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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.

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.

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.

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.

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.

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.

The uncertainties in scientific studies for climate risk management can be investigated at three levels of complexity: “ABC”. The most sophisticated involves “Analyzing” the full range of uncertainty with large multi-model ensemble experiments. The simplest is about “Bounding” the uncertainty by defining only the upper and lower limits of the likely outcomes. The intermediate approach, “Crystallizing” the uncertainty, distills the full range to improve the computational efficiency of the “Analyze” approach. Modelers typically dictate the study design, with decision-makers then facing difficulties when interpreting the results of ensemble experiments. We assert that to make science more relevant to decision-making, we must begin by considering the applications of scientific outputs in facilitating decision-making pathways, particularly when managing extreme events. This requires working with practitioners from outset, thereby adding “D” for “Decision-centric” to the ABC framework.

There is overwhelming evidence that the climate system has warmed since the instigation of instrumental meteorological observations. The Fifth Assessment Report of the Intergovernmental Panel on Climate Change concluded that the evidence for warming was unequivocal. However, owing to imperfect measurements and ubiquitous changes in measurement networks and techniques, there remain uncertainties in many of the details of these historical changes. These uncertainties do not call into question the trend or overall magnitude of the changes in the global climate system. Rather, they act to make the picture less clear than it could be, particularly at the local scale where many decisions regarding adaptation choices will be required, both now and in the future. A set of high-quality long-term fiducial reference measurements of essential climate variables will enable future generations to make rigorous assessments of future climate change and variability, providing society with the best possible information to support future decisions. Here we propose that by implementing and maintaining a suitably stable and metrologically well-characterized global land surface climate fiducial reference measurements network, the present-day scientific community can bequeath to future generations a better set of observations. This will aid future adaptation decisions and help us to monitor and quantify the effectiveness of internationally agreed mitigation steps. This article provides the background, rationale, metrological principles, and practical considerations regarding what would be involved in such a network, and outlines the benefits which may accrue. The challenge, of course, is how to convert such a vision to a long-term sustainable capability providing the necessary well-characterized measurement series to the benefit of global science and future generations.

This paper describes the development of the first operational seasonal hydrological forecasting service for the UK, the Hydrological Outlook UK (HOUK). Since June 2013, this service has delivered monthly forecasts of streamflow and groundwater levels, with an emphasis on forecasting hydrological conditions over the next three months, accompanied by outlooks over longer time horizons. This system is based on three complementary approaches combined to produce the outlooks: (i) national-scale modelling of streamflow and groundwater levels based on dynamic seasonal rainfall forecasts, (ii) catchment-scale modelling where streamflow and groundwater level models are driven by historical meteorological forcings (i.e. the Ensemble Streamflow Prediction, ESP, approach), and (iii) a catchment-scale statistical method based on persistence and historical analogues. This paper provides the background to the Hydrological Outlook, describes the various component methods in detail and then considers the impact and usefulness of the product. As an example of a multi-method, operational seasonal hydrological forecasting system, it is hoped that this overview provides useful information and context for other forecasting initiatives around the world.

Sound water policy and management rests on sound hydrometeorological and ecological data. Conversely, unrepresentative, poorly collected or erroneously archived data introduces uncertainty regarding the magnitude, rate and direction of environmental change, in addition to undermining confidence in decision-making processes. Unfortunately, data biases and errors can enter the information flow at various stages, starting with site selection, instrumentation, sampling/ measurement procedures, post-processing and ending with archiving systems. Techniques such as visual inspection of raw data, graphical representation and comparison between sites, outlier and trend detection, and referral to metadata can all help uncover spurious data. Tell-tale signs of ambiguous and/or anomalous data are highlighted using 12 carefully chosen cases drawn mainly from hydrology (‘the dirty dozen’). These include evidence of changes in site or local conditions (due to land management, river regulation or urbanisation); modifications to instrumentation or inconsistent observer behaviour; mismatched or misrepresentative sampling in space and time; treatment of missing values, post-processing and data storage errors. As well as raising awareness of pitfalls, recommendations are provided for uncovering lapses in data quality after the information has been gathered. It is noted that error detection and attribution are more problematic for very large data sets, where observation networks are automated, or when various information sources have been combined. In these cases, more holistic indicators of data integrity are needed that reflect the overall information life-cycle and application(s) of the hydrological data.

Meteorological agencies of Ireland and the UK have confirmed that winter (December to February) 2013–2014 (W2013–14) set records for precipitation totals and the occurrence of extreme wind speeds1–3. Less clear is whether storminess (characterized as the frequency and intensity of cyclones) during W2013–14 was equally unprecedented. We assessed multidecadal variations in storminess by considering frequency and intensity together and found that in the context of these large-scale cyclone characteristics, W2013–14 was indeed exceptional. Given the potential societal impacts, there is clearly a need to better understand the processes driving extreme cyclonic activity in the North Atlantic.

This paper introduces a reference hydrometric network for Ireland and examines the derived flow archive for evidence of climate-driven trends in mean and high river flows. The Mann-Kendall and Theil-Sen tests are applied to eight hydroclimatic indicators for fixed and variable (start and end date) records. Spatial coherence and similarities of trends with rainfall suggest they are climate driven; however, large temporal variability makes it difficult to discern widely-expected anthropogenic climate change signals at this point in time. Trends in summer mean flows and recent winter means are at odds with those expected for anthropogenic climate change. High-flow indicators show strong and persistent positive trends, are less affected by variability and may provide earlier climate change signals than mean flows. The results highlight the caution required in using fixed periods of record for trend analysis, recognizing the trade-off between record length, network density and geographic coverage. Edi...

Simulations of future climate change impacts are highly uncertain, particularly for catchment hydrology, where output from models of complex dynamic systems (global climate) are used as inputs to models of complex dynamic systems (hydrology models). This is problematic where decision-making for adaptation is underpinned by future climate predictions, and where policy-makers have opted to delay adaptation until either uncertainties are reduced, or climate change signals emerge from observations. This paper, using the Boyne catchment in the east of Ireland as a case study, discusses the uncertainties involved in climate change impact assessment for catchment hydrology and highlights why uncertainties are unlikely to be constrained or reduced in the time-scale required for adaptation. In addition, by calculating the time required for climate change signals to emerge from the observational record and the magnitude of change required for detection, it is highlighted that waiting for climate signals to be statistically detectable is not an option for effective adaptation. The paper concludes by considering how a paradigm shift in how we use the output from climate impact assessments can progress the adaptation agenda given the limits to prediction identified.