27 Oct 2021
27 Oct 2021
Status: this preprint is currently under review for the journal HESS.

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

Yohei Sawada1, Rin Kanai2, and Hitomu Kotani1,3 Yohei Sawada et al.
  • 1Institute of Engineering Innovation, the University of Tokyo, Tokyo, Japan
  • 2Department of Civil Engineering, the University of Tokyo, Tokyo, Japan
  • 3Department of Urban Management, Kyoto University, Kyoto, Japan

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.

Yohei Sawada et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-497', Anonymous Referee #1, 07 Dec 2021
    • AC1: 'Reply on RC1', Yohei Sawada, 27 Jan 2022
  • RC2: 'Comment on hess-2021-497', Anonymous Referee #2, 19 Dec 2021
    • AC2: 'Reply on RC2', Yohei Sawada, 27 Jan 2022

Yohei Sawada et al.

Yohei Sawada et al.


Total article views: 511 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
403 95 13 511 5 5
  • HTML: 403
  • PDF: 95
  • XML: 13
  • Total: 511
  • BibTeX: 5
  • EndNote: 5
Views and downloads (calculated since 27 Oct 2021)
Cumulative views and downloads (calculated since 27 Oct 2021)

Viewed (geographical distribution)

Total article views: 525 (including HTML, PDF, and XML) Thereof 525 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 28 May 2022
Short summary
Although flood early warning systems (FEWS) are promising, they inevitably issue false alarms. Many false alarms undermine the credibility of FEWS, which we call a cry wolf effect. Here we present a simple model that can simulate the cry wolf effect. Our model implies that the cry wolf effect is important if a community is heavily protected by infrastructure and few floods occur. The cry wolf effects get more important as the natural scientific skill to predict flood events is improved.