Total Water Storage Change Research Articles

Using Swarm to Detect Total Water Storage Changes in 26 Global Basins (Taking the Amazon Basin, Volga Basin and Zambezi Basin as Examples)

The Gravity Recovery and Climate Experiment (GRACE) satellite provides time-varying gravity field models that can detect total water storage change (TWSC) from April 2002 to June 2017, and its second-generation satellite, GRACE Follow-On (GRACE-FO), provides models from June 2018, so there is a one year gap. Swarm satellites are equipped with Global Positioning System (GPS) receivers, which can be used to recover the Earth’s time-varying gravitational field. Swarm’s time-varying gravitational field models (from December 2013 to June 2018) were solved by the International Combination Service for Time-variable Gravity Field Solutions (COST-G) and the Astronomical Institute of the Czech Academy of Sciences (ASI). On a timely scale, Swarm has the potential to fill the gap between the two generations of GRACE satellites. In this paper, using 26 global watersheds as the study area, first, we explored the optimal data processing strategy for Swarm and then obtained the Swarm-TWSC of each watershed based on the optimal results. Second, we evaluated Swarm’s accuracy in detecting regional water storage variations, analyzed the reasons for its superior and inferior performance in different regions, and systematically explored its potential in detecting terrestrial water storage changes in land areas. Finally, we constructed the time series of terrestrial water storage changes from 2002 to 2019 by combining GRACE, Swarm, and GRACE-FO for the Amazon, Volga, and Zambezi Basins. The results show that the optimal data processing strategy of Swarm is different from that of GRACE. The optimal results of Swarm-TWSC were explored in 26 watersheds worldwide; its accuracy is related to the area size, runoff volume, total annual mass change, and instantaneous mass change of the watershed itself, among which the latter is the main factor affecting Swarm-TWSC. Knowledge of the Swarm-TWSC of 26 basins constructed in this paper is important to study long-term water storage changes in basins.

An application of GRACE mission datasets for streamflow and baseflow estimation in the Conterminous United States basins

Runoff and baseflow estimates provide critical information on surface and groundwater flow dynamics, water resource availability, and water usage patterns for effective water resource planning and management. Developing a physical-based model at large spatial scales such as continental/global scales require extensive datasets, complex parameterization, and higher computational capacity. Recent advances in satellite datasets related to water cycle process modeling, on the other hand, involve less parameterization and more effective computation over larger spatial and finer temporal scales. Evapotranspiration (ET) is a critical flux in the water cycle that can be estimated with reasonable accuracy from satellite-based information by using complementary relationship (CR) concepts. Similarly, the Gravity Recovery and Climate Experiment (GRACE) mission satellite datasets provide useful estimates of total water storage change over large spatial extents. GRACE datasets have been extensively used in groundwater storage change and groundwater depletion studies. However, the application of GRACE data products for estimating runoff and baseflow is limited. Therefore, in this study, we used a CR approach to estimate ET and a water balance method for estimating runoff and baseflow over the Conterminous United States (CONUS). We evaluated the CR-based water balance model (CR-GRACE) with United States Geological Survey (USGS) runoff datasets based on hydrologic unit code level-2 (HUC-2) basin boundaries. The CR-GRACE model estimated annual runoff values agreed with USGS runoff values with root mean square error (RMSE) and correlation coefficient values of 14.7 mm/month and 0.81, respectively. We also evaluated the CR-GRACE model with two well-known Land Surface Models (LSM), Noah-LSM and Variable Infiltration Capacity LSM (VIC-LSM), from the Global Land Data Assimilation System (GLDAS) database. The comparison results for baseflow estimates from the CR-GRACE model have improved over Noah-LSM and VIC-LSM model estimates with RMSE values of 62.16, 101.00, and 207.32 mm/year, respectively. Furthermore, this study demonstrates the feasibility of using the CR-GRACE model to estimate runoff and baseflows with limited parameterization and datasets.

Attribution of growing season evapotranspiration variability considering snowmelt and vegetation changes in the arid alpine basins

Abstract. Previous studies have successfully applied variance decomposition frameworks based on the Budyko equations to determine the relative contribution of variability in precipitation, potential evapotranspiration (E0), and total water storage changes (ΔS) to evapotranspiration variance (σET2) on different timescales; however, the effects of snowmelt (Qm) and vegetation (M) changes have not been incorporated into this framework in snow-dependent basins. Taking the arid alpine basins in the Qilian Mountains in northwest China as the study area, we extended the Budyko framework to decompose the growing season σET2 into the temporal variance and covariance of rainfall (R), E0, ΔS,Qm, and M. The results indicate that the incorporation of Qm could improve the performance of the Budyko framework on a monthly scale; σET2 was primarily controlled by the R variance with a mean contribution of 63 %, followed by the coupled R and M (24.3 %) and then the coupled R and E0 (14.1 %). The effects of M variance or Qm variance cannot be ignored because they contribute 4.3 % and 1.8 % of σET2, respectively. By contrast, the interaction of some coupled factors adversely affected σET2, and the out-of-phase seasonality between R and Qm had the largest effect (−7.6 %). Our methodology and these findings are helpful for quantitatively assessing and understanding hydrological responses to climate and vegetation changes in snow-dependent regions on a finer timescale.

Estimation of total water storage changes in India

ABSTRACT Long-term droughts significantly impact surface and groundwater resources in India, however, observed changes in major river basins have not been well explored. Here we use Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) at three different time scales (24, 48, and 60 months) to identify long-term droughts in India for the observed record of 1951–2015. Drought characteristics (extent, events, frequency, and intensity) are analyzed for different river basins in India. Increasing trend in the areal extent of droughts is observed in two methods with three time scales in the maximum area (63.66%) in India. We use the data from the Gravity Recovery and Climate Experiment (GRACE) to estimate the changes in the terrestrial water storage (TWS) during the period 2002–2015. We identify that major long-term droughts in India occurred from 1966 to 1969, 1972, 1986–1987, and 2002–2004. The all-India average TWS shows a negative trend from 2002 to 2015 with prominent decline in north Indian river basins and positive trend in south Indian river basins. SPI and SPEI at longer time scales are positively associated with TWS indicating the adverse impacts of droughts on surface and groundwater resources in such a populated region.

Coherent Satellite Monitoring of the Water Cycle Over the Amazon. Part 2: Total Water Storage Change and River Discharge Estimation

AbstractIn companion paper 1, the SAtellite Water Cycle (SAWC) satellite data set integration approach was presented. SAWC accounts for (1) the closure of the water budget at the sub‐basin scale by (2) using upstream/downstream dependencies. Here, the SAWC database is used to reconstruct a missing water component. The total water storage change (dS) can be reconstructed prior to the Gravity Recovery and Climate Experiment (GRACE) period. In terms of seasonal and interannual variations, SAWC provides long‐term dS estimates that are highly consistent with the GRACE estimate (2002–2015). When compared to the extent of inundation, correlation of anomalies reaches 0.6 over sub‐basins presenting large floodplain. A strong relationship (correlation of 0.7) exists between the monthly dS and the El Niño index. The river discharge R can also be estimated using the SAWC approach during the GRACE period. A scheme was developed to estimate R continuously along the river through the indirect interpolation of discharge station measurements by using satellite estimates of dS, precipitation P, and evaporation E. These R estimates along the river were evaluated with 80 independent measurements and had a correlation of 0.86 and a Kling‐Gupta efficiency index of 0.71. A comprehensive monthly river discharge estimate was proposed for the first time across the Amazon basin over 13 years (2002–2015) at a 0.25° resolution. The SAWC framework is a pure observation tool for integrating a large range of satellite estimates, potentially facilitating the assimilation of such data into land surface models.

Downscaling GRACE total water storage change using partial least squares regression

The Gravity Recovery And Climate Experiment (GRACE) satellite mission recorded temporal variations in the Earth’s gravity field, which are then converted to Total Water Storage Change (TWSC) fields representing an anomaly in the water mass stored in all three physical states, on and below the surface of the Earth. GRACE provided a first global observational record of water mass redistribution at spatial scales greater than 63000 km2. This limits their usability in regional hydrological applications. In this study, we implement a statistical downscaling approach that assimilates 0.5° × 0.5° water storage fields from the WaterGAP hydrology model (WGHM), precipitation fields from 3 models, evapotranspiration and runoff from 2 models, with GRACE data to obtain TWSC at a 0.5° × 0.5° grid. The downscaled product exploits dominant common statistical modes between all the hydrological datasets to improve the spatial resolution of GRACE. We also provide open access to scripts that researchers can use to produce downscaled TWSC fields with input observations and models of their own choice.

An Analysis of Total Water Storage Changes Obtained from GRACE FO Observations over the Venezia Islands Area Supported with Additional Data

The Venezia Islands are a very special area from the hydrological point of view due to its water mass changes. Regular floods results in the need for the regular monitoring of water mass changes. For this purpose, a Gravity Recovery and Climate Experiment mission (GRACE) can be used as a source of data. The aim of the paper is to compare the latest results of the new GRACE FO observations. The comparisons were carried out all over Venezia Island using the L3 level, RL06 release data obtained with spherical harmonics degree and order extension of up to 120, by the three most important computational centres: JPL, GFZ, CSR. Results are compared to an average month values of precipitation and evapotranspiration and tide gauge data in the nearby area. Based on the research, no dependence between TWS and evapotranspiration and evapotranspiration change were found.

Drought Events over the Amazon River Basin (1993–2019) as Detected by the Climate-Driven Total Water Storage Change

The Gravity Recovery and Climate Experiment (GRACE) mission has measured total water storage change (TWSC) and interpreted drought patterns in an unparalleled way since 2002. Nevertheless, there are few sources that can be used to understand drought patterns prior to the GRACE era. In this study, we extended the gridded GRACE TWSC to 1993 by combining principal component analysis (PCA), least square (LS) fitting, and multiple linear regression (MLR) methods using climate variables as input drivers. We used the extended (climate-driven) TWSC to interpret drought patterns (1993–2019) over the Amazon basin. Results showed that, in the Amazon area with the resolution of 0.5°, GRACE, GRACE follow on, and Swarm had correlation coefficients of 0.95, 0.92, and 0.77 compared with climate-driven TWSCS, respectively. The drought patterns assessed by the climate-driven TWSC were consistent with those interpreted by the Palmer Drought Severity Index and GRACE TWSC. We also found that the 1998 and 2016 drought events in the Amazon, both induced by strong El Niño events, showed similar drought patterns. This study provides a new perspective for interpreting long-term drought patterns prior to the GRACE period.

Twentieth and Twenty-First Century Water Storage Changes in the Nile River Basin from GRACE/GRACE-FO and Modeling

This research assesses the changes in total water storage (TWS) during the twentieth century and future projections in the Nile River Basin (NRB) via TWSA (TWS anomalies) records from GRACE (Gravity Recovery and Climate Experiment), GRACE-FO (Follow-On), data-driven-reanalysis TWSA and a land surface model (LSM), in association with precipitation, temperature records, and standard drought indicators. The analytical approach incorporates the development of 100+ yearlong TWSA records using a probabilistic conditional distribution fitting approach by the GAMLSS (generalized additive model for location, scale, and shape) model. The model performance was tested using standard indicators including coevolution plots, the Nash–Sutcliffe coefficient, cumulative density function, standardized residuals, and uncertainty bounds. All model evaluation results are satisfactory to excellent. The drought and flooding severity/magnitude, duration, and recurrence frequencies were assessed during the studied period. The results showed, (1) The NRB between 2002 to 2020 has witnessed a substantial transition to wetter conditions. Specifically, during the wet season, the NRB received between ~50 Gt./yr. to ~300 Gt./yr. compared to ~30 Gt./yr. to ~70 Gt./yr. of water loss during the dry season. (2) The TWSA reanalysis records between 1901 to 2002 revealed that the NRB had experienced a positive increase in TWS of ~17% during the wet season. Moreover, the TWS storage had witnessed a recovery of ~28% during the dry season. (3) The projected TWSA between 2021 to 2050 unveiled a positive increase in the TWS during the rainy season. While during the dry season, the water storage showed insubstantial TWS changes. Despite these projections, the future storage suggested a reduction between 10 to 30% in TWS. The analysis of drought and flooding frequencies between 1901 to 2050 revealed that the NRB has ~64 dry-years compared to ~86 wet-years. The exceedance probabilities for the normal conditions are between 44 to 52%, relative to a 4% chance of extreme events. The recurrence interval of the normal to moderate wet or dry conditions is ~6 years. These TWSA trajectories call for further water resources planning in the region, especially during flood seasons. This research contributes to the ongoing efforts to improve the TWSA assessment and its associated dynamics for transboundary river basins.

Groundwater Depletion Signals in the Beqaa Plain, Lebanon: Evidence from GRACE and Sentinel-1 Data

Regions with high productivity of agriculture, such as the Beqaa Plain, Lebanon, often rely on groundwater supplies for irrigation demand. Recent reports have indicated that groundwater consumption in this region has been unsustainable, and quantifying rates of groundwater depletion has remained a challenge. Here, we utilize 15 years of data (June 2002–April 2017) from the Gravity Recovery and Climate Experiment (GRACE) satellite mission to show Total Water Storage (TWS) changes in Lebanon’s Beqaa Plain. We then obtain complimentary information on various hydrologic cycle variables, such as soil moisture storage, snow water equivalent, and canopy water storage from the Global Land Data Assimilation System (GLDAS) model, and surface water data from the largest body of water in this region, the Qaraaoun Reservoir, to disentangle the TWS signal and calculate groundwater storage changes. After combining the information from the remaining hydrologic cycle variables, we determine that the majority of the losses in TWS are due to groundwater depletion in the Beqaa Plain. Results show that the rate of groundwater storage change in the West Beqaa is nearly +0.08 cm/year, in the Rashaya District is −0.01 cm/year, and in the Zahle District the level of depletion is roughly −1.10 cm/year. Results are confirmed using Sentinel-1 interferometric synthetic aperture radar (InSAR) data, which provide high-precision measurements of land subsidence changes caused by intense groundwater usage. Furthermore, data from local monitoring wells are utilized to further showcase the significant drop in groundwater level that is occurring through much of the region. For monitoring groundwater storage changes, our recommendation is to combine various data sources, and in areas where groundwater measurements are lacking, we especially recommend the use of data from remote sensing.

Re-assessing global water storage trends from GRACE time series

Monitoring changes in freshwater availability is critical for human society and sustainable economic development. To identify regions experiencing secular change in their water resources, many studies compute linear trends in the total water storage (TWS) anomaly derived from the Gravity Recovery and Climate Experiment (GRACE) mission data. Such analyses suggest that several major water systems are under stress (Rodell et al 2009 Nature 460 999–1002; Long et al 2013 Geophys. Res. Lett. 40 3395–401; Richey et al 2015 Water Resour. Res. 51 5217–38; Voss et al 2013 Water Resour. Res. 49 904–14; Famiglietti 2014 Nat. Clim. Change. 4 945–8; Rodell et al 2018 Nature 557 651–9). TWS varies in space and time due to low frequency natural variability, anthropogenic intervention, and climate-change (Hamlington et al 2017 Sci. Rep. 7 995; Nerem et al 2018 Proc. Natl Acad. Sci.). Therefore, linear trends from a short time series can only be interpreted in a meaningful way after accounting for natural spatiotemporal variability in TWS (Paolo et al 2015 Science 348 327–31; Edward 2012 Geophys. Res. Lett. 39 L01702). In this study, we first show that GRACE TWS trends from a short time series cannot determine conclusively if an observed change is unprecedented or severe. To address this limitation, we develop a novel metric, trend to variability ratio (TVR), that assesses the severity of TWS trends observed by GRACE from 2003 to 2015 relative to the multi-decadal climate-driven variability. We demonstrate that the TVR combined with the trend provides a more informative and complete assessment of water storage change. We show that similar trends imply markedly different severity of TWS change, depending on location. Currently more than 3.2 billion people are living in regions facing severe water storage depletion w.r.t. past decades. Furthermore, nearly 36% of hydrological catchments losing water in the last decade have suffered from unprecedented loss. Inferences from this study can better inform water resource management.

Using GRACE and hydrological data to estimate changes of evapotranspiration in the Three Gorges Reservoir

The Three Gorges Project (TGP) has driven changes in water storage, which directly affected the migration and redistribution of land water in the Yangtze River Basin. As a result, a new water balance is established, regional or local evapotranspiration fluctuates, and it may change the climate and weaken geological stability. In this study, we use data from GRACE gravity satellites, hydrological models, remote sensing, and in-situ observations to monitor the spatiotemporal evolution of terrestrial water changes and in the Three Gorges Reservoir (TGR) area, and then to estimate the evapotranspiration induced by water storage fluctuations in the TGR. We calculated the downscaled GRACE results based on the Land Surface Models (LSMs) and scale factor method, which can improve the signal amplitude and spatial differences in the study area and its surrounding areas. Combining the scaled GRACE-derived total water storage changes, precipitation and surface runoff, our estimated evapotranspiration (ET) in the TGR area is highly consistent with the MOD16 global ET results from 2002 to 2016. Seasonal ET changes are mainly driven by climate and rainfall, but GRACE-derived ET shown that the fluctuations of evapotranspiration are also affected by artificial water storage directly, e.g., in the main three water impounding stages (2003, 2006, and 2008), the elevation of the water level caused an abnormal increase in regional ET, but during the water drainage period (spring and autumn), high precipitation did not have a great impact on ET. Our results show that the coverage area of the reservoir area and the short-term fluctuations of the reservoir capacity are the main factors determining the ET in the TGR area.

Refined estimation of lake water level and storage changes on the Tibetan Plateau from ICESat/ICESat-2

Lakes on the Tibetan Plateau (TP) are sensitive indicators of climate change due to minimal disturbance by human activities. Estimations of lake water levels and storage variations on the TP are essential in evaluating water resources and modeling hydrologic processes in high-altitude endorheic basins. By integrating the ICESat/ICESat-2, Global Surface Water dataset, and HydroLAKES dataset, this study puts forward plateau-scale research of lake level changes and further estimates water storage changes on the TP during 2003–2019. The number of lakes observed by ICESat-2 is nearly three times as large as that by ICESat. There are 242 lakes over 1 km2 that can be observed by combining both ICESat and ICESat-2. These lakes with available data present a mean water-level change rate of 0.20 ± 0.04 m/yr in 2003–2019. A total water storage change of 11.51 ± 2.26 Gt/yr for all lakes on the TP is estimated by combining the two-generation spaceborne laser altimetry missions. Besides, water-level change rates of the inner TP lakes during the ICESat era (2003–2009) and joint mission period (2003–2019) are compared to analyze the spatio-temporal variation characteristics of these lakes. The lakes of the inner TP show average rates of water-level change in two periods are 0.20 ± 0.05 m/yr and 0.26 ± 0.03 m/yr, respectively. The examination of water vapor transport over the inner TP indicates that the accelerated water-level rises in the northern TP, yet deceleration in the southern part may be tightly related to the stronger water vapor transport in the north.

Predicting Changes in Spatiotemporal Groundwater Storage Through the Integration of Multi-Satellite Data and Deep Learning Models

Continuous monitoring and accurate spatiotemporal groundwater storage change predictions can help support sustainable development and efficient groundwater resources management. In this study, remote sensing-based data were used to develop two deep learning predictive models, long short term memory (LSTM), and convolutional neural network-LSTM (CNN-LSTM) models. The input variables were terrestrial water storage anomaly (TWSA) recorded by the Gravity Recovery and Climate Experiment (GRACE) satellites and the GRACE-Follow-On (GRACE-FO) mission, precipitation measured by the Tropical Rainfall Measuring Mission (TRMM) satellite, average temperature and soil moisture obtained from the Global Land Data Assimilation System (GLDAS), and normalized difference vegetation index (NDVI) and modified normalized difference water index (MNDWI) acquired from the Landsat 5 and 8 satellites. A comparison of groundwater storage change predictions by the two deep learning predictive models with in situ measurements of the National Groundwater Monitoring Network (NGMN) in South Korea showed that the CNN-LSTM model was more accurate [root mean square error (RMSE) = 44.92 mm/month, correlation coefficient (r) = 0.70, index of agreement (IOA) = 0.81] than the LSTM model [RMSE = 47.44 mm/month, r = 0.62, and IOA = 0.77]. Furthermore, a comparison of the performance of the two models for three specific regions showed that the CNN-LSTM model captured spatiotemporal variations in groundwater storage better. Additionally, a comparison of cumulative change trends in groundwater storage with the NDVI, MNDWI, and TWSA data showed that changes in land cover and total water storage affect groundwater storage. These comparison results demonstrate that the use of parameters recorded by multiple satellites as training data for deep learning models can play an important role in the model’s performance.

Spatiotemporal Analysis of Hydrological Variations and Their Impacts on Vegetation in Semiarid Areas from Multiple Satellite Data

Understanding the spatiotemporal characteristics of hydrological components and their impacts on vegetation are critical for comprehending hydrological, climatological, and ecological processes under environmental change and solving future water management challenges. Innovative methods need to be developed in semiarid areas to analyze the special hydrological factors in the water resource systems of these areas. Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) were applied with the normalized difference vegetation index (NDVI) data in this paper to analyze spatiotemporal changes of hydrological factors in the Xiliaohe River Basin (XRB). The results showed that precipitation (P), evapotranspiration (ET) and temperature (T) had similar seasonal change patterns at rates of 0.05 cm/yr., 0.01 cm/yr. and −0.05 °C/yr., respectively. Total water storage change (TWSC) was consistent with the change trend of soil moisture change (SMC) and showed a fluctuating trend. Groundwater change (GWC) showed a decreasing trend at a rate of −0.43 cm/yr. P and ET had a greater impact on GLDAS data (R = 0.634, P < 0.05 and R = 0.686, P < 0.01, respectively) than on other factors. GWC was more sensitive to changes in T (R = 0.570, P < 0.05). Furthermore, a lag period of 0 to 1 months was observed for the effects of P and ET on TWSC and GLDAS. NDVI showed an upward trend at a rate of 0.001 yr−1 between 2002 and 2014. A spatial distribution of NDVI was heterogeneous in the study area. ET, GLDAS and GWC in growing season limited vegetation growth and were more important than other factors in XRB. The results may contribute to an understanding of the relationships between the hydrological cycle and climate change and provide scientific support for local environmental management.

Observing Snow Cover and Water Resource Changes in the High Mountain Asia Region in Comparison with Global Mountain Trends over 2000–2018

The quantification of snow cover changes and of the related water resources in mountain areas has a key role for understanding the impact on several sectors such as ecosystem services, tourism and energy production. By using NASA-Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2000 to 2018, this study analyzes changes in snow cover in the High Mountain Asia region and compares them with global mountain areas. Globally, snow cover extent and duration are declining with significant trends in around 78% of mountain areas, and the High Mountain Asia region follows similar trends in around 86% of the areas. As an example, Shaluli Shan area in China shows significant negative trends for both snow cover extent and duration, with −11.4% (confidence interval: −17.7%, −5.5%) and −47.3 days (confidence interval: −70.4 days, −24.4 days) at elevations >5500 m a.s.l. respectively. In spring, an earlier snowmelt of −13.5 days (confidence interval: −24.3 days, −2.0 days) in 4000–5500 m a.s.l. is detected. On the other side, Tien Shan area shows an earlier snow onset of −28.8 days (confidence interval: −44.3 days, −8.2 days) between 2500 and 4000 m a.s.l., governed by decreasing temperature and increasing snowfall. In the current analysis, the Tibetan Plateau shows no significant changes. Regarding water resources, by using Gravity Recovery and Climate Experiment (GRACE) data it was found that around 50% of areas in the High Mountain Asia region and 30% at global level are suffering from significant negative temporal trends of total water storage (including groundwater, soil moisture, surface water, snow, and ice) in the period 2002–2015. In the High Mountain Asia region, this negative trend involves around 54% of the areas during spring period, while at a global level this percentage lies between 25% and 30% for all seasons. Positive trends for water storage are detected in a maximum 10% of the areas in High Mountain Asia region and in around 20% of the areas at global level. Overall snow mass changes determine a significant contribution to the total water storage changes up to 30% of the areas in winter and spring time over 2002–2015.

Estimation of component contributions to total terrestrial water storage change in the Yangtze river basin

Terrestrial water storage (TWS) is a key variable in global and regional hydrological cycles. In this study, the TWS changes in the Yangtze River Basin (YRB) were derived using the Lagrange multiplier method (LMM) from Gravity Recovery and Climate Experiment (GRACE) data. To assess TWS changes from LMM, different GRACE solutions, different hydrological models, and in situ data were used for validation. Results show that TWS changes from LMM in YRB has the best performance with the correlation coefficients of 0.80 and root mean square error of 1.48 cm in comparison with in situ data. The trend of TWS changes over the YRB increased by 10.39 ± 1.27 Gt yr−1 during the 2003–2015 period. Moreover, TWS change is disintegrated into the individual contributions of hydrological components (i.e., glaciers, surface water, soil moisture, and groundwater) from satellite data, hydrologic models, and in situ data. The estimated changes in individual TWS components in the YRB show that (1) the contribution of glaciers, surface water, soil moisture, and groundwater to total TWS changes is 15%, 12%, 25% and 48%, respectively; (2) Geladandong glacier melt from CryoSat-2/ICESat data has a critical effect on TWS changes with a correlation coefficients of −0.51; (3) the Three Gorges Reservoir Impoundment has a minimal effect on surface water changes (mainly lake water storage), but it has a substantial effect on groundwater storage (GWS), (4) the Poyang and Doting Lake water storage changes are mainly caused by climate change, (5) soil moisture storage change is mainly influenced by surface water, (6) human-induced GWS changes accounted for approximately half of the total GWS. The results of this study can provide valuable information for decision-making in water resources management.

Dynamic simulation water storage of different parts in peach tree under drought stress

The simulation of stored water in various parts of fruit trees under drought stress could be described in detail by the application of mathematical modeling. Under drought stress, tissue water storage provides information on water status, water demand pattern and water regulation capacity by each part of the tree. In the present study, the potted peach (Prunus persica (L.) Batsch) tree was used as the test material, and the water operation model of each part of the tree was constructed based on the resistance-capacitance (RC) model. By analyzing transpiration rate, water potential and stored water during a drying cycle, we found that the water potential in different organs of the potted peach trees were all decreased with the increase in transpiration rate but showing an opposite trend to the transpiration. In the daytime, the stored water flowed out from various tissues, and at night, the water runs into the tissues via root absorption. With the worsening of drought, the regulation capacity of tissue water storage kept decreasing. There was a big difference in the trend of water storage change among different parts. The difference also existed in the amount of tissue stored water entering the transpiration and the amount of water absorption at night among different parts. With an adequate soil water supply, the water storage change of trunk (including the main root) accounted for 42.3% of the total water storage change, followed by 20.6%, 15.4%, 15.3% and 6.7% by the root system, fruits, branches and leaves, respectively. This difference was a result of cooperative actions of both water storage resistance and capacity. Estimated the total of absorbed water of tissue at night were approximately 10% of the daily total transpiration, and it could go above 20% for severely dry soil.

Comparative analysis of snowfall accumulation over Antarctica in light of Ice discharge and gravity observations from space

The remote and cold Antarctic continent presents unique challenges to quantify precipitation rates from space and in situ observations. This has resulted in large uncertainties in current estimates. In this study, we quantify annual precipitation rates over seven Antarctic basins using a novel mass budget (MB) approach, by building on the recent Landsat based estimate of ice discharge and changes in total water storage from GRACE. The MB precipitation rates are compared with those from CloudSat, GPCP, the Arthern precipitation climatology, the GPM constellation sensors, a few popular reanalysis products, and a regional climate model for two periods: 2007–2010 and 2013–2015. The new estimates are bounded by CloudSat precipitation rates with and without adjustment for the unmeasured near surface precipitation. GPM products significantly underestimate Antarctic precipitation rate, but capture spatial variability that is valuable for bias-adjustment. We find variable performance between products at basin scale, suggesting that an in-depth regional study of precipitation rates is necessary.

Assessing seasonal and interannual water storage variations in Taiwan using geodetic and hydrological data

We systematically investigate the spatiotemporal water storage changes in Taiwan using geodetic (GNSS and GRACE) and hydrological (precipitation, GLDAS and LSDM assimilation models, and in-situ groundwater level) datasets. We use GNSS-observed vertical deformation to estimate water storage changes based on elastic loading theory and weighted least-squares inversion, correcting for contributions from global loads using GRACE. The mean annual water-thickness change inferred from GNSS across Taiwan is 0.53 ± 0.17 m and the largest seasonal change of up to 0.91 m is estimated in southwest Taiwan. Comparison of the geodetic and hydrological data shows that the spatial pattern of annual water storage change estimated from GNSS, GLDAS, and precipitation data are generally consistent, indicating significant seasonal water-load fluctuations in Taiwan. However, the GRACE solution significantly underestimates the amplitude of water mass change in Taiwan due to leakage effect, but temporally correlates well with GNSS estimates. Hydrological assimilation model GLDAS, dominated by shallow soil moisture variations, predicts that the average seasonal variation of water thickness is only about 17% of GNSS estimates. This value is about half of the mean annual LSDM water storage change of 0.18 m including an estimate of both soil moisture and surface water. The discrepancy suggests that the contribution of groundwater is substantial and the total water storage change in the hydrological assimilation model is underestimated in Taiwan. The spatiotemporal distributions derived using independent component analysis (ICA) are generally consistent between the geodetic and hydrological data. However, comparisons of seasonal amplitudes and phases between all data pairs reveal different response times to precipitation, reflecting the complex nature of transient water storage due to variable rainfall patterns, infiltration rate, soil saturation, and runoff. The peak rainfall occurs in June-July, which is one-to-two months before the peak GNSS subsidence. Water storage of the GLDAS model also reaches its maximum in August, suggesting the water storage is controlled by the infiltration rate and capacity and the total water recharge from rainfall is generally larger than discharge in the summer. The highest groundwater levels lag one and two months behind the peak GNSS subsidence in western and eastern Taiwan, respectively, indicating a higher infiltration rate in western Taiwan.