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Soil moisture over the Brazilian semiarid region is presented in different visualizations that highlight spatial, temporal and short-term agricultural risk. The analysis used the Soil Moisture Index (SMI), which is based on a normalization of soil moisture by field capacity and wilting point. The index was used to characterize the actual soil moisture conditions into categories from severe drought to very wet. In addition, the temporal evolution of SMI was implemented to visualize recent trends in short-term drought and response to rainfall events at daily time steps, as new data are available. Finally, a visualization of drought risk was developed by considering a critical value of SMI (assumed as 0.4), below which water stress is expected to be triggered in plants. A novel index based on continuous exposure to critical SMI was developed to help bring awareness of real time risk of water stress over the region: the Index of Stress in Agriculture (ISA). The index was tested during a drought over the region and successfully identified locations under water stress for periods of three days or more. The monitoring tools presented here help to describe the real time conditions of drought over the region using daily observations. The information from those tools support decisions on agricultural management such as planting dates, triggering of irrigation, or harvesting.

The objective of this work was to investigate the mean and variability of a dataset of pollutant concentrations from measurements taken over the metropolitan area of São Paulo city, Brazil. Wavelet analysis was applied to the time series of pollutant concentrations, revealing the strongest harmonics influencing the signals. A mode of variability of 4-8 days was significant until the middle of the last decade and is likely associated with the approach and passage of meteorological systems. A dataset representing the number of frontal systems moving across the state helped to explain the interannual variability during wintertime. Years with fewer frontal systems had higher levels of pollutants in several locations. Weather events such as inversions and the passage of frontal systems influence the concentration of pollutants. Public policies on air quality should focus not only on reducing the long-term exposure of city-dwellers to the negative effects of pollutants but also account for the possible short-term effects of the weather on air quality.

Soil 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 duringThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

It is generally accepted that El Niño–Southern Oscillation (ENSO) is the main modulator of rainfall variability over Northern South America in the interannual scale. Assuming that, an index is proposed to quantify this expected interannual variability in time series of rainfall. The result is the exposure assessment to the effects of droughts, measured with the Standardized Precipitation Index (SPI) for the monthly scale. The SPI is calculated from rainfall series, and wavelet analysis is used to estimate the variance for different frequencies present in the signal. The Wavelet Interannual Variability Index (WIVI) is calculated as the sum of the wavelet coefficients over a predetermined range of modes of variability (with periods longer than 2 year and shorter than 8 years). The index was tested using a dataset of rainfall records from Tocantins state, Central Brazil. Most of the series ranged from 1974 to 2012. On average, the series had 3.2% of gaps which were not filled to avoid the effect of artificial trends on the data. The state lies mostly over the Cerrado biome and is a new frontier of agricultural development in Brazil. According to the results, the Northern region is under higher exposure of interannual variability, with higher values of WIVI. The assessment is in agreement with large‐scale features of South American climate, specifically considering the influences of the Pacific and Atlantic Oceans and their patterns of sea surface temperature (SST).

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