Non-stationary Investigation of Extreme Rainfall

Abstract

Natural or human-induced variability emerged from investigation of the traditional stationary assumption regarding extreme precipitation analyses. The frequency of extreme rainfall occurrence is expected to increase in the future and neglecting these changes will result in the underestimation of extreme events. However, applications of extremes accept the stationarity that assumes no change over time. Thus, non-stationarity of extreme precipitation of 5, 10, 15, and 30 minutes and 1-, 3-, 6-, and 24-hour data of 17 station in the Black Sea region were investigated in this study. Using one stationary and three non-stationary models for every station and storm duration, 136 stationary and 408 non-stationary models were constructed and compared. The results are presented as non-stationarity impact maps across the Black Sea Region to visualize the results, providing information about the spatial variability and the magnitude of impact as a percentage difference. Results revealed that nonstationary (NST) models outperformed the stationary model for almost all precipitation series at the 17 stations. The model in which time dependent location and scale parameter used (Model 1), performed better among the three different time variant non-stationary models (Model 1 as time variant location and scale parameters, Model 2 as time variant location parameter, and Model 3 as time variant scale parameter). Furthermore, non-stationary impacts exhibited site-specific behavior: Higher magnitudes of non-stationary impacts were observed for the eastern Black Sea region and the coastal line. Moreover, the non-stationary impacts were more explicit for the sub-hourly data, such as 5 minutes or 15 minutes, which can be one of the reasons for severe and frequent flooding events across the region. The results of this study indicate the importance of the selected covariate and the inclusion of it for the reliability of the model development. Spatial and temporal distribution of the nonstationary impacts and their magnitude also urges to further investigation of the impact on precipitation regime, intensification, severity. Doi: 10.28991/cej-2021-03091748 Full Text: PDF

Impact Evaluation Using Nonstationary Parameters for Historical and Projected Extreme Precipitation

“…The four distributions of generalized extreme value, Gumbel, normal, and lognormal, have been used in this study to carry out the return level for a specific return period. Numerous researchers used these probability distributions for the analysis of return levels of hydro-meteorological variables [39][40][41][60][61][62][63][64]. The analysis of precipitation was carried out for the historical and projected period.…”

“…For instance, Aziz et al [39] focuses on the transient inconsistency in yearly as well as seasonal extreme rainfall in Turkey, which was investigated by using stationary and nonstationary frequency approaches. Sertac et al [40] studied the nonstationary investigation of extreme rainfall and observed that nonstationary (NST) models outperformed the stationary model at the 17 stations. Nashwan et al [41] investigated how a change in the environment (nonstationarity) affects the average and intensity of annual and seasonal rainfall in Malaysia and the magnitude of maximum rainfall at 2-, 10-, 25-, 50-, and 100-year return periods has increased at most of the stations.…”

Long‐term evolution and non‐stationary behaviours of daily and subdaily precipitation extremes in China

“…Xavier et al (2019) used generalized extreme value (GEV) distribution with time‐varying parameters to study the trend for precipitation extremes over the last 30 years in the Paraná River basin, Brazil. Many studies have proved that time‐varying characteristics and return levels of extreme precipitation are better described by non‐stationary extreme value distributions compared with stationary distributions (Brown, 2018; Liu et al, 2022b; Oruc, 2021). However, there are still limited studies to apply non‐stationary extreme value distribution to investigate return levels of precipitation extremes at a large scale, especially for China.…”

Assessment of Parameters of the Generalized Extreme Value Distribution in Rainfall of the Peruvian North

“…Other investigations such as those of Rollenbeck et al (2021), Rollenbeck et al (2022) and, Vavrus et al (2022 also analyzed the maximum rainfall in Peru, specifically in the northern zone of Peruvian Pacific, but without reaching the contrast of any extreme value distribution with their respective parameters and the quantification of extreme events for different return periods. Due to the above, there is a need to know the parameters of the GEVD in rainfall of Peru and with greater scope in the northern zone, since as indicated by SENAMHI (2017b) the Pacific 5 and Pacific 6 hydrological regions of Peruvian northwest, they are the ones that are the most prone to extreme storm events, which are mainly influenced by the altitude and seasonality of rainfall (Oruc, 2021;Arriola et al, 2022;Fernandez-Palomino et al, 2022).…”