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Article

Downscaling of GRACE-Derived Groundwater Storage Based on the Random Forest Model

School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
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Remote Sens. 2019, 11(24), 2979; https://doi.org/10.3390/rs11242979
Received: 31 October 2019 / Revised: 3 December 2019 / Accepted: 9 December 2019 / Published: 11 December 2019
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
Groundwater is an important part of water storage and one of the important sources of agricultural irrigation, urban living, and industrial water use. The recent launch of Gravity Recovery and Climate Experiment (GRACE) Satellite has provided a new way for studying large-scale water storage. The application of GRACE in local water resources has been greatly limited because of the coarse spatial resolution, and low temporal resolution. Therefore, it is of great significance to improve the spatial resolution of groundwater storage for regional water management. Based on the method of random forest (RF), this study combined six hydrological variables, including precipitation, evapotranspiration, runoff, soil moisture, snow water equivalent, and canopy water to conduct downscaling study, aiming at downscaling the resolution of the total water storage and groundwater storage from 1° (110 km) and to 0.25° (approximately 25 km). The results showed that, from the perspective of long time series, the prediction results of the RF model are ideal in the whole research area and the observations wells area. From the perspective of space, the detailed changes of water storage could be captured in greater detail after downscaling. The verification results show that, on the monthly scale and annual scale, the correlation between the downscaling results and the observation wells is 0.78 and 0.94, respectively, and they both reach the confidence level of 0.01. Therefore, the RF downscaling model has great potential for predicting groundwater storage. View Full-Text
Keywords: GRACE; downscaling; total water storage; groundwater storage; random forest GRACE; downscaling; total water storage; groundwater storage; random forest
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MDPI and ACS Style

Chen, L.; He, Q.; Liu, K.; Li, J.; Jing, C. Downscaling of GRACE-Derived Groundwater Storage Based on the Random Forest Model. Remote Sens. 2019, 11, 2979. https://doi.org/10.3390/rs11242979

AMA Style

Chen L, He Q, Liu K, Li J, Jing C. Downscaling of GRACE-Derived Groundwater Storage Based on the Random Forest Model. Remote Sensing. 2019; 11(24):2979. https://doi.org/10.3390/rs11242979

Chicago/Turabian Style

Chen, Li, Qisheng He, Kun Liu, Jinyang Li, and Chenlin Jing. 2019. "Downscaling of GRACE-Derived Groundwater Storage Based on the Random Forest Model" Remote Sensing 11, no. 24: 2979. https://doi.org/10.3390/rs11242979

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