Forecasting GRACE Data over the African Watersheds Using Artificial Neural Networks
2019
Ahmed, Mohamed | Sultan, Mohamed | Elbayoumi, Tamer | Tissot, Philippe
The GRACE-derived terrestrial water storage (TWS<inf>GRACE</inf>) provides measurements of the mass exchange and transport between continents, oceans, and ice sheets. In this study, a statistical approach was used to forecast TWS<inf>GRACE</inf> data using 10 major African watersheds as test sites. The forecasted TWS<inf>GRACE</inf> was then used to predict drought events in the examined African watersheds. Using a nonlinear autoregressive with exogenous input (NARX) model, relationships were derived between TWS<inf>GRACE</inf> data and the controlling and/or related variables (rainfall, temperature, evapotranspiration, and Normalized Difference Vegetation Index). The performance of the model was found to be “very good” (Nash–Sutcliffe (NSE) > 0.75; scaled root mean square error (R*) < 0.5) for 60% of the investigated watersheds, “good” (NSE > 0.65; R* < 0.6) for 10%, and “satisfactory” (NSE > 0.50; R* < 0.7) for the remaining 30% of the watersheds. During the forecasted period, no drought events were predicted over the Niger basin, the termination of the latest (March–October 2015) drought event was observed over the Zambezi basin, and the onset of a drought event (January-March 2016) over the Lake Chad basin was correctly predicted. Adopted methodologies generate continuous and uninterrupted TWS<inf>GRACE</inf> records, provide predictive tools to address environmental and hydrological problems, and help bridge the current gap between GRACE missions.
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