TRMM satellite rainfall estimates for landslide early warning in Italy: preliminary results

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.

[1]  B. Enquist,et al.  On estimating the exponent of power-law frequency distributions. , 2008, Ecology.

[2]  Yang Hong,et al.  Advances in landslide nowcasting: evaluation of a global and regional modeling approach , 2012, Environmental Earth Sciences.

[3]  Pietro Aleotti,et al.  A warning system for rainfall-induced shallow failures , 2004 .

[4]  Ronald Aylmer Sir Fisher,et al.  020: The Goodness of Fit of Regression Formulae and the Distribution of Regression Coefficients. , 1922 .

[5]  Y. Hong,et al.  The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales , 2007 .

[6]  N. Caine,et al.  The Rainfall Intensity - Duration Control of Shallow Landslides and Debris Flows , 1980 .

[7]  Fausto Guzzetti,et al.  Societal landslide and flood risk in Italy. , 2010 .

[8]  M. Rossi,et al.  Rainfall thresholds for the initiation of landslides in central and southern Europe , 2007 .

[9]  E. Parzen.  On Estimation of a Probability Density Function and Mode , 1962 .

[10]  R. Gnanadesikan,et al.  Probability plotting methods for the analysis of data. , 1968, Biometrika.

[11]  M. Rossi,et al.  The rainfall intensity–duration control of shallow landslides and debris flows: an update , 2008 .

[12]  Fausto Guzzetti,et al.  Lithological and seasonal control on rainfall thresholds for the possible initiation of landslides in central Italy , 2012 .

[13]  R. Fisher,et al.  On the Mathematical Foundations of Theoretical Statistics , 1922 .

[14]  P. Reichenbach,et al.  Towards a definition of topographic divisions for Italy , 1994 .

[15]  Fausto Guzzetti,et al.  Information system on hydrological and geomorphological catastrophes in Italy (SICI): a tool for managing landslide and flood hazards , 2004 .

[17]  Fausto Guzzetti,et al.  The AVI project: A bibliographical and archive inventory of landslides and floods in Italy , 1994 .

[18]  Michele Brunetti,et al.  SANF: National warning system for rainfall-induced landslides in Italy , 2012 .

[19]  John L. Innes,et al.  Debris flows , 1983 .

[20]  J. R. Koehler,et al.  Modern Applied Statistics with S-Plus. , 1996 .