Temporal relationships between agricultural and meteorological drought over the Oum Er Rbia River, Morrocco

This study examines the temporal relationships between meteorological and agricultural drought indices using lagged and linear correlations, the Mann–Kendall trend test, and machine learning (random forest – RF and deep neural network – DNN). On a seasonal and annual scale, the results revealed that the resonance of agricultural drought is strongly synchronized with the temporal variability of meteorological drought. At the monthly scale, the resonance of agricultural drought reflected by the vegetation condition index and the soil moisture condition index (SMCI) has an obvious latency time of at least one month and is statistically significant up to three months. For both agricultural drought indices, their statistical relationships with meteorological drought indices are highly variable, depending on the month of the agricultural season, the time scale and the type of meteorological drought index. The correlations between the SMCI and Palmer drought severity index were the most stable. They ranged from 0.7 to 0.86, whereas the linear correlations between the SMCI and the precipitation conditions index varied from 0.5 to 0.16 in the first and last months of the agricultural season, respectively. Despite this high correlation variability, analysis of historical trends on an annual scale demonstrated the existence of obvious similarities of very negative trends in the spatiotemporal changes in agricultural and meteorological drought indices. Similarly, machine learning models highlighted the importance of the positive relative contribution of their joint occurrence to the annual variability in agricultural yields. Overall, the RF model achieved optimal performance with a relatively small number of predictors, whereas the DNN model was more dependent on the number of features used.