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Proper satellite-based crop monitoring applications at the farm-level often require near-daily imagery at medium to high spatial resolution. The combination of data from different ongoing satellite missions Sentinel 2 (ESA) and Landsat 7/8 (NASA) provides this unprecedented opportunity at a global scale; however, this is rarely implemented because these procedures are data demanding and computationally intensive. This study developed a robust stream processing for the harmonization of Landsat 7, Landsat 8 and Sentinel 2 in the Google Earth Engine cloud platform, connecting the benefit of coherent data structure, built-in functions and computational power in the Google Cloud. The harmonized surface reflectance images were generated for two agricultural schemes in Bekaa (Lebanon) and Ninh Thuan (Vietnam) during 2018–2019. We evaluated the performance of several pre-processing steps needed for the harmonization including the image co-registration, Bidirectional Reflectance Distribution Functions correction, topographic correction, and band adjustment. We found that the misregistration between Landsat 8 and Sentinel 2 images varied from 10 m in Ninh Thuan (Vietnam) to 32 m in Bekaa (Lebanon), and posed a great impact on the quality of the final harmonized data set if not treated. Analysis of a pair of overlapped L8-S2 images over the Bekaa region showed that, after the harmonization, all band-to-band spatial correlations were greatly improved. Finally, we demonstrated an application of the dense harmonized data set for crop mapping and monitoring. An harmonic (Fourier) analysis was applied to fit the detected unimodal, bimodal and trimodal shapes in the temporal NDVI patterns during one crop year in Ninh Thuan province. The derived phase and amplitude values of the crop cycles were combined with max-NDVI as an R-G-B false composite image. The final image was able to highlight croplands in bright colors (high phase and amplitude), while the non-crop areas were shown with grey/dark (low phase and amplitude). The harmonized data sets (with 30 m spatial resolution) along with the Google Earth Engine scripts used are provided for public use.

Terrestrial evaporation plays a crucial role in modulating climate and water resources. Here, we present a continuous, daily dataset covering 1980–2023 with a 0.1°spatial resolution, produced using the fourth generation of the Global Land Evaporation Amsterdam Model (GLEAM). GLEAM4 embraces developments in hybrid modelling, learning evaporative stress from eddy-covariance and sapflow data. It features improved representation of key factors such as interception, atmospheric water demand, soil moisture, and plant access to groundwater. Estimates are inter-compared with existing global evaporation products and validated against in situ measurements, including data from 473 eddy-covariance sites, showing a median correlation of 0.73, root-mean-square error of 0.95 mm d −1 , and Kling–Gupta efficiency of 0.49. Global land evaporation is estimated at 68.5 × 10 3 km 3 yr −1 , with 62% attributed to transpiration. Beyond actual evaporation and its components (transpiration, interception loss, soil evaporation, etc.), the dataset also provides soil moisture, potential evaporation, sensible heat flux, and evaporative stress, facilitating a wide range of hydrological, climatic, and ecological studies.

Climate change poses a serious risk for the freshwater ecosystem of Western Patagonia and threatens glacial and non‐glacial water resources. Here, we model the historical glacio‐hydrology of 2,236 catchments across the Western Patagonia, and project climate change impacts through the 21st century. To this end, we develop a novel modeling framework that combines Long Short‐Term Memory (LSTM) neural networks with ice‐dynamical glacier modeling using the Open Global Glacier Model (OGGM). We evaluate the ability of this hybrid framework to predict streamflow in ungauged basins (PUB) and regions (PUR) through 10‐fold cross‐validation and compare the results with those obtained with a LSTM model without a glacier component, and two process‐based coupled glacio‐hydrological models. The hybrid modeling approach outperforms all other approaches in 38% and 44% of the catchments considering PUB and PUR evaluations, respectively. Using our new hybrid approach, we estimate an average regional freshwater flux of 19,815 m3 s−1 for the period 2000–2019, with glacier melt contributing 29% during the summer season. Under a high‐emission scenario (Shared socioeconomic pathways 5‐8.5), the northern region (>46°S) is projected to experience the largest reductions in runoff, with dry season runoff decreasing by almost 50% by the end of the century. In contrast, runoff increases are projected for glacierized basins in the southern regions, with average relative changes of 10%–25% and a marked seasonality shift. The results highlight the potential of hybrid modeling in glacio‐hydrology and provide important information for climate change adaptation in Western Patagonia. Plain Language Summary This study investigates future glacial and non‐glacial runoff in Western Patagonia through the 21st century. We develop a novel approach that combines machine learning and a glacier model to predict runoff in approximately 2,200 river catchments under various climate change scenarios. This hybrid approach outperformed traditional process‐based modeling techniques, especially in glacier‐fed regions. Our new approach reveals that northern Patagonia is likely to experience sharp declines in water availability, especially during dry seasons, whereas increases in runoff and considerable shifts in timing of peak river flows (typically from summer to spring) are expected for some glacier‐fed catchments in central and southern Patagonia. These findings are critical to develop strategies for climate change adaptation in the region. Key Points Hybrid glacio‐hydrological models outperformed traditional approaches in predicting streamflow in ungauged basins and areas The regional freshwater flux was 19,815 m3 s−1 (2000–2019), with glacier melt contributing 29% during the summer season Northern basins may see streamflow declines up to 50% in the dry season, while glacierized southern basins could see increases up to 25%

Climate change includes the change of the long-term average values and the change of the tails of probability density functions, where the extreme events are located. However, obtaining average values are more straightforward than the high temporal resolution information necessary to catch the extreme events on those tails. Such information is difficult to get in areas lacking sufficient rain stations. Thanks to the development of Satellite Precipitation Estimates with a daily resolution, this problem has been overcome, so Extreme Precipitation Indices (EPI) can be calculated for the entire Colombian territory. However, Colombia is strongly affected by the ENSO (El Niño—Southern Oscillation) phenomenon. Therefore, it is pertinent to ask if the EPI’s long-term change due to climate change is more critical than the anomalies due to climate variability induced by the warm and cold phases of ENSO (El Niño and La Niña, respectively). In this work, we built EPI annual time series at each grid-point of the selected Satellite Precipitation Estimate (CHIRPSv2) over Colombia to answer the previous question. Then, the Mann-Whitney-Wilcoxon test was used to compare the samples drawn in each case (i.e., change tests due to both long-term and climatic variability). After performing the analyses, we realized that the importance of the change depends on the region analyzed and the considered EPI. However, some general conclusions became evident: during El Niño years (La Niña), EPI’s anomaly follows the general trend of reduction -drier conditions- (increase; -wetter conditions-) observed in Colombian annual precipitation amount, but only on the Pacific, the Caribbean, and the Andean region. In the Eastern plains of Colombia (Orinoquía and Amazonian region), EPI show a certain insensitivity to change due to climatic variability. On the other hand, EPI’s long-term changes in the Pacific, the Caribbean, and the Andean region are spatially scattered. Still, long-term changes in the eastern plains have a moderate spatial consistency with statistical significance.

There is a wide variety of drought indices, yet a consensus on suitable indices and temporal scales for monitoring streamflow drought remains elusive across diverse hydrological settings. Considering the growing interest in spatially distributed indices for ungauged areas, this study addresses the following questions: (i) What temporal scales of precipitation-based indices are most suitable to assess streamflow drought in catchments with different hydrological regimes? (ii) Do soil moisture indices outperform meteorological indices as proxies for streamflow drought? (iii) Are snow indices more effective than meteorological indices for assessing streamflow drought in snow-influenced catchments? To answer these questions, we examined 100 near-natural catchments in Chile with four hydrological regimes, using the standardised precipitation index (SPI), standardised precipitation evapotranspiration index (SPEI), empirical standardised soil moisture index (ESSMI), and standardised snow water equivalent index (SWEI), aggregated across various temporal scales. Cross-correlation and event coincidence analysis were applied between these indices and the standardised streamflow index at a temporal scale of 1 month (SSI-1), as representative of streamflow drought events. Our results underscore that there is not a single drought index and temporal scale best suited to characterise all streamflow droughts in Chile, and their suitability largely depends on catchment memory. Specifically, in snowmelt-driven catchments characterised by a slow streamflow response to precipitation, the SPI at accumulation periods of 12–24 months serves as the best proxy for characterising streamflow droughts, with median correlation and coincidence rates of approximately 0.70–0.75 and 0.58–0.75, respectively. In contrast, the SPI at a 3-month accumulation period is the best proxy over faster-response rainfall-driven catchments, with median coincidence rates of around 0.55. Despite soil moisture and snowpack being key variables that modulate the propagation of meteorological deficits into hydrological ones, meteorological indices are better proxies for streamflow drought. Finally, to exclude the influence of non-drought periods, we recommend using the event coincidence analysis, a method that helps assessing the suitability of meteorological, soil moisture, and/or snow drought indices as proxies for streamflow drought events.

We introduce Saudi Rainfall (SaRa), a gridded historical and near-real-time precipitation (P) product specifically designed for the Arabian Peninsula, one of the most arid, water-stressed, and data-sparse regions on Earth. The product has an hourly 0.1° resolution spanning 1979 to the present and is continuously updated with a latency of less than 2 h. The algorithm underpinning the product involves 18 machine learning model stacks trained for different combinations of satellite and (re)analysis P products along with several static predictors. As a training target, hourly and daily P observations from gauges in Saudi Arabia (n = 113) and globally (n = 14 256) are used. To evaluate the performance of SaRa, we carried out the most comprehensive evaluation of gridded P products in the region to date, using observations from independent gauges (randomly excluded from training) in Saudi Arabia as a reference (n = 119). Among the 20 evaluated P products, our new product, SaRa, consistently ranked first across all evaluation metrics, including the Kling–Gupta efficiency (KGE), correlation, bias, peak bias, wet-day bias, and critical success index. Notably, SaRa achieved a median KGE – a summary statistic combining correlation, bias, and variability – of 0.36, while widely used non-gauge-based products such as CHIRP, ERA5, GSMaP V8, and IMERG-L V07 achieved values of −0.07, 0.21, −0.13, and −0.39, respectively. SaRa also outperformed four gauge-based products such as CHIRPS V2, CPC Unified, IMERG-F V07, and MSWEP V2.8 which had median KGE values of 0.17, −0.03, 0.29, and 0.20, respectively. Our new P product – available at https://www.gloh2o.org/sara (last access: 24 September 2025) – addresses a crucial need in the Arabian Peninsula, providing a robust and reliable dataset to support hydrological modeling, water resource assessments, flood management, and climate research.

We introduce Saudi Rainfall (SaRa), a gridded historical and near-real-time precipitation (P) product specifically designed for the Arabian Peninsula, one of the most arid, water-stressed, and data-sparse regions on Earth. The product has an hourly 0.1° resolution spanning 1979 to the present and is continuously updated with a latency of less than 2 h. The algorithm underpinning the product involves 18 machine learning model stacks trained for different combinations of satellite and (re)analysis P products along with several static predictors. As a training target, hourly and daily P observations from gauges in Saudi Arabia (n = 113) and globally (n = 14 256) are used. To evaluate the performance of SaRa, we carried out the most comprehensive evaluation of gridded P products in the region to date, using observations from independent gauges (randomly excluded from training) in Saudi Arabia as a reference (n = 119). Among the 20 evaluated P products, our new product, SaRa, consistently ranked first across all evaluation metrics, including the Kling-Gupta efficiency (KGE), correlation, bias, peak bias, wet-day bias, and critical success index. Notably, SaRa achieved a median KGE - a summary statistic combining correlation, bias, and variability - of 0.36, while widely used non-gauge-based products such as CHIRP, ERA5, GSMaP V8, and IMERG-L V07 achieved values of -0.07, 0.21, -0.13, and -0.39, respectively. SaRa also outperformed four gauge-based products such as CHIRPS V2, CPC Unified, IMERG-F V07, and MSWEP V2.8 which had median KGE values of 0.17, -0.03, 0.29, and 0.20, respectively. Our new P product - available at

Western Patagonia (40–56°S) is a clear example of how the systematic lack of publicly available data and poor quality control protocols have hindered further hydrometeorological studies. To address these limitations, we present PatagoniaMet (PMET), a compilation of ground-based hydrometeorological data (PMET-obs; 1950–2020), and a daily gridded product of precipitation and temperature (PMET-sim; 1980–2020). PMET-obs was developed considering a 4-step quality control process applied to 523 hydrometeorological time series obtained from eight institutions in Chile and Argentina. Following current guidelines for hydrological datasets, several climatic and geographic attributes were derived for each catchment. PMET-sim was developed using statistical bias correction procedures, spatial regression models and hydrological methods, and was compared against other bias-corrected alternatives using hydrological modelling. PMET-sim was able to achieve Kling-Gupta efficiencies greater than 0.7 in 72% of the catchments, while other alternatives exceeded this threshold in only 50% of the catchments. PatagoniaMet represents an important milestone in the availability of hydro-meteorological data that will facilitate new studies in one of the largest freshwater ecosystems in the world.

We introduce Version 3 (V3) of the gridded near real-time Multi-Source Weighted-Ensemble Precipitation (MSWEP) product -- the first fully global, historical machine learning powered precipitation (P) dataset, developed to meet the growing demand for timely and accurate P estimates amid escalating climate challenges. MSWEP V3 provides hourly data at 0.1\\(^\\circ\\) resolution from 1979 to the present, continuously updated with a latency of approximately two hours. Development follows a two-stage process. First, baseline P fields are generated using machine learning model stacks that integrate satellite- and (re)analysis-based P and air-temperature products, along with static variables. The models are trained using hourly and daily observations from 15,959 P gauges worldwide. Second, these baseline P fields are corrected using daily and monthly gauge observations from 57,666 and 86,000 stations globally. To assess MSWEP V3's baseline performance, we evaluated 19 (quasi-) global gridded P products -- including both uncorrected and gauge-based products -- using observations from an independent set of 15,958 gauges excluded from the first training stage. The MSWEP V3 baseline achieved a median daily Kling-Gupta Efficiency (KGE) of 0.69, outperforming all evaluated products. Other uncorrected products achieved median daily KGE values of 0.61 (ERA5), 0.46 (IMERG-L V7), 0.38 (GSMaP V8), and 0.31 (CHIRP). Using leave-one-out cross-validation, the daily gauge correction was found to improve the median daily correlation by 0.09, constrained by the already strong baseline performance. We anticipate that MSWEP V3 -- accessible at www.gloh2o.org/mswep -- will enable more reliable monitoring, forecasting, and management of water-related risks in a variable and changing climate.