8 November 2012 TRMM satellite rainfall estimates for landslide early warning in Italy: preliminary results
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Abstract
Early warning systems can predict rainfall-induced landslides by comparing rainfall data with landslide rainfall thresholds. These systems are based on empirical rainfall thresholds defined using rain gauges data. Despite quantitative satellite rainfall estimates are currently available, limited research has compared satellite estimates and rain gauge measurements for the forecasting of possible landslide occurrence. In this work, we validate satellite estimates obtained for Italy by the NASA Tropical Rainfall Measuring Mission (TRMM) against rainfall measurements from the Italian rain gauge network (< 1950 rain gauges), in the period from 1 September 2009 to 31 August 2010. Using cumulative rainfall measurements/estimates, we: (i) evaluate the correlation between the rain gauge measurements and the satellite estimates in different morpho-climatological domains, (ii) analyse the distributions of the ground-based measurements and the satellite estimates using different statistical approaches, and (iii) compare rainfall events derived automatically from satellite and rain gauge rainfall series. We observe differences between satellite estimates and rain gauge measurements in different morpho-climatological domains. The differences are larger in mountain areas, and collectively reveal a complex relationship between the ground-based measurements and the satellite estimates. We find that a power law correlation model is appropriate to describe the relation between the two rainfall data series. We conclude that specific rainfall thresholds must be defined to exploit satellite rainfall estimates in existing landslide early warning systems.
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Mauro Rossi, Mauro Rossi, Dalia Kirschbaum, Dalia Kirschbaum, Silvia Luciani, Silvia Luciani, Alessandro C. Mondini, Alessandro C. Mondini, Fausto Guzzetti, Fausto Guzzetti, } "TRMM satellite rainfall estimates for landslide early warning in Italy: preliminary results", Proc. SPIE 8523, Remote Sensing of the Atmosphere, Clouds, and Precipitation IV, 85230D (8 November 2012); doi: 10.1117/12.979672; https://doi.org/10.1117/12.979672
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