Downscaled GRACE/GRACE-FO observations for spatial and temporal monitoring of groundwater storage variations at the local scale using machine learning

Ali，Shoaib1; Ran，Jiangjun1; Khorrami，Behnam2,3; Wu，Haotian1; Tariq，Aqil4; Jehanzaib，Muhammad5,6; Khan，Muhammad Mohsin7; Faisal，Muhammad8

2024-05-01

10.1016/j.gsd.2024.101100

Groundwater for Sustainable Development   影响因子和分区

2352-801X

Groundwater utilization for several purposes such as irrigation in agriculture, industry, and domestic use substantially impacts water storage. Groundwater Storage Anomaly (GWSA) estimates have improved owing to the Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GRACE-FO) advancements. However, the characterization of GWSA fluctuation hotspots has been hindered by the coarse resolution of GRACE data. To better measure groundwater storage and depletion variations throughout an area and identify GWSA variation hotspots, a fine spatial resolution of GWSA estimations is required. Therefore, due to the coarse resolution of GRACE measurements, the eXtreme Gradient Boosting (XGBoost) model was developed to simulate fine resolution 0.1° GWSA combining climatic variables (soil moisture storage, evapotranspiration, temperature, surface runoff, and rainfall) from improved spatial high resolution FLDAS (Famine Early Warning Systems Network Land Data Assimilation System) model derived data and geospatial variables (elevation, slope, and aspect) extracted from Digital Elevation Model (DEM). A correlation of 0.98 demonstrated that the XGBoost model successfully simulated groundwater storage at a finer scale over the Upper Indus Plain Aquifer (UIPA). The findings suggested that the UIPA's groundwater storage has been depleted at an annual rate of 0.44 km/yr which was 7.94 km in total between 2003 and 2020. According to the results, there seems to be consistency between the downscaled and original GWSA regarding temporal and spatial variability. The results were verified to show an improved correlation of 0.77 between the downscaled and the in-situ GWSA, compared to 0.75 between the GRACE-derived and the in-situ GWSA.

Downscaling GRACE Groundwater depletion GWSA Machine learning TWSA UIPA

ESCI ; EI

英语

第一 ; 通讯

2-s2.0-85184149311

Scopus

1.Department of Earth and Space Sciences,Southern University of Science and Technology,Shenzhen,518005,China
2.Department of Remote Sensing &GIS,University of Tabriz,Iran
3.Department of GIS,The Graduate School of Natural and Applied Sciences,Dokuz Eylul University,Izmir,Turkey
4.Department of Wildlife,Fisheries and Aquaculture,College of Forest Resources,Mississippi State University,775 Stone Boulevard,39762-9690,United States
5.Institute for Climate and Atmospheric Science,School of Earth and Environment,University of Leeds,Leeds,United Kingdom
6.Department of Civil Engineering & Technology,Qurtuba University of Science and Information Technology,Dera Ismail Khan,29050,Pakistan
7.Department of Agricultural Engineering,Muhammad Nawaz Shareef University of Agriculture Multan,Multan,Pakistan
8.Centre for Ports and Maritime Safety,Dalian Maritime University,Dalian,China

Ali，Shoaib,Ran，Jiangjun,Khorrami，Behnam,et al. Downscaled GRACE/GRACE-FO observations for spatial and temporal monitoring of groundwater storage variations at the local scale using machine learning[J]. Groundwater for Sustainable Development,2024,25.

Ali，Shoaib.,Ran，Jiangjun.,Khorrami，Behnam.,Wu，Haotian.,Tariq，Aqil.,...&Faisal，Muhammad.(2024).Downscaled GRACE/GRACE-FO observations for spatial and temporal monitoring of groundwater storage variations at the local scale using machine learning.Groundwater for Sustainable Development,25.

Ali，Shoaib,et al."Downscaled GRACE/GRACE-FO observations for spatial and temporal monitoring of groundwater storage variations at the local scale using machine learning".Groundwater for Sustainable Development 25(2024).