Next Article in Journal
A Null Space Sensitivity Analysis for Hydrological Data Assimilation with Ensemble Methods
Previous Article in Journal
A New Custom Deep Learning Model Coupled with a Flood Index for Multi-Step-Ahead Flood Forecasting
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Hydrology
Volume 12
Issue 5
10.3390/hydrology12050105
hydrology-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Responses of Terrestrial Water Storage to Climate Change in the Closed Alpine Qaidam Basin

by
Liang Chang
1,2,3,
Qunhui Zhang
1,*,
Xiaofan Gu
1,2,3,
Rui Duan
1,
Qian Wang
1 and
Xiangzhi You
1
1
Xi’an Geological Survey Center, China Geological Survey, Xi’an 710119, China
2
Shaanxi Water Resources and Environment Engineering Technology Research Center, Xi’an 710119, China
3
Key Laboratory of Groundwater and Ecology in Arid and Semi-Arid Areas, China Geological Survey, Xi’an 710119, China
*
Author to whom correspondence should be addressed.
Hydrology 2025, 12(5), 105; https://doi.org/10.3390/hydrology12050105
Submission received: 4 March 2025 / Revised: 22 April 2025 / Accepted: 25 April 2025 / Published: 28 April 2025
(This article belongs to the Special Issue GRACE Observations for Global Groundwater Storage Analysis)
Browse Figures
Versions Notes

Abstract

Terrestrial water storage (TWS) in the Qaidam Basin in western China is highly sensitive to climate change. The GRACE mascon products provide variations of TWS anomalies (TWSAs), greatly facilitating the exploration of water storage dynamics. However, the main meteorological factors affecting the TWSA dynamics in this region need to be comprehensively investigated. In this study, variations in TWSAs over the Qaidam Basin from 2002 to 2024 were analyzed using three GRACE mascon products with CSR, JPL, and GSFC. The groundwater storage anomalies (GWAs) were extracted through GRACE and GLDAS products. The impact of meteorological elements on TWSAs and GWAs was identified. The results showed that the GRACE mascon products showed a significant increasing trend with a rate of 0.51 ± 0.13 mm per month in TWSAs across the entire basin from 2003 to 2016. The groundwater part accounted for the largest proportion and was the main contributor to the increase in TWS for the entire basin. In addition to the dominant role of precipitation, other meteorological elements, particularly air humidity and solar radiation, were also identified as important contributors to TWSA and GWA variations. This study highlighted the climatic effect on water storage variations, which have important implications for local water resource management and ecological conservation under ongoing climate change.

1. Introduction

The Tibetan Plateau (TP), known as the “Third Pole” and “Asian Water Tower,” serves as the primary freshwater source for nearly 2 billion people in China and downstream Asian regions [1,2]. The TP has experienced substantial climate warming in recent decades, with profound impacts on its water storage dynamics, threatening the long-term sustainability of this vital resource [3,4,5]. Particularly, the northern TP has undergone pronounced warming since the 1960s, with rates much higher than other parts [6,7]. The Qaidam Basin, a hydrologically closed system on the TP’s northeastern margin, serves as a critical indicator of plateau-wide hydrological changes [8,9]. This closed basin, encircled by mountain ranges and characterized by limited water resources, is particularly sensitive to climatic warming [10,11]. Climate change, with shifts in precipitation patterns and temperature fluctuations, directly affects water availability and storage within the basin [12,13,14]. An accurate understanding of the Qaidam Basin’s water resource conditions is essential for assessing the basin’s hydrological response to climate change and formulating sustainable water resource management strategies in this vulnerable region. As the largest terminal basin in the plateau, its unique closed drainage system (endorheic basin) provides an ideal natural laboratory for studying climate–water storage interactions.
Terrestrial water storage (TWS) refers to the total amount of water resources, including various water components (groundwater, soil moisture, runoff, and other water bodies), and is a critical state variable in regional and global hydrological cycles [15,16,17]. The twin satellites of the Gravity Recovery and Climate Experiment (GRACE) have measured changes in the Earth’s gravitational field, which can be interpreted as changes in TWS, significantly advancing the ability to evaluate hydrologic features and variations [18,19,20]. The GRACE satellite mission, launched in 2002 and retired in October 2017, and the GRACE Follow-On mission (GRACE-FO), the successor of the erstwhile GRACE mission launched in May 2018, accurately measure month-to-month mass changes across the Earth and provide estimated changes in the amount of water stored beneath the surface. These two GRACE missions have been widely utilized in the research on global and regional TWS dynamics with high precision, contributing to a deeper understanding of water balance and water cycles.
In addition to the change in TWS, the GRACE satellites have also been used to explore the changes in various water components, such as groundwater, lakes, and glaciers. Land surface datasets provide important data support for separating water components from the total storage of the TWS. By combining satellite observations with ground-based measurements, datasets such as the Global Land Data Assimilation System (GLDAS) provide a comprehensive view of meteorological and hydrological conditions [21]. This integrated approach has been instrumental in quantifying the contributions of various components (e.g., groundwater) to TWS and offers valuable insights into hydrological processes under changing climatic conditions [22,23,24].
Previous results have demonstrated that TWS has been significantly increasing in the Northern TP and the Qaidam Basin [25,26,27]. The variations of groundwater storage were estimated based on the GRACE products and the GLDAS. These studies primarily focused on TWS and its components and precipitation impacts. However, under complex climate change, the potential roles of other critical meteorological drivers in modulating TWS dynamics remain systematically unexplored. Thus, in this study, based on a comprehensive understanding of the changes in TWS and its components, we intended to advance our understanding by thoroughly investigating the responses of TWS dynamics to ongoing climate change in the Qaidam Basin. We aimed to elucidate the main climatic factors driving TWS dynamics. This work is dedicated to providing new insights into the responses of TWS dynamics to climate change in water resources and risks.

2. Study Area, Data, and Methods

2.1. Study Area

The Qaidam Basin is situated on the Tibetan Plateau (TP), a region renowned as the “Roof of the World” and the “Asian Water Tower.” It lies between the Kunlun and Altun mountain ranges, spanning geographical coordinates of 34°41′–39°20′ N and 87°48′–99°18′ E (Figure 1). This endorheic basin is characterized by a wide range of elevations, from approximately 2600 to 6600 m above sea level. The climate of the Qaidam Basin is representative of a plateau continental climate, marked by arid conditions, with limited precipitation predominantly concentrated in the summer months. The region experiences intense solar radiation and high evaporation rates, which are typical of high-altitude desert environments. Given the ongoing trends of global warming associated with increased precipitation and rising temperatures, hydrological processes and water balance within the Qaidam Basin are increasingly being studied [12]. These climatic changes pose significant challenges to the ecological stability and water resources of the basin, underscoring the importance of detailed research to assess and predict the impact of climate variability in this sensitive and strategically important region.

2.2. Data

2.2.1. GRACE Mascon Products

Two forms of GRACE datasets are available: spherical harmonics (SH) and mass concentration (mascon) solutions. The GRACE mascon products computed from GRACE and GRACE-FO provide high-precision and high-resolution grids, which have been widely utilized and applied in hydrological studies. In this study, we used three GRACE mascon products generated from three processing centers: the Center for Space Research at the University of Texas (CSR) [28], the Jet Propulsion Laboratory (JPL) [29], and the Goddard Space Flight Center (GSFC) [30]. The three GRACE mascon products with the CSR, the JPL, and the GSFC provided monthly TWS anomalies (TWSAs) with an equivalent water thickness relative to the mean baseline from 2004 to 2009. The products from the CSR were obtained at 0.25°, and the JPL and GSFC products were gridded at 0.5°. Owing to technical issues, these GRACE mascon products have missing data for some months and an 11-month GRACE/GRACE-FO data gap between July 2017 and May 2018. The missing data for these three products were interpolated using linear interpolation, and the 11-month data gap was not processed.

2.2.2. GLDAS

The GLDAS products encompass global-scale and gridded land surface variables that are widely regarded as reliable and appropriate for validating and supplementing GRACE products [10,31]. We utilized monthly products with a spatial resolution of 0.25° derived from the Noah Land Surface Model (Noah-LSM). The specific variables extracted included soil moisture (0~200 cm) (SM), snow water equivalent (SWE), canopy interception (CI), and runoff (R). To align these data with the GRACE-based TWS anomaly dataset, we processed the GLDAS data by removing the monthly average for the period 2004–2009, thereby obtaining the GLDAS-based anomalies of the water storage components.

2.2.3. CMFD

The China Meteorological Forcing Dataset (CMFD), which provides high spatiotemporal resolution surface meteorological data, was employed for this study [32,33]. The CMFD integrates national meteorological station observations, reanalysis data, and satellite remote-sensing data, making it suitable for land surface hydrological simulations and research on data assimilation and related processes. It has a temporal resolution of 3 h and a spatial resolution of 0.1° and is extensively used in studies of regional land surface hydrological processes, ecological dynamics, and climate change in China [34,35]. Meteorological elements, including air temperature, air pressure, specific humidity, wind speed, downward shortwave radiation, downward longwave radiation, and precipitation, were used in this study. To assess the accuracy of the CMFD data, meteorological data from eight stations in the QB were used (Table 1). Previous studies have indicated that large uncertainties exist in precipitation, particularly in regions with rare observations. Thus, widely used and high-precision precipitation datasets were also obtained and compared with CMFD data from the Climatic Research Unit (CRU), the Tropical Rainfall Measuring Mission (TRMM), and the Global Precipitation Measurement (GPM).

2.3. Methods

2.3.1. Water Balance Analysis

A terrestrial water balance approach was utilized to estimate groundwater storage. According to the water budget analysis, TWS is the sum of all the components of different water bodies. Given that parts of the lakes, glaciers, and other water bodies account for less than 3% [10], a simple water balance equation for the Qaidam Basin is as follows:
TWS = GW + SM + SWE + CI + R
Because the GRACE mascon products provide anomalies of TWS, the groundwater storage anomalies (GWAs) can be obtained by subtracting the anomalies of SM, SWE, CI, and R from the TWSAs using Equation (2) below.
GWA = TWSA − (SMA + SWEA + CIA + RA)
where SMA, SWEA, CIA, and RA are the anomalies of SM, SWE, CI, and R, respectively.

2.3.2. Regression Model

Linear regression is a widely used statistical technique. It aims to establish a linear relationship between dependent and independent variables. Simple linear regression focuses on the relationship between two variables and finds the straight-line relationship that best fits the data. In addition, linear regression was applied to examine the effects of meteorological elements on the TWS variations. Considering the possible existence of multicollinearity among meteorological elements, ridge linear regression (RLR) was used to analyze their impacts. RLR is a regression technique that adds a penalty term to the least-squares cost function to shrink the regression coefficients towards zero. Its advantage lies in effectively handling multicollinearity, thus providing more stable and reliable coefficient estimates compared to ordinary linear regression. Ridge regression has a wide range of applications in dealing with multicollinearity problems, variable importance assessment, and constructing prediction models. In this study, we adopted the coefficient of determination R2 and the root mean square error (RMSE) to evaluate the performance of different datasets.

3. Results

3.1. Variations in TWSAs

The variations in TWSAs from the CSR, the JPL, and the GSFC from 2002 to 2024 are presented in Figure 2 and Table 2. The comparison between data with and without linear interpolation for missing values was also provided, which indicated that this simple and effective approach did not affect the change trend of TWSAs. From April 2002 to June 2017, the average values varied among the products, with the GSFC exhibiting relatively higher values than the CSR and the JPL. The GRACE TWSAs experienced a rapid increase with a change rate of 0.50 ± 0.13 mm per month. From June 2018 to March 2024, this disparity continued, highlighting the differences among products generated from different processing centers. Although there were differences, all three products, CSR, JPL, and GSFC, showed a significant increasing trend from April 2002 to June 2017, indicating an overall increase in TWS over the entire Qaidam Basin. From June 2018 to March 2024, the CSR and the JPL displayed negative trends, whereas the GSFC remained positive. The JPL products consistently showed a medium-level change, the GSFC had the largest degree of change, and the CSR had the smallest.
To further analyze the water balance and meteorological factors, considering the continuity of data, we focused on the analysis of spatial characteristics from 2003 to 2016. From the perspective of spatial comparison, the spatial distributions of the CSR and the JPL during this period were similar (Figure 3). In the southwestern region, there were more TWSAs, and the trend was the most obvious. The GSFC did not have obvious spatial heterogeneity in TWSAs, and its trend was similar to that of the CSR and the JPL. These GRACE products showed an increasing trend with a rate of 0.51 ± 0.13 mm per month, further indicating that the TWS increased from 2003 to 2016. Overall, such comparisons emphasize the importance of comparing multiple sources for a comprehensive understanding of TWS variations.

3.2. Variations of Individual Water Storage Components

The water storage values of SM, SWE, CI, and R were extracted from the GLDAS dataset. The results indicated that SM anomalies were more prevalent than those of SWE, CI, and R and had the greatest change trend, with a rate of 0.045 mm per month from 2003 to 2016 (Figure 4). There was a significant increasing trend in the northeastern, western, and southeastern areas in terms of water storage from soil moisture (Figure 5). By subtracting the SMAs, SWEAs, CIAs, and RAs from the TWSA, the GWAs were extracted and analyzed (Figure 6). The multi-year average GWAs from 2003 to 2016 from the CSR, the JPL, and the GSFC had similar spatial distributions, with lower values in the northeast and higher values in the southwest. The spatial distributions of the change trends were similar to those of the multi-year averages, with a significantly increasing rate in the southwest and a significantly decreasing rate in the northeast. Among these GRACE mascon products, the trend from the JPL was in the middle level (0.40 mm per month), and the CSR (0.29 mm per month) and the GSFC (0.71 mm per month) showed the lowest and highest values, respectively. The results indicated that water storage in the groundwater part of the Qaidam Basin accounted for the largest proportion (Figure 6). The increase in GW was the main contributor to the increase in TWS for the entire basin. These results aligned with previous research [14,36]. The substantial rise in GW has elevated groundwater levels, increasing the risk of hydrological hazards in this basin [37]. Specifically, the rising groundwater levels have caused urban flooding and widespread destruction of farmland and pastures, severely disrupting agricultural and livestock production systems [38].

3.3. The Impact of the Meteorological Elements on TWSAs and GWAs

To analyze the effect of meteorological elements on TWS dynamics, we first evaluated the reliability of the CMFD against limited station observations using correlation coefficients and RMSEs. Air temperature and wind speed from the CMFD showed very high correlations and low biases with the observations (Figure 7 and Figure 8). Even though there were certain differences in precipitation between the CMFD and the observations, when compared with other products such as the TRMM, the GPM, and the CRU, the CMFD showed a relatively better consistency and lower RMSEs with the observations (Figure 9). Thus, in the following analysis, the CMFD was used to review the climatic change in the Qaidam Basin (Figure 10 and Figure 11). We further utilized the CMFD to analyze the impacts of meteorological elements.
Spatial analysis of the correlation between TWSA and GWA changes and meteorological elements was conducted. Figure 12 shows that changes in TWSAs from the JPL exhibited strong and statistically significant correlations with precipitation and air humidity, particularly in the eastern and southern regions. Moderate but significant correlations were observed with solar radiation, air temperature, and pressure, whereas weaker associations were found for wind speed. TWSAs from the CSR and the GSFC and GWAs demonstrated a similar spatial correlation pattern, reinforcing the coherence between terrestrial water storage dynamics and meteorological drivers. Additionally, spatial differences in the correlation coefficients between meteorological variables and TWSA changes showed a relatively low variability. Therefore, we conducted an analysis of correlation and contribution of meteorological elements to changes in TWSAs and GWAs at the basin scale. Figure 13 presents the correlation coefficients on the TWSA and GWA changes and meteorological elements averaged for the whole basin. For these three products, precipitation showed the strongest correlation, followed by humidity. Changes in the TWS with the CSR, the JPL, and the GSFC had a relatively weak correlation with wind speed. While changes in TWSAs and GWAs with the GSFC had a relatively weak correlation with meteorological elements, the TWS and the GW with the CSR and the JPL showed a relatively high correlation with meteorological elements. The TWS changes with the JPL had the strongest correlation with meteorological elements. Its correlations with precipitation and humidity were both greater than 0.5, and its correlations with air temperature, air pressure, and solar radiation were all around 0.4. A similar pattern was observed for GWAs. Thus, considering that the variation range with the JPL was at an intermediate level and had the strongest correlation with meteorological elements compared to the CSR and the GSFC, we further utilized the JPL product to analyze the impact of meteorological elements.
Notably, there was a relatively strong correlation between the meteorological elements. Using simple linear regression, strong multicollinearity existed among the meteorological elements. The variance inflation factor (VIF) is a widely used measure of the degree of multicollinearity, with its value greater than 10 regarded as a sign of severe or serious multicollinearity. The VIFs of precipitation, air temperature, humidity, wind speed, air pressure, and solar radiation were 5.1, 62.1, 21.2, 2.7, 17.0, and 13.0, respectively. In addition, VIF analysis with and without variables was conducted. The results confirmed severe multicollinearity, particularly for air temperature and specific humidity. Air temperature exhibited extreme collinearity (minimum of 7.3), indicating inherent covariance with multiple predictors. Precipitation demonstrated robust stability (2.6~5.1), whereas wind speed remained independent (<3 in most cases). Notably, excluding precipitation caused solar radiation’s VIF to spike from 13 to 52, revealing precipitation’s moderating effect. Therefore, due to such obvious multicollinearity, a simple linear regression cannot be used to clarify the impact of meteorological elements on the dynamics of TWSAs and GWAs. To eliminate the problem of multicollinearity among meteorological elements, a regression model of RLR was applied to explore the impact of meteorological elements on TWSAs and GWAs. The results demonstrated that 76% and 65% of TWSA and GWA variabilities could be attributed to meteorological drivers for the whole basin. Among all the meteorological elements affecting TWSA dynamics, precipitation accounted for the largest relative contribution (46%), followed by air humidity (28%) and solar radiation with air pressure, temperature, and wind collectively contributing the remaining 26%. For GWA variations, precipitation remained dominant (46% relative contribution), while air humidity accounted for 34%, other factors—20%. Overall, these degrees of contribution provided valuable insights into the relative importance of different meteorological elements in relation to the dependent variables within the scope of this study.

4. Discussion

4.1. Uncertainties and Implications

TWSA datasets are fundamental to investigating regional and global changes in the hydrological cycles. While these three GRACE mascon products with the CSR, the JPL, and the GSFC are based on different regulations, parameterizations, and constraint schemes, the uncertainties of these datasets exist. Such uncertainties might differ over different basins across different climate zones [39]. We analyzed the differences among these GRACE mascon products (Table 2). For this basin, in terms of the magnitude of variations and trends, the CSR showed the smallest values, while the GSFC presented the largest, and the JPL lay in between. For example, the changing trends of CSR, JPL, and GSFC from April 2002 to June 2017 were 0.30, 0.45, and 0.74 mm per month, respectively. The uncertainties of the GRACE mascon products for this basin may provide insights into the research on TWSAs of other basins under similar climatic conditions. The results of this study were consistent with the conclusions of previous studies. The TWS oscillation was found to have an increasing trend of 0.43 mm per month from 2002 to 2020, especially in the southwest of the Qaidam Basin [27]. From 2002 to 2012, the TWS changed with a significant upward trend of 25.5 mm per year [12]. Thus, our study further supports the conclusion that the water storage in the Qaidam Basin has recently increased owing to climate change. The projected trends in the TWS under future climate scenarios suggest significant implications for ecosystems, water cycles, and human habitats [40,41,42].

4.2. The Impact of Human Activities

The focus of this study was to explore the impact of meteorological elements on variations in TWSAs and GWAs. Other factors that may affect the dynamics of TWS were also discussed. Understanding the factors that influence TWS is pivotal for assessing water availability and sustainability. This study revealed the complex interplay of meteorological factors affecting TWS dynamics in the Qaidam Basin. Precipitation is the primary determinant of water storage variability. This aligns with the findings from numerous global and regional studies, which consistently highlight this factor as a crucial driver of water storage changes [12]. The study further demonstrated that air humidity, solar radiation, air pressure, and air temperature could also play critical roles in TWS dynamics.
In addition to these meteorological factors, human activities can also have a noticeable impact [43,44,45]. The Qinghai Water Resource Bulletin was utilized to extract the amount of water consumption in the Qaidam Basin from 2000 to 2023, including farmland irrigation, forestry, husbandry, fishery, industry, residents, urban public use, and the ecological environment, which were used to determine the possible effects of human activities on TWS dynamics. For the whole basin, the total water consumption in the entire QB increased at a rate of 0.11 × 108 m3/yr from 2000 to 2023 and 0.18 × 108 m3/yr from 2003 to 2016, as shown in Figure 10. The ratio of the total water consumption to the total amount of water resources ranged from 6.80% to 13.78%. The water consumption for forestry, husbandry, fishery, and industry contributed the most to the variation in the total water consumption. Increased water consumption had a negative effect on the increase in TWS, indicating that human activities did not play a significant role in the increase in TWSAs for the whole basin [46]. Notably, water consumption for farmland irrigation decreased due to crop water management and the change in the type of grains grown to mainly cash crops, such as wolfberry, that reduced the irrigation amount. While with the demand for the economic development in the husbandry and the industry, water consumption showed an increasing trend. Thus, even though the proportion of water consumption was relatively small, the impact of human activities on the water storage should not be ignored at the local scale.

4.3. Limitations

This study has certain limitations. The changes in glacier and lake storage were not considered when analyzing the variations of individual water storage components. The Qaidam Basin is located in the northeastern Tibetan Plateau, with unique geographical features, where glaciers and lakes are an important part of the landscape. In this basin, glaciers are mainly distributed in the alpine mountains of the northern and southern areas, while lakes are mostly located in the plain areas of this inner basin. Previous research indicated that these glaciers have experienced significant shrinkage [47], and lakes have undergone expansion mostly due to climate change [1]. In the regions where glaciers and lakes are concentrated, GWAs would be overestimated. Even though the proportions of lakes and glaciers were only about 1.85% and 0.61% of the Qaidam Basin, respectively [10], more refined research should be conducted on the accurate changes in water storage components.

5. Conclusions

This study provided a comprehensive analysis of TWSA dynamics and factors in the Qaidam Basin by leveraging three GRACE mascon products. Although there were certain differences between these three products, the temporal trend indicated a notable increase in all TWSAs from 2003 to 2016, with GW accounting for a substantial portion of the TWS changes. This underscored the dominant role of groundwater in shaping the overall water storage dynamics in the Qaidam Basin. The spatial analysis of TWS changes highlighted higher average values in the southwestern area and lower values in the northeastern area. The effects of meteorological elements on TWSAs and GWAs were determined quantitatively. Overall, this study underscores the critical role of climatic variables in shaping water availability in the Qaidam Basin.
In this study, the coarse spatial resolution of the GRACE mascon dataset and the GLDAS may not have fully captured local or fine-scale variability. Future research should address this limitation by leveraging machine learning or deep learning techniques to downscale water storage models, thus enhancing the spatial resolution and prediction accuracy. Moreover, the parameterization of complex land surface processes in the land surface process models of the GLDAS may lead to deviations. Further exploration is needed to integrate hydrological models to investigate how altered climate patterns and groundwater recharge dynamics might affect regional water availability and ecosystems.

Author Contributions

Conceptualization, L.C. and Q.Z.; methodology, L.C. and Q.Z.; validation, L.C., Q.Z. and X.G.; writing—original draft preparation, L.C. and Q.Z.; writing—review and editing, L.C., Q.Z., X.G., R.D., Q.W. and X.Y.; funding acquisition, L.C. and X.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Geological Survey Project of China (grant Nos. DD20230301 and DD20250501404) and the Technology Project of the Qinghai Bureau of Environmental Geology Exploration of China (grant No. 2023-ZK-01). Qunhui Zhang was also supported by the Foundation of the Director of the Xi’an Center of Geological Survey, CGS (grant No. XACGS-2023-05).

Data Availability Statement

Datasets including GRACE/GRACE-FO, the CMFD, the CRU, the TRMM, the GPM, and the GLDAS were derived from public domain resources.

Acknowledgments

The CMFD used in this study was provided by the National Tibetan Plateau/Third Pole Environment Data Center (http://data.tpdc.ac.cn, accessed on 14 June 2024). The GRACE mascon products with the CSR, the JPL, and the GSFC were downloaded from http://www2.csr.utexas.edu/grace (accessed on 16 October 2024), http://grace.jpl.nasa.gov (accessed on 16 October 2024), and https://earth.gsfc.nasa.gov/geo/data/grace-mascon (accessed on 16 October 2024), respectively. The GLDAS data were obtained from https://ldas.gsfc.nasa.gov/gldas/ (accessed on 10 April 2024).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Xu, F.; Zhang, G.; Woolway, R.I.; Yang, K.; Wada, Y.; Wang, J.; Crétaux, J. Widespread societal and ecological impacts from projected Tibetan Plateau lake expansion. Nat. Geosci. 2024, 17, 516–523. [Google Scholar] [CrossRef]
  2. Wang, T.; Zhao, Y.; Xu, C.; Ciais, P.; Liu, D.; Yang, H.; Piao, S.; Yao, T. Atmospheric dynamic constraints on Tibetan Plateau freshwater under Paris climate targets. Nat. Clim. Change 2021, 11, 219–225. [Google Scholar] [CrossRef]
  3. Li, L.; Zhang, R.; Wen, M.; Lv, J. Regionally Different Precipitation Trends Over the Tibetan Plateau in the Warming Context: A Perspective of the Tibetan Plateau Vortices. Geophys. Res. Lett. 2021, 48, e2020GL091680. [Google Scholar] [CrossRef]
  4. Li, X.; Long, D.; Scanlon, B.R.; Mann, M.E.; Li, X.; Tian, F.; Sun, Z.; Wang, G. Climate change threatens terrestrial water storage over the Tibetan Plateau. Nat. Clim. Change 2022, 12, 801–807. [Google Scholar] [CrossRef]
  5. Wang, B.; Bao, Q.; Hoskins, B.; Wu, G.; Liu, Y. Tibetan Plateau warming and precipitation changes in East Asia. Geophys. Res. Lett. 2008, 35, L14702. [Google Scholar] [CrossRef]
  6. Li, L.; Yang, S.; Wang, Z.; Zhu, X.; Tang, H. Evidence of Warming and Wetting Climate over the Qinghai-Tibet Plateau. Arct. Antarct. Alp. Res. 2010, 42, 449–457. [Google Scholar] [CrossRef]
  7. Bibi, S.; Wang, L.; Li, X.; Zhou, J.; Chen, D.; Yao, T. Climatic and associated cryospheric, biospheric, and hydrological changes on the Tibetan Plateau: A review. Int. J. Climatol. 2018, 38, e1–e17. [Google Scholar] [CrossRef]
  8. Ehlers, T.A.; Chen, D.; Appel, E.; Bolch, T.; Chen, F.; Diekmann, B.; Dippold, M.A.; Giese, M.; Guggenberger, G.; Lai, H.; et al. Past, present, and future geo-biosphere interactions on the Tibetan Plateau and implications for permafrost. Earth-Sci. Rev. 2022, 234, 104197. [Google Scholar] [CrossRef]
  9. Wang, Y.; Xie, X.; Shi, J.; Zhu, B.; Jiang, F.; Chen, Y.; Liu, Y. Accelerated Hydrological Cycle on the Tibetan Plateau Evidenced by Ensemble Modeling of Long-term Water Budgets. J. Hydrol. 2022, 615, 128710. [Google Scholar] [CrossRef]
  10. Wang, J.; Chen, X.; Hu, Q.; Liu, J. Responses of terrestrial water storage to climate variation in the Tibetan Plateau. J. Hydrol. 2020, 584, 124652. [Google Scholar] [CrossRef]
  11. Yang, N.; Wang, G.; Liao, F.; Dang, X.; Gu, X. Insights into moisture sources and evolution from groundwater isotopes (2H, 18O, and 14C) in Northeastern Qaidam Basin, Northeast Tibetan Plateau, China. Sci. Total Environ. 2023, 864, 160981. [Google Scholar] [CrossRef] [PubMed]
  12. Bibi, S.; Wang, L.; Li, X.; Zhang, X.; Chen, D. Response of Groundwater Storage and Recharge in the Qaidam Basin (Tibetan Plateau) to Climate Variations from 2002 to 2016. J. Geophys. Res. Atmos. 2019, 124, 9918–9934. [Google Scholar] [CrossRef]
  13. Wang, Z.; Lei, Y.; Che, H.; Wu, B.; Zhang, X. Aerosol forcing regulating recent decadal change of summer water vapor budget over the Tibetan Plateau. Nat. Commun. 2024, 15, 2233. [Google Scholar] [CrossRef]
  14. Xu, P.; Yan, D.; Weng, B.; Bian, J.; Wu, C.; Wang, H. Evolution trends and driving factors of groundwater storage, recharge, and discharge in the Qinghai-Tibet Plateau: Study progress and challenges. J. Hydrol. 2024, 631, 130815. [Google Scholar] [CrossRef]
  15. Bringeland, S.; Fotopoulos, G. Analysis of gap filling techniques for GRACE/GRACE-FO terrestrial water storage anomalies in Canada. J. Hydrol. 2024, 630, 130644. [Google Scholar] [CrossRef]
  16. Wei, W.; Wang, J.; Wang, X.; Yan, P.; Xie, B.; Zhou, J.; Liu, T.; Lu, D. The response of global terrestrial water storage to drought based on multiple climate scenarios. Atmos. Res. 2024, 303, 107331. [Google Scholar] [CrossRef]
  17. Zhu, E.; Wang, Y.; Yuan, X. Changes of terrestrial water storage during 1981–2020 over China based on dynamic-machine learning model. J. Hydrol. 2023, 621, 129576. [Google Scholar] [CrossRef]
  18. Nourani, V.; Paknezhad, N.J.; Ng, A.; Wen, Z.; Dabrowska, D.; Üzelaltınbulat, S. Application of the machine learning methods for GRACE data based groundwater modeling, a systematic review. Groundwater Sust. Dev. 2024, 25, 101113. [Google Scholar] [CrossRef]
  19. Soltani, S.S.; Ataie-Ashtiani, B.; Simmons, C.T. Review of assimilating GRACE terrestrial water storage data into hydrological models: Advances, challenges and opportunities. Earth-Sci. Rev. 2021, 213, 103487. [Google Scholar] [CrossRef]
  20. Akl, M.; Thomas, B.F. Challenges in applying water budget framework for estimating groundwater storage changes from GRACE observations. J. Hydrol. 2024, 639, 131600. [Google Scholar] [CrossRef]
  21. Rodell, M.; Houser, P.R.; Jambor, U.; Gottschalck, J.; Mitchell, K.; Meng, C.J.; Arsenault, K.; Cosgrove, B.; Radakovich, J.; Bosilovich, M.; et al. The Global Land Data Assimilation System; American Meteorological Society: Boston, MA, USA, 2004; Volume 85, pp. 381–394. [Google Scholar]
  22. Gautam, P.K.; Chandra, S.; Henry, P.K. Monitoring of the Groundwater Level using GRACE with GLDAS Satellite Data in Ganga Plain, India to Understand the Challenges of Groundwater, Depletion, Problems, and Strategies for Mitigation. Environ. Chall. 2024, 15, 100874. [Google Scholar] [CrossRef]
  23. Nenweli, R.; Watson, A.; Brookfield, A.; Münch, Z.; Chow, R. Is groundwater running out in the Western Cape, South Africa? Evaluating GRACE data to assess groundwater storage during droughts. J. Hydrol. Reg. Stud. 2024, 52, 101699. [Google Scholar] [CrossRef]
  24. Viviers, C.; van der Laan, M.; Gaffoor, Z.; Dippenaar, M. Downscaling and validating GLDAS groundwater storage anomalies by integrating precipitation for recharge and actual evapotranspiration for discharge. J. Hydrol. Reg. Stud. 2024, 54, 101879. [Google Scholar] [CrossRef]
  25. Cheng, W.; Feng, Q.; Xi, H.; Yin, X.; Sindikubwabo, C.; Habiyakare, T.; Chen, Y.; Zhao, X. Spatiotemporal variability and controlling factors of groundwater depletion in endorheic basins of Northwest China. J. Environ. Manag. 2023, 344, 118468. [Google Scholar] [CrossRef] [PubMed]
  26. Meng, F.; Su, F.; Li, Y.; Tong, K. Changes in Terrestrial Water Storage During 2003–2014 and Possible Causes in Tibetan Plateau. J. Geophys. Res. Atmos. 2019, 124, 2909–2931. [Google Scholar] [CrossRef]
  27. Wei, L.; Jiang, S.; Ren, L.; Tan, H.; Ta, W.; Liu, Y.; Yang, X.; Zhang, L.; Duan, Z. Spatiotemporal changes of terrestrial water storage and possible causes in the closed Qaidam Basin, China using GRACE and GRACE Follow-On data. J. Hydrol. 2021, 598, 126274. [Google Scholar] [CrossRef]
  28. Save, H.; Bettadpur, S.; Tapley, B.D. High-resolution CSR GRACE RL05 mascons. J. Geophys. Res. Solid. Earth 2016, 121, 7547–7569. [Google Scholar] [CrossRef]
  29. Wiese, D.N.; Landerer, F.W.; Watkins, M.M. Quantifying and reducing leakage errors in the JPL RL05M GRACE mascon solution. Water Resour. Res. 2016, 52, 7490–7502. [Google Scholar] [CrossRef]
  30. Loomis, B.D.; Luthcke, S.B.; Sabaka, T.J. Regularization and error characterization of GRACE mascons. J. Geodesy 2019, 93, 1381–1398. [Google Scholar] [CrossRef]
  31. Zhao, K.; Fang, Z.; Li, J.; He, C. Spatial-temporal variations of groundwater storage in China: A multiscale analysis based on GRACE data. Resour. Conserv. Recycl. 2023, 197, 107088. [Google Scholar] [CrossRef]
  32. He, J.; Yang, K.; Tang, W.; Lu, H.; Qin, J.; Chen, Y.; Li, X. The first high-resolution meteorological forcing dataset for land process studies over China. Sci. Data 2020, 7, 25. [Google Scholar] [CrossRef] [PubMed]
  33. Yang, K.; He, J.; Tang, W.; Lu, H.; Qin, J.; Chen, Y.; Li, X. China Meteorological Forcing Dataset (1979–2018). National Tibetan Plateau Data Center: Beijing, China, 2015. [Google Scholar]
  34. Du, Y.; Wang, D.; Zhu, J.; Lin, Z.; Zhong, Y. Intercomparison of multiple high-resolution precipitation products over China: Climatology and extremes. Atmos. Res. 2022, 278, 106342. [Google Scholar] [CrossRef]
  35. Zhang, Q.; Jin, J.; Wang, X.; Budy, P.; Barrett, N.; Null, S.E. Improving lake mixing process simulations in the Community Land Model by using K profile parameterization. Hydrol. Earth Syst. Sci. 2019, 23, 4969–4982. [Google Scholar] [CrossRef]
  36. Zou, Y.; Kuang, X.; Feng, Y.; Jiao, J.J.; Liu, J.; Wang, C.; Fan, L.; Wang, Q.; Chen, J.; Ji, F.; et al. Solid Water Melt Dominates the Increase of Total Groundwater Storage in the Tibetan Plateau. Geophys. Res. Lett. 2022, 49, e2022GL100092. [Google Scholar] [CrossRef]
  37. Xiao, Y.; Liu, K.; Zhang, Y.; Yang, H.; Wang, S.; Qi, Z.; Hao, Q.; Wang, L.; Luo, Y.; Yin, S. Numerical investigation of groundwater flow systems and their evolution due to climate change in the arid Golmud river watershed on the Tibetan Plateau. Front. Earth Sci. 2022, 10, 943075. [Google Scholar] [CrossRef]
  38. Dang, X.; Gu, X.; Chang, L. Prevention and Cure Countermeasures on Groundwater Table Rising Disaster in the Qaidam Basin. J. Chongqing Jiaotong Univ. (Nat. Sci.) 2023, 42, 64–69, (In Chinese abstract). [Google Scholar]
  39. Ferreira, V.; Yong, B.; Montecino, H.; Ndehedehe, C.E.; Seitz, K.; Kutterer, H.; Yang, K. Estimating GRACE terrestrial water storage anomaly using an improved point mass solution. Sci. Data 2023, 10, 234. [Google Scholar] [CrossRef] [PubMed]
  40. Forootan, E.; Mehrnegar, N.; Schumacher, M.; Schiettekatte, L.A.R.; Jagdhuber, T.; Farzaneh, S.; van Dijk, A.I.J.M.; Shamsudduha, M.; Shum, C.K. Global groundwater droughts are more severe than they appear in hydrological models: An investigation through a Bayesian merging of GRACE and GRACE-FO data with a water balance model. Sci. Total Environ. 2024, 912, 169476. [Google Scholar] [CrossRef]
  41. Raju, A.; Singh, R.P.; Kannojiya, P.K.; Patel, A.; Singh, S.; Sinha, M. Declining groundwater and its impacts along Ganga riverfronts using combined Sentinel-1, GRACE, water levels, and rainfall data. Sci. Total Environ. 2024, 920, 170932. [Google Scholar] [CrossRef]
  42. Song, X.; Chen, H.; Chen, T.; Qin, Z.; Chen, S.; Yang, N.; Deng, S. GRACE-based groundwater drought in the Indochina Peninsula during 1979–2020: Changing properties and possible teleconnection mechanisms. Sci. Total Environ. 2024, 908, 168423. [Google Scholar] [CrossRef]
  43. Bao, J.; Wu, Y.; Huang, X.; Peng, Q.; Yuan, Y.; Li, T.; Tao, Y.; Wang, T.; Zhang, P.; Nzabarinda, V.; et al. Changes in Surface and Terrestrial Waters in the China–Pakistan Economic Corridor Due to Climate Change and Human Activities. Remote Sens. 2024, 16, 1437. [Google Scholar] [CrossRef]
  44. Liu, B.; Zou, X.; Yi, S.; Sneeuw, N.; Cai, J.; Li, J. Identifying and separating climate- and human-driven water storage anomalies using GRACE satellite data. Remote Sens. Environ. 2021, 263, 112559. [Google Scholar] [CrossRef]
  45. Wang, L.; Zhu, M.; Zhong, Y.; Sun, J.; Peng, Z. Impacts of artificial dams on terrestrial water storage changes and the Earth’s elastic load response during 1950–2016: A case study of medium and large reservoirs in Chinese mainland. Geod. Geodyn. 2024, 15, 252–263. [Google Scholar] [CrossRef]
  46. Yang, X.; Wang, N.; Chen, A.; Li, Z.; Liang, Q.; Zhang, Y. Impacts of Climate Change, Glacier Mass Loss and Human Activities on Spatiotemporal Variations in Terrestrial Water Storage of the Qaidam Basin, China. Remote Sens. 2022, 14, 2186. [Google Scholar] [CrossRef]
  47. Yao, T.; Bolch, T.; Chen, D.; Gao, J.; Immerzeel, W.; Piao, S.; Su, F.; Thompson, L.; Wada, Y.; Wang, L.; et al. The imbalance of the Asian water tower. Nat. Rev. Earth Environ. 2022, 3, 618–632. [Google Scholar] [CrossRef]
Hydrology 12 00105 g001
Figure 1. Geophysical map of the Qaidam Basin.
Figure 1. Geophysical map of the Qaidam Basin.
Hydrology 12 00105 g001
Hydrology 12 00105 g002
Figure 2. Time series of TWSAs from the CSR, the JPL, and the GSFC and the average TWSA series for the Qadiam Basin from 2002 to 2024. The light blue shaded area shows the data range. The grey shaded area shows the 11-month period of the data gap.
Figure 2. Time series of TWSAs from the CSR, the JPL, and the GSFC and the average TWSA series for the Qadiam Basin from 2002 to 2024. The light blue shaded area shows the data range. The grey shaded area shows the 11-month period of the data gap.
Hydrology 12 00105 g002
Hydrology 12 00105 g003
Figure 3. Averaged TWSAs from (a) the CSR, (b) the JPL, and (c) the GSFC and trends from (d) the CSR, (e) the JPL, and (f) the GSFC from 2003 to 2016. The black dots indicate passing the significance test of p < 0.01.
Figure 3. Averaged TWSAs from (a) the CSR, (b) the JPL, and (c) the GSFC and trends from (d) the CSR, (e) the JPL, and (f) the GSFC from 2003 to 2016. The black dots indicate passing the significance test of p < 0.01.
Hydrology 12 00105 g003
Hydrology 12 00105 g004
Figure 4. Time series of TWSAs averaged from the CSR, the JPL, and the GSFC, GWAs, SMAs, CIAs, SWEAs, and RAs for the Qaidam Basin. The light orange shaded area shows the data range of GWAs. The grey shaded area shows the 11-month period of the data gap in the GRACE mascon products.
Figure 4. Time series of TWSAs averaged from the CSR, the JPL, and the GSFC, GWAs, SMAs, CIAs, SWEAs, and RAs for the Qaidam Basin. The light orange shaded area shows the data range of GWAs. The grey shaded area shows the 11-month period of the data gap in the GRACE mascon products.
Hydrology 12 00105 g004
Hydrology 12 00105 g005
Figure 5. Multi-year average distributions of (a) SMAs, (b) SWEAs, (c) CIAs, and (d) RAs and their distributions of trends with (e) SMAs, (f) SWEAs, (g) CIAs, and (h) RAs. The black dots indicate passing the significance test of p < 0.01.
Figure 5. Multi-year average distributions of (a) SMAs, (b) SWEAs, (c) CIAs, and (d) RAs and their distributions of trends with (e) SMAs, (f) SWEAs, (g) CIAs, and (h) RAs. The black dots indicate passing the significance test of p < 0.01.
Hydrology 12 00105 g005
Hydrology 12 00105 g006
Figure 6. Multi-year average distributions of GWAs from (a) the CSR, (b) the JPL, and (c) the GSFC and trend distributions of GWAs from (d) the CSR, (e) the JPL, and (f) the GSFC. The black dots indicate passing the significance test of p < 0.01.
Figure 6. Multi-year average distributions of GWAs from (a) the CSR, (b) the JPL, and (c) the GSFC and trend distributions of GWAs from (d) the CSR, (e) the JPL, and (f) the GSFC. The black dots indicate passing the significance test of p < 0.01.
Hydrology 12 00105 g006
Hydrology 12 00105 g007
Figure 7. Comparisons of air temperature between the station data and the CMFD at five stations with (a) 52602, (b) 52713, (c) 52737, (d) 52818, and (e) 52836.
Figure 7. Comparisons of air temperature between the station data and the CMFD at five stations with (a) 52602, (b) 52713, (c) 52737, (d) 52818, and (e) 52836.
Hydrology 12 00105 g007
Hydrology 12 00105 g008
Figure 8. Comparisons of wind speed between the station data and the CMFD at five stations with (a) 52602, (b) 52713, (c) 52737, (d) 52818, and (e) 52836.
Figure 8. Comparisons of wind speed between the station data and the CMFD at five stations with (a) 52602, (b) 52713, (c) 52737, (d) 52818, and (e) 52836.
Hydrology 12 00105 g008
Hydrology 12 00105 g009
Figure 9. Comparisons of precipitation (a–c) between station data and CMFD at (a) 52818, (b) 52825, (c) 52707 and (d–f) between station data and CMFD, GLDAS, GPM, CRU, TRMM at (d) 52818, (e) 52825, (f) 52707.
Figure 9. Comparisons of precipitation (a–c) between station data and CMFD at (a) 52818, (b) 52825, (c) 52707 and (d–f) between station data and CMFD, GLDAS, GPM, CRU, TRMM at (d) 52818, (e) 52825, (f) 52707.
Hydrology 12 00105 g009
Hydrology 12 00105 g010
Figure 10. Distributions of trends of meteorological elements with (a) precipitation, (b) air temperature, (c) wind speed, (d) solar radiation, (e) specific humidity, and (f) air pressure from 2003 to 2016. The black dots indicate passing the significance test of p < 0.01.
Figure 10. Distributions of trends of meteorological elements with (a) precipitation, (b) air temperature, (c) wind speed, (d) solar radiation, (e) specific humidity, and (f) air pressure from 2003 to 2016. The black dots indicate passing the significance test of p < 0.01.
Hydrology 12 00105 g010
Hydrology 12 00105 g011
Figure 11. Time series of meteorological elements of (a) precipitation, (b) air temperature, (c) solar radiation, (d) wind speed, (e) specific humidity, and (f) air pressure.
Figure 11. Time series of meteorological elements of (a) precipitation, (b) air temperature, (c) solar radiation, (d) wind speed, (e) specific humidity, and (f) air pressure.
Hydrology 12 00105 g011
Hydrology 12 00105 g012
Figure 12. The correlation coefficients for the changes in TWSAs and meteorological elements of (a) precipitation, (b) air temperature, (c) wind speed, (d) solar radiation, (e) specific humidity, and (f) air pressure. The black dots represent passing the significance test with p < 0.01. Note: PREC: precipitation; TEMP: air temperature; WIND: wind speed; SRAD: solar radiation; SHUM: specific humidity; PRES: air pressure.
Figure 12. The correlation coefficients for the changes in TWSAs and meteorological elements of (a) precipitation, (b) air temperature, (c) wind speed, (d) solar radiation, (e) specific humidity, and (f) air pressure. The black dots represent passing the significance test with p < 0.01. Note: PREC: precipitation; TEMP: air temperature; WIND: wind speed; SRAD: solar radiation; SHUM: specific humidity; PRES: air pressure.
Hydrology 12 00105 g012
Hydrology 12 00105 g013
Figure 13. The correlation coefficients for the changes in TWSAs and GWAs from the CSR, the JPL, the GSFC, and their averaged values and meteorological elements. Note: ** p < 0.01.
Figure 13. The correlation coefficients for the changes in TWSAs and GWAs from the CSR, the JPL, the GSFC, and their averaged values and meteorological elements. Note: ** p < 0.01.
Hydrology 12 00105 g013
Table 1. Detailed information of the meteorological stations used in this study.
Table 1. Detailed information of the meteorological stations used in this study.
NumberStationCodeLatitude (°N)Longitude (°E)Altitude (m)
1Dachaidan5271337.8595.353173.20
2Delingha5273737.3797.382981.50
3Dulan5283636.3098.103189.00
4Golmud5281836.4294.922807.60
5Lenghu5260238.7593.332770.00
6Nuomuhong5282536.4396.432790.40
7Xiaozaohuo5270736.8093.682767.00
Table 2. Ranges, means, and trends of TWSAs from the CSR, the JPL, and the GSFC and these TWSAs averaged over the Qaidam Basin during the P1 period (April 2002 to June 2017) and the P2 period (June 2018 to March 2024). Note: ** p < 0.01.
Table 2. Ranges, means, and trends of TWSAs from the CSR, the JPL, and the GSFC and these TWSAs averaged over the Qaidam Basin during the P1 period (April 2002 to June 2017) and the P2 period (June 2018 to March 2024). Note: ** p < 0.01.
DataRange (mm)Mean (mm)Trend (mm/month)
P1P2P1P2P1P2
OriginalCSR–51.63~99.6949.74~156.4110.46 ± 2.0690.95 ± 2.820.31 **–0.43
JPL–64.14~97.4367.63~133.4013.89 ± 2.3597.83 ± 1.850.45 **–0.48
GSFC–53.87~99.2196.66~175.2025.76 ± 3.27146.07 ± 1.780.74 **0.16
Average–50.94~98.7877.66~146.2716.70 ± 2.45111.62 ± 1.700.50 ± 0.13 **–0.25 ± 0.21
InterpolationCSR–51.63~99.6949.74~156.4111.65 ± 1.9590.66 ± 2.770.30 **–0.38
JPL–64.14~97.4367.63~133.4015.39 ± 2.2497.83 ± 1.810.45 **–0.45
GSFC–53.87~99.2196.66~175.2028.99 ± 3.17145.36 ± 1.800.74 **0.20
Average–50.94~98.7877.66~146.2718.68 ± 2.34111.28 ± 1.680.50 ± 0.13 **–0.21 ± 0.21
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Chang, L.; Zhang, Q.; Gu, X.; Duan, R.; Wang, Q.; You, X. Responses of Terrestrial Water Storage to Climate Change in the Closed Alpine Qaidam Basin. Hydrology 2025, 12, 105. https://doi.org/10.3390/hydrology12050105

AMA Style

Chang L, Zhang Q, Gu X, Duan R, Wang Q, You X. Responses of Terrestrial Water Storage to Climate Change in the Closed Alpine Qaidam Basin. Hydrology. 2025; 12(5):105. https://doi.org/10.3390/hydrology12050105

Chicago/Turabian Style

Chang, Liang, Qunhui Zhang, Xiaofan Gu, Rui Duan, Qian Wang, and Xiangzhi You. 2025. "Responses of Terrestrial Water Storage to Climate Change in the Closed Alpine Qaidam Basin" Hydrology 12, no. 5: 105. https://doi.org/10.3390/hydrology12050105

APA Style

Chang, L., Zhang, Q., Gu, X., Duan, R., Wang, Q., & You, X. (2025). Responses of Terrestrial Water Storage to Climate Change in the Closed Alpine Qaidam Basin. Hydrology, 12(5), 105. https://doi.org/10.3390/hydrology12050105

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop