Machine Learning for Projecting Extreme Precipitation Intensity for Short Durations in a Changing Climate

Huiling Hu, Bilal M. Ayyub

Geosciences · 09 May 2019

In one sentence. Machine learning temporally downscales climate projections to estimate short-duration extreme precipitation IDF curves, trained on about 2,000 US stations.

Abstract

Climate change is one of the prominent factors that causes an increased severity of extreme precipitation which, in turn, has a huge impact on drainage systems by means of flooding. Intensity–duration–frequency (IDF) curves play an essential role in designing robust drainage systems against extreme precipitation. It is important to incorporate the potential threat from climate change into the computation of IDF curves. Most existing works that have achieved this goal were based on Generalized Extreme Value (GEV) analysis combined with various circulation model simulations. Inspired by recent works that used machine learning algorithms for spatial downscaling, this paper proposes an alternative method to perform projections of precipitation intensity over short durations using machine learning. The method is based on temporal downscaling, a downscaling procedure performed over the time scale instead of the spatial scale. The method is trained and validated using data from around two thousand stations in the US. Future projection of IDF curves is calculated and discussed.

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