Socio-meteorology: flood prediction, social preparedness, and cry wolf effects

TL;DR

This paper develops a socio-hydrological model to simulate how social trust and collective memory influence flood preparedness and the cry wolf effect, highlighting the importance of social factors in flood warning systems.

Contribution

It introduces a novel socio-hydrological model explicitly incorporating social trust dynamics and cry wolf effects in flood early warning systems.

Findings

Trust dynamics are more critical in infrequent flood societies.

Improved flood prediction skills increase sensitivity to social trust.

False alarms undermine credibility and preparedness actions.

Abstract

To improve the efficiency of flood early warning systems (FEWS), it is important to understand the interactions between natural and social systems. The high level of trust in authorities and experts is necessary to improve the likeliness of individuals to take preparedness actions responding to warnings. Despite a lot of efforts to develop the dynamic model of human and water in socio-hydrology, no socio-hydrological models explicitly simulate social collective trust in FEWS. Here we develop the stylized model to simulate the interactions of flood, social collective memory, social collective trust in FEWS, and preparedness actions responding to warnings by extending the existing socio-hydrological model. We realistically simulate the cry wolf effect, in which many false alarms undermine the credibility of the early warning systems and make it difficult to induce preparedness actions. We found (1) considering the dynamics of social collective trust in FEWS is more important in the technological society with infrequent flood events than in the green society with frequent flood events; (2) as the natural scientific skill to predict flood events is improved, the efficiency of FEWS gets more sensitive to the behavior of social collective trust, so that forecasters need to determine their warning threshold by considering the social aspects.