Simulating the dynamics of individual adaptation to floods

Abstract

Individual adaptation measures are an important tool for households to reduce the negative consequences of oods. Although people's motivations to adopt such measures are widely studied in the literature, the diusion of adaptations within a given population is less well described. In this paper, we build a dynamic agent based model which simulates the adoption of individual adaptation measures and enables evaluation of the eciency of dierent communication policies. We run our model using an original dataset, based on a survey in France. We test the importance of dierent parameters of our model by implementing a global sensitivity analysis. We then compare the ranking and performance of dierent communication policies under dierent model settings. We show that in all settings, targeted policies that deal with both risk and coping possibilities, perform best in supporting individual adaptation. Moreover, we show that dierent dynamic parameters are of particular importance, namely the delay between the motivation to act and the implementation of the measure and the time during which households stick to a given adaptation measure. Keywords:)ood riskD dpttionD gent sed modelD protetion motivtion theoryD soil networkD smllworldD risk ommunitionD)ood preventionD vulnerilityF

An integrated model of real estate market responses to coastal flooding

“…Such models are used for studying land use (An, ; Levy, Martens, & van der Heijden, ; Matthews, Gilbert, Roach, Polhill, & Gotts, ; Parker, Manson, Janssen, Hoffmann, & Deadman, ), the individual and social behaviors affecting coastal flood risk (Haer et al. ; Jenkins, Surminski, Hall, & Crick, ), the competing influences of storm risks and coastal amenities (Walls, Magliocca, & McConnell, ), individual adaptations (Erdlenbruch & Bonté, ), the operation of real estate markets in the coastal zone (Chandra‐Putra, Zhang, & Andrews, ), effects of flood insurance schemes (Dubbelboer, Nikolic, Jenkins, & Hall, ; Han & Peng, ), and interactions between insurance schemes and local government actions (Crick, Jenkins, & Surminski, ).…”

Combining social network analysis and agent-based modelling to explore dynamics of human interaction: A review

“…Marketing research addresses the spread of non-material processes when dealing with the diffusion of innovations (Kiesling et al, 2012;Peres et al, 2010). Agents exchange information with their peers which influences their decision towards a new product (Amini et al, 2012;Bohlmann et al, 2010;Goldenberg et al, 2007;Haenlein & Libai, 2013;Hu, et al, 2018;Janssen & Jager, 2001, 2003Libai et al, 2013;Negahban & Smith, 2018) or technology such as sustainable mobility (Huétink et al, 2010), solar photovoltaics (Pearce & Slade, 2018;Wang et al, 2018), water conservation (Rasoulkhani et al, 2018), smart metering (Zhang & Nuttall, 2011), flood prevention measures (Erdlenbruch & Bonte, 2018) or innovations like autonomous vehicles (Talebian & Mishra, 2018).…”

“…how many agents are of grey state at the end of the simulation. Examples from the literature include the comparison of agent properties like variable adoption thresholds (Bohlmann et al, 2010) or group identification of agents (Frank et al, 2018), or the inclusion of external influences such as policies accompanying the introduction of new products (Amini et al, 2012;Negahban & Smith, 2018), influencing risk prevention of individuals (Erdlenbruch & Bonte, 2018), mitigating influenza pandemics (Davey et al, 2008;Perlroth et al, 2010) or encouraging the transformation towards sustainable behaviour (Kaufmann et al, 2009;Rasoulkhani et al, 2018;Wang et al, 2018;Zhang & Nuttall, 2011).…”

“…In addition, the global clustering coefficient, network diameter and average path length fall under this category. For the comparison of network topologies, model outcomes emerge based on different network structures such as scale-free, small-world or regular networks (Bohlmann et al, 2010;Bravo et al, 2012;Chen et al, 2012;Chica et al, 2018;Erdlenbruch & Bonte, 2018;Fu et al, 2011;Hadzibeganovic et al, 2018;Heinrich, 2018;Huétink et al, 2010;Janssen & Jager, 2003;Ke et al, 2008;Keijzer et al, 2018;Lu et al, 2009;Moglia et al, 2018;Negahban & Smith, 2018;Rasoulkhani et al, 2018;Schlaile et al, 2018). Changes in link properties include strength (Goldenberg et al, 2007) and direction of social interaction (Flache & Macy, 2011).…”

Flood Vulnerability, Environmental Land Use Conflicts, and Conservation of Soil and Water: A Study in the Batatais SP Municipality, Brazil

“…For example, in the long term the conversion of forest into agricultural land reduces soil permeability, because the use of heavy machines over many years increases compaction of topsoil layers and deteriorates the soil structure [4,16,17]. The stability of the soil influences its infiltration capacity [3,4] and its ability to preserve structure under the impact rain drops [18,19]. The overall result is increased runoff and soil erosion.…”