Articles | Volume 26, issue 16
https://doi.org/10.5194/hess-26-4265-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-4265-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Aug 2022
Research article |
| 18 Aug 2022
| 18 Aug 2022
Impact of cry wolf effects on social preparedness and the efficiency of flood early warning systems
Yohei Sawada
CORRESPONDING AUTHOR
Institute of Engineering Innovation, University of Tokyo, Tokyo, Japan
Rin Kanai
Department of Civil Engineering, University of Tokyo, Tokyo,
Japan
Hitomu Kotani
Institute of Engineering Innovation, University of Tokyo, Tokyo, Japan
Department of Urban Management, Kyoto University, Kyoto, Japan
Department of Natural Resources, Graduate School of Global
Environmental Studies, Kyoto University, Kyoto, Japan
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Cited
19 citations as recorded by crossref.
- Check or reject? Trust and motivation development in app-based warning systems M. von der Linde et al. https://doi.org/10.1016/j.ssci.2024.106724
- Early warning response to rainstorm: Designing a model with incentive and supervision mechanisms based on the principal-agent theory R. Ma et al. https://doi.org/10.1016/j.ijdrr.2024.104683
- Towards reanalysis of human-flood interactions: case study in the United States M. Langhu & Y. Sawada https://doi.org/10.3178/hrl.24-00037
- The boy who cried hydrogen: Examining the cost impact of proactive vs reactive policy for electrolytic hydrogen D. Mathews & P. Deane https://doi.org/10.1016/j.ijhydene.2025.152350
- Socio-hydrological prediction of soft-path vs. hard-path in flood risk management under climate change: A case study from the Lower Kelani River Basin, Sri Lanka C. Perera et al. https://doi.org/10.1016/j.ejrh.2025.102230
- Thinking systemically about climate services: Using archetypes to reveal maladaptation R. Biella et al. https://doi.org/10.1016/j.cliser.2024.100490
- Assessing Storm Surge Multiscenarios Based on Ensemble Tropical Cyclone Forecasting M. Rezuanul Islam et al. https://doi.org/10.1029/2023JD038903
- Flood severity classification in Bangladesh: a comprehensive analysis of historical weather and water level data using machine learning approaches F. Nishat et al. https://doi.org/10.1007/s11069-025-07202-6
- Does the Performance of a Flood Early Warning System Affect Casualties and Economic Losses? Empirical Analysis Using Open Data from the 2018 Japan Floods H. KOTANI et al. https://doi.org/10.2151/jmsj.2025-025
- Conceptualizing the effectiveness of flood risk information with a socio-hydrological model: A case study in Lower Kelani River Basin, Sri Lanka C. Perera & S. Nakamura https://doi.org/10.3389/frwa.2023.1131997
- Short-term panel data analysis of the effect of flood risk communication on individual evacuation decisions M. Ikegai et al. https://doi.org/10.1016/j.ijdrr.2024.104433
- Assessing Driver’s Trust, Compliance, and Reliance in an Automated Flood Warning System: Effects of Error Types and System Reliability T. Mao et al. https://doi.org/10.1177/10711813241276493
- Developing an open-source flood forecasting system adapted to data-scarce regions: A digital twin coupled with hydrologic-hydrodynamic simulations L. M. C. Rápalo et al. https://doi.org/10.1016/j.jhydrol.2024.131929
- Balancing Safety and Mobility: Experience with Iceland’s SMS Avalanche Warning System M. Kokorsch https://doi.org/10.1007/s13753-026-00722-0
- Determining the threshold of issuing flash flood warnings based on people's response process simulation R. Zhang et al. https://doi.org/10.5194/hess-28-5229-2024
- Impact-Based Decision Making in Flood Risk Management: A Review of Behavioral and Technical Approaches Z. Bovermann et al. https://doi.org/10.1007/s13753-026-00712-2
- Using virtual reality to study human response to flood risk across controlled experiments Z. Zhang et al. https://doi.org/10.1016/j.ijdrr.2025.105956
- Form over Function: How design expectations shape what counts as a climate service R. Biella et al. https://doi.org/10.1016/j.cliser.2026.100674
- Torrential rainfall in Valencia, Spain, recorded by personal weather stations preceding and during the 29 October 2024 floods N. Rombeek et al. https://doi.org/10.5194/hess-29-6715-2025
19 citations as recorded by crossref.
- Check or reject? Trust and motivation development in app-based warning systems M. von der Linde et al. https://doi.org/10.1016/j.ssci.2024.106724
- Early warning response to rainstorm: Designing a model with incentive and supervision mechanisms based on the principal-agent theory R. Ma et al. https://doi.org/10.1016/j.ijdrr.2024.104683
- Towards reanalysis of human-flood interactions: case study in the United States M. Langhu & Y. Sawada https://doi.org/10.3178/hrl.24-00037
- The boy who cried hydrogen: Examining the cost impact of proactive vs reactive policy for electrolytic hydrogen D. Mathews & P. Deane https://doi.org/10.1016/j.ijhydene.2025.152350
- Socio-hydrological prediction of soft-path vs. hard-path in flood risk management under climate change: A case study from the Lower Kelani River Basin, Sri Lanka C. Perera et al. https://doi.org/10.1016/j.ejrh.2025.102230
- Thinking systemically about climate services: Using archetypes to reveal maladaptation R. Biella et al. https://doi.org/10.1016/j.cliser.2024.100490
- Assessing Storm Surge Multiscenarios Based on Ensemble Tropical Cyclone Forecasting M. Rezuanul Islam et al. https://doi.org/10.1029/2023JD038903
- Flood severity classification in Bangladesh: a comprehensive analysis of historical weather and water level data using machine learning approaches F. Nishat et al. https://doi.org/10.1007/s11069-025-07202-6
- Does the Performance of a Flood Early Warning System Affect Casualties and Economic Losses? Empirical Analysis Using Open Data from the 2018 Japan Floods H. KOTANI et al. https://doi.org/10.2151/jmsj.2025-025
- Conceptualizing the effectiveness of flood risk information with a socio-hydrological model: A case study in Lower Kelani River Basin, Sri Lanka C. Perera & S. Nakamura https://doi.org/10.3389/frwa.2023.1131997
- Short-term panel data analysis of the effect of flood risk communication on individual evacuation decisions M. Ikegai et al. https://doi.org/10.1016/j.ijdrr.2024.104433
- Assessing Driver’s Trust, Compliance, and Reliance in an Automated Flood Warning System: Effects of Error Types and System Reliability T. Mao et al. https://doi.org/10.1177/10711813241276493
- Developing an open-source flood forecasting system adapted to data-scarce regions: A digital twin coupled with hydrologic-hydrodynamic simulations L. M. C. Rápalo et al. https://doi.org/10.1016/j.jhydrol.2024.131929
- Balancing Safety and Mobility: Experience with Iceland’s SMS Avalanche Warning System M. Kokorsch https://doi.org/10.1007/s13753-026-00722-0
- Determining the threshold of issuing flash flood warnings based on people's response process simulation R. Zhang et al. https://doi.org/10.5194/hess-28-5229-2024
- Impact-Based Decision Making in Flood Risk Management: A Review of Behavioral and Technical Approaches Z. Bovermann et al. https://doi.org/10.1007/s13753-026-00712-2
- Using virtual reality to study human response to flood risk across controlled experiments Z. Zhang et al. https://doi.org/10.1016/j.ijdrr.2025.105956
- Form over Function: How design expectations shape what counts as a climate service R. Biella et al. https://doi.org/10.1016/j.cliser.2026.100674
- Torrential rainfall in Valencia, Spain, recorded by personal weather stations preceding and during the 29 October 2024 floods N. Rombeek et al. https://doi.org/10.5194/hess-29-6715-2025
Saved (final revised paper)
Latest update: 03 Jun 2026
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.
Although flood early warning systems (FEWS) are promising, they inevitably issue false alarms....
