Modeling of GRACE-Derived Groundwater Information in the Colorado River Basin
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-02-01
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| Series: | Hydrology |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5338/6/1/19 |
| _version_ | 1828155388912992256 |
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| author | Md Mafuzur Rahaman Balbhadra Thakur Ajay Kalra Sajjad Ahmad |
| author_facet | Md Mafuzur Rahaman Balbhadra Thakur Ajay Kalra Sajjad Ahmad |
| author_sort | Md Mafuzur Rahaman |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T22:52:40Z |
| format | Article |
| id | doaj.art-660f3471575d4eddba3af65673961940 |
| institution | Directory Open Access Journal |
| issn | 2306-5338 |
| language | English |
| last_indexed | 2024-04-11T22:52:40Z |
| publishDate | 2019-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Hydrology |
| spelling | doaj.art-660f3471575d4eddba3af656739619402022-12-22T03:58:32ZengMDPI AGHydrology2306-53382019-02-01611910.3390/hydrology6010019hydrology6010019Modeling of GRACE-Derived Groundwater Information in the Colorado River BasinMd Mafuzur Rahaman0Balbhadra Thakur1Ajay Kalra2Sajjad Ahmad3Department of Civil and Environmental Engineering, Southern Illinois University, 1230 Lincoln Drive, Carbondale, IL 62901, USADepartment of Civil and Environmental Engineering, Southern Illinois University, 1230 Lincoln Drive, Carbondale, IL 62901, USADepartment of Civil and Environmental Engineering, Southern Illinois University, 1230 Lincoln Drive, Carbondale, IL 62901, USADepartment of Civil and Environmental Engineering and Construction, University of Nevada, Las Vegas, NV 89154, USAGroundwater 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.https://www.mdpi.com/2306-5338/6/1/19ARIMAGRACEgroundwaterforecaststochastic model |
| spellingShingle | Md Mafuzur Rahaman Balbhadra Thakur Ajay Kalra Sajjad Ahmad Modeling of GRACE-Derived Groundwater Information in the Colorado River Basin Hydrology ARIMA GRACE groundwater forecast stochastic model |
| title | Modeling of GRACE-Derived Groundwater Information in the Colorado River Basin |
| title_full | Modeling of GRACE-Derived Groundwater Information in the Colorado River Basin |
| title_fullStr | Modeling of GRACE-Derived Groundwater Information in the Colorado River Basin |
| title_full_unstemmed | Modeling of GRACE-Derived Groundwater Information in the Colorado River Basin |
| title_short | Modeling of GRACE-Derived Groundwater Information in the Colorado River Basin |
| title_sort | modeling of grace derived groundwater information in the colorado river basin |
| topic | ARIMA GRACE groundwater forecast stochastic model |
| url | https://www.mdpi.com/2306-5338/6/1/19 |
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