Abstract
AbstractSoil moisture information is essential to monitoring of the intensity of droughts, the start of the rainy season, planting dates and early warnings of yield losses. We assess spatial and temporal trends of drought over the Brazilian semiarid region by combining soil moisture observations from 360 stations, root zone soil moisture from a leading land surface model, and a vegetation health index from remote sensing. The soil moisture dataset was obtained from the network of stations maintained by the National Center of Monitoring and Early Warning of Natural Disasters (Cemaden), in Brazil. Soil water content at 10 to 35 cm depth, for the period 1979–2018, was obtained from running the JULES land surface model (the Joint UK Land Environment Simulator). The modelled soil moisture was correlated with measurements in the common period of 2015–2018, resulting in an average correlation coefficient of 0.48 across the domain. The standardized soil moisture anomaly (SMA) was calculated for the long‐term modelled soil moisture and revealed strong negative values during well‐known drought periods in the region, especially during El‐Niño years. The performance of SMA in identifying droughts during the first 2 months of the raining and cropping season was similar to the Standardized Precipitation Index (SPI), commonly used for drought assessment: 12–14 events were identified by both indices. Finally, the temporal relationship between both SMA and SPI with the Vegetation Health Index (VHI) was assessed using the cross‐wavelet transform. The results indicated lagged correlations of 1 to 1.5 months in the annual scale, suggesting that negative trends in SMA and SPI can be an early warning to yield losses during the growing season. Public policies on drought assessment should consider the combination of multiple drought indices, including soil moisture anomaly.
Highlights
Drought is a natural disaster characterized by a slow onset and evolution (Marthews et al, 2019; Mishra & Singh, 2010; van Loon, 2015)
Spearman correlation coefficient was used for its robustness, since no assumption is made about the data having a normal distribution
Each one of those classes were observed over approximately 80 stations, accounting to 71% of the total; for those classes, median correlation between model and measured soil moisture was of 0.51 ± 0.15
Summary
Drought is a natural disaster characterized by a slow onset and evolution (Marthews et al, 2019; Mishra & Singh, 2010; van Loon, 2015). The impacts of droughts are experienced in several societal sectors and activities, such as agriculture, water supply, food security, tourism and energy generation (Mishra & Singh, 2010). Monitoring of drought conditions is a crucial and timely task, which is essential to mitigate the impacts of recurrent and unexpected events (Wilhite, 2018). For this reason, the improvement of existing drought monitoring tools and testing of new approaches should be carried out continuously (Svoboda & Fuchs, 2017)
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