Hydrological datasets

Over the last few years, significant progress has been made in the development of convection-permitting climate models (CPCMs), especially in improving precipitation estimates in regions with complex terrain. Recently, the South American Affinity Group (SAAG) developed a novel high-resolution dataset – hereafter referred to as the WRF-SAAG dataset – by dynamically downscaling the ERA5 reanalysis using the Weather Research and Forecasting (WRF) model over South America for the period 2000–2021. Here, we evaluate WRF-SAAG outputs across continental Chile and compare them with two widely used gridded meteorological products, CR2MET and RF-MEP, which combine reanalysis data with in situ measurements. In particular, we analyze spatial differences among climatological estimates from the three products, and we assess the quality of daily precipitation and daily maximum and minimum temperature simulations using observations from meteorological stations over the period April/2001–March/2018. We also compare the potential of these climate datasets for hydrological modeling using a conceptual, bucket-style rainfall-runoff model, evaluating the results against streamflow observations, satellite remote sensing (SRS) products, and a snow water equivalent (SWE) reanalysis product. We found that the detection accuracy varied within each Chilean macrozone, with worse performance in the Far North (17.5–26° S) and Patagonia (43.7–56° S). The evaluation of daily precipitation against station observations using Tang's Kling–Gupta efficiency (KGE T ) reveals that all datasets performed better in reproducing precipitation in rainy regions (median KGE T ≥ 0.65 in the Southern macrozone), while in arid areas such as the Near North during summer, the median KGE T was negative. The CR2MET product consistently provided the best performance for extreme precipitation and temperature, partly because it includes information from the stations used for evaluation. Finally, the application of the hydrological model shows that although WRF-SAAG simulations achieved runoff estimations comparable to the best observation-based gridded products (i.e., CR2MET or RF-MEP), with the best results in the southern macrozone they provided relatively lower performance in evapotranspiration (ET) and soil moisture estimates, as well as SWE simulations of competitive quality. More broadly, the results…

Spatially resolved, multi-year observations of stable water isotopes in precipitation remain scarce for high-relief maritime islands. We present a historical dataset of monthly precipitation isotopes collected between September 2000 and December 2003 at 15 stations on Jeju Island, South Korea, spanning elevations of 5–1230 ma.s.l. The dataset contains 462 paired δ 18 O and δ 2 H measurements, deuterium excess ( d -excess = δ 2 H − 8 × δ 18 O), station and sampling metadata, and 488 station-month records of precipitation and air-temperature estimates, including 26 records without corresponding isotope measurements. Missing isotope observations were not imputed. Individual δ 18 O and δ 2 H values range from −13.2 ‰ to −2.0 ‰ and from −93.3 ‰ to −5.8 ‰, respectively, and generally exhibit lower values in summer than during the cool season. Unweighted station means for the common 2002–2003 period yield elevation gradients of −0.146 ± 0.013 ‰ per 100 m for δ 18 O and −1.043 ± 0.128 ‰ per 100 m for δ 2 H, with uncertainties expressed as standard errors. An island–nearshore comparison with isotope-enabled model output provides context for broad seasonal variability. By recovering and openly publishing observations collected more than two decades ago, this dataset preserves a historical baseline for subsequent precipitation-isotope monitoring and hydrological studies on Jeju Island. The data are available at https://doi.org/10.22747/paper_data.20260219.42 (Lee et al., 2026).

Groundwater depletion poses a challenge to water security, but the coarse spatial resolution of Gravity Recovery and Climate Experiment (GRACE) observations limits the characterization of groundwater variability at regional and local scales. Here, we present a 1 km monthly global groundwater storage anomaly (GWSA) dataset covering April 2002–December 2020. The dataset was generated using Singular Spectrum Analysis (SSA) for temporal gap filling, 19 high-resolution hydroclimatic and geophysical predictors, and aquifer-stratified machine-learning downscaling. Extreme Gradient Boosting (XGBoost) and deep neural networks (DNN) were evaluated as candidate models, and XGBoost was selected for dataset production based on internal, cross-scale, and in-situ evaluations. When aggregated to the original GRACE scale, the 1 km product agreed closely with the original GWSA ( R 2 = 0.972, RMSE = 2.10 cm). Independent evaluation used a global archive of 152 069 groundwater-monitoring wells and a 1° spatial-block calibration–validation design. Effective specific yield ( S yeff ) was estimated from calibration blocks and transferred to held-out blocks before comparison at a common 0.25° grid. For XGBoost, the held-out evaluation yielded R 2 = 0.538, RMSE = 0.378 cm yr −1 , and Bias = 0.106 cm yr −1 . The association between modeled and observation-derived trends remained similar under ± 30 % perturbations in S yeff , although amplitude-related metrics were more sensitive to the conversion coefficient. Standardized time-series comparisons and monthly evaluations of representative aquifers showed better agreement for long-term variability than for seasonal variability. Comparisons with published regional estimates showed close agreement in the North China Plain, weaker depletion magnitudes in northwestern India and California, and a trend-sign mismatch in the High Plains Aquifer during 2003–2013. Experiments using CSR, JPL, H08, and WaterGAP also showed that reconstruction performance and predictor importance depend on the target product. The dataset provides kilometre-scale estimates of groundwater-storage variability under the large-scale GRACE constraint. Local interpretation of the reconstructed spatial patterns requires additional groundwater-level, abstraction, and hydrogeological observations. The 1 km monthly global GWSA dataset is openly available from the National…

The TV-SAT signals received by the ground antennas of Satellite Microwave Links (SMLs) can be opportunistically used for detecting and quantifying precipitation. Consequently, SMLs have the potential to serve as low-cost rainfall sensors complementing conventional instruments. However, a major challenge for opportunistic sensors, including both SMLs and their terrestrial counterpart – Commercial Microwave Links (CMLs) – is related to data ownership and accessibility constraints, possibly hindering progress in the development of processing tools and validation studies. This limitation highlights the importance of openly accessible datasets. While CML open datasets are already available, there are no large-scale SML datasets in public repositories. To address this gap, we introduce the OpenSat4Weather dataset, a comprehensive and openly accessible collection of measurements from 215 SML sensors deployed in Southern France, over a five-month period from August to December 2022. The dataset is accessible at https://doi.org/10.5281/zenodo.16530166 (Nebuloni et al., 2025b). OpenSat4Weather also includes concurrent conventional measurements, namely, 6 min rainfall depths from 113 operational rain gauges, and radar-based estimates of rainfall intensity along each SML path. The radar data were derived from the gauge-adjusted weather radar product PANTHERE provided by Météo-France. Additionally, the dataset stores ERA5 reanalysis data of the 0 °C isotherm height for rain height estimation, which are essential for accurately converting the received signal level into rainfall intensity. In this paper, we detail the OpenSat4Weather data preparation process and draw statistics of data availability. We then provide a descriptive analysis of the dataset, including rainfall characteristics, derived from rain gauges, statistical properties of the SML received signal levels, and a comparison between SML and radar data. In addition, we illustrate examples of disturbances and anomalous patterns observed in raw SML signals. Finally, we provide some practical guidelines for processing and analyzing SML data. Our ultimate goal is to promote open research initiatives aimed at accelerating the development of SML-based applications. Enhancing rainfall monitoring by opportunistic sensors has the potential to provide substantial benefits, particularly in regions where conventional…

Global warming is increasing the frequency and intensity of extreme droughts, threatening terrestrial ecosystem structure and function. However, existing indices often fail to provide a comprehensive representation of the water cycle, and high-resolution datasets for characterizing vegetation responses to drought remain limited. To address this gap, we first developed a monthly Vertical Composite Drought Index (VCDI) by integrating precipitation, surface runoff, soil moisture, and groundwater storage anomalies. This index summarizes multidimensional water constraints across the “atmosphere-surface-subsurface” continuum and captures cumulative effects of drought across multiple timescales. Furthermore, VCDI, land surface temperature, and vegetation greenness were combined to produce a monthly Vegetation Drought Resistance Index (VDRI) dataset for China at 250 m resolution from 2000 to 2022. Multi-source evaluations indicate that VDRI provides spatially detailed and temporally consistent information on vegetation conditions under concurrent hydroclimatic and thermal stress. The datasets support ecosystem assessment, ecological drought monitoring, wildfire risk analysis, and benchmarking for drought-impact studies and related ecological models.

Drought assessment is an extremely complex task that relies on high-quality datasets, posing a major challenge for scientists and water managers. Here, we present the Global Multi-Index Drought (GMID), a multiscalar high-resolution dataset that includes five standardised drought indices: the Standardised Precipitation Index (SPI), the Evaporative Drought Demand Index (EDDI), the Standardised Precipitation Evapotranspiration Index (SPEI), the Standardised Evapotranspiration Deficit Index (SEDI), and the Standardised Vapor Pressure Deficit Index (SVPDI). This dataset is available globally at multiple time scales (1- to 12-, 18-, 24-, 48-month) for the period 1980–2024, with high spatial (0.1°) and temporal (weekly and monthly) resolution. The GMID dataset contains two versions of the drought indices; one generated using a traditional single-distribution approach, and another using a new flexible-distribution approach allowing the selection of probability distributions providing the best-fitting normalised series in each case. GMID adds significant value to previous drought data products through its high resolution and flexible multi-index nature, representing a comprehensive and robust long-term dataset for drought (and flash drought) assessment at the global scale.

Accurate estimation of regional-scale terrestrial carbon budgets is of great importance but remains challenging. With particular advantages, the Long Short-Term Memory (LSTM) network method shows potential for improving regional carbon budget upscaling estimations. Here, using LSTM, we upscaled regional net ecosystem carbon exchange (NEE) with available flux tower measurements and satellite land surface observations in North America. With well-established ecosystem-specific LSTMs, we produced monthly NEE at a spatial resolution of 0.1 ° ×0.1 ° over 2001–2021 (labelled as MemoryFlux). Unlike existing upscaling estimates, our dataset properly identified the Midwest Corn Belt as a region of large seasonal carbon uptake during the peak growing season, a feature revealed by previous top-down studies and recognized as a model benchmark. Moreover, the seasonal variations in NEE estimated by MemoryFlux were strongly correlated with those from independent atmospheric inversions, including the ensemble mean of the Orbiting Carbon Observatory-2 Model Intercomparison Project (OCO-2 v10 MIP; r =0.96, p <0.001) and CarbonTracker2022 (CT2022) ( r =0.97, p <0.001). The mean annual NEE was estimated at −1.27 ± 0.12 Pg C yr −1 , which was closer in magnitude to inversions (−0.83 to −0.70 Pg C yr −1 ) than existing upscaling estimates (−3.30 to −1.68 Pg C yr −1 ). In addition, MemoryFlux captured spatial NEE anomaly patterns associated with six selected severe drought and flood events. We further found that explicitly incorporating historical predictor information improved the representation of NEE interannual variability and spatial anomalies associated with climate extremes. MemoryFlux provides an improved bottom-up estimation of North American NEE and shows greater consistency in magnitude with independent atmospheric inversion estimates than several existing EC-based upscaling products. The MemoryFlux dataset is available at https://doi.org/10.5281/zenodo.20482274 (Huang and He, 2026).

Accurate streamflow prediction is critical for effective water resource management, and recent advances in deep learning and multi‐basin data sets have significantly improved model skill. Global deep‐learning models, trained on thousands of catchments have demonstrated strong generalization across diverse climates and continents. However, these models rely solely on globally available meteorological inputs, potentially overlooking the value of nationally developed data sets with enhanced regional accuracy. In this study, we assess the benefit of using Canada's high‐resolution national reanalysis data set (CaSR) in long short‐term memory (LSTM) models for streamflow prediction. We compare model performance using different meteorological forcings, global (ERA5‐Land), continental (Daymet), national (CaSR), and a combined “Complete” model, across over 1,000 Canadian stream gauges. Model performance is evaluated using the Nash–Sutcliffe Efficiency (NSE), Kling‐Gupta Efficiency (KGE) and related metrics. We also benchmark our best‐performing model against Google's global LSTM streamflow model at 412 Canadian gauges. Results show that national‐scale deep‐learning models using CaSR outperforms deep‐learning models using only global or continental inputs. The Complete model achieved the highest performance, with a median KGE 0.886−exceeding the 0.839 achieved by Google's global model within Canada. These findings suggest that nationally trained models using a wide array of inputs, including national meteorological data sets, can outperform state‐of‐the‐art global deep‐learning models that do not use such data when applied within their region of origin. For countries with dense observational networks and distinct hydrological regimes, investing in national data sets and models can enhance deep‐learning model prediction accuracy, leading to improved resilience to climate extremes and local water management decisions.

For analysing karst hydrogeological systems, observations of karst springs and cave drips are considered indispensable. In addition to hydrometric observations, knowing the oxygen and hydrogen stable isotope ratios has improved the understanding of vadose zone and aquifer dynamics, likewise supporting system characterisation and modelling. However, limited accessibility and high costs of the analysis of stable isotopes in karst aquifers have hindered progress in karst research and impeded the accurate understanding of karst processes especially when it comes to comparative or large-scale studies. In this study, we present our workflow to compile the WoKaS-Iso database, the first extensive collection of time series data for Oxygen-18 and Deuterium isotopes in karst springs and cave drip water from diverse sources, encompassing publications, theses, reports, online archives, and collaborative initiatives worldwide. The database incorporates data sourced from 241 springs and 74 caves, of which 73 caves contain 365 individual drip sites and one cave contains input data only. In total, the database comprises 1007 time series, including 389 spring time series and 618 cave drip water time series. The compiled isotope records span the period from 1967 to 2024, with the strongest temporal coverage during the 2010s and continuing coverage into the early 2020s. Of these sites, 148 springs and 64 caves contain both δ 18 O and δ 2 H records, while 93 springs and 9 caves contain only one isotope tracer. These datasets provide coverage across significant karst regions globally, spanning China, the USA, Europe, the Middle East, and Australia. Within datasets, 79 % for springs and 68 % for cave drip water exhibit resolutions finer than monthly intervals. In addition, by integrating isotopic records with ancillary environmental variables including spring discharge, cave drip rate, precipitation, and rainwater isotopes, the database offers a more comprehensive perspective on hydrological behaviours in karst aquifers, hence advancing hydrogeological characterisation and modelling. The WoKaS-Iso database not only deepens the understanding of the complex systems but also promotes sustainable water resource management as well as the potential to foster collaborative research. The database can be accessed at: https://doi.org/10.25532/OPARA-909 (Zang, 2025).

Seasonal snow is a critical component of the water cycle in the Southern Andes of Chile and Argentina. Quantitative assessments of snow accumulation remain constrained by the scarcity, heterogeneity, and inconsistency of in situ observations, resulting in substantial uncertainties in mountain hydrological modeling. To address this gap, we compiled and quality-controlled snow depth observations to produce a consistent daily dataset from 81 stations between 21 and 54° S covering 2010–2024. Our quality-control procedure involved data compilation and preprocessing, followed by harmonization of snow depth observations, primarily by correcting the ground reference level relative to the soil surface during snow-free periods and removing anomalous spikes and observations outside physically plausible ranges. This process improved data reliability, increasing the Physical Consistency Index (PCI) from 87 % to 95 % at selected stations in the Maipo River Basin, while reducing the median number of days with data across stations by 23 % (from 1392 to 1074 d). The snow depth data availability increased from one station in 2010 to 57 stations in 2024, largely driven by the expansion of the monitoring network operated by the General Directorate of Water (DGA), Chile. However, this expansion remains spatially uneven across the Andean zones. The Mediterranean Andes have the largest number of stations (39), as well as the largest number of stations with highly complete records with 17 stations reaching 80 %–100 % data coverage, compared with only nine stations each in the Arid and Wet Andes, highlighting persistent spatial and temporal gaps. Using this newly quality-controlled dataset, we find that snow depth generally increases from the Arid to the Wet Andes in association with increasing precipitation, whereas its relationship with elevation is not consistently positive. The snow depth-elevation relationship is nonlinear in the Arid and Mediterranean Andes, with maximum observed snow depths at 4300 m a.s.l. in the Elqui River Basin and 3300 m a.s.l. in the Maipo River Basin. In contrast, the available observations indicate a positive snow depth-elevation relationship in the Wet Andes (Maule–Itata River Basin). However, these relationships should be interpreted in the context of the specific spatial and temporal conditions represented by the available observations. This…

The Canadian Surface Reanalysis (CaSR) includes an offline, high-resolution gridded total precipitation reanalysis designed to provide accurate estimates across North America. This product, referred to as CaPA-24h, builds on the Canadian Precipitation Analysis (CaPA) system of Environment and Climate Change Canada (ECCC). It combines a dense network of daily surface observations with a background field from the CaSR dynamical component, using updated quality-control and assimilation procedures to filter spurious observations. This study evaluates the CaPA-24h fields produced in CaSR v3.2, together with their background field, and compares them with the previous version (v2.1) as well as with two independent datasets, ERA5-Land and PRISM. Results show substantial improvements in v3.2, particularly in data-sparse regions, with an enhanced representation of precipitation events of different intensities. Compared to ERA5-Land, CaPA-24h v3.2 provides more accurate seasonal and regional precipitation patterns, while evaluations against PRISM confirm this improved performance. However, biases persist in southern and western mountainous areas, especially for orographic precipitation. A first-time assessment of the hourly disaggregated product reveals limitations in the diurnal cycle representation, indicating the need for refined disaggregation methods and background field generation. Overall, CaPA-24h v3.2 delivers a reliable and well-established gridded precipitation dataset, offering a valuable resource for hydrological, climatological, and impact studies across North America.

Floods are among the most damaging disasters worldwide, necessitating high-resolution modeling of where terrain and environmental conditions predispose societies to flooding. While geospatial data are invaluable for flood susceptibility mapping, comprehensive and globally consistent datasets remain scarce. We present the Global Flood Susceptibility Map (GFSM v1), a globally harmonized 30 m flood susceptibility dataset produced by incorporating multi-source flood conditioning factors accounting for topographic, hydrological, meteorological, and anthropogenic characteristics within a machine learning framework. A gradient-boosted tree model was trained on ~17,000 curated grid tiles with ~30.45 million training samples, using a global flood inundation dataset as training labels and nine conditioning factors (elevation, slope, aspect, wetness index, vegetation index, height above nearest drainage, water/roads proximity, and rainfall frequency). The model was deployed based on geopolitical and climate strata (units n = 192) to generate global pixel-level susceptibility. Rigorous cross-validation indicates strong model predictive accuracy (median area-under-curve ~0.95), and the output captures localized flood-prone regions worldwide. GFSM v1 offers a global open-source, high-resolution resource to support informed flood risk management, particularly in data-sparse regions.

Long-term monitoring of global river discharge has been hindered by the uneven distribution of gauging stations and limited data accessibility, a challenge that is particularly acute in China. Although China contains one of the world's densest river networks, high-frequency in situ discharge observations remain largely unavailable in the international public domain. To address this gap, we compiled daily discharge records from 1196 gauges across China, comprising approximately 2.33 ×10 6 observations – 39 times as many gauges as are currently available for the region in the Global Runoff Data Centre (GRDC). Leveraging this unprecedented collection of in situ discharge records, along with river width time series derived from Landsat and Sentinel-2 imagery and gauge-specific hydraulic geometry relationships, we reconstructed and extended daily river discharge observations for 310 gauges from 1990 to 2024, resulting in the China Daily River Discharge Records (CDR 2 ) dataset. Compared with existing global satellite-derived discharge products, CDR 2 expands the number of available gauges in China by more than fivefold while delivering substantially improved performance, achieving a median Kling–Gupta efficiency of 0.66 during validation. Moreover, uncertainties propagated from the fitted hydraulic geometry parameters remain low, with a median relative uncertainty of only 11.89 % in the reconstructed discharge estimates. Sensitivity analyses further indicate that discharge estimation accuracy increases markedly with greater river width variability and stronger hydraulic sensitivity. Trend analysis reveals that nearly 65 % of gauges exhibit declining discharge over 1990–2024, with a median relative trend of −0.21 % yr −1 , most pronounced in the Haihe, Liaohe, Yellow River, and middle Yangtze River basins. As the most extensive satellite-derived, gauge-constrained river discharge dataset currently available for China at daily resolution, CDR 2 bridges a critical geographic gap in global river monitoring and provides a valuable benchmark for future discharge estimation, hydrological research, water resources management, and the calibration and evaluation of satellite missions. The CDR 2 dataset is publicly available on Zenodo at https://doi.org/10.5281/zenodo.22231453 (Wang and Li, 2026).

The lack of reliable, spatiotemporally-completed and millennium-long drought dataset for China has limited a full-field and accurate understanding of drought variability over the past millennium. Here, we present a millennium-long gridded dataset of summer (June-August) Standardized Precipitation Evapotranspiration Index (SPEI) for China at 1.875° spatial resolution and annual temporal resolution. This dataset was generated using the paleoclimate data assimilation (PDA) approach by assimilating an expanded proxy network comprising 186 tree rings, 69 historical documents, three stalagmites and two ice cores from China and adjacent regions - addressing the limitations of previous gridded drought reconstructions that lacked proxies in eastern China and had sparsely-distributed proxies in northwestern China. Validation indicated that this dataset exhibits substantially-improved reliability compared to model-only calculations and previous PDA-based reconstruction. As a significantly-improved, millennium-long gridded SPEI dataset for China, this product provides essential support for elucidating the spatiotemporal evolution of drought variability over the past millennium, contextualizing current droughts within that period and evaluating climate model performance in simulating long-term drought events.

The availability of large-sample hydrometeorological datasets, now widespread across many regions worldwide, has changed hydrological catchment modelling. Assessing model performance is an essential component of any modelling exercise, and an important question is how to interpret performance measure values. Performances of uncalibrated bucket-type models vary significantly across regions and can reach NSE values of 0.8 or higher, particularly in humid or snow-dominated catchments. This implies that using a fixed value for a performance measure to judge model performance, as sometimes suggested in the literature, is inappropriate. Instead, one should consider that, given local hydroclimatic conditions and the quality of the available data, the performance we should expect from any model in a particular catchment can vary widely. At the same time, a perfect fit (NSE value of 1) is usually impossible to achieve due to errors and uncertainties in the model and data. Therefore, it is helpful to compare model performances to lower and upper benchmarks. The purpose of this study was two-fold. First, we examined how to compute lower bounds, including determining appropriate ensemble sizes, assessing the effects of parameter ranges, deciding whether to use random or regional parameter sets, and evaluating how best to aggregate the ensemble of simulations. We also examined the relationships between lower and upper benchmarks and catchment characteristics. Secondly, we utilised these findings to compute both lower and upper benchmarks for many of the existing large sample datasets. By providing these values to the modelling community, we aim to facilitate the broader use of lower and upper benchmarks in large-sample hydrological modelling studies. We argue that these values are valuable as they provide a basis for evaluating model performance across the various large-sample datasets. This will allow assessment of model performance, considering what one could and should expect for a particular catchment.

Latent heat flux ( L E ) provides a direct representation of terrestrial evapotranspiration (ET) and plays a critical role in hydrological cycle studies, land surface model development, and the evaluation of remotely sensed evapotranspiration products. Although flux observations based on the eddy covariance technique are widely regarded as essential benchmark data for evapotranspiration estimation, existing ChinaFlux observations are generally limited by short observation periods and extensive data gaps, which substantially constrain their applicability in long-term change analyses and multi-scale studies. To address these limitations, we developed a gap-filling and temporal prolongation framework specifically designed for half-hourly L E and established a continuous ground-based benchmark dataset covering China for the period 2000–2024 based on observations from 50 ChinaFlux sites. The framework is built upon an automated machine learning approach (AutoML) and integrates ERA5-Land reanalysis data with MODIS vegetation indices, enabling accurate gap-filling within observation periods and reliable prolongation beyond observation intervals. Comprehensive evaluations demonstrate that the AutoML framework achieves high accuracy at the half-hourly scale across different gap-length scenarios, with an overall correlation coefficient (CC) of 0.862 and a root mean square error (RMSE) of 33.75 W m −2 , and it substantially outperforms conventional methods under long-gap conditions (i.e., 7 and 30 d) introduced in the artificial gap experiments. The forward and backward prolongation results show high consistency (CC values of 0.902 and 0.896, respectively) and exhibit robust temporal stability under varying training data lengths. Multi-timescale validations further indicate that the prolonged L E data reasonably reproduce diurnal variations, seasonal cycles, and interannual variability from half-hourly to daily and monthly scales. Comparisons with ChinaFlux observations under strict quality control (half-hourly L E observations with QA/QC = 0) reveal good consistency across different temporal scales, underlying surface types, and climate zones. Interpretability analysis based on Shapley Additive Explanations (SHAP) indicates that energy supply consistently dominates L E variability, while vegetation state and water availability modulate their relative importance…

Access to high-resolution long-term earth system projections is essential for advancing research in hydrology, agriculture, disaster management, and adaptation planning. This Data Descriptor presents an Artificial Intelligence (AI)-refined ensemble of Earth system model (ESM) projections for the conterminous United States at 1/24° (~4 km) spatial resolution. The dataset was generated by downscaling ten Coupled Models Intercomparison Project phase 6 (CMIP6) Earth system models (ESMs) under two emissions scenarios (SSP245 and SSP585) for an 80-year period (1980–2059). Two AI-driven methods, Super-Resolution Convolutional Neural Networks (SRCNN) and Super-Resolution Generative Adversarial Networks (SRGAN), were applied to produce high-resolution daily precipitation and minimum/maximum temperature. This descriptor documents the input datasets, processing workflow, bias-correction steps, file structure, variables, spatial and temporal coverage, and technical validation of the released data. Validation analyses compare the generated products with Daymet observations and existing downscaled datasets to characterize spatial patterns, biases, temporal consistency, and differences among products. The dataset provides daily high-resolution historical and future earth system projections that can support regional, impact assessment, and related applications, and details the AI downscaling framework, training protocols, and evaluation strategies to ensure reproducibility and usability.

Sub-daily climate data are increasingly important for climate-impact assessments because many processes, such as heat stress, hydrological extremes, land–surface energy balance, and renewable-energy production, respond non-linearly to intra-day variability. Daily data miss short-duration events and obscure sub-daily inter-variable interactions, creating biases in impact estimates. To address these limitations and provide consistent forcing across sectors, we generated a global hourly climate dataset by temporally disaggregating the Inter-Sectoral Impact Model Intercomparison Project Phase 3 (ISIMIP3) daily climate archives using the Temporal Disaggregation Tool (Teddy). The approach uses analogue-based hourly profiles from the bias-corrected WFDE5 (WATCH Forcing Data methodology applied to ERA5) reanalysis, preserves daily mass and energy, and maintains temporal coherence between variables. We illustrate the utility of the hourly data with four applications using the MPI Earth System Model (MPI-ESM) under ScenarioMIP pathway SSP3–7.0: (1) the fraction of wet hours, revealing rainfall intermittency not captured by daily wet-day metrics; (2) the number of hours with dangerous heat-index values, capturing joint diurnal cycles of temperature and humidity; (3) hours with wind speeds suitable for onshore wind-power generation; and (4) photovoltaic power potential calculated from radiation, temperature, and wind speed at hourly resolution. We discuss the benefits of preserving inter-variable timing, along with limitations such as reduced spatial coherence at sub-daily scales and potential constraints under strong climate-change signals. The resulting hourly ISIMIP3 dataset provides a harmonized foundation for more realistic sub-daily climate-impact modeling across sectors. The dataset is openly available through the ISIMIP repository under the following DOIs: https://doi.org/10.48364/ISIMIP.736682 (Bechtold et al., 2026a) and https://doi.org/10.48364/ISIMIP.170328 (Bechtold et al., 2026b).

We present the globally available dataset of Line-of-sight Gravity Differences (LGD) as a new data product to fill the long-standing gap of investigating sub-monthly surface mass change from the satellite gravimetry along-track perspective. The dataset is generated directly from Gravity Recovery and Climate Experiment Follow-on (GRACE-FO) Level-1B intersatellite ranging observations, positioned conceptually between Level-1B measurements and Level-2 monthly gravity field solutions. Provided as along-track time series, LGD delivers instantaneous gravity change at satellite altitude, thus ensuring the fidelity of measurements by avoiding the complex preprocessing, regularization, and filtering applied to monthly products. The instantaneous LGD measurements enable monitoring of sub-monthly terrestrial water storage variability and provide accurate information on timing and magnitude of water mass changes. GRACE-FO's ground track allows LGD retrievals every 5–6 d in most low and mid latitude regions, while the revisit interval becomes shorter toward higher latitudes. Our LGD data products also contain climatology in the form of along-track gravity variations. We demonstrate its potential through case studies, including along-track diagnosis of flash drought evolution in the southeastern United States and the characterization of sub-monthly hydrological extremes in global hotspots. Results show that the LGD dataset has the potential to resolve the critical timing (e.g., onset and peak) of hydrological extremes, which is unattainable from monthly products. The LGD dataset is available at https://doi.org/10.5281/zenodo.17766177 (Tang et al., 2025a), which is expected to pave the way for advances in sub-monthly hydrological monitoring and can serve as a unique constraint for hydrological and geophysical models, opening new opportunities for Earth system sciences.

Copernicus Land Monitoring Service (CLMS) produces biogeophysical maps of the global land surface. The CLMS portfolio so far did not include actual evapotranspiration (ETa), despite it being a direct link between the energy, water and carbon cycles and its importance for global food security, efficient water resources management and weather forecasting. However, a global CLMS ETa product was recently developed and entered operational production at the end of 2025. It consists of water flux (ETa, evaporation and transpiration) and energy flux (latent and sensible heat) sub-products, has a spatial resolution of 300 m, dekadal (10 d mean) temporal resolution for water fluxes and daily temporal resolution for instantaneous energy fluxes, is produced in near-real-time, and (like all other CLMS products) is distributed under free and open data policy. It is based mainly on Copernicus input data with primary satellite imagery coming from the observations of OLCI and SLSTR sensors on board of Sentinel-3 satellites. Such product fills a gap in previously existing global and operational ETa products, thus satisfying a wide range of potential users' needs. In this paper, we describe, evaluate and justify the various design choices taken during the development of the ETa product, ranging from cloud masking and gap-filling, through derivation of biophysical traits, radiation components and weather forcings to spatial sharpening of the land surface temperature observations. Those data were then used to drive two ETa models: TSEB-PT and ETLook. A prototype implementation of the ETa processing chain was used to produce ETa data (using both models individually and their ensemble) across a globally representative range of climatic zones and plant functional types, which was validated against measurements from 206 Eddy Covariance flux tower sites. The ensemble resulted in the all-site root mean squared error (RMSE) of 0.87 mm d −1 (relative RMSE of 47 %, site range: 0.31–1.96 mm d −1 ), bias of 0.01 mm d −1 (relative bias of 0.6 %, site range: −1.39–1.50 mm d −1 ) and correlation coefficient of 0.81 (site mean of 0.77), which compares well with WaPOR global ETa dataset and therefore supports the operational production of global, near-real time, Copernicus-based 300 m ETa maps. Finally, we propose a number of potential evolutions of the CLMS ETa product, including enlarging…

Reservoir hypsometric relationships are crucial in determining water storage changes and modeling hydrological processes. Previous studies have demonstrated that uncertainties in reservoir surface area observations are a primary source of error in satellite‐derived storage estimates. However, little research has examined how variability in hyposometric data sets influences storage estimation accuracy. In addition to evaluating the impacts of hypsometric data set variability on storage estimation, this study introduced a new, simple hypsometric satellite and geometry‐based (SAGE) approach. SAGE combines remote sensing and geometry‐based methods to generate a global hypsometric data set for 7,250 reservoirs worldwide using the Global Reservoir and Dam (GRanD) database, and its performance is compared with existing data sets. The SAGE‐based results were validated against in situ Elevation‐Volume (E‐V), Area‐Volume (A‐V) relationships and storage dynamics from 132 reservoirs in the United States and Vietnam. Validation results showed strong performance, with median Normalized Root Mean Square Error (NRMSE) of 9.2% for A‐V curves and 12.4% for E‐V curves, compared to higher median NRMSE values from other global data sets, exceeding 20% for E‐V curves and ranging from 12.5% to 32.3% for A‐V curves. Reservoir storage dynamics were then estimated by integrating satellite‐derived surface areas from the Global Reservoir Surface Area Data set (GRSAD) with the SAGE‐derived AEV curves. The SAGE‐based approach significantly improved storage estimates, reducing errors by over 20% compared to other global AEV products, based on in situ storage data from 109 reservoirs in the United States and Vietnam. Overall, this study improved the accuracy of hypsometric relationships and storage dynamics, producing a more robust product for global hydrological applications.

Reported values of average annual peak snow water equivalent (SWE) in the mountains of the western United States differ by more than a factor of five, from not enough water to fill Lake Mead, the largest reservoir in the region, to enough water to fill Lake Mead five times. These products are not created equal. Many vastly underestimate snow water storage, and guidelines regarding which products are more accurate are crucial for using these products in any scientific analysis. Here, we review existing products and compare them to each other and to SWE derived from 26 aerial LiDAR acquisitions in Colorado, California, and Washington mountains of the western U.S. Products that match well with observations include (a) resolution of 9 km or finer, (b) reliance on observations of either fractional snow‐covered area or in situ SWE measurements, and (c) a well‐calibrated model framework that includes both quality precipitation estimates and accurate snowmelt. Many of the energy balance models reviewed had too much melt, likely from too much incoming radiation. Over all three regions, for the 14 flights that occurred close to peak SWE (March–April), the Western U.S. SWE Reanalysis (WUS‐SR) is least biased in reproducing near‐peak SWE (bias of 1.6%) and also best represented the spatial distribution of SWE (smallest mean absolute error). SNODAS and both of the University of Arizona products provide reasonable estimates (biases of 7.8%–10.2%). At the basin scale, ERA5‐Land also has small bias (−17.5%), but due to its coarser resolution, less accurate spatial representations.

Snow plays a critical role in cold-region environments, from climate regulation to water storage and from spring freshet to ecosystem services. However, quantification of snow accumulation and redistribution across spatio-temporal scales is challenging, owing to non-uniform snowfall, topographic attributes, wind redistribution, and interception and sublimation by vegetation. Manual snow surveys are often conducted to improve understanding of snow processes across scales and to support societal needs (e.g., seasonal water supply projections, flood forecasting, reservoir filling, etc.). Nevertheless, manual snow surveys are time-consuming and expensive, which limits their temporal frequency and are rarely conducted in remote locations. This manuscript presents snow data from manual snow surveys conducted from 2009 to 2024 across four small watersheds in high-latitude boreal plains of northern Alberta in Canada. The dataset includes various snow properties, including depth, density and snow water equivalent across different watersheds and land cover types (i.e., Open, Flat Low Lying, Coniferous and Mixed Deciduous). The dataset is expected to be useful in understanding snow processes in high-latitude boreal plains, to help calibrate snow models and to validate airborne lidar or satellite-derived snow depth measurements.

We present a 225-year (1799–2024) homogenized daily water level series for the Vistula River in Warsaw, comprising 82 453 observations. The construction of this consistent dataset required adjustments for changes in gauge location, shifts in gauge zero, differences in historical measurement units, and calendar discrepancies between the Julian and Gregorian systems. A small number of missing measurements was completed using the Long Short-Term Memory (LSTM) method, applied to measurements taken at the Warsaw gauge and parallel measurements from downstream water level gauge stations along the Vistula. The resulting dataset offers a robust foundation for long-term hydrological, climatic, and socio-environmental research. The dataset is openly available at Zenodo repository: https://doi.org/10.5281/zenodo.16919654 (Sobechowicz et al., 2025a).

Land surface temperature (LST) is an essential climate variable in geophysical, ecological, and environmental research. Remote sensing provides a unique observation approach for obtaining large-scale LST products. However, current official LST datasets (such as FY-4A) are limited by the unaddressed thermal radiation directionality effect and suffer from the spatial discontinuities due to the pervasive presence of clouds. Moreover, geostationary LST products have relatively coarser resolution than those of polar-orbiting satellites due to trade-off between spatial and temporal resolutions. Based on the official hourly FY-4A LST dataset, this study proposes a novel framework for generating angular-normalized, cloud-filled, and 0.01°-downscaled LST (ANCFDS-LST) product, encompassing directional ( T dir ), nadir ( T nadir ), and hemispherical ( T hemi ) LST layers. First, the angular-normalized T nadir and T hemi were generated using a time-evolving kernel driven model (TEKDM) with the inputs of multi-temporal FY-4A T dir . Subsequently, hypothetical clear-sky LST was predicted using a CatBoost model optimized via Bayesian methods. The cloudy-sky LST values were then derived through a cloud radiation force (CRF) correction. Finally, the 0.05° all-weather T dir , T nadir , and T hemi values were downscaled to 0.01° resolution using an improved hybrid downscaling algorithm (IHDA) combining kernel- and fusion-based methods. Taking the daytime clear-sky near-nadir VNP21A1 LST as reference, the 0.05° T dir before angular-normalization has a root mean squared difference (RMSD) of 4.53 K and a mean bias difference (MBD) of −1.85 K, whereas the angularly normalized T nadir has a much smaller RMSD of 2.56 K and a better MBD of 0.06 K. For the all-weather T hemi , temperature-based validation over 8 sites in the Heihe River Basin and Australia shows a root mean squared error (RMSE) and mean bias error (MBE) of 2.48 and −0.83 K under clear-sky conditions, 3.65 and −1.43 K under cloudy-sky conditions. After the spatial downscaling, the 0.01° all-weather T hemi exhibits abundant texture details. The generated LST products over the FY-4A disk from 2018 to 2023 exhibit enhanced angular consistency, spatial continuity, and finer resolution, offering valuable support for subsequent LST-related applications. The ANCFDS-LST data is freely available at…

Snowmelt runoff onset timing represents a critical hydrological parameter, particularly in mountainous regions where seasonal snow serves as a natural reservoir for downstream water resources. Despite this importance, high-resolution observations of snowmelt runoff onset across complex terrain are limited, due to challenges from sparse in situ monitoring networks, intermittent optical remote sensing data, and coarse passive microwave remote sensing data. To address this gap, we prepared a global snowmelt runoff onset timing dataset (https://doi.org/10.5281/zenodo.16953614, Gagliano et al., 2026) for the 10-year period spanning 2015–2024, with 80 m spatial resolution and 9.5 d average temporal resolution. We created this dataset by identifying backscatter minima empirically associated with runoff onset in a time series of Sentinel-1 C-band SAR images, with detection constrained by a custom MODIS-derived snow phenology dataset. We evaluated our dataset using in situ snow pillow estimates of runoff onset from 1116 automated weather stations across the Western United States, British Columbia, Norway, and Nepal, finding a median timing difference of −2.0 d and a median absolute deviation of 10.0 d. The local agreement between our runoff onset estimates and snow pillow runoff onset estimates varies with site-specific variables like forest cover fraction, SWE, and dataset temporal resolution. We characterized these dependencies to provide empirically-derived thresholds for quality filtering as well as guidance for interpretation and use of our products. The dataset includes global annual runoff onset products for each water year, annual local temporal resolution products for each water year, and 10-year composites of median runoff onset, median absolute deviation, and local temporal resolution. This unique combination of high spatial resolution, global coverage, and decade-long temporal coverage provides unprecedented detail for the study of snowmelt runoff onset across snow-covered regions. Our snowmelt runoff onset dataset enables an improved understanding of mountain hydrological processes and informs water resource management in snow-dominated watersheds.

Agricultural weather data are fundamental for the accurate estimate of evapotranspiration (ET), irrigation scheduling, and water-use accounting. In particular, reference ET provides a standardized atmospheric demand for water loss from a hypothetical well-watered grass (ETo) or alfalfa (ETr); however, weather stations may not adequately represent such climatic conditions. Weather data commonly contain errors from poor siting, sensor drift, and network management deficiencies. No standardized dataset exists over the contiguous United States (CONUS). Systematic errors affect ETo/ETr calculations and derived products. Notably, satellite-based platforms like OpenET require agricultural weather data to bias correct gridded reference ET to interpolate between satellite overpasses. CONUS-AgWeather is a benchmark dataset of daily agricultural weather data (precipitation, solar radiation, air temperature, humidity, wind speed, ETo, ETr) from 793 stations. This dataset contains 4,191,808 days (11,484 station-years, 1981–2020) and was produced through standardized and systematic quality control procedures and open-source software packages for time series inspection, outlier detection, corrections, and ETo/ETr calculations. CONUS-AgWeather is intended primarily to support OpenET in the Western U.S. but has broader applications.

We introduce OpenMesh, a publicly available dataset of wireless signal measurements from the NYC Mesh, a community-run communication network in New York City (NYC). While originally designed for affordable internet access, these links can be used opportunistically for high-resolution weather monitoring in diverse settings, including dense urban areas, providing 1 min sampling of signal strength measurements with dense spatial coverage across the study region. Spanning eight months of measurements (November 2023 to June 2024), the dataset comprises 103 wireless links in Lower Manhattan and Brooklyn, operating in three primary frequency ranges: 5–6 GHz (C-band), 24 GHz (K-band), and 58–70 GHz (V-band), part of the millimeter-wave (mmWave) spectrum. Our analysis incorporates meteorological records from two primary sources: (1) 37 Weather Underground (WUnderground) personal weather stations (PWS) across Manhattan and Brooklyn with 5 and 15 min sampling, and (2) three Automated Surface Observing System (ASOS) stations. The study period saw total rainfall of approximately 900 mm and included diverse weather events, from intense rain to snowstorms in winter 2023–2024. These events were captured by the NYC Mesh wireless network, with intense rainfall causing attenuation up to 30 dB and frequent outages, especially on high-frequency V-band links. This highlights the trade-off of using high-frequency bands in wireless networks: while precipitation might cause severe channel disruptions, this greater sensitivity simultaneously makes them effective opportunistic weather sensors. The OpenMesh dataset is available at https://doi.org/10.5281/zenodo.15287692 (Jacoby et al., 2025b). By publishing the dataset and demonstrating its usability, we aim to encourage further research that leverages wireless networks for real-time urban weather sensing.

In the development of satellite precipitation climate data records, enormous effort has been expended to ensure that satellite precipitation products (SPPs) match the most accurate in situ gauge measurements. In this work, we evaluate the accuracy of satellite precipitation estimates covering a range of rainfall accumulation from the subdaily to the monthly scales. Using the U.S. Climate Reference Network (USCRN) as a reference, results show that Climate Prediction Center morphing technique (CMORPH) and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) satellite products performed better at accumulation scales greater than 12 h than at smaller time scales. Both products exhibited strong contrast between the western and eastern halves of the contiguous United States (CONUS), with the best results obtained over the eastern United States. Normalized SPP estimations of rainfall event maxima for heavy precipitation events—those at or above the 90th percentile at each station—are consistent across the CONUS, with SPP estimates generally below the station observations. Residual analysis at the 90th percentile indicates that SPPs tend to underestimate extreme precipitation when compared to in situ observations, with very few exceptions. Generally, the SPPs underestimate the top-10 percentile observations by a factor of around 0.25–0.5 with respect to the in situ 90th percentile threshold. For maximum precipitation, SPP estimates diverge from gauges significantly at the 1–3-h accumulation time scales. Between 6 and 12 h, the SPP estimates tend to stabilize, and the differences between the SPPs and USCRN are reduced thereafter regardless of the location or for the entire CONUS. The use of satellite precipitation estimates for extreme event quantification at time scales shorter than 12 h should be done with caution.

The Penman–Monteith–Leuning (PML) model is a widely recognized diagnostic framework for estimating coupled terrestrial evapotranspiration (ET) and gross primary production (GPP). To address the critical need for high-fidelity, long-term, and near-present eco-hydrological records, we developed the PML-V2.2 dataset, spanning from 1982 to 2025. Driven by observation-constrained Multi-Source Weighted-Ensemble Precipitation (MSWEP) and Multi-Source Weather (MSWX) meteorological variables, the dataset comprises three complementary products: (1) PML-V2.2a, an 8 d 500 m MODIS/VIIRS satellite-based product (2000–2024 and 2012–2025) optimized for near-present monitoring (updated annually); (2) PML-V2.2b, a half-month 0.1° AVHRR-based product (1982–2020) anchoring long-term climate attribution; and (3) PML-V2.2c, a consolidated half-month 0.1° record integrating the above products for seamless 44-year continuity (1982–2025). Our methodological framework features an expanded bottom-up calibration using 208 flux sites (∼ 1400 site-years) across various plant functional types (PFTs) and a refined parameterization that explicitly distinguishes between irrigated and rainfed croplands. This distinction effectively mitigated systematic biases in agricultural regions, reducing ET and GPP estimation errors by 8.7 % and 16.2 %, respectively. Performance evaluation reveals high accuracy across PFTs (cross-validation Nash-Sutcliffe Efficiency, NSE > 0.60, absolute bias < 5 %), while top-down water-balance validation across 56 large river basins during 1982–2016 and 152 basins during 2003–2020 confirms high reliability (NSE: 0.89–0.91) as compared with other products. The MODIS- and VIIRS-based PML-V2.2a datasets are internally consistent, and exhibit high agreement with PML-V2.2b during their overlapping period (NSE = 0.90 and 0.79 for annual ET and GPP anomalies), ensuring a seamless transition across satellite epochs. Based on the consolidated PML-V2.2c dataset, global terrestrial ET and GPP during 1982–2025 are estimated at 65.8 × 10 3 km 3 yr −1 (with 58.2 % from transpiration) and 143.4 PgC yr −1 , respectively. Long-term analysis reveals significant ( p < 0.05) increasing trends in GPP (0.343 PgC yr −2 ) and ET (0.019 × 10 3 km 3 yr −2 ) during 1982–2025, where vegetation greening impact on ET is partially offset by physiological water saving under rising atmospheric CO 2…

South and Southeast Asia is a major global hub for paddy rice cultivation, with the highest rice cropping intensity worldwide owing to its favorable hydrothermal conditions. The region has also experienced considerable spatiotemporal changes driven by climate change and anthropogenic activities. However, the absence of spatially explicit long-term datasets on paddy rice distribution and cropping intensity hinders effective agricultural and environmental management. This gap is particularly critical in the 21st century, as changes in climate, water resources, and food trade patterns increasingly reshape regional rice production systems. Using all available Landsat and Sentinel-2 archives, we refined a phenology-based algorithm to generate 30 m multi-year composite rice distribution and cropping-intensity products across South and Southeast Asia for four nominal reference years: 1995, 2005, 2015, and 2024. The algorithm addresses the challenge of detecting rice cropping intensity from long-term satellite time series and comprises three core steps: (1) identifying pixel-level rice phenological peaks using an enhanced peak detection method, thereby defining potential transplanting windows and minimizing monsoon-induced cloud and precipitation interference; (2) detecting paddy flooding signals and delineating rice cultivation areas based on phenological rules derived from the relationship between the Land Surface Water Index (LSWI) and Enhanced Vegetation Index (EVI); and (3) determining rice cropping intensity according to the number of valid crop peaks and associated flooding signals detected within the corresponding composite-period time series. The resulting maps were validated using 23 396 samples derived from a field photo library, visual interpretation of Sentinel-1/2 satellite imagery, and a sample migration algorithm. Across the four periods, the maps achieved overall accuracies ranging from 83.74 % to 87.60 %. In addition, the resulting products were compared with existing regional and period-specific rice datasets (e.g., NESEA-Rice10 and Open-SEA-Rice-10) for further evaluation. The comparisons demonstrated that the refined approach achieved higher accuracy and robustness in mapping both rice distribution and cropping intensity. When compared with official FAO statistics for South and mainland Southeast Asian countries, the derived maps yielded R 2…

A 40-year gridded meteorological forcing dataset spanning the water years from 1 October 1983 to 30 September 2023 was compiled for the Reynolds Creek Experimental Watershed (RCEW) in southwest Idaho, USA. This Reynolds Creek Long-Term (RCLT) dataset consists of hourly, 10 m resolution grids of air temperature, vapor pressure, precipitation mass and phase, incoming shortwave and longwave radiation, visible and infrared snow albedo, and wind speed and direction. These variables were interpolated and calculated from hourly measurements from the dense meteorological station network within the mountainous 239 km 2 RCEW, which contains elevations that span the historical winter rain-to-snow transition. The observations are foundational for many ecological and hydrological Land Surface Models (LSMs) used in research and operational applications. Additionally, an example use case is presented in which we show how the snow-dominated area of the basin has evolved over the data record. This 13 TB dataset, stored in cloud-optimized Zarr format, enables future model development, benchmarking, uncertainty analyses of existing models, independent validation of gridded atmospheric reanalysis datasets, and novel investigations of hydroclimatic variability across snow-dominated semi-arid environments. Data access is available via the following repository: https://doi.org/10.15482/USDA.ADC/30199954 (Hedrick et al., 2025).