Abstract
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.
Highlights
Climate variability and extreme weather events threaten many populations around the world [1]
An intercomparison of 5-day accumulated rainfall estimates derived from SM2RAIN-Climate Change Initiative (CCI) with ones from the GBGR dataset were carried out in order to assess the quality of their rainfall estimates
In light of the daily comparisons, we found that CMORPH performed rather poorly (Table 10, and Figures 13 and 14), coinciding with findings from previous studies [48,78], possibly because it was only based on rainfall estimates from microwave sensors and geostationary satellite infrared sensors
Summary
Climate variability and extreme weather events threaten many populations around the world [1]. The accurate estimation of rainfall is of paramount importance for analyzing the spatial and temporal patterns of rainfall at various scales [7,8,9], and advancing our understanding of the effect of droughts and floods in Brazil. In NEB, conventional rain gauges are the main source of rainfall data [14,15]. Despite the efforts of the National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN), and other state climate agencies, most of the rain-gauge networks currently available are inadequate to produce reliable rainfall analysis, due to their scarce spatial coverage, high-proportion of missing data, and short-length records [16,17,18]
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