Changes In Storage Research Articles

Estimation of water storage changes in a tropical lake-floodplain system through remote sensing

Estimation of water storage changes in a tropical lake-floodplain system through remote sensing

Effects of 24-h sleep deprivation on whole-body heat exchange in young men during exercise in the heat.

Sleep deprivation has been associated with impaired thermoregulatory function. However, whether these impairments translate to changes in whole-body heat exchange during exercise-heat stress remains unknown. Therefore, following either a night of normal sleep or 24 h of sleep deprivation, 10 young men (mean (SD): 23 (3) years) completed three 30-min bouts of semi-recumbent cycling at increasing fixed rates of metabolic heat production (150, 200, 250 W/m2), each separated by a 15-min rest in dry heat (40°C, ~ 13% relative humidity). Rates (W/m2) of whole-body total heat exchange (dry + evaporative) were measured continuously and expressed as peak responses [mean of the final 5-min of exercise at the highest metabolic heat production (250 W/m2)]. Body heat storage was quantified as the temporal summation of heat production and loss. Core temperature, indexed by rectal temperature, was measured continuously. Relative to normal sleep, sleep deprivation did not modify whole-body heat exchange (evaporative (-6 [-18, 5] W/m2; P = 0.245), or dry (7 [-5, 19] W/m2; P = 0.209; sleep deprivation-normal sleep mean difference [95%CIs]) and therefore total heat loss (1 [-14, 15] W/m2; P = 0.917). There were no differences in either the change in body heat storage (-9 [-67, 49] kJ; P = 0.732) or change in core temperature (0.1 [-0.1, 0.3] °C; P = 0.186) between conditions. Overall, we showed that 24-h sleep deprivation did not influence whole-body dry or evaporative heat exchange, resulting in no differences in total whole-body heat exchange or body heat storage in young adults during exercise under hot-dry conditions.

Inferring the energy cost of resistance to parasitic infection and its link to a trade-off

BackgroundIn infected hosts, immune responses trigger a systemic energy reallocation away from energy storage and growth, to fuel a costly defense program. The exact energy costs of immune defense are however unknown in general. Life history theory predicts that such costs underpin trade-offs between host disease resistance and other fitness related traits, yet this has been seldom assessed. Here we investigate immune energy cost induced by infection, and their potential link to a trade-off between host resistance and fat storage that we previously exposed in sheep divergently selected for resistance to a pathogenic helminth.ResultsTo this purpose, we developed a mathematical model of host-parasite interaction featuring individual changes in energy allocation over the course of infection. The model was fitted to data from an experimental infectious challenge in sheep from genetically resistant and susceptible lines to infer the magnitude of immune energy costs. A relatively small and transient immune energy cost in early infection best explained within-individual changes in growth, energy storage and parasite burden. Among individuals, predicted responses assuming this positive energy cost conformed to the observed trade-off between resistance and storage, whereas a cost-free scenario incorrectly predicted no trade-off.ConclusionsOur mechanistic model fitting to experimental data provides novel insights into the link between energy costs and reallocation due to induced resistance within-individual, and trade-offs among individuals of selected lines. These will be useful to better understand the exact role of energy allocation in the evolution of host defenses, and for predicting the emergence of trade-offs in genetic selection.

Using Integrated Geodetic Data for Enhanced Monitoring of Drought Characteristics Across Four Provinces and Municipalities in Southwest China

This paper presents an analysis of regional terrestrial water storage (TWS) changes and drought characteristics in Southwest China, encompassing Sichuan Province, Chongqing Municipality, Yunnan Province, and Guizhou Province. Existing geodetic datasets, such as those from the Gravity Recovery and Climate Experiment (GRACE) and its successor satellites (GRACE Follow-On), as well as Global Navigation Satellite System (GNSS) data, face significant challenges related to limited spatial resolution and insufficient station distribution. To address these issues, we propose a novel inversion method that integrates GNSS and GRACE/GFO data by establishing virtual stations for GRACE/GFO data and determining the weight values between GNSS and GRACE/GFO using the Akaike Bayesian Information Criterion (ABIC). This method allows for estimating the TWS changes from December 2010 to June 2023 and monitoring drought conditions in conjunction with hydrometeorological data (precipitation, evapotranspiration, and runoff). The results show strong correlations between TWS changes from the joint inversion and GNSS (0.98) and GRACE/GFO (0.69). The Joint Drought Severity Index (Joint-DSI) indicates five major drought events, with the most severe occurring from July 2022 to June 2023, with an average deficit of 86.133 km³. Extreme drought primarily impacts Sichuan and Yunnan, driven by abnormal precipitation deficits. The joint inversion methodology presented in this study provides a practical approach for monitoring TWS changes and assessing drought characteristics in Southwest China. This paper leverages the complementary strengths of GNSS and GRACE/GFO data and offers new insights into regional water resource management and drought detection.

Deep reinforcement learning for multiple reservoir operation planning in the Chao Phraya River Basin

This study demonstrates application of Deep Deterministic Policy Gradient (DDPG)-based algorithm to provide comprehensive and flexible plans for reservoir operation planning of the multiple reservoir system in the Chao Phraya River Basin (CPYRB), Thailand aiming to mitigate flood and drought risks in the region. The multi-agent-based Deep Reinforcement Learning (DRL) model is accordingly constructed considering 7-D predicted inflow, reservoir water released from adjacent reservoir, downstream flow condition, and changes in reservoir water storage, as state variables. The desired goal is to increase water storage levels in all reservoirs by 10–15% to ensure higher potential in supplying water for crop cultivation over the dry seasons and preventing flood occurrences during wet season. Simulation results from 2009 to 2022 indicate that DRL–DDPG-based algorithm can perform well in solving sequential decision problems for optimal operation of multiple reservoir system to achieve the desired water storage goal. It can offer realistic simulation results of seasonal and annual release schemes and reservoir release ratios among reservoirs in the system compared to actual operation and Fmincon and ANFIS optimizations. Importantly, DRL model demonstrates a significant advantage in view of increasing the long-term water storage levels in all reservoirs as targeted in the modelling process while maintaining the similar and consistent release schemes in the reservoir system. For the multipurpose multiple reservoir system operation, adjusting the dynamic desired goals within multi-agent-based RL model is advisable to attain the specific desired outcomes and address various water scenarios.

Impacts of Future Climate Change and Xiamen’s Territorial Spatial Planning on Carbon Storage and Sequestration

The intensification of climate change and the implementation of territorial spatial planning policies have jointly increased the complexity of future carbon storage changes. However, the impact of territorial spatial planning on carbon storage under future climate change remains unclear. Therefore, this study aims to reveal the potential impacts of future climate change and territorial spatial planning on carbon storage and sequestration, providing decision support for addressing climate change and optimizing territorial spatial planning. We employed the FLUS model, the InVEST model, and the variance partitioning analysis (VPA) method to simulate carbon storage under 15 different scenarios that combine climate change scenarios and territorial spatial planning for Xiamen in 2035, and to quantify the individual and combined impacts of territorial spatial planning and climate change on ecosystem carbon sequestration. The results showed that (1) by 2035, Xiamen’s carbon storage capacity is expected to range from 32.66 × 106 Mg to 33.00 × 106 Mg under various scenarios, reflecting a decrease from 2020 levels; (2) the implementation of territorial spatial planning is conducive to preserving Xiamen’s carbon storage, with the urban development boundary proving to be the most effective; (3) carbon storage is greatly affected by climate change, with RCP 4.5 more effective than RCP 8.5 in maintaining higher levels of carbon storage; and (4) the influence of territorial spatial planning on carbon sequestration consistently exceeds that of climate change, particularly under high-emission scenarios, where the regulatory effect of planning is especially significant.

Can system dynamics explain long-term hydrological behaviors? The role of endogenous linking structure

Abstract. Hydrological models with conceptual tipping bucket and process-based evapotranspiration formulations are the most common tools in hydrology. However, these models consistently fail to replicate long-term and slow dynamics of a hydrological system, indicating the need for model augmentation and a shift in formulation approach. This study employed an entirely different approach – system dynamics – towards more realistic replication of the observed slow hydrological behaviors at inter-annual and inter-decadal scales. Using the headwaters of Baiyang Lake in China as a case study, the endogenous linking structure of the hydrological system was gradually unraveled from 1982 to 2015 through wavelet analysis, Granger's causality test, and a system dynamics model. The wavelet analysis and Granger's causality test identified a negatively correlated and bidirectional causal relationship between actual evapotranspiration and catchment water storage change across distinct climatic periodicities, and the system dynamics approach suggested a combined structure of a vegetation reinforcing feedback and a soil water–vegetation balancing feedback in the hydrological system. The system dynamics' structure successfully captured the slow hydrological behaviors under both natural and human-intervention scenarios, demonstrating a self-sustained oscillation arising within the system's boundary. Our results showed that the interaction between the vegetation structure and the soil-bound water dominates the hydrological process at an inter-annual scale, while the interaction between the climatic oscillation and the soil-water-holding capacity dominates the hydrological process at an inter-decadal scale. Conventional hydrological models, which typically employ physiological-based evapotranspiration formulations and assume invariable soil characteristics, ignore vegetation structure change at the inter-annual scale and soil-water-holding capacity change at the inter-decadal scale, leading to failure in predicting the observed long-term hydrological behaviors. The system dynamics model is in its early stage with applications primarily confined to water-stressed regions and long-term scales. However, the novel insights proposed in our study, including the different hierarchies corresponding to distinct mechanisms and timescales and the endogenous linking structure among stocks being a more important driver of the hydrological behaviors, offer potential solutions for better understanding a hydrological system and guidelines for improving the configuration and performance of conventional hydrological models.

Sub-District Level Spatiotemporal Changes of Carbon Storage and Driving Factor Analysis: A Case Study in Beijing

Analyzing the current trends and causes of carbon storage changes and accurately predicting future land use and carbon storage changes under different climate scenarios is crucial for regional land use decision-making and carbon management. This study focuses on Beijing as its study area and introduces a framework that combines the Markov model, the Patch-based Land Use Simulation (PLUS) model, and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model to assess carbon storage at the sub-district level. This framework allows for a systematic analysis of land use and carbon storage spatiotemporal evolution in Beijing from 2000 to 2020, including the influence of driving factors on carbon storage. Moreover, it enables the simulation and prediction of land use and carbon storage changes in Beijing from 2025 to 2040 under various scenarios. The results show the following: (1) From 2000 to 2020, the overall land use change in Beijing showed a trend of “Significant decrease in cropland area; Forest increase gradually; Shrub and grassland area increase first and then decrease; Decrease and then increase in water; Impervious expands in a large scale”. (2) From 2000 to 2020, the carbon storage in Beijing showed a “decrease-increase” fluctuation, with an overall decrease of 1.3 Tg. In future carbon storage prediction, the ecological protection scenario will contribute to achieving the goals of carbon peak and carbon neutrality. (3) Among the various driving factors, slope has the strongest impact on the overall carbon storage in Beijing, followed by Human Activity Intensity (HAI) and Nighttime Light Data (NTL). In the analysis of carbon storage in the built-up areas, it was found that HAI and DEM (Digital Elevation Model) have the strongest effect, followed by NTL and Fractional Vegetation Cover (FVC). The findings from this study offer valuable insights for the sustainable advancement of ecological conservation and urban development in Beijing.

Assessment of human-induced evapotranspiration with GRACE satellites in the catchment area of lake Baikal

Evapotranspiration is an integral part of the Earth system studies, but it is challenging to measure it on regional scales. One estimation technique is a terrestrial water budget, i.e., total precipitation minus the sum of evapotranspiration and net runoff equals the change in water storage. Gravity Recovery and Climate Experiment (GRACE) satellite gravity observations are now enabling the closure of this equation by providing information on the terrestrial water storage changes. The main objective of this study was to estimate human induced evapotranspiration (HET) using the water budget and Remote Sensing-Based Vegetation Interface Processes (VIP-RS) model. We compare VIP-RS model ET estimates with Gravity Recovery and Climate Experiment and Moderate Resolution Imaging Spectroradiometer satellite-based estimates in the intensively managed Lake Baikal basin. The GRACE-based ET (0,534–133,570 mm/yr.), considerably higher than VIP-RS ET (0–94,319 mm/yr.), agrees well with existing estimates found in the literature and indicates that human activities contribute to an increase in ET. The evaluated uncertainty of monthly precipitation, runoff, GRACE based terrestrial water storage, ET-GRACE, and VIP-RS is 1,56, 0,04, 1,3, 0,89, and 0,8 km3 month-1, respectively. The differences may be utilized as an indicator of water management impacts on ET. We argue that satellite-based ET should yield larger seasonal amplitudes in the lake basin due to the impacts of anthropogenic activities. To date, no such investigation has been available in the existing literature for the Lake Baikal basin. Therefore, the adopted approaches for HET and its result will be regarded as a new and honest contribution to the Lake Baikal basin.

Decoupling of surface water storage from precipitation in global drylands due to anthropogenic activity

The availability of surface water in global drylands is essential for both human society and ecosystems. However, the long-term drivers of change in surface water storage, particularly those related to anthropogenic activities, remain unclear. Here we use multi-mission remote sensing data to construct monthly time series of water storage changes from 1985 to 2020 for 105,400 lakes and reservoirs in global drylands. An increase of 2.20 km3 per year in surface water storage is found primarily due to the construction of new reservoirs. For lakes and old reservoirs (constructed before 1983), conversely, the trend in storage is minor when aggregated globally, but they dominate surface water storage trends in 91% of individual global dryland basins. Further analysis reveals that long-term storage changes in these water bodies are primarily linked to anthropogenic factors—including human-induced warming and water-management practices—rather than to precipitation changes, as previously thought. These findings reveal a decoupling of surface water storage from precipitation in global drylands, raising concerns about societal and ecosystem sustainability.

Spatiotemporal Nonlinear Characteristics and Threshold Effects of China's Water Resources

China faces shortage of water resources, particularly in the context of rapid population growth and accelerating urbanization, making the changes in its water resources among the most pronounced on Earth. Additionally, the complex interplay between climate change and human activities leads to nonlinear and non-stationary patterns in China's water resources. This study utilizes high-resolution water storage monitoring data to comprehensively analyze the nonlinear changes in water storage and its relationships with human footprint, precipitation, and temperature, revealing the complex dynamics of water storage changes across China. We used advanced data analysis techniques to identify critical points where water storage patterns shifted significantly, with categories of nonlinear changes that first decrease then increase and those that first increase then decrease together accounting for 55.62% of the observations. The northeastern and western border areas of China are hotspots for these nonlinear changes, and the analysis identifies 2019 as a year with a high frequency of turning points. The piecewise linear regression analysis found that when the human footprint exceeds a specific threshold, its negative impact on water storage significantly intensifies; when precipitation and temperature exceed certain thresholds, their impact on water storage shifts from negative to positive. These findings not only reveal the complex spatiotemporal distribution characteristics of water storage change turning points across China but also emphasize that water resource management strategies in China and globally need to adopt more comprehensive and dynamic approaches in the context of global climate change. To meet future challenges, it is essential to integrate multiple variables and develop flexible, adaptive management strategies to ensure the sustainable use and effective management of water resources.

Carbon Storage Response to Land Use/Land Cover Changes and SSP-RCP Scenarios Simulation: A Case Study in Yunnan Province, China.

Changes in terrestrial ecosystem carbon storage (CS) affect the global carbon cycle, thereby influencing global climate change. Land use/land cover (LULC) shifts are key drivers of CS changes, making it crucial to predict their impact on CS for low-carbon development. Most studies model future LULC by adjusting change proportions, leading to overly subjective simulations. We integrated the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, the Patch-generating Land Use Simulation (PLUS) model, and the Land Use Harmonization 2 (LUH2) dataset to simulate future LULC in Yunnan under different SSP-RCP scenarios of climate and economic development. Within the new PLUS-InVEST-LUH2 framework, we systematically analyzed LULC alterations and their effects on CS from 1980 to 2040. Results demonstrated that: (1) Forestland had the highest CS, whereas built-up land and water showed minimal levels. Western areas boast higher CS, while the east has lower. From 1980 to 2020, CS continuously decreased by 29.55 Tg. In the wake of population increase and economic advancement, the area of built-up land expanded by 2.75 times. Built-up land encroaches on other land categories and is a key cause of the reduction in CS. (2) From 2020 to 2040, mainly due to an increase in forestland, CS rose to 3934.65 Tg under the SSP1-2.6 scenario, whereas under the SSP2-4.5 scenario, primarily due to a reduction in forestland and grassland areas, CS declined to 3800.86 Tg. (3) Forestland is the primary contributor to CS, whereas the ongoing enlargement of built-up land is causing a sustained decline in CS. Scenario simulations indicate that future LULC changes under different scenarios will have a significant impact on CS in Yunnan. Under a green sustainable development pathway, Yunnan can exhibit significant carbon sink potential. Overall, this research offers a scientific reference for optimizing land management and sustainable development in Yunnan, aiding China's "double carbon" goals.

Corrigendum to “Estimation of Groundwater Storage Change and Groundwater Recharge Using GRACE data in Jedeb Watershed Under the Application of Google Earth Engine”

Corrigendum to “Estimation of Groundwater Storage Change and Groundwater Recharge Using GRACE data in Jedeb Watershed Under the Application of Google Earth Engine”

A Half‐Order Derivative Based Model of Lake Heat Storage Change

A Half‐Order Derivative Based Model of Lake Heat Storage Change

GNSS2TWS_Slepian: A software to recover GNSS-inverted terrestrial water storage changes based on Slepian basis functions

GNSS2TWS_Slepian: A software to recover GNSS-inverted terrestrial water storage changes based on Slepian basis functions

Temporal and Spatial Characteristics and Influencing Factors of Carbon Storage in Black Soil Area Under Topographic Gradient

Exploring the characteristics and driving factors of carbon storage change in different terrain gradient variations can provide important insights for formulating the agricultural ecological protection policy for regional development. Previous studies have used the fixed value of carbon density to evaluate the change characteristics of carbon storage but ignored the spatio-temporal heterogeneity of carbon storage at the block scale and the impact of policy factors. Thus, this paper takes Sanjiang Plain, Heilongjiang Province, China, as a study area, and the spatio-temporal variation of carbon storage at different topographic gradients was revealed using hot and cold spot analysis and zonal statistics. Through the geographic detector and estimation of the soil carbon density model, the driving factors and intensity of carbon storage spatial distribution are revealed from 1990 to 2020. We conducted analyses on aboveground biomass, underground biomass, and soil carbon storage across three elevation levels (0–200 m, 200–500 m, 500–999 m) to reveal the quantitative distribution features of carbon storage. The study analysis finds that carbon storage indicates a sawtooth evolution during the study period. Carbon storage was dominant at elevation I (range is 0–200 m), slope I (range is 0–2°), and relief amplitude I (range is 0–30 m). Additionally, the carbon storage losses were severe at elevation II (range is 200–500 m), slope II (2–6°), and relief amplitude II (30–70 m). In contrast, the carbon storage losses at elevation III (500–999 m), slope III (6–15°), and relief amplitude III (70–186 m) were insignificant. The spatial pattern of carbon storage varies significantly under different topographic gradients from 1990 to 2020. The most critical driving factors influencing the spatial distribution pattern of carbon storage were land use and annual average temperature. Distance to urban centers and soil texture also moderately influence the distribution of carbon storage. As the topographic gradient increases, the dominant factors of carbon storage gradually change from annual mean temperature and the extent of land use to policy factors and other socio-economic factors. Therefore, this study emphasizes the importance of implementing policies that convert farmland to forests and wetlands and promote the green transformation of agriculture.

Internal climate variability modulates decadal changes in ocean anthropogenic carbon storage

Abstract The ocean removes man-made (anthropogenic) carbon from the atmosphere and thereby mitigates climate change. Observations from global hydrographic surveys reveal the spatial and temporal evolution of the ocean inventory of anthropogenic carbon and suggest substantial decadal variability in historical storage rates. Here, we use a 100-member ensemble of an Earth system model to investigate the influence of external forcing and internal climate variability on historical changes in ocean anthropogenic carbon storage over 1994 to 2014. Our findings reveal that the externally forced, decadal changes in storage are largest in the Atlantic (2-4 mmol m-3 decade-1) and positive nearly everywhere. Internal climate variability modulates regional ocean anthropogenic carbon storage trends by up to 10 mmol m-3 decade-1. The influence of internal climate variability on decadal storage changes is most prominent at depths of ~300 m and at the edges of the subtropical gyres. Internal variability in anthropogenic carbon in the extratropics has high spectral power on decadal to multi-decadal timescales, indicating that the approximately decadal repetitions of hydrographic surveys may produce storage change estimates that are heavily influenced by internal climate variability.

Assessing the impact of land use and cover change on above-ground carbon storage in subtropical forests: a case study of Zhejiang Province, China

ABSTRACT Land Use and Cover Change (LUCC) has emerged as a primary driver of terrestrial carbon storage changes. However, the contributions of LUCC to Above-Ground Carbon (AGC) storage in subtropical forests remain unclear due to the complex and diverse LUCC trajectory. Quantitative assessment of the impact of different LUCC trajectories on carbon storage is essential for regional carbon cycle mechanisms. Therefore, this study focuses on Zhejiang Province, a representative subtropical forest region in China, to accurately assess the contribution of LUCC to AGC storage changes from 1984 to 2019. We first mapped the land cover patterns using the random forest and spatiotemporal filtering algorithm and then applied these patterns to drive an optimized BIOME-BGC model to simulate the spatiotemporal distribution of AGC density. Finally, the LUCC trajectories were classified into three categories: afforestation, deforestation, and forest type transformations. Their contributions to AGC changes were isolated and analyzed through the trajectory analysis. The results demonstrated that the forest area of Zhejiang Province increased from 5.35 × 106 ha to 6.83 × 106 ha (+27.66%) and the total forest AGC storage increased from 80.52 Tg C to 124.16 Tg C (+54.19%) between 1984 and 2019. The increase in forest AGC due to LUCC amounted to 31.26 Tg C, contributing 71.63% to the total. Specifically, the afforestation, deforestation, and forest type transformations contributed 82.37%, −17.27%, and 6.53% to the change in AGC, respectively. Overall, the afforestation within the LUCC trajectories was the primary contributing factor to the growth of forest AGC in Zhejiang Province from 1984 to 2019. This study obtained accurate LUCC and AGC data, clarifying the contribution of different LUCC trajectories and providing a better understanding of the responses of the forest carbon storage to LUCC dynamics.

The Analysis of the Spatial–Temporal Evolution and Driving Effect of Land Use Change on Carbon Storage in the Urban Agglomeration in the Middle Reaches of the Yangtze River

Studying the temporal and spatial variation characteristics and driving factors of carbon reserves in the middle reaches of the Yangtze River urban agglomeration is crucial for achieving sustainable development and regional ecological conservation against the backdrop of the “double carbon” plan. Based on three periods of land use data from 2000 to 2020, combined with the InVEST model(Version 3.14.2), the spatiotemporal changes in carbon storage in the urban agglomeration in the middle reaches of the Yangtze River were analyzed. The PLUS model (Version 1.3.5) was used to predict three scenarios of natural development, urban development, and eco-development in the urban agglomeration in the middle reaches of the Yangtze River in 2035 and estimate the carbon storage of the ecosystems under different scenarios, and it used optimal parameter GeoDetectors (Version 4.4.2) to reveal the driving factors affecting the spatiotemporal differentiation of carbon storage. The results show that farmland and construction land area increased and forestland area continued to decrease from 2000 to 2020. Carbon storage decreased by 1 × 106 t, with forestland conversion to farmland and construction land being the main decreasing drivers. The carbon storage of natural and urban developments decreased by 0.26 × 106 t and 0.32 × 106 t, while it increased by 0.16 × 106 under ecological development. The results of the factor detector showed that the NDVI (Normalized Difference Vegetation Index) had the highest explanatory power on the spatiotemporal variation in carbon storage (q = 0.588), followed by the slope (q = 0.454) and elevation (q = 0.391), and the explanatory power of natural environmental factors on the spatiotemporal variation in of carbon storage was dominant. The interaction detector results showed that the spatiotemporal variation in carbon storage was affected by multiple factors, the interaction intensity between each driving factor was stronger than that of a single factor, and the synergy between the NDVI and slope was the strongest, at q = 0.646.

Contribution of the GRACE Satellite Mission to Mapping Water Mass Variations in River Basins

Abstract. In the context of climate change, there is a need to understand the dynamics and temporal fluctuations of continental water. Such knowledge is essential for the development of more effective and sustainable management strategies for this indispensable resource. The Earth's gravitational field is not constant but varies over time due to factors such as mass redistribution during the hydrological cycle. The GRACE satellite mission (Gravity Recovery and Climate Experiment) provides valuable information about the geodynamic behaviour of our planet by estimating variations in the continental water storage from the temporal changes in the Earth’s gravity field.The aim of this work is to demonstrate the contribution of satellite gravimetry to identify patterns of variation in continental water masses within large river basins. For this purpose, a regional study of the TWS (Total Water Storage) derived from GRACE was carried out, focusing on the spatial and temporal changes detected in the La Plata Basin in South America. The periods with the most extreme variations between 2019 and 2023 were analyzed, and the TWS was compared with satellite precipitation data, to subsequently relate them to ENSO events that occurred in the region. The results show that GRACE detected the significant changes in water storage, highlighting in particular the severe drought that occurred in this basin in 2022.