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DOI | 10.3390/rs11070824 |
Downscaling GRACE TWSA Data into High-Resolution Groundwater Level Anomaly Using Machine Learning-Based Models in a Glacial Aquifer System | |
Seyoum, Wondwosen M.1; Kwon, Dongjae1; Milewski, Adam M.2 | |
发表日期 | 2019 |
ISSN | 2072-4292 |
卷号 | 11期号:7 |
英文摘要 | With continued threat from climate change and human impacts, high-resolution and continuous hydrologic data accessibility has a paramount importance for predicting trends and availability of water resources. This study presents a novel machine learning (ML)-based downscaling algorithm that produces a high spatial resolution groundwater level anomaly (GWLA) from the Gravity Recovery and Climate Experiment (GRACE) data by utilizing the relationship between Terrestrial Water Storage Anomaly (TWSA) from GRACE and other land surface and hydro-climatic variables (e.g., vegetation coverage, land surface temperature, precipitation, streamflow, and in-situ groundwater level data). The predicted downscaled GWLA data were tested using monthly in-situ groundwater level observations. Of the 32 groundwater monitoring wells available in the study site, 21 wells were used to develop the ML-based downscaling model, while the remaining 11 wells were used to assess the performance of the ML-based downscaling model. The test results showed that the model satisfactorily reproduces the spatial and temporal variation of the GWLA in the area, with acceptable correlation coefficient and Nash-Sutcliffe Efficiency values of similar to 0.76 and similar to 0.45, respectively. GRACE TWSA was the most influential predictor variable in the models, followed by stream discharge and soil moisture storage. Though model limitations and uncertainty could exist due to high spatial heterogeneity of the geologic materials and omission of human impact (e.g., abstraction), the significance of the result is undeniable, particularly in areas where in-situ well measurements are sparse. |
WOS研究方向 | Remote Sensing |
来源期刊 | REMOTE SENSING
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文献类型 | 期刊论文 |
条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/95701 |
作者单位 | 1.Illinois State Univ, Dept Geog Geol & Environm, Normal, IL 61790 USA; 2.Univ Georgia, Dept Geol, Athens, GA 30602 USA |
推荐引用方式 GB/T 7714 | Seyoum, Wondwosen M.,Kwon, Dongjae,Milewski, Adam M.. Downscaling GRACE TWSA Data into High-Resolution Groundwater Level Anomaly Using Machine Learning-Based Models in a Glacial Aquifer System[J],2019,11(7). |
APA | Seyoum, Wondwosen M.,Kwon, Dongjae,&Milewski, Adam M..(2019).Downscaling GRACE TWSA Data into High-Resolution Groundwater Level Anomaly Using Machine Learning-Based Models in a Glacial Aquifer System.REMOTE SENSING,11(7). |
MLA | Seyoum, Wondwosen M.,et al."Downscaling GRACE TWSA Data into High-Resolution Groundwater Level Anomaly Using Machine Learning-Based Models in a Glacial Aquifer System".REMOTE SENSING 11.7(2019). |
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