Nonstationary Generalized Extreme Value Research Articles

A Non-Stationarity Analysis of Annual Maximum Floods: A Case Study of Campaspe River Basin, Australia

A design flood is an essential input for water infrastructure design and flood protection. A flood frequency analysis has been traditionally performed under stationarity assumption indicating that the statistical properties of historical flooding will not change over time. Climate change and variability challenges the stationarity assumption, and a flood frequency analysis without consideration of non-stationarity can result in under- or overestimation of the design floods. In this study, non-stationarity of annual maximum floods (AMFs) was investigated through a methodology consisting of trend and change point tests, and non-stationary Generalized Extreme Value (NSGEV) models, and the methodology was applied to Campaspe River Basin as a case study. Statistically significant decreasing trends in AMFs were detected for almost all stations at the 0.01 significance level in Campaspe River Basin. NSGEV models outperformed the stationary counterparts (SGEV) for some stations based on statistical methods (i.e., Akaike information criterion (AIC) and Bayesian information criterion (BIC)) and graphical approaches (i.e., probability and quantile plots). For example, at Station 406235, AIC and BIC values were found to be 334 and 339, respectively, for the SGEV model, whereas AIC and BIC values were calculated as 330 and 334, respectively, for the NSGEV 15 model with time-varying location and scale parameters. Deriving a design flood from conventional stationary models will result in uneconomical water infrastructure design and poor water resource planning and management in the study basin.

Nonstationary quantity-duration-frequency (QDF) relationships of lowflow in the source area of the Yellow River basin, China

Study regionThe source area of the Yellow River (SAYR) located in the northeastern part of the Qinghai-Tibet Plateau, China Study focusThis study attempts to produce the quantity-duration-frequency (QDF) relationships of lowflow through a nonstationary approach of the generalized extreme value (GEV) models. Detailed derivation of theoretical model of different structures and parameter estimation is presented as the central part, followed by model evaluation and recommendation. Time and two climate indices, i.e., Arctic Oscillation (AO) and Eastern Asia (EA), are selected as covariates. New hydrological insights for the regionFor the SAYR, nonstationary GEV models incorporating additional climate-informed covariates show apparent strengths over stationary GEV model. Using AO and EA as covariates, dual-covariate model has not shown apparent advantages over single-covariate model. The introduction of climate indices as critical covariates, in addition to time, is found to be a good choice in the construction of nonstationary GEV models for obtaining the QDF relationships and analyzing multivariate properties of lowflow. The results could help to guide the emergency management and regulation of water resources during the lowflow season, and to assistant the design of water engineering for the future period.

Analysis of maximum precipitation in Thailand using non‐stationary extreme value models

AbstractNon‐stationarity in heavy rainfall time series is often apparent in the form of trends because of long‐term climate changes. We have built non‐stationary (NS) models for annual maximum daily (AMP1) and 2‐day precipitation (AMP2) data observed between 1984 and 2020 years by 71 stations and between 1960 and 2020 by eight stations over Thailand. The generalized extreme value (GEV) models are used. Totally, 16 time‐dependent functions of the location and scale parameters of the GEV model are considered. On each station, a model is selected by using Bayesian and Akaike information criteria among these candidates. The return levels corresponding to some years are calculated and predicted for the future. The stations with the highest return levels are Trad, Samui, and Narathiwat, for both AMP1 and AMP2 data. We found some evidence of increasing (decreasing) trends in maximum precipitation for 22 (10) stations in Thailand, based on NS GEV models.

Importance of non-stationary analysis for assessing extreme sea levels under sea level rise

Abstract. Increased coastal flooding caused by extreme sea levels (ESLs) is one of the major hazards related to sea level rise. Estimates of return levels obtained under the framework provided by extreme-event theory might be biased under climatic non-stationarity. Additional uncertainty is related to the choice of the model. In this work, we fit several extreme-value models to two long-term sea level records from Venice (96 years) and Marseille (65 years): a generalized extreme-value (GEV) distribution, a generalized Pareto distribution (GPD), a point process (PP), the joint probability method (JPM), and the revised joint probability method (RJPM) under different detrending strategies. We model non-stationarity with a linear dependence of the model's parameters on the mean sea level. Our results show that non-stationary GEV and PP models fit the data better than stationary models. The non-stationary PP model is also able to reproduce the rate of extremes occurrence fairly well. Estimates of the return levels for non-stationary and detrended models are consistently more conservative than estimates from stationary, non-detrended models. Different models were selected as being more conservative or having lower uncertainties for the two datasets. Even though the best model is case-specific, we show that non-stationary extremes analyses can provide more robust estimates of return levels to be used in coastal protection planning.

The role of teleconnection in the occurrence probability of extreme precipitation over China based on extreme value theory

Non-stationarity of extreme climate events has been reported worldwide in recent decades, and traditional stationary analysis methods are no longer sufficient to properly reveal the occurrence probability of climate extremes. Based on the 0.25°C × 0.25°C gridded precipitation data (i.e., CN05.1), stationary and non-stationary models of generalized extreme value (GEV) and generalized Pareto (GP) distributions are adopted to estimate the occurrence probability of extreme precipitation over China during 1961–2018. Low-frequency oscillation (LFO) indices, such as El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO), Southern Annular Mode (SAM), and Pacific Decadal Oscillation (PDO), are included as time-varying covariates in the non-stationary GEV and GP models. Results illustrate that the occurrence probability of extreme precipitation estimated from the stationary GEV and GP distributions shows a significant increasing trend in northwestern and southeastern China, and the opposite trend in southwestern, central, and northeastern China. In comparison with stationary model, the fitness of extreme precipitation series is improved for both the GEV and GP distributions if these LFO indices are used as time-varying covariates. Positive ENSO, IOD and PDO tend to cause negative anomalies in the occurrence probability of extreme precipitation in northeastern China and Tibet Plateau, and positive anomalies in southern China. Positive NAO and SAM phases mainly tend to cause positive anomalies in southern China. The circulation patterns of extreme precipitation anomalies associated with these LFO indices are discussed from aspects of precipitable water, vertical integrated moisture transport, 500-hPa geopotential height and 850-hPa wind field.

Estimating the impact of urbanization on non-stationary models of extreme precipitation events in the Yangtze River Delta metropolitan region

Rapid urbanization has significantly increased the proportion of impervious surfaces in Yangtze River Delta urban agglomeration (YRDUA), hence quantifying its impact on extreme precipitation events is essential for waterlogging control and urban planning. The spatiotemporal evolution patterns of extreme precipitation indices (EPIs) based on a non-stationary Generalized Extreme Value (GEV) model are analyzed in urban, suburban, and rural stations. Additionally, the degree of change of the recurrence levels are calculated to evaluate the effect of the urbanization process on EPIs in these stations. The results indicated that (1) Except for CWD, other EPIs show an increasing trend and EPIs with significantly increasing trend distributed in the downwind direction of the YRD. The percentages of significantly positive trends in EPIs, accounted for more than 85%, are mainly in the urban and suburban stations. (2) The optimal non-stationary GEV model with urbanization factor is predominately distributed in the highly urbanized areas. Urbanization factor has a significant impact on short-term, high-intensity precipitation and PRCPTOT in urban areas. (3) While the proportion of urban impervious areas in the urban area increased from 0.11 in 1985 to 0.63 in 2016, the effect of urbanization on EPIs changes from negative to positive and R95p, R25, R50, PRCPTOT in urban are much higher than those in rural after 2000. Urbanization significantly affects the recurrence levels of Rx5day in the rural, urban, and suburban stations, which increased by 27%, 29%, and 29%, respectively. The degree of change in the recurrence level in the suburban and urban areas is greater than that in the rural areas.

Investigation of the Effect of Climate Change on Extreme Precipitation: Capital Ankara Case

This study examines the potential impacts of climate change on extreme precipitation. Rainfall analysis with stationary and nonstationary approach for observed and future conditions is performed for (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. 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 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.

Improvement of Extreme Value Modeling for Extreme Rainfall Using Large-Scale Climate Modes and Considering Model Uncertainty

Extreme value modeling for extreme rainfall is one of the most important processes in the field of hydrology. For the improvement of extreme value modeling and its physical meaning, large-scale climate modes have been widely used as covariates of distribution parameters, as they can physically account for climate variability. This study proposes a novel procedure for extreme value modeling of rainfall based on the significant relationship between the long-term trend of the annual maximum (AM) daily rainfall and large-scale climate indices. This procedure is characterized by two main steps: (a) identifying significant seasonal climate indices (SCIs), which impact the long-term trend of AM daily rainfall using statistical approaches, such as ensemble empirical mode decomposition, and (b) selecting an appropriate generalized extreme value (GEV) distribution among the stationary GEV and nonstationary GEV (NS-GEV) using time and SCIs as covariates by comparing their model fit and uncertainty. Our findings showed significant relationships between the long-term trend of AM daily rainfall over South Korea and SCIs (i.e., the Atlantic Meridional Mode, Atlantic Multidecadal Oscillation in the fall season, and North Atlantic Oscillation in the summer season). In addition, we proposed a model selection procedure considering both the Akaike information criterion and residual bootstrap method to select an appropriate GEV distribution among a total of 59 GEV candidates. As a result, the NS-GEV with SCI covariates generally showed the best performance over South Korea. We expect that this study can contribute to estimating more reliable extreme rainfall quantiles using climate covariates.

Non-stationary extreme value analysis of sea states based on linear trends. Analysis of annual maxima series of significant wave height and peak period in the Mediterranean Sea

Non-stationary Extreme Value Analysis (NEVA) allows to determine the probability of exceedance of extreme sea states taking into account trends in the time series of data at hand. In this work, we analyse the reliability of NEVA of significant wave height (Hs) and peak period (Tp) under the assumption of linear trend for time series of annual maxima (AM) Hs in the Mediterranean Sea. A methodology to assess the significance of the results of the non-stationary model employed is proposed. Both the univariate long-term extreme value distribution of Hs and the bivariate distribution of Hs and Tp are considered. For the former, a non-stationary Generalized Extreme Value (GEV) probability is used, and a methodology to compute the parameters of the distribution based on the use of a penalty function is explored. Then, non-stationary GEV is taken as a reference to compute the Environmental Countours of Hs and Tp, assuming a conditional model for the latter parameter. Several methods to compute linear trends are analysed and cross-validated on the series of AM Hs at more than 20,000 hindcast nodes. Results show that the non-stationary analysis provides advantages over the stationary analysis only when all the considered metrics are consistent in indicating the presence of a trend. Moreover, both the univariate return levels of Hs and bivariate return levels of Hs and Tp show a marked dependence to the time window considered in the GEV distribution formulation. Therefore, when applying NEVA for coastal and marine applications, the hypothesis of linear trend and the length of the reference data used for the non-stationary distribution should be carefully considered.

Assessment of Nonstationarity and Uncertainty in Precipitation Extremes of a River Basin Under Climate Change

In this study, an uncertainty analysis of extreme precipitation return levels was performed for the Chaliyar river basin, India, under representative concentration pathways (RCPs) 4.5 and 8.5. Weighted average projections of various climate models (for RCPs 4.5 and 8.5) using reliability ensemble averaging were used in the analysis for projecting the future extremes. To start with, the presence of nonstationarity in the observed annual maximum precipitation (AMP) series and the future ensemble averaged AMP projections were investigated. For this purpose, three generalized extreme value (GEV) models—one stationary model with constant parameters and two nonstationary models with trends in location and scale parameters—were applied to assess the goodness of fit using Akaike information criterion and likelihood ratio test. The best fit model was used in the uncertainty analysis, and the confidence bounds of extreme precipitation return levels were estimated. A nonparametric bootstrapping approach was followed in the uncertainty analysis. Results of the study suggest that a nonstationary GEV distribution with linear trend in location parameter and constant scale and shape parameters are the best fit distribution for the AMP series under the RCP scenarios, whereas the stationary GEV distribution fits the observed AMP series the best. The expected values and confidence bounds of return levels obtained from the uncertainty analysis reveal that precipitation extremes in the river basin would intensify under the projected climate change scenarios. Compared with the RCP4.5 scenario, the confidence intervals of return levels under the RCP8.5 scenario were wider, implying that uncertainty in the latter scenario is higher.

Uncertainty of Rate of Change in Korean Future Rainfall Extremes Using Non-Stationary GEV Model

Interest in future rainfall extremes is increasing, but the lack of consistency in the future rainfall extremes outputs simulated in climate models increases the difficulty of establishing climate change adaptation measures for floods. In this study, a methodology is proposed to investigate future rainfall extremes using future surface air temperature (SAT) or dew point temperature (DPT). The non-stationarity of rainfall extremes is reflected through non-stationary frequency analysis using SAT or DPT as a co-variate. Among the parameters of generalized extreme value (GEV) distribution, the scale parameter is applied as a function of co-variate. Future daily rainfall extremes are projected from 16 future SAT and DPT ensembles obtained from two global climate models, four regional climate models, and two representative concentration pathway climate change scenarios. Compared with using only future rainfall data, it turns out that the proposed method using future temperature data can reduce the uncertainty of future rainfall extremes outputs if the value of the reference co-variate is properly set. In addition, the confidence interval of the rate of change of future rainfall extremes is quantified using the posterior distribution of the parameters of the GEV distribution sampled using Bayesian inference.

Nonstationary extreme value analysis for event attribution combining climate models and observations

Abstract. We develop an extension of the statistical approach by Ribes et al. (2020), which was designed for Gaussian variables, for generalized extreme value (GEV) distributions. We fit nonstationary GEV distributions to extremely hot temperatures from an ensemble of Coupled Model Intercomparison Project phase 5 (CMIP) models. In order to select a common statistical model, we discuss which GEV parameters have to be nonstationary and which do not. Our tests suggest that the location and scale parameters of GEV distributions should be considered nonstationary. Then, a multimodel distribution is constructed and constrained by observations using a Bayesian method. The new method is applied to the July 2019 French heat wave. Our results show that both the probability and the intensity of that event have increased significantly in response to human influence. Remarkably, we find that the heat wave considered might not have been possible without climate change. Our results also suggest that combining model data with observations can improve the description of hot temperature distribution.

Rainfall Modelling using Generalized Extreme Value Distribution with Cyclic Covariate

Increased flood risk is recognized as one of the most significant threats in most parts of the world, resulting in severe flooding events which have caused significant property and human life losses. As there is an increase in the number of extreme flash flood events observed in Klang Valley, Malaysia recently, this paper focuses on modelling extreme daily rainfall within 30 years from year 1975 toyear 2005 in Klang Valley using generalized extreme value (GEV) distribution. Cyclic covariate is introduced in the distribution because of the seasonal rainfall variation in the series. One stationary (GEV) and three nonstationary models (NSGEV1, NSGEV2, and NSGEV3) are constructed to assess the impact of cyclic covariates on the extreme daily rainfall events. The better GEV model is selected using Akaike's information criterion (AIC), bayesian information criterion (BIC) and likelihood ratio test (LRT). The return level is then computed using the selected fitted GEV model. Results indicate that the NSGEV3 model with cyclic covariate trend presented in location and scale parameters provides better fits the extreme rainfall data. The results showed the capability of the nonstationary GEV with cyclic covariates in capturing the extreme rainfall events. The findings would be useful for engineering design and flood risk management purposes.

Nonstationary bayesian modeling of precipitation extremes in the Beijing-Tianjin-Hebei Region, China

This paper investigates the nonstationarity of precipitation extremes by incorporating time-varying and physical-based explanatory covariates, using daily precipitation data across the Beijing-Tianjin-Hebei (BTH) region, China. We perform the stationary and nonstationary generalized extreme value (GEV) models based on the Bayesian framework to estimate the expected return levels of precipitation extremes with the 90% credible intervals. Results reveal that the nonstationarity of precipitation extremes is not prominently visible for the majority of sites in BTH. However, the nonstationary GEV models exhibit better performance to capture the variations of precipitation extremes by comparison to the stationary models based on four evaluation criteria. Further, this work attempts to determine the best covariate to illustrate the possible effects of environmental changes on the frequency analysis. Results indicate that the El Nino-Southern Oscillation (ENSO) is the top of the best covariates, followed by the East Asian summer monsoon, North Atlantic Oscillation (NAO) and local temperature anomaly. Moreover, the best covariates are dominated by the physical-based covariates, and the best models with nonlinear functions of covariates are found in the majority of sites. Finally, the best-fitted models are used to estimate the design values of return levels in precipitation extremes. Results illustrate that the differences between the stationary modeling and nonstationary modeling in the median condition of covariates are not significant for most of the sites. But the discrepancies will be enhanced if the covariates locate in a high (95-percentile) or low (5-percentile) value. Our findings suggest that the nonstationary modeling of precipitation extremes might prove more useful and reliable, especially in the uncommon conditions of physical-based covariates.

Modeling power loss during blackouts in China using non-stationary generalized extreme value distribution

Power loss during blackouts shows an increasing complexity with the increase in the size of power grids. Traditional statistical methods based on the assumption of stationarity can lead to inappropriate assessments of blackouts. This study builds on the generalized extreme value (GEV) distribution by introducing time as a covariate to account for possible non-stationarities. The location, scale, and shape parameters of GEV are modeled step-by-step through the one-factor-at-a-time experiment. The results indicate that the GEV model of the annual maximum power loss in China exhibits a significant change of the scale parameter. By contrast, changes in location and shape parameters are not significant. Overall, the power loss during blackouts is tested to change with time, which reflects the sustained increase in the variability of power loss due to the growing size of power grids. The mean tends to be stable and the distribution type remains the same, which can be related to technological advances that improve the reliability of power grids. The non-stationary GEV model with time as a covariate can effectively analyze power loss during blackouts in China. It has the potential to be extended to describe non-stationary high-dimensional characteristics of power grids.

21st Century flood risk projections at select sites for the U.S. National Park Service

Assessing flood risk using stationary flood frequency analysis techniques is commonplace. However, it is increasingly evident that the stationarity assumption of these analyses does not hold as anthropogenic climate change could shift a site’s hydroclimate beyond the range of historical behaviors. We employ nonstationary flood frequency models using the generalized extreme value (GEV) distribution to model changing flood risk for select seasons at twelve National Parks across the U.S. In this GEV model, the location and/or scale parameters of the distribution are allowed to change as a function of time-variable covariates. We use historical precipitation and modeled flows from the Variable Infiltration Capacity model (VIC), a land-surface model that simulates land–atmosphere fluxes using water and energy balance equations, as covariates to fit a best nonstationary GEV model to each site. We apply climate model projections of precipitation and VIC flows to these models to obtain future flood probability estimates. Our model results project a decrease in flood risk for sites in the southwestern U.S. region and an increase in flood risk for sites in northern and eastern regions of the U.S. for the selected seasons. The methods and results presented will enable the NPS to develop strategies to ensure public safety and efficient infrastructure management and planning in a nonstationary climate.

Detecting the impact of climate and reservoirs on extreme floods using nonstationary frequency models

The annual maximum flood records of the Danjiangkou reservoir displayed significant decreasing trends. The upper stream of the reservoir was characterized by minor changes in urbanization in recent decades, while several large reservoirs were built since 1975. To better explain the changes in floods, this paper used the nonstationary generalized extreme value (GEV) distribution by considering the impacts of climate change and human activity. The GEV parameters were expressed as functions of time, climate indices, and climate-reservoir index (CRI). By using CRI as the covariate, the performance of the nonstationary GEV distribution was much better than others. This paper also analyzed the risk of floods using design life level and annual average reliability methods. The reliability obtained from these methods can describe the effects of external indices on floods. Finally, the uncertainty analysis indicated that a nonstationary model might not be practical, and the large confidence intervals of the design flood implied that the results were meaningless. It is essential to build a deterministic relationship between parameters and covariates, and we should be prudent when using general circulation models outputs in extreme statistics.

Stationary and Nonstationary Generalized Extreme Value Models for Monthly Maximum Rainfall in Sabah

Generalized Extreme Value (GEV) model is the combination of three types of distribution class namely Gumbel, Fréchet and Weibull distributions of the Extreme Value Theory (EVT). In hydrological studies, GEV model is widely applied in the modelling of extreme rainfall. The nature of hydrological variables is highly complex, especially with the changing climate and frequent occurrences of extreme events. As such, some rainfall models assume rainfall series as stationary, while some as nonstationary. In this study, GEV models based on stationary and nonstationary data are used to assess monthly maximum rainfall data within Sabah, Malaysia and performance comparison between both GEV models is conducted. Theoretically both stationary and nonstationary GEV models are based on the same foundation, however nonstationary GEV model allows the location and scale parameters to be expressed as cyclic function of time. In this study, the stationary and the nonstationary GEV models are individually fitted to rainfall data at selected stations. Monthly maximum rainfall is blocked, and the estimated location (μ), scale (σ) and shape (ξ) parameters are estimated by using Maximum Likelihood Estimation method. Performance of both GEV models are compared based on the Akaike Information Criterion, Bayesian Information Criterion and the likelihood ratio goodness of fit tests. Results showed that nonstationary GEV model is the best fit. The inclusion of cyclic covariates in the GEV model gives improvement on the stationary GEV model at the study region. It is also concluded that, Gumbel is identified as the significant distribution for monthly maximum rainfall in Sabah at 5% significance level.

Modelling spatial variation of extreme precipitation over Ho Chi Minh City under nonstationary condition

Understanding the magnitude and spatial variation of extreme rainfall events are required for decision making and adaptation strategies for flood risk. In Ho Chi Minh City (HCMC), heavy rainfall, which is considered as a main cause of floods, witnessed an increase in frequency and magnitude in last few decades. Although nonstationarity in extreme rainfall has been proved in many places of the world, research into nonstationarity feature in extreme rainfall in HCMC has not been paid attention thoroughly. In this study, the spatial variation of extreme precipitation over Ho Chi Minh City is modelled under nonstationary condition. The generalized extreme value (GEV) distribution with location made a nonlinear function of time is applied to annual maximum daily rainfall. The study results show that the nonstationary GEV model is found to be superior in capturing extreme precipitation events when compared to the stationary GEV model. The extreme rainfall estimates under the stationary condition are lower than those under the nonstationary condition in most stations. Besides, the spatial variation of extreme rainfall under nonstationary condition shows a significant difference in extreme estimates between the periods of 1980–1984 and 2010–2014 in study area.

The assessment of return probability of maximum ozone concentrations in an urban environment of Delhi: A Generalized Extreme Value analysis approach

High ozone episodes have become a serious issue specially in megacities of developing countries. In this study an attempt has been made to understand the changes in the extreme ozone concentration due to different precursor compounds and meteorological variables in a given time period by applying Generalized Extreme Value (GEV) theory. The return probabilities of the extreme ozone concentrations were estimated for both classical stationary assumption as well as nonstationary assumption given the fact that ozone time series have both trend and multiple periodicities. Under stationary case, the distribution parameters were allowed to remain stationary, but in nonstationary case, distribution parameters were allowed to vary as a function of precursor compounds (Benzene, Toluene, mp-Xylene, NO, NO2) and meteorological variables (temperature and relative humidity). Daily maximum ozone concentrations were found to follow heavy tailed Fréchet distribution in both stationary and nonstationary conditions. The inclusion of covariate into the classical model explains the dynamic nature of ozone depending on its precursor variables. Principal components were also used as covariates in nonstationary GEV distribution model. The estimated return levels of ozone from the stationary model, were found to be 97.89, 144.34, 188.44, 232.10, 310.33, 366.47 and 429.97 μg/m3 for 3, 7, 15, 30, 90, 180 and 365-day return period, respectively. In case of non-stationary GEV model the estimated return levels for 3, 7, 15, 30, 90, 180 and 365-day period were in the range 38.38–140.96, 84.31–184.35, 115.92–225.26, 166.61–337.21, 189.46–388.30 and 214.98–445 μg/m3, respectively. The highest median return level of O3 for 3-day return period (100.35 μg/m3) was observed due to temperature as covariate, for 7-day (146.04 μg/m3) and 15- day (189.85 μg/m3) return period, it was observed due to Benzene as a covariate and for rest of the 4 different return periods, NO was found responsible for the highest median return level of O3 (233.52, 312.40, 369.02 and 433.06 μg/m3 for 30, 90, 180 and 365-day respectively). Seasonal analysis finds O3 extremes to be high in monsoon and premonsoon seasons and low in winter period. The impact of nonstationary condition is exemplified by the fact that the 365 day return level of maximum ozone concentration was found to exceed within just 20 days for a lower concentration of precursor Benzene (i.e. 214.48 μg/m3). Results underline the role of the principal components of the precursor compounds in governing the maximum ozone concentration in any city.