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Spatial predictive modeling for outlining areas prone to torrential flows in the Colombian Andes

Moreno Zapata, Mateo (2021) Spatial predictive modeling for outlining areas prone to torrential flows in the Colombian Andes.

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Abstract:Historical records show the highly destructive power that torrential flows have had in the Colombian Andes. In the current climate change scenario, frequencies and intensities of extreme events are expected to increase in the upcoming years, likely leading to an increase in torrential flow events. Despite each municipality in Colombia requires susceptibility assessment of torrential flow as a basis for the land use planning, very few studies have been done at a national scale in Colombia. Besides, a recent methodological guide for assessing torrential flow describes methods that require detailed information which cannot be applied to the entire Colombian territory n. Therefore, prioritizing the watersheds where detailed torrential flow hazard analysis should be applied is a crucial first step for spatial planning purposes. This research applied Generalized Additive Models in a Bayesian framework to model torrential flow susceptibility at a national scale in Colombia. Different watershed levels were considered to find a suitable representation of these phenomena. Two inventories, DesInventar and SIMMA were used for the susceptibility model. The predisposing and triggering factors were grouped into morphometric indices, lithology, land cover-land use, and rainfall. Validation and performance estimations were assessed with the Area Under the Receiver Operating Characteristics (AUROC) using a k-fold cross-validation. The results were classified into five classes according to the success rate curves. Afterward, the selected levels of watersheds were combined with different Elements at Risk (urban centers and small settlements) to prioritize areas prone to torrential flows. In terms of the predictor variables, slope and maximum daily rainfall showed the highest contributions to the susceptibility models. Also, the obtained performances (median AUROC from 0.82 to 0.87) suggest a relatively high predictive power for all the watershed levels. The integration with the EaR showed a total of 871 watersheds out of 32,293 (with an area of 21,600 km2) for the most detailed level (Level 1-1,000 ) were in the highest priority class. At the second level of detail (Level 2 -5,000) the results showed that in 429 watersheds out of 6,906 with an estimated area of 51,900 km2 where more detailed studies should be carried out.
Item Type:Essay (Master)
Faculty:ITC: Faculty of Geo-information Science and Earth Observation
Programme:Geoinformation Science and Earth Observation MSc (75014)
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