MSWEP : 3-hourly 0.25° global gridded precipitation (1979-2015) by merging gauge, satellite, and reanalysis data

Current global precipitation (P) datasets do not take full advantage of the complementary nature of satellite and reanalysis data. Here, we present Multi-Source Weighted-Ensemble Precipitation (MSWEP) version 1.1, a global P dataset for the period 1979-2015 with a 3hourly temporal and 0.25 degrees ffi spatial resolution, specifically designed for hydrological modeling. The design philosophy of MSWEP was to optimally merge the highest quality P data sources available as a function of timescale and location. The long-term mean of MSWEP was based on the CHPclim dataset but replaced with more accurate regional datasets where available. A correction for gauge under-catch and orographic effects was introduced by inferring catchment-average P from streamflow (Q) observations at 13 762 stations across the globe. The temporal variability of MSWEP was determined by weighted averaging of P anomalies from seven datasets; two based solely on interpolation of gauge observations (CPC Unified and GPCC), three on satellite remote sensing (CMORPH, GSMaP-MVK, and TMPA 3B42RT), and two on atmospheric model reanalysis (ERA-Interim and JRA-55). For each grid cell, the weight assigned to the gauge-based estimates was calculated from the gauge network density, while the weights assigned to the satellite-and reanalysis-based estimates were calculated from their comparative performance at the surrounding gauges. The quality of MSWEP was compared against four state-of-the-art gauge-adjusted P datasets (WFDEI-CRU, GPCP-1DD, TMPA 3B42, and CPC Unified) using independent P data from 125 FLUXNET tower stations around the globe. MSWEP obtained the highest daily correlation coefficient (R) among the five P datasets for 60.0% of the stations and a median R of 0.67 vs. 0.44-0.59 for the other datasets. We further evaluated the performance of MSWEP using hydrological modeling for 9011 catchments (< 50 000 km(2)) across the globe. Specifically, we calibrated the simple conceptual hydrological model HBV (Hydrologiska Byrans Vattenbalansavdelning) against daily Q observations with P from each of the different datasets. For the 1058 sparsely gauged catchments, representative of 83.9% of the global land surface (excluding Antarctica), MSWEP obtained a median calibration NSE of 0.52 vs. 0.29-0.39 for the other P datasets. MSWEP is available via http://www.gloh2o.org

Water scenarios for the Danube River Basin: Elements for the assessment of the Danube agriculture-energy-water nexus

This report provides background material for the identification and elicitation of scenarios relevant for the futures of the agriculture-energy-ecosystems-water nexus in the Danube region. We present a summary of the regional climate scenarios available as input for water resources simulations, and the consequent long term average water balance figures estimated using a Budyko framework. Then we introduce the LUISA model for the simulation of land use-related variables in the region. Finally, we include a contribution by a water expert from the Danube region, presenting an initial reasoning on important elements to be addressed in scenario simulations. This report is intended as a reader for water professionals, stakeholders and decision makers in the Danube region, in order to stimulate the foresight of scenarios worth being simulated with JRC models, so to further our understanding of the water-energy-agriculture-ecosystems nexus and its management in the mid- and long-term.JRC.H.1-Water Resource

Global-scale regionalization of hydrologic model parameters

Current state-of-the-art models typically applied at continental to global scales (hereafter called macroscale) tend to use a priori parameters, resulting in suboptimal streamflow (Q) simulation. For the first time, a scheme for regionalization of model parameters at the global scale was developed. We used data from a diverse set of 1787 small-to-medium sized catchments ( 10-10,000 km(2)) and the simple conceptual HBV model to set up and test the scheme. Each catchment was calibrated against observed daily Q, after which 674 catchments with high calibration and validation scores, and thus presumably good-quality observed Q and forcing data, were selected to serve as donor catchments. The calibrated parameter sets for the donors were subsequently transferred to 0.5 degrees grid cells with similar climatic and physiographic characteristics, resulting in parameter maps for HBV with global coverage. For each grid cell, we used the 10 most similar donor catchments, rather than the single most similar donor, and averaged the resulting simulated Q, which enhanced model performance. The 1113 catchments not used as donors were used to independently evaluate the scheme. The regionalized parameters outperformed spatially uniform (i.e., averaged calibrated) parameters for 79% of the evaluation catchments. Substantial improvements were evident for all major Koppen-Geiger climate types and even for evaluation catchments>5000 km distant from the donors. The median improvement was about half of the performance increase achieved through calibration. HBV with regionalized parameters outperformed nine state-of-the-art macroscale models, suggesting these might also benefit from the new regionalization scheme. The produced HBV parameter maps including ancillary data are available via

GLEAM v3 : satellite-based land evaporation and root-zone soil moisture

The Global Land Evaporation Amsterdam Model (GLEAM) is a set of algorithms dedicated to the estimation of terrestrial evaporation and root-zone soil moisture from satellite data. Ever since its development in 2011, the model has been regularly revised, aiming at the optimal incorporation of new satellite-observed geophysical variables, and improving the representation of physical processes. In this study, the next version of this model (v3) is presented. Key changes relative to the previous version include (1) a revised formulation of the evaporative stress, (2) an optimized drainage algorithm, and (3) a new soil moisture data assimilation system. GLEAM v3 is used to produce three new data sets of terrestrial evaporation and root-zone soil moisture, including a 36-year data set spanning 1980-2015, referred to as v3a (based on satellite-observed soil moisture, vegetation optical depth and snow-water equivalent, reanalysis air temperature and radiation, and a multi-source precipitation product), and two satellite-based data sets. The latter share most of their forcing, except for the vegetation optical depth and soil moisture, which are based on observations from different passive and active C-and L-band microwave sensors (European Space Agency Climate Change Initiative, ESA CCI) for the v3b data set (spanning 2003-2015) and observations from the Soil Moisture and Ocean Salinity (SMOS) satellite in the v3c data set (spanning 2011-2015). Here, these three data sets are described in detail, compared against analogous data sets generated using the previous version of GLEAM (v2), and validated against measurements from 91 eddy-covariance towers and 2325 soil moisture sensors across a broad range of ecosystems. Results indicate that the quality of the v3 soil moisture is consistently better than the one from v2: average correlations against in situ surface soil moisture measurements increase from 0.61 to 0.64 in the case of the v3a data set and the representation of soil moisture in the second layer improves as well, with correlations increasing from 0.47 to 0.53. Similar improvements are observed for the v3b and c data sets. Despite regional differences, the quality of the evaporation fluxes remains overall similar to the one obtained using the previous version of GLEAM, with average correlations against eddy-covariance measurements ranging between 0.78 and 0.81 for the different data sets. These global data sets of terrestrial evaporation and root-zone soil moisture are now openly available at www.GLEAM.eu and may be used for large-scale hydrological applications, climate studies, or research on land-atmosphere feedbacks

Modelling water demand and availability scenarios for current and future land use and climate in the Sava River Basin

170 Simulations with the LISFLOOD water resources for 30-year periods with various combinations of land use change and climate change have been evaluated for their impact on the water-food-energy-environment nexus in the Sava river basin. For the Sava river basin, we found in this study that more intense irrigated agriculture does have the potential to increase crop yields considerably, but there are not sufficient water resources available to realise this. Also, if irrigation would be increased drastically, other sectors would be negatively influenced, such as the energy sector (reduced cooling water availability, potentially less water at times produce hydropower), navigation (more frequent and lower low-flows), and the environment (breaches of environmental or minimum flow conditions). Effects on water resources would be more significant with increased irrigation to increase the crop yield of e.g maize. This would lead to an increase in water demand from 2216 Mm3/year to 3337 Mm3/year. Overall water demand in the Sava basin would further increase to around 6000 Mm3/year if we combine both increased irrigation and climate projections until 2100. The average simulated maize yield could increase from 5.7 tons/ha at present conditions to 9.9 tons/ha in case of increased and optimum irrigation. These substantial increases in irrigation, which would lead to substantial crop yield increases as well, would lead to water scarcity in parts of the Sava basin. Also, there just is not sufficient water to irrigate all areas which are water-limited for crop growth. Existing irrigation plans and irrigating the areas which were previously equipped for irrigation (according to FAO) seems more feasible from a water resources perspective. Flood peaks are projected to remain unchanged as a consequence of projected land use changes until 2050 for the Sava basin. However, with climate change projections we do simulate an overall increase in the flood peaks with 13% for the 2011-2040 period and a 23% increase for the 2071-2100 period. River low-flows decrease moderately for the 2011-2040 scenarios. For the end of the century 2071-2100, lowflow values are projected to moderately increase as compared to the control 1981-2010 climate. Excessive irrigation would result in a severe decrease of the lowflow discharges with 50-60%. As for ecological flows, similar observations can be made. Navigation in the main Sava river may be affected by these trends. Water availability for energy production - hydropower and cooling water for thermal and nuclear power stations – is projected to decrease by an average of 3.3% for 2030 under RCP4.5, whereas RCP8.5 would result in a 1.3% increase. End of the century simulations yield a 17.6% higher Q50 for RCP4.5 and 23.1% higher for RCP8.5. Excessive irrigation could affect the water availability for power production, especially for cooling thermal power stations. Hydropower reservoirs could be turned into multi-functional reservoirs, also serving downstream irrigation needs and flood control, and thus serve multiple purposes.JRC.D.2-Water and Marine Resource

Towards an online GIS platform to enhance data and research sharing among meteorologists, natural hazard experts, governments, and the public

The “open era” of climate science is marked by an abundance of datasets across various environmental variables. While there are many evaluation studies, researchers and practitioners often still struggle to select the most suitable dataset or product for their study. The year 2023 marked the hottest year on record, resulting in a series of destructive hazard events, including heatwaves, wildfires, and floods. These conditions underscore the urgent need for enhanced preparedness in disaster risk reduction (DRR). In the field of natural hazards, environmental data are crucial for building more accurate models. We will take 'P' (precipitation) as an example in the presentation, as it's a major trigger for multiple hazards such as floods and landslides. There are dozens of publicly freely available global gridded P products available (including satellite, (re)analysis, gauge, and combinations thereof), but estimates from different products at the same time and location can differ significantly. Currently, there is no effective platform that facilitates the sharing of quantitative information on the relative strengths and weaknesses of these P products between meteorologists and other stakeholders. To address this challenge, we propose the development of a web-based GIS platform which allows users to interactively explore the globe, click on different locations, and access various statistics and databases. Multiple P products and evaluation statistics can be accessed via the platform. We hope this platform will host multiple hazards-related datasets, fostering better collaboration between scientists in the fields of DRR and meteorology. Initially focusing on P data based on our expertise in precipitation and landslide hazard modeling, we aim to expand this resource by involving more scientists from related fields. Additionally, we plan to integrate a ChatGPT-based extension to streamline data access and enhance efficiency for researchers, practitioners, and laypeople. We want to contribute to the collective effort in creating a dynamic, accessible repository of resources and initiatives for the wider geoscience community

Global analysis of seasonal streamflow predictability using an ensemble prediction system and observations from 6192 small catchments worldwide

Key Points Global bimonthly streamflow forecasts show potentially valuable skill Initial catchment conditions are responsible for most skill Skill can be estimated from model performance and theoretical skill Ideally, a seasonal streamflow forecasting sy

The impact of lake and reservoir parameterization on global streamflow simulation

Lakes and reservoirs affect the timing and magnitude of streamflow, and are therefore essential hydrological model components, especially in the context of global flood forecasting. However, the parameterization of lake and reservoir routines on a global scale is subject to considerable uncertainty due to lack of information on lake hydrographic characteristics and reservoir operating rules. In this study we estimated the effect of lakes and reservoirs on global daily streamflow simulations of a spatially-distributed LISFLOOD hydrological model. We applied state-of-the-art global sensitivity and uncertainty analyses for selected catchments to examine the effect of uncertain lake and reservoir parameterization on model performance. Streamflow observations from 390 catchments around the globe and multiple performance measures were used to assess model performance. Results indicate a considerable geographical variability in the lake and reservoir effects on the streamflow simulation. Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE) metrics improved for 65% and 38% of catchments respectively, with median skill score values of 0.16 and 0.2 while scores deteriorated for 28% and 52% of the catchments, with median values −0.09 and −0.16, respectively. The effect of reservoirs on extreme high flows was substantial and widespread in the global domain, while the effect of lakes was spatially limited to a few catchments. As indicated by global sensitivity analysis, parameter uncertainty substantially affected uncertainty of model performance. Reservoir parameters often contributed to this uncertainty, although the effect varied widely among catchments. The effect of reservoir parameters on model performance diminished with distance downstream of reservoirs in favor of other parameters, notably groundwater-related parameters and channel Manning’s roughness coefficient. This study underscores the importance of accounting for lakes and, especially, reservoirs and using appropriate parameterization in large-scale hydrological simulations

The impact of forest regeneration on streamflow in 12 mesoscale humid tropical catchments

Although regenerating forests make up an increasingly large portion of humid tropical landscapes, little is known of their water use and effects on streamflow (Q). Since the 1950s the island of Puerto Rico has experienced widespread abandonment of pastur