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Environment and Climate Change Canada (ECCC) is in the process of deploying a continental-scale hydrological prediction system known as the National Surface and River Prediction System (NSRPS). Operating at a resolution of 30 arc seconds, NSRPS currently generates Ensemble Streamflow Predictions (ESPs) for over four million grid points. Issued once per day, the ensemble is composed of twenty members and provides 16-day forecasts. NSRPS differs from other hydrological forecasting systems in operational use in Canada by being continental in scope and by relying on an Earth System Modelling (ESM) approach for prediction. In order to assess the value of forecasts issued by NSRPS, a comparison is performed with a similar ESP system available over all of Canada: the Global Flood Awareness System (GloFAS) from the European Centre for Medium-Range Weather Forecasts (ECMWF). The evaluation focusses on the Great Lakes and St. Lawrence watershed as well as the Nelson and Churchill watersheds, each over one million km² in size. 393 streamflow stations are identified where NSRPS and GloFAS agree on the watershed delineation. The comparison is limited to the Spring, Summer and Fall of 2022 due to NSRPS forecast data availability. For most stations, NSRPS performs better than GloFAS in terms of Continuous Ranked Probability Score (CRPS), but the median of the potential CRPS across the 393 stations is very similar for days 3-16. Both systems suffer from a lack of spread, particularly for short lead times, but the problem is slightly more acute for GloFAS. Bayesian Model Averaging (BMA) is explored in order to obtain calibrated probabilistic forecasts that perform better than both NSRPS and GloFAS.

The Global Flood Awareness System (GloFAS, https://www.globalfloods.eu/) is a freely available flood forecasting service that is running fully operational as part of the Copernicus Emergency Management Service since April 2018. GloFAS offers a number of products, which are tailored to give an overview of the current and future hydro-meteorological situation. The GloFAS dataset includes medium-range and seasonal discharge forecasts, as well as storages (e.g. soil moisture, snow cover, lakes volumes) and main fluxes (e.g. surface and sub-surface runoff, actual evapotranspiration).The GloFAS dataset is generated using the open source hydrological model OS LISFLOOD (https://ec-jrc.github.io/lisflood/). OS LISFLOOD is a distributed, physically based rainfall-runoff model, which has been designed for the modelling of rainfall-runoff processes in large and transnational catchments for a variety of applications including flood simulation and forecasting; water resources assessment (drought forecast); analysis of the impacts of land use changes, river regulation measures, and other water management plans; or climate change analysis. The recent high-resolution global implementation of OS LISFLOOD allowed the delivery of the newest GloFAS set-up, namely GloFAS v4.0 which is foreseen to become operational in Q2 2023. This latest set-up has a 0.05 degrees resolution (~5km), 4 times higher than the previous version. Moreover, a crucial feature of the high-resolution implementation is the use of the latest research findings and remote sensing datasets to prepare the set of high-resolution input maps for the hydrological model. These maps allow to account more accurately for the morphological, physical, and land use characteristics of the catchments and thus enable an improved representation of the rainfall-runoff processes in different climates and socio-economic contexts at global scale.This presentation provides an overview (i) of the GloFAS v4.0 OS-LISFLOOD high-resolution implementation, (ii) of the model calibration incorporating almost 2000 gauging stations and a pragmatic regionalization approach, and (iii) of the technological solutions adopted to limit the computational time of global high-resolution simulations.OS-LISFLOOD, the high-resolution implementation maps, and GloFAS v4.0 are publicly available and they disclose opportunities for further analysis of the terrestrial water cycle fluxes and storages, and of the current and future state of global water resources.

Historically, Switzerland and the nearby alpine countries have not been associated with major droughts. However, in recent years, the European Alpine space has experienced several unprecedented low-flow conditions and drought events. As many economic sectors in the region depend heavily on sufficient water availability, such as hydropower production, navigation and transportation, agriculture, and tourism, it is important for decision-makers to have early warnings of drought tailored to their needs and geographical conditions.The European Flood Awareness System (EFAS) has been in operation since 2012 providing flood risk overviews for Europe up to 15 days in advance. More recently, it has also run long-range hydrological outlooks for sub-seasonal to seasonal horizons. While EFAS early flood warnings have been extensively evaluated in the past years, less attention has been paid to evaluating the system’s ability to detect upcoming drought conditions. In this study, we turn our focus to this other extreme of the spectrum and on EFAS’ predictability of drought events in large Alpine catchments. Our goal is to investigate how hydrological patterns of skill at a large spatial scale can be combined with local model outputs to more accurately inform decision makers on droughts and their spatio-temporal evolution.For this, we evaluate the performance of EFAS comparatively to that of a local model in terms of the ability to simulate drought conditions. The Precipitation-Runoff-Evapotranspiration HRU (PREVAH) local model was set up for 59 stations in Switzerland. The PREVAH model is a distributed conceptual hydrological model that accounts for processes such as evapotranspiration, interception, snow- and ice-melt, soil moisture storage, groundwater storage, and runoff generation. We analyse 25 overlapping stations between the local model and the EFAS reporting stations (river network points where EFAS outputs are available to users), and compare the drought simulation performances of the two models. We focus on evaluating the duration, deficit, and magnitude of the drought events, as well as metrics including Nash–Sutcliffe model efficiency coefficient (NSE) and Kling-Gupta efficiency (KGE).The outcome of this study will lay a foundation for how a large-scale hydrological model like EFAS can complement a local model like PREVAH to improve the predictability of sub-seasonal drought forecasting and provide more reliable early warnings for better water resources management.

The unprecedented progress in ensemble hydro-meteorological modelling and forecasting on a range of temporal and spatial scales, raises a variety of new challenges which formed the theme of the Joint Virtual Workshop,‘Connecting global to local hydrological modelling and forecasting: challenges and scientific advances’. Held from 29 June to 1 July 2021, this workshop was co-organised by the European Centre for Medium-Range Weather Forecasts (ECMWF), the Copernicus Emergency Management (CEMS) and Climate Change (C3S) Services, the Hydrological Ensemble Prediction EXperiment (HEPEX), and the Global Flood Partnership (GFP). This article aims to summarise the state-of-the-art presented at the workshop and provide an early career perspective. Recent advances in hydrological modelling and forecasting,reflections on the use of forecasts for decision-making across scales, and meansto minimise new barriers to communication in the virtual format are also dis-cussed. Thematic foci of the workshop included hydrological model develop-ment and skill assessment, uncertainty communication, forecasts for early action, co-production of services and incorporation of local knowledge, Earth observation, and data assimilation. Connecting hydrological services to societalneeds and local decision-making through effective communication, capacity-building and co-production was identified as critical. Multidisciplinary collaborations emerged as crucial to effectively bring newly developed tools to practice.

Operational global-scale hydrological forecasting systems are widely used to help manage hydrological extremes such as floods and droughts. The vast amounts of raw data that underpin forecast systems and the ability to generate information on forecast skill have, until now, not been publicly available. As part of the Global Flood Awareness System (GloFAS; https://www.globalfloods.eu/) service evolution, in this paper daily ensemble river discharge reforecasts and real-time forecast datasets are made free and openly available through the Copernicus Climate Change Service (C3S) Climate Data Store (CDS). They include real-time forecast data starting on 1 January 2020 updated operationally every day and a 20-year set of reforecasts and associated metadata, available through the dedicated GloFAS FTP service. This paper describes the model components and configuration used to generate the real-time river discharge forecasts and the reforecasts. An evaluation of ensemble forecast skill using the Continuous Ranked Probability Skill Score (CRPSS) was also undertaken for river points around the globe. Results show that GloFAS is skilful in over 93thinsp;% of catchments in the short- (1- to 3-days) and medium-range (5- to 15-days) against a persistence benchmark forecast, and skilful in over 80 % of catchments out to the extended-range (16- to 30-days) against a climatological benchmark forecast. However, the strength of skill varies considerably by location with GloFAS found to have no or negative skill at longer lead times in broad hydroclimatic regions in tropical Africa, western coast of South America, and catchments dominated by snow and ice in high northern latitudes. Forecast skill is summarised as a new headline skill score to be added as a layer on the GloFAS forecast Web Map Viewer at the next GloFAS cycle release, expected Autumn 2020, to aid user’s interpretation and understanding of forecast quality.

Precipitation is a key component of the global water cycle and plays a crucial role in flooding, droughts, and water supply. One way to manage its socioeconomic effects is based on precipitation forecasts from numerical weather prediction (NWP) models, and an important step to improve precipitation forecasts is by diagnosing NWP biases. In this study, we investigate the biases in precipitation forecasts from the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS). Using the IFS control forecast from 12 June 2019 to 11 June 2020 at 5219 stations globally, we show that in each of the boreal winter and summer half years, the IFS 1) has an average global wet bias and 2) displays similar bias patterns for forecasts starting at 0000 and 1200 UTC and across forecast days 1–5. These biases are dependent on observed (climatological) precipitation; stations with low observed precipitation have an IFS wet bias, while stations with high observed precipitation have an IFS dry bias. Southeast Asia has a wet bias of 1.61 mm day−1 (in boreal summer) and over the study period the precipitation is overestimated by 31.0% on forecast day 3. This is the hydrological signature of several hypothesized processes including issues specifying the IFS snowpack over the Tibetan Plateau, which may affect the mei-yu front. These biases have implications for IFS land–atmosphere feedbacks, river discharge, and for ocean circulation in the Southeast Asia region. Reducing these biases could lead to more accurate forecasts of the global water cycle.

Ensemble streamflow prediction (ESP) is a well-established and widely used approach to hydrological forecasting, the application of which requires a hydrological model that can contribute to forecast skill by providing: (i) accurate initial hydrological conditions; and (ii) accurate transformation of climate to river flow signals. It is widely known that there exists a relationship between ESP skill and the hydrological regime of a catchment, and several studies have correlated forecast quality with sets of catchment descriptors. The choice of hydrological model is therefore significant. Whilst a parsimonious structure may be preferable for efficiency, potential skill could be lost if the model’s simplicity means it cannot adequately reproduce key hydrological processes in the catchment. This work seeks to examine the contribution of hydrological model complexity to forecast skill. Using a parsimonious model as a reference, we investigate if additional model complexity adds forecast skill at different lead times and initialisation months through the use of models with different structures and parametric complexity. Forecast skill is evaluated within a hindcast experiment for a selection of Irish river catchments using the continuous ranked probability skill score. Results are presented for our reference model, GR4J (Génie Rural à 4 paramètres Journalier), and our complex model, SMART (Soil Moisture Accounting and Routing for Transport). The performance of each model is viewed in the context of its ability to reproduce key hydrological signatures known to control forecast quality in Ireland (e.g., baseflow index).

The frequency and magnitude of flood events in Kenya have increased over the past decade. Observations show a shift in timing and variability in flood occurrences in most parts of the country. Trend analysis is useful in detecting and supporting the evidence of change in flow series, as well as variability in flood timing. In this study, the frequency and magnitude of floods observed in the annual maximum flood (AMAX) and peak over threshold (POT) flood series from 1981 to 2016 are compared in 19 Kenyan catchments. Flood peaks are identified using a threshold technique from Kenyan daily discharge data, and notable patterns in the AMAX series are compared to those in the POT series, which is created for three distinct exceedance criteria. The timing and variability of the annual floods is determined from the AMAX flow. Our findings show that, the AMAX series detects more trends in flood magnitude than the POT series, while the POT series detects more significant trends in flood frequency than flood magnitude. Sensitivity of trends to different exceedance thresholds selection reveal variable trend patterns across the stations. The timing of inter-annual floods occurs in peak rainfall months of April, May and November and shows a higher variability index in most of the coastal and western stations, and a low variability in stations whose annual floods occur in dry months of June, July, and August. This information is useful to hydrological applications such as flood protection facility design, risk assessment, and risk management for improved livelihoods in Kenya

This study aims to evaluate the comparative suitability of a global hydrological forecasting and monitoring system, the Copernicus-Emergency Management Service - Global Flood Awareness System (GloFAS), and a local catchment-based model (GR4J) as possible alternative or complementary flood forecasting tools in Uganda. Local stakeholders and end-users in Uganda need to understand whether flood forecasts from ready-to-use global systems can be relied on as one of the available tools to inform flood preparedness actions or whether other easy-to-set-up local hydrological models can provide more reliable information at the catchment scale or some advantages in particular regions. While GloFAS provides probabilistic extended-range forecasts, it has only been calibrated at a few locations in Africa and remains uncalibrated at most locations in Uganda and eastern Africa. A simpler catchment-based model can be calibrated more easily by local national authorities using observed hydrological data. This research investigates whether the reanalysis data from GloFAS can perform satisfactorily in Uganda with respect to the simulation of a lumped catchment-based model (GR4J) using the same meteorological inputs across Uganda.Results are presented for four Ugandan catchments with different morphological and hydrological characteristics. An evaluation of both GloFAS reanalysis (GloFAS-ERA5) and extended-range (re-)forecasts has been carried out against observed streamflow data, analysing performance statistics including the Kling-Gupta Efficiency (KGE) for the reanalysis, and the False Alarm Ratio and Probability of Detection for forecasts at short lead times (< 15 days). The GR4J model simulations were run using the ERA5 meteorological reanalysis as input. In both calibration and validation mode, on average, the calibrated GR4J model provides better KGE scores than GloFAS, especially for the smaller catchments (< 2000 km2). However, GloFAS performance is relatively good for the two largest basins (>2200 km2) and is acceptable with respect to a mean flow benchmark for all catchments, except the smallest (500 km2). Our results suggest that in small- to medium-size basins in Uganda, a simple lumped catchment-based model may outperform GloFAS, but even without calibration GloFAS performs satisfactorily in larger basins. Thus, GloFAS can be relied on as interim solution for flood forecasting in Uganda, especially for larger river catchments. An evaluation of the accuracy of the rainfall reanalysis (ERA5) with respect to local rainfall observations showed significant differences in biases and correlation of rainfall input data across catchments and this can explain the different performance of the hydrological models across Uganda. Finally, the importance of assessing and calibrating flood forecasting models with action-relevant scores to support humanitarian actions is highlighted by analysing the discrepancies between traditional general scores (as the KGE) with other more specific flood event-based scores.

In recent years, the Amazon forest has experienced several major droughts (2010, 2015/16) and floods (2012, 2014, 2021). Extreme events represent a threat to the Amazons important functions, but these perturbations also provide valuable insights into the underlying mechanisms. Here we studied the most recent massive drought and flood events in detail, and quantified their severity and spatiotemporal extent relative to a multi-year baseline.First, we describe the anomalous hydrological status of these events, by bringing together a large variety of data sets, including in-situ observations and reanalysis products for precipitation, discharge, vapor pressure deficit and soil moisture. During the strong El Niño conditions following the dry season of 2015, the precipitation fell below its climatological values. This was soon reflected in low discharge rates and soil moisture levels, persisting far into the year 2016 for some regions. In contrast, we find anomalously high precipitation over the northern Amazon during the first months of 2021, resulting in high discharge rates, and rising river levels that have led to massive floods in downstream regions.Finally, we quantified the impact of the 2015/16 drought on vegetation using the inverse model CarbonTacker South America (CT-SAM) and remote sensing proxies for photosynthesis. To address the uncertainty in prior emission estimates, we have used a range of different biosphere models (SiBCASA, SiB4), including a biosphere model linked to a detailed hydrological model (PCR-GLOBWB). For the fire flux we used multiple data sets (GFAS, SiBCASA-GFED4), including a modified version based on CO inversions performed with the TM5-4DVAR system. We find that photosynthesis was reduced during the 2015 drought, especially in the drier, southern part of the Amazon. This was followed by a recovery in the first months of 2016, but during the subsequent dry season a secondary impact on photosynthesis was found. The inversely derived net CO2 fluxes do not have the same high resolution as the satellite products, but when assessed over larger scales, a consistent drought signal is derived.

Seasonal precipitation forecasting is highly challenging for the northwest fringes of Europe due to complex dynamical drivers. Hybrid dynamical-statistical approaches offer potential to improve forecast skill. Here, hindcasts of mean sea level pressure (MSLP) from two dynamical systems (GloSea5 and SEAS5) are used to derive two distinct sets of indices for forecasting winter (DJF) and summer (JJA) precipitation over lead-times of 1-4 months. These indices provide predictors of seasonal precipitation via a multiple linear regression model (MLR) and an artificial neural network (ANN) applied to four Irish rainfall regions and the Island of Ireland. Forecast skill for each model, lead time, and region was evaluated using the correlation coefficient (

Knowledge of the key drivers of the severity of river flooding from tropical cyclones (TCs) is vital for emergency preparedness and disaster risk reduction activities. This global study examines landfalling TCs in the decade from 2010 to 2019, to identify those characteristics that influence whether a storm has an increased flood hazard. The highest positive correlations are found between flood severity and the total precipitation associated with the TC. Significant negative correlations are found between flood severity and the translation speed of the TC, indicating that slower moving storms, that rain over an area for longer, tend to have higher flood severity. Larger and more intense TCs increase the likelihood of having a larger area affected by severe flooding but not its duration or magnitude, and it is found that the fluvial flood hazard can be severe in all intensity categories of TC, including those of tropical storm strength. Catchment characteristics such as antecedent soil moisture and slope also play a role in modulating flood severity, and severe flooding is more likely in cases where multiple drivers are present. The improved knowledge of the key drivers of fluvial flooding in TCs can help to inform research priorities to help with flood early warning, such as increasing the focus on translation speed in model evaluation and impact-based forecasting.

Framed within the Copernicus Climate Change Service (C3S) of the European Commission, the European Centre for Medium-Range Weather Forecasts (ECMWF) is producing an enhanced global dataset for the land component of the fifth generation of European ReAnalysis (ERA5), hereafter referred to as ERA5-Land. Once completed, the period covered will span from 1950 to the present, with continuous updates to support land monitoring applications. ERA5-Land describes the evolution of the water and energy cycles over land in a consistent manner over the production period, which, among others, could be used to analyse trends and anomalies. This is achieved through global high-resolution numerical integrations of the ECMWF land surface model driven by the downscaled meteorological forcing from the ERA5 climate reanalysis, including an elevation correction for the thermodynamic near-surface state. ERA5-Land shares with ERA5 most of the parameterizations that guarantees the use of the state-of-the-art land surface modelling applied to numerical weather prediction (NWP) models. A main advantage of ERA5-Land compared to ERA5 and the older ERA-Interim is the horizontal resolution, which is enhanced globally to 9 km compared to 31 km (ERA5) or 80 km (ERA-Interim), whereas the temporal resolution is hourly as in ERA5. Evaluation against independent in situ observations and global model or satellite-based reference datasets shows the added value of ERA5-Land in the description of the hydrological cycle, in particular with enhanced soil moisture and lake description, and an overall better agreement of river discharge estimations with available observations. However, ERA5-Land snow depth fields present a mixed performance when compared to those of ERA5, depending on geographical location and altitude. The description of the energy cycle shows comparable results with ERA5. Nevertheless, ERA5-Land reduces the global averaged root mean square error of the skin temperature, taking as reference MODIS data, mainly due to the contribution of coastal points where spatial resolution is important. Since January 2020, the ERA5-Land period available has extended from January 1981 to the near present, with a 2- to 3-month delay with respect to real time. The segment prior to 1981 is in production, aiming for a release of the whole dataset in summer/autumn 2021. The high spatial and temporal resolution of ERA5-Land, its extended period, and the consistency of the fields produced makes it a valuable dataset to support hydrological studies, to initialize NWP and climate models, and to support diverse applications dealing with water resource, land, and environmental management. The full ERA5-Land hourly (Muñoz-Sabater, 2019a) and monthly (Muñoz-Sabater, 2019b) averaged datasets presented in this paper are available through the C3S Climate Data Store at https://doi.org/10.24381/cds.e2161bac and https://doi.org/10.24381/cds.68d2bb30, respectively.

As part of the phase 2 of the CMEMS Service Evolution, the BRONCO project (Benefits of dynamically modelled River discharge input for OceaN and COupled atmosphere-land-ocean systems) led to the creation of a new river discharge dataset that can be used as input for the NEMO ocean model. River runoffs into the ocean are taken from a global river discharge reanalysis dataset produced by the CEMS Global Flood Awareness System (GloFAS) driven by ERA5 forcing and called GloFAS-ERA5. This new reanalysis dataset has been evaluated using the latest ECMWF ocean analysis system - Ocean5 - over the 1979-2017 period. Comparisons to ocean observations, showed improved ocean state in the Atlantic Ocean in areas affected by large rivers such as the Amazon, the Mississippi and the St Lawrence, but also in the Mediterranean and the Baltic seas. Positive impact on the representation of the Atlantic Merdional Overturning Circulation is also seen. However, degradation of the ocean state can be detected over the Maritime Continent and on the west coast of Central America and Alaska. Such degradation of the ocean state can be alleviated via a retuning of the GloFAS-ERA5 river runoffs. The need for retuning suggests the existence of biases in the GloFAS-ERA5 reanalysis. Further investigation allowed to attribute those biases to spurious signals in both precipitation and snowmelt in the ERA5 atmospheric reanalysis. This result suggests that, the ocean analysis system can help evaluate the water cycle over land in atmospheric reanalysis products through river-ocean coupling further showcasing the value of an Earth system approach to reanalysis.

Global ensemble forecast models have been shown to have good skill in forecasting the track probabilities of tropical cyclones worldwide, but less well-studied is their ability to predict the hazards resulting from tropical cyclones, which in the case of fluvial flooding can extend far from the landfall location traditionally focussed on in operational tropical cyclone warnings. This work aims to investigate the key factors that influence the predictability of fluvial flood severity from tropical cyclones, using forecasts from the Global Flood Awareness System (GloFAS). GloFAS is jointly developed by the European Commission and the European Centre for Medium-Range Weather Forecasts (ECMWF) and is designed to provide a global overview of upcoming flood events to decision makers as part of the Copernicus Emergency Management Service, producing probabilistic river discharge forecasts driven by global ECMWF ensemble forecasts coupled to a hydrological model. This presentation will explore the chain of uncertainty through the forecasting process for several recent tropical cyclone flood events including Hurricane Iota and Cyclone Nivar. It investigates the influence on the overall predictability and uncertainty of the fluvial flood forecasts of various components of the forecasting chain, including the track, intensity, and precipitation forecasts for the tropical cyclone, and the hydrological catchment conditions and modelling.

Riverine plastic pollution is of global concern due to its negative impact on ecosystem health and human livelihood. Recent studies show a strong link between river discharge and plastic transport, but the role of floods is still unresolved. We combined high resolution mismanaged plastic waste data and river flood extents with increasing return periods to estimate flood-driven plastic mobilisation, aggregated at administration unit, country and global scale. We show that 10-year return period floods already tenfold the global plastic mobilisation potential compared to non-flood conditions. In the worst affected regions, plastic mobilisation increases up to five orders of magnitude. Our results suggest a high inter-annual variability in plastic mobilisation, previously ignored by global plastic transport models. Flood defences reduce plastic mobilisation substantially, but regions vulnerable to flooding often coincide with high plastic mobilisation potential during floods. Consequentially, clean-up and mitigation measures and flood risk management are inherently interdependent and need to be managed holistically.

This study assesses the seasonal forecast skill of river flow persistence in 46 catchments representing a range of hydrogeological conditions across Ireland. Skill is evaluated against a climatology benchmark forecast and by examining correlations between predicted and observed flow anomalies. Forecasts perform best when initialized in drier summer months, 87% of which show greater skill relative to the benchmark at a 1-month horizon. Such skill declines as forecast horizon increases due to the longer time a catchment has to “forget” initial anomalous flow conditions and/or to be impacted by “new” events. Skill is related to physical catchment descriptors such as the baseflow index (correlation ρ = 0.86) and is greatest in permeable high-storage catchments. The distinct seasonal and spatial variations in persistence skill allow us to pinpoint when and where this method can provide a useful benchmark in the future development of more complex seasonal hydrological forecasting approaches in Ireland.

River freshwater input is crucial in modelling global ocean. Most ocean models used in CMEMS services rely on climatological river discharge data with various deficiencies, which can lead to biased simulated ocean states. The Copernicus Emergency Management Service (CEMS) Global Flood Awareness System (GloFAS) provides state-of-the-art global flood forecasts and downstream river discharge. A GloFAS-ERA5 global river discharge reanalysis dataset has been produced using the same system, modelled by routing runoff from ECMWF’s (European Centre for Medium- Range Weather Forecasts) atmospheric reanalysis ERA5 via a river network. As a global gridded data set that covers from 1979 to near-real-time, GloFAS-ERA5 reanalysis can provide an improved and standardized input of land freshwater input for global, regional and coastal ocean models. Evaluation results suggest that the overall performance of this new river discharge reanalysis is reasonably good in general when verified against a global network of 1801 discharge observation stations. A new method has been developed for conversion of the GloFAS-ERA5 reanalysis data into land freshwater input for the NEMO ocean model. This method has been tested with a climatology of GloFAS-ERA5 river discharge. Compared to the DRAKKAR climatology of land freshwater input (BT06, hereafter) used by most CMEMS services, this new data set has an increased global mean value of ~1.33 Sv, but with reduced seasonal variations. Assessment of this GloFAS-ERA5 land freshwater input has been carried out with the operational ECMWF ocean analysis system-OCEAN5, driven by the same ERA5 atmospheric forcing. Evaluation of simulated ocean state against in-situ observations show improvements in regions affected by Amazon freshwater input when use GloFAS-ERA5 instead of BT06, by reducing a negative sea surface salinity bias in these regions. However, negative impact from switching to GloFAS-ERA5 land freshwater input is also visible in several regions, e.g. in the Maritime Continent and west coast of central America, which is associated with a large positive bias in the GloFAS-ERA5 river discharge at these regions. This issue can be mitigated by applying bias-correction to the GloFAS-ERA5 land freshwater input, and by adding extra vertical mixing in several affected regions that are close to the river mouth. Assessments of module simulated ocean Essential Climate Variables (ECVs) have been carried out to quantify the benefit of this realistic freshwater time series input. Improvements in climate signals like the Atlantic Meridional Overturning transports is also recorded.

In this study we assess the seasonal hydrological forecast skill of river flow persistence across a sample of 46 catchments representative of Ireland’s diverse range of hydrogeological conditions. This statistical approach is straightforward to implement as it uses a river’s most recently observed flow anomaly (calculated over a predictor period of a given duration) as its forecasted flow anomaly (for a given horizon). In our hindcast experiment, persistence skill is evaluated against a streamflow climatology benchmark and by assessing the correlations between predicted and observed anomalies. Using the most skilful predictor period of 1-week, we find that the majority of persistence forecasts outperform the benchmark between April and September at the 1-month forecast horizon. However, this narrows to solely the summer months when using 2- and 3-month horizons. Skill declines with increasing durations of the predictor and/ or forecast horizon period as a catchment is given more time to “forget” initial anomalous streamflow conditions and/or to be impacted by “new” anomalies. High rainfall events, for example, tend to disrupt the persistence of flows and greater forecast skill is thus found in the relatively drier months.The degree of persistence skill is also strongly conditional on the “memory” inherent to each catchment (i.e. their storage capacity), as indicated by physical catchment descriptors such as the Base Flow Index (correlation ρ with skill = 0.86). Persistence skill is greatest in lowland regions characterised by permeable lithologies, well drained soils and lower annual average rainfall totals. Physical descriptors can thus be used to anticipate the likely performance of river flow persistence as a forecasting tool in rivers outside the catchment sample. Through multiple linear regression analysis, we identified the combination of predictors that produced the best-performing model (adjusted R2= 0.89) and used it to predict the persistence forecast skill level expected in 215 catchments across the country at different horizons and seasons. Highlighting exactly when and where persistence provides higher predictive skill than the reference climatology forecast, we show the value of statistical flow persistence methods as a tougher-to-beat benchmark in the development of more sophisticated seasonal river flow forecasting methods at the catchment-scale. This research also underscores the scope for development of dynamical hydrological forecasting approaches in the wetter, poorly drained catchments underlain by impermeable lithologies, found mainly in the north-western and south-western regions of Ireland.

Plastic pollution of aquatic ecosystems is an emerging environmental risk. Land-based plastics are considered the main source of plastic litter in the world’s oceans. Quantifying the emission from rivers into the oceans is crucial to optimize prevention, mitigation and cleanup strategies. Although several studies have focused on estimating annual plastic emission based on average hydrology, the role of extreme events remains underexplored. Recent work has demonstrated that floods can mobilize additional plastics. For example, the 2015/2016 UK floods resulted in a 70% decrease of microplastic sediments in several catchments. In this project, the use of the Global Flood Awareness System (GloFAS) flood forecasting system to assess additional mobilization of plastic pollution will be explored.

International audience; IMproving PRedictions and management of hydrological EXtremes (IMPREX) was a European Union Horizon 2020 project that ran from September 2015 to September 2019. IMPREX aimed to improve society's ability to anticipate and respond to future extreme hydrological events in Europe across a variety of uses in the water-related sectors (flood forecasting, drought risk assessment, agriculture, navigation, hydropower and water supply utilities). Through the engagement with stakeholders and continuous feedback between model outputs and water applications, progress was achieved in better understanding the way hydrological predictions can be useful to (and operationally incorporated into) problem-solving in the water sector. The work and discussions carried out during the project nurtured further reflections toward a common vision for hydrological prediction. In this article, we summarized the main findings of the IMPREX project within a broader overview of hydrological prediction, providing a vision for improving such predictions. In so doing, we first presented a synopsis of hydrological and weather forecasting, with a focus on medium-range to seasonal scales of prediction for increased preparedness. Second, the lessons learned from IMPREX were discussed. The key findings were the gaps highlighted in the global observing system of the hydrological cycle, the degree of accuracy of hydrological models and the techniques of post-processing to correct biases, the origin of seasonal hydrological skill in Europe and user requirements of hydrometeorological forecasts to ensure their appropriate use in decision-making models and practices. Last, a vision for how to improve these forecast systems/products in the future was expounded, including advancing numerical weather and hydrological models, improved earth monitoring and more frequent interaction between forecasters and users to tailor the forecasts to applications. We conclude that if these improvements can be implemented in the coming years, earth system and hydrological modelling will become more skillful, thus leading to socioeconomic benefits for the citizens of Europe and beyond.

Highlights • We produced a global daily streamflow reanalysis for 1980–2018. • The hydrological model is calibrated with observed discharge at 1226 river sections. • Considerable improvements compared to the operational setup of GloFAS v2.1. • GloFAS streamflow reanalysis v3.0 is freely available for use in large scale applications., Global and continental scale hydrological reanalysis datasets receive growing attention due to their increasing number of applications, ranging from water resources management, climate change studies, water related hazards and policy support. Until recently, their use was mostly limited to qualitative assessments, due to their coarse spatial and temporal resolution, large uncertainty and bias in the model output, and limited extent of the dataset in space and time. This research reports on the setup of a gridded hydrological model with quasi-global coverage, able to reproduce a seamless 39-year streamflow simulation in all world’s medium to large river basins. The model was calibrated at 1226 river sections with a total drainage area of 51 million km2 within 66 countries, using ECMWF’s latest atmospheric reanalysis ERA5. A performance assessment revealed large improvements in reproducing past discharge observations, in comparison to the calibration used in the current operational setup of the hydrological model as part of the Copernicus – Global Flood Awareness System (GloFAS, www.globalfloods.eu), with median scores of Kling-Gupta Efficiency KGE = 0.67 and correlation r = 0.8. The simulation bias was also dramatically reduced and narrowed around zero, with more than 60% of stations showing percent bias within ±20%. Pronounced regional differences in the simulation results remain, pointing out the need for detailed investigation of the hydrological processes in specific regions, including parts of Africa and South Asia. In addition, observed discharges with high data quality is key to achieving skillful model output. The new calibrated model will become part of the operational runs of GloFAS in the next system release foreseen for Spring 2020, together with a near real time extension of the streamflow reanalysis.

Globally, few precipitation records extend to the 18th century. The England Wales Precipitation (EWP) series is a notable exception with continuous monthly records from 1766. EWP has found widespread use across diverse fields of research including trend detection, evaluation of climate model simulations, as a proxy for mid‐latitude atmospheric circulation, a predictor in long‐term European gridded precipitation data sets, the assessment of drought and extremes, tree‐ring reconstructions and as a benchmark for other regional series. A key finding from EWP has been the multi‐centennial trends towards wetter winters and drier summers. We statistically reconstruct seasonal EWP using independent, quality‐assured temperature, pressure and circulation indices. Using a sleet and snow series for the UK derived by Profs. Gordon Manley and Elizabeth Shaw to examine winter reconstructions, we show that precipitation totals for pre‐1870 winters are likely biased low due to gauge under‐catch of snowfall and a higher incidence of snowfall during this period. When these factors are accounted for in our reconstructions, the observed trend to wetter winters in EWP is no longer evident. For summer, we find that pre‐1820 precipitation totals are too high, likely due to decreasing network density and less certain data at key stations. A significant trend to drier summers is not robustly present in our reconstructions of the EWP series. While our findings are more certain for winter than summer, we highlight (a) that extreme caution should be exercised when using EWP to make inferences about multi‐centennial trends, and; (b) that assessments of 18th and 19th Century winter precipitation should be aware of potential snow biases in early records. Our findings underline the importance of continual re‐appraisal of established long‐term climate data sets as new evidence becomes available. It is also likely that the identified biases in winter EWP have distorted many other long‐term European precipitation series., The trend towards wetter winters and drier summers in EWP can be explained by changes in measurement practice and data biases. For winter (Figure below), the timing of divergence aligns with the introduction of the Snowdon Pattern Rain Gauge in the UK around 1863. Given the widespread use of EWP across environmental and climate research, we expect that our findings will challenge understanding of multi‐centennial trends in precipitation in the UK. These changes in measurement practice are also likely to affect other long‐term precipitation records in north‐west Europe.

Climate change has led to concerns about increasing river floods resulting from the greater water-holding capacity of a warmer atmosphere1. These concerns are reinforced by evidence of increasing economic losses associated with flooding in many parts of the world, including Europe2. Any changes in river floods would have lasting implications for the design of flood protection measures and flood risk zoning. However, existing studies have been unable to identify a consistent continental-scale climatic-change signal in flood discharge observations in Europe3, because of the limited spatial coverage and number of hydrometric stations. Here we demonstrate clear regional patterns of both increases and decreases in observed river flood discharges in the past five decades in Europe, which are manifestations of a changing climate. Our results—arising from the most complete database of European flooding so far—suggest that: increasing autumn and winter rainfall has resulted in increasing floods in northwestern Europe; decreasing precipitation and increasing evaporation have led to decreasing floods in medium and large catchments in southern Europe; and decreasing snow cover and snowmelt, resulting from warmer temperatures, have led to decreasing floods in eastern Europe. Regional flood discharge trends in Europe range from an increase of about 11 per cent per decade to a decrease of 23 per cent. Notwithstanding the spatial and temporal heterogeneity of the observational record, the flood changes identified here are broadly consistent with climate model projections for the next century4,5, suggesting that climate-driven changes are already happening and supporting calls for the consideration of climate change in flood risk management. Analysis of a comprehensive European flood dataset reveals regional changes in river flood discharges in the past five decades that are consistent with models suggesting that climate-driven changes are already happening.

Inferring the mechanisms causing river flooding is key to understanding past, present, and future flood risk. However, a quantitative spatially distributed overview of the mechanisms that drive flooding across Europe is currently unavailable. In addition, studies that classify catchments according to their flood‐driving mechanisms often identify a single mechanism per location, although multiple mechanisms typically contribute to flood risk. We introduce a new method that uses seasonality statistics to estimate the relative importance of extreme precipitation, soil moisture excess, and snowmelt as flood drivers. Applying this method to a European data set of maximum annual flow dates in several thousand catchments reveals that from 1960 to 2010 relatively few annual floods were caused by annual rainfall peaks. Instead, most European floods were caused by snowmelt and by the concurrence of heavy precipitation with high antecedent soil moisture. For most catchments, the relative importance of these mechanisms has not substantially changed during the past five decades. Exposing the regional mechanisms underlying Europe's most costly natural hazard is a key first step in identifying the processes that require most attention in future flood research.

Highlights • We present a novel approach to estimate flood warning thresholds in GloFAS. • It preserve statistical consistency between thresholds and operational forecasts. • Time-invariant thresholds are consistent with 6-week forecasts only in 27% of points. • Range-dependent thresholds are a better alternative to time-invariant thresholds., Early warning systems (EWS) for river flooding are strategic tools for effective disaster risk management in many world regions. When driven by ensemble Numerical Weather Predictions (NWP), flood EWS can provide skillful streamflow forecasts beyond the monthly time scale in large river basins. Yet, effective flood detection is challenged by accurate estimation of warning thresholds that identify specific hazard levels along the entire river network and forecast horizon. This research describes a novel approach to estimate warning thresholds which retain statistical consistency with the operational forecasts at all lead times. The procedure is developed in the context of the Global Flood Awareness System (GloFAS). A 21-year forecast-consistent dataset is used to derive thresholds with global coverage and forecast range up to six weeks. These are compared with thresholds derived from ERA5, a state of the art atmospheric reanalysis used to run the baseline simulation for the years 1986–2017 and to give a best guess of the present hydrological states. Findings show that the use of constant thresholds for 30-day flood forecasting, as in the current operational GloFAS setup, is consistent throughout the entire forecast range in only 30% to 40% of the river network, depending on the flood return period. Findings show that range-dependent thresholds, of weekly duration, are a more suitable alternative to time-invariant thresholds, as they improve the model consistency as well as the skills in flood monitoring and early warning, particularly over longer forecasting range.

The open-source programming language R has gained a central place in the hydrological sciences over the last decade, driven by the availability of diverse hydro-meteorological data archives and the development of open-source computational tools. The growth of R's usage in hydrology is reflected in the number of newly published hydrological packages, the strengthening of online user communities, and the popularity of training courses and events. In this paper, we explore the benefits and advantages of R's usage in hydrology, such as: the democratization of data science and numerical literacy, the enhancement of reproducible research and open science, the access to statistical tools, the ease of connecting R to and from other languages, and the support provided by a growing community. This paper provides an overview of important packages at every step of the hydrological workflow, from the retrieval of hydro-meteorological data, to spatial analysis and cartography, hydrological modelling, statistics, and the design of static and dynamic visualizations, presentations and documents. We discuss some of the challenges that arise when using R in hydrology and useful tools to overcome them, including the use of hydrological libraries, documentation and vignettes (long-form guides that illustrate how to use packages); the role of Integrated Development Environments (IDEs); and the challenges of Big Data and parallel computing in hydrology. Last, this paper provides a roadmap for R's future within hydrology, with R packages as a driver of progress in the hydrological sciences, Application Programming Interfaces (APIs) providing new avenues for data acquisition and provision, enhanced teaching of hydrology in R, and the continued growth of the community via short courses and events.

13 Pags.- 4 Figs. The work contributes to the UNESCO‐IHP‐VIII EURO FRIEND‐Water Programme and to the IAHS Panta Rhei initiative and the Drought in the Anthropocene Working Group. The raw streamflow data used in this study can be obtained from the web sites of the different water management agencies of Western Europe. In France from the Ministère de la Transition écologique et solidaire (http://www.hydro.eaufrance.fr/). In the United Kingdom, from the National River Flow Archive (https://nrfa.ceh.ac.uk/). In Ireland from the Office of Public Works (http://waterlevel.ie/hydro‐data/home.html) and the Environmental Protection Agency (http://www.epa.ie/hydronet/). In Portugal from the Sistema Nacional de Informaçao de Recursos Hídricos (https://snirh.apambiente.pt/). In Spain from the Ministry of Agriculture's CEDEX (http://ceh‐flumen64.cedex.es/anuarioaforos/default.asp) and the Catalan (http://aca.gencat.cat/ca/inici), Basque (http://www.uragentzia.euskadi.eus/u81‐0002/es/), and Andalusian (https://www.agenciamedioambienteyagua.es/) agencies. The temperature and precipitation gridded data can be obtained from the ECA&D site (https://www.ecad.eu/). The ERA climate data is available in the European Centre for Medium‐Range Weather Forecasts (https://www.ecmwf.int/en/forecasts/datasets/browse‐reanalysis‐datasets). Land cover data and NDVI were available from the European Union (https://land.copernicus.eu/pan‐european/corine‐land‐cover) and NASA (https://ecocast.arc.nasa.gov/data/pub/gimms/3g.v1/), respectively., Attribution of trends in streamflow is complex, but essential, in identifying optimal management options for water resources. Disagreement remains on the relative role of climate change and human factors, including water abstractions and land cover change, in driving change in annual streamflow. We construct a very dense network of gauging stations (n = 1,874) from Ireland, the United Kingdom, France, Spain, and Portugal for the period of 1961–2012 to detect and then attribute changes in annual streamflow. Using regression‐based techniques, we show that climate (precipitation and atmospheric evaporative demand) explains many of the observed trends in northwest Europe, while for southwest Europe human disturbances better explain both temporal and spatial trends. For the latter, large increases in irrigated areas, agricultural intensification, and natural revegetation of marginal lands are inferred to be the dominant drivers of decreases in streamflow., This work was supported by the research projects PCIN‐2015‐220, PCIN‐2017‐020, CGL2014‐52135‐C03‐ 01, CGL2017‐83866‐C3‐3‐R, and CGL2017‐82216‐R financed by the Spanish Commission of Science and Technology and FEDER, CROSSDRO financed by the AXIS (Assessment of Cross(X)—sectorial climate Impacts and pathways for Sustainable transformation) JPI‐Climate co‐funded call of the European Commission, IMDROFLOOD financed by the WaterWorks 2014 cofunded call of the European Commission and INDECIS, which is part of ERA4CS, an ERA‐NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), and ANR (FR) with cofunding by the European Union (Grant 690462). J. H.'s contribution was supported by the Natural Environment Research Council Grant “Historic Droughts,” Grant NE/ L01016X/1. C. M. was funded by a Science Foundation Ireland Career Development Award (SFI/17/CDA/ 4783). I. N. is beneficiary of a predoctoral fellowship awarded by the Government of Aragón (Spain). S. B. and J.‐P. V.'s contribution was supported by the project EFA210/16 PIRAGUA, cofounded by the European Regional Development Fund (ERDF) through the Interreg V Spain‐France‐Andorre Programme (POCTEFA 2014‐2020) of the European Union.

Global hydrological forecasts are now produced operationally on a daily basis. However, the lack of global river discharge observations precludes routine flood forecast evaluation, an essential step in providing more skilful and reliable forecasts. A vision is expounded for greater and more timely exchange of global river discharge observations, which would result in improved flood awareness and socioeconomic benefits in some of the World’s most vulnerable countries.

Earth and environmental models are relied upon to investigate system responses that cannot otherwise be examined. In simulating physical processes, models have adjustable parameters which may, or m...

A continuous 305-year (1711–2016) monthly rainfall series is created for the Island of Ireland. Two overlapping data sources are employed: i) a previously unpublished UK Meteorological Office note containing annual rainfall anomalies and corresponding proportional monthly totals based on weather diaries and early observational records for the period 1711–1977 and; ii) a long-term, homogenised monthly rainfall series for the island of Ireland for the period 1850–2016. Using estimates of long-term average precipitation from the homogenised series to merge these sources, the new 305-year record is constructed and insights drawn about notable extremes, climate variability and change. The consistency of the resulting series was evaluated by comparison with independent long-term observations and reconstructions of precipitation, temperature and circulation indices from across the British-Irish Isles. Strong decadal consistency is evident throughout the record amongst all series in spring, summer and autumn. The winter series is probably too dry from the 1740s to the 1780s, but strong consistency with other records strengthens confidence from 1790 onwards. The new Island of Ireland series reveals remarkably wet winters during the 1730s, concurrent with a period of strong westerly airflow, glacial advance throughout Scandinavia and near unprecedented warmth in the Central England Temperature record – all consistent with the strong phase of the North Atlantic Oscillation. The driest winter decade in the series coincides with the Laki eruption of 1783–1784. Unusually wet summers occurred in the 1750s, consistent with proxy (tree-ring) reconstructions of summer precipitation in the region. In the annual series, the most recent decade (2006–2015) is found to be the wettest in over 300 years. The new series reveals statistically significant (0.05 level) multi-centennial trends in winter (increasing) and summer (decreasing) seasonal precipitation. However, given uncertainties in the early winter record, the former should be treated as tentative. Importantly, we show that the years 1940 to present – the period with the most widely available digitised records – is unrepresentative of long-term changes in all seasons. Although there are recognized uncertainties in the early record, the derived series offers valuable insights for understanding multi-decadal and centennial rainfall variability in Ireland, and provides a firm basis for benchmarking other long-term records and future reconstructions.