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CLIMATE CHANGE AND FUTUREPROOFING INFRASTRUCTURE
Sertac Oruc 1 , Ismail Yucel 2 , Aysen Yilmaz 3
1  Earth System Science (ESS) Interdisciplinary Program, Middle East Technical University, Ankara, Turkey
2  Dept. of Civil Eng., Water Resources Division, Middle East Technical University, Ankara, Turkey,Earth System Science (ESS) Interdisciplinary Program, Middle East Technical University, Ankara, Turkey
3  Institute of Marine Sciences, Middle East Technical University, Ankara, Turkey,Earth System Science (ESS) Interdisciplinary Program, Middle East Technical University, Ankara, Turkey

Published: 15 November 2018 by MDPI AG in Proceedings of 3rd International Electronic Conference on Water Sciences (ECWS-3) in 3rd International Electronic Conference on Water Sciences (ECWS-3) session Submission
MDPI AG, 10.3390/ECWS-3-05807
Abstract:

As the alteration of the precipitation regime due to climate change, extreme precipitation events causing floods with the negative impacts on urban water infrastructure are observed today and expected to be observed in the future. This study examines the potential impacts of climate change and investigates the impact of these changes into urban stormwater network design. Rainfall analysis with stationary and nonstationary approach for observed and future conditions is performed for the (1950-2015 period) observed data of 5, 10, 15, 30 minutes and 1, 2, 3, 6 hour and projections (2015-2098 period) of 10, 15 minutes and 1, 6 hour for Ankara province, Turkey. Daily projections are disaggregated to finer scales, 5 minutes storm durations, then five minutes time series aggregated to the storm durations that are subject of interest and used for future period. Nonstationary Generalized Extreme Value (GEV) models and stationary GEV models for observed and future data are obtained. Nonstationary model results are in general exhibited smaller return level values with respect to stationary model results of each storm duration for the observed data driven model results. Considering the projected data driven model results; on average nonstationary models produce mostly lower return levels for mid and longer return periods for all storm durations and return periods except one hour storm duration. Depending on the models and Representative Concentration Pathways (RCP), there are different results for the future extreme rainfall input; yet all results indicate a decreasing extreme trend. The magnitude of future period extreme rainfall decreases with respect to observations. Return periods of the extreme rainfall increase in the future period therefore, not considering these trends may lead to overdesign of the stormwater network.

Keywords: rainfall, models, Extreme, respect, Observed Data, minutes, Storm Durations, Stormwater Network
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