A coupled agent-based model for France for simulating adaptation and migration decisions under future coastal flood risk

Abstract

In this study, we couple an integrated flood damage and agent-based model (ABM) with a gravity model of internal migration and a flood risk module (DYNAMO-M) to project household adaptation and migration decisions under increasing coastal flood risk in France. We ground the agent decision rules in a framework of subjective expected utility theory. This method addresses agent’s bounded rationality related to risk perception and risk aversion and simulates the impact of push, pull, and mooring factors on migration and adaptation decisions. The agents are parameterized using subnational statistics, and the model is calibrated using a household survey on adaptation uptake. Subsequently, the model simulates household adaptation and migration based on increasing coastal flood damage from 2015 until 2080. A medium population growth scenario is used to simulate future population development, and sea level rise (SLR) is assessed for different climate scenarios. The results indicate that SLR can drive migration exceeding 8000 and 10,000 coastal inhabitants for 2080 under the Representative Concentration Pathways 4.5 and 8.5, respectively. Although household adaptation to flood risk strongly impacts projected annual flood damage, its impact on migration decisions is small and falls within the 90% confidence interval of model runs. Projections of coastal migration under SLR are most sensitive to migration costs and coastal flood protection standards, highlighting the need for better characterization of both in modeling exercises. The modeling framework demonstrated in this study can be upscaled to the global scale and function as a platform for a more integrated assessment of SLR-induced migration.

Exploring spatial feedbacks between adaptation policies and internal migration patterns due to sea-level rise

“…To account for migration decisions at the individual level, top-down modeling approaches like CONCLUDE can be combined with bottom-up approaches such as agent-based models (ABMs) 36 , 100 . According to ref.…”

“…While ABMs have been primarily applied at local to national scales (e.g., refs. 42 , 100 , 105 ) due to the high computational demands and data requirements 104 , a recent study has applied an ABM at supra-national scale to assess the future risks of river flooding in Europe 47 .…”

“…Similarly, empirical surveys can help establish drivers of SLR-related migration, thereby providing a basis for calibrating and validating migration models based on observed as well as anticipated migration and adaptation behavior due to SLR 84 (as recently done in ref. 100 ). Furthermore, it is worth exploring the individual effect of each adaptation measure (i.e., hard protection; coastline buffer; restoration of coastal wetlands; managed retreat) on the number and spatial patterns of migration, which we did not assess in this study as our aim was to explore three integrated adaptation policy scenarios of combined adaptation measures (Fig.…”

Future Population Exposure to Flood Risk: A Decomposition Approach across Shared-Socioeconomic Pathways (SSPs)

“…Climate adjusted flood models are early in development within peer-reviewed literature, and are not widely used (Wing et al, 2022). Additionally, most models are unable to estimate risk at a high-resolution (Ward et al, 2013;Alfieri et al, 2017), fail to consider adaptation (Scussolini et al, 2016;Wing et al, 2019), cannot accurately capture extreme precipitation (Kim et al, 2022), and often focus on single flood sources of either pluvial, fluvial, or coastal (Kulp & Strauss, 2019;Marsooli et al, 2019;Wing et al, 2022).…”

“…Advancements in agent based modeling (ABM) are moving towards modeling household level migration under climate change (Bell et al, 2021;Tierolf et al, 2023;Entwisle et al, 2016), but these developments have generally been limited to flood risk forecasts which only consider SLR, and are computationally intensive due to the reliance on individual flood simulations. Other research has looked at population exposure to flooding, but has existed primarily outside of the US or at a global scale using country level projections, and has been focused only on SLR and riverine flooding (Jongman et al, 2012;Merkens et al, 2018;Neumann et al, 2015).…”