Articles | Volume 26, issue 16
https://doi.org/10.5194/hess-26-4265-2022
https://doi.org/10.5194/hess-26-4265-2022
Research article
 | 
18 Aug 2022
Research article |  | 18 Aug 2022

Impact of cry wolf effects on social preparedness and the efficiency of flood early warning systems

Yohei Sawada, Rin Kanai, and Hitomu Kotani

Related authors

Ensemble Kalman–Guided Model Predictive Path Integral Control for Spatially Localized Suppression of Extremes in Chaotic Geophysical Flows
Haru Kuroki, Kazumune Hashimoto, Yuki Uehara, Yohei Sawada, Duc Le, and Masashi Minamide
EGUsphere, https://doi.org/10.5194/egusphere-2026-419,https://doi.org/10.5194/egusphere-2026-419, 2026
This preprint is open for discussion and under review for Nonlinear Processes in Geophysics (NPG).
Short summary
Technical Note: Benefits of Bayesian estimation of model parameters in a large hydrological model ensemble
Yohei Sawada and Shinichi Okugawa
EGUsphere, https://doi.org/10.5194/egusphere-2025-4984,https://doi.org/10.5194/egusphere-2025-4984, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Global assessment of socio-economic drought events at the subnational scale: a comparative analysis of combined versus single drought indicators
Sneha Kulkarni, Yohei Sawada, Yared Bayissa, and Brian Wardlow
Hydrol. Earth Syst. Sci., 29, 4341–4370, https://doi.org/10.5194/hess-29-4341-2025,https://doi.org/10.5194/hess-29-4341-2025, 2025
Short summary
Ensemble Kalman filter in geoscience meets model predictive control
Yohei Sawada
EGUsphere, https://doi.org/10.48550/arXiv.2403.06371,https://doi.org/10.48550/arXiv.2403.06371, 2024
Preprint archived
Short summary
A signal-processing-based interpretation of the Nash–Sutcliffe efficiency
Le Duc and Yohei Sawada
Hydrol. Earth Syst. Sci., 27, 1827–1839, https://doi.org/10.5194/hess-27-1827-2023,https://doi.org/10.5194/hess-27-1827-2023, 2023
Short summary

Cited articles

Albertini, C., Mazzoleni, M., Totaro, V., Iacobellis, V., Di Baldassarre, G.: Socio-Hydrological Modelling: The Influence of Reservoir Management and Societal Responses on Flood Impacts, Water, 12, 1384, https://doi.org/10.3390/w12051384, 2020. 
Barendrecht, M. H., Viglione, A., Kreibich, H., Merz, B., Vorogushyn, S., and Blöschl, G.: The Value of Empirical Data for Estimating the Parameters of a Sociohydrological Flood Risk Model, Water Resour. Res., 55, 1312–1336, https://doi.org/10.1029/2018WR024128, 2019. 
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956, 2015. 
Ciullo, A., Viglione, A., Castellarin, A., Crisci, M., and Di Baldassarre, G.: Socio-hydrological modelling of flood-risk dynamics: comparing the resilience of green and technological systems, Hydrolog. Sci. J., 62, 880–891, https://doi.org/10.1080/02626667.2016.1273527, 2017. 
Cloke, H. L. and Pappenberger, F.: Ensemble flood forecasting: A review, J. Hydrol., 375, 613–626, https://doi.org/10.1016/j.jhydrol.2009.06.005, 2009. 
Download
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.
Share