Spatially and temporally consistent prediction of heavy precipitation from mean values View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2012-07

AUTHORS

R. E. Benestad, D. Nychka, L. O. Mearns

ABSTRACT

Extreme precipitation can cause flooding, result in substantial damages and have detrimental effects on ecosystems1,2. Climate adaptation must therefore account for the greatest precipitation amounts that may be expected over a certain time span3. The recurrence of extreme-to-heavy precipitation is notoriously hard to predict, yet cost–benefit estimates of mitigation and successful climate adaptation will need reliable information about percentiles for daily precipitation. Here we present a new and simple formula that relates wet-day mean precipitation to heavy precipitation, providing a method for predicting and downscaling daily precipitation statistics. We examined 32,857 daily rain-gauge records from around the world and the evaluation of the method demonstrated that wet-day precipitation percentiles can be predicted with high accuracy. Evaluations against independent data demonstrated high skill in both space and time, indicating a highly robust methodology. More... »

PAGES

544

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/nclimate1497

DOI

http://dx.doi.org/10.1038/nclimate1497

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1025496914


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