A data-driven impact-based analysis stemming from first responders reports to predict wind damage to urban trees

Abstract

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Transitioning from weather forecasts and warnings to impact-based forecast and warning services represents a paradigm shift in service delivery for many national hydrological and meteorological services (NHMS). NHMS typically excel at delivering information about hazardous weather, but are less experienced at inferring measures of risk of impact of extreme weather. Severe wind storms are high-impact weather phenomena that generally have a detrimental effect on distinct socio-economic sectors. In the Netherlands, the emergency services record locations where wind damage occurred to public or private property. In this work, we take 10 years of damage locations (2013–2023) provided by two safety regions in the Dutch province of Noord-Brabant. Each of the reports is enriched with an array of weather and environmental features, intended to describe the local conditions where wind damage was recorded. We model the wind reports using an ensemble of data-driven methods (i.e., One-Class Support Vector Machine) which are capable of learning from these hyper local conditions and predict for the rest of the study area. Results show how the ensemble of data-driven models are able to skillfully map locations where wind-induced damages are likely at spatial resolutions of 1 km and 5 km under high and low wind conditions scenarios. These results are encouraging for NHMS to strengthen national multi-hazard early warning systems by providing a new range of services at the urban scales in collaboration with external partners. As a consequence, the transition of scientific knowledge towards society would accelerate, hence helping at better protecting communities and livelihoods.

Keywords

• wind damage prediction
• impact-based analysis
• machine learning
• climate action
• climate adaptation