Improving the spatial resolution of GRACE-based groundwater storage estimates using a machine learning algorithm and hydrological model

The low-resolution characteristic of Gravity Recovery and Climate Experiment (GRACE) satellite data greatly limits their application in many fields at regional or local scales. Aiming to overcome this limitation, the partial least squares regression (PLSR) model is firstly utilized to assess the imp...

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Vydané v:	Hydrogeology journal Ročník 30; číslo 3; s. 947 - 963
Hlavní autori:	Yin, Wenjie, Zhang, Gangqiang, Liu, Futian, Zhang, Dasheng, Zhang, Xiuping, Chen, Sheming
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2022 Springer Nature B.V
Predmet:	Air temperature Algorithms Anomalies Aquatic Pollution China climate Correlation coefficient Correlation coefficients data collection Earth and Environmental Science Earth Sciences Estimates Evapotranspiration Geology Geophysics/Geodesy GRACE (experiment) Gravity Groundwater Groundwater levels Groundwater storage Heterogeneity Hydrogeology Hydrologic models Hydrology Hydrology/Water Resources Independent variables Land surface temperature Least squares method Machine learning Modelling Moisture effects Regression models remote sensing Resolution River basins Rivers Satellite data Soil moisture Soil temperature soil water Spatial discrimination Spatial distribution Spatial resolution Surface temperature Waste Water Technology Water Management Water Pollution Control Water Quality/Water Pollution Water resources Water resources management Water storage watersheds wavelet Wavelet transforms Haihe River Basin China Groundwater storage anomalies Downscaling methods China Remote and satellite sensing GRACE satellites
ISSN:	1431-2174, 1435-0157
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Abstract	The low-resolution characteristic of Gravity Recovery and Climate Experiment (GRACE) satellite data greatly limits their application in many fields at regional or local scales. Aiming to overcome this limitation, the partial least squares regression (PLSR) model is firstly utilized to assess the importance of some independent variables that are commonly employed in GRACE downscaling research. Three kinds of downscaling models are chosen to improve the resolution of GRACE-based water storage estimates from 1 to 0.25°, namely: multivariable linear regression, random forest (RF), and NoahV2.1. Results indicate that terrestrial water storage anomalies are more closely related to four independent variables in the Haihe River Basin, China: these variables are evapotranspiration, land surface temperature, air temperature, and soil moisture. With respect to the spatial distribution, the downscaled results based on the NoahV2.1 and RF models can effectively capture the subgrid heterogeneity while preserving the water storage characteristics at the original scale. By verifying the downscaled results with measured groundwater levels, it can be observed that the correlation coefficient between the RF-based downscaled groundwater storage anomalies (GWSA) and in-situ measurements is increased by 20.55% (Beijing), 9.13% (Tianjin), and 10.48% (Hebei) relative to the downscaled results based on the NoahV2.1 model. The cross wavelet transform illustrates that the meteorological factors have a strong influence on the GWSA series in the Haihe River Basin with an approximately 12-month signal during 2003–2016. This study can provide high-resolution GWSA datasets for water resources management and also provide a reference for the selection of dominant independent variables.
AbstractList	The low-resolution characteristic of Gravity Recovery and Climate Experiment (GRACE) satellite data greatly limits their application in many fields at regional or local scales. Aiming to overcome this limitation, the partial least squares regression (PLSR) model is firstly utilized to assess the importance of some independent variables that are commonly employed in GRACE downscaling research. Three kinds of downscaling models are chosen to improve the resolution of GRACE-based water storage estimates from 1 to 0.25°, namely: multivariable linear regression, random forest (RF), and NoahV2.1. Results indicate that terrestrial water storage anomalies are more closely related to four independent variables in the Haihe River Basin, China: these variables are evapotranspiration, land surface temperature, air temperature, and soil moisture. With respect to the spatial distribution, the downscaled results based on the NoahV2.1 and RF models can effectively capture the subgrid heterogeneity while preserving the water storage characteristics at the original scale. By verifying the downscaled results with measured groundwater levels, it can be observed that the correlation coefficient between the RF-based downscaled groundwater storage anomalies (GWSA) and in-situ measurements is increased by 20.55% (Beijing), 9.13% (Tianjin), and 10.48% (Hebei) relative to the downscaled results based on the NoahV2.1 model. The cross wavelet transform illustrates that the meteorological factors have a strong influence on the GWSA series in the Haihe River Basin with an approximately 12-month signal during 2003–2016. This study can provide high-resolution GWSA datasets for water resources management and also provide a reference for the selection of dominant independent variables.
Author	Zhang, Gangqiang Yin, Wenjie Chen, Sheming Zhang, Dasheng Zhang, Xiuping Liu, Futian
Author_xml	– sequence: 1 givenname: Wenjie surname: Yin fullname: Yin, Wenjie organization: Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology – sequence: 2 givenname: Gangqiang surname: Zhang fullname: Zhang, Gangqiang email: 211804020040@home.hpu.edu.cn organization: Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, School of Surveying and Landing Information Engineering, Henan Polytechnic University – sequence: 3 givenname: Futian surname: Liu fullname: Liu, Futian organization: Tianjin Center, China Geological Survey – sequence: 4 givenname: Dasheng surname: Zhang fullname: Zhang, Dasheng organization: Hebei Provincial Institute of Water Science – sequence: 5 givenname: Xiuping surname: Zhang fullname: Zhang, Xiuping organization: Jiangxi Academy of Water Science and Engineering – sequence: 6 givenname: Sheming surname: Chen fullname: Chen, Sheming organization: Tianjin Center, China Geological Survey
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Snippet	The low-resolution characteristic of Gravity Recovery and Climate Experiment (GRACE) satellite data greatly limits their application in many fields at regional...
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StartPage	947
SubjectTerms	Air temperature Algorithms Anomalies Aquatic Pollution China climate Correlation coefficient Correlation coefficients data collection Earth and Environmental Science Earth Sciences Estimates Evapotranspiration Geology Geophysics/Geodesy GRACE (experiment) Gravity Groundwater Groundwater levels Groundwater storage Heterogeneity Hydrogeology Hydrologic models Hydrology Hydrology/Water Resources Independent variables Land surface temperature Least squares method Machine learning Modelling Moisture effects Regression models remote sensing Resolution River basins Rivers Satellite data Soil moisture Soil temperature soil water Spatial discrimination Spatial distribution Spatial resolution Surface temperature Waste Water Technology Water Management Water Pollution Control Water Quality/Water Pollution Water resources Water resources management Water storage watersheds wavelet Wavelet transforms
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Title	Improving the spatial resolution of GRACE-based groundwater storage estimates using a machine learning algorithm and hydrological model
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