Search Results (144)

The fragile karst ecosystem in Southwest China faces severe water scarcity. Since 2000, large-scale ecological restoration programs (e.g., the “Grain for Green” Program) have substantially increased vegetation coverage. Concurrently, climate change has manifested as a distinct warming trend and heightened drought risk in recent decades. Therefore, understanding the synergistic and competing effects of climate change and vegetation restoration on regional evapotranspiration (ET) is critical for projecting water budgets and ensuring the sustainability of ecosystems and water resources within this vital ecological barrier region. This study employs a dual-scenario PT-JPL model (simulating natural vegetation dynamics versus constant coverage) integrated with Sen + MK trend analysis to quantify the spatiotemporal patterns of ET and its components—canopy transpiration (ETc), interception evaporation (ETi), and soil evaporation (ETs)—in Southwest China’s karst region (2000–2018). Furthermore, multiple regression analysis and SEM were utilized to investigate the driving mechanisms of vegetation and climatic factors (temperature, precipitation, radiation, and relative humidity) on changes in ET and its components. The key results demonstrate the following: (1) Vegetation restoration exerted a net positive effect on total ET (+0.44 mm/a) through enhanced ETi (+0.22 mm/a) and ETs (+0.37 mm/a), despite reducing ETc (−0.08 mm/a), revealing trade-offs in water allocation. (2) Radiation dominated ET variability (66.45% of the area exhibiting >50% contribution), while temperature exhibited the most extensive spatial dominance (44.02% of the region), and relative humidity exhibited drought-mediated dual effects (promoting ETi while suppressing ETc). (3) Precipitation exhibited minimal direct influence. Vegetation restoration and climate change collectively drive ET dynamics, with ETc declines indicating potential water stress. These findings elucidate the synergistic regulation of vegetation restoration and climate change on karst ecohydrology, providing critical insights for water resource management in fragile ecosystems globally. Full article

The cislunar space navigation satellite system is essential infrastructure for lunar exploration in the next phase. It relies on high-precision orbit determination to provide the reference of time and space. This paper focuses on constructing a navigation constellation using special orbital locations such as Earth–Moon libration points and distant retrograde orbits (DRO), and it discusses the simplification of planetary perturbation models for their autonomous orbit determination on board. The gravitational perturbations exerted by major solar system bodies on spacecraft are first analyzed. The minimum perturbation required to maintain a precision of 10 m during a 30-day orbit extrapolation is calculated, followed by a simulation analysis. The results indicate that considering only gravitational perturbations from the Moon, Sun, Venus, Saturn, and Jupiter is sufficient to maintain orbital prediction accuracy within 10 m over 30 days. Based on these findings, a method for simplifying the ephemeris is proposed, which employs Hermite interpolation for the positions of the Sun and Moon at fixed time intervals, replacing the traditional Chebyshev polynomial fitting used in the JPL DE ephemeris. Several simplified schemes with varying time intervals and orders are designed. The simulation results of the inter-satellite links show that, with a 6-day orbit arc length, a 1-day lunar interpolation interval, and a 5-day solar interpolation interval, the accuracy loss for cislunar space navigation satellites remains within the meter level, while memory usage is reduced by approximately 60%. Full article

Accurately monitoring terrestrial water storage (TWS) variations is essential due to global climate change and growing water demands. This study investigates TWS changes in Oregon, USA, using Global Navigation Satellite System (GNSS) data from the Nevada Geodetic Laboratory, Gravity Recovery and Climate Experiment (GRACE) level-3 mascon data from the Jet Propulsion Laboratory (JPL), and Noah model data from the Global Land Data Assimilation System (GLDAS) data. The results show that the GNSS inversion offers superior spatial resolution, clearly capturing a water storage gradient from 300 mm in the Cascades to 20 mm in the basin and accurately distinguishing between mountainous and basin areas. However, the GRACE data exhibit blurred spatial variability, with the equivalent water height amplitude ranging from approximately 100 mm to 145 mm across the study area, making it difficult to resolve terrestrial water storage gradients. Moreover, GLDAS exhibits limitations in mountainous regions. The GNSS can provide continuous dynamic monitoring, with results aligning well with seasonal trends seen in GRACE and GLDAS data, although with a 1–2 months phase lag compared to the precipitation data, reflecting hydrological complexity. Future work may incorporate geological constraints, region-specific elastic models, and regularization strategies to improve monitoring accuracy. This study demonstrates the strong potential of GNSS technology for monitoring TWS dynamics and supporting environmental assessment, disaster warning, and water resource management. Full article

In water-scarce regions, natural ecosystems and agriculture increasingly compete for limited water resources, intensifying stress during periods of drought. To assess these competing demands, we applied a modified PT-JPL model that incorporates the thermal inertial approach as a substitute for relative humidity (RH) in estimating soil evaporation—a method that significantly outperforms the original PT-JPL formulation in Mediterranean semi-arid irrigated areas. This remote sensing framework enabled us to quantify spatial and temporal variations in water use across both natural and agricultural systems within the UNESCO World Heritage site of Doñana. Our analysis revealed an increasing evapotranspiration (ET) trend in intensified agricultural areas and rice fields surrounding the National Park (R = 0.3), contrasted by a strong negative ET trend in wetlands (R < −0.5). These opposing patterns suggest a growing diversion of water toward irrigation at the expense of natural ecosystems. The impact was especially marked during droughts, such as the 2011–2016 period, when precipitation declined by 16%. In wetlands, ET was significantly correlated with precipitation (R > 0.4), highlighting their vulnerability to reduced water inputs. These findings offer crucial insights to support sustainable water management strategies that balance agricultural productivity with the preservation of ecologically valuable systems under mounting climatic and anthropogenic pressures typical of semi-arid Mediterranean environments. Full article

To address the limited inversion accuracy caused by low-fidelity data in satellite gravimetry, this study proposes a data preprocessing framework based on iterative residual correction. Utilizing Level-1B observations from the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) satellite (January 2020), outliers were systematically detected and removed, while data gaps were compensated through spline interpolation. Experimental results demonstrate that the proposed method effectively mitigates data discontinuities and anomalous perturbations, achieving a significant improvement in data quality. Furthermore, a 60-order Earth gravity field model derived via the energy balance approach was validated against contemporaneous models published by the University of Texas Center for Space Research (CSR), German Research Centre for Geosciences (GFZ), and Jet Propulsion Laboratory (JPL). The results reveal a two-order-of-magnitude enhancement in inversion precision, with model accuracy improving from 10⁻⁶–10⁻⁷ to 10⁻⁸–10⁻⁹. This method provides a robust solution for enhancing the reliability of gravity field recovery in satellite-based geodetic missions. Full article

An event is an occurrence that takes place at a specific time and location that can be either weather-related (snowfall), social (crime), natural (earthquake), political (political unrest), or medical (pandemic) in nature. These events do not belong to the “normal” or “usual” spectrum and result in a change in a given situation; thus, their prediction would be very beneficial, both in terms of timely response to them and for their prevention, for example, the prevention of traffic accidents. However, this is currently challenging for researchers, who are called upon to manage and analyze a huge volume of data in order to design applications for predicting events using artificial intelligence and high computing power. Although significant progress has been made in this area, the heterogeneity in the input data that a forecasting application needs to process—in terms of their nature (spatial, temporal, and semantic)—and the corresponding complex dependencies between them constitute the greatest challenge for researchers. For this reason, the initial forecasting applications process data for specific situations, in terms of number and characteristics, while, at the same time, having the possibility to respond to different situations, e.g., an application that predicts a pandemic can also predict a central phenomenon, simply by using different data types. In this work, we present the forecasting applications that have been designed to date. We also present a model for predicting traffic accidents using categorical logic, creating a Knowledge Base using the Resolution algorithm as a proof of concept. We study and analyze all possible scenarios that arise under different conditions. Finally, we implement the traffic accident prediction model using the Prolog language with the corresponding Queries in JPL. Full article

The Tibetan Plateau (TP) serves as a vital ecological safeguard and water conservation region in China. In recent decades, substantial efforts have been made to promote vegetation greening across the TP; however, these interventions have added complexity to the local water balance and evapotranspiration (ET) processes. To investigate these dynamics, we apply the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model to simulate ET components in the TP. Through model sensitivity experiments, we isolate the contribution of vegetation greening to ET variations. Furthermore, we analyze the role of climatic drivers on ET using a suite of statistical techniques. Based on satellite and climate data from 1982 to 2018, we found the following: (1) The PT-JPL model successfully captured ET trends over the TP, revealing increasing trends in total ET, canopy transpiration, interception loss, and soil evaporation at rates of 0.06, 0.39, 0.005, and 0.07 mm/year, respectively. The model’s performance was validated using eddy covariance observations from three flux tower sites, yielding R² values of 0.81–0.86 and RMSEs ranging from 6.31 to 13.20 mm/month. (2) Vegetation greening exerted a significant enhancement on ET, with the mean annual ET under greening scenarios (258.6 ± 120.9 mm) being 2.9% greater than under non-greening scenarios (251.2 ± 157.2 mm) during 1982–2018. (3) Temperature and vapor pressure deficit were the dominant controls on ET, influencing 53.5% and 23% of the region, respectively, as identified consistently by both multiple linear regression and dominant factor analyses. These findings highlight the net influence of vegetation greening and offer valuable guidance for water management and sustainable ecological restoration efforts in the region. Full article

For the management of the water cycle, it is essential to comprehend evapotranspiration (ET) and how it changes over time and space, especially in relation to vegetation. Here, using the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model, we explored the spatiotemporal variations in ET across different time scales during 1982–2018 in the Wuding River Basin. We also quantitatively evaluated the driving mechanisms of climate and vegetation changes on ET changes. Results showed that the ET estimate by the PT-JPL model showed good agreement (R² = 0.71–0.84) with four ET products (PML, MOD16A2, GLASS, FLDAS). Overall, the ET increased significantly at a rate of 3.11 mm/year (p < 0.01). Spatially, ET in the WRB is higher in the southeast and lower in the northwest. Attribution analysis indicated that vegetation restoration (leaf area index) was the dominant driver of ET changes (99.93% basin area, p < 0.05), exhibiting both direct effects and indirect mediation through the Vapor Pressure Deficit. Temperature influences emerged predominantly through vegetation feedbacks rather than direct climatic forcing. These findings establish vegetation restoration as a key driver of regional ET, providing empirical support for optimizing revegetation strategies in semi-arid environments. Full article

Terrestrial water storage (TWS) in the Qaidam Basin in western China is highly sensitive to climate change. The GRACE mascon products provide variations of TWS anomalies (TWSAs), greatly facilitating the exploration of water storage dynamics. However, the main meteorological factors affecting the TWSA dynamics in this region need to be comprehensively investigated. In this study, variations in TWSAs over the Qaidam Basin from 2002 to 2024 were analyzed using three GRACE mascon products with CSR, JPL, and GSFC. The groundwater storage anomalies (GWAs) were extracted through GRACE and GLDAS products. The impact of meteorological elements on TWSAs and GWAs was identified. The results showed that the GRACE mascon products showed a significant increasing trend with a rate of 0.51 ± 0.13 mm per month in TWSAs across the entire basin from 2003 to 2016. The groundwater part accounted for the largest proportion and was the main contributor to the increase in TWS for the entire basin. In addition to the dominant role of precipitation, other meteorological elements, particularly air humidity and solar radiation, were also identified as important contributors to TWSA and GWA variations. This study highlighted the climatic effect on water storage variations, which have important implications for local water resource management and ecological conservation under ongoing climate change. Full article

Electric vehicles (EVs) play a crucial role in achieving sustainability goals, mitigating energy crises, and reducing air pollution. However, their rapid adoption poses significant challenges to the power grid, particularly during peak charging periods, necessitating advanced load management strategies. This study introduces an artificial intelligence (AI)-integrated optimal charging framework designed to facilitate fast charging and mitigate grid stress by smoothing the “duck curve”. Data from Caltech’s Adaptive Charging Network (ACN) at the National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL) site was collected and categorized into day and night patterns to predict charging duration based on key features, including start charging time and energy requested. The AI-driven charging strategy developed optimizes energy management, reduces peak loads, and alleviates grid strain. Additionally, the study evaluates the impact of integrating 1.5 million, 3 million, and 5 million EVs under various AI-based charging strategies, demonstrating the framework’s effectiveness in managing large-scale EV adoption. The peak power consumption reaches around 22,000 MW without EVs, 25,000 MW for 1.5 million EVs, 28,000 MW for 3 million EVs, and 35,000 MW for 5 million EVs without any charging strategy. By implementing an AI-driven optimal charging optimization strategy that considers both early charging and duck curve smoothing, the peak demand is reduced by approximately 16% for 1.5 million EVs, 21.43% for 3 million EVs, and 34.29% for 5 million EVs. Full article

Iraq and other semi-arid regions are facing severe climate change impacts, including increased temperatures and decreased rainfall. Changes to climate variables have posed a significant challenge to groundwater storage dynamics. In this regard, the Gravity Recovery and Climate Experiment (GRACE) mission permits novel originate groundwater storage variations. This study used the monthly GRACE satellite data for 2002–2023 to determine variations in groundwater storage (GWS). Changes in GWS were implied by extracting soil moisture, acquired from the Global Land Data Assimilation System (GLDAS), from the extracted Territorial Water Storage (TWS). The results demonstrated that an annual average ΔGWS trend ranged for the Goddard Space Flight Center (GSFC) mascon and Jet Propulsion Laboratory (JPL) mascon was from 0.94 to −1.14 cm/yr and 1.64 to −1.36 cm/yr, respectively. Also, the GSFC illustrated superior performance in estimating ΔGWS compared with the JPL in Iraq, achieving the lowest root mean square error at 0.28 mm and 0.60 mm and the highest coefficient of determination (R²) at 0.92 and 0.89, respectively. These data are critical for identifying areas of depletion, especially in areas where in situ data are lacking. These data allows us to fill the knowledge gaps; provide critical scientific information for monitoring and managing dynamic variations. Full article

Given that effective angular momentum (EAM) data demonstrate a strong correlation with length of day (LOD) data and are extensively utilized in the prediction of the universal time (UT1), this research integrated the EAM into the design of a Kalman filter. At the solution combination level, the UT1, LOD, and EAM were merged to derive a UT1/LOD sequence featuring higher accuracy and enhanced continuity. To begin with, a comprehensive evaluation of the three datasets was conducted to identify the systematic biases and periodic components of the LOD. Subsequently, geodetic angular momentum (GAM) data were employed to rectify the EAM data spanning from 2019 to 2022. Finally, the corrected EAM was combined with the UT1 and LOD through Kalman modeling. To evaluate the capability of this EAM-aided Kalman filter, Jet Propulsion Laboratory (JPL) and Wuhan University (WHU) LOD data, International Very Long Baseline Interferometry (VLBI) Service for Geodesy and Astrometry (IVS) intensive and National Time Service Center (NTSC) UT1 data, and German Research Centre for Geosciences (GFZ) EAM data were used for combination experiments. The final estimations of the UT1 and LOD were compared with the International Earth Rotation Service (IERS) Earth-orientation parameter (EOP) 20 C04 series. From July to September 2021, the root mean square (RMS) of the combined UT1 series was reduced from 38 µs to 26 µs for the IVS intensive UT1, with an improvement of 30%. The RMS of the combined UT1 series was reduced from 102 µs to 47 µs for the NTSC UT1 measurement, with an improvement of 54%. The bias of the LOD was effectively corrected and the RMS of the LOD improved by 60–70% and the standard deviation of the LOD improved by 11–30%. Further, the final estimated uncertainties of the UT1 and LOD are, in general, consistent with the estimated RMS, indicating a reasonable estimation of uncertainties. Comparative experiments with and without the EAM show that using EAM data can effectively reduce the extreme values, especially for the NTSC UT1 series with large uncertainties. In summary, this EAM-aided Kalman filter can produce UT1 and LOD series with improved accuracy, and with reasonable uncertainties. Full article

Evapotranspiration (ET) and water availability (WA) are critical components of the global water cycle. Although the effects of ecological restoration on ET and WA have been widely investigated, quantifying the impacts of multiple environmental factors on plant water consumption and regional water balance in dryland areas remains challenging. In this study, we investigated the spatial and temporal trends of ET and WA and isolated the contributions of vegetation restoration and climate change to variations in ET and WA in the Beijing–Tianjin Sand Source Region (BTSSR) in Northern China from 2001 to 2021, using the remote sensing-based Priestley–Taylor-Jet Propulsion Laboratory (PT-JPL) model and scenario simulation experiments. The results indicate that the estimated ET was consistent with field observations and state-of-the-art ET products. The annual ET in the BTSSR increased significantly by 1.28 mm yr⁻¹ from 2001 to 2021, primarily driven by vegetation restoration (0.78 mm yr⁻¹) and increased radiation (0.73 mm yr⁻¹). In contrast, the drier climate led to a decrease of 0.56 mm yr⁻¹ in ET. In semiarid areas, vegetation and radiation were the dominant factors driving the variability of ET, while in arid areas, relative humidity played a more critical role. Furthermore, reduced precipitation and increased plant water consumption resulted in a decline in WA by −0.91 mm yr⁻¹ during 2001–2021. Climate factors, rather than vegetation greening, determined the WA variations in the BTSSR, accounting for 77.6% of the total area. These findings can provide valuable insights for achieving sustainable ecological restoration and ensuring the sustainability of regional water resources in dryland China under climate change. This study also highlights the importance of simultaneously considering climate change and vegetation restoration in assessing their negative impacts on regional water availability. Full article

Accurate data collection and time series creation are crucial for understanding these changes. However, many areas lack reliable data due to geopolitical issues and government permissions. Urgent action is needed for sustainable water management. This study uses Gravity Recovery and Climate Experiment (GRACE) data to analyze monthly fluctuations in groundwater storage in the Missan region of Iraq from January 2022 to December 2023, using Goddard Space Flight Center (GSFC) mascon, Jet Propulsion Laboratory Downscaled (JPL_D), and Catchment Land Surface Model (CLSM). This study revealed the variability in GWS over the area using RS data and in integration with available monitoring wells. To investigate GWS variability, GSFC, JPL_D, and CLSM observed a downward trend in GWS in 2022; GSFC exhibits the highest negative groundwater trend, while CLSM has the lowest negative trend. Then, from January to June 2023, GSFC had the highest positive trend, while CLSM had the lowest positive trend. Most of the study period has a negative trend for remote sensing that matches the monitoring well data in situ, in which wells 1, 2, and 4 are negative trends of the study period. In conclusion, these results improve the role of remote sensing in groundwater monitoring in small-scale region unconfined aquifers, which supports decision-making in water resource management. The findings illustrated a match between the results derived from the GRACE data and monitoring well data. Full article

With the application and promotion of space geodesy, the popularization of remote sensing technology, and the development of artificial intelligence, a more accurate and stable Terrestrial Reference Frame (TRF) has become more urgent. For example, sea level change detection, crustal deformation monitoring, and driverless cars, among others, require the accuracy of the terrestrial reference frame to be better than 1 mm in positioning and 0.1 mm/a in velocity, respectively. However, the current frequently used ITRF2014 and ITRF2020 do not satisfy such requirements. Therefore, this paper analyzes the coordinate residual time series data of linear TRFs and finds there are still some unlabeled jumps and time-dependent periodic signals, especially in the GNSS coordinate residuals, which can lead to incorrect station epoch coordinates and velocities, further affecting the accuracy and stability of the TRF. The unlabeled jumps could be detected by the sequential t-test analysis of regime shifts (STARS) combined with the generalized extreme Studentized deviate (GESD) algorithms introduced in our earlier paper. These nonlinear time-dependent periodic signals could be modeled better by singular spectrum analysis (SSA) with respect to least squares fitting; the fitting period is no longer composed of semi-annual and annual items, as with ITRF2014. The periods of continuous coordinate residual time series data longer than 5 years are obtained by FFT. The results show that there are no period signals for individual SLR/VLBI sites, and there are still other period terms, such as 34 weeks, 20.8 weeks and 17.3 weeks, in addition to semi-annual and annual items for some GNSS sites. Moreover, after SSA corrections, the re-calculated TRF and the corresponding EOP could be obtained, based on data from the Chinese Earth Rotation and Reference System Service (CERS) TRF and the Earth Orientation Parameter (EOPs) multi-technique determination software package (CERS TRF&EOP V2.0) developed by the Shanghai Astronomical Observatory (SHAO). Their accuracy could be evaluated with respect to the ITRF2014 and the IERS 14 C04, respectively. The results show that the accuracy and stability of the newly established a nonlinear TRF and EOP based on SSA have been greatly improved and better than a linear TRF and EOP. SSA is better than least squares fitting, especially for those coordinate residual time series with varying amplitude and phase. For GPS, comparing with the ITRF2014, the station coordinate accuracy of 10.8% is better than 1 mm, and the station velocity accuracy of 4.4% is better than 0.1 mm/year. There are 3.1% VLBI stations, for which coordinate accuracy is better than 1 mm and velocity accuracy is better than 0.1 mm/year. However, there are no stations with coordinates and velocities better than 1 mm and 0.1 mm/year for the SLR and DORIS. The WRMS values of polar motion x, polar motion y, LOD, and UT1-UTC are reduced by 2.4%, 3.2%, 2.7%, and 0.96%, respectively. The EOP’s accuracy in SOL-B, in addition to LOD, is better than that of the JPL. Full article