The GWR model-based regional downscaling of GRACE/GRACE-FO derived groundwater storage to investigate local-scale variations in the North China Plain

Shoaib Ali; Jiangjun Ran; Yi Luan; Behnam Khorrami; Yun Xiao; Natthachet Tangdamrongsub

doi:10.1016/j.scitotenv.2023.168239

The GWR model-based regional downscaling of GRACE/GRACE-FO derived groundwater storage to investigate local-scale variations in the North China Plain

Sci Total Environ. 2024 Jan 15:908:168239. doi: 10.1016/j.scitotenv.2023.168239. Epub 2023 Nov 4.

Authors

Shoaib Ali¹, Jiangjun Ran², Yi Luan³, Behnam Khorrami⁴, Yun Xiao⁵, Natthachet Tangdamrongsub⁶

Affiliations

¹ Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen 518005, China. Electronic address: shoaib@sustech.edu.cn.
² Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen 518005, China. Electronic address: ranjj@sustech.edu.cn.
³ Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen 518005, China. Electronic address: luany@sustech.edu.cn.
⁴ Department of GIS, The Graduate School of Natural and Applied Sciences, Dokuz Eylul University, Izmir, Türkiye. Electronic address: behnam.khorrami@ogr.deu.edu.tr.
⁵ Xi'an Research Institute of Surveying and Mapping, Xi'an, China.
⁶ Water Engineering and Management, School of Engineering and Technology, Asian Institute of Technology, Pathum Thani 12120, Thailand. Electronic address: natthachet@ait.ac.th.

PMID: 37931810
DOI: 10.1016/j.scitotenv.2023.168239

Abstract

Groundwater storage and depletion fluctuations in response to groundwater availability for irrigation require understanding on a local scale to ensure a reliable groundwater supply. However, the coarser spatial resolution and intermittent data gaps to estimate the regional groundwater storage anomalies (GWSA) prevent the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GARCE-FO) mission from being applied at the local scale. To enhance the resolution of GWSA measurements using machine learning approaches, numerous recent efforts have been made. With a focus on the development of a new algorithm, this study enhanced the GWSA resolution estimates to 0.05° by extensively investigating the continuous spatiotemporal variations of GWSA based on the regional downscaling approach using a regression algorithm known as the geographically weighted regression model (GWR). First, the modified seasonal decomposition LOESS method (STL) was used to estimate the continuous terrestrial water storage anomaly (TWSA). Secondly, to separate GWSA from TWSA, a water balance equation was used. Third, the continuous GWSA was downscaled to 0.05° based on the GWR model. Finally, spatio-temporal properties of downscaled GWSA were investigated in the North China Plain (NCP), China's fastest-urbanizing area, from 2003 to 2022. The results of the downscaled GWSA were spatially compatible with GRACE-derived GWSA. The downscaled GWSA results are validated (R = 0.83) using in-situ groundwater level data. The total loss of GWSA in cities of the NCP fluctuated between 2003 and 2022, with the largest loss seen in Handan (-15.21 ± 7.25 mm/yr), Xingtai (-14.98 ± 7.25 mm/yr), and Shijiazhuang (-14.58 ± 7.25 mm/yr). The irrigated winter-wheat farming strategy is linked to greater groundwater depletion in several cities of NCP (e.g., Xingtai, Handan, Anyang, Hebi, Puyang, and Xinxiang). The study's high-resolution findings can help with understanding local groundwater depletion that takes agricultural water utilization and provide quantitative data for water management.

Keywords: Downscaling; GRACE; GWR; GWSA; North China Plain; STL.