Water Resources Management Planning Research Articles

Construction of a quantitative model for ski resort water demand and preliminary exploration of drainage irrigation pathways

In China, natural snowfall is insufficient, and ski resorts often require artificial snowmaking in winter and turf management in summer, which results in high overall water consumption and considered as one of the high water-consuming service industries, with an urgent need for theoretical foundations related to water management. Based on life cycle theory, we examined the characteristics of the water systems of ski resorts in winter and summer. A Monte Carlo simulation was used to construct a stochastic model to quantify the theoretical water consumption for snowmaking at ski resorts and to investigate the mechanisms by which various factors influence this consumption. We summarize the necessity of summer turf management and irrigation requirements at ski resorts, explore the interaction between ski resorts and agricultural irrigation. The results show that the theoretical water demand of ski resorts is basically consistent with the actual water consumption of ski resorts, that the constructed model is feasible, and that snowmaking water is the most important water use of ski resorts. Exploring the collection and reuse of meltwater or rainwater from ski resorts for agricultural irrigation in the spring and summer could provide more possibilities for the sustainable management of regional water resources. The research findings can provide a theoretical basis for scientifically and reasonably setting the norm of water intake for ski resorts and for developing comprehensive water resource management plans for ski resorts and agricultural irrigation.

Integration of Generative-Adversarial-Network-Based Data Compaction and Spatial Attention Transductive Long Short-Term Memory for Improved Rainfall–Runoff Modeling

This work presents a novel approach to rainfall–runoff modeling. We incorporate GAN-based data compaction into a spatial-attention-enhanced transductive long short-term memory (TLSTM) network. The GAN component reduces data dimensions while retaining essential features. This compaction enables the TLSTM to capture complex temporal dependencies in rainfall–runoff patterns more effectively. When tested on the CAMELS dataset, the model significantly outperforms benchmark LSTM-based models. For 8-day runoff forecasts, our model achieves an NSE of 0.536, compared to 0.326 from the closest competitor. The integration of GAN-based feature extraction with spatial attention mechanisms improves predictive accuracy, particularly for peak-flow events. This method offers a powerful solution for addressing current challenges in water resource management and disaster planning under extreme climate conditions.

Runoff Estimation in Kajurli Watershed Using SCS-CN and GIS Techniques

The study focuses on estimating surface runoff using the Soil Conservation Service Curve Number (SCS-CN) method, combined with Remote Sensing (RS) and Geographic Information System (GIS) techniques, for the Kajurli Watershed in Ratnagiri District, Maharashtra. Surface runoff is a key factor influencing agricultural productivity, soil erosion, and water management at the watershed level. The SCS-CN method, a widely used tool for runoff estimation, utilizes rainfall, land use, and soil type data. By integrating RS and GIS, the study achieves a more accurate assessment of land use and soil characteristics, which are critical inputs for runoff calculations. The Kajurli Watershed falls under hydrological soil group B, known for moderate runoff potential. The study uses 33 years of historical rainfall data (1990-2022) to compute annual runoff using the SCS-CN method. Key parameters such as Curve Numbers (CN) are derived based on land use, hydrological soil groups, and antecedent moisture conditions (AMC). The average annual rainfall in the watershed is found to be 3392.8 mm, with 39.69% of this rainfall contributing to surface runoff. The integration of RS and GIS simplifies spatial analysis, enabling accurate and efficient runoff estimation. The study highlights the sensitivity of the SCS-CN method to CN values, emphasizing the importance of precise land use and soil data for reliable hydrological modelling. These findings offer crucial insights for water resource management, flood control, and agricultural planning in the region.

Climate change impacts on hydrology and water resources in East Africa considering CMIP3, CMIP5, and CMIP6

Climate change impacts on the hydrology and water resources in East Africa require proper planning of regional adaptation measures. Past studies on the subject matter tended to focus on catchment instead of regional scale. To support actionable policy regarding regional planning of water resources management, this paper reviewed studies conducted in East Africa based on CMIP3, CMIP5, and CMIP6. Compared to the historical condition, temperature of East Africa over the mid- to late 21st century will increase by varying extents over the range 0.17–7°C with about 2.6°C on average. Future precipitation over East Africa will increase by 8.5% on average. Changes in discharge based on CMIP3, CMIP5, and CMIP6 will vary from −25.3 to +60.5%, −42.5 to 129%, and −23.4 to 69%, respectively. Ensemble means of the changes in discharge across the study area based on CMIP3, CMIP5, and CMIP6 will be by 25.2, 19.2, and 19.1%, respectively. Future peak river discharge across East Africa will increase by 8% on average. These results are important for planning regional climate change adaptation across East Africa. To support local or catchment adaptation, results of climate change impacts of temperature, precipitation and discharge of about 50 catchments across East Africa can be found summarized in this review paper. It is recommended that both regional and local policies or approaches should be considered for climate change adaptation to ensure a balanced, effective and sustainable framework for climate resilience in support of measures for tackling broad and specific needs cohesively.

Enhancing LSPIV accuracy in low-speed flows and heterogeneous seeding conditions using image gradient

Flow measurement in rivers and channels is crucial for water resource management and infrastructure planning, especially under the context of climate change. However, traditional methods like mechanical current meters and hydroacoustic instruments face limitations in terms of cost, intrusiveness, and accessibility. In recent years, image-based velocimetry techniques have emerged as promising alternatives due to their non-contact nature and cost-effectiveness. Nevertheless, persistent challenges remain, particularly concerning the uniform distribution of surface tracers necessary for precise measurements. These challenges are particularly pronounced in cases involving artificial seeding, where ensuring uniform distribution poses a significant obstacle. To address this issue, this study presents a novel methodology for filtering Large Scale Particle Image Velocimetry (LSPIV) data based on indicators of pixel intensity gradients. The methodology was evaluated across various field measurements under low flow conditions, encompassing a wide range of seeding characteristics. The evaluations demonstrated improvements in mean surface velocity profile estimation, showing reductions of up to 70 % in normalized root mean square error compared to not using filters. Additionally, the results were compared with filters typically employed by experienced LSPIV users, such as background subtraction and cross-correlation coefficient thresholds, showing improvements with the proposed filter. Implementation of the proposed strategy reduces the subjectivity in LSPIV implementation, particularly for users with limited knowledge of the technique, but also require minimal post-processing efforts. The methodology is anticipated to be integrated into existing software tools, thereby enhancing the accessibility of LSPIV for individuals with limited expertise in image velocimetry. Overall, this methodology facilitates cost-effective expansion of hydrological information availability, particularly in resource-constrained regions.

Could California's groundwater resource management lessons be beneficial to England?

In California, Groundwater Sustainability Agencies (GSAs) are basin/catchment-specific water organizations mandated by the Sustainable Groundwater Management Act of 2014 to manage groundwater sustainably by 2040/2042. A GSA can charge replenishment fees for pumped groundwater and these fees can be used to: (a) monitor basin hydrologic conditions; (b) improve the water budget accuracy; (c) increase water availability through management actions and projects; (d) mitigate undesirable results; and/or (e) pay for activities including issuing allocations and limiting production to allocations. In England, housing is frequently proposed in river basin catchments that face water resource challenges. High profile examples are the Arun Valley and Cambridge. The current National Planning Policy Framework manages water resource planning strategically through water company Water Resource Management Plans or Environment Agency River Basin Management Plans. These processes can be slow to respond to development requirements. England might look to California's practices that require water users and developers to pay for ‘water credits’ or fees. This could involve a ‘Watermaster’ charged with local or catchment scale resource management and project implementation. This Watermaster could engage all stakeholders including the local water company, farmers, the Environment Agency and the local authority, none of whom is well positioned to act in isolation.

Spatial and Temporal Variability of Rainfall Erosivity in the Niyang River Basin

Rainfall erosivity is a crucial factor in the evaluation of soil erosion, significantly influencing the complex relationships among water, soil, and the environment. Understanding its attributes and variations in space and time is essential for effective water resource management, erosion mitigation, and land-use planning. This paper utilizes daily precipitation data from 123 grid points in the Niyang River Basin, spanning from 2008 to 2016, to calculate rainfall erosivity using a straightforward algorithmic model. Ordinary Kriging was used to examine the spatial and temporal variations in rainfall erosivity, while Spearman’s correlation analysis was employed to examine the relationships between annual rainfall erosivity and various factors, including multi-year average precipitation, erosive rainfall, dry-season rainfall, wet-season rainfall, temperature, and elevation. The results indicate a year-by-year increase in rainfall erosivity in the basin, with a trend towards stabilization. The average annual rainfall erosivity over the years is 711 MJ·mm·hm−2·h−1, peaking at 1098 MJ·mm·hm−2·h−1 in 2014. A significant 93.9% of rainfall erosivity is concentrated in the wet season, with a maximum of 191 MJ·mm·hm−2·h−1 in July. The left bank of the mainstream, especially the central and lower sections of the main river and its tributaries, experiences the highest rainfall erosivity. Rainfall factors predominantly influence erosivity, with erosive rainfall showing the strongest correlation (rho = 0.93), while temperature and elevation have relatively minor effects. This study enhances the understanding of rainfall erosive forces in the plateau region and provides a scientific basis for predicting soil loss, developing effective erosion control measures, and ensuring sustainable land use.

Spatiotemporal forecasting of water change trends in Urmia Lake through to 2030, using STC-based models

ABSTRACT The purpose of this paper is to forecast the spatiotemporal water change trends in Urmia Lake through 2030. Three space–time cube-based models were applied. The forest-based forecast model, with a mean of 0.14 forecast root mean square error (RMSE) and 0.39 validation RMSE, had a better performance. According to the model’s results, the peripheral parts of the lake will mainly stay completely arid with a 42.5% extension in the entire period, 21.7% in spring, and a 27% reduction in winter. In the middle parts, the aridity will increase by 121% in summer, 93% in fall, and 38% in spring. Eventually, the centric areas will not be completely arid; however, the water patterns’ extent will decrease by 53.46% in fall, 34.6% in spring, and 28.5% in summer. Additionally, southern, eastern, western, and northern areas will experience drier conditions, respectively. The findings can be used for water resource management and water restoration plans for the lake.

Impact of land use/land cover change on surface water hydrology in Akaki river catchment, Awash basin, Ethiopia

Watershed reactions to environmental changes is critical for water resource use and management planning. The change in land use/land cover (LUCC) alters surface water hydrology and water balance, subsequently it affects the availability of water resources. This study examined Akaki River catchment surface hydrological responses to future land use scenarios. The future LULC simulated using Cellular Automata-Markov Chain (CA-Markov) model in IDRISI software for 2031, 2041, and 2051 under Business as Usual (BAU) and Forest Patch Protection (FPP) scenarios. Water yield (WY), sediment export (SE), and soil loss (SL) were simulated using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. The findings indicate that under BAU, forest and settlement areas increased 42.64% and 31.47%, whereas water body, grass land, and agriculture dropped 31.99%, 25.72%, and 10.4%, respectively, from 2021 to 2051. The FPP scenario shows a 66.42% increase in forest coverage and a 31.97% decrease in cultivated land between 2021 and 2051. Overall settlement expansion will expand by 31.44%, while water body and grassland will decline by 11.12% and 24.9%. As a result of LULC changes in BAU scenario, the proportions of WY (4.36%), SE (26%), and SL (22.45%) increased. The hydrological components exhibited more gradual variations of 0.55%, 18.8%, and 14.24% for the WY, SE, and SL, respectively, under the FPP scenario. Therefore, the alteration has resulted in a concerning state within the area, necessitating the implementation of comprehensive management techniques to restore the hydrology of the catchment.

A geospatial modelling for glacial lake outburst floods susceptibility analysis in the Central Himalayas

ABSTRACT Glacial lakes have increased throughout a significant portion of the Himalayan region, and hence the present study focuses on a geospatial modelling approach for analysing the susceptibility to glacier lake outburst flood (GLOF) in the central Himalayan region. Multitemporal satellite imageries such as Landsat-TM and Sentinel-2 were taken. The hydrodynamic compatibility tool, i.e. HEC-RAS 5.0, was used for GLOF prediction, and the lake volume and surface area were calculated using established empirical equations. The breach fails and flood flow were approximated, and arising uncertainties were analysed with three outburst scenarios. The number of lakes and their sizes have significantly increased according to the findings of the study. Of these, the Vasundhara Lake (VL), which is located at 4,676 m above the sea level, is assigned to the hazardous category for more examination. It has been found that the lake's area expanded over the past three decades (i.e. 1994–2020), rising from 0.15 to 0.29 km2. The study demonstrates that, in the worst-case situation, infrastructure along riverbanks may be susceptible to harm. The study provides information on the potential effects of GLOF events in the study region that might be used in early warning systems and water resource management plans.

Analysis of Spatial and Temporal Variations in Evapotranspiration and Its Driving Factors Based on Multi-Source Remote Sensing Data: A Case Study of the Heihe River Basin

The validation of remotely sensed evapotranspiration (ET) products is important for the development of ET estimation models and the accuracy of the scientific application of the products. In this study, different ET products such as HiTLL, MOD16A2, ETMonitor, and SoGAE were compared using multi-source remote sensing data and ground-based data to evaluate their applicability in the Heihe River Basin (HRB) during 2010–2019. The results of the comparison with the site observations show that ETMonitor provides a more stable and reliable estimation of ET than the other three products. The ET exhibited significant variations over the decade, characterized by a general increase in rates across the HRB. These changes were markedly influenced by variations in land use and topographical features. Specifically, the analysis showed that farmland and forested areas had higher ET rates due to greater vegetation cover and moisture availability, while grasslands and water bodies demonstrated lower ET rates, reflecting their respective land cover characteristics. This study further explored the influence of various factors on ET, including land use changes, NDVI, temperature, and precipitation. It was found that changes in land use, such as increases in agricultural areas or reforestation efforts, directly influenced ET rates. Moreover, meteorological conditions such as temperature and precipitation patterns also played crucial roles, with warmer temperatures and higher precipitation correlating with increased ET. This study highlights the significant impact of land use and climatic factors on spatiotemporal variations in ET within the HRB, underscoring its importance for optimizing water resource management and land use planning in arid regions.

Morphometric analysis in the sub basins of the Kali River using Geographic Information System, Karnataka, India

Morphometric analysis serves as a crucial tool for quantitatively characterizing drainage basins and exploring the intricate relationships between hydraulic parameters and geomorphological characteristics. This study presents a comprehensive morphometric analysis of the Kali Subbasin in Karnataka, covering an area of 1175.45 km². Utilizing remote sensing data and GIS tools, we quantified various morphometric parameters, including stream order, stream length, bifurcation ratio, drainage density, stream frequency, form factor, and circularity ratio, to understand the basin's geomorphological and hydrological characteristics. The study area is classified as a seventh-order basin displaying a dendritic drainage pattern with a drainage density of 2.853 km/km². Notably, the bifurcation ratio ranges from 4.396 to 3, indicating low to moderate structural control within the basin. The subbasin's elongated shape, with an elongation ratio of 0.604 and a circularity ratio of 0.298, indicates moderate peak flow and high susceptibility to erosion. Relief parameters showed significant elevation variations, contributing to intense erosion and heightened flood risks in lower-lying areas. The study identifies the basin's transition to a mature geomorphic stage, evidenced by an escalating stream length ratio from lower to higher orders. These findings underscore the importance of tailored watershed management strategies, including floodplain zoning, retention basins, afforestation, and terracing, to mitigate flooding and soil erosion. The study's insights are crucial for developing effective, localized water resource management plans and enhancing the resilience of the Kali Subbasin to future hydrological challenges.

Scalable and Interpretable Forecasting of Hydrological Time Series Based on Variational Gaussian Processes

Accurate streamflow forecasting is crucial for effectively managing water resources, particularly in countries like Colombia, where hydroelectric power generation significantly contributes to the national energy grid. Although highly interpretable, traditional deterministic, physically-driven models often suffer from complexity and require extensive parameterization. Data-driven models like Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) networks offer simplicity and performance but cannot quantify uncertainty. This work introduces Sparse Variational Gaussian Processes (SVGPs) for forecasting streamflow contributions. The proposed SVGP model reduces computational complexity compared to traditional Gaussian Processes, making it highly scalable for large datasets. The methodology employs optimal hyperparameters and shared inducing points to capture short-term and long-term relationships among reservoirs. Training, validation, and analysis of the proposed approach consider the streamflow dataset from 23 geographically dispersed reservoirs recorded during twelve years in Colombia. Performance assessment reveals that the proposal outperforms baseline Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) models in three key aspects: adaptability to changing dynamics, provision of informative confidence intervals through Bayesian inference, and enhanced forecasting accuracy. Therefore, the SVGP-based forecasting methodology offers a scalable and interpretable solution for multi-output streamflow forecasting, thereby contributing to more effective water resource management and hydroelectric planning.

High-resolution estimates of water availability for the Iberian Peninsula under climate scenarios

Water availability is of paramount importance for sustainable development and environmental planning, specifically in regions such as the Iberian Peninsula, renowned for diverse landscapes and varying climatic conditions. Due to climate change, understanding the potential impacts on water resources becomes essential for effective water management strategies. This research effort aims to assess future potential water availability for the Iberian Peninsula in different climate scenarios, employing cutting-edge water resource modelling techniques integrated within a geographic information system (GIS) framework. In this study, potential water availability is defined as the annual demand for water that can be satisfied at a specific point in the fluvial network with certain reliability. An ensemble of state-of-the-art climate models is utilised to project runoff for the Iberian Peninsula during the mid- and late-twenty-first century periods. These climate projections were subsequently processed using the GIS-based water resource management model, WAAPA, to derive potential water availability under a range of realistic hypotheses. The results indicate that anticipated shifts in precipitation patterns will lead to alterations in hydrological regimes across the region, significantly impacting future water availability. By using GIS-based methodologies, we can facilitate the identification of vulnerable areas susceptible to changes in water availability, offering spatially explicit information along the main rivers of the Iberian Peninsula for decision-makers and stakeholders. High-resolution spatial outputs from this research and detailed water availability estimates serve as valuable input for integrated water resource management and climate change adaptation planning. By combining advanced GIS-based hydrological modelling with climate scenarios, this research presents a robust framework for assessing water resources amidst a changing climate, applicable to other regions struggling with analogous challenges. Ultimately, our study provides vital insights for policymakers and stakeholders, empowering them to make informed decisions and devise adaptive measures to ensure sustainable use of water resources despite uncertain future climatic conditions.

Impact assessment of agricultural droughts on water use efficiency in different climatic regions of Punjab Province Pakistan using MODIS time series imagery

AbstractDrought is the most destructive phenomenon that distresses the terrestrial carbon cycle balance and crop production. The variation in evapotranspiration (ET) and gross primary productivity (GPP) is a significant cause of agricultural drought effects on water use efficiency. This study aims to evaluate the impact of agricultural drought on WUE and it's anomalies in different climate regions. The standard vegetation index was used to measure the extent of agricultural drought. WUE was calculated using the ratio of ET, GPP, and climate classification using the De Martonne method. This study was conducted over the last 22 years, from 2001 to 2022. Meanwhile, 2001, 2002, 2014, and 2018 were considered high drought years based on a 22‐year analysis. According to remote sensing analysis of ET and GPP, the WUE increased throughout drought in all climate regions and more strongly in the arid zone than in humid regions. Humid areas were more vital due to drought than arid ones. WUE anomalies badge with increased drought severity across all climates except the very humid zone. Humid climates saw faster recovery times than arid ones, and areas experienced severe droughts. The findings of this research are essential for understanding the carbon and water cycles for agriculture management. The analysis of drought severity and WUE across arid climate regions helped analyse the varying impact of drought on WUE due to climate change. The significance of this study includes informing agricultural, water resource, and drought management planning in the Punjab Province, an agricultural region vulnerable to climate change. This study holds important learnings for agricultural regions worldwide. It has practical and scientific importance regarding agricultural systems' specific stresses and responses to droughts.

Unlocking policy effects: Water resources management plans and urban water pollution

Water resource management, as a foundation for supporting sustainable urban development, has garnered increasing attention from scholars. Developing effective water resource management plans is a major challenge faced by countries worldwide. This study uses the 2015 Water Pollution Control and Prevention Action Plan (WPCAP) in China as a natural experiment and employs a Difference-in-Differences (DID) model to estimate the relationship between WPCAP and urban water pollution from 2010 to 2021. The findings are as follows: 1) WPCAP reduces water pollution. 2) WPCAP decreases water pollution in high-policy-pressure cities but increases water pollution in low-policy-pressure cities within a 60 km radius, particularly having a significantly negative impact on water pollution in low-policy-pressure cities with low altitude. 3) optimizing industrial and domestic water use, as well as enhancing sewage treatment capabilities, are crucial pathways through which WPCAP reduces water pollution. Additionally, WPCAP significantly improves water pollution control capabilities in cities with abundant water resources, large cities, and industrialized cities. 4) although WPCAP's ability to control water pollution increases management costs, it also raises residential income and promotes population growth. These findings have important implications for the sustainable development of water resources in emerging countries, including China.

Estimating Return Flow Rate from Water Demand Using Optimization Approach

Water budget analysis involves assessing available water resources and demand in specific regions or watersheds in order to efficiently manage the already-limited water resources, ensure supply stability, and formulate relevant plans. In this analysis, the quantity of return flow is as crucial as supply and demand, necessitating diverse approaches for accurate estimation. Hence, this study employed a gradient descent-based optimization approach to determine a reliable return flow rate. Unlike traditional unidimensional rates, this approach considers scenarios with varied analysis points and periods within the target watershed. The research findings indicated that the optimized return flow rate varied with the analysis location and period, underscoring the potential of this optimization approach to enhance the accuracy of water budget analysis compared to conventional rates in runoff simulations using water supply networks. Integrating the optimization approach proposed in this study with water budget analysis is expected to significantly contribute to the development of effective water resource management plans.

Population regeneration of two Haloxylon species in central Asian deserts as affected by groundwater depth

Haloxylon ammodendron and Haloxylon persicum are the dominant species in the deserts of Central Asia. As groundwater exploration has resulted in a decline in groundwater depth in this region, the impact on the regeneration of these two Haloxylon populations calls for urgent attention. Therefore, we conducted an intensive vegetation survey, including population density, age structure and seed production and seed characteristics, along a groundwater depth gradient in the Gurbantunggut Desert of Central Asia. The result shows that for H. ammodendron, groundwater depth of 15 m is its survival and regeneration limit, beyond which this species cannot grow. However, for H. persicum, this limit does not apply. Instead, declined groundwater depth promotes a reproduction strategy with low seed quantity, high quality, and a high germination rate for H. ammodendron. Ultimately, H. ammodendron exhibits a tighter regeneration trait coordination, with a reasonable age structure being crucial for its population continuation and environmental adaptation. In contrast, H. persicum demonstrates higher resource acquisition and reproductive ability, along with tolerance to groundwater depth decline. In conclusion, if the groundwater depth decline continues, the continuation of H. ammodendron will be seriously threatened but H. persicum will not be. This study is beneficial for evaluating the future trend of vegetation cover in desert region and planning water resources management in Central Asia.

Evaluation of hydrological responses to climate change in the Gibe Gojeb catchment, southwestern Ethiopia

ABSTRACT The expected influences of climate alteration on hydrological responses in the Gibe Gojeb catchment, southwestern Ethiopia, were evaluated using the Soil and Water Assessment Tool (SWAT) model and regional climate models (RCMs). This study emphasizes the evaluation of the climate change impact on hydrological responses. The RCM data were downloaded from CORDEX-Africa. Power transformation and delta change methods were used for bias correction of precipitation and temperature, respectively. The calibration and validation of the SWAT model showed good agreement, and climate change impact was simulated. Accordingly, precipitation, soil water content, percolation, water yield, and groundwater are projected to decrease by 28.18, 7.5, 9.1, 5.4, and 9.2%, respectively, under RCP4.5 in the future (2021–2050). Besides, surface runoff and potential and actual evapotranspiration are projected to increase by 17.4, 11.32, and 5.06%, respectively. Soil water content, percolation, water yield, and precipitation are projected to decrease by 10.51, 5.63, 2.76, and 21.57%, respectively, under RCP8.5 in the future. Rain-fed crop production and hydropower generation in the catchment would be vulnerable to the effects of climate change. The results help to develop effective adaptation measures to reduce the impacts of climate change and draft long-term water resource management plans in the Gibe Gojeb catchment.

Changes in terrestrial water storage in the Three-North region of China over 2003–2021: Assessing the roles of climate and vegetation restoration

Large-scale vegetation restoration has markedly enhanced ecosystem services in the Three-North (TN) region, concurrently exacerbating the pre-existing water resource crisis stemming from climate change in this arid domain. However, the specific contributions of climate and vegetation changes to the evolution of water resources in the TN region have yet to be elucidated. This study leverages data from the Gravity Recovery and Climate Experiment (GRACE) and its Follow-on mission (GRACE-FO), specifically the terrestrial water storage anomaly (TWSA) from 2003 to 2021. We identify two TWSA rising (TWSA_rⅠ and TWSA_rⅡ) and two declining (TWSA_dⅠ and TWSA_dⅡ) hotspots in the TN region. By exploring the relative importance of climate drivers and vegetation dynamics in influencing the temporal dynamics of TWSA, we found that vegetation restoration assumes a more pivotal role than climate factors in 45.2% of the total area within the identified hotspots, predominantly evident in TWSA declining hotspots. The heightened TWSA in TWSA_rⅠ and TWSA_rⅡ primarily stems from precipitation recharge, with precipitation-dominated areas constituting 19.3% of the area within the identified hotspots. Potential evapotranspiration dominates TWSA changes in only 4.6% of the region within the identified hotspots, primarily scattered in TWSA_dⅠ and TWSA_dⅡ. Furthermore, a comparative assessment of soil moisture and groundwater responses to environmental factors indicates that water storage at shallower layers is more responsive to climate factors, while the impacts of vegetation changes on TWSA are more discernibly reflected in water storage at deeper layers. These findings furnish scientific guidance for future ecological restoration planning and sustainable water resources management.