A soil moisture dataset over the Brazilian semiarid region

This dataset contains soil moisture measured over the Brazilian Semiarid region from 2015 to 2019. An observational network of 595 automatic ground stations was established by the National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN) with the aim of supporting monitoring activities over the driest and most drought-affected Brazilian region – the Brazilian Semiarid. Stations sites are mostly characterized by family farming and rainfed agriculture. Soil volumetric water content is obtained by measuring the dielectric constant of the soil at 70 MHz frequency, which minimizes salinity and textural effects (model EC-5, Decagon Devices, Pullman, WA, USA). Probes use the default calibration, resulting in a precision of approximately ±0.03 m3 m-3. The network stations collected measurements hourly at 0.1 m and 0.2 m depths; a subset of stations (10% of the original network, evenly distributed) had measurements at 0.1 m, 0.2 m, 0.3 m, and 0.4 m depths. A quality control process was applied and selected 357 stations that provided soil moisture time series that spans from August 2015 to April 2019. Data availability is approximately 2-year-long on average, reaching up to 4-year-long in some stations. Gaps caused by instrument malfunction or data transmission problems are occasional on the data. Validation and consistency were assessed by comparing trends in soil vertical neighbor data and rainfall records collected by each station (not available in this dataset). Minimal of two-month long valid data, for at least one vertical level, were kept on the database. Each file available here contains the time series and the metadata with information on the city, the Brazilian state, the geographic coordinates, and the soil texture (fraction of sand, silt and clay). Soil information was derived from 1 kg disturbed samples collected at each site. Data for individual stations are in the compressed file Data.zip while a list summarising stations information is found in the file Stations.xlsx. First applications of this dataset and further details on methodology and site can be found in Zeri et al. (2018, https://doi.org/10.3390/w10101421)