Enhanced Flood Monitoring in the Pearl River Basin via GAIN-Reconstructed GRACE Terrestrial Water Storage Anomalies DOI Creative Commons
Jing Wang,

Haiyang Li,

Shuguang Wu

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(24), P. 4727 - 4727

Published: Dec. 18, 2024

Floods are a significant and pervasive threat globally, exacerbated by climate change increasing extreme weather events. The Gravity Recovery Climate Experiment (GRACE) its follow-on mission (GRACE-FO) provide crucial insights into terrestrial water storage anomalies (TWSA), which vital for understanding flood dynamics. However, the observational gap between these missions presents challenges monitoring, affecting estimation of long-term trends limiting analysis interannual variability, thereby impacting overall accuracy. Reconstructing missing data GRACE GRACE-FO is essential systematically spatiotemporal distribution characteristics driving mechanisms changes in regional reserves. In this study, Generative Adversarial Imputation Network (GAIN) applied to improve monitoring capability events Pearl River Basin (PRB). First, GRACE/GRACE-FO TWSA imputed with GAIN compared long short-term memory (LSTM) k-Nearest Neighbors (KNN) methods. Using reconstructed data, we develop Flood Potential Index (FPI) integrating GRACE-based precipitation analyze key FPI variability against actual results indicate that effectively predicts gap, an average improvement approximately 50.94% over LSTM 68.27% KNN. proves effective PRB, validating reliability TWSA. Additionally, achieves predictive accuracy 79.7% real events, indicating better captured using This study demonstrates effectiveness enhancing continuity, providing reliable framework large-scale risk assessment offering valuable management vulnerable regions.

Language: Английский

Disentangling Ecological Restoration's Impact on Terrestrial Water Storage DOI Creative Commons
Xiaozhe Shen,

Liantao Niu,

Xiaoxu Jia

et al.

Geophysical Research Letters, Journal Year: 2025, Volume and Issue: 52(4)

Published: Feb. 12, 2025

Abstract Large‐scale ecological restoration (ER) in semiarid regions is often associated with substantial terrestrial water storage (TWS) depletion. This study challenged previous estimates by demonstrating the critical importance of considering other human activities when assessing ER impacts on TWS. Using a novel analytical framework integrating GRACE satellite data and ground observations, we analyzed TWS changes China's Mu Us Sandyland under two scenarios: without mining farming activities. Our results show that consumed at an average rate 11.7 ± 12.2 mm yr −1 from 2003 to 2022. Neglecting led 251% overestimation ER's effect provided more nuanced understanding resource dynamics restored ecosystems, emphasizing need for comprehensive approaches assessments informing sustainable land management strategies globally.

Language: Английский

Citations

2
Assessing long-term water storage dynamics in Afghanistan: An integrated approach using machine learning, hydrological models, and remote sensing DOI Creative Commons
Abdul Haseeb Azizi, Fazlullah Akhtar, Bernhard Tischbein

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 370, P. 122901 - 122901

Published: Oct. 21, 2024

Language: Английский

Citations

4
Combining machine learning algorithms for bridging gaps in GRACE and GRACE Follow-On missions using ERA5-Land reanalysis DOI Creative Commons
Jaydeo K. Dharpure, I. M. Howat, Saurabh Kaushik

et al.

Science of Remote Sensing, Journal Year: 2025, Volume and Issue: unknown, P. 100198 - 100198

Published: Jan. 1, 2025

Language: Английский

Citations

0
Monsoon-Based Linear Regression Analysis for Filling Data Gaps in Gravity Recovery and Climate Experiment Satellite Observations DOI Creative Commons
Hussein A. Mohasseb, Wenbin Shen, Jiashuang Jiao

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(8), P. 1424 - 1424

Published: April 17, 2024

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

Language: Английский

Citations

3
Comparison of three spatial downscaling concepts of GRACE data using random forest model DOI Open Access

Chu Jiangdong,

Xiaoling Su,

Zhang Te

et al.

Journal of Lake Sciences, Journal Year: 2024, Volume and Issue: 36(3), P. 951 - 962

Published: Jan. 1, 2024

陆地水储量是赋存在陆地上各种形式水的综合体现,研究其时空变化对认识区域水循环过程和水资源调控等具有重要意义。然而现有陆地水储量变化数据实际分辨率较低,限制了其在中小流域或地区中的应用。针对这一问题,本文基于GRACE重力卫星和其后续卫星GRACE-FO反演的陆地水储量变化数据,首先采用随机森林模型,分别基于格点、区域(流域)和区域(全国)3种空间降尺度思路将GRACE数据降尺度至0.25°×0.25°,后结合GLDAS模型数据,基于水量平衡原理计算得到地下水储量变化数据,最后基于降尺度模型模拟效果和实测地下水位数据评估3种降尺度思路在全国的适用性。结果表明:随机森林模型能够较好地模拟驱动数据(降水、气温、植被条件指数和土壤水储量)与GRACE数据的统计关系,验证期格点降尺度思路的平均相关系数总体在0.6左右,区域降尺度思路的平均纳什效率系数、相关系数和均方根误差分别>0.5、>0.75和<6.6 cm,3种空间降尺度思路的模拟精度均满足基本要求;2003—2021年间,GRACE数据、格点降尺度、区域降尺度(流域)和区域降尺度(全国)得到的我国陆地水储量亏缺量分别约为119.5×108、62.4×108、121.1×108和121.8×108 m3/a,地下水储量亏缺量分别约为230.0×108、171.8×108、235.6×108和236.4×108 m3/a,受制于样本数较少等原因,格点降尺度结果精度较差;两种区域降尺度思路得到的水储量变化速率均和原始GRACE数据基本一致,模拟结果均优于格点降尺度,且细化到流域的区域降尺度对地下水储量变化验证精度有一定的改善。区域降尺度具有适用性强、模拟精度高、计算效率高的优势,研究结果可为流域水资源可持续利用以及水资源规划等提供精细化的水储量变化数据。;Terrestrial water storage is a comprehensive manifestation of land water. Analyzing the spatio-temporal changes terrestrial vital for improving understanding hydrological processes and resource management. However, low spatial resolution existing anomalies derived from GRACE limits their applications in small medium basins. To improve resolution, random forest models were utilized to downscale data satellites its follow-up mission GRACE-follow on into 0.25°×0.25° at three scales, including grid cell, regional (basin) (China). Groundwater calculated by combining vertical budget GLDAS model output. The performance downscaling was evaluated based models' indicators in-situ groundwater levels across China. Results show that can accurately establish statistical relationship between input variables (precipitation, temperature, vegetation condition index, soil storage) data. average correlation coefficient cell method during validation period generally around 0.6. Nash efficiency coefficient, root mean square error are greater than 0.5, 0.75 less 6.6 cm, respectively. Overall, accuracy different downscaled promising. From 2003 2021, deficit China's original, downscaling-based, downscaling-based (China) about 119.5×108, 62.4×108, 121.1×108 121.8×108 m3/a, approximately 230.0×108, 171.8×108, 235.6×108 236.4×108 simulation results relatively poor due sample size. Change rates obtained methods consistent with original data, indicating better grid-cell method. Compared method, smoother space, refined basin could anomalies. Regional has advantages strong applicability, high computational downscaling. Findings this study provide sustainable utilization resources planning basin-scale.

Language: Английский

Citations

3
Monitoring terrestrial water storage changes using GNSS vertical coordinate time series in Amazon River basin DOI Creative Commons
Yifu Liu,

Keke Xu,

Zengchang Guo

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 15, 2024

Aiming at the Terrestrial Water Storage(TWS) changes in Amazon River basin, this article uses coordinate time series data of Global Navigation Satellite System (GNSS), adopts Variational Mode Decomposition and Bidirectional Long Short Term Memory(VMD-BiLSTM) method to extract vertical crustal deformation series, then Principal Component Analysis(PCA) invert terrestrial water storage Basin from July 15, 2012 25, 2018. Then, GNSS inversion results were compared with equivalent height retrieved Gravity Recovery Climate Experiment (GRACE) data. The show that (1) extraction proposed has better denoising effect than traditional method; (2) surface hydrological load can be well calculated using regional TWS inverted, which a good consistency result GRACE storage, almost same seasonal variation characteristics; (3) There is strong correlation between by based on characteristics mass gravitational field changes, but satellite's all-weather measurement finer scale results. In summary, used as supplementary technology for monitoring complement advantages technology.

Language: Английский

Citations

1
Analysis of watershed terrestrial water storage anomalies by Bi-LSTM with X-11 time series prediction combined model DOI
Yongyao Su,

Lei Feng,

Jiancheng Li

et al.

Geosciences Journal, Journal Year: 2024, Volume and Issue: 28(6), P. 941 - 958

Published: Oct. 8, 2024

Language: Английский

Citations

0
Monitoring Terrestrial Water Storage Changes Using GNSS Vertical Coordinate Time Series in Amazon River Basin DOI Creative Commons
Yifu Liu,

Keke Xu,

Zengchang Guo

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 29, 2024

Abstract Aiming at the Terrestrial Water Storage(TWS) changes in Amazon River basin, this article uses coordinate time series data of Global Navigation Satellite System (GNSS), adopts Variational Mode Decomposition and Bidirectional Long Short Term Memory(VMD-BiLSTM) method to extract vertical crustal deformation series, then Principal Component Analysis(PCA) invert terrestrial water storage Basin from July 15, 2012 25, 2018. Then, GNSS inversion results were compared with equivalent height retrieved Gravity Recovery Climate Experiment (GRACE) data. The show that (1) extraction proposed has different advantages traditional methods; (2) surface hydrological load can be well calculated using regional TWS inverted, which a good consistency result GRACE storage, almost same seasonal variation characteristics; (3) There is strong correlation between by based on characteristics mass gravitational field changes, but satellite's all-weather measurement finer scale results. In summary, used as supplementary technology for monitoring complement technology.

Language: Английский

Citations

0
Enhanced Flood Monitoring in the Pearl River Basin via GAIN-Reconstructed GRACE Terrestrial Water Storage Anomalies DOI Creative Commons
Jing Wang,

Haiyang Li,

Shuguang Wu

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(24), P. 4727 - 4727

Published: Dec. 18, 2024

Floods are a significant and pervasive threat globally, exacerbated by climate change increasing extreme weather events. The Gravity Recovery Climate Experiment (GRACE) its follow-on mission (GRACE-FO) provide crucial insights into terrestrial water storage anomalies (TWSA), which vital for understanding flood dynamics. However, the observational gap between these missions presents challenges monitoring, affecting estimation of long-term trends limiting analysis interannual variability, thereby impacting overall accuracy. Reconstructing missing data GRACE GRACE-FO is essential systematically spatiotemporal distribution characteristics driving mechanisms changes in regional reserves. In this study, Generative Adversarial Imputation Network (GAIN) applied to improve monitoring capability events Pearl River Basin (PRB). First, GRACE/GRACE-FO TWSA imputed with GAIN compared long short-term memory (LSTM) k-Nearest Neighbors (KNN) methods. Using reconstructed data, we develop Flood Potential Index (FPI) integrating GRACE-based precipitation analyze key FPI variability against actual results indicate that effectively predicts gap, an average improvement approximately 50.94% over LSTM 68.27% KNN. proves effective PRB, validating reliability TWSA. Additionally, achieves predictive accuracy 79.7% real events, indicating better captured using This study demonstrates effectiveness enhancing continuity, providing reliable framework large-scale risk assessment offering valuable management vulnerable regions.

Language: Английский

Citations

0