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!

Schreiben Sie den ersten Kommentar!