Erosive processes play an important role in environmental degradation. Rain is the main erosive agent in the Metropolitan Region of Sao Paulo. This study characterized the erosion events caused by precipitation leveraging the accumulated daily precipitation estimate generated by the Climate Prediction Center Morphing Method (CMORPH) and integrating the surface telemetric network using the Statistical Objective Analysis method (SOAS). From the Civil Defense database, 400 events were identified in the Metropolitan region of Sao Paulo (MRSP) area between 2000 and 2013 and, of these, 3 were chosen to carry out meteorological and climatological analyses. In an initial observation, 58% of them were found to occur in summer. Two regions with the highest number of erosion events were observed, in the Serra do Mar and Serra da Cantareira. In the Serra do Mar core, the municipality of São Bernardo do Campo was the one with the greatest amount of erosion. Precipitation volumes were estimated for accumulations of 30 minutes, 1 day, 1 month, and 1 year. The results, from the 3 events, indicate accumulated precipitation in 30 minutes from 10 mm to 19.8 mm, daily from 30.8 mm to 69.5 mm, and 1 month from 369.7 mm to 742.5 mm, and 1 year (2010) from 1712.9 mm to 1961.8 mm. In these events, it was noted that there were heavy rains in December 2009 and January 2010. It was also noted that the CMORPH and SOAS identify the rain events found by the São Paulo meteorological radar. The meteorological analyzes of the events based on images from the São Paulo meteorological radar and the Meteosat-9 satellite indicate that the active precipitation systems are associated with cold fronts, instability lines, and isolated convection.
Extreme rainfall events cause diverse loss of life and economic losses. These disasters include flooding, landslides, and erosion. For these intense rainfall events, one can statistically estimate the time when a given rainfall volume will occur. Initially, this work estimated rainfall volumes for the mountainous region of Rio de Janeiro, and the frequency with which rainfall events occur. For this, we analyzed daily precipitation data using the ANOBES method and the Gumbel statistical distribution to estimate return times. Extreme prec’ipitation volumes of up to 240 mm per day were identified in some locations, with 100 years or more return periods. On 11 January 2011 precipitation volumes were high, but on 12 January they were extreme, similar to the 100-year return time data. The analysis method presented enables the determination of the return time of heavy rainfall, assisting in the prevention of its effects. Knowledge of the atmospheric configuration enables decision support. The atmospheric systems that combined to cause the event were local circulations (orographic and sea breeze) and large-scale systems (SACZ and frontal systems).
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