Satellite-Observed Arid Vegetation Greening and Terrestrial Water Storage Decline in the Hexi Corridor, Northwest China

Abstract

The interplay between terrestrial water storage and vegetation dynamics in arid regions is critical for understanding ecohydrological responses to climate change and human activities. This study examines the coupling between total water storage anomaly (TWSA) and vegetation greenness changes in the Hexi Corridor, an arid region in northwestern China consisting of three inland river basins—Shule, Heihe, and Shiyang—from 2002 to 2022. Utilizing TWSA data from GRACE/GRACE-FO satellites and MODIS Enhanced Vegetation Index (EVI) data, we applied a trend analysis and partial correlation statistical techniques to assess spatiotemporal patterns and their drivers across varying aridity gradients and land cover types. The results reveal a significant decline in TWSA across the Hexi Corridor (−0.10 cm/year, p < 0.01), despite a modest increase in precipitation (1.69 mm/year, p = 0.114). The spatial analysis shows that TWSA deficits are most pronounced in the northern Shiyang Basin (−600 to −300 cm cumulative TWSA), while the southern Qilian Mountain regions exhibit accumulation (0 to 800 cm). Vegetation greening is strongest in irrigated croplands, particularly in arid and hyper-arid regions of the study area. The partial correlation analysis highlights distinct drivers: in the wetter semi-humid and semi-arid regions, precipitation plays a dominant role in driving TWSA trends. Such a rainfall dominance gives way to temperature- and human-dominated vegetation greening in the arid and hyper-arid regions. The decoupling of TWSA and precipitation highlights the importance of human irrigation activities and the warming-induced atmospheric water demand in co-driving the TWSA dynamics in arid regions. These findings suggest that while irrigation expansion cause satellite-observed greening, it exacerbates water stress through increased evapotranspiration and groundwater depletion, particularly in most water-limited arid zones. This study reveals the complex ecohydrological dynamics in drylands, emphasizing the need for a holistic view of dryland greening in the context of global warming, the escalating human demand of freshwater resources, and the efforts in achieving sustainable development.

1. Introduction

Drylands cover approximately 45% of the Earth’s terrestrial surface and host fragile ecosystems that are highly susceptible to environmental change [1]. As the largest biome on Earth, drylands exert a dominant influence on interannual variability and long-term carbon sink trends [2,3,4]. Dryland vegetation delivers essential ecosystem services, including carbon sequestration, biodiversity conservation, livelihood sustenance, and food production [4,5,6]. Furthermore, dryland ecosystems are crucial for wind erosion mitigation and climate regulation, underscoring the urgency of their conservation. Water availability remains the primary limiting factor for dryland ecosystems, while the escalating water demand driven by global warming and anthropogenic activities poses a severe threat to these fragile systems [7]. Projections indicate that the anthropogenic water demand in drylands will surge by approximately 270% by the late 21st century, further intensifying water scarcity [8]. Owing to severe water limitations, dryland vegetation dynamics exhibit pronounced sensitivity to fluctuations in precipitation and water availability [4,9].

The Hexi Corridor, situated in the arid and semi-arid regions of northwestern China, represents one of the world’s highest-latitude inland arid zones [10]. This region experiences a typical temperate continental climate characterized by four distinct seasons, pronounced interannual temperature variability, and scarce precipitation. Furthermore, the Hexi Corridor is influenced by multiple factors, including the topographical effects of the Tibetan Plateau, anthropogenic activities, land–atmosphere interactions, dust aerosols, and global warming [11,12]. These factors contribute to the pronounced spatial and temporal heterogeneity of precipitation and surface water resources, rendering the region highly sensitive to global climate change [10,13].

As a critical component of the global water cycle, terrestrial water storage (TWS) is an indispensable natural resource for dryland ecosystem functioning and human societal development [14]. Due to the scarcity of precipitation in dryland, groundwater and surface water are both integrative components of total water storage anomalies (TWSAs) that supply groundwater-dependent ecosystems, providing domestic water to nearly half of the global population [15] and contributing to food security by meeting 38% of the world’s consumptive irrigation demand [14,16]. The Gravity Recovery and Climate Experiment (GRACE) twin satellites measure variations in Earth’s gravitational field, which can be interpreted as changes in TWS, offering a novel approach to assessing hydrological dynamics [17]. This observational framework provides critical insights into how global water resources respond to anthropogenic influences and climate change, enabling the assessment and prediction of emerging threats to water and food security [16].

Numerous studies have indicated that the global decline in TWS is primarily concentrated in arid regions [16,18,19]. At global or broader continental scales, several studies have demonstrated that in semi-arid regions, vegetation expansion tends to reduce runoff [20,21,22] and soil moisture [23,24], whereas the opposite trend has been observed in wetter regions [25]. Similarly, An et al. (2021) [26] found that in global humid regions, climate change is the dominant factor driving the TWSA trend, but human land use activities become the dominant factor over dryland regions. In Asian endorheic basins, Zhang et al. (2024) [7] reported that the rapid human expansion of irrigation agriculture caused large-scale greening at the cost of a persistent and significant decline in TWSAs over the past two decades.

In northwestern China, Zhao, Zhang, Velicogna, Liang, and Li [27] reported that ecological restoration programs in the Mu Us Sandy Land have led to a significant depletion of regional terrestrial water reserves over time. Kang, Lu, and Xu [28] examined the Hei River Basin in the arid inland region of Northwest China, focusing on water resources and vegetation growth conditions, and found that the vegetation ecosystems in the basin have degraded over the past two decades. Wu, Bai, Li, Liu, Zhao, and Ma [29] reported that a significant fraction of woody plants in the arid land of northwestern China is dependent on groundwater for maintaining healthy functioning, hence underscoring the importance of assessing the water storage dynamics associated with climate change and human overextraction. These results suggest that large-scale vegetation greening could exacerbate the conflict between ecosystems and human water demands in water-scarce, densely populated regions. However, existing studies have primarily focused on the correlation between TWSAs and vegetation growth, with limited exploration of the synergistic change patterns and coupling mechanisms between the two at different time scales, particularly in typical inland river basins like the Hexi Corridor. The high intensity of human activities in this region combined with the risk of groundwater depletion due to agricultural expansion pose significant threats to ecosystem stability and sustainable socioeconomic development.

The overarching aim of this study is to investigate the spatial and temporal coupling between vegetation greenness and terrestrial water storage in the inland river basins of the Hexi Corridor over the past two decades using long-term satellite observations in the context of climate change and intensified human activities. Specifically, the objectives are (1) to quantify the temporal trends in vegetation greenness and TWSAs in the inland river basins of the Hexi Corridor over the past two decades; (2) to analyze the changes in the spatial patterns of vegetation greenness and TWSAs in the region over the same period; and (3) to investigate the correlation and underlying factors driving the spatial–temporal coupling relationship between vegetation greenness and terrestrial water storage in these basins. This study contributes to the existing understanding of the dryland vegetation greening and water storage decline, the so-called “greening despite drying” dilemma, by moving beyond the simplistic “greening = good” or “greening vs. browning” narratives and achieving a holistic view of dryland sustainability in the context of global warming and the escalating human demand for freshwater resources. This contribution was achieved through the innovative and synthetic use of multi-source satellite observations (vegetation, precipitation, water storage, and land cover), in particular a down-scaled long-term TWSA dataset, as well as a statistical approach such as the second-order partial correlation analysis to disentangle the relative importance of multiple environmental drivers. A unique feature of this study is that it not only offers new insights into the eco-hydrological interactions in arid inland river basins but also provides a much-needed scientific basis for sustainable ecosystem management and water resource planning in water-scarce regions.

2. Data

2.1. Study Area

The Hexi Corridor is situated in the arid to semi-arid region of northwestern China, spanning from 93°23′E to 104°12′E and 37°17′N to 42°48′N (Figure 1). Stretching from the Ussuri Mountains in the east to the ancient Yumen Pass in the west, the Hexi Corridor covers an area of 215,000 km². As a key passageway along the ancient Silk Road and the modern Belt and Road Initiative, the Hexi Corridor has connected China with central and western Eurasia in both historical and contemporary times [30]. Today, the main livelihood of residents relies on oasis-based agriculture and animal husbandry [31], with the urban and agricultural expansion primarily limited by the availability of critical water resources. The Hexi Corridor faces significant ecological degradation pressure, primarily due to the scarcity of natural precipitation and the fact that more than 50% of the area in each watershed is desert [32]. Three major inland rivers—the Shiyang River, the Heihe River, and the Shule River—flow through the Hexi Corridor, from east to west (and from south to north) (Figure 1).

The Shiyang River Basin lies at the eastern edge of the Hexi Corridor to the west of the Wushao Mountains and north of the Qilian Mountains (101°41′E–104°16′E, 36°29′N–39°27′N), covering a total area of 4.16 × 10⁴ km² [33]. The basin experiences a mean annual precipitation of 197.957 mm and a mean annual temperature of 7.745 °C. Annual evaporation ranges from 700 mm to 2600 mm, with values reaching as high as 2000 mm to 2600 mm in the northern arid zone [34,35]. The Heihe River Basin is located between the Dahuangshan Mountains and the Jiayuguan Pass (97.1°E–102.0°E, 37.7°N–42.7°N), covering an area of approximately 143 × 10³ km² [36]. The basin experiences a mean annual precipitation of 108 mm and a mean annual evaporation of 84 mm [37]. The Shule River is situated at the western end of the Hexi Corridor in northwestern China (Figure 1). The Shule River originates in the Qilian Mountains, flowing through the Yumen Zaxizi Basin to the lower reaches of the Gobi Desert, where it eventually disappears [38], spanning from 93°10′ to 99°00′ and 38°00′ to 42°48′N. The Shule River lies in the western part of the Gobi Desert (Figure 1). The basin covers an area of approximately 4.13 × 10⁴ km² [39]. The basin experiences mean annual precipitation ranging from 47 mm to 63 mm, with annual evapotranspiration rates between 2897 mm and 3042 mm, resulting in very high evapotranspiration intensity. However, the average annual evaporation is more than forty times greater than precipitation, exacerbating the region’s acute water scarcity [40].

2.2. GRACE/GRACE-FO TWSA Data

We used the downscaled high-resolution (0.05°) GRACE and GRACE-FO terrestrial water storage anomaly (TWSA) data obtained from the National Tibetan Plateau Data Center (https://data.tpdc.ac.cn/en/data/42176bad-0d38-4a84-9f87-3c2c06eb19b8) (accessed on 24 January 2025) [41,42]. This dataset was developed using a machine learning downscaling framework with a physically constrained sliding window (MLDF-PCSW) [42]. It was first derived by applying the Bayesian-based three-cornered hat (BTCH) method to merge low-uncertainty GRACE-derived terrestrial water storage anomaly (TWSA) and groundwater storage anomaly (GWSA) products at a coarse 0.5° resolution. The resulting fused TWSA/GWSA data, combined with multi-source datasets, were then downscaled to produce high-resolution TWSA data.

We also calculated the cumulative TWSAs, or cTWSAs, to represent the long-term deviation of TWSAs from their “normal” cycle. This method has been widely used to quantitatively estimate changes in TWSAs in response to environmental and/or anthropogenic disturbances [7]. cTWSAs were calculated as follows:

$${cTWSA}_k={\sum }_{i=1}^k{TWSA}_i$$

(1)

where k is the kth year of the time series and i is the sequence number, i.e., 1–21 from 2002 to 2022.

2.3. MODIS EVI Data

In this study, we used the Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) product (MYD13C2, Collection 6 monthly 0.05°) [43], and datasets were accessed through the USGS (United States Geological Survey) repository (https://lpdaac.usgs.gov/)(accessed on 8 December 2024). To ensure data reliability, we conducted quality control by retaining only high-quality pixels based on the VI_Quality flag (VI_Quality = 0, use with confidence). Pixels with low-quality flags or missing values were excluded from the analysis. For the spatial trend analysis, no further interpolation or gap filling was applied to avoid introducing artificial signals. The EVI serves as a commonly used indicator of canopy “greenness” and reflects an integrated measure of green leaf area, foliage coverage, structural characteristics, and the leaf chlorophyll concentration [43]. The EVI calculation using the following equation:

$$\mathrm{E}\mathrm{V}\mathrm{I}=2.5\times {\displaystyle \frac{{\rho }_{NIR}-{\rho }_{red}}{{\rho }_{NIR}+{6\rho }_{red}-7.5{\rho }_{blue}+1}}$$

(2)

where ${\rho }_{NIR}$, ${\rho }_{red},$ and ${\rho }_{blue}$ are reflectance in the near-infrared, red, and blue bands, respectively [43]. Figure 2 shows that multiple-year average EVI values ranged from 0 to 0.31 across the Hexi Corridor, indicating a relatively low vegetation cover in the study area.

2.4. Land Cover Data

We used the GLC-FCS30 global 30 m resolution land cover map from the Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS) [44]. The dataset is at 30 m resolution and updated every 5 years from 2000 to 2020. The extent of irrigated cropland was also obtained from GLC FCS30 dataset. The dataset can be accessed at https://doi.org/10.5281/zenodo.3986872 (accessed on 22 May 2024). The dataset includes 29 distinct land surface cover types. This product is derived from continuous time series of Landsat surface reflectance data spanning from 1984 to 2020, combined with locally adaptive algorithms, resulting a global 30 m dynamic land cover monitoring dataset. The accuracy of the GLC_FCS30 land cover map was evaluated using several independent datasets, including Global Cropland reference data, Global Observation for Forest Cover and Land Dynamics (GOFC_GOLD) reference data, high-resolution Google Earth imagery, and time series of NDVI [7]. Evaluations confirmed that GLC_FCS30 had good accuracy (OA = 72.55% ± 9%) compared to other GLC products, such as GlobeLand30 and FROM_GLC [45,46]. The irrigated farmland data in the 30 m land cover data were aggregated to match the 0.05° resolution of GPP using the “majority” method. This method assigns the attribute value of the dominant category (i.e., the category with the highest numerical aspect ratio) within the specified coarse resolution scale. The area percentage of irrigated farmland within each coarse-resolution pixel was then computed. The computed area percentages were assigned as attribute values to generate the irrigated farmland percentage raster layer, which was subsequently used for further analysis.

2.5. GPM Precipitation Data

We obtained the GPM precipitation data from NASA’s Goddard Earth Sciences Data and Information Services Center (https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGM_07) (accessed on 13 October 2024). The dataset has a spatial resolution of 0.1° × 0.1° and a temporal resolution of monthly, with the unit expressed in millimeters per hour (mm/h). We synthesized monthly precipitation (mm/month) by adjusting for the number of days in each month, and subsequently computed the total annual precipitation through accumulation for the period from 2002 to 2022. The precipitation estimates, derived from various satellite-based passive microwave (PMW) sensors within the GPM constellation, were processed using the 2021 version of the Goddard Profiling Algorithm (GPROF2021) [47].

2.6. Air Temperature Data

We used the air temperature at 2 m above the surface (Ta) from the ECMWF (European Centre for Medium-Range Weather Forecasts) Reanalysis v5 (ERA5) dataset. The dataset is provided at a monthly temporal resolution and a spatial resolution of 0.1° × 0.1°. The dataset can be accessed through the Copernicus Climate Data Store (https://cds.climate.copernicus.eu/) (accessed on 8 May 2024). ERA5-Land is a high-resolution reanalysis dataset that provides a consistent depiction of the evolution of land surface variables over decades. With a finer resolution compared to ERA5, it offers a reliable archive of historical climatic conditions [48]. We resampled the data to 0.05° in order to match the spatial resolution of the other data consistently.

3. Methodology

3.1. Spatiotemporal Trends of TWSAs and Environmental Variables

We assessed the time series of TWSAs, EVI, Ta, and precipitation for the presence of monotonic trends using the rank-based Mann–Kendall trend test [49] and computed the slope of each series using the non-parametric Theil–Sen estimator. This approach for robust trend assessments accounts for potential temporal autocorrelation and has been employed in previous studies analyzing changes in target variables, including remote sensing and climate data.

The slope of the linear trend is calculated as

$$\mathrm{s}\mathrm{l}\mathrm{o}\mathrm{p}\mathrm{e}=\mathrm{M}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{a}\mathrm{n}\left({\displaystyle \frac{x_j-x_i}{j-i}}\right),\hspace{1em}\hspace{1em}n\ge j\ge i,\hspace{1em}\hspace{1em}i\ne j$$

(3)

where n is the length of the time series, and x_j and x_i are the sample values at times j and i, respectively. The sign of the slope indicates whether the trend is upward (+) or downward (−) for TWSAs, EVI, Ta, and precipitation over time.

3.2. Attribution Analysis of TWSA Changes

This study utilizes a partial correlation analysis to investigate the relationship between variations in TWSAs and EVI, Ta, and precipitation. Partial correlation analysis effectively removes the confounding effects of covariance between EVI, Ta, or precipitation, thus providing a clearer assessment of their independent influences on TWSAs [7]. Specifically, we conducted a second-order partial correlation analysis to explore the relationship between TWSAs and EVI, Ta, or precipitation, while controlling for the effects of the other two variables:

$$r_{xy,z}={\displaystyle \frac{r_{xy}-r_{xz}{r}_{yz}}{\sqrt{\left(1-r_{xz}^2\right)\left(1-r_{yz}^2\right)}}}$$

(4)

where r_xy,z is the first-order partial correlation coefficient, x is TWSAs, and y and z are EVI, Ta or precipitation. r_xy, r_xz, and r_yz are correlation coefficients. The coefficient representing the relationship between TWSAs and EVI, Ta or precipitation, with the other two being fixed, was calculated as the second-order partial correlation coefficient (r_xy,zw) [50]:

$$r_{xy,zw}={\displaystyle \frac{r_{xy,z}-r_{xw,z}{r}_{yw,z}}{\sqrt{\left(1-r_{xw,z}^2\right)\left(1-r_{yw,z}^2\right)}}}$$

(5)

where r_xy,z, r_xw,z, and r_yw,z represent partial correlations among variables x and y, when controlling variables z and w in the first order. Data processing, statistical analysis and visualization were performed using the R scientific computing environment (version 4.3.1, R Core Team, 2023) and related packages contributed by the user community (http://cran.r-project.org).

4. Results

4.1. Spatial and Temporal Variations in TWSAs in the Inland River Basin of the Hexi Corridor

From 2002 to 2022, the mean annual temperature in the Hexi Corridor increased significantly at a rate of 0.04 °C/year (p < 0.01) (Figure 3a). Although annual precipitation showed a slight rise of 1.69 mm/year, this trend was not statistically significant (p = 0.114), suggesting a modest wetting of the region (Figure 3a). Over the same period, terrestrial water storage anomalies (TWSAs) in the Hexi Corridor exhibited a consistent downward trend, declining at an average rate of −0.10 cm/year (p < 0.01) (Figure 3b). Despite seasonal fluctuations, long-term terrestrial water storage decreased, with cumulative TWSAs (cTWSAs) dropping below −140 cm by 2022 (Figure 3b). A brief TWSA recovery occurred between 2018 and 2020, followed by a sharp decline thereafter.

From 2002 to 2022, the average TWSA in the Hexi Corridor ranged from −3 to 3 cm (Figure 4a), with higher values concentrated in the southern Qilian Mountains. The spatial distribution of the cumulative TWSAs (cTWSAs) over this period, shown in Figure 4b, spanned from −720 to 800 cm. In the southern study area, cTWSAs ranged from 0 to 800 cm, indicating a net water gain. The central region exhibited cTWSA values from −500 to 0 cm, reflecting a net water loss. In the northern Heihe River Basin, cTWSAs varied between −600 and −200 cm, denoting a moderate water loss, while the northern Shiyang River Basin showed a severe deficit, with cTWSAs ranging from −600 to −300 cm (Figure 4b).

For individual basins, TWSAs fluctuated around zero from 2002 to 2012 but declined significantly thereafter, particularly in the Shiyang and Heihe River Basins (Figure 5). In contrast, the Shule River Basin experienced a milder TWSA decline, with signs of recovery after 2020, possibly due to water management policies and climatic factors (Figure 5a). The Shiyang River Basin saw the steepest cTWSA drop, exceeding −300 cm by 2023, followed by the Heihe River Basin at over −200 cm (Figure 5b). The Shule River Basin exhibited the slowest decline (−0.03 cm/year) (Figure 5b).

We conducted a trend analysis to evaluate TWSA, temperature, and precipitation trends from 2002 to 2022 at a consistent scale, generating spatial maps (Figure 6). As depicted in Figure 6a, TWSA trends in inland river basins closely mirrored the cTWSA spatial pattern (Figure 4b). The upper reaches of the Shule and Heihe Rivers showed a notable increase, peaking at 5 cm/year, while downstream areas, especially the northeastern Heihe and Shiyang Rivers, exhibited a pronounced decrease, with a maximum rate of −5.4 cm/year.

4.2. Spatial and Temporal Trends of EVI in the Inland River Basins of the Hexi Corridor

Over the past two decades, Enhanced Vegetation Index (EVI) values in the Hexi Corridor’s inland river basins have generally remained below 0.12, reflecting sparse vegetation cover (Figure 7a). Both the overall EVI and that of the three major basins—Shiyang, Shule, and Heihe—showed an increasing (greening) trend from 2002 to 2022 (Figure 7b). The Shiyang River Basin exhibited the steepest rise at 0.02 EVI/year (p < 0.01), while the Shule and Heihe River Basins displayed slower, nearly identical increases of 0.01 EVI/year (p < 0.01) (Figure 7b).

Changes in the annual integrated EVI (IntEVI) across vegetation types in the Hexi Corridor were influenced by both aridity gradients and land use (Figure 8). Along the aridity gradient, IntEVI trends were most pronounced in arid and hyper-arid regions, where vegetation greenness exhibited significant increases from 2002 to 2022 (Figure 8b). In contrast, semi-arid and semi-humid areas showed more moderate, largely non-significant IntEVI trends across all land cover types (Figure 8b). Among land cover types, irrigated cropland (ICR) displayed the strongest IntEVI increase, while grassland (GRA) and sparse vegetation (SPA) exhibited minimal changes (Figure 8b, c).

From 2000 to 2020, the area of irrigated cropland in the Hexi Corridor rose from 3401.71 km² to 4011.69 km², a 17.93% increase over two decades (Figure 9). Regions with rapid irrigation expansion aligned closely with those exhibiting the most pronounced greening trends (Figure 9a), as further evidenced by the integrated aridity gradient and land cover analyses (Figure 9). The greatest expansion of irrigated cropland occurred in arid and hyper-arid zones, underscoring irrigation growth in the water-scarce regions of the Hexi Corridor (Figure 9b, c).

4.3. Partial Correlation Analysis Between TWSAs and Multiple Driving Factors

The relative influences of temperature, precipitation, and vegetation greening on TWSA changes were mapped using an RGB plot (Figure 10a). In the upper and middle reaches of the Heihe River Basin (near the cities Zhangye and Wuwei), precipitation and vegetation greening predominantly drove TWSA changes, whereas temperature exerted a greater influence on the drier downstream regions of the Shiyang and Shule River Basins. Partial correlation coefficients between TWSAs and temperature, precipitation, and EVI revealed distinct patterns along the aridity gradient (Figure 10b). In semi-humid regions, TWSA correlated most strongly with precipitation, indicating a reliance on precipitation dynamics. As aridity increased (lower aridity index values), precipitation’s dominance peaked in mid-semi-arid zones, then the dominance gave way to temperature, where the influence of precipitation kept declining toward arid areas (Figure 10b). Notably, vegetation greening played a minor role across most of the aridity gradient but became as important as temperature in arid and hyper-arid zones, underscoring human-induced irrigation expansion as a key driver of TWSA trends in the driest regions of the Hexi Corridor (Figure 10b). The increased importance of agricultural expansion is visually evident in Figure 10c, where a 2D heatmap shows that in arid and hyper-arid pixels dominated by irrigated cropland (upper-left corner), vegetation greening—tied to irrigation—primarily drove TWSA declines.

5. Discussion

This study examines trends in climate and vegetation greenness within the Hexi Corridor region and explores their relationships with terrestrial water storage anomalies (TWSAs) over the period from 2002 to 2022. The findings indicate that the region experienced a significant warming trend, accompanied by a minor wetting trend, while TWSAs continued to decline, with the cumulative deficit becoming more pronounced after 2011. This pattern suggests that the primary driver of the observed decline in water storage may not be a reduction in precipitation but rather an increase in evapotranspiration driven by warming, coupled with a greater rate of water depletion resulting from human activities, such as groundwater extraction for irrigation purposes. Furthermore, despite an overall upward trend in EVI, which indicates vegetation greening, changes in vegetation within regions characterized by relatively scarce water supplies are influenced by a more complex interplay of climatic and anthropogenic factors. The findings presented in this study contribute to a deeper understanding of the driving mechanisms behind changes in terrestrial water storage in the arid regions of northwestern China, thereby providing a robust scientific basis for informing strategies related to regional water resource management, ecological conservation efforts, and the pursuit of sustainable development.

This study reveals a decreasing trend in TWSAs across the three major inland or endorheic river basins of the Hexi Corridor over the past two decades. Recent satellite gravimetry studies have documented a significant decline in water storage within global endorheic basins during the early 21st century, potentially driven by climate change and human activities [19], and our findings are generally consistent with this global pattern but add more regional details. Climate change and, more prominently, agricultural activities such as irrigation [32] are considered key drivers of this trend. A decline in water storage may exacerbate water stress on natural vegetation, thereby reducing ecosystem resilience to drought [51]. Dryland ecosystems, characterized by limited water availability, exhibit vegetation dynamics that are highly sensitive to moisture levels [2,4,9]. Furthermore, in this region, where water resources are scarce, both vegetation conservation and expansion are constrained, with agriculture representing the predominant human activity [52]. Consequently, a reduction in water resources could negatively impact agricultural yields, posing threats to the social and economic stability of the study area.

Our study reveals that within the Hexi Corridor, precipitation and vegetation in the middle and upper reaches of the Heihe River Basin exert more significant influences on changes in TWSAs. In the drier regions, notably the Shiyang and Shule River Basins, temperature plays a more dominant role in driving TWSA changes, potentially due to water deficits resulting from increased evapotranspiration. The marked expansion of irrigated cropland combined with the overexploitation of groundwater have led to a sustained water imbalance, further aggravating regional water stress [19]. Consequently, while the growth of irrigated agriculture in these areas promotes vegetation greening, it may also intensify the overconsumption of water resources, imposing a long-term ecological burden [7,53].

Between 2000 and 2020, the Hexi Corridor saw a net increase of 609.98 km² in irrigated cropland, reflecting intensified human land use activities amid significant climate and water-deficit pressures. Although the arid zone of northwestern China has experienced a gradual shift toward warming and increased humidity in recent decades, precipitation in the Hexi Corridor has shown only a weak wetting trend that is insufficient to offset the rising atmospheric evaporative demand or to counterbalance the extraction of surface and groundwater for irrigation by human activities. Rapid population growth, oasis expansion, and urbanization have further exacerbated the imbalance between the water resource supply and demand in the region [10]. Consequently, the widespread reliance on surface and groundwater for agricultural irrigation has contributed to substantial groundwater depletion across northwestern China, leading to a decline in TWSAs in recent years [7,54].

Terrestrial water storage changes represent the vertical integration of variations across multiple components, including groundwater, glacier and snow masses, surface water, soil water, and biological water [16]. While TWSAs provide a broad understanding of water storage dynamics in the Hexi Corridor, the specific contributions of each component remain unclear. Therefore, integrating additional data sources, such as groundwater measurements and river discharge observations, is essential to identify the dominant hydrological components driving TWSA changes. Secondly, the vegetation index employed in this study may not fully capture the complex interactions between groundwater and ecosystems. The greenness index serves as a comprehensive proxy for the canopy structure, leaf characteristics, and chlorophyll content [55]; however, it is not easy to distinguish vegetation responses to changes in surface water, soil water, and groundwater using only greenness observation. Recent advancements in remote sensing technology offer promising solutions to address this limitation. For instance, solar-induced chlorophyll fluorescence (SIF) can assess canopy photosynthesis and transpiration dynamics [56,57,58,59]. When combined with satellite gravimetry, precipitation, and soil moisture retrievals [60], SIF could provide insights into how vegetation responds to individual hydrological components [61]. Future integration of these advanced techniques will enable a more comprehensive understanding of how terrestrial water storage changes influence vegetation structure and function, thereby deepening our knowledge of coupled water–ecosystem relationships in arid regions. Thirdly, the anthropogenic factor considered in this study is limited to changes in the area of irrigated cropland, excluding other human activities such as industrial and domestic water use. Looking forward, hydrological models like WaterGAP could be employed to address this gap. These models provide a detailed representation of direct human impacts on the water cycle, encompassing irrigation, reservoir regulation, groundwater pumping, and multisectoral water use [26,62]. Incorporating such models will enhance our understanding of the intricate relationship between human activities and the water cycle in the Hexi Corridor and other drylands worldwide. Finally, it should also be noted that some methodological limitations exist in this study. In the spatial trend analysis, we did not conduct significance tests on the slope values of each grid cell, which may introduce some uncertainties in interpreting spatial patterns. Future research could apply spatial significance testing or more advanced statistical methods to improve the robustness of trend detection.

6. Conclusions

This study has thoroughly investigated changes in TWSAs and the associated environmental and anthropogenic influences within the Hexi Corridor region. Our findings reveal that over the past two decades, the region has undergone significant shifts in climate, water resources, and vegetation dynamics. Although precipitation has exhibited an increasing trend, rapid warming and heightened evapotranspiration—combined with human activities such as excessive groundwater extraction—have resulted in a substantial decline in terrestrial water storage. This growing imbalance between water availability and demand has intensified water stress in the region, adversely affecting both ecosystem services and agricultural productivity. The results of this study establish a scientific foundation for future research into the impacts of climate change and human activities on terrestrial water storage and support the development of more effective strategies for regional water resource management, ecological conservation, and sustainable development.