%0 Journal Article %1 Wi2016 %A Wi, Sungwook %A Valdés, Juan B. %A Steinschneider, Scott %A Kim, Tae-Woong %D 2016 %J Stochastic Environmental Research and Risk Assessment %K extreme gauge non-stationary ppt %N 2 %P 583--606 %R 10.1007/s00477-015-1180-8 %T Non-stationary frequency analysis of extreme precipitation in South Korea using peaks-over-threshold and annual maxima %U https://doi.org/10.1007/s00477-015-1180-8 %V 30 %X The conventional approach to the frequency analysis of extreme precipitation is complicated by non-stationarity resulting from climate variability and change. This study utilized a non-stationary frequency analysis to better understand the time-varying behavior of short-duration (1-, 6-, 12-, and 24-h) precipitation extremes at 65 weather stations scattered across South Korea. Trends in precipitation extremes were diagnosed with respect to both annual maximum precipitation (AMP) and peaks-over-threshold (POT) extremes. Non-stationary generalized extreme value (GEV) and generalized Pareto distribution (GPD) models with model parameters made a linear function of time were applied to AMP and POT respectively. Trends detected using the Mann--Kendall test revealed that the stations showing an increasing trend in AMP extremes were concentrated in the mountainous areas (the northeast and southwest regions) of South Korea. Trend tests on POT extremes provided fairly different results, with a significantly reduced number of stations showing an increasing trend and with some stations showing a decreasing trend. For most of stations showing a statistically significant trend, non-stationary GEV and GPD models significantly outperformed their stationary counterparts, particularly for precipitation extremes with shorter durations. Due to a significant-increasing trend in the POT frequency found at a considerable number of stations (about 10 stations for each rainfall duration), the performance of modeling POT extremes was further improved with a non-homogeneous Poisson model. The large differences in design storm estimates between stationary and non-stationary models (design storm estimates from stationary models were significantly lower than the estimates of non-stationary models) demonstrated the challenges in relying on the stationary assumption when planning the design and management of water facilities. This study also highlighted the need of caution when quantifying design storms from POT and AMP extremes by showing a large discrepancy between the estimates from those two approaches.

@article{Wi2016, abstract = {The conventional approach to the frequency analysis of extreme precipitation is complicated by non-stationarity resulting from climate variability and change. This study utilized a non-stationary frequency analysis to better understand the time-varying behavior of short-duration (1-, 6-, 12-, and 24-h) precipitation extremes at 65 weather stations scattered across South Korea. Trends in precipitation extremes were diagnosed with respect to both annual maximum precipitation (AMP) and peaks-over-threshold (POT) extremes. Non-stationary generalized extreme value (GEV) and generalized Pareto distribution (GPD) models with model parameters made a linear function of time were applied to AMP and POT respectively. Trends detected using the Mann--Kendall test revealed that the stations showing an increasing trend in AMP extremes were concentrated in the mountainous areas (the northeast and southwest regions) of South Korea. Trend tests on POT extremes provided fairly different results, with a significantly reduced number of stations showing an increasing trend and with some stations showing a decreasing trend. For most of stations showing a statistically significant trend, non-stationary GEV and GPD models significantly outperformed their stationary counterparts, particularly for precipitation extremes with shorter durations. Due to a significant-increasing trend in the POT frequency found at a considerable number of stations (about 10 stations for each rainfall duration), the performance of modeling POT extremes was further improved with a non-homogeneous Poisson model. The large differences in design storm estimates between stationary and non-stationary models (design storm estimates from stationary models were significantly lower than the estimates of non-stationary models) demonstrated the challenges in relying on the stationary assumption when planning the design and management of water facilities. This study also highlighted the need of caution when quantifying design storms from POT and AMP extremes by showing a large discrepancy between the estimates from those two approaches.}, added-at = {2021-04-15T15:59:06.000+0200}, author = {Wi, Sungwook and Vald{\'e}s, Juan B. and Steinschneider, Scott and Kim, Tae-Woong}, biburl = {https://www.bibsonomy.org/bibtex/20432f0b6d137a92ba8dc8375a95f9f1c/simon.brown}, day = 01, description = {Non-stationary frequency analysis of extreme precipitation in South Korea using peaks-over-threshold and annual maxima | SpringerLink}, doi = {10.1007/s00477-015-1180-8}, interhash = {ea953ae302f492df22fe642270cc03e8}, intrahash = {0432f0b6d137a92ba8dc8375a95f9f1c}, issn = {1436-3259}, journal = {Stochastic Environmental Research and Risk Assessment}, keywords = {extreme gauge non-stationary ppt}, month = feb, number = 2, pages = {583--606}, timestamp = {2021-04-15T15:59:06.000+0200}, title = {Non-stationary frequency analysis of extreme precipitation in South Korea using peaks-over-threshold and annual maxima}, url = {https://doi.org/10.1007/s00477-015-1180-8}, volume = 30, year = 2016 }