TRMM rainfall estimative coupled with Bell (1969) methodology for extreme rainfall characterization

Abstract

Abstract. The lack of rainfall data in Brazil, and, in particular, in Rio Grande do Sul State (RS), hinders the understanding of the spatial and temporal distribution of rainfall, especially in the case of the more complex extreme events. In this context, rainfall's estimation from remote sensors is seen as alternative to the scarcity of rainfall gauges. However, as they are indirect measures, such estimates needs validation. This paper aims to verify the applicability of the Tropical Rainfall Measuring Mission (TRMM) satellite information for extreme rainfall determination in RS. The analysis was accomplished at different temporal scales that ranged from 5 min to daily rainfall while spatial distribution of rainfall was investigated by means of regionalization. An initial test verified TRMM rainfall estimative against measured rainfall at gauges for 1998-2013 period considering different durations and return periods (RP). Results indicated that, for the RP of 2, 5, 10 and 15 years, TRMM overestimated on average 24.7 % daily rainfall. As TRMM minimum time-steps is 3 h, in order to verify shorter duration rainfall, the TRMM data were adapted to fit Bell's (1969) generalized IDF formula (based on the existence of similarity between the mechanisms of extreme rainfall events as they are associated to convective cells). Bell's equation error against measured precipitation was around 5-10 %, which varied based on location, RP and duration while the coupled BELL+TRMM error was around 10-35 %. However, errors were regionally distributed, allowing a correction to be implemented that reduced by half these values. These findings in turn permitted the use of TRMM+Bell estimates to improve the understanding of spatiotemporal distribution of extreme hydrological rainfall events.

Influence of the rain gauge network on the performance of a hydrological lumped model applied at different basin scales

“…It is noteworthy that in most cases there was a rainfall overestimation, when compared with the real scenario. The TRMM satellite overestimated rainfall by approximately 15% in all river basins, except for the Ijuí River Basin, in which it was approximately 7.5% in agreement with results presented by Fensterseifer, Allasia and Paz (2016) and Schiavo Bernardi et al (2015) in the same region.…”

Satellite data planning for flood mapping activities based on high rainfall events generated using TRMM, GEFS and disaster news

“…Mitra et al (2013) have merged the TRMM data with rainfall data of 14 monsoon seasons collected from ground using Indian Metrological Department (IMD) Gauge stations to generate gridded daily Indian monsoon rainfall. Researchers have successfully used TRMM products for soil moisture prediction, dry/wet conditioning monitoring, risk assessment, extreme rainfall characterization and hydrological modelling (Gupta et al 2014;Li, Zhang, and Ye 2013;Dinis et al 2013;Bernardi et al 2015;Liu et al 2015;He et al 2017;Robbins 2016;Cabrera, Yupanqui, and Rau 2016). The above-mentioned studies proved the potential of TRMM data usage for high rainfall event detection.…”