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Hydrology 2019, 6(1), 19; https://doi.org/10.3390/hydrology6010019

Modeling of GRACE-Derived Groundwater Information in the Colorado River Basin

1
Department of Civil and Environmental Engineering, Southern Illinois University, 1230 Lincoln Drive, Carbondale, IL 62901, USA
2
Department of Civil and Environmental Engineering and Construction, University of Nevada, Las Vegas, NV 89154, USA
*
Author to whom correspondence should be addressed.
Received: 30 December 2018 / Revised: 25 January 2019 / Accepted: 17 February 2019 / Published: 18 February 2019
(This article belongs to the Special Issue Remote Sensing in Hydrological Modelling)
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Abstract

Groundwater depletion has been one of the major challenges in recent years. Analysis of groundwater levels can be beneficial for groundwater management. The National Aeronautics and Space Administration’s twin satellite, Gravity Recovery and Climate Experiment (GRACE), serves in monitoring terrestrial water storage. Increasing freshwater demand amidst recent drought (2000–2014) posed a significant groundwater level decline within the Colorado River Basin (CRB). In the current study, a non-parametric technique was utilized to analyze historical groundwater variability. Additionally, a stochastic Autoregressive Integrated Moving Average (ARIMA) model was developed and tested to forecast the GRACE-derived groundwater anomalies within the CRB. The ARIMA model was trained with the GRACE data from January 2003 to December of 2013 and validated with GRACE data from January 2014 to December of 2016. Groundwater anomaly from January 2017 to December of 2019 was forecasted with the tested model. Autocorrelation and partial autocorrelation plots were drawn to identify and construct the seasonal ARIMA models. ARIMA order for each grid was evaluated based on Akaike’s and Bayesian information criterion. The error analysis showed the reasonable numerical accuracy of selected seasonal ARIMA models. The proposed models can be used to forecast groundwater variability for sustainable groundwater planning and management. View Full-Text
Keywords: ARIMA; GRACE; groundwater; forecast; stochastic model ARIMA; GRACE; groundwater; forecast; stochastic model
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Rahaman, M.M.; Thakur, B.; Kalra, A.; Ahmad, S. Modeling of GRACE-Derived Groundwater Information in the Colorado River Basin. Hydrology 2019, 6, 19.

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