Water Storage Changes Research Articles

Assessing long-term water storage dynamics in Afghanistan: An integrated approach using machine learning, hydrological models, and remote sensing

Assessment of Terrestrial Water Storage (TWS) components is crucial for understanding regional climate and water resources, particularly in arid and semi-arid regions like Afghanistan. Given the scarcity of ground-based data, this study leverages remote sensing datasets to quantify water storage changes. We integrated Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on (GRACE-FO) data with WaterGap, Global Land Water Storage (GWLS), Catchment Land Surface Model (CLSM), and climate variables (precipitation, temperature, potential evapotranspiration) using artificial neural networks (ANN) and random forests (RF). Additionally, Ice, Cloud, and Land Elevation Satellite (ICESat-1,2) data were utilized to estimate glacier mass changes. Seasonal trend decomposition using Loess (STL) was applied to assess TWS changes from 2003 to 2022. Our methodology reveals a high correlation (R = 0.90–0.97) between reconstructed and observed TWS anomalies across Afghanistan's major basins. Glacier mass decreased by −0.59 and −1.17 Gt/year during 2003–2009 and 2018–2022, respectively, while overall TWS declined by −2.46 Gt/year. The HRB experienced the largest TWS loss (−1.47 Gt/year), primarily due to groundwater depletion (−1.18 Gt/year). These findings underscore the importance of our approach for assessing water resources, providing vital insights for sustainable management under a changing climate in a data-scarce country.

Generating Long-term GRACE-like Total Water Storage Change Products using Conditional Generative Adversarial Networks

Abstract. Since 2002, the Gravity Recovery And Climate Experiment (GRACE) and its Follow-On (GRACE-FO, hereafter GRACE) missions have offered global observations of total water storage (TWS). However, the relatively short record of GRACE data poses a significant challenge for researchers to investigate the full range and long-term variability in TWS. In this study, we present RecGAN, a novel Conditional Generative Adversarial Network (CGAN) comprising a RecNet generator and pixel discriminator. Our approach aims to generate long-term GRACE-like TWS observations by calibrating the WaterGAP Global Hydrology Model (WGHM). The generator is trained to produce observations conforming to the distribution of GRACE data, while the discriminator is trained to assess whether each generated pixel resembles GRACE data. Our results show that RecGAN effectively enhances the consistency between GRACE observations and WGHM-derived TWS changes, achieving improved correlation coefficients, Nash-Sutcliffe Efficiency, and Normalized Root-Mean-Square Error. In addition, RecGAN is robust to different GRACE mascon data, crop sizes used during the training period, and hydrological models targeted for calibration. This study illustrates a promising application of employing CGANs to fine-tune the WGHM output to match GRACE observations. This approach enables the generation of longterm TWS change datasets, which are invaluable for evaluating long-term water storage fluctuations, allocating water resources, and forecasting future hydrological extremes.

Monitoring terrestrial water storage changes using GNSS vertical coordinate time series in Amazon River basin

Aiming at the Terrestrial Water Storage(TWS) changes in the Amazon River basin, this article uses the coordinate time series data of the Global Navigation Satellite System (GNSS), adopts the Variational Mode Decomposition and Bidirectional Long and Short Term Memory(VMD-BiLSTM) method to extract the vertical crustal deformation series, and then adopts the Principal Component Analysis(PCA) method to invert the changes of terrestrial water storage in the Amazon Basin from July 15, 2012 to July 25, 2018. Then, the GNSS inversion results were compared with the equivalent water height retrieved from Gravity Recovery and Climate Experiment (GRACE) data. The results show that (1) the extraction method proposed in this article has better denoising effect than the traditional method; (2) the surface hydrological load deformation can be well calculated using GNSS coordinate vertical time series, and then the regional TWS changes can be inverted, which has a good consistency with the result of GRACE inversion of water storage, and has almost the same seasonal variation characteristics; (3) There is a strong correlation between TWS changes retrieved by GNSS based on surface deformation characteristics and water mass changes calculated by GRACE based on gravitational field changes, but GNSS satellite’s all-weather measurement results in a finer time scale compared with GRACE inversion results. In summary, GNSS can be used as a supplementary technology for monitoring terrestrial water storage changes, and can complement the advantages of GRACE technology.

Low-degree gravity field coefficients based on inverse GNSS method: insights into hydrological and ice mass change studies

The relative displacements of stations from a global network of Global Navigation Satellite System (GNSS) sites provide information on global mass transport. In this study, we use 19 years of global GNSS station displacements from the 3rd International GNSS Service reprocessing campaign to estimate the coefficients of the spherical harmonics of the Earth’s gravity field up to degree and order 8 using the inverse GNSS method based on elastic loading theory. The results indicate that the C30 coefficient can be derived based on GNSS station displacements as an alternative to solutions provided by Satellite Laser Ranging (SLR) and Gravity Recovery and Climate Experiment (GRACE). GNSS may support GRACE solutions that face the problems of deriving C30, which has fundamental meaning in estimating ice mass changes in polar regions. The recovery of Antarctic ice sheet mass change from January 2007 to December 2020 based on coefficients replaced by GNSS estimates results in a linear trend of − 152 ± 4 Gt/year, in comparison to − 149 ± 2 Gt/year for the replacement based on SLR from GRACE Technical Note #14. The results indicate that the spatial and seasonal patterns of terrestrial water storage changes derived from GNSS are consistent with those estimated using GRACE/GRACE Follow-On and SLR at a few-millimeter level in the Amazon and Brahmaputra River basin regions.

Towards sustainable development goals: Leveraging multi-data remote sensing fusion for monitoring groundwater-induced bedrock subsidence dynamics in Egypt's Nile Valley

Egypt's Golden Triangle megaproject within Egypt's vision 2030, involving land reclamation in Qena Bend's densely populated governorate, develops sustainable land management strategies. Advanced technologies and low-cost remote sensing multi-data fusion are utilized to understand subsidence dynamics influenced by geologic structure, groundwater, climate change, and human activities in Egypt's Nile Valley. This approach identifies environmental hazards and provides a detailed explanation for groundwater-induced bedrock subsidence, aiding in informed decision-making and risk avoidance. Landsat images reveal 13% increased cultivation, 28.28% urban-growth, and decreased water by 8.46%, impacting groundwater resources and controlling the situation. The Gravity Recovery and Climate Experiment(GRACE) and Global Land Data Assimilation System (GLDAS) satellite observations reveal changes in water storage, impacting climate change, groundwater storage dynamics, and aquifer behavior. Historical data indicates a significant southwest-northeast gradient in precipitation from 5 to 60 mm. GLDAS shows soil moisture decline from 0.25 to 0.23 mm. GRACE (total water storage) depleting, then slightly increasing from 2020 to 2023 with an average value (−5 cm/yr). Groundwater storage increases in wet seasons, in 2015 showing (+3–4 mm), less than (+1 mm) in (2018), and (+6–8 mm) in (2020–2023). The NE-SW and NW-SE faults increase hydraulic connection and recharge from aquifers, causing groundwater circulation and karstification in Eocene limestone aquifers, posing risks to urban development and human safety. The InSAR (Synthetic Aperture Radar) measures ground subsidence over time, revealing a range of (−0.04 to −0.07m) in the northwest to (+0.03m) in the southeast, with average subsidence (-4 cm), primarily associated with increased groundwater storage motivate the interaction between the carbonate and groundwater. The ArcGIS overlay model divides the region into three zones: northern, middle, and southern, each with varying degrees of displacement and groundwater storage. The findings emphasize the significance of remote sensing in hazard evaluation for development planning due to its cost-effectiveness and accuracy, applicable globally in hydrogeologically similar areas.

Lake water storage changes and their cause analysis in Mongolia

Lake is an important water resources in Mongolia, which has undergone a large variation in past decades. However, it is still challenging to monitor long-term changes in lake water storage (LWS) due to the lack of lake level monitoring and long-term satellite altimetry data for Mongolian lakes. According to the Advanced Land Observing Satellite Digital Elevation Model (ALOS DEM) and the Joint Research Centre (JRC) dataset, we estimated the LWS changes of 55 Mongolian lakes (> 10 km2) from 1991 to 2020. The results showed that the LWS increased by 40.24 km3 from 1991 to 1997, especially for northwestern Mongolia with 31.47 km3. However, the LWS decreased by 32.44 km3 from 1998 to 2010, and the lakes in the northwestern and southern decreased by 20.24 km3 (62%) and 7.38 km3 (23%), respectively, and then the LWS continued to decrease by 10.22 km3 from 2011 to 2020. The precipitation was the primary cause of lake change, which could explain 45.13% of LWS change based on Generalized Linear Model, followed by temperature (26.33%) and irrigated area (16.72%). Analyzing the changing characteristic and driving mechanisms of LWS can provide a scientific basis for local water resources management and planning.

Deep learning-aided temporal downscaling of GRACE-derived terrestrial water storage anomalies across the Contiguous United States

The Gravity Recovery And Climate Experiment (GRACE) and GRACE-FollowOn (GRACE(−FO)) satellites have been monitoring Earth’s changes in terrestrial water storage (TWS) or surficial mass changes at monthly sampling and a spatial scale longer than ∼330 km (half wavelength) over the past two decades. At monthly sampling or revisit time, the use of satellite gravimetry is difficult to effectively monitor abrupt extreme weather events which are high-frequency, including the climate-induced hurricanes/cyclones, flash floods and droughts. The majority of the contemporary studies have focused on satellite gravimetry spatial downscaling, and not on reducing the temporal resolution of Earth’s mass change. Here, we developed a Deep Learning (DL) algorithm to downscale monthly GRACE/ GRACE(−FO) Mass Concentration (Mascon) TWS anomaly (TWSA) solutions to daily sampling over the Contiguous United States (CONUS), with the aim of monitoring rapidly evolving natural hazard episodes. The simulative performance of the DL algorithm is validated by comparing the modeling to an independent observation and the land hydrology model (LHM) predicted TWSA. To confirm that our daily and monthly simulations captured the climatic variations, we first compared our simulations with El Niño/La Niña Southern Oscillation (ENSO) circulation system index, which has a dominant climatological and socioeconomic impact across CONUS, and results reveal high correlations which are statistically significant. Next, we assessed the feasibilities to detect long- and short-term variations in the TWSA signals triggered by hydrological extremes, including the 2011 and 2019 Missouri River Floods, the August 2017 Atlantic Hurricane Harvey landfalls in Texas, the 2012–2017 drought in California, and the flash drought in the Northern Great Plains in 2017. Additional validation results using independent in situ observations reveal that our DL-aided gravimetry downscaled daily simulations are capable of elucidating hazards and water cycle evolutions at high temporal resolution.

Responses of vegetation to hydroclimatic variables on the Loess Plateau after large scale vegetation restoration

AbstractUnderstanding plant‐water relations is essential for effective regional water management and promoting ecologically sustainable development on the Loess Plateau, especially in the context of the Grain for Green project initiated in 1999. This study evaluated the variations in vegetation variables (leaf area index, enhanced vegetation index, solar‐induced chlorophyll fluorescence and gross primary production) and hydroclimatic variables (precipitation, total water storage, aridity index and standardized precipitation evapotranspiration index) from 2003 to 2020, along with their interactions across the Loess Plateau. Our analysis revealed a general increase in vegetation variables, with the largest increase observed in the forest expansion areas. Precipitation and the aridity index exhibited significant upwards trends, while total water storage showed a significant decline, particularly in the forest expansion areas. Vegetation variables were more sensitive to changes in total water storage across the Loess Plateau. In the northwest region, where large‐scale croplands and grasslands expansion occurred, vegetation variables also showed sensitivity to precipitation. Lag effect analysis revealed short time lags (1–3 months) between vegetation and hydroclimatic variables, expect for total water storage (6 months). Overall, human activities and climate factors contributed 58.4% and 41.6% to the increase in leaf area index, and 52.2% and 47.8% to the increase in gross primary production, respectively. In relatively arid environments, precipitation contributed over 50% to the observed vegetation greening. This study underscores the increasingly significant role of human activities in driving vegetation greening on the Loess Plateau, particularly in large‐scale afforestation areas.

Quantifying Water Storage Changes and Groundwater Drought in the Huaihe River Basin of China Based on GRACE Data

Quantifying Water Storage Changes and Groundwater Drought in the Huaihe River Basin of China Based on GRACE Data

Water budgets in an arid and alpine permafrost basin: Observations from the High Mountain Asia

Ground freeze‒thaw processes have significant impacts on infiltration, runoff and evapotranspiration. However, there are still critical knowledge gaps in understanding of hydrological processes in permafrost regions, especially of the interactions among permafrost, ecology, and hydrology. In this study, an alpine permafrost basin on the northeastern Qinghai‒Tibet Plateau was selected to conduct hydrological and meteorological observations. We analyzed the annual variations in runoff, precipitation, evapotranspiration, and changes in water storage, as well as the mechanisms for runoff generation in the basin from May 2014 to December 2015. The annual flow curve in the basin exhibited peaks both in spring and autumn floods. The high ratio of evapotranspiration to annual precipitation (>1.0) in the investigated wetland is mainly due to the considerably underestimated ‘observed’ precipitation caused by the wind-induced instrumental error and the neglect of snow sublimation. The stream flow from early May to late October probably came from the lateral discharge of subsurface flow in alpine wetlands. This study can provide data support and validation for hydrological model simulation and prediction, as well as water resource assessment, in the upper Yellow River Basin, especially for the headwater area. The results also provide case support for permafrost hydrology modeling in ungauged or poorly gauged watersheds in the High Mountain Asia.

A dataset of soil water storage of typical ecosystems in arid and semi-arid areas in China, 2005-2017

Soil water is one of the important components of the earth's water resources, and also the main source of water directly consumed by vegetation and crops. Therefore, the storage, recharge, consumption, renewal and balance of soil water are of great significance to agriculture, animal husbandry, forestry, ecological environment and water resources balance, especially for the arid and semi-arid region of China. We collect and sort out the long-term observation data of soil moisture from eight ecosystem observation and research stations of the Chinese Ecosystem Research Network (CERN) located in the arid and semi-arid region of China during 2005 - 2017, and generates the corresponding soil water storage dataset through quality control, spatial and temporal normalization, statistical calculation, in order to provide long-term data support for understanding distribution and change of soil water storage in typical ecosystems in arid and semi-arid areas of China.

Estimating the Ebro river discharge at 1 km/daily resolution using indirect satellite observations

Estimating river discharge Q at global scale from satellite observations is not yet fully satisfactory in part because of limited space/time resolution. Furthermore, on highly anthropized basins, it is essential to anchor the analysis to reliable Q measurements. Gauge networks are however very sparse and limited in time, and SWOT (Surface Water Ocean Topography) river discharge estimates at global scale are not yet available. The method proposed here is able to obtain continuous daily Q estimates at 1 km/daily resolution, using indirect satellite data and ground-based estimates. We focus here on the Ebro. Over such an anthropized basin (e.g. change of land use, irrigation), the exploitation of 205 available gauges at their nominal resolution (i.e., daily point measurements) is a necessity. The hydrological Continuum model is used to help interpolate spatially and temporally the observations into our optimal interpolation scheme. The proposed Q-mapping is similar to an assimilation scheme were Earth observations (precipitation, evapotranspiration and total water storage change) and model simulations are constrained by in situ gauge measurements. The Q estimates are evaluated using a rigorous leave-one-out experiment, showing a good agreement with the in situ data: a correlation of 0.72 (median), and a 75th percentile of Nash-Sutcliffe Efficiency up to 0.62. Our spatio-temporal continuous Q estimates at high spatial/temporal resolution can describe complex continental water dynamics, including extreme events. SWOT estimates will soon be available, at the global scale but with irregular space/time sampling: our method should help exploit them to obtain a regular space-temporal description of the water cycle at high resolution.

Synergizing AI and Physical Expertise to Close the Water Budget from Satellite Data

Abstract A multitude of Earth observation (EO) products are available for monitoring the terrestrial water cycle. These EO datasets have resulted in a multiplicity of datasets for the same geophysical variable. Furthermore, inconsistencies between the water components prevent the water budget closure. A maximum a posteriori (MAP) estimator has been used in the past to optimally combine EO datasets. This framework has many advantages, but it can only be utilized when all four water components are available (precipitation P, evapotranspiration E, total water storage change dS, and river discharge R) and solely at the basin scale. By combining physical expertise with the statistical inference of neural networks (NNs), we designed a custom deep learning scheme to optimize EO data. This hybrid approach benefits from the optimization capabilities of NNs to estimate the parameters of interconnected physical modules. The NN is trained using basin-scale data (from MAP results) over 38 basins to obtain optimized EOs globally. The NN integration offers several enhancements compared to MAP: Independent calibration/mixing models are obtained with imbalance reduction and optimization at the pixel level, and environmental variables can be used to extrapolate results to unmonitored regions. The NN integration enables combining EO estimates of individual water components (P, E, dS, and R) in a hydrologically coherent manner, resulting in a significant decrease in the water budget imbalance at the global scale. Mean imbalance errors can be significant on raw EOs, but they become negligible when EOs are integrated. The standard deviation (STD) of the imbalance is around 26 mm month−1 for raw EOs, and they decrease to 21 when combined and 19 when mixed.

Long-term trends in human-induced water storage changes for China detected from GRACE data

Terrestrial Water Storage (TWS) plays a pivotal role in water resource management by providing a comprehensive measure of both surface water and groundwater availability. This study investigates changes in TWS driven by human activities from 2003 to 2023, and forecasts future TWS trends under various climate change and development scenarios. Our findings reveal a continuous decline in China's TWS since 2003, with an average annual decrease of approximately 1.36 mm. This reduction is primarily attributed to the combined effects of climate change and human activities, including irrigation, industrial water use, and domestic water consumption. Notably, TWS exhibits significant seasonal and annual fluctuations, with variations ranging ±10 mm. For the future period (2024–2030), we project greater disparities between water resource supply and demand in specific years for the Songliao, Southwest, and Yangtze basins. Consequently, future water resource management must prioritize water conservation during wet seasons, particularly in years when supply-demand conflicts for limited water resources intensify. This study is valuable for effective planning and sustainable utilization of water resources.

Modelling Soil Moisture Balance for Okra Cultivars in Makurdi Agro Climate Using Decision Support System for Agro Technology Transfer (DSSAT)

Abstract: Soil moisture balance from okra (abelmoschus esculentus) field was performed on experimental plot of the Department of Agricultural and Environmental Engineering, Joseph Sanwuan Tarka University Makurdi -Nigeria. Field and laboratory experiments were conducted on four okra cultivars planted and irrigated by drip system at different levels (80% (I1), 60% (I2), 45% (I3), and 15% (I4)) according to the agronomic practices of okra. Decision Support System for agro technology Transfer (DSSAT) model for the Crop Environment Resource Synthesis (CERES) was used to model soil moisture balance by linear regression (multivariate analysis of variance, (MANOVA)) and validated. The soil was sandy loam with high field capacity (FC) at I4 and nearly uniform drainage (D) except for I4.. Runoff (R) decreases from 35.71 for I1 to 0.07 for I4 implying that R, D and change in water storage (∆S) are functions of the amount and duration of irrigation. The models reported an acceptable deviation from the ideal line of the R and ∆S at higher values confirming degree of the correlation between the observed and predicted dataset but experienced difficulties with estimating lower values due to lower magnitudes in irrigation. From the foregoing it is concluded that decreasing water application results in an increase in irrigation and the reverse is also true.

Monthly Monitoring of Inundated Areas and Water Storage Dynamics in China's Large Reservoirs Using Multisource Remote Sensing

AbstractHigh‐frequency monitoring of reservoir inundation and water storage changes is crucial for reservoir functionality assessment and hydrological model calibration. Although the integration of optical data with synthetic aperture radar (SAR) backscattering coefficients (backscatters) offers an effective approach, conventional methods struggle to consistently provide accurate retrievals over diverse regions and seasons. In this study, we introduce reservoir‐ and monthly‐specific classification models to enhance the integration of Sentinel‐1 SAR backscatters with optical‐based water dynamics. Our method covers 721 reservoirs with a capacity greater than 0.1 km3 in China during 2017–2021. Furthermore, we leverage multisource satellite altimetry records (e.g., ICESat‐2, CryoSat‐2, and GEDI) and digital elevation models to derive hypsometry relationship (i.e., water level–water area relationship) for reservoirs, enabling the transformation of inundated areas into monthly water storage changes for 662 reservoirs, representing 93% of the total storage capacity of large reservoirs. Validation against in‐situ measurements at 80 reservoirs reveals improved monthly inundated area monitoring compared to existing data sets. Additionally, our reservoir water storage change estimates exhibit an average R2 of 0.79 and a mean relative root mean square error (rRMSE) of 21%. Our findings highlight reservoir water increases from May/June to November and declines in winter–spring in most regions. However, the inter‐annual patterns vary among regions, with increases in Northeast China, the Yellow River basin (YR), and Southwest China, contrasted by declines in Eastern and Northwest China. Inter‐ and intra‐annual variability in reservoir water storage is mainly influenced by natural inflow in Northeast and Northwest China, while anthropogenic factors dominate in the YR, Eastern, and Southwest China.

Assessing GNSS hydrological monitoring capability across different climatic settings in China

Vertical position changes of ground-based Global Navigation Satellite System (GNSS) stations have been used to study regional terrestrial water storage (TWS) changes. However, the feasibility is still unclear in many areas due to diverse local effects. This study aims to evaluate the capability of GNSS vertical displacements in monitoring hydrological variations in four climate settings over Chinese mainland. The spatial and temporal variations of hydrological load-induced (HYDL) vertical displacements at 208 GNSS sites during 2011–2020 were analyzed by comparing with Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-On (GFO) and Global Land Data Assimilation System (GLDAS) derived TWS changes. The results indicate that GNSS vertical positions show different capabilities in capturing seasonal and non-seasonal hydrological dynamics in different climate regions. Among the four climatic settings, the subtropical monsoon climate (SMC) region, with the largest deformation fluctuation (the regional mean root mean square (RMS) is 7.97 mm), has the highest regional mean HYDL-GRACE and HYDL-GLDAS anti-correlation coefficients (CCs) of −0.47 and −0.45 at the seasonal scale, respectively. For the individual GNSS site, the number of the sites with CC <−0.40 between HYDL and GRACE/GLDAS-derived TWS changes accounts for 55.1% and 55.1% (SMC), 13.0% and 7.4% (temperate monsoon climate, TMC), 6.7% and 13.3% (temperate continental climate, TCC), 32.3% and 38.7% (plateau climate, PC), respectively. For the non-seasonal term, although the proportion with CC <−0.40 in each climate type decreases mainly due to the influence of local geodynamic and human activities, especially in the SMC and PC regions, GNSS site vertical deformations still show good capability in monitoring hydrological extremes. The results provide valuable information for better application of GNSS to hydrology.

Extent of gross underestimation of precipitation in India

Abstract. The underestimation of precipitation (UoP) in the hilly and mountainous parts of South Asia is estimated by some studies to be as large as the observed precipitation (P). However, UoP has been analyzed to only a limited extent across India. To help bridge this gap, watershed-scale UoP was analyzed using various P datasets within a water imbalance analysis. Among these P datasets, the often-used Indian Meteorological Department (IMD) dataset is of primary interest. The gross UoP was identified by analyzing the extent of the imbalance in the annual water budget of watersheds corresponding to 242 river gauging stations for which quality-controlled data on catchment boundaries and streamflow are available. The water year (WY)-based volume of observed annual P was compared against the observed annual streamflow (R) and the satellite-based actual evapotranspiration (ET). Across many watersheds of both Northern and Peninsular India, spurious water imbalance scenarios (P≤R or P≪R+ET) were realized. It is shown that the management of water, such as groundwater extraction, reservoir storage and water diversion, is generally minimal compared to the annual P in such watersheds. It is also shown that annual changes in terrestrial water storage are minimal compared to the annual P in such watersheds. Assuming that data on R (and, to a lesser extent, ET) are reliable, it is concluded that UoP is very likely the cause of this imbalance. Inter-watershed groundwater flow (IGF) is assumed to be negligible. While the effect of IGF on R is unknown, examples are provided which show that IGF is unlikely to be the cause of the observed imbalance in certain watersheds. All 12 of the P datasets analyzed here suffer from UoP, but the extent of the UoP varies by dataset and region. The reanalysis-based datasets ERA5-Land and IMDAA are less affected by UoP than the IMD dataset. Based on the 30-year period of WY 1985–2014, P for the whole of India could be as much as 19 % (ERA5-Land) to 37 % (IMDAA) higher than that from the IMD, with substantial variability within years and river basins. The actual magnitude of UoP is speculated to be even greater. Moreover, trends seen in the IMD's P are not always present in ERA5-Land and IMDAA. Studies using IMD should exercise caution since UoP could lead to the misrepresentation of water budgets and long-term trends. Limitations of this study are discussed.

The energy-limited water loss of an alpine shrubland on the northeastern Qinghai-Tibetan Plateau, China

Study regionAlpine shrubland on the northeastern Qinghai-Tibetan Plateau. Study focusWater provision ability is a pivotal ecological service of high-altitude alpine regions and is controlled by precipitation, evapotranspiration (ET), and soil water storage whereas the underlying ecohydrological processes remain highly unquantified. Here, we investigated continuous 19-year flux measurements to quantify the temporal patterns of ET and water budget (precipitation minus ET, P−ET), as well as 0-20 cm soil water storage change (ΔSWS). New hydrological insights for the regionAt a monthly scale, ET peaked in July (96.7 ± 26.4 mm, Mean ± S.D.) and averaged 41.7 ± 31.9 mm, whose variations were determined by the slope of the saturation vapor pressure curve at air temperature, air and soil temperatures, regardless of vegetation growth stage. P−ET averaged 18.3 ± 26.3 mm in August and September while stayed deficit during the other months. The variations in P−ET were controlled by precipitation in the May-October growing season whereas by ET in the non-growing season from November to April. ΔSWS peaked in May (28.8 ± 11.2 mm) and September (3.0 ± 2.7 mm) and almost accumulated to zero over the whole season. At annual scales, none of ET, P−ET, and ΔSWS changed significantly. ET averaged 512.2 ± 68.4 mm and exceeded precipitation (459.1 ± 58.4 mm), likely due to the lateral flow supply of uphill locations. The variations in ET were regulated directly by bulk canopy resistance and indirectly by net radiation. P−ET averaged −53.2 ± 95.4 mm and demonstrated a clear water deficit (−51.6 ± 21.0 mm) during the non-growing season. The variations of P−ET were driven jointly by precipitation and ET, with opposite but equivalent effects. The dominance of thermal conditions and energy availability on ET variability manifested an energy-limited feature of water loss in the alpine shrubland. The temporal patterns in P−ET elucidated that the alpine shrubland plays the water retention rather than water provision function through transforming variable precipitation input into stable ET loss.

Learning-based Reconstruction of GRACE Data Based on Changes in Total Water Storage and Its Accuracy Assessment

Learning-based Reconstruction of GRACE Data Based on Changes in Total Water Storage and Its Accuracy Assessment