Gravity Recovery And Climate Experiment Satellite Research Articles

The Influence of Climate Change on Forest Fires in Yunnan Province, Southwest China Detected by GRACE Satellites

Yunnan province in China has rich forest resources but high forest fire frequency. Therefore, a better understanding of the relationship between climate change and forest fires in this region is important for forest fire prevention. This study used the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage change (TWSC) data to analyze the influence of climate change on forest fires in the region during 2003–2016. To improve the accuracy and reliability of GRACE TWSC data, we used the generalized three-cornered hat (GTCH) and the least square method to fuse TWSC data from six GRACE solutions. The spatiotemporal variation of forest fires during 2003–2016 was investigated using burned area data. Then, the relationship between burned area and hydrological and climatic factors was analyzed. The results indicate that more than 90% of burned areas are located in northwestern and southern Yunnan (NW and S). On the seasonal scale, forest fires are mainly concentrated in January–April (dry season) and the burned area is negatively correlated with precipitation (correlation coefficient r = −0.83 (NW) and −0.51 (S)), relative humidity (r = −0.79 (NW) and −0.92 (S)), GRACE TWSC (r = −0.57 (NW) and −0.73 (S)) and evapotranspiration (r = −0.90 (NW) and −0.35 (S)). However, the burned area has no significant correlations with the above four factors on the interannual scale. The composite analysis suggests that the extreme climate affects precipitation, evapotranspiration and TWSC in this region, thereby changing water storage of the air in this region, leading to the formation of an environment prone to forest fires. Such conditions have led to an increase in the burned area in the above region. We also found that the difference between TWSC in high- and low-fire years is much greater than the precipitation in the same period. The above results show that GRACE satellites can detect the influence of climate change on forest fires in Yunnan province.

Geodetic observations for constraining mantle processes in Antarctica

Abstract Geodynamic processes in Antarctica such as glacial isostatic adjustment (GIA) and post-seismic deformation are measured by geodetic observations such as global navigation satellite systems (GNSS) and satellite gravimetry. GNSS measurements have comprised both continuous measurements and episodic measurements since the mid-1990s. The estimated velocities typically reach an accuracy of 1 mm a −1 for horizontal velocities and 2 mm a −1 for vertical velocities. However, the elastic deformation due to present-day ice-load change needs to be considered accordingly. Space gravimetry derives mass changes from small variations in the inter-satellite distance of a pair of satellites, starting with the GRACE (Gravity Recovery and Climate Experiment) satellite mission in 2002 and continuing with the GRACE-FO (GRACE Follow-On) mission launched in 2018. The spatial resolution of the measurements is low (about 300 km) but the measurement error is homogeneous across Antarctica. The estimated trends contain signals from ice-mass change, and local and global GIA signals. To combine the strengths of the individual datasets, statistical combinations of GNSS, GRACE and satellite altimetry data have been developed. These combinations rely on realistic error estimates and assumptions of snow density. Nevertheless, they capture signals that are missing from geodynamic forward models such as the large uplift in the Amundsen Sea sector caused by a low-viscous response to century-scale ice-mass changes.

Regional Crustal Vertical Deformation Driven by Terrestrial Water Load Depending on CORS Network and Environmental Loading Data: A Case Study of Southeast Zhejiang.

Monitoring regional terrestrial water load deformation is of great significance to the dynamic maintenance and hydrodynamic study of the regional benchmark framework. In view of the lack of a spatial interpolation method based on the GNSS (Global Navigation Satellite System) elevation time series for obtaining terrestrial water load deformation information, this paper proposes to employ a CORS (Continuously Operating Reference Stations) network combined with environmental loading data, such as ECMWF (European Centre for Medium-Range Weather Forecasts) atmospheric data, the GLDAS (Global Land Data Assimilation System) hydrological model, and MSLA (Mean Sea Level Anomaly) data. Based on the load deformation theory and spherical harmonic analysis method, we took 38 CORS stations in southeast Zhejiang province as an example and comprehensively determined the vertical deformation of the crust as caused by regional terrestrial water load changes from January 2015 to December 2017, and then compared these data with the GRACE (Gravity Recovery and Climate Experiment) satellite. The results show that the vertical deformation value of the terrestrial water load in southeast Zhejiang, as monitored by the CORS network, can reach a centimeter, and the amplitude changes from −1.8 cm to 2.4 cm. The seasonal change is obvious, and the spatial distribution takes a ladder form from inland to coastal regions. The surface vertical deformation caused by groundwater load changes in the east–west–south–north–central sub-regions show obvious fluctuations from 2015 to 2017, and the trends of the five sub-regions are consistent. The amplitude of surface vertical deformation caused by groundwater load change in the west is higher than that in the east. We tested the use of GRACE for the verification of CORS network monitoring results and found a relatively consistent temporal distribution between both data sets after phase delay correction on GRACE, except for in three months—November in 2015, and January and February in 2016. The results show that the comprehensive solution based on the CORS network can effectively improve the monitoring of crustal vertical deformation during regional terrestrial water load change.

Comparison of decadal water storage trends from common GRACE releases (RL05, RL06) using spatial diagnostics and a modified triple collocation approach

GRACE (Gravity Recovery and Climate Experiment) and GRACE-FO (Follow-On) satellites have provided unique insights into the evolution of Terrestrial Water Storage (TWS) in space and time. Despite such advancements, various GRACE solutions produced by different data centers display uneven spatial attributes with varying associated uncertainties. Via spatial diagnostics tools and a modified triple collocation (MTC) approach, this research evaluates the TWS (terrestrial water storage) trend estimations “on the grid-scale” from 11 gridded GRACE products of RL05 and RL06 releases between 2002 and 2017. Distinct from classic TCA (triple collocation analysis), the MTC employs a GWR (geographically weighted regression) scaling scheme with distinctive spatial coefficients. The spatial diagnostics analyses identified different autocorrelation patterns, clustering tendencies of hot (positive) and cold (negative) spots agglomeration at varying spatial width, and unique frequency distributions. The results indicated that within a 10-degree spatial radius the SHs (Spherical Harmonics) of RL05 and RL06 are highly autocorrelated compared to the mascons (mass concentration blocks) solutions. The spatial clustering results revealed that many solutions agreed on the overall directions and distribution of the hot and cold spots. The clustering among mascon products, however, reflected more localized mass anomalies. At the scale of drainage basins, the trend magnitude, as well as their associated uncertainties appeared to be driven by the occurrence of spatial clusters within the basin area. The MTC results showed that the uncertainty patterns follow the same spatial extent within each cluster. The MTC analysis underscored the added benefits of cluster analysis and the GWR scaling over the classic OLS approach.

Monitoring land subsidence induced by groundwater change using satellite gravimetry and radar interferometry measurements. Case study: Surabaya city, Indonesia

Land subsidence is considered a potential hazard often occurring in densely populated urban areas due to increasing freshwater demands from groundwater pumping. The Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry combined with Sentinel 1 interferometric satellite radar measurement has provided the possibility to monitor land subsidence induced by groundwater change. This study monitored land subsidence induced by groundwater change through satellite observations over Surabaya City, Indonesia, from 2014 to 2019. Persistent Scattered InSAR (PSInSAR) measurement was used to monitor land subsidence by using 114 SLC pairs. As for the groundwater perspective, Global Land Data Assimilation System (GLDAS v.2.2), which contains the Groundwater Storage Anomaly (GWS) derived from GRACE satellite observation, was used to understand groundwater’s spatial and temporal variation. The results show a satisfactory agreement of satellite radar measurement with ground measurement (R = 0.96, RMSE = 4.92cm), while satellite gravimetry measurement showed reasonably good agreement with radar measurement as well (R = 0.25). Regarding the magnitude and occurrence of land subsidence over Surabaya City, the result shows that, over the past 5 years, the southern part of the city had the highest subsidence ranging from -10 mm/year to -40 mm/year. Therefore, the results conclude the capability of both satellite gravimetry and radar measurements to monitor land subsidence over an urban area. Thus, this information could be considered as an important decision-making process for disaster management purposes.

Effects of New Level-1B Data on GRACE Temporal Gravity Field Models and Precise Orbit Determination Solutions

The quality of Gravity Recovery and Climate Experiment (GRACE) observation is the prerequisite for obtaining the high-precision GRACE temporal gravity field model. To study the influence of new-generation GRACE Level-1B Release 03 (RL03) data and the new atmosphere and ocean de-aliasing (AOD1B) products on recovering temporal gravity field models and precise orbit determination (POD) solutions, we combined the global positioning system and K-band ranging-rate (KBRR) observations of GRACE satellites to estimate the effect of different data types on these solutions. The POD and monthly gravity field solutions are obtained from 2005 to 2010 by SHORDE software developed by the Shanghai Astronomical Observatory. The post-fit residuals of the KBRR data were decreased by approximately 10%, the precision of three-direction positions of the GRACE POD was improved by approximately 5%, and the signal-to-noise ratio of the monthly gravity field model was enhanced. The improvements in the new release of monthly gravity field model and POD solutions can be attributed to the enhanced Level-1B KBRR data and the AOD1B model. These improvements were primarily due to the enhanced of KBRR data; the effect of the AOD1B model was not significant. The results also showed that KBRR data slightly improve the satellite orbit precision, and obviously enhance the precision of the gravity field model.

Support vector machine and data assimilation framework for Groundwater Level Forecasting using GRACE satellite data

Groundwater (GW) is a vital resource for human consumption, agriculture, environmental sustainability, and socio-economic development in different parts of the world. Besides, GW plays a crucial role in minimizing the impacts during extreme drought events. However, there is a fast decline in groundwater resources due to increased sectoral water demand compounded by reduced rainfall and rising temperature. Therefore, it is essential to develop appropriate tools to study spatio-temporal dynamics and predict groundwater levels to improve water resource management, especially during drought events. In this study, we applied machine learning algorithms based on support vector machines (SVMs), combined with data assimilation (DA) technique to predict the change in groundwater levels (CGWLs) at 1 to 3-month time scales for 46 GW wells located at the northeast United States. The in-situ climate variables and the Gravity Recovery and Climate Experiment (GRACE) mission-informed groundwater anomalies data (GWA) are used to develop the models. The results suggest that SVMs (SVM-DA) models forced with limited climate variables (i.e., precipitation, solar radiation, air temperature, infrared surface temperature) can forecast the CGWLs up to 3-month lead times at most of the locations. The addition of GRACE data as a forcing variable can improve the performance of SVMs at most of the stations, where a strong relationship exists between the CGWLs and the GWA. The SVM-DA model comparatively performed better than SVMs at most of the stations.

Appraisal of Remote Sensing Technology for Groundwater Resource Management Perspective in Indus Basin

The dynamic nature and unsustainable exploitation of groundwater aquifers pose a range of management challenges. The accurate basin-wide hydrological assessment is very critical for the quantification of abstraction rates, spatial patterns of groundwater usage, recharge and discharge processes, and identification of critical areas having groundwater mining. This study provides the appraisal of remote sensing technology in comparison with traditionally prevailing tools and methodologies and introduces the practical use of remote sensing technology to bridge the data gaps. It demonstrates the example of Gravity Recovery and Climate Experiment (GRACE) satellite inferred Total Water Storage (TWS) information to quantify the behavior of the Upper Indus Plain Aquifer. The spatio-temporal changes in aquifer usage are investigated particularly for irrigation and anthropogenic purposes in general. The GRACE satellite is effective in capturing the water balance components. The basin-wide monthly scale groundwater storage monitoring is a big opportunity for groundwater managers and policymakers. The remote sensing integrated algorithms are useful tools to provide timely and valuable information on aquifer behavior. Such tools are potentially helpful to support the implementation of groundwater management strategies, especially in the developing world where data scarcity is a major challenge. Groundwater resources have not grown to meet the growing demands of the population, consequently, overexploitation of groundwater resources has occurred in these decades, leading to groundwater decline. However, future developments in the field of space technology are envisioned to overcome the currently faced spatio-temporal challenges.

Effects of climate and irrigation on GRACE-based estimates of water storage changes in major US aquifers

Understanding climate and human impacts on water storage is critical for sustainable water-resources management. Here we assessed climate and human drivers of total water storage (TWS) variability from Gravity Recovery and Climate Experiment (GRACE) satellites compared with drought severity and irrigation water use in 14 major aquifers in the United States. Results show that long-term variability in TWS tracked by GRACE satellites is dominated by interannual variability in most of the 14 major US aquifers. Low TWS trends in the humid eastern U.S. are linked to low drought intensity. Although irrigation pumpage in the humid Mississippi Embayment aquifer exceeded that in the semi-arid California Central Valley, a surprising lack of TWS depletion in the Mississippi Embayment aquifer is attributed to extensive streamflow capture. Marked storage depletion in the semi-arid southwestern Central Valley and south-central High Plains totaled ∼90 km3, about three times greater than the capacity of Lake Mead, the largest U.S. reservoir. Depletion in the Central Valley was driven by long-term droughts (⩽5 yr) amplified by switching from mostly surface water to groundwater irrigation. Low or slightly rising TWS trends in the northwestern (Columbia and Snake Basins) US are attributed to dampening drought impacts by mostly surface water irrigation. GRACE satellite data highlight synergies between climate and irrigation, resulting in little impact on TWS in the humid east, amplified TWS depletion in the semi-arid southwest and southcentral US, and dampened TWS deletion in the northwest and north central US Sustainable groundwater management benefits from conjunctive use of surface water and groundwater, inefficient surface water irrigation promoting groundwater recharge, efficient groundwater irrigation minimizing depletion, and increasing managed aquifer recharge. This study has important implications for sustainable water development in many regions globally.

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.

Identifying and separating climate- and human-driven water storage anomalies using GRACE satellite data

Hydrological changes, which are affected by climate variability and human activities, pose challenges to the sustainable management and conservation of water resources. As such, it is important to improve our understanding of how climate and anthropogenic activities impact land water storage. Previous studies using Gravity Recovery and Climate Experiment (GRACE) satellite data have struggled to effectively separate the impacts of climate variability and human activities on water storage. Here, we propose a statistical model that simulates natural dynamically driven changes in water storage to reconstruct climate-driven water storage anomalies at basin scales and discuss the possibility of differentiating climate- and human-driven water storage anomalies. The prediction and reconstruction performance of this method shows optimal robustness. Compared with water storage anomalies calculated using land surface models, our reconstruction results are closer to GRACE observations, especially in humid basins with few human interventions. Mismatches are found in basins with intensive human interventions. Isolated human-driven water storage anomalies in four representative basins correlate well with anthropogenic water usage in each basin. The results of this study provide a method to effectively separate and monitor climate- and human-driven water storage anomalies, thereby providing information for better protection and utilization of water resources.

Decreasing water resources in Southeastern U.S. as observed by the GRACE satellites

AbstractChanging water quantities and location can be estimated using the Gravity Recovery and Climate Experiment (GRACE) satellites. By measuring differences in the Earth's gravity, the satellites provide monthly data on regional changes in the Earth's mass resulting from the movement of water. Studying the Southeast U.S., using the full record of the original GRACE satellites (2002–2016), a significant trend of declining water quantities appears in west-central Alabama, extending into eastern Mississippi. These findings confirm earlier research which indicates declining streamflow levels but develops this research further by estimating the amount lost as 11.6 km3. Considering the different terrestrial water storages by analyzing data from the National Climate Assessment – Land Data Assimilation System Noah 3.3 Version 2 (NCA-LDAS) indicates that the majority of this loss can be attributed to groundwater losses, a finding that is further confirmed by well records throughout the region.

Effects of Extreme Climate Change in 2016 and Its Correlation with EWH Variability in Indonesia

Gravity Recovery and Climate Experiment (GRACE) satellite mission, which is dedicated specifically to the Earth’s gravity change monitoring, also can be used to observe water mass variations. GRACE data is provided through temporal mass variations, primarily water volume variations such as surface water and groundwater changes. GRACE satellite data can be obtained in monthly spherical harmonics coefficients depicting the change of surface mass density. This study investigates the effects of extreme climate change during 2016 in Indonesia by utilizing the GRACE data. Here, the surface mass density change is converted into equivalent water height (EWH). Since the amount of rainfall in Indonesia indirectly dominates the water mass variability, the EWH is then compared with the precipitation over the same period of this study. The result showed that during 2016, the highest and lowest EWH was presented in August with the highest value of 23.527 cm over the Aceh region and the lowest EWH of 0.665 cm over the Nusa Tenggara region. The most increased precipitation during 2016 is in the Kalimantan region, with a value of 0.610 mm/hr in February. The lowest one is in the Nusa Tenggara region, with a value of 0.009 mm/hr in August. For comparison with the EWH, the precipitation rates were converted into monthly precipitation in cm. The correlations between EWH and precipitations variabilities showed different results in different study zones, with the highest correlation in zone 6, which covers North Kalimantan. In comparison, the lowest correlation occurred in zone 25, which covers Yamdena and Tragan islands_ area in the Moluccas

A novel method for cold-region streamflow hydrograph separation using GRACE satellite observations

Abstract. Streamflow hydrograph analysis has long been used for separating streamflow into baseflow and surface runoff components, providing critical information for studies in hydrology, climate and water resources. Issues with established methods include the lack of physics and arbitrary choice of separation parameters, problems in identifying snowmelt runoff, and limitations on watershed size and hydrogeological conditions. In this study, a Gravity Recovery and Climate Experiment (GRACE)-based model was developed to address these weaknesses and improve hydrograph separation. The model is physically based and requires no arbitrary choice of parameters. The new model was compared with six hydrograph separation methods provided with the U.S. Geological Survey Groundwater Toolbox. The results demonstrated improved estimates by the new model particularly in filtering out the bias of snowmelt runoff in baseflow estimate. This new model is specifically suitable for applications over large watersheds which is complementary to the traditional methods that are limited by watershed size. The output from the model also includes estimates for watershed hydraulic conductivity and drainable water storage, which are useful parameters in evaluating aquifer properties, calibrating and validating hydrological and climate models, and assessing regional water resources.

GRACE Satellites Enable Long-Lead Forecasts of Mountain Contributions to Streamflow in the Low-Flow Season

Terrestrial water storage (TWS) in high mountain areas contributes large runoff volumes to nearby lowlands during the low-flow season when streamflow is critical to downstream water supplies. The potential for TWS from GRACE (Gravity Recovery and Climate Experiment) satellites to provide long-lead streamflow forecasting in adjacent lowlands during the low-flow season was assessed using the upper Yellow River as a case study. Two linear models were trained for forecasting monthly streamflow with and without TWS anomaly (TWSA) from 2002 to 2016. Results show that the model based on streamflow and TWSA is superior to the model based on streamflow alone at up to a five-month lead-time. The inclusion of TWSA reduced errors in streamflow forecasts by 25% to 50%, with 3–5-month lead-times, which represents the role of terrestrial hydrologic memory in streamflow changes during the low-flow season. This study underscores the high potential of streamflow forecasting using GRACE data with long lead-times that should improve water management in mountainous water towers and downstream areas.

Global Freshwater Storage Capability across Time Scales in the GRACE Satellite Era

Freshwater is recharged mainly by rainfall and stored inland for a period of time, which is directly affected by its storage capability. The storage capability of river basins has different spatiotemporal features that are important for the predictability of freshwater resources. However, the estimation of freshwater storage capability (FSC) remains a challenge due to the lack of observations and quantification indices. Here, we use a metric that characterizes hydrological “inertia” after rainfalls to analyze FSC over the 194 largest global major river basins based on satellite observations from the Gravity Recovery and Climate Experiment (GRACE) and simulations from the Community Land Model version 5 (CLM5). During 2003–16, the global land was observed to retain 28% of precipitation after one month based on GRACE observations, and the simulation depicts that the retained proportions decrease from 42% after one day to 26% after one month, with smaller FSC partly attributed to wetter conditions and higher vegetation densities. The root zone contributes about 40% to the global land FSC on daily to monthly time scales. As the time scale increases, the contribution from the surface soil decreases from 26% to 14%, while the contribution from the deep soil increases from 4% to 10%. Snow contributes over 20% of land FSC, especially over high latitudes. With six decades of CLM5 long-term simulations, it is revealed that the change of FSC in most basins is related to internal climate variability. The FSC of river basins which displays the proportion of precipitation retained on land is worthy of further attention regarding the predictability of water resources.

The influence of Antarctic ice loss on polar motion: an assessment based on GRACE and multi-mission satellite altimetry

Increasing ice loss of the Antarctic Ice Sheet (AIS) due to global climate change affects the orientation of the Earth’s spin axis with respect to an Earth-fixed reference system (polar motion). Here the contribution of the decreasing AIS to the excitation of polar motion is quantified from precise time variable gravity field observations of the Gravity Recovery and Climate Experiment (GRACE) and from measurements of the changing ice sheet elevation from altimeter satellites. While the GRACE gravity field models need to be reduced by noise and leakage effects from neighboring subsystems, the ice volume changes observed by satellite altimetry have to be converted into ice mass changes. In this study we investigate how much individual gravimetry and altimetry solutions differ from each other. We show that due to combination of individual solutions systematic and random errors of the data processing can be reduced and the robustness of the geodetic derived AIS polar motion excitations can be increased. We investigate the interannual variability of the Antarctic polar motion excitation functions by means of piecewise linear trends. We find that the long-term behavior of the three ice sheet subregions: EAIS (East Antarctic Ice Sheet), WAIS (West Antarctic Ice Sheet) and APIS (Antarctic Peninsula Ice Sheet) is quite different. While APIS polar motion excitations show no significant interannual variations during the study period 2003-2015, the trend of the WAIS and EAIS polar motion excitations increased in 2006 and again in 2009 while it started slightly to decline in 2013. AIS mass changes explain about 45% of the observed magnitude of the polar motion vector (excluding glacial isosatic adjustment). They cause the pole position vector to drift along 59^{circ } East longitude with an amplitude of 2.7 mas/yr. Thus the contribution of the AIS has to be considered to close the budget of the geophysical excitation functions of polar motion.

Two Severe Prolonged Hydrological Droughts Analysis over Mainland Australia Using GRACE Satellite Data

In recent years, many droughts have happened over mainland Australia, especially the two severe prolonged droughts, from 2006 to 2009 and 2018 to 2020, resulting in serious water scarcity. Therefore, using the Total Storage Deficit Index (TSDI) from the Gravity Recovery and Climate Experiment (GRACE), we analyzed the two severe prolonged droughts from the perspective of the affected area, spatial evolution, frequency, severity and drought driving factors. The results show that the affected area of Drought 2006–2009 ranged from 57% to 95%, and that of Drought 2018–2020 ranged from 45% to 95%. Drought 2006–2009 took its rise in southeastern Australia and gradually spread to the central part. Drought 2018–2020 originated in the southwest corner of the Northern Territory and northern New South Wales, and gradually expanded to Western Australia and the whole New South Wales respectively. During Drought 2006–2009, Victoria suffered drought all months, including 59% mild drought and 41% moderate drought, North Territory had the highest drought severity of 44.26 and Victoria ranked the second high with the severity of 35.51 (cm months). For Drought 2018–2020, Northern Territory was also dominated by drought all months, including 92% mild drought and 8% moderate drought, the drought severities were in North Territory and Western Australia with 52.19 and 31.44 (cm months), respectively. Finally, the correlation coefficients between the two droughts and Indo-Pacific climate variability including El Niño-Southern Oscillation and Indian Ocean Dipole (IOD) are computed. By comparing the correlation coefficients of Drought 2018–2020 with Drought 2006–2009, we find that the impact of the El Niño on the hydrological drought becomes weaker while IOD is stronger, and the role of Southern Oscillation on droughts is diverse with the quite different spatial patterns. The results from Fourier analysis confirm that the two hydrological droughts are all related to Indo-Pacific climate variability but with slightly different driving mechanisms.

Estimation of surface density changes using a mascon method in GRACE-like missions

Gravity Recovery and Climate Experiment (GRACE) data are a valuable source of information for estimating hydrological mass changes. Several approaches have been conducted to investigate surface density changes from satellite-based observations. The traditional approaches are mainly based on the Stokes coefficients, related to a spherical harmonic representation of the gravitational potential. This study aims to develop an alternative method to estimate the temporal variations in water storage. It is based on a specific type of mascon technique that investigates the possibility of obtaining a solution without Stokes coefficients. The method uses a piecewise constant surface density function to estimate surface density changes based on the GRACE satellite-to-satellite tracking (SST) data. The surface density changes are directly obtained from the variations in positions and velocities of the two GRACE satellites. We therefore avoid the series truncation and aim to improve the leakage problem at the price of higher numerical burden. The proposed method is numerically tested on synthetic data similar to level-1 GRACE data for a period of one month. Two regularization methods, the well-known Tikhonov solution and a method that accounts for the areas of different patches, are employed to obtain a stable solution. The accuracy assessment over the Greenland area indicates that the estimated values are reliable and statistically significant, a further confirmation of the efficacy and stability of the method.

Temporal and spatial variations in the terrestrial water storage across Central Asia based on multiple satellite datasets and global hydrological models

Arid regions of Central Asia have sensitive ecosystems that rely heavily on terrestrial water storage which is composed of surface water storage, soil moisture storage and groundwater storage. Therefore, we employed three Gravity Recovery and Climate Experiment (GRACE) satellite datasets and five global hydrological models (GHMs) to explore the terrestrial water storage (TWS) changes over arid regions of Central Asia from 2003 to 2014. We observed significantly decreasing water storage trends in the GRACE data, which were underestimated by the GHMs. After averaging the three GRACE satellite datasets, we found that the water storage was decreasing at a rate of −4.74 mm/year. Contrary to the prevailing declining water storage trends, northeastern Kazakhstan (KAZ), and southern Xinjiang increased their water storage over the same period. The GRACE data showed that Turkmenistan (TKM), Uzbekistan (UZB) and KAZ experienced the most severe water depletions, while Tajikistan (TJK) and northwest China (NW) experienced the least significant depletions. With respect to the major river and lake basins, the Aral Sea Basin exhibited the most serious water loss (-0.60 mm/month to −0.38 mm/month). The water storage positively correlates with the precipitation; and negatively correlates, with a three-month lag, with temperature and potential evapotranspiration (PET). Partial least square regression (PLSR) had the high capability in simulating and predicting the TWS. These results provide scientific evidence and guidance for local policy makers working toward sustainable water resource management, and the resolution of international water resource disputes among Central Asian countries.