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Existing approaches for predicting total water storage (TWS) rely on land surface or hydrological models using meteorological forcing data. Yet, such models are more adept at predicting specific water compartments, such as soil moisture, rather than others, which consequently impedes accurately forecasting of TWS. Here we show that machine learning can be used to uncover relations between nonseasonal terms of Gravity Recovery and Climate Experiment (GRACE) derived total water storage and the preceding hydrometeorological drivers, and these relations can subsequently be used to predict water storage up to 12 months ahead, and even exceptional droughts on the basis of near real‐time observational forcing data. Validation by actual GRACE observations suggests that the method developed here has the capability to forecast trends in global land water storage for the following year. If applied in early warning systems, these predictions would better inform decision‐makers to improve current drought and water resource management. Plain Language Summary: Traditional methods for predicting short‐term/seasonal variations in land total water storages rely on hydrological models. However, these models have a drawback—they are better at predicting water stored in specific parts of the land system like soil moisture than giving an accurate forecast for the overall integrated land total water storage. In this study, we demonstrate the applicability of machine learning in uncovering relationships between the de‐season and de‐linearized terms of global water storage variability as observed by the Gravity Recovery and Climate Experiment (GRACE) satellites, and the preceding hydrometeorological variables such as sea surface temperature. These relationships can then be utilized to forecast monthly changes in land total water storage up to 1 year ahead, and even to predict exceptional drought events based on near real‐time observational forcing data. The validation by actual GRACE observations, lends further credence to the effectiveness of the method developed here, showcasing its potential to forecast trends in global land total water storage for the upcoming year. The potential applications of these predictions in early warning systems are highly promising, and we anticipate that they can assist decision‐makers in enhancing current drought and water resource management practices. Key Points: We identify a stable lag relationship between hydrometeorological variables and the GRACE derived total water storage change (TWSC)Using this stable lag relationship, we are able to forecast the global TWSC up to 1 year ahead with solely observational data as inputsOur forecasts exhibit high consistency with actual GRACE data in terms of global mean land water storage trends for the following year [ABSTRACT FROM AUTHOR]

Groundwater potential maps are crucial tools for effectively managing water resources, particularly in agriculturally focused countries such as Vietnam. However, creating these maps is a challenging task that requires reliable data and methods. In this study, we integrated the Split‐Point Sampling and Node Attribute Subsampling Classifier (SPAARC) with the Bagging (B), MultiBoostAB (MBAB), and Random Subspace (RSS) ensemble learning techniques and developed three ensemble models: B‐SPAARC, MBAB‐SPAARC, and RSS‐SPAARC. We selected 13 geoenvironmental factors based on their availability, relevance, and association with groundwater potential in the Sesan River basin of Vietnam. We assessed the models' performance using various metrics such as area under the curve (AUC), accuracy, sensitivity, specificity, and RMSE. The findings indicated that the ensemble models performed better than the single SPAARC model in mapping groundwater potential. The MBAB‐SPAARC model demonstrated the highest accuracy with an AUC value of 0.891, followed by B‐SPAARC (AUC = 0.844), RSS‐SPAARC (AUC = 0.871), and the single SPAARC (AUC = 0.853) models. The results also highlighted that elevation, rainfall, land use/cover, and altitude were the most significant factors for mapping groundwater potential in the Sesan River basin. The innovative ensemble models and reliable potential maps developed in this study assist water resource managers in planning water usage based on the benefits and costs for various users and in devising sustainable strategies for using, protecting, and managing groundwater. Key Points: Groundwater potential mapping was carried out at the Central Highlands of VietnamHybrid Machine learning Models based on Naïve Bayes Tree were developed and usedResults showed that the proposal models are potential and accurate tools [ABSTRACT FROM AUTHOR]

The Yellow River Basin (YRB) plays a very important role in China's economic and social development and ecological security, so studying the spatiotemporal variation characteristics of net primary productivity (NPP) and its influencing factors is of great significance for protecting the stable development of its ecological environment. This article takes the YRB as the research area, based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, climate data, terrain data, land data, social data, and the gravity recovery and climate experiment (GRACE) data. The spatiotemporal evolution characteristics of vegetation NPP in the YRB from 2000 to 2022 were explored using methods such as trend analysis, correlation analysis, and geographic detectors, and the correlation characteristics of NPP with meteorological factors, social factors, and total water storage (TWS) were evaluated. The results indicate that the NPP of vegetation in the YRB showed an increasing trend (4.989 gC·m−2·a−1) from 2000 to 2022, with the most significant changes occurring in the middle reaches of the YRB. The correlation coefficient indicates that temperature and accumulated temperature have a significant positive impact on the change of NPP, while TWS has a significant negative impact. In the study of the factors affecting vegetation NPP in the YRB, the most influential factors are soil type (0.48), precipitation (0.46), and temperature (0.32). The strong correlation between TWS and vegetation NPP in the YRB is about 39%, with a contribution rate of about 0.12, which is a factor that cannot be ignored in studying vegetation NPP changes in the YRB. [ABSTRACT FROM AUTHOR]