Major River Basins Research Articles

A decade-long meta-analysis of risks posed by pesticides in Chinese surface waters.

A decade-long meta-analysis of risks posed by pesticides in Chinese surface waters.

Global space-time patterns of sub-daily extreme precipitation and its relationship with temperature and wind speed

Abstract Sub-daily extreme precipitation events can present significant risks such as flooding and soil erosion. Yet the space time pattern of such events on a global scale and their relationships with temperature and wind speed are not well understood. Based on over 9000 global meteorological stations and Multi-Source Weighted-Ensemble Precipitation (MSWEP) covering the warm seasons of 1980-2022, the characteristics and trends in sub-daily extreme precipitation (3h time interval) were examined and their relationships with temperature and wind speed were explored across global major river basins. The results revealed a similar spatial pattern in the annual frequency, intensity, duration, and total amount of 3h extreme precipitation events, though notable discrepancies were observed in specific regions such as Southeast Asia and eastern North America. Over half of the stations, particularly in South America, exhibited decreasing trends in extreme precipitation frequency, while significant increases were documented in Europe and Southeast Asia. The relationship between extreme precipitation intensity and temperature over different river basins exhibited diverse behaviors, characterized by monotonic increase (sub-daily Clausius-Clapeyron scaling, mostly in Europe), hook structure (mostly in North America), monotonic decrease (mostly in Africa), and nearly constant (mostly in Mid East, South Africa and South America). Moreover, wind speed generally increased with extreme precipitation intensity for tropical and subtropical basins, while in high-latitude basins strong winds tended to occur with lower precipitation intensity. These insights are vital for improving resilience against sub-daily extreme precipitation.

Quantifying the Added Values of a Merged Precipitation Product in Streamflow Prediction over the Central Himalayas

Gridded precipitation datasets (GPDs) have complemented gauge-based measurements in global hydrology by providing spatiotemporally continuous rainfall estimates for streamflow prediction. However, these datasets suffer from uncertainties in space and time, particularly in complex terrains like the Himalayas. Merging multi-GPDs offers a potential solution to reduce such uncertainties, but the actual contribution of the merged product to hydrological modeling remains underexplored in data-scarce and topographically complex regions. Here, we applied a gauge-independent merging technique called Signal-to-Noise Ratio optimization (SNR-opt) to merge three precipitation products: ERA5, SM2RAIN, and IMERG-late. The resulting Merged Gridded Precipitation Dataset (MGPD) was evaluated using the hydrological model (HYMOD) across three major river basins in the Central Himalayas (Koshi, Narayani, and Karnali). The results show that MGPD significantly outperforms the individual GPDs in streamflow simulation. This is evidenced by higher Nash–Sutcliffe Efficiency (NSE) values, 0.87 (Narayani) and 0.86 (Karnali), compared to ERA5 (0.83, 0.82), SM2RAIN (0.83, 0.85), and IMERG-Late (0.82, 0.78). In Koshi, the merged product (NSE = 0.80) showed slightly lower performance than SM2RAIN (NSE = 0.82) and ERA5 (NSE = 0.81), likely due to the poor performance of IMERG-Late (NSE = 0.69) in this basin. These findings underscore the value of merging precipitation datasets to enhance the accuracy and reliability of hydrological modeling, especially in ungauged or data-scarce mountainous regions, offering important implications for water resource management and forecasting.

Remote Sensing‐Based Assessment of Carbon and Water Ecosystem Services in Response to Climate and Vegetation Cover Changes Across Global Major River Basins

ABSTRACTImproving our understanding of terrestrial carbon‐water coupling processes at the basin scale is crucial for assessing the impacts of global change on river basin's ecosystem services. Supported by remote sensing technology, previous studies have investigated the variability of carbon sequestration or water yield of a basin separately. However, the integration of these two components in an assessment system has rarely been considered, which may lead to partial appraisals of eco‐hydrological services. To address this knowledge gap, we have developed a new remotely sensed index, the Carbon‐Water Efficiency Index (CWEI), which is derived as the product of the normalized precipitation use efficiency (PUE) and water yield efficiency (WYE) during a specified period for a given basin, to quantify the coupling carbon‐water ecosystem service. In this study, the spatial and temporal variabilities of CWEI were investigated using satellite‐based products across the 38 major river basins from 1982 to 2016. Furthermore, the response patterns of CWEI to its components were explored, and the contributions of the three variables (i.e., temperature, precipitation, vegetation coverage) on changes in CWEI were quantified for each of the basins. The results demonstrated that the CWEI exhibited an increase in 31.6% of the world's major river basins, of which 83.3% with an increasing CWEI demonstrated a response pattern of increasing PUE and decreasing WYE. Furthermore, temperature was identified as the primary contributor to CWEI in 50% of the basins. It is noteworthy that green hotspot basins, where afforestation projects were underway, exhibited enhancements in reduced water yield pressure and demonstrable benefits to local ecohydrology. The proposed CWEI provides a new framework for comprehending the sustainable development of river basins worldwide.

Cross-regional and multi-entity resource coordination can enhance the supply of disaster relief materials during flood events in China

Municipalities face growing challenges in independently managing frequent and severe external shocks, often constrained by limited relief supplies and inefficient allocation strategies. Here we develop and implement hybrid coordination strategies that integrate cross-regional and multi-entity resource allocation to enhance national flood resilience across China’s eight major river basins. By incorporating cross-regional coordination among government reserves, we demonstrate that the 12-h demand coverage rate (DCR) for 1-in-100-year basin flood events can increase from 15% to 51%. Expanding to multi-entity coordination, which integrates enterprise reserves, further improves the 12-h DCR to 76%. Optimizing enterprise contractual reserve proportions can achieve complete demand coverage, even in the Yangtze and Pearl River basins with dense populations and large relief shortages. Our work provides insights into operational disaster relief coordination and informs flood management strategies for other countries facing extreme flooding challenges.

Machine-learning-based reconstruction of long-term global terrestrial water storage anomalies from observed, satellite and land-surface model data

Abstract. Understanding long-term terrestrial water storage (TWS) variations is vital for investigating hydrological extreme events, managing water resources and assessing climate change impacts. However, the limited data duration from the Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission (GRACE-FO) poses challenges for comprehensive long-term analysis. In this study, we reconstruct TWS anomalies (TWSAs) for the period from January 1960 to December 2022, thereby filling data gaps between the GRACE and GRACE-FO missions and generating a complete dataset for the pre-GRACE era. The workflow involves identifying optimal predictors from land surface model (LSM) outputs, meteorological variables and climatic indices using a novel Bayesian network (BN) technique for raster-based TWSA simulations. Climate indices, like the Oceanic Niño Index and Dipole Mode Index, are selected as optimal predictors for a large number of grid cells globally, along with TWSAs from LSM outputs. The most effective machine learning (ML) algorithms among convolutional neural network (CNN), support vector regression (SVR), extra trees regressor (ETR) and stacking ensemble regression (SER) models are evaluated at each grid cell to achieve optimal reproducibility. Globally, ETR performs best for most of the grid cells; this is also noticed at the river basin scale, particularly for the Ganga–Brahmaputra–Meghna, Godavari, Krishna, Limpopo and Nile river basins. The simulated TWSAs (BNML_TWSA) outperformed the TWSAs from LSM outputs when evaluated against GRACE datasets. Improvements are particularly noted in river basins such as the Godavari, Krishna, Danube and Amazon, with median correlation coefficient, Nash–Sutcliffe efficiency, and RMSE values for all grid cells in the Godavari Basin, India, being 0.927, 0.839 and 63.7 mm, respectively. A comparison with TWSAs reconstructed in recent studies indicates that the proposed BNML_TWSA outperforms them globally as well as for all of the 11 major river basins examined. Furthermore, the uncertainty of BNML_TWSA is assessed for each grid cell in terms of the standard error. Results show smaller standard error magnitudes in grid cells in arid regions compared to other regions. The presented gridded dataset is published at https://doi.org/10.6084/m9.figshare.25376695 (Mandal et al., 2024), featuring a spatial resolution of 0.50° × 0.50° and offering global coverage.

Machine Learning and Morphometric Analysis for Runoff Dynamics: Enhancing Flood Management and Catchment Prioritization in Bayelsa, Nigeria

Flooding is a recurring environmental hazard with devastating socio-economic and ecological impacts, especially in vulnerable regions like Bayelsa State, Nigeria. The state’s low-lying terrain, dense river networks, and poor drainage infrastructure exacerbate its flood susceptibility. This study employs morphometric analysis to assess flood-prone areas across major river basins using Shuttle Radar Topographic Mission (SRTM) data, Geographic Information Systems (GIS), and remote sensing techniques. Key morphometric parameters stream order, drainage density (2.41-3.57 km/km²), bifurcation ratio (1.84-2.84), relief ratio (0.03-0.15), stream frequency (5.00-11.71 streams/km²), infiltration number, and form factor (0.64-1.04) were extracted and analyzed using ArcGIS 10.5, Arc Hydro tools, and Python. Results indicate significant spatial heterogeneity in flood susceptibility. The Forcados River catchment recorded the highest flood risk, with a priority score of 3.4/5, a relief ratio of 0.15, drainage density of 3.57 km/km², and stream frequency of 11.71 streams/km². This aligns with 78% of historical flood event locations. Conversely, the Ekole and Seibri catchments exhibited the lowest susceptibility, with priority scores of 2.0-2.1, relief ratios below 0.05, and drainage densities under 0.9 km/km². The Nun River catchment showed moderate risk (priority score: 2.4), with a stream frequency of 3.2/km² and elongation ratio of 0.6. To enhance predictive capacity, machine learning models were employed. The Random Forest classifier achieved 89% accuracy and an AUC-ROC of 0.93, outperforming the Support Vector Machine model. This study offers a scalable flood assessment framework for data-scarce regions and recommends targeted structural interventions and nature-based solutions tailored to each catchment’s flood profile.

Conflicting results of ecological and health risk assessment of perfluorinated compounds in major river basins in China

Conflicting results of ecological and health risk assessment of perfluorinated compounds in major river basins in China

Seeing China's Invisible Groundwater: Advances and Challenges

Abstract In 2021, China adopted comprehensive “Groundwater Management Regulations” to address critical groundwater issues, building upon the foundation of a national groundwater monitoring network established by 2020. This commentary reviews the development of China's National Groundwater Monitoring Network, examines its current monitoring capacity, and highlights ongoing groundwater issues and related ecological and environmental challenges. The network now comprises over 20,000 monitoring wells across 10 major river basins in China. Despite this progress, three key challenges persist in advancing groundwater knowledge: (a) insufficient data from the Tibetan Plateau, a crucial headwater region for many of Asia's major rivers; (b) limited understanding of how groundwater systems affect ecohydrological processes and ecosystem services; and (c) the identification and mitigation of environmental geological hazards driven by groundwater changes. We advocate for the integration of the groundwater observational data with physics‐based groundwater models, artificial intelligence, and deep learning techniques to deepen our understanding of these complex, largely hidden groundwater systems, providing critical insights for both scientific research and regulatory decision‐making.

Deep Learning Advances Arctic River Water Temperature Predictions

Abstract The accelerated warming in the Arctic poses serious risks to freshwater ecosystems by altering streamflow and river thermal regimes. However, limited research on Arctic River water temperatures exists due to data scarcity and the absence of robust methodologies, which often focus on large, major river basins. To address this, we leveraged the newly released, extensive AKTEMP data set and advanced machine learning techniques to develop a Long Short‐Term Memory (LSTM) model. By incorporating ERA5‐Land reanalysis data and integrating physical understanding into data‐driven processes, our model advanced river water temperature predictions in ungauged, snow‐ and permafrost‐affected basins in Alaska. Our model outperformed existing approaches in high‐latitude regions, achieving a median Nash‐Sutcliffe Efficiency of 0.95 and root mean squared error of 1.0°C. The LSTM model learned air temperature, soil temperature, solar radiation, and thermal radiation—factors associated with energy balance—were the most important drivers of river temperature dynamics. Soil moisture and snow water equivalent were highlighted as critical factors representing key processes such as thawing, melting, and groundwater contributions. Glaciers and permafrost were also identified as important covariates, particularly in seasonal river water temperature predictions. Our LSTM model successfully captured the complex relationships between hydrometeorological factors and river water temperatures across varying timescales and hydrological conditions. This scalable and transferable approach can be potentially applied across the Arctic, offering valuable insights for future conservation and management efforts.

Analysis of extreme precipitation in eastern China from 1960 to 2020 and future projections

This study analyzes extreme precipitation in eastern China over the past 60 years through climatology and trend detection, with future projections using 10 indices. Data from 1310 meteorological stations and 20 CMIP6 models were employed to examine the spatiotemporal evolution of these indices from 1961 to 2020 during summer, both regionally and across eight major river basins. Key findings include: (1) the spatial distribution of extreme precipitation generally decreases from south to north and east to west. High-value centers for extreme precipitation are found south of the Yangtze River, while the maximum consecutive wet days decline from the southeast coast inland. (2) Extreme summer precipitation in eastern China increased over six decades, with intensity, 1-day max, heavy, extreme heavy precipitation, and very heavy rain days rising at 0.03, 0.11, 0.22, 0.13 mm yr–1, and 0.02 d yr–1, respectively. Trends show growth in the Yangtze River Basin and the northeast, but declines in the Hai and Yellow River basins, with decreases up to 0.50 mm yr–1. (3) Multi-model ensemble simulations reliably capture summer extreme precipitation trends, indicating that by 2060, heavy and extreme precipitation amounts will increase by up to 10% under SSP-1.26. Higher carbon emissions will further accelerate this increase, particularly in the southeast rivers, the Pearl River, and the Yangtze River basins. These results provide important references for predicting extreme precipitation in eastern China.

A New Methodology to Estimate the Level of Water Stress (SDG 6.4.2) by Season and by Sub-Basin Avoiding the Double Counting of Water Resources

While at the global level, water stress does not seem to present a serious threat to the sustainability of freshwater withdrawal and use, the situation appears much grimmer if a closer look is given to the status of the freshwater resources at basin and sub-basin levels. Unfortunately, such information is often not available to water managers and decision-makers, due both to the scarcity of sufficient data and also to the lack of methods capable of transforming the existing data into usable information. Hence, disaggregating water stress at basin and sub-basin levels is fundamental to provide a finer view of both its causes and effects, allowing the targeting of interventions at areas with high water stress and sectors with high water use. The spatial disaggregation of SDG indicator 6.4.2 by major river basin already implemented at a global scale showed the existence of a water stress belt running across the globe approximately between 10 and 45 degrees north, with a few other areas above and below it. The value of SDG indicator 6.4.2 at the country level is influenced by its size: the larger the country, the more the national average masks local variability. When the disaggregation is performed at sub-basin level, there is the possibility that the same amount of water is counted twice or even more (double counting), as it flows from one sub-basin to the neighbouring ones. Current water accounting methods do not allow this issue to be overcome. This causes an underestimation of water stress and an overestimation of the water resources available for human use in a given area. This paper presents a new methodology to assess SDG indicator 6.4.2 (water stress) seasonally and at the sub-basin level, addressing double counting by factoring in water demands between upstream and downstream sub-basins. This approach supports more informed water management. A corresponding plugin for the WEAP tool was developed, tested in the Senegal River basin countries, and is available online with a user manual in English, French, and Spanish.

Coupled Risk Assessment of Flood Before and During Disaster Based on Machine Learning

Currently, regional flood research often lacks a synergistic assessment of both flood occurrence risk and flood duration, limiting the comprehensive understanding needed for sustainable disaster risk reduction. To address this gap, this study applies advanced machine learning approaches to assess flood hazards in the Yangtze River Delta, one of China’s most economically and environmentally significant regions. Specifically, XGBoost is employed to evaluate flood occurrence risk, while LSTM is used to predict flood duration. A novel flood risk index (FRI) is proposed to quantify the integrated risk by combining these two dimensions, supporting more sustainable and effective flood risk management strategies. Furthermore, SHAP analysis is conducted to identify the most critical factors contributing to flooding. The results demonstrate that XGBoost delivers strong predictive performance, with average precision, recall, F1-score, accuracy, and AUC values of 0.823398, 0.831667, 0.827090, 0.826435, and 0.871062, respectively. Areas with high flood risk, long duration, and elevated FRI values are mainly concentrated in major river basins and coastal zones. The range of flood risk spans from 0.000073 to 0.998483 (mean: 0.237031), flood duration from 0.223598 to 2.077040 (mean: 0.940050), and FRI from 0 to 0.934256 (mean: 0.091711). Cities with over 40% of their areas falling in medium to high FRI zones include Suzhou (48.99%), Jiaxing (48.07%), Yangzhou (46.87%), Suqian (44.19%), Changzhou (43.43%), Wuxi (43.20%), Lianyungang (42.21%), Yancheng (40.88%), Huai’an (40.73%), and Bengbu (40.06%). SHAP analysis reveals that elevation and rainfall are the most critical factors influencing flood occurrence, underscoring the importance of integrating environmental variables into sustainable flood risk governance.

δ-MedBioclim: A New Dataset Bridging Current and Projected Bioclimatic Variables for the Euro-Mediterranean Region

This data descriptor presents δ-MedBioclim, a newly developed dataset for the Euro-Mediterranean region. This dataset applies the delta-change method by comparing the values of 25 General Circulation Models (GCMs) for the reference period (1981–2010) with their projections for future periods (2026–2050, 2051–2075, and 2076–2100) under the SSP1-RCP2.6, SSP2-RCP4.5, and SSP5-RCP8.5 scenarios. These anomalies are added to two pre-existing datasets, ERA5-Land and CHELSA, yielding resolutions of 0.1° and 0.01°, respectively. Additionally, this manuscript provides a ranking of GCMs for each major river basin within the study area to guide model selection. δ-MedBioclim includes, for all the aforementioned scenarios, monthly mean temperature, total monthly precipitation, and 23 bioclimatic variables, including 9 (biorm1 to biorm9) from the Worldwide Bioclimatic Classification System (WBCS) that are not available in other databases. It also provides two bioclimatic classifications: Köppen–Geiger and WBCS. This dataset is expected to be a valuable resource for modeling the distribution of Mediterranean species and habitats, which are highly affected by climate change.

Decelerating Response of Western US Runoff to Shrinking Snowpacks

Abstract Climate warming threatens snowmelt‐derived water supplies in the western US (WUS) by reducing snowfall and snowmelt runoff, yet future rates of these declines remain highly uncertain in an evolving climate. Here, we analyze historical data, land surface model warming experiments, and climate projections across three major WUS river basins. We find that runoff loss become less sensitive to warming as snowpack shrinks, stemming from reduced snowmelt‐radiation feedback, a consequence of smaller snow‐cover changes and shifts in snowmelt timing to lower‐energy periods. Near‐linear projected warming with time (IPCC SSP245) exhibit a stable, possibly decelerating decline in runoff ratios. Although decelerating runoff declines do not eliminate broader water‐management challenges under continued warming, our findings complement the view that snowmelt‐radiation feedback drives runoff decline by highlighting the negative feedback from a shrinking snowpack on runoff warming sensitivity. Our findings should facilitate more comprehensive future water supply assessments in snow‐affected regions.

Spatial and Temporal Variability of River Water Quality

ABSTRACTThe deterioration of stream water quality threatens ecosystems and human water security worldwide. Effective risk assessment and mitigation requires spatial and temporal data from water quality monitoring networks (WQMNs). However, it remains challenging to quantify how well current WQMNs capture the spatiotemporal variability of stream water quality, making their evaluation and optimisation an important task for water management. Here, we investigate the spatial and temporal variability of concentrations of three constituents, representing different input pathways: anthropogenic (NO3−), geogenic (Ca2+) and biogenic (total organic carbon, TOC) at 1215 stations in three major river basins in Germany. We present a typology to classify each constituent on the basis of magnitude, range and dominance of spatial versus temporal variability. We found that mean measures of spatial variability dominated over those for temporal variability for NO3− and Ca2+, while for TOC they were approximately equal. The observed spatiotemporal patterns were robustly explained by a combination of local landscape composition and network‐scale landscape heterogeneity, as well as the degree of spatial auto‐correlation of water quality. Our analysis suggests that river network position systematically influences the inference of spatial variability more than temporal variability. By employing a space–time variance framework, this study provides a step towards optimising WQMNs to create water quality data sets that are balanced in time and space, ultimately improving the efficiency of resource allocation and maximising the value of the information obtained.

Direct and Indirect Effects of Large-Scale Forest Restoration on Water Yield in China’s Large River Basins

Emerging evidence indicates that large-scale forest restoration exhibits dual hydrological effects: direct reduction of local water availability through elevated evapotranspiration (ET) and indirect augmentation of water resources via enhanced atmospheric moisture recycling. However, the quantitative assessment of these counteracting effects remains challenging due to the limited observational constraints on moisture transport. Here, we integrate the Budyko model with the Lagrangian-based UTrack moisture-tracking dataset to disentangle the direct (via ET) and indirect (via precipitation) large-scale hydrological impacts of China’s four-decade forest restoration campaign across eight major river basins. Multisource validation datasets, including gauged runoff records, hydrological reanalysis products, and satellite-derived forest cover maps, were systematically incorporated to verify the Budyko model at the nested spatial scales. Our scenario analyses reveal that during 1980–2015, extensive afforestation individually reduced China’s terrestrial water yield by −28 ± 25 mm yr−1 through dominant ET increases. Crucially, atmospheric moisture recycling mechanisms attenuated this water loss by 12 ± 5 mm yr−1 nationally, with marked spatial heterogeneity across the basins. In some moisture-limited watersheds in the Yellow River Basin, the negative ET effect was compensated for to a certain extent by precipitation recycling, demonstrating net positive hydrological outcomes. We conclude that China’s forest expansion imposes local water stress (direct effect) by elevating ET, while the concomitant strengthening of continental-scale moisture recycling generates compensatory water gains (indirect effect). These findings advance the mechanistic understanding of the vegetation-climate-water nexus, providing quantitative references for optimizing forestation strategies under atmospheric water connectivity constraints.

GRILSS: opening the gateway to global reservoir sedimentation data curation

Abstract. Reservoir sedimentation poses a significant challenge to freshwater management, leading to declining storage capacity and inefficient reservoir operations for various purposes. However, trustworthy and independently verifiable information on declining storage capacity or sedimentation rates around the world is sparse and suffers from inconsistent metadata and curation to allow global-scale archiving and analyses. The Global Reservoir Inventory of Lost Storage by Sedimentation (GRILSS) dataset addresses this challenge by providing organized, well-curated, and open-source data on sedimentation rates and capacity loss for 1013 reservoirs in 75 major river basins across 54 countries. This publicly accessible dataset captures the complexities of reservoir sedimentation, influenced by regional factors such as climate, topography, and land use. By curating the information from numerous sources with disparate formats in a homogenized data structure, GRILSS serves as an invaluable resource for water managers, policymakers, and researchers for improved sediment management strategies. The open-source nature of GRILLS promotes collaboration and contributions from the global community to grow the dataset. By providing essential reference data on sedimentation to understand the global challenge of reservoir sedimentation, the GRILLS dataset represents a gateway for the global community to share sedimentation and storage loss data for sustainable operation of world's reservoirs for future generations. The dataset is publicly available at OSFHome (https://doi.org/10.17605/OSF.IO/W4UG8, Minocha and Hossain, 2025).

Assessing groundwater drought in Iran using GRACE data and machine learning

Groundwater serves as a critical freshwater reservoir globally, essential for ecosystem conservation and human well-being. Drought conditions adversely impact groundwater systems by first reducing recharge, followed by declines in groundwater levels and withdrawal potential, which can result in agricultural setbacks and irreversible consequences such as land subsidence. The introduction of the Gravity Recovery and Climate Experiment (GRACE) project marked a significant advancement in monitoring terrestrial water storage anomalies (TWSA), encompassing both surface and subsurface water. Traditional methods for assessing groundwater storage anomalies (GWSA), such as piezometric wells, have proven to be costly and inefficient, often lacking sufficient spatial and temporal coverage. Although GRACE data offers valuable insights, its large-scale nature presents challenges for localized basin and aquifer studies, compounded by data gaps resulting from a 15-month interruption during the transition to the GRACE-FO project. This study investigates the status of groundwater across six major river basins in Iran utilizing data from GRACE and its complementary Global Land Data Assimilation System (GLDAS) over a 255-month period from 2002 to 2023. The Extreme Gradient Boosting (XGBoost) algorithm is employed for downscaling TWSA to a resolution of 0.25°, achieving a high Pearson correlation (R) of 0.99 and a root mean square error (RMSE) of 22 mm. The downscaled GWSA, derived from the balance equation, exhibits an average correlation (R) of 0.93 and RMSE of 39 mm with observational data. Following the application of the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to fill GWSA time series gaps, this study models and forecasts GWSA trends through 2030 using historical data and SSP2 scenario projections of the canESM5 climate model. Results indicate an average groundwater depletion of 29 cm per year across Iran’s aquifers from 2002 to 2023, with the Caspian Sea basin experiencing the most significant decline. The GRACE Groundwater Drought Index (GGDI) is calculated and compared with the Standardized Precipitation Index (SPI), revealing an 8-month lag in drought propagation from meteorological to groundwater sources in Iran. Furthermore, correlations between the GGDI and teleconnection indices highlight their substantial influence on drought conditions in basins adjacent to major water sources. The results of this study, by emphasizing the reliability of satellite data and machine learning models in groundwater drought monitoring, can assist policymakers in enhancing groundwater resource management, strategic planning, and identifying critical basins, particularly in regions with limited observational data.

Glacier meltwater has limited contributions to the total runoff in the major rivers draining the Tibetan Plateau

The Tibetan Plateau is the headwaters of several major river basins, but uncertainties exist in the estimated contributions of glacial melt and groundwater to runoff. We present a new tracer-aided glacio-hydrological model constrained by multiple datasets for five major river basins of the Tibetan Plateau. We show that the contribution of glacier melt to the annual runoff is less than 5% in all the five basins at the outlets—much less than previous estimates. Our secondary finding is that the partitioning between surface runoff and groundwater flow varied greatly across the watersheds, with groundwater runoff contributing 35–75% of the annual runoff. The contribution of glacier melt has a strong spatial variability and scale dependency, but the population heavily dependent on it is limited, so a potential significant decrease in water resources due to glacier shrinkage is not a problem that should raise public worries in the Tibetan Plateau.