An Agent-Based Model for Simulating Flood Governance and Community Resilience

Anqi Zhu; Wenhan Feng; Huan Zheng; Yingxin Huang; Xin He; Chengying Zhou; Liang Emlyn Yang

doi:10.1016/j.mex.2026.103820

An Agent-Based Model for Simulating Flood Governance and Community Resilience

MethodsX. 2026 Feb 13:16:103820. doi: 10.1016/j.mex.2026.103820. eCollection 2026 Jun.

Authors

Anqi Zhu¹, Wenhan Feng¹, Huan Zheng², Yingxin Huang³, Xin He⁴, Chengying Zhou⁵, Liang Emlyn Yang¹

Affiliations

¹ Department of Geography, Ludwig Maximilian University of Munich (LMU), Munich, Germany.
² School of Humanities and Social Science, The Chinese University of Hong Kong, Shenzhen, China.
³ Harmony Community Foundation, Guangzhou, China.
⁴ Conservatoire National des Arts et Métiers, Paris, France.
⁵ Guangzhou University, Guangzhou, China.

Abstract

Agent-based modeling (ABM) is a unique tool for understanding social mechanisms and emergent phenomena. The paper presents an empirically grounded agent-based model that simulates how stakeholders embedded in flood governance networks facilitate community loss-sharing and post-flood recovery. The model is designed and calibrated using extensive empirical data from communities in Guangzhou, China. Modeled agents include multi-level government agencies, NGOs, private sector entities, and local clans, among others. The model integrates core processes (rainfall and flood impacts, network-based loss sharing and recovery, and the implementation of resilience measures) with modules for trust evolution and resource constraints. The purpose of this model is to evaluate the effects of different network structures, inter-stakeholder trust, and the diffusion of flood resilience measures on community flood resilience, and to advance the understanding of how resilience emerges as a macro-level attribute from micro-level interactions. Innovations are twofold: First, it moves beyond static analysis to simulate the dynamic, network-based collaborative processes among diverse institutional stakeholders; Second, it implements a process-based framework to measure community robustness and adaptivity, using these metrics to evaluate overall community resilience to floods. Key parameters, derived from literature and empirical research, were validated and tested via sensitivity analysis. The model serves as an accessible tool for researchers and practitioners interested in stakeholder collaborations in community-level climate governance and identifying optimal intervention strategies. • The model is described using the ODD protocol. • Validation, sensitivity analysis, and the number of minimum simulation runs are explained. • Complete NetLogo code and a brief user guide are provided.

Keywords: China; Community resilience; Emergency response; Flood resilience; Governance network; Post-disaster recovery; Social simulation.