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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Hydrogeology journal Jg. 30; H. 3; S. 947 - 963
Hauptverfasser: Yin, Wenjie, Zhang, Gangqiang, Liu, Futian, Zhang, Dasheng, Zhang, Xiuping, Chen, Sheming
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2022
Springer Nature B.V
Schlagworte:
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
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1431-2174
1435-0157
DOI:10.1007/s10040-021-02447-4