Social multi-criteria evaluation to identify appropriate disaster risk reduction measures: application to landslides in the Rwenzori Mountains, Uganda

Abstract

Over the past decades the global exposure of persons and assets to natural hazards, like 5 landslides, has increased faster than that their vulnerability has decreased. Therefore the Sendai 6Framework for Disaster Risk Reduction (DRR) made a call in 2015 for more investment in 7 DRR. Recent studies exposed the lack of scientific evaluation data for selecting DRR measures. 8The objective of this article is to develop a participatory methodology for identifying 9 appropriate DRR measures based on stakeholder groups' perceptions. More specifically, we 10 propose a social multi-criteria evaluation which combines a two-phased participatory multi-11 criteria analysis with an institutional analysis for its design and a discourse analysis for 12 interpreting its outcomes. This method is then tested for a case study focusing on landslides in 13 the Rwenzori Mountains region (Uganda) to illustrate both its relevance and practical 14 challenges. The results from the case study highlight the perceived importance of the 15 acceptability of measures above cost, feasibility and effectiveness criteria, by district and 16 village stakeholder groups. Frequently recommended DRR actions, such as disaster risk 17 zonation or relocation, are shown to be perceived negatively by local decision-makers despite 18 being put forward in national policies. This article contributes to the DRR literature as it 19proposes a new method to support DRR-related decision-making. It also contributes to the 20 understanding of landslide risk reduction in the Rwenzori Mountains region as it presents the 21 first attempt to identify appropriate landslide risk reduction measures for the region.

Testing Interscience in Understanding and Tackling Disaster Risk

“…The proposed independent unit would also give a formal representation to the CAR, enabling them to take part in negotiating and controlling the implementation of DRR strategies, with continuous attention to their culture. This agrees with what is currently emphasized among some disaster risk researchers: CAR will consider a measure seriously and support its implementation if it considers their livelihood and culture; but also if they have been involved in the design and decision process (Cannon, 2015;Maes et al, 2019).…”

“…Two additional factors make the Rwenzori interesting for this study. First, it is a region where a recent study highlights the perceived importance of the acceptability of DRR measures by the CAR (Maes et al, 2019). Second, it is also a region in a context of a least developed economy, with limited resources to implement and sustain highly specialized technologies for DRR.…”

“…These categories are, in this case study, respectively represented by: the indigenous practitioners and specialists on flood DRR, representing CAR; the policymakers, i.e., politicians charged with the responsibility of developing and approving policies on DRR under the governmental platform called Disaster Management Committee; and the scientists as well as technocrats or scientific advisers, who inform the policy development and DRR implementation (Maes et al, 2017). The selection targeting representatives knowledgeable of the DRR perspectives within their circles was conducted through a triangulated institutional stakeholder analysis following the methods described in Maes et al (2019). For the scientists, attention was paid to identifying researchers familiar with the Rwenzori as well as scientists holding technical positions in the district local government.…”

Building Resilience to Chronic Landslide Hazard Through Citizen Science

“…We define local knowledge as unique, experiential knowledge developed over an extended period of time and held by a group of people in a specific location (Roncoli et al, 2002). Particularly in the case of water-induced hazards like floods and landslides, local knowledge offers invaluable insights about the disaster dynamics and frugal preventive measures (Alcántara-Ayala, 2004;Maes et al, 2019; see also Shaw et al, 2008). Current studies in disaster risk reduction bring evidence that it is in fact the over-reliance on top-down engineering structures and expert solutions that has an adverse effect on community resilience, creating dependencies and incapacitating local-level stakeholders (Wisner, 2006;Munroe et al, 2013).…”

Risk Assessment of Resources Exposed to Rainfall Induced Landslide with the Development of GIS and RS Based Ensemble Metaheuristic Machine Learning Algorithms

“…Research on landslide risk assessment is not new work. Many pieces of research on landslide risk assessment have already been conducted in mountainous regions [95][96][97][98][99][100].…”