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Study Region: Forty-one river basins in Brazil and neighboring countries in South America. Study Focus: In large river basins, on countrywide or continental scales, it is often difficult to have consistent and accurate long time series of spatially distributed precipitation data available. However, these are needed to calibrate hydrological models and to run hydrological simulations continuously in real-time streamflow forecasting. In this study, we assess two real-time precipitation products based on rain gauges and satellite data (TRMM-MERGE and CPC-NOAA) for their use in streamflow forecasting in the hydropower sector in Brazil. To take advantage of each precipitation data source and derive a unique dataset, a methodology is proposed to combine, extend, and validate the datasets. We consider the discharges at the river basin outlets as an independent and robust reference for hydrological applications. Observed discharges are used to quantify precipitation uncertainties and to weight the blending, while discharges obtained from hydrological modeling are used to validate the final precipitation product. New Hydrological Insights for the Region: The proposed blending method, which uses the uncertainty of the original datasets to define the weighting factors, was efficient in generating a precipitation product that performs better than each dataset separately when used to force a hydrological model. The use of the double-mass curve correlation to extend the time series of the datasets beyond their common period allowed us to produce long time series of precipitation for South American basins and hydrological applications. The study shows that it is possible to rely on river discharge data and hydrological modeling to select and combine different precipitation products in the region and presents a step-by-step methodology to do so.

Estimating how much water is flowing through rivers at the global scale is challenging due to a lack of observations in space and time. A way forward is to optimally combine the global network of earth system observations with advanced numerical weather prediction (NWP) models to generate consistent spatio-temporal maps of land, ocean, and atmospheric variables of interest, which is known as a reanalysis. While the current generation of NWP models output runoff at each grid cell, they currently do not produce river discharge at catchment scales directly and thus have limited utility in hydrological applications such as flood and drought monitoring and forecasting. This is overcome in the Global Flood Awareness System (GloFAS; http://www.globalfloods.eu/, last access: 28 June 2020) by coupling surface and sub-surface runoff from the Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL) land surface model used within ECMWF's latest global atmospheric reanalysis (ERA5) with the LISFLOOD hydrological and channel routing model. The aim of this paper is to describe and evaluate the GloFAS-ERA5 global river discharge reanalysis dataset launched on 5 November 2019 (version 2.1 release). The river discharge reanalysis is a global gridded dataset with a horizontal resolution of 0.1 degrees at a daily time step. An innovative feature is that it is produced in an operational environment so is available to users from 1 January 1979 until near real time (2 to 5 d behind real time). The reanalysis was evaluated against a global network of 1801 daily river discharge observation stations. Results found that the GloFAS-ERA5 reanalysis was skilful against a mean flow benchmark in 86% of catchments according to the modified Kling-Gupta efficiency skill score, although the strength of skill varied considerably with location. The global median Pearson correlation coefficient was 0.61 with an interquartile range of 0.44 to 0.74. The long-term and operational nature of the

One of the most effective strategies to reduce the impacts of drought is by issuing a timely and targeted warning from month to seasons ahead to end users. Yet to accurately forecast the drought hazard on a sub-seasonal to seasonal time scale remains a challenge, and usually, meteorological drought is forecasted instead of hydrological drought, although the latter is more relevant for several impacted sectors. Therefore, we evaluate the hydro-meteorological drought forecast skill for the pan-European region using categorical drought classification method. The results show that the hydrological drought forecasts outperform the meteorological drought forecasts. Hydrological drought forecasts even show predictive power (area with perfect prediction >50%) beyond 2 months ahead. Our study also concludes that dynamical forecasts, derived from seasonal climate forecasts, have higher predictability than ensemble streamflow predictions. The results suggest that further development of seasonal hydrological drought forecasting systems are beneficial, particularly important in the context of global warming, where drought hazard will become more frequent and severe in multiple regions in the world.

Central America is frequently affected by droughts that cause significant socio-economic and environmental problems. Drought characterisation, monitoring and forecasting are potentially useful to support water resource management. Drought indices are designed for these purposes, but their ability to characterise droughts depends on the characteristics of the regional climate and the quality of the available data. Local comprehensive and high-quality observational networks of meteorological and hydrological data are not available, which limits the choice of drought indices and makes it important to assess available datasets. This study evaluated which combinations of drought index and meteorological dataset were most suitable for characterising droughts in the region. We evaluated the standardised precipitation index (SPI), a modified version of the deciles index (DI), the standardised precipitation evapotranspiration index (SPEI) and the effective drought index (EDI). These were calculated using precipitation data from the Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS), the CRN073 dataset, the Climate Research Unit (CRU), ECMWF Reanalysis (ERA-Interim) and a regional station dataset, and temperature from the CRU and ERA-Interim datasets. The gridded meteorological precipitation datasets were compared to assess how well they captured key features of the regional climate. The performance of all the drought indices calculated with all the meteorological datasets was then evaluated against a drought index calculated using river discharge data. Results showed that the selection of database was more important than the selection of drought index and that the best combinations were the EDI and DI calculated with CHIRPS and CRN073. Results also highlighted the importance of including indices like SPEI for drought assessment in Central America.

This paper considers whether there is any added value in using seasonal climate forecasts instead of historical meteorological observations for forecasting streamflow on seasonal timescales over Europe. A Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) seasonal streamflow forecasts (produced by forcing the Lisflood model with the ECMWF System 4 seasonal climate forecasts), benchmarked against the ensemble streamflow prediction (ESP) forecasting approach (produced by forcing the Lisflood model with historical meteorological observations), is undertaken. The results suggest that, on average, the System 4 seasonal climate forecasts improve the streamflow predictability over historical meteorological observations for the first month of lead time only (in terms of hindcast accuracy, sharpness and overall performance). However, the predictability varies in space and time and is greater in winter and autumn. Parts of Europe additionally exhibit a longer predictability, up to 7 months of lead time, for certain months within a season. In terms of hindcast reliability, the EFAS seasonal streamflow hindcasts are on average less skilful than the ESP for all lead times. The results also highlight the potential usefulness of the EFAS seasonal streamflow forecasts for decision-making (measured in terms of the hindcast discrimination for the lower and upper terciles of the simulated streamflow). Although the ESP is the most potentially useful forecasting approach in Europe, the EFAS seasonal streamflow forecasts appear more potentially useful than the ESP in some regions and for certain seasons, especially in winter for almost 40 % of Europe. Patterns in the EFAS seasonal streamflow hindcast skill are however not mirrored in the System 4 seasonal climate hindcasts, hinting at the need for a better understanding of the link between hydrological and meteorological variables on seasonal timescales, with the aim of improving climate-mod