#1irpi ITALIAN NATIONAL RESEARCH COUNCIL GS GRAN SASSO SI RESEARCH INSTITUTE FOR GEO-HYDROLOGICAL PROTECTION SCIENCE INSTITUTE SCHOOL OF ADVANCED STUDIES Scuola Universitaria Superiore PROTEZIO CIVILE NAZIONALE AS CATHOLICA SACRI CO UNIVERSITÀ CATTOLICA del Sacro Cuore Consiglio Nazionale delle Ricerche RCES ISTITUTO di RICERCA sulla CRESCITA ECONOMICA SOSTENIBILE RESEARCH INSTITUTE on SUSTAINABLE ECONOMIC GROWTH ECONOMIC LANDSLIDE SUSCEPTIBILITY UNDER A SOCIO-ECONOMIC PERSPECTIVE: AN APPLICATION TO UMBRIA REGION (CENTRAL ITALY) Donnini M.[1], Modica M. [2], Salvati P.[1], Marchesini I.[1], Rossi M.[1], Guzzetti F.[3], Zoboli R.[4,5] [1] CNR-IRPI (Istituto di Ricerca per la Protezione Idrogeologica). Perugia, Italy [2] Gran Sasso Science Institute - Social Sciences. L'Aquila, Italy [3] Dipartimento della Protezione Civile. Roma, Italy [4] Università Cattolica del Sacro Cuore. Milano, Italy [5] CNR-IRCRES (Istituto di Ricerca sulla Crescita Economica Sostenibile). Milano, Italy jrpi Marco Donnini - CNR-IRPI - ([email protected]) EGUASsembly EGU General 2020 CC 1 BY#24800000 Economic landslide susceptibility. Some questions What is it? The economic landslide susceptibility is the probability of landslide occurrence in an area weighted for its socio-economic exposure in term of real estate market value. How can I do it? The economic landslide susceptibility is estimated trough a pixel-based method designed for large areas. The method needs the maps of: Landslide susceptibility Real estate market value € Building density A 4500000 250000 300000 Città di Castello A Gubbio Lake Trasimeno Perugia Assisi Foligno 350000 nine Elevation 100 2400 4700000 4750000 Todi Orvieto 0 10 20 km کی ہے Spoleto Monti Sibillini Terni 4750000 4700000 250000 Where did we realize it? We used Umbria Region (Central Italy) as test area. Umbria is rich in historical cities, industrial plants and agricultural areas. jrpi Marco Donnini - CNR-IRPI - ([email protected]) 300000 350000 EGUASsembly EGU General 2020 CC 2 BY#3Economic landslide susceptibility. Umbria Region application Landslide susceptibility Raster 1 0.80 0.55 0.45 0.20 0.00 Real estate market value [€/m²] € € Perugia (FC) Vai Cancella Building density [#/m²] Vectorial Vectorial fasce OMI Download perimetri Help E:12.4633 N: 45.47247 Coordinate יוי Istat From Mateos et al. (2014) From ISTAT 1000mm From Italian Fiscal Agency Data aggregation (Italian National Institute of Statistics) Data aggregation irpi Marco Donnini - CNR-IRPI - ([email protected]) EGUASsembly EGU General 2020 CC 3 BY#4Economic landslide susceptibility. Landslide susceptibility map Landslide susceptibility is the probability of a landslide occurring in an area based on local terrain conditions (Brabb, 1984) expressing "where" landslides could occur (Guzzetti et al., 1999; 2005; 2006a; 2006b) The landslide susceptibility map was realized using: the landslide inventory map of Umbria region (Antonini et al., 2002); SRTM-DEM version 2.1 (http://dds.cr.usgs.gov/srtm/); the Corine Land Cover 2006; the Geological Map of Italy (ISPRA); 140000 60000 100000 -100000-60000 -20000 20000 .1 0.80 0.55 0.45 0.20 L0.00 the Soil map of Italy (Mancini, 1966). From Mateos et al. (2014) Landslide susceptibility ranges from 0 (minimum susceptibility) to 1 (maximum susceptibility) irpi Marco Donnini - CNR-IRPI - ([email protected]) EGUASsembly EGU General 2020 CC อ 4 BY#5Economic landslide susceptibility. ISTAT and fiscal data adaptation 1° step - adaptation of ISTAT data with Italian Fiscal Agency data Italian Fiscal Agency INTENDED USE CATEGORY TIPOLOGY Villa Refined house DATA ADAPTATION Non-residential Istat Residential Residential Commercial Service sector Residential unit Economical residential unit Box and garage Covered/unovered parking Shop Shopping center Warehouse Office Productive irpi Marco Donnini. CNR-IRPI ([email protected]) Structured office Shed Industrial shed EGU General 2020 CC อ 5 BY#6Economic landslide susceptibility. Residential / Non-residential mean real estate market values 2° step-calculation of the mean real estate market values 750000.000 800000.000 (a) 850000.000 750000.000 800000.000 (b) 0-600 600-712 712-938 938 1176 1176-2492 no data 850000.000 750000.000 800000.000 850000.000 (c) ПО-333 333-393 393-455 455-500 500-650 ПО-1100 1100 1300 1300-1500 1500 1636 1636 - 2900 no data no data for residential and for non-residential buildings [€/m²] V(commercial)mean 750000.000 800000.000 850000.000 Istat Non-res Res Italian Fiscal Agency 750000.000 (d) 800000.000 850000.000 10-809 809-980 980 1097 1097-1204 1204-2650 no data Residential Commercial Service [€/m²] V(residential)mean 750000.000 800000.000 [€/m²] V(service)mean 750000.000 800000.000 8500 750000.000 800000.000 [€/m²] V(productive)mean 750000.000 800000.000 850000.000 10-408 850000.000 408-613 613-811 811 1067 1067 - 2696 no data MEAN [€/m²] V(non-residential)mean 750000.000 800000.000 850 Productive irpi V(res)mean,i ; V(non-res)mean,i Marco Donnini - CNR-IRPI - ([email protected]) [€/m²] EGU General 2020 CC + BY 6#7Economic landslide susceptibility. Weighted mean real estate market value 3° step- the mean 750000.000 800000.000 (d) ПО-809 809-980 850000.000 980-1097 1097 1204 1204-2650 no data 750000.000 800000.000 A 850000.000 ПО-408 408-613 613-811 811 1067 1067 - 2696 no data WEIGHTED MEAN real estate market values for residential and for non-residential buildings were weighted for residential and non- residential building density (a) [€/m²] V(residential)mean 750000.000 750000.000 800000.000 850000.000 [€/m²] V(non-residetial)mean 750000.000 800000.000 850000.000 (b) 750000.000 0 0-5 5-100 > 100 no data 800000.000 850000.000 800000.000 850000.000 750000 800000 0-408 408-613 613-811 811-1067 850000 1067 - 2696 no data 0 0-5 5-100 > 100 no data [€/m²] V(weight)mean 750000 800000 850000 D(res) [build/km²] Istat 750000.000 800000.000 850000.000 V(weight mean),i = D(non-res) [build/km²] 750000.000 800000.000 Istat 850000.000 [V(res)mean,i x D(res),i] + [V(non-res)mean,i x D(non-res),i] [D(res),i+D(non-res),i] irpi Marco Donnini - CNR-IRPI - ([email protected]) [€/m²] EGU General 2020 CC 7 BY#8Economic landslide susceptibility. Normalized weighted mean real estate market value 4° step - the weighted mean real estate market values were normalized to obtain 0 to 1 values 750000 800000 850000 0 10-408 [€/m²] V(weight)mean 408-613 613-811 811 1067 1067-2696 no data NORMALIZATION 750000.000 (f) 800000.000 850000.000 По.о 0.0-0.27 0.27-0.34 0.34 0.39 0.39 -0.44 0.44 1.00 no data VN(weight mean),i = 750000 800000 850000 irpi Marco Donnini - CNR-IRPI - ([email protected]) 4700000.000 4750000.000 4800000.000 VN(weight)mean 750000.000 800000.000 V(weight mean),i - V(weight mean), min V(weight mean),max - V(weight mean),min [Normalized, unitless] EGU General 2020 CC + 850000.000 8 BY 4700000.000 4750000.000 4800000.000#9Economic landslide susceptibility. Calculation 5° step - the economic landslide susceptibility comes from the combination of normalized weighted mean real estate market values and landslide susceptibility ECONOMIC LANDSLIDE SUSCEPTIBILITY CALCULATION 750000.000 800000.000 (f) VN(weight)mean 750000.000 850000.000 П 0.0 0.0-0.27 0.27-0.34 0.34 0.39 0.39 0.44 0.44 1.00 no data 800000.000 85:00:00.000 From 0 to 1 4700000.000 4750000.000 4700000.000 4800000.000 S(econ land) 750000.000 750000.000 800000.000 800000.000 850000.000 From 0 to 1 S(econ land),i= VN(weight mean),i x S(land),i irpi Marco Donnini - CNR-IRPI - ([email protected]) 850000.000 0.01 -0.06 0.06 -0.09 0.09 0.15 0.15-0.20 0.020 -0.62 No data 4700000.000 4750000.000 4800000.000 750000.000 Susc. land 750000.000 800000.000 [Normalized, unitless] EGU General 2020 CC อ BY From 0 to 1 9 800000.000 850000.000 1 E -0.80 -0.55 -0.45 -0.20 -0.00 850000.000#10Economic landslide susceptibility. A combination of two «0 to 1» variables The color and the sizes of the circles in (a) represent the S(econ) values. The contour lines in (b) give a measure of the distribution of the S(econ) values. 4750000.000 4800000.000 850000.000 VN(weight mean) 1.00 0.75 0.50 0.25 750000.000 800000.000 0.01 -0.06 0.00- 0.06 -0.09 0.00 0.25 0.50 0.75 1.00 0.09 -0.15 S(land) 0.15-0.20 0.020 -0.62 No data 4800000.000 4750000.000 VN(weight mean) 1.00- 0.75 0.50 0.25 S(econ) 0.2 0.4 0.6 S(econ) 0.6 level 0.4 0.2 10 5 jrpi S(econ land) 750000.000 800000.000 850000.000 4700000.000 0.00 0.00 0.25 0.50 S(land) 0.75 1.00 Economic landslide susceptibility ranges from 0 (minimum) to 1 (maximum) Marco Donnini - CNR-IRPI - ([email protected]) General EGU 2020 CC 10 BY#11References Antonini, G., Cardinali, M., Guzzetti, F., Reichenbach, P., & Sorrentino, A. (1993). Carta inventario dei fenomeni franosi della regione Marche ed aree limitrofe. CNR Gruppo per la Difesa dalle Catastrofi Idrogeologiche Publication, 580(2). Brabb, E.E. (1984). Innovative approach to landslide hazard and risk mapping. Proceedings of the 4th International Symposium on Landslides, Toronto, vol. 1, pp. 307-324 Guzzetti, F., Carrara, A., Cardinali, M., & Reichenbach, P. (1999). Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology, 31(1-4), 181-216. Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M., & Ardizzone, F. (2005). Probabilistic landslide hazard assessment at the basin scale. Geomorphology, 72(1-4), 272-299. Guzzetti, F., Reichenbach, P., Ardizzone, F., Cardinali, M., & Galli, M. (2006a). Estimating the quality of landslide susceptibility models. Geomorphology, 81(1-2), 166-184. Guzzetti, F., Galli, M., Reichenbach, P., Ardizzone, F., & Cardinali, M. J. N. H. (2006b). Landslide hazard assessment in the Collazzone area, Umbria, Central Italy. Natural Hazards and Earth System Science, 6(1), 115-131. Mancini, F. (1966). Carta Dei Suoli d'Italia 1: 1 000 000: Soil Map of Italy. Società geografica. Mateos, R.M., Garcia, I., Del Ventisette, C., Ciampalini, A., Arizzone, F., Rossi, M., Simoniello, T., Malamud B.D. (2014) D6.1. Landslide susceptibility models and maps. LAMPRE Project available at www.lampre- project.eu/index.php?option=com_phocadownload&view=category&download=33:d6-1-report-on-landslide-susceptibility-models-and-maps- pdf-1-7-mb&id=7:wp6-preparedeness-prevention-recovery-reconstruction irpi Marco Donnini - CNR-IRPI - ([email protected]) General EGUAS 2020 CC + 11 BY