Constructing GRACE-Based 1 km Resolution Groundwater Storage Anomalies in Arid Regions Using an Improved Machine Learning Downscaling Method: A Case Study in Alxa League, China
文献类型：期刊论文
| 作者 | Wang, Jie; Xu, Duanyang; Li, Hongfei |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2023-06-02 |
| 卷号 | 15期号:11页码:2913 |
| ISSN号 | 2072-4292 |
| 关键词 | groundwater storage anomalies GRACE spatial downscaling time-lag effect arid region |
| DOI | 10.3390/rs15112913 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Using the Gravity Recovery and Climate Experiment (GRACE) satellite to monitor groundwater storage (GWS) anomalies (GWSAs) at the local scale is difficult due to the low spatial resolution of GRACE. Many attempts have been made to downscale GRACE-based GWSAs to a finer resolution using statistical downscaling approaches. However, the time-lag effect of GWSAs relative to environmental variables and optimal model parameters is always ignored, making it challenging to achieve good spatial downscaling, especially for arid regions with longer groundwater infiltration paths. In this paper, we present a novel spatial downscaling method for constructing GRACE-based 1 km-resolution GWSAs by using the back propagation neural network (BPNN) and considering the time-lag effect and the number of hidden neurons in the model. The method was validated in Alxa League, China. The results show that a good simulation performance was achieved by adopting varying lag times (from 0 to 4 months) for the environmental variables and 14 hidden neurons for all the networks, with a mean correlation coefficient (CC) of 0.81 and a mean root-mean-square error (RMSE) of 0.70 cm for each month from April 2002 to December 2020. The downscaled GWSAs were highly consistent with the original data in terms of long-term temporal variations (the decline rate of the GWSAs was about -0.40 +/- 0.01 cm/year) and spatial distribution. This study provides a feasible approach for downscaling GRACE data to 1 km resolution in arid regions, thereby assisting with the sustainable management and conservation of groundwater resources at different scales. |
| 学科主题 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS关键词 | ARTIFICIAL NEURAL-NETWORKS ; RECHARGE |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/193769]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 作者单位 | 1.University of Chinese Academy of Sciences, CAS 2.Chinese Academy of Sciences 3.Institute of Geographic Sciences & Natural Resources Research, CAS 4.Liaoning Normal University |
| 推荐引用方式 GB/T 7714 | Wang, Jie,Xu, Duanyang,Li, Hongfei. Constructing GRACE-Based 1 km Resolution Groundwater Storage Anomalies in Arid Regions Using an Improved Machine Learning Downscaling Method: A Case Study in Alxa League, China[J]. REMOTE SENSING,2023,15(11):2913. |
| APA | Wang, Jie,Xu, Duanyang,&Li, Hongfei.(2023).Constructing GRACE-Based 1 km Resolution Groundwater Storage Anomalies in Arid Regions Using an Improved Machine Learning Downscaling Method: A Case Study in Alxa League, China.REMOTE SENSING,15(11),2913. |
| MLA | Wang, Jie,et al."Constructing GRACE-Based 1 km Resolution Groundwater Storage Anomalies in Arid Regions Using an Improved Machine Learning Downscaling Method: A Case Study in Alxa League, China".REMOTE SENSING 15.11(2023):2913. |
入库方式： OAI收割
来源：地理科学与资源研究所
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