Implication Of Gravity Recovery And Climate Experiment Grace To Monitor Groundwater Storage Variability

Download or read book Implication of Gravity Recovery and Climate Experiment (GRACE) to Monitor Groundwater Storage Variability written by Md. Mafuzur Rahaman and published by . This book was released on 2019 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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). Besides, the application of GRACE has been constrained in a local scale due to lower spatial resolution. 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. Besides, the current study proposes Random Forest (RF), a recently developed unsupervised machine learning method to downscale the GRACE-derived groundwater storage anomaly (GWSA) from 1° X 1° to 0.25° X 0.25° in the Northern High Plains Aquifer. RF algorithm integrated the GRACE to other satellite-based geospatial and hydroclimatological variables, obtained from the Noah Land Surface Model, to generate high-resolution GWSA map from 2009 to 2016. The error analysis showed the reasonable numerical accuracy of selected seasonal ARIMA models. Additionally, the RF approach replicates local groundwater variability (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 mm ~ 46.69 mm), Nash-Sutcliffe Efficiency (0.58 ~ 0.84). Developed RF model has significant potential to generate finer scale GWSA map for managing groundwater at both local and regional scale, especially the areas with sparse groundwater monitoring wells. The proposed ARIMA and RF models can be used to forecast groundwater variability for sustainable groundwater planning and management.