Congo Basin Hydrology, Climate, and Biogeochemistry. Группа авторов

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Congo Basin Hydrology, Climate, and Biogeochemistry - Группа авторов

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was employed (e.g., Barnett & Preisendorfer, 1987; Bretherton et al., 1992; Ndehedehe et al., 2018b). This resulted in significant modes of SST variability from the respective oceans, which were then used as predictands in the SVMR model. Specifically, a linear SVM regression model was trained to fit the data. The SVMR technique evaluates each run of the experiment using regression, by partitioning the data internally into training, validation, and testing components (i.e., 65% of the total data). The remaining 35% of the observed data were thereafter used for forward prediction based on the hold‐out method of cross‐validation (e.g., Haley, 2017). The stratified partitioning of the data using this approach ensures that each partition includes similar amount of observations from each group. The predicted and observed discharge were then compared using Pearson correlation.

      5.3.1. Characteristics of Extreme Events in the Congo Basin

Schematic illustration of spatiotemporal SPEI patterns of the Congo basin using 12-month gridded SPEI values (a–f). Schematic illustration of estimated areas affected by various drought intensities (extreme, severe, and moderate) over the Congo basin during 1980–2000 (a) and 2001–2015 (b) periods.

      5.3.2. Surface Water Hydrology of the Congo Basin

      

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