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Global hydrological reanalyses are modelled datasets providing information on river discharge evolution everywhere in the world. With multi‐decadal daily timeseries, they provide long‐term context to identify extreme hydrological events such as floods and droughts. By covering the majority of the world's land masses, they can fill the many gaps in river discharge in‐situ observational data, especially in the global South. These gaps impede knowledge of both hydrological status and future evolution and hamper the development of reliable early warning systems for hydrological‐related disaster reduction. River discharge is a natural integrator of the water cycle over land. Global hydrological reanalysis datasets offer an understanding of its spatio‐temporal variability and are therefore critical for addressing the water–energy–food–environment nexus. This paper describes how global hydrological reanalyses can fill the lack of ground measurements by using earth system or hydrological models to provide river discharge time series. Following an inventory of alternative sources of river discharge datasets, reviewing their advantages and limitations, the paper introduces the Copernicus Emergency Management Service (CEMS) Global Flood Awareness System (GloFAS) modelling chain and its reanalysis dataset as an example of a global hydrological reanalysis dataset. It then reviews examples of downstream applications for global hydrological reanalyses, including monitoring of land water resources and ocean dynamics, understanding large‐scale hydrological extreme fluctuations, early warning systems, earth system model diagnostics and the calibration and training of models, with examples from three Copernicus Services (Emergency Management, Marine and Climate Change). Global hydrological reanalyses are powerful datasets that can fill the observational gap in river discharge observation. They make wide ranging downstream applications possible worldwide, from water resources to ocean monitoring and early warning systems, through earth system model diagnostic, hydrological extreme understanding and model calibration and training. The GloFAS hydrological reanalysis dataset is a product of the Copernicus Emergency Management Service freely available from the Copernicus Climate Data store, offering daily time series from early 1980 until recent, updated daily with a 3‐ to 5‐day delay.

The land-surface developments of the European Centre for Medium-range Weather Forecasts (ECMWF) are based on the Carbon-Hydrology Tiled Scheme for Surface Exchanges over Land (CHTESSEL) and form an integral part of the Integrated Forecasting System (IFS), supporting a wide range of global weather, climate and environmental applications. In order to structure, coordinate and focus future developments and benefit from international collaboration in new areas, a flexible system named ECLand, which would facilitate modular extensions to support numerical weather prediction (NWP) and society-relevant operational services, for example, Copernicus, is presented. This paper introduces recent examples of novel ECLand developments on (i) vegetation; (ii) snow; (iii) soil; (iv) open water/lake; (v) river/inundation; and (vi) urban areas. The developments are evaluated separately with long-range, atmosphere-forced surface offline simulations and coupled land-atmosphere-ocean experiments. This illustrates the benchmark criteria for assessing both process fidelity with regards to land surface fluxes and reservoirs of the water-energy-carbon exchange on the one hand, and on the other hand the requirements of ECMWF’s NWP, climate and atmospheric composition monitoring services using an Earth system assimilation and prediction framework.

Streamflow forecasts provide vital information to aid emergency response preparedness and disaster risk reduction. Medium-range forecasts are created by forcing a hydrological model with output from numerical weather prediction systems. Uncertainties are unavoidably introduced throughout the system and can reduce the skill of the streamflow forecasts. Post-processing is a method used to quantify and reduce the overall uncertainties in order to improve the usefulness of the forecasts. The post-processing method that is used within the operational European Flood Awareness System is based on the model conditional processor and the ensemble model output statistics method. Using 2 years of reforecasts with daily timesteps, this method is evaluated for 522 stations across Europe. Post-processing was found to increase the skill of the forecasts at the majority of stations in terms of both the accuracy of the forecast median and the reliability of the forecast probability distribution. This improvement is seen at all lead times (up to 15 d) but is largest at short lead times. The greatest improvement was seen in low-lying, large catchments with long response times, whereas for catchments at high elevation and with very short response times the forecasts often failed to capture the magnitude of peak flows. Additionally, the quality and length of the observational time series used in the offline calibration of the method were found to be important. This evaluation of the post-processing method, and specifically the new information provided on characteristics that affect the performance of the method, will aid end users in making more informed decisions. It also highlights the potential issues that may be encountered when developing new post-processing methods.

Climate change has resulted in more frequent occurrences of extreme events, such as flooding and heavy snowfall, which can have a significant impact on densely populated or industrialised areas. Numerical models are used to simulate and predict these extreme events, enabling informed decision-making and planning to minimise human casualties and to protect costly infrastructure. LISFLOOD is an integrated hydrological model underpinning the European Flood Awareness System and Global Flood Awareness System (EFAS and GloFAS, respectively), developed by the Copernicus Emergency Management Service (CEMS). The CEMS_SurfaceFields_2022 dataset is a new set of high-resolution surface fields at 1 and 3 arcmin resolution (approximately 2 and 6 km at the Equator, respectively) based on a wide variety of high-resolution and up-to-date data sources. The 1 arcmin fields cover Europe, while the surface fields at 3 arcmin cover the global land surface (excluding Antarctica). The dataset encompasses (i) catchment morphology and river networks, (ii) land use, (iii) vegetation cover type and properties, (iv) soil properties, (v) lake information, and (vi) water demand. This paper details the complete workflow used to generate the CEMS_SurfaceFields_2022 fields, including the data sources and methodology. Whilst created together with upgrades to the open source LISFLOOD code, the CEMS_SurfaceFields_2022 fields can be used independently for a wide range of applications, including as input to hydrological, Earth system, or environmental models or for carrying out general analyses across spatial scales, ranging from global and regional levels to local levels (especially useful for regions outside Europe), expected to improve the accuracy, detail and realism of applications.

In practice, several mechanisms are employed to facilitate the required collaboration including webinars, online feedback forms, training sessions, user surveys, workshops, working groups, and the EFAS annual meetings. [...]hydrographs created from the raw (or non-postprocessed) hydrological model output must be considered carefully. [...]we discuss the lessons learnt regarding stakeholder engagement in a “post-pandemic” world. The usability of forecasts is dependent on the relevance of the forecast information content, the communication channel, the forecast visualization (also known as the forecast product), the quality of the forecast, and the expertise of the user (Vincent et al. 2020; WMO 2022). [...]the workshop was organized around these five topics (Table 1). Results of the latest forecast evaluations were presented, showing that the postprocessed forecasts could predict the exceedance probability of the flood thresholds more reliably (i.e., the forecast probabilities more accurately represented the exceedances of the thresholds) than the raw ensemble forecasts (Matthews et al. 2022), and that the update to 6-hourly time steps increased the skill of the forecast probability distribution at shorter lead times.

Since 1950, anthropogenic activities have altered the climate, land cover, soil properties, channel morphologies, and water management in the river basins of Europe. This has resulted in significant changes in hydrological conditions. The availability of consistent estimates of river flow at the global and continental levels is a necessity for assessing changes in the hydrological cycle. To overcome limitations posed by observations (incomplete records, inhomogeneous spatial coverage), we simulate river discharge for Europe for the period 1951–2020 using a state-of-the-art hydrological modelling approach. We use the new European set-up of the OS LISFLOOD model, running at 1 arcmin (≈1.8 km) with 6-hourly time steps. The hydrological model is forced by climate reanalysis data (ERA5-Land) that are bias-corrected and downscaled to the model resolution with gridded weather observations. The model also incorporates 72 surface field maps representing catchment morphology, vegetation, soil properties, land use, water demand, lakes, and reservoirs. Inputs related to human activities are evolving through time to emulate societal changes. The resulting Hydrological European ReAnalysis (HERA) provides 6-hourly river discharge for 282 521 river pixels with an upstream area >100 km2. We assess its skill using 2448 river gauging stations distributed across Europe. Overall, HERA delivers satisfying results (median KGE′=0.55), despite a general underestimation of observed mean discharges (mean bias=-13.1 %), and demonstrates a capacity to reproduce statistics of observed extreme flows. The performance of HERA increases through time and with catchment size, and it varies in space depending on reservoir influence and model calibration. The fine spatial and temporal resolution results in an enhanced performance compared to previous hydrological reanalysis based on OS LISFLOOD for small- to medium-scale catchments (100–10 000 km2). HERA is the first publicly available long-term, high-resolution hydrological reanalysis for Europe. Despite its limitations, HERA enables the analysis of hydrological dynamics related to extremes, human influences, and climate change at a continental scale while maintaining local relevance. It also creates the opportunity to study these dynamics in ungauged catchments across Europe. The HERA hydrological reanalysis and its climate and dynamic socio-economic inputs are available via the JRC data catalogue: https://doi.org/10.2905/a605a675-9444-4017-8b34-d66be5b18c95 (Tilloy et al., 2024).

IntroductionAmyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder causing progressive motor neuron death in cortex, brainstem and spinal cord. The most common genetic cause is the G4C2 hexanucleotide repeat expansion in the non-coding region of exon 1 of C9ORF72, accounting for ~40% of familial and ~7% of sporadic ALS. RNA dysregulation is increasingly recognized as a key contributor to ALS pathogenesis. This study aimed to identify specific microRNAs (miRNAs) involved in motor neuron degeneration in C9ORF72-ALS.MethodsWe profiled 754 miRNAs in human post-mortem spinal cord tissue from C9ORF72-ALS patients and healthy donors. Laser capture microdissection isolated ventral horn regions, and in silico target prediction identified potential genes and pathways regulated by differentially expressed miRNAs. Target genes were validated by Real time PCR.ResultsTwo subsets of miRNAs were exclusively expressed in ventral horn regions: miR-200b-3p and miR-346 in C9ORF72-ALS patients, and miR-30d-5p, miR-106b-5p and miR-135a-5p in healthy donors. Target prediction and molecular analysis identified putative genes and pathways linked to cell death, inflammation, protein metabolism, DNA modification, excitotoxicity, autophagy and vesicles trafficking.DiscussionThis study identifies specific miRNAs and their target genes as key molecules in motor neuron degeneration in C9ORF72-ALS. Restoring their expression could represent a therapeutic approach for ALS.