Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> 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 many 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 that (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; and (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.

Highlights

  • The number of severe flood events is expected to increase in many regions due to climate change (Hirabayashi et al 2013, 2021)

  • 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 Flood Early Warning Systems (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

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Summary

Introduction

The number of severe flood events is expected to increase in many regions due to climate change (Hirabayashi et al 2013, 2021). In the literature of weather forecasting, the “cry wolf effect” has been intensively investigated as an important interaction between weather prediction and social systems. Simmons and Sutter (2009) performed econometric analysis of a disaster database and revealed that tornadoes that occurred in areas with higher false alarm ratio killed and injured more people. Trainor et al (2015) performed large-scale telephone interviews and revealed the significant relationship between actual false alarm ratio and behavioral responses to tornado warnings. They found that there is a wide variation in public definition of false alarms and actual false alarm ratio does not predict perception of false alarm ratio, which illustrated the significant complexity associated with the analysis of false alarms

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