European Geosciences Union General Assembly 2012 Vienna, Austria, 22 – 27 April 2012

1. 22-December-2008 Pmax-1h + API API dH SWI Ptot + + API + European Geosciences Union General Assembly 2012 Vienna, Austria, 22 – 27 April 2012 IMPROVING LANDSLIDE MOVEMENT FORECASTING USING ASCAT SOIL MOISTURE DATA IMPROVING LANDSLIDE MOVEMENT FORECASTING USING ASCAT SOIL MOISTURE DATA http://hydrology.irpi.cnr.it http://hydrology.irpi.cnr.it Luca Brocca (1), Francesco Ponziani (2), Nicola Berni (2), Florisa Melone (1), Tommaso Moramarco (1), Wolfgang Wagner (3) INTRODUCTION Predicting the spatial and temporal occurrence of rainfall triggered landslides represents an important scientific and operational issue due to their high threats to human lives and properties. This study investigates the relationship between rainfall, soil moisture conditions and landslide movement by using recorded movements of a rock slope located in central Italy, the Torgiovannetto landslide. This landslide is a very large rock slide, threatening county and state roads. Data acquired by a network of extensometers and a meteorological station clearly indicate that the movements of the unstable wedge, firstly detected in 2003, are still proceeding and the alternate phases of quiescence and reactivation are associated with rainfall patterns. By using a multiple linear regression approach, the opening of the tension cracks (as recorded by the extensometers) as a function of rainfall and soil moisture conditions prior the occurrence of rainfall, are predicted for the period 2007-2009. Specifically, soil moisture indicators are obtained through the Soil Water Index, SWI, product derived by the Advanced SCATterometer (ASCAT) on board the MetOp (Meteorological Operational) satellite and by an Antecedent Precipitation Index, API. ARE COARSE-RESOLUTION SATELLITE SOIL MOISTURE DATA USEFUL FOR THE PREDICTION OF THE MOVEMENT OF SMALL-SCALE LANDSLIDES? TORGIOVANNETTO LANDSLIDE MULTIPLE REGRESSION ANALYSIS RESULTS Based on a previous study (Ponziani et al., 2012) indicating the significant effect of initial soil moisture conditions on landslide triggering, the relationship between rainfall, ASCAT soil moisture and the slope movement was investigated here by applying a multiple linear regression analysis for a sequence of rainfall events occurred in the period 2007-2009. Specifically, 46 rainfall events were extracted and for each of them the main characteristic of rainfall (total rainfall, Ptot, and maximum rainfall over a duration of 1h, Pmax-1h), the initial Soil Water Index obtained by ASCAT with T=75 days, SWI75, and the cumulated antecedent precipitation over 20 days, API20, were computed. Finally, the extensometer crack aperture, dH, computed as the difference between the recorded displacement between the end and the start of each rainfall event, was considered as the predictand. ................ The multiple linear regression equation was written as: The Torgiovannetto rock slope is located in an abandoned stone quarry close to Assisi town in central Italy. The main front of the quarry, oriented approximately along the SE-NW direction, has an average dip of about 38°. In this area, the rock mass is composed of regular stratifications of limestone, with intercalations of thin, weak clay layers. Due to the orientation of the bedding planes and to the presence of the weak clay layers between the hard calcareous strata, the upper part of the slope is in marginal stability conditions. A limited number of slope failures have been reported on several occasions, and several tension cracks running parallel to the quarry face have been observed on the upper part of the slope.The monitoring network is composed of 13 extensometers, 5 inclinometers and one meteorological station. The data used for this study covers the period 2007-2009 Successively, different configurations of the model are analysed with the aim of understanding the different impact of rainfall and soil moisture conditions on the landslide movement. The first configuration uses as predictors only the rainfall variables (Pmax-1h and Ptot), the second and the third configurations the rainfall variables together with the API20 and the SWI75, respectively; and the last configuration all the predictors. • The obtained results can be summarized as follows: • for the 1st configuration (only rainfall), the estimated crack aperture poorly follows the observations (r=0.219) • for the 2nd configuration (rainfall + API20) results significantly improve (r=0.635) with a better reproduction of the seasonal magnitude of crack aperture. • the 3rd configuration (rainfall + SWI75) provides a further significant improvement (r=0.821) • for the 4th configuration (all the predictors) the agreement is reasonably good for the whole period resulting in r=0.877. • Moreover, the relative weight of each predictor is quantitatively determined by analysing the standardized coefficients of the linear regression. In the configuration 4 the coefficients are found to be equal to 0.13, 0.04, 0.36 and 0.74 for Pmax-1h, Ptot; API20and SWI75, respectively. Thus, the SWI75 is the most significant predictor with a weight ~50% and ~80% higher than the API20 and Pmax-1h, respectively. ASCAT Soil Water Index (SWI) ASCAT is a C-band scatterometer on-board METOP satellite and operating since 2006. The spatial resolution is 50/25 km and a daily coverage for the 80% of the Earth is provided. CONCLUSIONS The influence of rainfall and soil moisture conditions in the estimation of the movements of a well monitored slope located in central Italy, the Torgiovannetto landslide, was here investigated. The results of the multiple regression analysis clearly indicate that ASCAT-derived soil moisture estimates can be effectively used to predict the crack aperture of the slope with reasonable accuracy (r=0.821). The regression model implemented in this study, coupled with quantitative precipitation forecasts, can be used to predict the crack aperture of the Torgiovannetto slope in an operational context even though it is only based on a simple empirical relationship. On the basis of these encouraging results, the use of more complex physically-based models linking the rainfall and soil moisture conditions with the landslide movement will be the object of future investigations. Moreover, the availability of ASCAT satellite soil moisture data at global scale present new opportunities for the integration of this data set in landslide forecasting systems worldwide. TU-Wien algorithm (Wagner et al., 1999) Surface soil moisture (SSM) is retrieved from the ASCAT backscatter measurements using a time series-based change detection approach. The surface roughness is assumed to have a constant contribution in time, and by knowing the typical yearly vegetation cycle and how it influences the backscatter-incidence angle relationship, the vegetation effects are removed revealing the soil moisture variations. The Soil Water Index (SWI) is then obtained by applying an exponential filter to the SSM data and depends on a single parameter T (characteristic time length). For more details see Brocca et al. (2011). References Brocca, L., Hasenauer, S., Lacava, T., Melone, F., Moramarco, T., Wagner, W., Dorigo, W., Matgen, P., Martínez-Fernández, J., Llorens, P., Latron, J., Martin, C., Bittelli, M. (2011). Soil moisture estimation through ASCAT and AMSR-E sensors: an intercomparison and validation study across Europe. Remote Sensing of Environment, 115, 3390-3408, doi:10.1016/j.rse.2011.08.003. Brocca L., Ponziani, F., Moramarco, T., Melone, F., Berni, N., Wagner, W. (2012). Improving landslide forecasting using ASCAT-derived soil moisture data: a case study of the Torgiovannetto landslide in central Italy. Remote Sensing, in press. Ponziani, F., Pandolfo, C., Stelluti, M., Berni, N., Brocca, L., Moramarco, T. (2012). Assessment of rainfall thresholds and soil moisture modelling for operational hydrogeological risk prevention in the Umbria region (central Italy). Landslides, in press, doi:10.1007/s10346-011-0287-3. Wagner, W., Lemoine, G., Rott, H. (1999). A method for estimating soil moisture from ERS scatterometer and soil data. Remote Sensing of Environment, 70, 191-207, doi:10.1016/S0034-4257(99)00036-X. Contact (1) Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy (2)Umbria Region Functional Centre, Foligno (Perugia), Italy (3) Institute of Photogrammetry and Remote Sensing, Vienna University of Technology, Vienna, Austria E-MAIL: luca.brocca@irpi.cnr.it URL: http://hydrology.irpi.cnr.it/people/l.brocca