Abstract
Nonstationarity is one major issue in hydrological models, especially in design rainfall analysis. Design rainfalls are typically estimated by annual maximum rainfalls (AMRs) of observations below 50 years in many parts of the world, including South Korea. However, due to the lack of data, the time-dependent nature may not be sufficiently identified by this classic approach. Here, this study aims to explore design rainfall with nonstationary condition using century-long reanalysis products that help one to go back to the early 20th century. Despite its useful representation of the past climate, the reanalysis products via observational data assimilation schemes and models have never been tested in representing the nonstationary behavior in extreme rainfall events. We used daily precipitations of two century-long reanalysis datasets as the ERA-20c by the European Centre for Medium-Range Weather Forecasts (ECMWF) and the 20th century reanalysis (20CR) by the National Oceanic and Atmospheric Administration (NOAA). The AMRs from 1900 to 2010 were derived from the grids over South Korea. The systematic errors were downgraded through quantile delta mapping (QDM), as well as conventional stationary quantile mapping (SQM). The evaluation result of the bias-corrected AMRs indicated the significant reduction of the errors. Furthermore, the AMRs present obvious increasing trends from 1900 to 2010. With the bias-corrected values, we carried out nonstationary frequency analysis based on the time-varying location parameters of generalized extreme value (GEV) distribution. Design rainfalls with certain return periods were estimated based on the expected number of exceedance (ENE) interpretation. Although there is a significant range of uncertainty, the design quantiles by the median parameters showed the significant relative difference, from −30.8% to 42.8% for QDM, compared with the quantiles by the multi-decadal observations. Even though the AMRs from the reanalysis products are challenged by various errors such as quantile mapping (QM) and systematic errors, the results from the current study imply that the proposed scheme with employing the reanalysis product might be beneficial to predict the future evolution of extreme precipitation and to estimate the design rainfall accordingly.
Highlights
Design rainfall plays an essential role in planning a water-related infrastructure and it has been commonly estimated from the precipitation intensity–duration–frequency (IDF) relationship based on the historical records with the stationary assumption [1,2]
The outputs by three different distributions were denoted as gevQM, gamQM and gumQM
For 20th century reanalysis (20CR), NSE values for gevQM and gamQM were similar but RMSE for gevQM, 16.69 mm, was slightly smaller than that of gamQM, 17.48 mm. These results suggest that the applied quantile mapping (QM) approaches can significantly reduce the error in the annual maximum rainfalls (AMRs) of reanalyses (i.e., ERA-20c and 20CR), and Atmoaspmheroe 2n02g1,t1h2,rxeFeORdPifEfEeRrReEnVtIEtWransfer functions, generalized extreme value (GEV) distribution could be the best option for 1b1ioafs24 correction of the AMRs, especially for ERA-20c
Summary
Design rainfall plays an essential role in planning a water-related infrastructure and it has been commonly estimated from the precipitation intensity–duration–frequency (IDF) relationship based on the historical records with the stationary assumption [1,2]. Recalling that a water-related project is designed under stationary condition in practice, the temporal change of extreme rainfalls, so-called ‘nonstationarity’, may significantly affect the safety of the infrastructure. The other major issue in rainfall frequency analysis is the lack of the gauge data resulting in significant errors in hydrological modellings. In a block maxima (BM) approach typically used in practical application, one generally collects annual maximum rainfalls (AMRs) from historical records to derive IDF relationships. The rainfall quantiles are derived from less than 40-year data and necessarily contain significant uncertainties, which are associated with the sampling errors [6,7,8,9,10]
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