Cited by Anthropogenic coal mining reducing groundwater storage in the Yellow River Basin

Groundwater Storage Loss in the Central Valley Analysis Using a Novel Method based on In Situ Data Compared to GRACE-Derived Data DOI

M. L. Stevens, Saul G. Ramirez,

E. Martin

et al.

Environmental Modelling & Software, Journal Year: 2025, Volume and Issue: unknown, P. 106368 - 106368

Published: Feb. 1, 2025

Language: Английский

Citations

A novel generative adversarial network and downscaling scheme for GRACE/GRACE-FO products: Exemplified by the Yangtze and Nile River Basins DOI

Jielong Wang, Yunzhong Shen, Joseph L. Awange

et al.

The Science of The Total Environment, Journal Year: 2025, Volume and Issue: 969, P. 178874 - 178874

Published: Feb. 24, 2025

Language: Английский

Citations

Improving understanding of drought using extended and downscaled GRACE data in the Pearl River Basin DOI

Xiangyu Wan,

Wei You, Xinchun Yang

et al.

Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 58, P. 102277 - 102277

Published: March 8, 2025

Language: Английский

Citations

A fusion strategy for terrestrial water storage anomaly inversion using joint GNSS and GRACE for Southwest China DOI

Yifan Shen, Wenzhu Hou,

Huizhong Zhu

et al.

Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 59, P. 102391 - 102391

Published: April 21, 2025

Language: Английский

Citations

Machine learning assessment of hydrological model performance under localized water storage changes through downscaling DOI

Ikechukwu Kalu, Christopher E. Ndehedehe, Vagner G. Ferreira

et al.

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 628, P. 130597 - 130597

Published: Dec. 10, 2023

The coarse spatial resolution of the Gravity Recovery and Climate Experiment (GRACE) data has limited its application in management local-scale water resources. To address this limitation, we developed a new downscaling approach using predictors from regional global hydrological models for 15-year period (2002–2017) tested it northern Great Artesian Basin, Australia. We used four different machine learning algorithms (support vector machine, partial least squares, gaussian process random forest) to downscale original GRACE estimate 0.5° grain size 0.1° (global) 0.05° (regional). This was based on precipitation, evapotranspiration runoff estimates Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) Australian Water Outlook (AWO) models, respectively. downscaled products were validated 42 in-situ precipitation observations spread across test region. further evaluated which best mimicked hydrology range statistical metrics. Our results showed that characterized dynamics local scale (rainfall v. product), regression algorithm made predictions both models. correlation coefficients raw values varied 0.45 0.49 while standardized 0.46 0.52 with forest model providing fitting regional-based products. employed study may be readily integrated into resources planning programs.

Language: Английский

Citations

HRU-based Downscaling of GRACE-TWS to Quantify the Hydrogeological Fluxes and Specific Yield in the Lower Middle Ganga Basin DOI

Ranveer Kumar,

Shishir Gaur,

Pramod Soni

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 639, P. 131591 - 131591

Published: July 3, 2024

Language: Английский

Citations

Assessing and attributing flood potential in Brazil using GPS 3D deformation DOI

Xinghai Yang, Linguo Yuan, Miao Tang

et al.

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 318, P. 114535 - 114535

Published: Dec. 5, 2024

Language: Английский

Citations

Automatic error correction: Improving annotation quality for model optimization in oil-exploration related land disturbances mapping DOI

Y. Cai,

Bingxu Hu,

Hongjie He

et al.

The Egyptian Journal of Remote Sensing and Space Science, Journal Year: 2024, Volume and Issue: 27(1), P. 108 - 119

Published: Feb. 4, 2024

The manual extraction of land disturbances associated with oil exploration, which normally includes resource roads, mining facilities, and well pads, presents significant challenges in terms cost time. Accurate monitoring mapping resulting from exploration plays a crucial role conducting comprehensive environmental assessments facilitating effective reclamation initiatives. However, prevailing deep learning methodologies the realm gas primarily focus on spill detection, neglecting critical aspect thus overlooking impact land. Furthermore, given that sites are scattered relatively diminutive compared to other covers, their detection poses substantial difficulties. This paper proposes an automatic error-correcting (AEC) algorithm address deficiencies ground truth data quality. AEC method was integrated into deep-learning framework for disturbance extraction, specifically tailored analysis exploration. efficacy our validated dataset collected Alberta covering area sand sites. application significantly enhanced accuracy analysis, thereby contributing more hydrocarbon timely planning by government. results demonstrate notable improvements both average pixel (AA) mean intersection over union (mIoU), ranging 8.3% 15.4% 0.5% 5.8%, respectively. These enhancements, have profound implications precision prove proposed can serve dual purpose: correcting errors efficiently detecting features area.

Language: Английский

Citations

Monitoring Terrestrial Water Storage Using GRACE/GRACE-FO Data over India: A Review DOI

Maniranjan Kumar, Pramod Soni, Debshri Swargiary

et al.

Iranian Journal of Science and Technology Transactions of Civil Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 12, 2024

Language: Английский

Citations

Meteorological Drought Analysis in Kızılırmak Basin, Türkiye DOI

Hamza Barkad Robleh,

Mehmet İshak Yüce, Musa Eşit

et al.

Environmental earth sciences, Journal Year: 2024, Volume and Issue: unknown, P. 97 - 108

Published: Oct. 15, 2024

Language: Английский

Citations