. Here we apply a simplified approach to large-area fluvial flood inundation modelling which combines a solution to the inertial form of the shallow water equations at 1 km horizontal resolution, with two alternative, simplified representations of sub-grid floodplain topography. One of these uses a fitted sub-grid probability distribution, the other a quantile-based representation of the floodplain. We evaluate the model’s steady-state performance when used with flood depth estimates corresponding to the 0.01 Annual Exceedance Probability (AEP; ‘100-year’) flood and compare the results with published benchmark data for England. The quantile-based method accurately predicts flood inundation in 86% of locations, with a domain-wide hit rate of 95% and false alarm rate of 10%. These performance measures compare with a hit rate of 71%, and false alarm rate of 9% for the simpler, distribution-based method. We suggest that these approaches are suitable for rapid, wide-area flood forecasting and climate change impact assessment.

The Copernicus Climate Change Service aims at facilitating the emergence of a downstream market of climate services with the ultimate goal of supporting the development of a climate-smart society. Central to this vision is the free and unrestricted distribution of high-quality climate data through the Climate Data Store [1], with seasonal meteorological predictions among them. Within this unique and challenging framework, ULYSSES [2] will provide the first "seamless'' multi-model hydrological seasonal prediction system, with a global coverage at a spatial resolution of 0.1° The ULYSSES modeling chain is based on the successfully tested EDgE proof of concept [3] using four state-of-the-art hydrological models (Jules, HTESSEL, mHM, and PCR-GLOBWB). A unique feature of this production chain consists of using the same land surface datasets (e.g. DEM, soil properties) with identical spatio-temporal resolutions and forecast inputs for all HMs, and the same river routing scheme (i.e., the multi-scale routing model mRM).The initial conditions of the production chain will be based on ERA5-Land dataset and the seasonal forecasts will be driven by a 25-member ensemble generated by the ECMWF-SEAS5 model. ULYSSES aims at generating six essential hydrological variables: snow-water equivalent, snowmelt, evapotranspiration, soil moisture, total runoff, and streamflow with a lead-time of up to six months. The seasonal forecast was verified at 250+ gauges distributred in all continents during the hind-casting period from 1993 to 2019. The operational forecasting period —in testing phase— started in January 2021 and be extended through until July 2021. The first operational ULYSSES forecast will be made available by the 10th of each month starting in January 2021.All input data sets (ERA5-Land), seasonal forecasts (SEAS5) and ULYSSES outputs will be made available in the Copernicus Climate Data Store [1] and will be open access. We aim to engage institutions and researchers around the world that are willing to evaluate the forecasts model performance, with the aim of improving the system in the future. In this talk, the modelling chain concept, model setup and verification of initial results will be presented.[1] https://cds.climate.copernicus.eu [2] https://www.ufz.de/ulysses [3] https://doi.org/10.1175/BAMS-D-17-0274.1

Timely predictions of fluvial flooding are important for national and regional planning and real-time flood response. Several new computational techniques have emerged in the past decade for making rapid fluvial flood inundation predictions at time and space scales relevant to early warning, although their efficient use is often constrained by the trade-off between model complexity, topographic fidelity and scale. Here we apply a simplified approach to large-area fluvial flood inundation modelling which combines a 5 solution to the inertial form of the shallow water equations at 1 km horizontal resolution, with two alternative representations of sub-grid floodplain topography. One of these uses a fitted sub-grid probability distribution, the other a quantile-based representation of the floodplain. We evaluate the model's performance when used to simulate the 0.01 Annual Exceedance Probability (AEP; 100-year) flood and compare the results with published benchmark data for England. The quantile-based method accurately predicts flood inundation in 86 % of locations, with a domain-wide hit rate of 95 % and 10 false alarm rate of 10 %. These performance measures compare with a hit rate of 71 %, and false alarm rate of 9 % for the simpler, but faster, distribution-based method. We suggest that these approaches are suitable for rapid, wide-area flood forecasting and climate change impact assessment.

The COSMOS-UK observation network has been providing field scale soil moisture and hydrometeorological measurements across the UK since 2013. At the time of publication a total of 51 COSMOS-UK sites have been established, each delivering high temporal resolution data in near-real time. Each site utilises a cosmic-ray neutron sensor, which counts fast neutrons at the land surface. These measurements are used to derive field scale near-surface soil water content, which can provide unique insight for science, industry, and agriculture by filling a scale gap between localised point soil moisture and large-scale satellite soil moisture datasets. Additional soil physics and meteorological measurements are made by the COSMOS-UK network including precipitation, air temperature, relative humidity, barometric pressure, soil heat flux, wind speed and direction, and components of incoming and outgoing radiation. These near-real time observational data can be used to improve the performance of hydrological models, validate remote sensing products, improve hydro-meteorological forecasting and underpin applications across a range of other scientific fields. The most recent version of the COSMOS-UK dataset is publically available at https://doi.org/10.5285/37702a54-b7a4-40ff-b62e-d14b161b69ca (Stanley et al., 2020).

Soil moisture predictions from land surface models are important in hydrological, ecological and meteorological applications. In recent years the availability of wide-area soil-moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in-situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the JULES land surface model using field scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way can improve the performance of land surface models, leading to the potential for better flood, drought and climate projections.

The 2018 drought and heatwave over northern Europe were exceptional, with unprecedented forest fires in Sweden, searing heat in Germany and water restrictions in England. Monthly, daily, and hourly data from ERA5, verified with in situ soil water content and surface flux measurements, are examined to investigate the subseasonal‐to‐seasonal progression of the event and the diurnal evolution of tropospheric profiles over Britain to quantify the anomalous land surface contribution to heat and drought. Data suggest the region entered an unprecedented condition of becoming a “hot spot” for land‐atmosphere coupling, which exacerbated the heatwave across much of northern Europe. Land‐atmosphere feedbacks were prompted by unusually low soil water over wide areas, which generated moisture limitations on surface latent heat fluxes, suppressing cloud formation, increasing surface net radiation, and driving temperatures higher during several multiweek episodes of extreme heat. We find consistent evidence in field data and reanalysis of a threshold of soil water content at most locations, below which surface fluxes and daily maximum temperatures become hypersensitive to declining soil water. Similar recent heatwaves over various parts of Europe in 2003, 2010, and 2019, combined with dire climate change projections, suggest such events could be on the increase. Land‐atmosphere feedbacks may play an increasingly important role in exacerbating extremes, but could also contribute to their predictability on subseasonal time scales.

Key Points The editors thank the 2019 peer reviewers

Consistent hydrological status and outlook information across transboundary basins or regions of shared hydrological interest are not often available. Furthermore, whilst large-scale modelling capabilities are continually improving, there is an information and confidence gap between locally informed hydrological status information products and those developed globally.HydroSOS is World Meteorological Organisation initiative that aims to increase global resilience to hydro-climatic risks through the production of hydrological status and outlooks assessments at different scales around the world. Currently in a pilot phase, HydroSOS is being developed through a collaboration between National Hydrometeorological Services, transboundary basin organisations, global modelling centres and the research community. The system will provide an appraisal of where current hydrological status is different from “normal”, as well as sub-seasonal to seasonal outlooks indicating whether this is likely to get better or worse over the coming weeks and months.The HydroSOS programme consists of five main activity streams:Increasing the interoperability of hydrological status and outlook products through Common Technical Specifications. Increasing national capabilities to generate hydrological status and sub-seasonal to seasonal outlook products through Guidance on Methods and Tools. Increasing the utility of large-scale hydrological status and outlook modelling through Co-design of Global Products, with international partners working from local to global scale. Increasing shared production of transboundary hydrological status and outlook products through Regional Pilots, initially in South Asia and the Lake Victoria Basin. Integration of hydrological status and outlook products for national, regional and global users through a Demonstration Portal. This PICO contribution will present progress in the pilot project to date, including a hands-on demonstration of the web portal.

Soil moisture predictions are increasingly important in hydrological, ecological and agricultural applications. In recent years the availability of wide-area assessments of current and future soil-moisture states has grown, yet few studies have combined model-based assessments with observations beyond the point scale. Here we use the JULES land surface model together with COSMOS-UK data to evaluate the extent to which data assimilation can improve predictions of soil moisture across the United Kingdom.COSMOS-UK is a network of soil moisture sensors run by UKCEH. The network provides soil moisture measurements at around 50 sites throughout the UK using innovative Cosmic Ray Neutron Sensors (CRNS). Half hourly measurements of the meteorological variables that the Joint UK Land Environment Simulator (JULES) requires as driving data are also recorded at COSMOS-UK sites, allowing us to run JULES at observation locations. This provides a unique opportunity to compare soil moisture outputs from JULES with CRNS observations; these measurements have a footprint of up to 12 ha (approx 30 acres) and are therefore better scale matched with JULES outputs than those from point sensors.We have used the Land Variational Ensemble Data Assimilation Framework (LaVEnDAR) to combine soil moisture estimates from JULES with daily CRNS observations from one year at a number of COSMOS-UK sites. We show that this results in improved soil moisture predictions from JULES over several years. This has been achieved by optimising parameters in the pedo-transfer function used to derive JULES soil physics parameters from soil texture information. Using data assimilation with LaVEnDAR in this way allows us to explore the relationships between soil moisture estimates, soil physics parameters and soil texture, as well as improving the agreement between JULES model outputs and observations.

Soil moisture is an important component of the Earth system and plays an important role in land-atmosphere interactions. Remote sensing of soil moisture is of great scientific interest and the scientific community has made significant progress in soil moisture estimation using Earth observations. Currently, several operational coarse spatial resolution soil moisture datasets have been produced and widely used for various applications such as climate, hydrology, ecosystem and agriculture. Due to the strong demand for high spatial resolution soil moisture in regional applications, much effort has been recently devoted to the generation of high spatial resolution soil moisture from either Sentinel-1 observations or downscaling of existing coarse resolution soil moisture datasets. The aim of this study is to evaluate high spatial resolution soil moisture products derived from multisource satellite observations. First, the COSMOS-UK measured soil moisture was used to validate existing satellite-based soil moisture datasets including SMAP_9km, SMOS_1km, Sentinel-1, and Sentinel-1/SMAP combined products. Second, an approach based on triple collocation was applied to inter compare these satellite products in the absence of a reference dataset. Third, two merging schemes including a simple average and a triple collocation method were used to develop a combined satellite soil moisture product based on existing satellite soil moisture datasets. From the above analysis, it is found that merging all the above soil moisture data provides a better estimate of soil moisture than any of them alone. Therefore, we conclude that combining existing satellite-based soil moisture products has the potential to provide the best estimate of high spatial resolution soil moisture in the UK.

Monitoring of soil water is essential to assess drought risk over rainfed agriculture. Soil water indicates the onset or progress of dry spells, the start of the rainy season and good periods for sowing or harvesting. Monitoring soil water over rainfed agriculture can be a valuable tool to support field activities and the knowledge of climate risks.A network of soil moisture sensors was established over the Brazilian North East semiarid region in 2015 with measurements at 10 and 20 cm, together with rainfall and other variables in a subset of locations. The data are currently being used to assess the available water over the region in monthly bulletins and reports of potential impacts on yields.In this work, we present a comparison of a dataset of observations from 2015 to 2019 with the soil water estimated by the JULES land surface model (the Joint UK Land Environment Simulator). Overall, the model captures the spatial and temporal variability observed in the measured data well, with an average correlation coefficient of 0.6 across the domain. The performance was compared for each station, resulting in a selection of locations with significant correlation.Based on the regression results, we derive modelled soil moisture for the time span of the JULES run (1979 to 2016). The modeled data enabled the calculation of a standardized soil moisture anomaly (SSMA). The values of SSMA in the period were in agreement with the patterns of drought in the region, especially the recent long-term drought in the Brazilian semiarid region, with significant dry years in 2012, 2013 and 2015. Further analysis will focus on comparisons with other drought indices and measures of impacts on yields at the municipality level.

Soil organic matter (SOM) is an indicator of sustainable land management as stated in the global indicator framework of the United Nations Sustainable Development Goals (SDG Indicator 15.3.1). Improved forecasting of future changes in SOM is needed to support the development of more sustainable land management under a changing climate. Current models fail to reproduce historical trends in SOM both within and during transition between ecosystems. More realistic spatio-temporal SOM dynamics require inclusion of the recent paradigm shift from SOM recalcitrance as an 'intrinsic property' to SOM persistence as an 'ecosystem interaction'. We present a soil profile, or pedon-explicit, ecosystem-scale framework for data and models of SOM distribution and dynamics which can better represent land use transitions. Ecosystem-scale drivers are integrated with pedon-scale processes in two zones of influence. In the upper vegetation zone, SOM is affected primarily by plant inputs (above- and belowground), climate, microbial activity and physical aggregation and is prone to destabilization. In the lower mineral matrix zone, SOM inputs from the vegetation zone are controlled primarily by mineral phase and chemical interactions, resulting in more favourable conditions for SOM persistence. Vegetation zone boundary conditions vary spatially at landscape scales (vegetation cover) and temporally at decadal scales (climate). Mineral matrix zone boundary conditions vary spatially at landscape scales (geology, topography) but change only slowly. The thicknesses of the two zones and their transport connectivity are dynamic and affected by plant cover, land use practices, climate and feedbacks from current SOM stock in each layer. Using this framework, we identify several areas where greater knowledge is needed to advance the emerging paradigm of SOM dynamics-improved representation of plant-derived carbon inputs, contributions of soil biota to SOM storage and effect of dynamic soil structure on SOM storage-and how this can be combined with robust and efficient soil monitoring.

Soil moisture is an important component of the Earth system and plays a key role in land-atmosphere interactions. Remote sensing of soil moisture is of great scientific interest and the scientific community has made significant progress in soil moisture estimation using Earth observations. Currently, several satellite-based coarse spatial resolution soil moisture datasets have been produced and widely used for various applications in climate science, hydrology, ecosystem research and agriculture. Owing to the strong demand for soil moisture data with high spatial resolution for regional applications, much effort has recently been devoted to the generation of high spatial resolution soil moisture data from either high-resolution satellite observations or by downscaling existing coarse-resolution satellite-based soil moisture datasets. In addition, land surface models provide an alternative way to obtain consistent high-resolution soil moisture information when forced with high-resolution inputs. The aim of this study is to create and evaluate high-resolution soil moisture products derived from multiple sources including satellite observations and land surface model simulations. The JULES-CHESS simulated soil moisture and satellite-based soil moisture datasets including SMAP L3E, SMAP L4, SMOS L4, Sentinel 1, ASCAT, and Sentinel 1/SMAP combined products were first validated against observed soil moisture from COSMOS-UK, a network of in-situ cosmic-ray based sensors. Second, an approach based on triple collocation was applied to compare these satellite products in the absence of a known reference dataset. Third, a combined soil moisture product was generated to integrate the better-performing soil moisture estimates based on triple collocation error estimation and a least-squares merging scheme. From further evaluation, it is found that the merged soil moisture integrates the characteristics of model simulation and satellite observations and particularly improves the limited temporal variability of the JULES-CHESS simulation. Therefore, we conclude that the triple collocation merging scheme is a simple and reliable way to combine satellite-based soil moisture products with outputs from the JULES-CHESS simulation for estimating model-data fused high-resolution soil moisture for the British mainland.

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 negative contribution to the trend in PET. The trend in spring PET was particularly strong due to a strong decrease in relative humidity and increase in downward shortwave radiation in the spring.

Hydrology is still, and for good reasons, an inexact science, even if evolving hydrological understanding has provided a basis for improved water management for at least the last three millennia. The limitations of that understanding have, however, become much more apparent and important in the last century as the pressures of increasing populations, and the anthropogenic impacts on catchment forcing and responses, have intensified. At the same time, the sophistication of hydrological analyses and models has been developing rapidly, often driven more by the availability of computational power and geographical data sets than any real increases in understanding of hydrological processes. This sophistication has created an illusion of real progress but a case can be made that we are still rather muddling along, limited by the significant uncertainties in hydrological observations, knowledge of catchment characteristics and related gaps in conceptual understanding, particularly of the sub-surface. These knowledge gaps are illustrated by the fact that for many catchments we cannot close the water balance without significant uncertainty, uncertainty that is often neglected in evaluating models for practical applications.

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.

Land surface models (LSMs) represent terrestrial hydrology in weather and climate modelling operational systems and research studies. We aim to improve hydrological performance in the Joint UK Land Environment Simulator (JULES) LSM that is used for distributed hydrological modelling within the new land–atmosphere–ocean coupled prediction system UKC2 (UK regional Coupled environmental prediction system 2). Using river flow observations from gauge stations, we study the capability of JULES to simulate river flow at 1 km2 spatial resolution within 13 catchments in Great Britain that exhibit a variety of climatic and topographic characteristics. Tests designed to identify where the model results are sensitive to the scheme and parameters chosen for runoff production indicate that different catchments require different parameters and even different runoff schemes for optimal results. We introduce a new parameterisation of topographic variation that produces the best daily river flow results (in terms of Nash–Sutcliffe efficiency and mean bias) for all 13 catchments. The new parameterisation introduces a dependency on terrain slope, constraining surface runoff production to wet soil conditions over flatter regions, whereas over steeper regions the model produces surface runoff for every rainfall event regardless of the soil wetness state. This new parameterisation improves the model performance across Great Britain. As an example, in the Thames catchment, which has extensive areas of flat terrain, the Nash–Sutcliffe efficiency exceeds 0.8 using the new parameterisation. We use cross-spectral analysis to evaluate the amplitude and phase of the modelled versus observed river flows over timescales of 2 days to 10 years. This demonstrates that the model performance is modified by changing the parameterisation by different amounts over annual, weekly-to-monthly and multi-day timescales in different catchments, providing insights into model deficiencies on particular timescales, but it reinforces the newly developed parameterisation.

A key aspect of the land surface response to the atmosphere is how quickly it dries after a rainfall event. It is key because it will determine the intensity and speed of the propagation of drought and also affects the atmospheric state through changes in the surface heat exchanges. Here, we test the theory that this response can be studied as an inherent property of the land surface that is unchanging over time unless the above- and below-ground structures change. This is important as a drydown metric can be used to evaluate a landscape and its response to atmospheric drivers in models used in coupled land&ndash, atmosphere mode when the forcing is often not commensurate with the actual atmosphere. We explore whether the speed of drying of a land unit can be quantified and how this can be used to evaluate models. We use the most direct observation of drying: the rate of change of evapotranspiration after a rainfall event using eddy-covariance observations, or commonly referred to as flux tower data. We analyse the data and find that the drydown timescale is characteristic of different land cover types, then we use that to evaluate a suite of global hydrological and land surface models. We show that, at the site level, the data suggest that evapotranspiration decay timescales are longer for trees than for grasslands. The studied model&rsquo, s accuracy to capture the site drydown timescales depends on the specific model, the site, and the vegetation cover representation. A more robust metric is obtained by grouping the modeled data by vegetation type and, using this, we find that land surface models capture the characteristic timescale difference between trees and grasslands, found using flux data, better than large-scale hydrological models. We thus conclude that the drydown metric has value in understanding land&ndash, atmosphere interactions and model evaluation.

Knowledge of how uncertainty propagates through a hydrological land surface modelling sequence is of crucial importance in the identification and characterisation of system weaknesses in the prediction of droughts and floods at global scale. We evaluated the performance of five state-of-the-art global hydrological and land surface models in the context of modelling extreme conditions (drought and flood). Uncertainty was apportioned between model used (model skill) and also the satellite-based precipitation products used to drive the simulations (forcing data variability) for extreme values of precipitation, surface runoff and evaporation. We found in general that model simulations acted to augment uncertainty rather than reduce it. In percentage terms, the increase in uncertainty was most often less than the magnitude of the input data uncertainty, but of comparable magnitude in many environments. Uncertainty in predictions of evapotranspiration lows (drought) in dry environments was especially high, indicating that these circumstances are a weak point in current modelling system approaches. We also found that high data and model uncertainty points for both ET lows and runoff lows were disproportionately concentrated in the equatorial and southern tropics. Our results are important for highlighting the relative robustness of satellite products in the context of land surface simulations and identifying areas where improvements may be made in the consistency of simulation models.

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.

In a warming climate, the water budget of the land is subject to varying forces such as increasing evaporative demand, mainly through the increased temperature, and changes to the precipitation, which might go up or down. Using a verified, physically based model with 55 years of observation-based meteorological forcing, an analysis of the water budget demonstrates that Great Britain is getting warmer and wetter. Increases in precipitation (3.0 ± 2.0 mm yr−1 yr−1) and air temperature (0.20 ± 0.13 K decade−1) are driving increases in river flow (2.16 mm yr−1 yr−1) and evapotranspiration (0.87 mm yr−1 yr−1), with no significant trend in the soil moisture. The change in evapotranspiration is roughly constant across the regions whereas runoff varies greatly between regions: the biggest change is seen in Scotland (4.56 mm yr−1 yr−1), where precipitation increases were also the greatest (5.4 ± 3.0 mm yr−1 yr−1) and smallest trend (0.29 mm yr−1 yr−1) is seen in the English Lowlands (East Anglia and Midlands), where the increase in rainfall is not statistically significant (1.1 ± 0.7 mm yr−1 yr−1). Relative to their contribution to the evapotranspiration budget, the increase in interception is higher than the other components. This is due to the fact that it correlates strongly with precipitation which is seeing a greater increase than the potential evapotranspiration. This leads to a higher increase in actual evapotranspiration that the potential evapotranspiration, and a negligible increase in soil moisture or groundwater store.

Wetlands are generally accepted as being the largest but least well quantified single source of methane (CH4). The extent of wetland or inundation is a key factor controlling methane emissions, both in nature and in the parameterisations used in large-scale land surface and climate models. Satellite-derived datasets of wetland extent are available on the global scale, but the resolution is rather coarse (>25 km). The purpose of the present study is to assess the capability of active microwave sensors to derive inundation dynamics for use in land surface and climate models of the boreal and tundra environments. The focus is on synthetic aperture radar (SAR) operating in C-band since, among microwave systems, it has comparably high spatial resolution and data availability, and long-term continuity is expected. C-band data from ENVISAT ASAR (Advanced SAR) operating in wide swath mode (150 m resolution) were investigated and an automated detection procedure for deriving open water fraction has been developed. More than 4000 samples (single acquisitions tiled onto 0.5° grid cells) have been analysed for July and August in 2007 and 2008 for a study region in Western Siberia. Simple classification algorithms were applied and found to be robust when the water surface was smooth. Modification of input parameters results in differences below 1 % open water fraction. The major issue to address was the frequent occurrence of waves due to wind and precipitation, which reduces the separability of the water class from other land cover classes. Statistical measures of the backscatter distribution were applied in order to retrieve suitable classification data. The Pearson correlation between each sample dataset and a location specific representation of the bimodal distribution was used. On average only 40 % of acquisitions allow a separation of the open water class. Although satellite data are available every 2–3 days over the Western Siberian study region, the irregular acquisition intervals and periods of unsuitable weather suggest that an update interval of 10 days is more realistic for this domain. SAR data availability is currently limited. Future satellite missions, however, which aim for operational services (such as Sentinel-1 with its C-band SAR instrument), may provide the basis for inundation monitoring for land surface and climate modelling applications.

A parameterisation to incorporate the effects of frozen soil on modelled hydrology is described and implemented within a land surface model, the Joint UK Land Surface Environment Simulator. It is shown to generally improve the modelled flow of Siberian rivers compared to observations, specifically in seasons of freezing or thawing soil. Most noticeably, the revised model increases the snowmelt flow peak by 26–100% compared to the control model thereby better matching observed flows. The model physics resulting in the changes to river flow are discussed and attention is given to the effect of inaccuracies in snowfall driving data which can hinder the comparison of new model processes.

Land surface models (LSMs) represent terrestrial hydrology in weather and climate modelling operational systems and research studies. Using river flow observations from gauge stations, we study the capability of the Joint UK Land Environment Simulator (JULES) LSM to simulate river flow over 13 catchments in Great Britain, each representing different climatic and topographic characteristics at the 1 km2 spatial resolution. A series of tests, carried out to identify where the model results are sensitive to the scheme and parameters chosen for runoff production, suggests that different catchments require different parameters and even different runoff schemes to produce the best results. From these results, we introduce a new topographical parametrization that produces the best daily river flow results (in terms of Nash-Sutcliffe efficiency and mean bias) for all 13 catchments. The new parametrization introduces a dependency on terrain slope, constraining surface runoff production to wet soil conditions over flatter regions (like the Thames catchment; Nash-Sutcliffe efficiency above 0.8), whereas over steeper regions the model produces surface runoff for every rainfall event regardless of the soil wetness state. This new parametrization improves the model capability in regional (Great Britain wide) assessments. The new choice of parameters is reinforced by examining the amplitude and phase of the modelled versus observed river flows, via cross-spectral analysis for time scales longer than daily.

It is hypothesized that more accurate prediction and warning of natural hazards, such as of the impacts of severe weather mediated through various components of the environment, require a more integrated Earth System approach to forecasting. This hypothesis can be explored using regional coupled prediction systems, in which the known interactions and feedbacks between different physical and biogeochemical components of the environment across sky, sea and land can be simulated. Such systems are becoming increasingly common research tools. This paper describes the development of the UKC2 regional coupled research system, which has been delivered under the UK Environmental Prediction Prototype project. This provides the first implementation of an atmosphere-land-ocean-wave modelling system focussed on the United Kingdom and surrounding seas at km-scale resolution. The UKC2 coupled system incorporates models of the atmosphere (Met Office Unified Model), land surface with river routing (JULES), shelf-sea ocean (NEMO) and ocean waves (WAVEWATCH III). These components are coupled, via OASIS3-MCT libraries, at unprecedentedly high resolution across the UK within a north-western European regional domain. A research framework has been established to explore the representation of feedback processes in coupled and uncoupled modes, providing a new research tool for UK environmental science. This paper documents the technical design and implementation of UKC2, along with the associated evaluation framework. An analysis of new results comparing the output of the coupled UKC2 system with relevant forced control simulations for six contrasting case studies of 5-day duration is presented. Results demonstrate that performance can be achieved with the UKC2 system that is at least comparable to its component control simulations. For some cases, improvements in air temperature, sea surface temperature, wind speed, significant wave height and mean wave period highlight the potential benefits of coupling between environmental model components. Results also illustrate that the coupling itself is not sufficient to address all known model issues. Priorities for future development of the UK Environmental Prediction framework and component systems are discussed. Fil: Lewis, Huw W.. Exeter Met Office; Reino Unido Fil: Manuel Castillo Sanchez, Juan. Exeter Met Office; Reino Unido Fil: Graham, Jennifer. Exeter Met Office; Reino Unido Fil: Saulter, Andrew. Exeter Met Office; Reino Unido Fil: Bornemann, Jorge. Exeter Met Office; Reino Unido Fil: Arnold, Alex. Exeter Met Office; Reino Unido Fil: Fallmann, Joachim. Exeter Met Office; Reino Unido Fil: Harris, Chris. Exeter Met Office; Reino Unido Fil: Pearson, David. Exeter Met Office; Reino Unido Fil: Ramsdale, Steven. Exeter Met Office; Reino Unido Fil: Martínez De La Torre, Alberto. Centre For Ecology & Hydrology; Reino Unido Fil: Bricheno, Lucy. National Oceanography Centre; Reino Unido Fil: Blyth, Eleanor. Centre For Ecology & Hydrology; Reino Unido Fil: Bell, Victoria A.. Centre For Ecology & Hydrology; Reino Unido Fil: Davies, Helen. Centre For Ecology & Hydrology; Reino Unido Fil: Marthews, Toby R.. Centre For Ecology & Hydrology; Reino Unido Fil: O'Neill, Clare. Exeter Met Office; Reino Unido Fil: Rumbold, Heather. Exeter Met Office; Reino Unido Fil: O'Dea, Enda. Exeter Met Office; Reino Unido Fil: Brereton, Ashley. National Oceanography Centre; Reino Unido Fil: Guihou, Karen. National Oceanography Centre; Reino Unido. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Hines, Adrian. Exeter Met Office; Reino Unido Fil: Butenschon, Momme. Plymouth Marine Laboratory; Reino Unido Fil: Dadson, Simon J.. University of Oxford; Reino Unido Fil: Palmer, Tamzin. Exeter Met Office; Reino Unido Fil: Holt, Jason. National Oceanography Centre; Reino Unido Fil: Reynard, Nick. Centre For Ecology & Hydrology; Reino Unido Fil: Best, Martin. Exeter Met Office; Reino Unido Fil: Edwards, John. Exeter Met Office; Reino Unido Fil: Siddorn, John. Exeter Met Office; Reino Unido