Cost estimation for the monitoring instrumentation of landslide early warning systems

<p>Landslides are socio-natural hazards. In Colombia, for example, these are the most frequent hazards. The interplay of climate change and the mostly informal growth of cities in landslide-prone areas increases the associated risks. Landslide early warning systems (LEWSs) are essential for di...

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Published in:Natural Hazards and Earth System Sciences
Main Authors: M. Sapena, M. Gamperl, M. Kühnl, C. Garcia-Londoño, J. Singer, H. Taubenböck
Format: Article
Language:English
Published: Copernicus Publications 2023-12-01
Online Access:https://nhess.copernicus.org/articles/23/3913/2023/nhess-23-3913-2023.pdf
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author M. Sapena
M. Gamperl
M. Kühnl
M. Kühnl
C. Garcia-Londoño
C. Garcia-Londoño
J. Singer
H. Taubenböck
H. Taubenböck
author_facet M. Sapena
M. Gamperl
M. Kühnl
M. Kühnl
C. Garcia-Londoño
C. Garcia-Londoño
J. Singer
H. Taubenböck
H. Taubenböck
author_sort M. Sapena
collection DOAJ
container_title Natural Hazards and Earth System Sciences
description <p>Landslides are socio-natural hazards. In Colombia, for example, these are the most frequent hazards. The interplay of climate change and the mostly informal growth of cities in landslide-prone areas increases the associated risks. Landslide early warning systems (LEWSs) are essential for disaster risk reduction, but the monitoring component is often based on expensive sensor systems. This study presents a data-driven approach to localize landslide-prone areas suitable for low-cost and easy-to-use LEWS instrumentation, as well as to estimate the associated costs. The approach is exemplified in the landslide-prone city of Medellín, Colombia. A workflow that enables decision-makers to balance financial costs and the potential to protect exposed populations is introduced. To achieve this, city-level landslide susceptibility is mapped using data on hazard levels, landslide inventories, geological and topographic factors, and a random forest model. Then, the landslide susceptibility map is combined with a population density map to identify highly exposed areas. Subsequently, a cost function is defined to estimate the cost of LEWS monitoring sensors at the selected sites, using lessons learned from a pilot LEWS in Bello Oriente, a neighbourhood in Medellín. This study estimates that LEWS monitoring sensors could be installed in several landslide-prone areas with a budget ranging from EUR 5 to EUR 41 per person (roughly COP 23 000 to 209 000), improving the resilience of over 190 000 exposed individuals, 81 % of whom are located in precarious neighbourhoods; thus, the systems would particularly reduce the risks of a social group of very high vulnerability. The synopsis of all information allows us to provide recommendations for stakeholders on where to proceed with LEWS instrumentation. These are based on five different cost-effectiveness scenarios. This approach enables decision-makers to prioritize LEWS deployment to protect exposed populations while balancing the financial costs, particularly for those in precarious neighbourhoods. Finally, the limitations, challenges, and opportunities for the successful implementation of a LEWS are discussed.</p>
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spelling doaj-art-e8f4b2cbc73149ce9d8f8bf6e8d3eed92025-08-19T22:30:12ZengCopernicus PublicationsNatural Hazards and Earth System Sciences1561-86331684-99812023-12-01233913393010.5194/nhess-23-3913-2023Cost estimation for the monitoring instrumentation of landslide early warning systemsM. Sapena0M. Gamperl1M. Kühnl2M. Kühnl3C. Garcia-Londoño4C. Garcia-Londoño5J. Singer6H. Taubenböck7H. Taubenböck8German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Weßling, GermanyChair of Engineering Geology, Technical University of Munich, 80333 Munich, GermanyGerman Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Weßling, GermanyCompany for Remote Sensing and Environmental Research (SLU), 81243 Munich, GermanyInstitute for Landscape Architecture, Leibniz University Hanover, 30419 Hanover, GermanyGeological Society of Colombia, 111321 Bogotá, ColombiaAlpGeorisk, 86609 Donauwörth, GermanyGerman Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Weßling, GermanyInstitute for Geography and Geology, Julius-Maximilians-Universität Würzburg, 97074 Würzburg, Germany<p>Landslides are socio-natural hazards. In Colombia, for example, these are the most frequent hazards. The interplay of climate change and the mostly informal growth of cities in landslide-prone areas increases the associated risks. Landslide early warning systems (LEWSs) are essential for disaster risk reduction, but the monitoring component is often based on expensive sensor systems. This study presents a data-driven approach to localize landslide-prone areas suitable for low-cost and easy-to-use LEWS instrumentation, as well as to estimate the associated costs. The approach is exemplified in the landslide-prone city of Medellín, Colombia. A workflow that enables decision-makers to balance financial costs and the potential to protect exposed populations is introduced. To achieve this, city-level landslide susceptibility is mapped using data on hazard levels, landslide inventories, geological and topographic factors, and a random forest model. Then, the landslide susceptibility map is combined with a population density map to identify highly exposed areas. Subsequently, a cost function is defined to estimate the cost of LEWS monitoring sensors at the selected sites, using lessons learned from a pilot LEWS in Bello Oriente, a neighbourhood in Medellín. This study estimates that LEWS monitoring sensors could be installed in several landslide-prone areas with a budget ranging from EUR 5 to EUR 41 per person (roughly COP 23 000 to 209 000), improving the resilience of over 190 000 exposed individuals, 81 % of whom are located in precarious neighbourhoods; thus, the systems would particularly reduce the risks of a social group of very high vulnerability. The synopsis of all information allows us to provide recommendations for stakeholders on where to proceed with LEWS instrumentation. These are based on five different cost-effectiveness scenarios. This approach enables decision-makers to prioritize LEWS deployment to protect exposed populations while balancing the financial costs, particularly for those in precarious neighbourhoods. Finally, the limitations, challenges, and opportunities for the successful implementation of a LEWS are discussed.</p>https://nhess.copernicus.org/articles/23/3913/2023/nhess-23-3913-2023.pdf
spellingShingle M. Sapena
M. Gamperl
M. Kühnl
M. Kühnl
C. Garcia-Londoño
C. Garcia-Londoño
J. Singer
H. Taubenböck
H. Taubenböck
Cost estimation for the monitoring instrumentation of landslide early warning systems
title Cost estimation for the monitoring instrumentation of landslide early warning systems
title_full Cost estimation for the monitoring instrumentation of landslide early warning systems
title_fullStr Cost estimation for the monitoring instrumentation of landslide early warning systems
title_full_unstemmed Cost estimation for the monitoring instrumentation of landslide early warning systems
title_short Cost estimation for the monitoring instrumentation of landslide early warning systems
title_sort cost estimation for the monitoring instrumentation of landslide early warning systems
url https://nhess.copernicus.org/articles/23/3913/2023/nhess-23-3913-2023.pdf
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