Generalized Extreme Value Model Research Articles

Nonstationary frequency analysis of extreme precipitation: Embracing trends in observations

Knowledge of the recurrence intervals of precipitation extremes is vital for infrastructure design, risk assessment, and insurance planning. However, trends and shifts in rainfall patterns globally pose challenges to the application of extreme value analysis (EVA) which relies critically on the assumption of stationarity. In this paper, we explore: (1) the suitability of nonstationary (NS) models in the presence of statistically significant trends, and (2) their potential in modeling out-of-sample data to improve frequency analysis of extreme precipitation. We analyze the benefits of using a nonstationary Generalized Extreme Value (GEV) model for annual extreme precipitation records from Southern Brazil. The location of the GEV distribution is allowed to change with time. The unknown GEV model parameters are estimated using Bayesian techniques coupled with Markov chain Monte Carlo simulation. Next, we use GAME sampling to compute the evidence (and their ratios, the so-called Bayes factors) for stationary and nonstationary models of annual maximum precipitation. Our results show that the presence of a statistically significant trend in annual maximum precipitation alone does not justify the use of a NS model. The location parameter of the GEV distribution must also be well defined, otherwise, stationary models of annual maximum precipitation receive more support by the data. These findings reiterate the importance of accounting for GEV model parameters and predictive uncertainty in frequency analysis and hypothesis testing of annual maximum precipitation data records. Furthermore, a meaningful EVA demands detailed knowledge about the origin and persistence of observed changes.

Variability of Extreme Climate Events and Prediction of Land Cover Change and Future Climate Change Effects on the Streamflow in Southeast Queensland, Australia

The severity and frequency of extremes are changing; thus, it is becoming necessary to evaluate the impacts of land cover changes and urbanisation along with climate change. A framework of the Generalised Extreme Value (GEV) method, Google Earth Engine (GEE), and land cover patterns’ classification including Random Forest (RF) and Support Vector Machine (SVM) can be useful for streamflow impact analysis. For this study, we developed a unique framework consisting of a hydrological model in line with the Process-informed Nonstationary Extreme Value Analysis (ProNEVA) GEV model and an ensemble of General Circulation Models (GCMs), mapping land cover patterns using classification methods within the GEE platform. We applied these methods in Southeast Queensland (SEQ) to analyse the maximum instantaneous floods in non-stationary catchment conditions, considering the physical system in terms of cause and effect. Independent variables (DEM, population, slope, roads, and distance from roads) and an integrated RF, SVM methodology were utilised as spatial maps to predict their influences on land cover changes for the near and far future. The results indicated that physical factors significantly influence the layout of landscapes. First, the values of projected evapotranspiration and rainfall were extracted from the multi-model ensemble to investigate the eight GCMs under two climate change scenarios (RCP4.5 and RCP8.5). The AWBM hydrological model was calibrated with daily streamflow and applied to generate historical runoff for 1990–2010. Runoff was projected under two scenarios for eight GCMs and by incorporating the percentage of each land cover into the hydrological model for two horizons (2020–2065 and 2066–2085). Following that, the ProNEVA model was used to calculate the frequency and magnitude of runoff extremes across the parameter space. The maximum peak flood differences under the RCP4.5 and RCP8.5 scenarios were 16.90% and 15.18%, respectively. The outcomes of this study suggested that neglecting the non-stationary assumption in flood frequency can lead to underestimating the amounts that can lead to more risks for the related hydraulic structures. This framework is adaptable to various geographical regions to estimate extreme conditions, offering valuable insights for infrastructure design, planning, risk assessment, and the sustainable management of future water resources in the context of long-term water management plans.

Convection‐Permitting Climate Models Can Support Observations to Generate Rainfall Return Levels

AbstractInformation about the frequency and intensity of extreme precipitation is generally derived from fitting extreme value models using point‐observations, but the regionalization of these models is challenging. Here we propose using high‐resolution convection‐permitting climate model output as covariates for the estimation of observation‐based spatial rainfall return levels. We apply the Weather and Forecasting Research (WRF) model at a 1.5 km resolution driven by ERA5 reanalysis data over southern Germany, where 1,132 rain gauges provide observations of daily rainfall. For this complex topography, we build three different smooth spatial Generalized Extreme Value (GEV) models: (a) a reference model using latitude, longitude and elevation as covariates; (b) a model adding mean annual precipitation from the WRF; (c) a model adding extreme value statistical model estimates using WRF output. We show that the additional information provided by the WRF model can improve the representation of 10‐year and 100‐year return levels of daily rainfall by lowering the percentage bias, mean absolute error, and root‐mean‐square error. Furthermore, we conduct an extensive cross‐validation, where only 5%, 10%, 20%, 50%, 80%, 90%, and 95% of all rain gauges are considered when building spatial GEV models. Again, the additional information provided by the WRF model can improve results here. This cross‐validation study also highlights the robustness of our approach, showing great potential for use in data‐scarce regions.

Modeling rainfall extremes along the coastal and Northern parts of Ghana

The main objective of the study was to determine the appropriate distribution for extreme rainfall along the coastal and northern sectors of Ghana. For stakeholders and policymakers to make appropriate risk-mitigating measures to lessen the damage caused by flood and drought, it is necessary to make proper inferences about extreme rainfall. In this study, we used both the multivariate and univariate extreme value data analysis approaches. The Generalized Extreme Value (GEV) with the Block Maxima approach and Generalized Pareto Distribution (GPD) with the Peak over the threshold (that is all excesses and decluster peaks approaches) were used in this study. Historical gridded monthly maximum rainfall data from 1970 to 2020 were obtained from the Climatic Research Unit and were grouped as the coastal and northern stations. The Maximum Likelihood Estimation method was used to estimate the model parameters, and both the unit root test and the Mann-Kendall tests were used to test for trend in the data. With the multivariate extreme modelling approach, the logistic bivariate GEV model was chosen as the “best” model. However, the dependence value was 0.965, so the extreme rainfall should be modelled independently using the univariate extreme value approaches. Hence, based on the information criteria and analysis of deviance approaches, the GEV distribution was considered the “best” fit for the extreme rainfall dataset for the northern part of Ghana. In contrast, the GPD distribution was the “best” fit for the coastal station. Comparatively, for the volume of rainfall in the year 2020, the extreme rainfall is expected to be higher in the coastal station of Ghana in the next two years. Also, extreme rainfall in 2 years would not exceed the maximum occurrence of rainfall (279.267), which happened in September 2020 at the northern station of Ghana.

A bi-level framework for real-time crash risk forecasting using artificial intelligence-based video analytics.

This study proposes a bi-level framework for real-time crash risk forecasting (RTCF) for signalised intersections, leveraging the temporal dependency among crash risks of contiguous time slices. At the first level of RTCF, a non-stationary generalised extreme value (GEV) model is developed to estimate the rear-end crash risk in real time (i.e., at a signal cycle level). Artificial intelligence techniques, like YOLO and DeepSort were used to extract traffic conflicts and time-varying covariates from traffic movement videos at three signalised intersections in Queensland, Australia. The estimated crash frequency from the non-stationary GEV model is compared against the historical crashes for the study locations (serving as ground truth), and the results indicate a close match between the estimated and observed crashes. Notably, the estimated mean crashes lie within the confidence intervals of observed crashes, further demonstrating the accuracy of the extreme value model. At the second level of RTCF, the estimated signal cycle crash risk is fed to a recurrent neural network to predict the crash risk of the subsequent signal cycles. Results reveal that the model can reasonably estimate crash risk for the next 20-25min. The RTCF framework provides new pathways for proactive safety management at signalised intersections.

GEV Analysis of Extreme Rainfall: Comparing Different Time Intervals to Analyse Model Response in Terms of Return Levels in the Study Area of Central Italy

The extreme rainfall events of recent years in central Italy are producing an increase in hydrogeological risk, with disastrous flooding in terms of human lives and economic losses, as well as triggering landslide phenomena in correspondence with these events. A correct prediction of 100-year return levels could encourage better land planning, sizing works correctly according to the expected extreme events and managing emergencies more consciously through real-time alerts. In the recent period, it has been observed that the return levels predicted by the main forecasting methods for extreme rainfall events have turned out to be lower than observed within a few years. In this context, a model widely used in the literature, the generalised extreme value (GEV) with the “block maxima” approach, was used to assess the dependence of this model on the length of the collected precipitation time series and the possible addition of years with extreme events of great intensity. A total of 131 rainfall time series were collected from the Adriatic slope in central Italy comparing two periods: one characterised by 70 years of observations (1951–2020), the other by only 30 years (1991–2020). At the same time, a decision was made to analyse what the effect might be—in terms of the 100-year return level—of introducing an additional extreme event to the 1991–2020 historical series, in this case an event that actually occurred in the area on 15 September 2022. The results obtained were rather surprising, with a clear indication that the values of the 100-year return level calculated by GEV vary according to the length of the historical series examined. In particular, the shorter time series 1991–2020 provided higher return level values than those obtained from the 1951–2020 period; furthermore, the addition of the extreme event of 2022 generated even higher return level values. It follows that, as shown by the extreme precipitation events that have occurred in recent years, it is more appropriate to consider a rather short period because the ongoing climate change does not allow true estimates to be obtained using longer time series, which are preferred in the scientific literature, or possibly questioning the real reliability of the GEV model.

Bayesian hierarchical modelling of sea-level extremes in the Finnish coastal region

Abstract. Occurrence probabilities of extreme sea levels required in coastal planning, e.g. for calculating design floods, have been traditionally estimated individually at each tide-gauge location. However, these estimates include uncertainties, as sea-level observations typically have only a small number of extreme cases such as annual maxima. Moreover, exact information on sea-level extremes between the tide-gauge locations and incorporation of dependencies between the adjacent stations is often lacking in the analysis. In this study, we use Bayesian hierarchical modelling to estimate return levels of annual maxima of short-term sea-level variations related to storm surges in the Finnish coastal region. We use the generalised extreme value (GEV) distribution as the basis and compare three hierarchical model structures of different complexity against tide-gauge-specific fits. The hierarchical model structures allow us to share information on annual maximum sea levels between the neighbouring stations and also provide a natural way to estimate uncertainties in the theoretical estimates. The results show that compared to the tide-gauge-specific fits, the hierarchical models, which pool information across the tide gauges, provide narrower uncertainty ranges for both the posterior parameter estimates and the corresponding return levels in most locations. The estimated shape parameter of the GEV model is systematically negative for the hierarchical models, which indicates a Weibull type of behaviour for the extremes along the Finnish coast. The negative shape parameter also allows us to calculate the theoretical upper limit for the annual maximum sea levels on the Finnish coast. Depending on the tide gauge and hierarchical model considered, the median value of the theoretical upper limit was 47–73 cm higher than the highest observed sea level.

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.

Changes in the distribution of annual maximum temperatures in Europe

Abstract. In this study we detect and quantify changes in the distribution of the annual maximum daily maximum temperature (TXx) in a large observation-based gridded data set of European daily temperature during the years 1950–2018. Several statistical models are considered, each of which analyses TXx using a generalized extreme-value (GEV) distribution with the GEV parameters varying smoothly over space. In contrast to several previous studies which fit independent GEV models at the grid-box level, our models pull information from neighbouring grid boxes for more efficient parameter estimation. The GEV location and scale parameters are allowed to vary in time using the log of atmospheric CO2 as a covariate. Changes are detected most strongly in the GEV location parameter, with the TXx distributions generally shifting towards hotter temperatures. Averaged across our spatial domain, the 100-year return level of TXx based on the 2018 climate is approximately 2 ∘C (95 % confidence interval of [2.03,2.12] ∘C) hotter than that based on the 1950 climate. Moreover, averaged across our spatial domain, the 100-year return level of TXx based on the 1950 climate corresponds approximately to a 6-year return level in the 2018 climate.

Dynamic flood frequency analysis for west flowing rivers of Kerala, India

Conventional Flood Frequency Analysis (FFA) may underestimate flood quantiles and increase hydraulic infrastructure vulnerability in changing climates. This study uses annual maximum streamflow data from 17 hydrologic stations along west-flowing rivers in Kerala, India, for Non-Stationary (NS) FFA. The Generalized Extreme Value model with a linear temporal location parameter worked effectively for five stations. Kidangoor and Pattazhy stations must account for non-stationarity for longer return periods (RPs) (>50 years), whereas Neeleeswaram and Perumannu stations must for shorter RPs (<50 years). An extensive study was conducted for Neeleswaram station (Periyar basin) by simulating NS models incorporating four large-scale climate oscillations as covariates. The stationary assumption underestimated flood return levels of 2-year RP by about 61% which increases the flood risk leading to failure of hydraulic infrastructures. It was observed that the best fitted climate-based NS model achieves stationary return level of 150-years RP at 25-years RP itself. The study proved that the climate-based NS models captured the 2018 August Floods in Periyar basin better than stationary and time-based models. The regional variability in FF curve behaviour concludes that NSFFA for Kerala cannot be generalised and must be done at a local-scale.

Smooth copula‐based generalized extreme value model and spatial interpolation for extreme rainfall in Central Eastern Canada

AbstractThis paper proposes a smooth copula‐based Generalized Extreme Value (GEV) model to map and predict extreme rainfall in Central Eastern Canada. The considered data contains a large portion of missing values, and one observes several nonconcomitant record periods at different stations. The proposed two‐step approach combines GEV parameters' smooth functions in space through the use of spatial covariates and a flexible hierarchical copula‐based model to take into account dependence between the recording stations. The hierarchical copula structure is detected via a clustering algorithm implemented with an adapted version of the copula‐based dissimilarity measure recently introduced in the literature. Finally, we compare the classical GEV parameter interpolation approaches with the proposed smooth copula‐based GEV modeling approach.

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.

Statistical Modeling on the Severity of Unhealthy Air Pollution Events in Malaysia

This study proposes the concept of severity as an alternative measure of extreme air pollution events. Information about severity can be derived from the cumulative effect of air pollution events, which can be determined from unhealthy Air Pollution Index (API) values that occur for a consecutive period. On the basis of the severity, an analysis of extreme air pollution events can be obtained through the application of the generalized extreme-value (GEV) model. A case study was conducted using hourly API data in Klang, Malaysia, from 1 January 1997 to 31 August 2020. The block-maxima approach was integrated with information about monsoon seasons to determine suitable data points for GEV modeling. Based on the GEV model, the estimated severity levels corresponding to their return periods are determined. The results reveal that pollution severity in Klang tends to rise with increases in the length of return periods that are measured based on seasonal monsoons as a temporal scale. In conclusion, the return period for severity provides a good basis for measuring the risk of recurrence of extreme pollution events.

Versatile Modelling of Extreme Surges in Connection with Large-Scale Circulation Drivers

In this article, we investigate the dependence of extreme surges on the North Atlantic weather regime variability across different timescales using the North Atlantic Oscillation (NAO) and Scandinavian blocking (SCAND) indices. The analysis was done using time series of surges along the North French Coast, covering long time periods (43 to 172 years of data). Time series that exhibited gaps were filled using linear interpolation to allow spectral analyses to be conducted. First, a continuous wavelet analysis on monthly maxima surges in the North French Coast was conducted to identify the multi-timescale variability. Second, a wavelet coherence analysis and maximum overlap discrete wavelet transform (MODWT) were used to study the timescale-dependent relationships between maxima surges and NAO or SCAND. Finally, NAO and SCAND were tested as physical covariates for a nonstationary generalized extreme value (GEV) distribution to fit monthly maxima surge series. Specific low-frequency variabilities characterizing these indices (extracted using MODWT) were also used as covariates to determine whether such specific variabilities would allow for even better GEV fitting. The results reveal common multi-annual timescales of variability between monthly maxima surge time series along the North French coasts: ~2–3 years, ~5–7 years, and ~12–17 years. These modes of variability were found to be mainly induced by the NAO and the SCAND. We identified a greater influence of the NAO on the monthly maxima surges of the westernmost stations (Brest, Cherbourg, Le Havre), while the SCAND showed a greater influence on the northernmost station (Dunkirk). This shows that the physical climate effects at multi-annual scales are manifested differently between the Atlantic/English Channel and the North Sea regions influenced by NAO and SCAND, respectively. Finally, the introduction of these two climate indices was found to clearly enhance GEV models as well as a few timescales of these indices.

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.

Effect of the tide on flood modeling and mapping in Kota Tinggi, Johor, Malaysia

This study aimed at mapping the Kota Tinggi flood event in 2006/2007 that had caused massive damages to properties and the environment. The flood was associated with unusually high intensity and continuous rainfall, and high tide. Therefore, a reliable technique of floodplain mapping is crucial for the improvement of flood control strategies and for preparing an evacuation plan. The main objective of this study is to incorporate the effect of tide on flood modeling analysis. The inundated areas were mapped for various annual recurrent intervals (ARI) using peak flow data from 1965 to 2010. The study used Light Detection and Ranging data for flood modeling using HEC-HMS, HEC-RAS, and HEC-GeoRAS. The Generalized Extreme Value model was found to be the best fit for the annual flood simulation. The HEC-HMS hydrologic model was calibrated and validated using observed hydrographs in September 2002 and January 2003, respectively. Due to riverbank overflow, the level-discharge rating curve during flood events is not valid which causes underestimation of the peak flow in the observed flow. Therefore, the simulated hydrographs which model the actual peak flow provide more reasonable results of 625.3 m3/s for the December 2006 flood and 743.9 m3/s for the January 2007 flood. The modeling took into account the tidal effect. When the tidal effect was not considered, the simulated flood depth was 43% lower than the observed flood. However, the inclusion of the tidal effect has reduced the simulation error with an average similarity with the observed flood at 91.4% based on site verification. The simulation results show that the river flow starts to over bank for ARIs exceeding 25 years.

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.

Climate change impact on extreme value and its frequency distribution function in a karst basin, Southwest China

Abstract Studying extreme meteorology and its frequency under climate change is helpful to guide flood and drought control. The original achievements and objective of this study are to further contribute to the literature on how to analyze the impact of climate change on extreme rainfall and extreme temperature more reasonably and comprehensively for a karst basin. The Mann–Kendall method, Heuristic segmentation method, cross-wavelet analysis method, generalized extreme value (GEV) model, and generalized Pareto distribution (GPD) model were applied in this paper. The 55-year (1963–2017) extreme rainfall and temperature data recorded in the Chengbi River Basin were applied. The results show that extreme rainfall showed a downward trend (−0.169 and −8.735 mm/10a), while the trends of extreme temperatures were not obvious (Sen's slope estimate is 0). The mutation points range from 1981 to 2002 and the mutation point of extreme rainfall series is earlier than that of extreme temperatures. Compared with the GEV model, the parameters of the GPD model show a smaller variation before and after climate change, and the extreme meteorology values corresponding to the same recurrence period show a decreasing trend after climate change. The performance of GEV and GPD models after climate change is generally more fit than that before climate change.