Increasing Extreme Hourly Precipitation Risk for New York City after Hurricane Ida
In this paper we aim to contextualize Ida’s precipitation intensity within the historical record by showing how the risk of an event of this magnitude would have changed year to year in the decades prior to its occurrence. We use a suite of traditional statistical tests to show how the significance of the extreme precipitation trend and the return period of Ida change with each year in the historical dataset. To complement existing climate modeling endeavors, we take a stochastic modeling approach to calculate nonstationary risk. We present a climate change-informed nonstationary method based on the Generalized Extreme Value distribution to project the risk of exceedance of another event of Ida’s magnitude and compare these values to a stationary one.
Our results show that the risk of hazards from precipitation is increasing in NYC. The framework we present is one way to inform mitigation measures and design alternatives adaptively on the decadal to multi-decadal time scales. The framework is also one of dynamic risk; our models show a non-linear change in risk year to year. Results like these provide a broader and more nuanced lens with which to predict and plan for the erratic response of regional precipitation trends to a warming climate. Our results can be translated to potential future damage costs and encourage planning for such events that would be expected under climate change, reinforcing the pressing need for improved urban stormwater management systems that can handle higher-intensity rainfall.
The likelihood of a storm like that due to Hurricane Ida was slowly increasing even before it happened, but the projected aggregate reoccurrence risk of an event of Ida’s magnitude over time from the non-stationary models ranges from 4 to 52 times higher than the risk given by the stationary model. Using Cooling Degree Days as a covariate resulted in risks that were more than twice the magnitude than when using Tavg. Presenting both covariates provides a broader envelope of uncertainty, which highlights the importance and nuances in the choice of a regionally appropriate covariate for non-stationary risk analysis.