Next Article in Journal
Management of Tropical River Basins and Reservoirs under Water Stress: Experiences from Northeast Brazil
Previous Article in Journal
Designer Biochars Impact on Corn Grain Yields, Biomass Production, and Fertility Properties of a Highly-Weathered Ultisol
Open AccessArticle

Estimating High-Resolution Groundwater Storage from GRACE: A Random Forest Approach

1
Department of Civil and Environmental Engineering, Southern Illinois University, 1230 Lincoln Drive, Carbondale, IL 62901, USA
2
Department of Geography and Environmental Resources, Southern Illinois University, 1000 Faner Drive, Carbondale, IL 62901, USA
3
Transportation Engineer, Louis Berger, 444 E. Warm Springs Road, Suite 118, Las Vegas, NV 89119, USA
*
Author to whom correspondence should be addressed.
Environments 2019, 6(6), 63; https://doi.org/10.3390/environments6060063
Received: 10 May 2019 / Revised: 30 May 2019 / Accepted: 1 June 2019 / Published: 4 June 2019
Gravity Recovery and Climate Experiment (GRACE) data have become a widely used global dataset for evaluating the variability in groundwater storage for the different major aquifers. Moreover, the application of GRACE has been constrained to the local scale due to lower spatial resolution. The current study proposes Random Forest (RF), a recently developed unsupervised machine learning method, to downscale a GRACE-derived groundwater storage anomaly (GWSA) from 1° × 1° to 0.25° × 0.25° in the Northern High Plains aquifer. The RF algorithm integrated GRACE to other satellite-based geospatial and hydro-climatological variables, obtained from the Noah land surface model, to generate a high-resolution GWSA map for the period 2009 to 2016. This RF approach replicates local groundwater variability (the combined effect of climatic and human impacts) with acceptable Pearson correlation (0.58 ~ 0.84), percentage bias (−14.67 ~ 2.85), root mean square error (15.53 ~ 46.69 mm), and Nash-Sutcliffe efficiency (0.58 ~ 0.84). This developed RF model has significant potential to generate finer scale GWSA maps for managing groundwater at both local and regional scales, especially for areas with sparse groundwater monitoring wells. View Full-Text
Keywords: GRACE; Random Forest; Northern High Plains aquifer; land surface model GRACE; Random Forest; Northern High Plains aquifer; land surface model
Show Figures

Figure 1

MDPI and ACS Style

Rahaman, M.M.; Thakur, B.; Kalra, A.; Li, R.; Maheshwari, P. Estimating High-Resolution Groundwater Storage from GRACE: A Random Forest Approach. Environments 2019, 6, 63.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop