Preprints
https://doi.org/10.5194/nhess-2021-25
https://doi.org/10.5194/nhess-2021-25

  09 Feb 2021

09 Feb 2021

Review status: this preprint is currently under review for the journal NHESS.

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

Colin Keating1,2, Donghoon Lee1,3, Juan Bazo4,5, and Paul Block1 Colin Keating et al.
  • 1Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, USA
  • 2Nelson Institute for Environmental Studies, University of Wisconsin-Madison, Madison, USA
  • 3Climate Hazards Center, Department of Geography, University of California, Santa Barbara, USA
  • 4Red Cross Red Crescent Climate Centre, The Hague, 2521 CV, the Netherlands
  • 5Universidad Tecnológica del Perú (UTP), Lima, Perú

Abstract. Disaster planning has historically allocated minimal effort and finances toward advanced preparedness, however evidence supports reduced vulnerability to flood events, saving lives and money, through appropriate early actions. Among other requirements, effective early action systems necessitate the availability of high-quality forecasts to inform decision making. In this study, we evaluate the ability of statistical and physically based season-ahead prediction models to appropriately trigger flood early preparedness actions based on a 75 % or greater probability of surpassing the 80th percentile of historical seasonal streamflow for the flood-prone Marañón River and Piura River in Peru. The statistical prediction model, developed in this work, leverages the asymmetric relationship between seasonal streamflow and the ENSO phenomenon. Additionally, a multi-model (least squares combination) is also evaluated against current operational practices. The statistical and multi-model predictions demonstrate superior performance compared to the physically based model for the Marañón River by correctly triggering preparedness actions in all four historical occasions. For the Piura River, the statistical model proves superior to all other approaches, and even achieves an 86 % hit rate when the required threshold exceedance probability is reduced to 50 %, with only one false alarm. Continued efforts should focus on applying this season-ahead prediction framework to additional flood-prone locations where early actions may be warranted and current forecast capacity is limited.

Colin Keating et al.

Status: open (until 27 Mar 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-25', Anonymous Referee #1, 17 Feb 2021 reply

Colin Keating et al.

Model code and software

Peru Streamflow Prediction Colin Keating https://gitlab.com/ckeating/peru_streamflow_prediction

Colin Keating et al.

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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.
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