Abstract
To reduce the impacts of drought, developing an integrated drought monitoring tool and early warning system is crucial and more effective than the crisis management approach that is commonly used in developing countries like Ethiopia. The overarching goal of this study was to develop a higher-spatial-resolution vegetation outlook (VegOut-UBN) model that integrates multiple satellite, climatic, and biophysical input variables for the Upper Blue Nile (UBN) basin. VegOut-UBN uses current and historical observations in predicting the vegetation condition at multiple leading time steps of 1, 3, 6, and 9 dekades. VegOut-UBN was developed to predict the vegetation condition during the main crop-growing season locally called “Kiremt” (June to September) using historical input data from 2001 to 2016. The rule-based regression tree approach was used to develop the relationship between the predictand and predictor variables. The results for the recent historic drought (2009 and 2015) and non-drought (2007) years are presented to evaluate the model accuracy during extreme weather conditions. The result, in general, shows that the predictive accuracy of the model decreases as the prediction interval increases for the cross-validation years. The coefficient of determination (R2) of the predictive and observed vegetation condition shows a higher value (R2 > 0.8) for one-month prediction and a relatively lower value (R2 ≅ 0.70) for three-month prediction. The result also reveals strong spatial integrity and similarity of the observed and predicted maps. VegOut-UBN was evaluated and compared with the Standardized Precipitation Index (SPI) (derived from independent rainfall datasets from meteorological stations) at different aggregate periods and with a food security status map. The result was encouraging and indicative of the potential application of VegOut-UBN for drought monitoring and prediction. The VegOut-UBN model could be informative in decision-making processes and could contribute to the development of operational drought monitoring and predictive models for the UBN basin.
Highlights
A vegetation-condition-based drought monitoring and prediction model is vital to enhance knowledge-based decision-making processes in areas where economic growth is dependent on rain-fed agriculture
The models were developed for all dekades; the resulting graphs obtained for the last dekades of June, July, August, and September are presented for demonstration and further discussion (Figure 3)
The rule-based regression tree data mining approach was adapted to develop a VegOut-Upper Blue Nile (UBN) prediction model that could potentially be used to monitor and mitigate the adverse impacts of drought. Such a drought model is still lacking for the UBN basin, which is commonly regarded as the main source of water for the main Nile River; the drainage basin of the Nile River covers eleven riparian countries
Summary
A vegetation-condition-based drought monitoring and prediction model is vital to enhance knowledge-based decision-making processes in areas where economic growth is dependent on rain-fed agriculture. Other notable historic drought events (e.g., 1983–1984, 1994–1995, 2003–2004, and 2009–2010) have occurred in the country and caused devastation in terms of the loss of human lives and reduction in annual crop production [5,6]. This indicates the existence of a strong linkage between crop production and drought in Ethiopia. Developing a vegetation condition prediction model greatly enhances the potential to estimate the annual crop yield production that, in turn, supports developing food security and drought early warning systems [7,8,9]
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