Articles | Volume 21, issue 7
Nat. Hazards Earth Syst. Sci., 21, 2215–2231, 2021
Nat. Hazards Earth Syst. Sci., 21, 2215–2231, 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,, 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,, 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,, 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,, 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,, 2013. 
Short summary
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.
Final-revised paper