Leveraging multi-model season-ahead streamflow forecasts to trigger advanced flood preparedness in Peru
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Published:2021-07-23
Issue:7
Volume:21
Page:2215-2231
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ISSN:1684-9981
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Container-title:Natural Hazards and Earth System Sciences
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language:en
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Short-container-title:Nat. Hazards Earth Syst. Sci.
Author:
Keating Colin, Lee DonghoonORCID, Bazo JuanORCID, Block PaulORCID
Abstract
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 prediction
demonstrates superior performance compared to the physically based model for
the Marañón River by correctly triggering preparedness actions in
three out of four historical occasions, while both the statistical and
multi-model predictions capture all four historical events when the required
threshold exceedance probability is reduced to 50 %, with only one false
alarm. For the Piura River, the statistical model proves superior to all
other approaches, correctly triggering 28 % more often in the hindcast
period. 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.
Funder
Wisconsin Alumni Research Foundation Office of the Vice Chancellor for Research and Graduate Education, University of Wisconsin-Madison
Publisher
Copernicus GmbH
Subject
General Earth and Planetary Sciences
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