Articles | Volume 21, issue 7
Nat. Hazards Earth Syst. Sci., 21, 2215–2231, 2021
https://doi.org/10.5194/nhess-21-2215-2021
Nat. Hazards Earth Syst. Sci., 21, 2215–2231, 2021
https://doi.org/10.5194/nhess-21-2215-2021

Research article 23 Jul 2021

Research article | 23 Jul 2021

Leveraging multi-model season-ahead streamflow forecasts to trigger advanced flood preparedness in Peru

Colin Keating et al.

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Cited articles

Aguirre, J., De La Torre Ugarte, D., Bazo, J., Quequezana, P., and Collado, M.: Evaluation of early action mechanisms in Peru regarding preparedness for El Niño, Int. J. Disaster Risk Sci., 10, 493–510, https://doi.org/10.1007/s13753-019-00245-x, 2019. 
Ali, M., Prasad, R., Xiang, Y., and Mundher Yaseen, Z.: Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts, J. Hydrol., 584, 1–15, https://doi.org/10.1016/j.jhydrol.2020.124647, 2020. 
Asefa, T., Kemblowski, M., Mckee, M., and Khalil, A.: Multi-time scale stream flow predictions: The support vector machines approach, J. Hydrol., 318, 7–16, https://doi.org/10.1016/j.jhydrol.2005.06.001, 2006. 
Aybar, C., Fernández, C., Huerta, A., Lavado, W., Vega, F., and Felipe-Obando, O.: Construction of a high-resolution gridded rainfall dataset for Peru from 1981 to the present day, Hydrol. Sci. J., 65, 770–785, https://doi.org/10.1080/02626667.2019.1649411, 2020. 
Badr, H. S., Zaitchik, B. F., and Guikema, S. D.: Application of statistical models to the prediction of seasonal rainfall anomalies over the Sahel, J. Appl. Meteorol. Climatol., 53, 614–636, https://doi.org/10.1175/JAMC-D-13-0181.1, 2013. 
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Disaster planning has historically underallocated resources for flood preparedness, but evidence supports reduced vulnerability via early actions. We evaluate the ability of multiple season-ahead streamflow prediction models to appropriately trigger early actions for the flood-prone Marañón River and Piura River in Peru. Our findings suggest that locally tailored statistical models may offer improved performance compared to operational physically based global models in low-data environments.
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