Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
A temporal downscaling approach for sub-daily gridded extreme rainfall intensity estimation under climate change
Study region: Canada. Study focus: Intensity-duration-frequency curves need to be updated to account for the potential effect of climate change. However, sub-daily regional climate model (RCM) simulations required for this purpose are commonly unavailable. The scaling approach is often applied for temporal downscaling of point daily precipitation; nevertheless, difference in spatial scale between simulations and observations may complicate its application at the grid scale. This study proposes a grid-scale temporal downscaling approach for estimating sub-daily gridded extreme rainfall intensity under climate change at regions with a relatively high station density. The approach uses daily annual maximum rainfall intensity series from RCM simulations and grid-scale scaling properties from observations. The study evaluates two methods for estimating gridded scaling exponents needed in temporal downscaling. The assessment is performed on observation-based and RCM-based temporally downscaled quantiles. The effect of temporal downscaling and bias correction on sub-daily quantiles and relative changes is also analysed. The approach is applied to three case studies across Canada. New hydrological insights for the region: Piecewise simple scaling is found over the study domains, indicating caution in using simple scaling. The performance of the approach in estimating sub-daily observation-based gridded rainfall intensity quantiles is suitable, with preference for point to grid transfer at the annual maximum series stage. Bias correction is necessary for properly estimating sub-daily RCM-based quantiles, presenting less impact on relative changes.
A temporal downscaling approach for sub-daily gridded extreme rainfall intensity estimation under climate change
Study region: Canada. Study focus: Intensity-duration-frequency curves need to be updated to account for the potential effect of climate change. However, sub-daily regional climate model (RCM) simulations required for this purpose are commonly unavailable. The scaling approach is often applied for temporal downscaling of point daily precipitation; nevertheless, difference in spatial scale between simulations and observations may complicate its application at the grid scale. This study proposes a grid-scale temporal downscaling approach for estimating sub-daily gridded extreme rainfall intensity under climate change at regions with a relatively high station density. The approach uses daily annual maximum rainfall intensity series from RCM simulations and grid-scale scaling properties from observations. The study evaluates two methods for estimating gridded scaling exponents needed in temporal downscaling. The assessment is performed on observation-based and RCM-based temporally downscaled quantiles. The effect of temporal downscaling and bias correction on sub-daily quantiles and relative changes is also analysed. The approach is applied to three case studies across Canada. New hydrological insights for the region: Piecewise simple scaling is found over the study domains, indicating caution in using simple scaling. The performance of the approach in estimating sub-daily observation-based gridded rainfall intensity quantiles is suitable, with preference for point to grid transfer at the annual maximum series stage. Bias correction is necessary for properly estimating sub-daily RCM-based quantiles, presenting less impact on relative changes.
A temporal downscaling approach for sub-daily gridded extreme rainfall intensity estimation under climate change
Ana I. Requena (Autor:in) / Truong-Huy Nguyen (Autor:in) / Donald H. Burn (Autor:in) / Paulin Coulibaly (Autor:in) / Van-Thanh-Van Nguyen (Autor:in)
2021
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
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