Evaluating and comparing methods of sinkhole susceptibility mapping in the Ebro Valley evaporite karst (NE Spain)

• Published in: Geomorphology (Amsterdam, Netherlands) Vol. 111; no. 3; pp. 160 - 172
• Main Authors: Galve, J.P., Gutiérrez, F., Remondo, J., Bonachea, J., Lucha, P., Cendrero, A.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 15.10.2009; Elsevier
• Subjects: Earth sciences; Earth, ocean, space; Engineering and environment geology. Geothermics; Exact sciences and technology; Geomorphology, landform evolution; GIS; Marine and continental quaternary; Model evaluation; Natural hazards: prediction, damages, etc; Spatial analysis; Subsidence hazard; Surficial geology; Iberian Peninsula; Ebro Basin; Ebro River; Europe; Saragossa Spain; Southern Europe; Aragon Spain; Spain; Spain, Aragon, Zaragoza; GIS; Spatial analysis; Model evaluation; Subsidence hazard; models; maps; density; geographic information systems; sinkholes; karst; quality; bedrock; subsidence; strategy; valleys; landform evolution
• ISSN: 0169-555X; 1872-695X
• DOI: 10.1016/j.geomorph.2009.04.017

Multiple sinkhole susceptibility models have been generated in three study areas of the Ebro Valley evaporite karst (NE Spain) applying different methods (nearest neighbour distance, sinkhole density, heuristic scoring system and probabilistic analysis) for each sinkhole type separately (cover collapse sinkholes, cover and bedrock collapse sinkholes and cover and bedrock sagging sinkholes). The quantitative and independent evaluation of the predictive capability of the models reveals that: (1) The most reliable susceptibility models are those derived from the nearest neighbour distance and sinkhole density. These models can be generated in a simple and rapid way from detailed geomorphological maps. (2) The reliability of the nearest neighbour distance and density models is conditioned by the degree of clustering of the sinkholes. Consequently, the karst areas in which sinkholes show a higher clustering are a priori more favourable for predicting new occurrences. (3) The predictive capability of the best models obtained in this research is significantly higher (12.5–82.5%) than that of the heuristic sinkhole susceptibility model incorporated into the General Urban Plan for the municipality of Zaragoza. Although the probabilistic approach provides lower quality results than the methods based on sinkhole proximity and density, it helps to identify the most significant factors and select the most effective mitigation strategies and may be applied to model susceptibility in different future scenarios.