Gravity Recovery And Climate Experiment Research Articles

Colored noise in GRACE total water storage time series: Its impact on trend significance in the Türkiye region and major world river basins

Temporal correlations are prevalent in most geophysical time series data, leading to what is known as colored noise, which become more apparent in the frequency domain rather than time domain. Neglecting this type of noise in modeling can result in underestimated standard errors for the parameters of the model used to analyze the time series. As a result, statistical tests misinterpret the significance of these parameters, such as the overall trend rate, because the “signal to noise” ratio becomes incorrectly larger than it should be. In this study, we investigate the temporal correlations in the time series data from the Gravity Recovery and Climate Experiment (GRACE) mission. GRACE and its successor mission GRACE Follow-On (GRACE-FO) have been successfully employed to monitor global total water storage anomalies over two decades since its initial launch in 2002. We primarily utilize monthly GRACE mascon (mass concentration) solutions provided by NASA Goddard Space Flight Center (GSFC), examining both regional and global scales. The power spectrum density of the residuals, calculated after applying standard harmonic regression to each mascon time series on Earth’s land, reveals an average spectral index of κ = −1. This indicates the predominance of flicker noise, one of the most common forms of colored noise in geodetic time series. Notably, the noise becomes more Brownian (κ < −1) in regions with intense hydrological events, while it tends to be more white noise (κ → 0) in areas with less water circulation compared to other land regions. This observation suggests that unmodelled hydrological events, such as droughts, floods, and ones associated with ice-melting, contribute to the residuals as colored noise in the time series. To account for this noise in our time series modeling, we primarily consider autoregressive (AR) moving average (MA) process, commonly known as ARMA, in addition to the standard variance component estimation for noise amplitudes. We focus on individual mascon blocks in the Türkiye Region and mascon solutions for 24 world’s major river basins. Our findings demonstrate that an ARMA(1,1) model is sufficient for describing the noise in these regions. The results underscore the importance of considering colored noise, as it leads to approximately three times larger standard errors for the overall trend rate. This substantially impacts the significance of the trend rates.

Significant Local Sea Level Variations Caused by Continental Hydrology Signals

AbstractSpace gravity missions have enabled the quantification of the mass component of sea‐level rise over the past two decades. Barystatic sea‐level rise is predominantly driven by melting polar ice sheets and mountain glaciers. However, continental hydrological processes also contribute to global sea level change at significant magnitudes. We show that for most coastal areas in low‐to‐mid latitudes, up to half of manometric sea‐level rise is due to changes in water storage in ice‐free continental regions. At other locations the direct attraction effect of anthropogenic pumping of groundwater over the duration of the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On (GRACE‐FO) mission offsets sea‐level rise from ice sheet and glacier melt. If these trends in continental hydrological storage were to slow or stop, these regions would experience greatly accelerated sea‐level rise, posing a risk to coastal settlements and infrastructure, however, for most coastal communities current rates of sea‐level rise would be significantly reduced.

Revealing the Hidden Consequences of Increased Soil Moisture Storage in Greening Drylands

Vegetation primarily draws water from soil moisture (SM), with restoration in drylands often reducing SM storage (SMS). However, anomalies have been detected in the Beijing–Tianjin Sand Source Region (BTSSR) of China via the Global Land Data Assimilation System (GLDAS) and Gravity Recovery and Climate Experiment (GRACE). This study quantified the sources of increased SMS in drylands to elucidate the effects of vegetation restoration on SMS. The results indicated the following: (1) In vegetated drylands, 46.2% experienced a significant increase in SMS while 53.8% remained stable; both were positively correlated with the normalised difference vegetation index (NDVI). (2) The increase in SMS was accompanied by a decrease in groundwater storage (GWS), as indicated by the significant correlation coefficients of −0.710 and −0.569 for SMS and GWS, respectively. Furthermore, GWS served as the primary source of water for vegetation. (3) The results of the redundancy analysis (RDA) indicated that the initial vegetation, the driver of the observed trend of increased SMS and decreased GWS, accounted for 50.3% of the variability in water storage. Therefore, to sustain dryland ecosystems, we recommend that future vegetation restoration projects give due consideration to the water balance while concurrently strengthening the dynamic monitoring of SMS and GWS.

Temporal and spatial variations of terrestrial water storage in the northeastern Tibetan Plateau retrieved by GNSS observations

The variation of solid Earth's hydrologic loading could cause the elastic vertical deformation of the crust, and the Global Navigation Satellite System (GNSS) could effectively monitor the vertical displacement of surface loads. However, the widely used Green's function method does not work well in areas where GNSS sites are sparse. Here, the vertical displacement time series of GNSS stations and the Slepian basis function method have been applied to convert displacement signals into spatial spectrum signals. The elastic mass load theory is used to study the changes in terrestrial water storage on the Northeastern Tibetan Plateau (NETP). The temporal and spatial characteristics of seasonal water changes are well-represented by the GNSS, the Gravity Recovery and Climate Experiment (GRACE), and the Global Land Data Assimilation System (GLDAS). Several data points suggest that the change in water storage shows a gradual increase from the northeast to the southwest. The greatest annual amplitude of water storage retrieved by GNSS is ∼159 mm, which is greater than the ∼47 mm and ∼44 mm obtained by GRACE and GLDAS. These results demonstrate that GNSS is capable of capturing small-scale hydrological changes in this region, whereas GRACE and GLDAS data tend to underestimate seasonal variations in water storage. We also used GNSS to describe the hydrological drought conditions in NETP, showing that GNSS could be used as an independent method to characterize hydrological drought events. The findings suggest it could observe water storage with high spatial and temporal resolution and aid in comprehending regional hydrological trends with a sparse GNSS station network.

Estimation of groundwater storage variations in African river basins: Response to global climate change using GRACE and GRACE-FO among past two decades

Groundwater Storage (GWS) is an important component of the water cycle and a vital resource for supplying water to millions of people as well as sustaining domestic and economic activities, especially in arid and semi-arid regions such as Africa. This study aims to estimate and analyze the recent groundwater changes (ΔGW) for 11 major African basins in light of climate changes and global warming utilizing the data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) from January 2003 to December 2021. We proposed a new gap-filling technique to calculate the missing months between GRACE and GRACE-FO missions for the continuous estimation of groundwater changes. Then, we establish a new framework to analyze and investigated the climate parameters that could affect groundwater changes using Empirical Orthogonal Function (EOF) technique. Our analysis reveals that the new gap-filling technique is significantly consistent with the TWS time-series based on the trend line analysis. The lowest GPCC drought index value occurs in the lower Nile basin by approximately −0.93, while the highest occurs in the middle of Africa in Congo basin by approximately −0.02. The precipitation rate is mostly concentrated in mid-Africa while North and South Africa shows a weak rate. Meanwhile, according to the analysis of dependent climate parameters, climate change has had a negative impact on groundwater. However, the long-term effect on the groundwater does not clearly appear because most of Africa is located within semi-arid and arid regions. The effect of rain on groundwater was evident through EOF analysis, which shows GWS EOF Mode-2 (36.28%) and precipitation EOF Mode-1 (78.34%) were found to be in significant agreement in spatio-temporal domains.

Spatiotemporal coupling analysis of surface water and groundwater in Tibet based on multi-source sensors

Abstract. Lakes and groundwater are two crucial components of the global terrestrial water cycle, collectively forming a vital network for Earth's water resources. However, in the Tibetan region, the spatiotemporal relationship between lakes and groundwater and their impact on the local hydrological cycle remain inadequately understood. Satellite remote sensing serves as an effective observational tool, enabling comprehensive investigation and analysis of surface lakes and groundwater in Tibet with high spatial resolution. Therefore, this study integrates Landsat and Cryosat-2 satellite data to examine the spatiotemporal patterns of surface lake extents and water levels in Tibet. Additionally, combining Gravity Recovery and Climate Experiment (GRACE) satellite observations with the Global Land Data Assimilation System (GLDAS) model data, we quantitatively analyze the spatiotemporal variations in groundwater storage and its correlation with lake water. The results indicate that: 1) Rivers and lakes in Tibet are mainly located in the central and northwest regions, displaying noticeable intra-annual variations; 2) Substantial lagged relationships exist between groundwater storage and lake water levels and areas, revealing that lakes contribute significantly to groundwater replenishment, especially in the Ngari prefecture and Lhokha prefecture. This study comprehensively utilizes multi-source remote sensing data to dynamically monitor surface and groundwater in Tibet, providing robust support for a better understanding of the interaction between groundwater and surface water.

Understanding the shift in drivers of terrestrial water storage decline in the central Inner Mongolian steppe over the past two decades

Terrestrial water storage (TWS) in mid-latitude arid and semiarid Eurasia steppe is under threat from climate change and human activities. The central Inner Mongolian steppe is a hotspot of this change, and in addition serves as an important ecological barrier in North China. However, the limitations of scarce observations make the variations of TWS in this study area have not been well tracked and quantified yet, and the drivers of TWS remain unclear. Here we use the gravity recovery and climate experiment (GRACE) satellites and its follow-on (GRACE-FO) missions, combined with data-driven models to fill the data gaps between GRACE and GRACE-FO. A significant TWS decrease (−2.7 ± 0.2 mm/yr, p < 0.05) was captured over the past two decades (2002–2020), which is consistent with land surface models (LSMs) that assimilated with GRACE data (R = 0.81, p < 0.01). By integrating GRACE satellites and open-loop LSMs, the contrary trends of TWS were identified over the period, and GRACE contrasts with the increase in TWS simulated by LSMs that excludes human activities. We observe a shift in the dominant factor driving the TWS decline from climatic (2002–2010) to anthropogenic factors (2011–2020). This shift accounts for the anthropogenic-driven increase in TWS decline from −2.0 mm/yr to −4.4 mm/yr, indicating the increasingly significant impact of human activities on TWS. We further reveal the climate-driven mechanisms of TWS using wavelet analysis and multiple drought indicators. The results showed that the TWS decline in the first period was mainly caused by continuous droughts, which were driven by the deficit in precipitation-minus-evapotranspiration. This study highlights the risk of TWS depletion in the central Inner Mongolian steppe that is eco-fragile, providing insights into the water resources sustainability and ecosystem conservation in vast dryland ecosystems of the world.

Changes in terrestrial water storage in the Three-North region of China over 2003–2021: Assessing the roles of climate and vegetation restoration

Large-scale vegetation restoration has markedly enhanced ecosystem services in the Three-North (TN) region, concurrently exacerbating the pre-existing water resource crisis stemming from climate change in this arid domain. However, the specific contributions of climate and vegetation changes to the evolution of water resources in the TN region have yet to be elucidated. This study leverages data from the Gravity Recovery and Climate Experiment (GRACE) and its Follow-on mission (GRACE-FO), specifically the terrestrial water storage anomaly (TWSA) from 2003 to 2021. We identify two TWSA rising (TWSA_rⅠ and TWSA_rⅡ) and two declining (TWSA_dⅠ and TWSA_dⅡ) hotspots in the TN region. By exploring the relative importance of climate drivers and vegetation dynamics in influencing the temporal dynamics of TWSA, we found that vegetation restoration assumes a more pivotal role than climate factors in 45.2% of the total area within the identified hotspots, predominantly evident in TWSA declining hotspots. The heightened TWSA in TWSA_rⅠ and TWSA_rⅡ primarily stems from precipitation recharge, with precipitation-dominated areas constituting 19.3% of the area within the identified hotspots. Potential evapotranspiration dominates TWSA changes in only 4.6% of the region within the identified hotspots, primarily scattered in TWSA_dⅠ and TWSA_dⅡ. Furthermore, a comparative assessment of soil moisture and groundwater responses to environmental factors indicates that water storage at shallower layers is more responsive to climate factors, while the impacts of vegetation changes on TWSA are more discernibly reflected in water storage at deeper layers. These findings furnish scientific guidance for future ecological restoration planning and sustainable water resources management.

Regionalization of GRACE data in shorelines by ensemble of artificial intelligence methods

Groundwater (GW) plays a crucial role in coastal aquifers and arid regions, serving as a lifeline for communities by providing a reliable and resilient water source, making its monitoring essential for sustainable water management. This study aimed at modeling GW via regionalization of the Gravity Recovery and Climate Experiment (GRACE) data based on two methods. The first method directly regionalized the GRACE data for modeling GW via in situ measurements, including the lake level, precipitation, temperature, observed GW and Penman-Monteith-Leuning (PML) evapotranspiration data. The second method included two stages, in the first stage, the GRACE data were downscaled via the Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) data which contains satellite based precipitation, temperature, soil moisture, and snow water equivalent data. In the second stage, the downscaled GRACE was bias corrected to provide regionalized data. Artificial intelligence models consist of shallow networks (Feed Forward Neural Network (FFNN), Adaptive neuro fuzzy (ANFIS), Support Vector Machine (SVR)), the ensemble of shallow networks and Long-Short Term Memory (LSTM) deep learning method were employed in the modeling process and the observed GW level data were targeted for the regionalization. The Link CluE clustering ensemble method was implemented to cluster the piezometers of the aquifer to separate different GW patterns in the area. The proposed methodology was examined over the Miandoab plain, one of the sub-basins of the Lake Urmia, located in Northwest Iran. The modeling results demonstrated that the first method could exhibit superior performance with the Nash-Sutcliffe Efficiency (NSE) of up to 17% higher than the second method. Thus, using in situ observed data for downscaling proved to be more accurate than relying on the data based on the satellite imagery. The results indicated that the ensemble of shallow networks could lead to more precise results than using the deep and shallow learning models, individually, where the NSE for the ensemble of shallow networks was up to 50% higher compared to the LSTM model.

Statistical downscaling of GRACE terrestrial water storage changes based on the Australian Water Outlook model

The coarse spatial resolution of the Gravity Recovery and Climate Experiment (GRACE) dataset has limited its application in local water resource management and accounting. Despite efforts to improve GRACE spatial resolution, achieving high resolution downscaled grids that correspond to local hydrological behaviour and patterns is still limited. To overcome this issue, we propose a novel statistical downscaling approach to improve the spatial resolution of GRACE-terrestrial water storage changes (ΔTWS) using precipitation, evapotranspiration (ET), and runoff data from the Australian Water Outlook. These water budget components drive changes in the GRACE water column in much of the global land area. Here, the GRACE dataset is downscaled from the original resolution of 1.0° × 1.0° to 0.05° × 0.05° over a large hydro-geologic basin in northern Australia (the Cambrian Limestone Aquifer—CLA), capturing sub- grid heterogeneity in ΔTWS of the region. The downscaled results are validated using data from 12 in-situ groundwater monitoring stations and water budget estimates of the CLA’s land water storage changes from April 2002 to June 2017. The change in water storage over time (ds/dt) estimated from the water budget model was weakly correlated (r = 0.34) with the downscaled GRACE ΔTWS. The weak relationship was attributed to the possible uncertainties inherent in the ET datasets used in the water budget, particularly during the summer months. Our proposed methodology provides an opportunity to improve freshwater reporting using GRACE and enhances the feasibility of downscaling efforts for other hydrological data to strengthen local-scale applications.

Optimal combinations of global evapotranspiration and terrestrial water storage products for catchment water balance

ABSTRACT During the last two decades, a great number of global products about evapotranspiration ( E ) and terrestrial water storage ( S ) have been released. This has led to numerous combinations for describing catchment water balance, and hence determining an appropriate combination has become an important issue. The main objective of this study is to evaluate various global products of E and S at the catchment scale and determine the most appropriate combination, taking two Korean catchments as examples. For E , we evaluated global evapotranspiration (Global ET), Global Land Evaporation Amsterdam Model (GLEAM), and Penman–Monteith–Leuning (PML). For S , six individual datasets of Gravity Recovery and Climate Experiment (GRACE) were evaluated, with Global Land Data Assimilation System (GLDAS) products used for comparison. We found that the performance is sensitive to the choice of E product while various S products displayed minimal differences in results. Based on four evaluation criteria, the combination of PML and GRACE-SH-GFZ is suggested as the most suitable pair. For the period between 2003 and 2013, these data exhibit noteworthy trends of increasing E , offset by decreasing S . Warming climate is suspected to be behind these trends. Our research presents an approach that allows for the estimation of monthly streamflow exclusively with global data products, which is an advancement in hydrological analysis and particularly useful for regions that lack in-situ data networks. This approach provides a new perspective in the application of global datasets for the assessment of water balance and could significantly improve predictions in ungauged basins.

Study on the vertical deformations induced by terrestrial water storage changes in Huang-Huai-Hai river basin

Terrestrial water storage (TWS) variations are associated with water mass movements, which may cause the deformation displacements of the Global Navigation Satellite System (GNSS) stations. This study investigates the spatio-temporal TWS variations and addresses the relationship between deformation variations observed in the Huang-Huai-Hai River Basin (HHHRB) and local hydrological features. Results indicate that the vertical velocities at the GNSS stations induced by TWS changes are relatively small, and the impacts of the terrestrial water storage changes are mainly reflected in the changes of seasonal characteristics. Although there is a downward TWS trend from 2011 to 2022 in most HHHRB areas, velocities from the vertical displacements of both Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GFO) and the GNSS reflect that the HHHRB is undergoing an uplift process, while the magnitude of the GRACE/GFO derived velocities is much smaller than that of the GNSS solutions. Common hydrological deformations estimated from GRACE/GFO and GNSS measurements reveal that the TWS-derived displacements can explain 54.5% of the GNSS seasonal variations, with the phases of terrestrial water storage advancing by about one month relative to GNSS common signal phases. Moreover, the decrease of the groundwater storage in the HHHRB has been accelerating since 2008. After reaching its lowest level around mid-2020, it began to rise rapidly, which might be closely related to the implementation of the South-North Water Transfer Central Project.

Validation of the IRI-2020 topside ionosphere options through in-situ electron density observations by low-Earth-orbit satellites

The topside ionosphere extends from the F2-layer peak, where the electron density reaches its absolute maximum in the ionosphere, to the overlying plasmasphere and magnetosphere. In the topside ionosphere, the electron density decreases with height with a vertical variation rate strongly dependent on height itself. The last version of the International Reference Ionosphere (IRI) model, i.e., IRI-2020, describes this complex behavior through four topside options based on different sub-models (i.e., options) developed from the 1970s to the present. All these options have in common the F2-layer peak as an anchor point, while they differ in their topside electron density profile and/or plasma effective scale height formulations. In this work, we perform a validation of the accuracy of the four IRI-2020 topside options based on the comparison against in-situ electron density observations by Gravity Recovery and Climate Experiment (GRACE), Ionospheric Connection Explorer (ICON), and Defense Meteorological Satellite Program (DMSP) F15 low-Earth-orbit satellites. Datasets used in this study encompass observations recorded from 1999 to 2022, covering different diurnal, seasonal, and solar activity conditions, on a global basis and for the height range 400–850 km above the ground. The nearly two solar cycles dataset facilitated the evaluation of IRI-2020 topside options ability to reproduce the spatial and time variations of the topside ionosphere for different solar activity conditions. The weaknesses and strengths of each IRI-2020 topside option are highlighted and discussed, and suggestions on how to improve the modeling of the challenging topside ionosphere region within the IRI model are provided for future reference.

Assessment of the Added Value of the GOCE GPS Data on the GRACE Monthly Gravity Field Solutions

The time-varying gravity field models derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission suffer from pronounced longitudinal stripe errors in the spatial domain. A potential way to mitigate such errors is to combine GRACE data with observations from other sources. In this study, we investigate the impacts on GRACE monthly gravity field solutions of incorporating the GPS data collected by the Gravity Field and Steady-State Ocean Circulation Explorer (GOCE) mission. To that end, we produce GRACE/GOCE combined monthly gravity field solutions through combination on the normal equation level and compare them with the GRACE-only solutions, for which we have considered the state-of-the-art ITSG-Grace2018 solutions. Analysis in the spectral domain reveals that the combined solutions have a notably lower noise level beyond degree 30, with cumulative errors up to degree 96 being reduced by 31%. A comparison of the formal errors reveals that the addition of GOCE GPS data mainly improves (near-) sectorial coefficients and resonant orders, which cannot be well determined by GRACE alone. In the spatial domain, we also observe a significant reduction by at least 30% in the noise of recovered mass changes after incorporating the GOCE GPS data. Furthermore, the signal-to-noise ratios of mass changes over 180 large river basins were improved by 8–20% (dependent on the applied Gaussian filter radius). These results demonstrate that the GOCE GPS data can augment the GRACE monthly gravity field solutions and support a future GOCE-type mission for tracking more accurate time-varying gravity fields.

Water availability identification from GRACE dataset and GLDAS hydrological model over data-scarce river basins of Ethiopia

ABSTRACT The nature of the heterogeneity of terrestrial water storage in the main river basins of Ethiopia is poorly understood. In river basins, measurements of terrestrial water storage changes are exceedingly rare in all temporal dimensions. This study investigates the spatiotemporal variation of water storage in the main river basins of Ethiopia using the Gravity Recovery and Climate Experiment (GRACE) with Global Land Data Assimilation System (GLDAS). The results revealed significant variation in liquid water equivalents from GRACE and terrestrial water storage from GLDAS for each river basin, following a seasonal pattern. I found that GRACE and GLDAS datasets can be combined effectively to evaluate the long-term water storage in large-scale basins with limited hydrological data, and the obtained result provides a reliable outcome that will be essential for the establishment of climate change adaptation pathways in the future for essential water resource management and optimization.

Preliminary Evidence for the Decline in Discharge at Falaj Daris, Nizwa, Oman

Falaj Daris is one of the oldest active aflāj located in Oman and the largest in the Dakhiliyah governorate. It is a 7,999-meter long Dāwūdī-type falaj. Ninety-five percent of water drawn from falaj Daris is used for agricultural irrigation and the remaining five percent is used for domestic purposes. In this paper, we analyzed several datasets including falaj Daris discharge, borehole water levels in wadi al-Abyadh, whose aquifer falaj Daris taps, and GRACE satellite data to determine the historical trend of falaj Daris discharge during 1982-2020. A set of standard statistical methods and data visualization techniques were applied including the Mann-Kendall trend test. Preliminary evidence shows a decline of 226,000 L/day in falaj Daris discharge between 1982-2020. This finding is supported by the overall trends in borehole water levels in wadi al-Abyadh. All these trends were confirmed using GRACE satellite data which shows a decrease in water volume of 6 (WET cm) in the larger basin between 2002-2021. Keywords: qanat; falaj Daris; wadi al-Abyadh; Nizwa, Oman; GRACE satellite.

VARIABILITY OF EQUIVALENT WATER HEIGHT (EWH) IN INDONESIA DURING 14 YEARS OF GRACE GRAVITY SATELLITE MISSION

GRACE Satellite (Gravity Recovery and Climate Experiment) is a gravity monitoring satellite, that is very sensitive to mass changes, especially the signals that produce by redistribution of water masses. Data from this satellite can be used to observe water distribution in the form of spherical harmonic coefficients. Water mass variations are presented as Equivalent Water Height (EWH). The results of GRACE processing showed that the largest EWH was 27.298 cm in January 2015 and the smallest EWH was 29.816 cm in June 2004 in Sumatera Island. The positive trend occurred in Sumatra island and the negative trend occurred in eastern Indonesia. Generally, the trend of Indonesian rainfall throughout 2002 to 2016 was constant. However, there was a change in seasonal patterns in 2014. This research also use Mascon data from GRACE satellite to determine the radius of the gaussian filter and TRMM satellite’s data to observe rainfall data to be compared with the EWH variability from GRACE.

Assessing the impact of agriculture, coal mining, and ecological restoration on water sustainability in the Mu Us Sandyland

Balancing water demand for socio–economic development and ecosystem stability presents a challenge for regional sustainable management, especially in drylands. Previous studies have indicated that large–scale ecological restoration projects (ERPs) lead to a decline in terrestrial water storage (TWS) in the Mu Us Sandyland (MUS). However, the effects of other human activities (e.g., cropland reclamation, coal mining) on water resources remain unclear, raising concerns regarding water crisis and human–natural system sustainability. Through the utilization of coal mine location data, we found that the impact of coal mass loss on the Gravity Recovery and Climate Experiment (GRACE) products cannot be ignored in MUS, especially in the coal–rich northeastern part. Combining these data with auxiliary datasets, we observed a significant (p < 0.05) decrease in TWS (−0.85 cm yr−1) and groundwater storage (GWS, −0.95 cm yr−1) in the MUS, with human activities accounting for 79.23 % of TWS and 90.45 % of GWS reductions, primarily due to increased agricultural and industrial water consumption. Agricultural water consumption increased 2.23 times from 2001 to 2020, attributed to enhanced water use intensity (62.6 %) and cropland expansion (37.4 %). Industrial water consumption in Shenmu, a representative coal county, experienced a 4.16–fold rise between 2001 and 2020. Despite these challenges, local governments have alleviated water stress, ensured food security, and increased household income by comprehensive management strategies, such as enhancing water–saving technology and enforcing stringent policies. Previous studies have overestimated the amount of water resources consumed by ERPs. However, ERPs has played a critical role in stabilizing the regional ecological environment and ensuring the region as a vital food and energy supplier. Our findings can guide for socio-economic development and water management policies in similar regions.

Reconstruction of Past Water Levels in Data-Deficient Karst Springs

Karst aquifers are crucial for providing fresh water worldwide but are also incredibly sensitive to human impact and climate change. This study aims to reconstruct the historical water levels of karst springs, despite the lack of data. By combining collected data, we have created a detailed numerical model to understand the complex behavior of karst aquifers. Our research reveals significant drops in the water levels at Longtan Spring, mainly due to the overuse of groundwater and inadequate water recharge, which is critical for the success of the Springs Resurgence project. We have also mapped out historical groundwater levels and identified the necessary conditions to get the spring flowing again. The model proved to be reliable during its calibration from 2000 to 2007, with an average Nash–Sutcliffe efficiency coefficient of 0.52 for the monitoring wells. For the period from 1960 to 2019, our model showed a strong correlation coefficient of over 0.97 when compared with data from the GRACE satellite mission, demonstrating its high accuracy. The approach we have taken in this study provides a feasible way to figure out historical water levels in karst springs, which is vital for protecting these essential fresh water sources. This work will provide a strong basis for policies to restore the spring.

Monsoon-Based Linear Regression Analysis for Filling Data Gaps in Gravity Recovery and Climate Experiment Satellite Observations

Over the past two decades, the Gravity Recovery and Climate Experiment (GRACE) satellite mission and its successor, GRACE-follow on (GRACE-FO), have played a vital role in climate research. However, the absence of certain observations during and between these missions has presented a persistent challenge. Despite numerous studies attempting to address this issue with mathematical and statistical methods, no definitive optimal approach has been established. This study introduces a practical solution using Linear Regression Analysis (LRA) to overcome data gaps in both GRACE data types—mascon and spherical harmonic coefficients (SHCs). The proposed methodology is tailored to monsoon patterns and demonstrates efficacy in filling data gaps. To validate the approach, a global analysis was conducted across eight basins, monitoring changes in total water storage (TWS) using the technique. The results were compared with various geodetic products, including data from the Swarm mission, Institute of Geodesy and Geoinformation (IGG), Quantum Frontiers (QF), and Singular Spectrum Analysis (SSA) coefficients. Artificial data gaps were introduced within GRACE observations for further validation. This research highlights the effectiveness of the monsoon method in comparison to other gap-filling approaches, showing a strong similarity between gap-filling results and GRACE’s SHCs, with an absolute relative error approaching zero. In the mascon approach, the coefficient of determination (R2) exceeded 91% for all months. This study offers a readily usable gap-filling product—SHCs and smoothed gridded observations—with accurate error estimates. These resources are now accessible for a wide range of applications, providing a valuable tool for the scientific community.