Soil moisture (SM), as a crucial variable in the soil-vegetation-atmosphere continuum, plays an important role in the terrestrial water cycle. Analyzing SM's variation and driver factors is crucial to maintaining ecosystem diversity on the Tibetan Plateau (TP) and ensuring food security as well as water supply balance in developing countries. Gradual wetting of the soil has been detected and attributed to precipitation in this area. However, there is still a gap in understanding the potential mechanisms. It is unclear whether the greening, glacier melting, and different vegetation degradation caused by asymmetrical climate change and intensified human activities have significantly affected the balance of SM. Here, to test the hypothesis that heterogeneous SM caused by precipitation was subject to temperatures and anthropogenic constraints, GLDAS-2.1 (Global Land Data Assimilation System-2.1) SM products combined with the statistical downscaling and Geographic detectors were applied. The results revealed that: (1) Seasonal SM gradually increased (p < 0.05), while SM deficit frequently appeared with exposure to extreme climates, such as in the summer of 2010 and 2013, and changed into a pattern of precipitation transport to western dry lands in autumn. (2) There was a synergistic reaction between greening and local moisture in autumn. SM was dominated by low temperature (TMN) in winter, warming indirectly regulated SM by exacerbating the thawing of glaciers and permafrost. The spatial coupling between the faster rising rate of TMN and the frozen soil might further aggravate the imbalance of SM. (3) The land cover's mutual transformation principally affected SM in spring and autumn, and degradation accelerated the loss of SM replenished by precipitation. (4) Land cover responses were different; SM in grassland was less affected by external disturbance, while degraded woodland and shrub performed adaptive feedback under dry environments, SM increased by 0.05 and 0.04 m(3)/(m(3) 10a), respectively. Our research provides a scientific basis for improving hydrological models and developing vegetation restoration strategies for long-term adaptation to TP-changing environments.

Long-term and high-quality surface soil moisture (SSM) and root-zone soil moisture (RZSM) data is crucial for understanding the land-atmosphere interactions of the Qinghai-Tibet Plateau (QTP). More than 40% of QTP is covered by permafrost, yet few studies have evaluated the accuracy of SSM and RZSM products derived from microwave satellite, land surface models (LSMs) and reanalysis over that region. This study tries to address this gap by evaluating a range of satellite and reanalysis estimates of SSM and RZSM in the thawed soil overlaying permafrost in the QTP, using in-situ measurements from sixteen stations. Here, seven SSM products were evaluated: Soil Moisture Active Passive L3 (SMAP L3) and L4 (SMAP-L4), Soil Moisture and Ocean Salinity in version IC (SMOS IC), Land Parameter Retrieval Model (LPRM) Advanced Microwave Scanning Radiometer 2 (AMSR2), European Space Agency Climate Change Initiative (ESA CCI), Advanced Scatterometer (ASCAT), and the fifth generation of the land component of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERAS-Land). We also evaluated three RZSM products from SMAP-L4, ERA5-Land, and the Noah land surface model driven by Global Land Data Assimilation System (GLDAS-Noah). The assessment was conducted using five statistical metrics, i. e. Pearson correlation coefficient (R), bias, slope, Root Mean Square Error (RMSE), and unbiased RMSE (ubRMSE) between SSM or RZSM products and in-situ measurements. Our results showed that the ESA CCI, SMAP-L4 and SMOS-IC SSM products outperformed the other SSM products, indicated by higher correlation coefficients (R) (with a median R value of 0.63, 0.44 and 0.57, respectively) and lower ubRMSE (with a median ubRMSE value of 0.05, 0.04 and 0.07 m(3)/m(3), respectively). Yet, SSM overestimation was found for all SSM products. This could be partly attributed to ancillary data used in the retrieval (e.g. overestimation of land surface temperature for SMAP-L3) and to the fact that the products (e.g. LPRM) more easily overestimate the in-situ SSM when the soil is very dry. As expected, SMAP-L3 SSM performed better in areas with sparse vegetation than with dense vegetation covers. For RZSM products, SMAP-L4 and GLDAS-Noah (R = 0.66 and 0.44, ubRMSE = 0.03 and 0.02 m(3)/m(3), respectively) performed better than ERAS-Land (R = 0.46; ubRMSE = 0.03 m(3)/m(3)). It is also found that all RZSM products were unable to capture the variations of in-situ RZSM during the freezing/thawing period over the permafrost regions of QTP, due to large deviation for the ice-water phase change simulation and the lack of consideration for unfrozen-water migration during freezing processes in the LSMs.

As the Water Tower of Asia and The Third Pole of the world, the Qinghai-Tibet Plateau (QTP) shows great sensitivity to global climate change, and the change in its terrestrial water storage has become a focus of attention globally. Differences in multi-source data and different calculation methods have caused great uncertainty in the accurate estimation of terrestrial water storage. In this study, the Yarlung Zangbo River Basin (YZRB), located in the southeast of the QTP, was selected as the study area, with the aim of investigating the spatio-temporal variation characteristics of terrestrial water storage change (TWSC). Gravity Recovery and Climate Experiment (GRACE) data from 2003 to 2017, combined with the fifth-generation reanalysis product of the European Centre for Medium-Range Weather Forecasts (ERA5) data and Global Land Data Assimilation System (GLDAS) data, were adopted for the performance evaluation of TWSC estimation. Based on ERA5 and GLDAS, the terrestrial water balance method (PER) and the summation method (SS) were used to estimate terrestrial water storage, obtaining four sets of TWSC, which were compared with TWSC derived from GRACE. The results show that the TWSC estimated by the SS method based on GLDAS is most consistent with the results of GRACE. The time-lag effect was identified in the TWSC estimated by the PER method based on ERA5 and GLDAS, respectively, with 2-month and 3-month lags. Therefore, based on the GLDAS, the SS method was used to further explore the long-term temporal and spatial evolution of TWSC in the YZRB. During the period of 1948-2017, TWSC showed a significantly increasing trend; however, an abrupt change in TWSC was detected around 2002. That is, TWSC showed a significantly increasing trend before 2002 (slope = 0.0236 mm/month, p < 0.01) but a significantly decreasing trend (slope = -0.397 mm/month, p < 0.01) after 2002. Additional attribution analysis on the abrupt change in TWSC before and after 2002 was conducted, indicating that, compared with the snow water equivalent, the soil moisture dominated the long-term variation of TWSC. In terms of spatial distribution, TWSC showed a large spatial heterogeneity, mainly in the middle reaches with a high intensity of human activities and the Parlung Zangbo River Basin, distributed with great glaciers. The results obtained in this study can provide reliable data support and technical means for exploring the spatio-temporal evolution mechanism of terrestrial water storage in data-scarce alpine regions.

Long-term and high-quality surface soil moisture (SSM) and root-zone soil moisture (RZSM) data is crucial for understanding the land-atmosphere interactions of the Qinghai-Tibet Plateau (QTP). More than 40% of QTP is covered by permafrost, yet few studies have evaluated the accuracy of SSM and RZSM products derived from microwave satellite, land surface models (LSMs) and reanalysis over that region. This study tries to address this gap by evaluating a range of satellite and reanalysis estimates of SSM and RZSM in the thawed soil overlaying permafrost in the QTP, using in-situ measurements from sixteen stations. Here, seven SSM products were evaluated: Soil Moisture Active Passive L3 (SMAP L3) and L4 (SMAP-L4), Soil Moisture and Ocean Salinity in version IC (SMOS IC), Land Parameter Retrieval Model (LPRM) Advanced Microwave Scanning Radiometer 2 (AMSR2), European Space Agency Climate Change Initiative (ESA CCI), Advanced Scatterometer (ASCAT), and the fifth generation of the land component of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERAS-Land). We also evaluated three RZSM products from SMAP-L4, ERA5-Land, and the Noah land surface model driven by Global Land Data Assimilation System (GLDAS-Noah). The assessment was conducted using five statistical metrics, i. e. Pearson correlation coefficient (R), bias, slope, Root Mean Square Error (RMSE), and unbiased RMSE (ubRMSE) between SSM or RZSM products and in-situ measurements. Our results showed that the ESA CCI, SMAP-L4 and SMOS-IC SSM products outperformed the other SSM products, indicated by higher correlation coefficients (R) (with a median R value of 0.63, 0.44 and 0.57, respectively) and lower ubRMSE (with a median ubRMSE value of 0.05, 0.04 and 0.07 m(3)/m(3), respectively). Yet, SSM overestimation was found for all SSM products. This could be partly attributed to ancillary data used in the retrieval (e.g. overestimation of land surface temperature for SMAP-L3) and to the fact that the products (e.g. LPRM) more easily overestimate the in-situ SSM when the soil is very dry. As expected, SMAP-L3 SSM performed better in areas with sparse vegetation than with dense vegetation covers. For RZSM products, SMAP-L4 and GLDAS-Noah (R = 0.66 and 0.44, ubRMSE = 0.03 and 0.02 m(3)/m(3), respectively) performed better than ERAS-Land (R = 0.46; ubRMSE = 0.03 m(3)/m(3)). It is also found that all RZSM products were unable to capture the variations of in-situ RZSM during the freezing/thawing period over the permafrost regions of QTP, due to large deviation for the ice-water phase change simulation and the lack of consideration for unfrozen-water migration during freezing processes in the LSMs.

The Qinghai-Tibetan Plateau (QTP), named the Asian Water Towers, feeds more than 2.5 billion people in downstream regions. It is still unknown how much water outflows from this region owing to lack of observations. The main objective of this study is to clarify availability of water flowed out of this region and is contribution to large Asian rivers. The Global Land Data Assimilation System (GLDAS) products are evaluated with the help of observations of the QTP. In addition, a velocity-based Fouling method is embedded into the GLDAS model W route runoff products W the basin outlet in this study. The results show that the simulated dry season runoff in the GLDAS model is generally lower than the observed value, which is mainly because most hydrological models only consider the potential evapotranspiration (ET) when simulating ET, while ignoring the water constraint factor. Noah I 0 v2.0 has the highest precision at the QTP. For the monthly precipitation and runoff series, the relative error is within 5%, the correlation coefficient is greater than 0.90, and the Nash -Sutcliffe efficiencies are 0.95 and 0.76, respectively. Glacier melt runoff plays an important role in the QTP runoff, with a proportion of approximately 22%. It is relatively high in the Tarim River basin (83%), Syr Darya River and Amu Darya River basins (69%), and Indus River basin (60%,). The contribution ratio also reaches 23% in the Yarlung ZangboBrahmaputra River and Ganges River basins, whereas it is the lowest in the Irrawaddy River basin (2%,). According W the NoahlO_v2.0 simulations, the mean annual runoff provided by the QTP exceeds 620 billion cubic metres, of which approximately 440 billion cubic metres flow out of the QTP and supply downstream regions of international rivers. The contribution ratio of the QTP runoff to the total runoff of its affected basins is approximately 16%. (C) 2020 The Authors. Published by Elsevier B.V.

Gravity Recovery and Climate Experiment (GRACE) and satellite altimetry are suitable for the precise measurement of terrestrial water storage (TWS) and lake water level variations from space. In this study, two GRACE solutions, namely, spherical harmonics (SH) and mascon (MSC), are utilized with the Global Land Data Assimilation System (GLDAS) model to estimate the spatial and temporal variations of TWS in the Upper Indus Basin (UIB) for the study period of January 2003 to December 2016. The TWS estimated by SH, MSC, and the GLDAS model are consistent and generally show negative trends of & x2212;4.47 & x00B1; 0.38 mm/year, & x2212;4.81 & x00B1; 0.49 mm/year, and & x2212;3.77 & x00B1; 0.46 mm/year, respectively. Moreover, we use the GLDAS model data to understand the roles of variations in land surface state variables (snow water equivalent (SWE), soil moisture, and canopy water storage) in enhancing or dissipating the TWS in the region. Results indicate that SWE, which has a significant contribution to GRACE TWS variability, is an important parameter. Spearman & x2019;s rank correlations are calculated to demonstrate the relationship of the GLDAS land surface state variables and the GRACE signals. A highly positive correlation between SWE with TWS is estimated by SH and MSC as 0.691 and 0.649, respectively, indicating that the TWS signal is mainly reliant on snow water in the study region. The ground water storages estimated by SH and MSC solutions are nearly stable with slight increasing trends of 0.63 & x00B1; 0.48 mm/year and 0.29 & x00B1; 0.51 mm/year, respectively. We also take advantage of the potential of satellite altimetry in measuring lake water level variations, and our result indicates that Cryosat-2 SARin mode altimetry data can be used in estimating small water bodies accurately in the high mountainous region of the UIB. Moreover, the climate indices data of El-Ni & x00F1;o Southern Oscillation and Pacific Decadal Oscillation are analyzed to determine the influence of pacific climatic variability on TWS.

Continental or regional water storage variations (WSVs) are crucial to regional economic development and human society and play an important role in coping with global change. Water scarcity is currently an especially key issue in Central Asia (CA), and therefore the study of WSVs can aid in the adoption of measures for mitigating pressures from contemporary environmental changes and economic development in CA. Based on Gravity Recovery and Climate Experiment (GRACE), Global Land Data Assimilation System (GLDAS), and CRU meteorological datasets and a proposed combined filter strategy, WSVs in Central Asia and its surrounding areas over 30 years are investigated in this paper. The results indicate that the WSVs derived from GRACE and GLDAS over CA generally show a decreasing tendency. CRU data demonstrated that CA has been undergoing a warming trend. The water loss in CA may be caused by warming, which will lead to the loss of soil moisture. Moreover, the water mass in the Tibetan Plateau and Tarim basin increases, which may be caused by glacier melting in the Pamirs and Himalaya. The precipitation contributed little to changes in water storage, but at the basin scale, the precipitation anomalies were very similar to the GRACE and GLDAS data, which can be viewed as an indicator of WSVs.