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![FIG. 3. Extreme precipitation [RX1 (green line) and RX5 (orange line)],...](profile/Thomas-Scholten-3/publication/258663770/figure/fig3/AS:341350592598026@1458395731422/Extreme-precipitation-RX1-green-line-and-RX5-orange-line-economic-flood-losses-in_Q320.jpg)
In a changing climate, understanding the frequency of weather extremes is crucial to improving the management of the associated risks. The concept of weather index based insurance is introduced as a new approach in weather risk adaptation. It can decrease the vulnerability to precipitation extremes that cause floods and economic losses in the Zhuji...
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... These PDFs are widely used in the literature for maximum daily rainfall estimation in various regions (Koutsoyiannis et al. 1998;Katz 2010;Rulfová et al. 2016;Yuan et al. 2018;Xavier et al. 2019a;Lima et al. 2021). The Kolmogorov-Smirnov (KS) and Anderson-Darling (AD) tests were employed to assess the goodness of fit of each PDF (Fischer et al. 2012;Ye et al. 2018;Xavier et al. 2019b;Moccia et al. 2021). The Akaike information criterion (AIC), Bayesian information criterion (BIC), and log-likelihood function (LLF) validation metrics were used to compare and select the best-fitting PDF (Coles ...
This study assesses the frequency and intensity of rainfall and determines the optimal methods for estimating extreme rainfall in the Itacaiúnas River watershed (IRW), situated in the eastern Brazilian Amazon. Daily rainfall data from 1988 to 2018 were acquired from the Brazilian National Water Agency (ANA) and the National Institute of Meteorology (INMET), whereas hourly data from 2016 to 2018 were obtained from the Vale Institute of Technology and INMET. To fit the annual maximum daily rainfall data, we employed 11 probability distribution functions (PDFs) and evaluated their efficacy using the Kolmogorov-Smirnov (KS) and Anderson-Darling (AD) tests, as well as the Akaike information criterion (AIC), Bayesian information criterion (BIC), and log-likelihood function (LLF). The Gumbel and gamma distributions yielded superior results, as evidenced by the AIC and BIC criteria. The LLF demonstrated that the GEV and Weibull 3 PDFs better fit the maximum annual rainfall series. We calculated rainfall disaggregation coefficients from the rainfall schedule data to estimate maximum rainfall for different duration periods. A comparison with CETESB coefficients revealed that updating these estimates is necessary for accurately representing intense rainfall events in the eastern Amazon. Our analysis estimated upper hourly maximum rainfall of up to 115 mm/h for a return period of 100 years.
... The concept of stationarity does not consider, whether the probability of occurrence of a certain extreme event and the corresponding probability distribution are connected to temporal changes (Coles (2001), Jacob (2013)). Due to the long service life of structures of 50 or 100 years, the validity of stationary assumptions may therefore be scrutinized, see, e.g., Coles (2001), Fischer et al. (2012), Soukissian and Tsalis (2015), and Milly et al. (2008). This is supported by numerous studies, illustrating fundamental changes in frequencies and intensities of extreme events connected to trends and shifts in corresponding data (Melillo et al. (2014), Coumou and Rahmstorf (2012), Mazdiyasni et al. (2017), Mallakpour and Villarini (2017), Wahl et al. (2015), Vahedifard et al. (2016), andJongman et al. (2014)). ...
A safe and economic structural design based on the semi-probabilistic concept requires statistically representative safety elements, such as characteristic values, design values, and partial safety factors. Regarding climate loads, the safety levels of current design codes strongly reflect experiences based on former measurements and investigations assuming stationary conditions, i.e. involving constant frequencies and intensities. However, due to climate change, occurrence of corresponding extreme weather events is expected to alter in the future influencing the reliability and safety of structures and their components. Based on established approaches, a systematically refined data-driven methodology for the determination of design parameters considering nonstationarity as well as standardized targets of structural reliability or safety, respectively, is therefore proposed. The presented procedure picks up fundamentals of European standardization and extends them with respect to nonstationarity by applying a shifting time window method. Taking projected snow loads into account, the application of the method is exemplarily demonstrated and various influencing parameters are discussed.
... The Generalized Extreme Value (GEV) and Generalized Pareto (GP) distributions are two broadly applied probability distributions for studying extreme climatic variables (Cooley, 2009;Jaiswal et al., 2021;Serinaldi and Kilsby, 2014). In this study, we used the GEV to calculate the probability distribution of extreme precipitation indices (Fischer et al., 2012). It should be highlighted that the GEV distribution is used to simulate four extreme precipitation indices (i.e., CWD, Rx1 day, R10, and SDII), assuming that these four are constant random variables. ...
The phenomena of climate change and increase in warming conditions across the globe causes changes in the frequency and severity of extreme weather events. The present study analyzed extreme precipitation weather events across four Indian cities in different climatic conditions. The study uses IMD precipitation datasets from 1900 to 2004 to analyze different atmospheric influencing parameters like ENSO, AMO, and IOD on future extreme precipitation conditions. The Bayesian analysis is carried out for nonstationary analysis of extreme indices like Rx1 Day, SDII, R10, and CWD with a 10, 20, 50, and 100 years return period. Significant outcomes of the comparative study of the stationary and nonstationary analysis showed an intensification in extreme precipitation across Indian cities for all return periods using the CWD indicator. The remaining three indicators of the nonstationary study suggested intensifying extreme rainfall across all Indian cities except Guwahati.
... that the concentrations of ∑ 12 NEOs were significantly different between for the high-level NEOs in WW is probably related to the fact that more NEOs are accumulated in the larger catchment area of WW than in the smaller catchment area of EW(Fischer et al., 2012). The low detection of NEOs in deep sediment layers in EW was most likely due to the slow sedimentary rate in EW (approximately 2.56 cm/yr, Text S1 in Supplementary Materials) and the short consumption history of NEOs (three decades). ...
Neonicotinoids (NEOs), as the most-consumed pesticides on a global scale, have posed a serious threat to human health and ecological environment. Information regarding the emission history of NEOs is of great importance to improve the prediction of their environmental loading and biological risk potential. In the present study, contamination levels and compositions of 12 NEOs were identified in 8 sediment cores from the Lingdingyang Estuary, which was impacted by agricultural emissions in riverine runoff of the Pearl River Basin for centuries. The total concentration of 12 target NEOs (∑12NEOs) ranged from 0.02 to 69.5 ng/g dw along the sediment core profile, with a mean of 12.9 ± 15.9 ng/g dw. Net deposition fluxes and concentrations of 5 parent NEOs experienced a remarkable exponential increase in the vertical profile of sediment cores, except for imidacloprid (IMI). Despite the similar exponential growth before 2012, subsequent decreased levels of IMI in historical sediment indicated its gradual replacement by other NEOs. IMI was the NEO with the highest frequency of 80.3% and the highest mean concentration of 7.66 ± 8.76 ng/g dw. The ecological risk assessment of NEOs suggests that 65.1% of sediment samples exceeded the chronic threshold for aqueous organisms using equilibrium partitioning approach. Since downward diffusion of NEOs in the Lingdingyang Estuary was rectified by their rapid desorption, the sedimentary record probably provided an accurate illustration of agricultural NEO emissions in the Pearl River Basin, China. The recent NEO inventory in the adjacent waters of core sites was estimated with a mean of 76.8 tons/yr. This study provides insights into the role of agricultural emission in riverine runoff in the environmental loads of NEOs in the historical sediment.
... By fitting G E V , they assessed the effects of anthropogenic forcings and other external factors on observed increases in extreme precipitation. In addition to G E V and P E 3, distributions such as L N 3 and the five-parameter Wakeby (W A K ) have also been used to study the statistical characteristics of AM series in China Su et al., 2009;Fischer et al., 2012;Zhang et al., 2017a;Lu et al., 2016). Su et al. (2009) used the G E V , Generalized Pareto (G P ), Generalized Logistic (G L O ), and W A K distributions with AM series of observed data in the Yangtze River Basin and grid data derived from the ECHAM5/MPI-OM climate model. ...
... Even for the parameter estimation of small samples, these parameters are unbiased (Chen et al., 2014). At present, L-moments are widely used in research in China (Cai et al., 2007;Jiang et al., 2009;Su et al., 2009;Yang et al., 2010;Fischer et al., 2012;Chen et al., 2014;Yuan et al., 2017a). Cai et al. (2007) applied the G distribution to fit the AM series of 210 stations in eastern China. ...
... They compared the parameters estimated by L-moments and MOM and found that the parameters estimated by the former were more accurate, unbiased, and robust. The Maximum likelihood (ML) (Fisher and Russell, 1922) method is also frequently used for parameter estimation in China (Song et al., 2007;Fischer et al., 2012;. However, Huang et al. (1994) revealed that ML is very sensitive to the sample's minimum value when estimating parameters of the P E 3. Also, when the coefficient of variation of the series is greater than 2, the P E 3 parameters have no solution. ...
In recent years China has witnessed massive economic losses and fatalities by natural disasters caused by extreme precipitation including floods and landslides. Alleviating the adverse effects of extreme precipitation in modern societies relies on building protection infrastructure. In turn, cost-effective and robust infrastructure design requires the identification of the statistical properties and laws that govern extreme precipitation. China is a vast country with diverse climates and terrains, and as such, different analysis and research methods have been applied in its regions. In this paper, we provide a comprehensive review and synthesis of techniques and methods used in research and engineering practice for extreme precipitation analysis across the country. Specifically, we focus on: (1) annual maximum methods, (2) peaks over threshold methods, (3) probable maximum precipitation estimates, and (4) non-stationary analysis of precipitation extremes. Research on extreme precipitation in China is generally based on the above four approaches and this review aims to provide a detailed timeline of the evolution and application of these methods. Finally, we stress ideas for further research on frequency analysis of extreme precipitation in response to climate change and human activities in a changing environment.
... The LPIII was adopted by the federal agencies of the United States of America and Australia for the frequency analysis of discharges (Gordon et al. 2004; et al. (2018) have shown that the LPIII distribution provides a good fit to precipitation data. The GEV distribution is widely used in the field of hydrology (Rao and Hamed 2000) and provides good fit to maximum precipitation data (e.g., Fischer et al. 2012;Mamoon and Rahman 2017;Ogarekpe et al. 2020), and its use is recommended by European countries such as Austria, Italy, Germany, and Spain (Asikoglu 2018). The other three distributions are two-parameter distributions which are each related to one of the three-parameter distributions that were selected. ...
Hydrologic design requires estimation of extreme hydrological events, whose magnitude and probability are estimated using a probability distribution function (PDF). Since estimations can vary considerably depending on the PDF that is used, the selection of an appropriate PDF is essential. This study compares the performance of four different methods for selecting probability distributions analyzing 56 data series of annual maximum precipitation in Mexico: the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), the Kolmogorov–Smirnov (KS) test, and the standard error of fit (SEF) statistic. Comparison of these methods was done by means of Monte Carlo simulations, in which data were generated from a mother distribution, and the ability of the different selection procedures to choose the right distribution was observed. Several different simulation scenarios were analyzed, varying the mother distribution and the sample size. None of the selection criteria proved to be superior to the others in all cases. AIC and BIC performed better when the mother distribution had two parameters, while the KS and SEF performed better when the mother distribution had three parameters. It was observed that the SEF and KS tend to select three- parameter distributions even when a third parameter is not justified, whereas the AIC and BIC tend to penalize excessively the addition of a third parameter, even when it is necessary.
... Statistical methods can be used to predict the rainfall in different return periods so as to provide a reference for flood prevention in the future. The most commonly used probability distribution models are Gumbel, P-III, generalized extreme value (GEV), and generalized Pareto (GP) (Fischer et al. 2012;Xia et al. 2012;Coronado-Hern A Ndez et al. 2020;Samuel et al. 2020). For instance, Samuel et al. (2020) reported that Kaduna's monthly rainfall data are fitted to a generalized extreme value distribution. ...
... Chaudhuri and Sharma (2020) indicated that compared with Gumbel, GEV predicts the higher intensity of extreme rainfall with different return periods and duration in Delhi, India. The extreme rainfall distribution functions widely used in China in recent decades were Gamma and P-III type distribution functions (Hanson and Vogel 2008;Fischer et al. 2012;Xia et al. 2012). used Gamma distribution and K-S test to detect the variation of extreme precipitation in southern China. ...
The North Anhui Province Plain (NAHPP), an important food production plain in China, is prone to frequent droughts and floods. To better understand the extreme events and mitigate their effects, this paper explores the spatiotemporal variation of precipitation extremes in the NAHPP during 1976 and 2018. Variation trends and spatial distributions of the annual maximum 1-day, 3-day, 7-day, and 15-day-rainfall were analyzed, and the probability distribution of rainfall extremes in the NAHPP was calculated by three distribution functions (Gumbel, P-III, and generalized extreme value). The optimal fitting function was selected based on the Kolmogorov–Smirnov test, and the rainfall in different return periods was calculated according to the optimal fitting function. The results indicate that rainfall extreme showed a 2- to 3-year periodicity on the interannual scale and 21-year periodicity on the chronological scale in the NAHPP. The rainfall extremes showed nonsignificant increase trend over the NAHPP, and some stations showed no significant decrease trend. The P-III distribution function best fit to the rainfall extremes (the maximum 1-day rainfall: 59%). The spatial distributions of rainfall extremes were similar in different return periods. As the return period increased, the estimated rainfall by the three distribution functions were slightly larger than that in the empirical return period. The findings will benefit regional water resources management and water-related risk control.
... Recently, a number of studies have used a variety of other pdfs with different levels of success (Faragó and Katz 1990;Kharin and Zwiers 2000;Fischer et al. 2012;Hossian et al. 2016;Vivekanandan 2018). The pdfs used include the gamma (GAM), Gaussian (GAU), inverse Gaussian (IGAM), twoand three-parameter lognormal (LN2, LN3), logistic (L), log-logistic (LL), Gumbel (EV1), GEV, generalized Pareto (GP), three-parameter beta (BE), Pearson type III (P3), log-Pearson type III (LP3), Burr (BR), Kappa (KAP) and Wakeby (WAK) distributions. ...
This paper explores different goodness-of-fit (GOF) criteria’s used in the various fields of science to compare candidate probability density functions (pdfs) to annual maximum temperature records and discusses their usefulness and drawbacks. The L-moment ratio diagram method is also proposed as alternative approach for the GOF of the pdfs. The advantage this method allows for an easy comparison of the fit of many pdfs for several stations on a single diagram. To gain knowledge about higher order moments (i.e. shape, skewness and kurtosis) of the station data set, plotting the position of a given temperature data set in L-moment ratio diagram space is prompt and effective and can provide a useful addition to the GOF criterion. Both the L-moment ratio diagrams and many GOF criteria are used on real data to assess the fit of the pdfs for temperature data in the state of New South Wales, Australia. The analysis of the L-moment ratio diagrams reveals that the generalized extreme value and normal distributions generally fit best the annual maximum temperature series. The other two- and three-parameter distributions also showed viable fits in some instances. Results obtained from L-moment diagrams, temperature frequency histograms, cumulative density plots and the simulation study are compared with those obtained from GOF statistics, and a good agreement is generally observed between all these approaches. In conclusion the L-moment ratio diagram can represent a simple, effective and efficient approach to be used as a complementary method along with the traditional GOF criteria.
... Reviewing the general situation in Figure 12, the F I G U R E 1 0 Regional growth curve (blue) and 99% error bounds (red) for seven sub-regions. Panels a-g are for regions I-VII of RX1DAY series, respectively extreme precipitation distribution is uneven and can be approximately divided into three comparative zones: the "arid zone" in the upper reaches (the source area), the "wet zone" covering the most area of the basin, and the "special wet zone" (Fischer et al., 2012). ...
The Yangtze River Basin has periodically been subject to torrential rains and floods. It is of great significance to characterize the extreme precipitation patterns to learn about frequent flood characteristics in the Yangtze River Basin. Commonly, spatiotemporal characteristics of extreme precipitation was studied by regional frequency analysis method with site data. Spatial sparse site data may cause imprecise divisions of homogeneous regions. In this paper, the spatiotemporal characteristics of extreme precipitation was studied by regional frequency analysis with corrected satellite‐based grid precipitation data (Global Land Data Assimilation System, GLDAS) rather than site data. The results show that: (1) The corrected GLDAS daily precipitation data had greatly improved its ability to capture extreme precipitation events in Yangtze River Basin, as the data average accuracy increased from 0.215 before correction to 0.849 after correction. It is feasible to use satellite‐based grid precipitation data to replace the site data for the regional frequency analysis of extreme precipitation. (2) The Yangtze River Basin was categorized into seven homogeneous regions for the annual maximum 1‐day (RX1DAY) index with an automatic subjective adjustment method. (3) The regional growth curves and quantiles of the Yangtze River Basin were drawn for the return period for 2–100 years. (4) Spatial patterns of extreme daily precipitation series with a return period of 100 years indicated that the precipitation amount increases gradually from the upper to the lower Yangtze River Basin, from the “arid zone” to the “wet zone” and then to the “special wet zone”, and the 100‐year return level of RX1DAY varied from 30.3 to 301.8 mm. There were three main precipitation centres, the Sichuan Basin, Dongting Lake Basin, and a great triangle area covering the Poyang Lake Basin and the south foot of Dabie Mountain.
... The use of meteorological indices including the SPI has been explored for crop insurance by Leblois and Quirion (2013) and for weather index insurance by Fischer et al., (2012). In Argentina, a pilot insurance program was based on 6-months SPIs calculated from weather station data to compensate dairy farmers for reduced milk production in case of drought occurrence (SPI <-2) and/or excessive rainfall events (SPI >+2) (Mercosur Group, 2016). ...
Examine the usability of rainfall and temperature outputs of a regional climate model (RCM) and meteorological drought indices to develop a macro-level risk transfer product to compensate the government of Central Java, Indonesia, for drought-related disaster payments to rice farmers.
