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.

Microwave-based satellite rainfall products offer an opportunity to assess rainfall-related events for regions where rain-gauge stations are sparse, such as in Northeast Brazil (NEB). Accurate measurement of rainfall is vital for water resource managers in this semiarid region. In this work, the SM2RAIN-CCI rainfall data obtained from the inversion of the microwave-based satellite soil moisture (SM) observations derived from the European Space Agency (ESA) Climate Change Initiative (CCI), and ones from three state-of-the-art rainfall products (Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), Climate Prediction Center Morphing Technique (CMORPH), and Multi-SourceWeighted-Ensemble Precipitation (MSWEP)) were evaluated against in situ rainfall observations under different bioclimatic conditions at the NEB (e.g., AMZ, Amazônia; CER, Cerrado; MAT, Mata Atlântica; and CAAT, Caatinga). Comparisons were made at daily, 5-day, and 0.25 • scales, during the time-span of 1998 to 2015. It was found that 5-day SM2RAIN-CCI has a reasonably good performance in terms of the correlation coefficient over the CER biome (R median: 0.75). In terms of the root mean square error (RMSE), it exhibits better performance in the CAAT biome (RMSE median: 12.57 mm). In terms of bias (B), the MSWEP, SM2RAIN-CCI, and CHIRPS datasets show the best performance in MAT (B median: −8.50%), AMZ (B median: −0.65%), and CER (B median: 0.30%), respectively. Conversely, CMORPH poorly represents the rainfall variability in all biomes, particularly in the MAT biome (R median: 0.43; B median: −67.50%). In terms of detection of rainfall events, all products show good performance (Probability of detection (POD) median > 0.90). The performance of SM2RAIN-CCI suggests that the SM2RAIN algorithm fails to estimate the amount of rainfall under very dry or very wet conditions. Overall, results highlight the feasibility of SM2RAIN-CCI in those poorly gauged regions in the semiarid region of NEB.

This work analyzes the quality of the soil moisture L3 (25 km) and L4 (1 km) products generated at the Barcelona Expert Center (BEC) in two sites located in distinct semi-arid regions, where measurement networks are installed: "Red de Estaciones de Medición de la Humedad del Suelo" (REMEDHUS), which is located in the central part of the Duero basin (Spain), and National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN), at Northeast of the Brazil. REMEDHUS has been used as a calibration/validation site for SMOS and SMAP missions. It is a dense network covering 35 x 35 km2 and it has 22 stations. The CEMADEN network provides a database for the development of early warnings for natural disasters that occurs in Brazil, such as droughts and floods. It is a sparse network covering 1000 x 1000 km2 with more than 500 station. Results show good correspondence between SMOS L4 data and the in situ soil moisture data for REMEDHUS and CEMADEN networks. Correlations mean range from 0.3 to 0.8, and depend mainly on the station situation, soil type, and land cover on both Spain and Brazil networks. For the average data of both series, the correlation coefficient is higher than 0.6. Results show that the L4 product are better correlated than the L3 product, although L4 and L3 products are quite similar. This indicates that both products are ready for its operational use, with L4 providing a better representation of the soil moisture status at finer scales.

estratégias de gestão e mitigação do risco sísmico: da avaliação ao planeamento de emergência Rui Maio, Tiago M.

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