scholarly journals High-resolution analysis of 1 day extreme precipitation in Sicily

2015 ◽  
Vol 15 (10) ◽  
pp. 2347-2358 ◽  
Author(s):  
M. Maugeri ◽  
M. Brunetti ◽  
M. Garzoglio ◽  
C. Simolo
Keyword(s):  
High Resolution ◽  
Extreme Precipitation ◽  
Human Life ◽  
Extreme Value Distribution ◽  
Generalized Extreme Value Distribution ◽  
Return Level ◽  
Regional Frequency Analysis ◽  
Economic Losses ◽  
Data Set ◽  
High Quantiles

Abstract. Sicily, a major Mediterranean island, has experienced several exceptional precipitation episodes and floods during the last century, with serious damage to human life and the environment. Long-term, rational planning of urban development is indispensable to protect the population and to avoid huge economic losses in the future. This requires a thorough knowledge of the distributional features of extreme precipitation over the complex territory of Sicily. In this study, we perform a detailed investigation of observed 1 day precipitation extremes and their frequency distribution, based on a dense data set of high-quality, homogenized station records in 1921–2005. We estimate very high quantiles (return levels) corresponding to 10-, 50- and 100-year return periods, as predicted by a generalized extreme value distribution. Return level estimates are produced on a regular high-resolution grid (30 arcsec) using a variant of regional frequency analysis combined with regression techniques. Results clearly reflect the complexity of this region, and show the high vulnerability of its eastern and northeastern parts as those prone to the most intense and potentially damaging events.

scholarly journals High-resolution analysis of 1 day extreme precipitation in Sicily

2015 ◽  
Vol 3 (4) ◽  
pp. 2247-2281 ◽  
Author(s):  
M. Maugeri ◽  
M. Brunetti ◽  
M. Garzoglio ◽  
C. Simolo
Keyword(s):  
High Resolution ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Human Life ◽  
Regional Climate ◽  
Generalized Extreme Value Distribution ◽  
Return Level ◽  
Regional Frequency Analysis ◽  
Economic Losses ◽  
Data Set

Abstract. Sicily, the major Mediterranean island, experienced several exceptional precipitation episodes and floods during the last century, with dramatic consequences on human life and environment. A long term, rational planning of urban development is mandatory for protecting population and avoiding huge economic losses in the future. This requires a deep knowledge of the distributional features of extreme precipitation over the complex territory of Sicily. In the present study, we address this issue, and attempt a detailed investigation of observed 1-day precipitation extremes and their frequency distribution, based on a dense data-set of high-quality, homogenized station records in 1921–2005. We extrapolate very high quantiles (return levels) corresponding to 10-, 50- and 100-year return periods, as predicted by a generalized extreme value distribution. Return level estimates are produced on a regular high-resolution grid (30 arcsec) using a variant of regional frequency analysis combined with regression techniques. Results clearly reflect the complexity of this region, and make evident the high vulnerability of its eastern and northeastern parts as those prone to the most intense and potentially damaging events. This analysis thus provides an operational tool for extreme precipitation risk assessment and, at the same time, is an useful basis for validation and downscaling of regional climate models.

Explaining recent trends in extreme precipitation in the Southwestern Alps by changes in atmospheric influences 

2021 ◽  
Author(s):  
Juliette Blanchet ◽  
Antoine Blanc ◽  
Jean-Dominique Creutin
Keyword(s):  
Extreme Precipitation ◽  
Extreme Value Distribution ◽  
Mediterranean Area ◽  
Return Level ◽  
French Alps ◽  
Weather Patterns ◽  
Recent Trends ◽  
The Mediterranean ◽  
Increasing Trends ◽  
Southwestern Alps

<p>We analyze recent trends in extreme precipitation in the Southwestern Alps and link these trends to changes in the atmospheric influences triggering extremes. We consider a high-resolution precipitation dataset of 1x1 km2 for the period 1958-2017. A robust method of trend estimation is considered, based on nonstationary extreme value distribution and a homogeneous neighborhood approach. The results show contrasting extreme precipitation trends depending on the season. Excluding autumn, the significant trends are mostly negative in the Mediterranean area, while the French Alps show more contrasted trends, in particular in winter with significant increasing extremes in the Western and Southern French Alps and decreasing extremes in the Northern French Alps and Swiss Valais. In autumn, most of Southern France shows significant increasing trends, with up to 100% increase in the 20-year return level between 1958 and 2017, while the Northern French Alps show decreasing extremes.<br>By comparing these trends to changes in the occurrence of the dominant weather patterns triggering the extremes, we show that part of the significant changes in extremes can be explained by changes in the dominant influences, particularly in the Mediterranean influenced region. We also show that part of the trends in extremes are explained by a shift in the seasonality of maxima. </p>

scholarly journals Maximum rainfall probability distributions pattern in Haryana –A case study

2016 ◽  
Vol 8 (4) ◽  
pp. 2029-2036
Author(s):  
Manoj Kumar ◽  
Chander Shekhar ◽  
Veena Manocha
Keyword(s):  
Probability Distribution ◽  
Coefficient Of Variation ◽  
Probability Distributions ◽  
Monsoon Season ◽  
Daily Rainfall ◽  
Extreme Value Distribution ◽  
Generalized Extreme Value Distribution ◽  
Data Set ◽  
Maximum Rainfall ◽  
Maximum Daily Rainfall

The present study has been undertaken to fit best probability distribution of rainfall in Ambala District of Haryana State. The analysis showed that the maximum daily rainfall among the years ranged between 41mm (1980) to 307.9mm (2009) indicating a very large variation during the period of study. The mean of maximum daily rainfall of all years annually is 112.13mm. The means of monthly and weekly values ranged from 33.10-88.92mm and 8.77- 46.28 mm, respectively. The maximum daily rainfall in a year/monsoon season was307.9 mm and monthly maximum daily rainfall in monsoon season ranged from 105 -307.9mm. The weekly maximum daily rainfall ranged from48 mm-307.9 mm. It was also observed that the minimum among the maximum daily rainfall was 41mm for annual, 34mm for season and 0 in all the months and weeks. The maximum value of coefficient of variation was observed in the first week which indicated a large fluctuation in the rainfall data set and minimum value of coefficient of variation 0.464 was observed for the whole year which shows that fluctuation was minimum for the whole year. Generalized extreme value distribution was found to be best fit probability distribution for most of the periods.

scholarly journals Return levels of sub-daily extreme precipitation over Europe

2020 ◽  
Author(s):  
Benjamin Poschlod ◽  
Ralf Ludwig ◽  
Jana Sillmann
Keyword(s):  
Observational Data ◽  
Extreme Precipitation ◽  
Climate Model ◽  
Internal Variability ◽  
Return Level ◽  
Data Set ◽  
Model Simulations ◽  
Return Levels ◽  
Climate Model Simulations ◽  
Precipitation Events

Abstract. Information on the frequency and intensity of extreme precipitation is required by public authorities, civil security departments and engineers for the design of buildings and the dimensioning of water management and drainage schemes. Especially for sub-daily resolution, at which many extreme precipitation events occur, the observational data are sparse in space and time, distributed heterogeneously over Europe and often not publicly available. We therefore consider it necessary to provide an impact-orientated data set of 10-year rainfall return levels over Europe based on climate model simulations and evaluate its quality. Hence, to standardize procedures and provide comparable results, we apply a high-resolution single-model large ensemble (SMILE) of the Canadian Regional Climate Model version 5 (CRCM5) with 50 members in order to assess the frequency of heavy precipitation events over Europe between 1980 and 2009. The application of a SMILE enables a robust estimation of extreme rainfall return levels with the 50 members of 30-year climate simulations providing 1500 years of rainfall data. As the 50 members only differ due to the internal variability of the climate system, the impact of internal variability on the return level values can be quantified. We present 10-year rainfall return levels of hourly to 24-hourly duration with a spatial resolution of 0.11° (12.5 km), which are compared to a large data set of observation-based rainfall return levels of 16 European countries. This observation-based data set was newly compiled and homogenized for this study from 32 different sources. The rainfall return levels of the CRCM5 are able to reproduce the general spatial pattern of extreme precipitation for all sub-daily durations with centred Pearson product-moment coefficients of linear correlation > 0.7 for the area covered with observations. Also, the rainfall intensity of the observational data set is in the range of the climate model generated intensities in 52 % (77 %, 79 %, 84 %, 78 %) of the area for hourly (3-hourly, 6-hourly, 12-hourly, 24-hourly) durations. This results in biases between −19.3 % (hourly) to +8.0 % (24-hourly) averaged over the study area. The range, which is introduced by the application of 50 members, shows a spread of −15 % to +18 % around the median. We conclude that our data set shows good agreement with the observations for 3-hourly to 24-hourly durations in large parts of the study area. Though, for hourly duration and topographically complex regions such as the Alps and Norway, we argue that higher-resolution climate model simulations are needed to improve the results. The 10-year return level data are publicly available (Poschlod, 2020; https://doi.org/10.5281/zenodo.3878887).

scholarly journals The evaluation of regional frequency analyses methods for nonstationary data

2015 ◽  
Vol 371 ◽  
pp. 95-98 ◽  
Author(s):  
W. Nam ◽  
S. Kim ◽  
H. Kim ◽  
K. Joo ◽  
J.-H. Heo
Keyword(s):  
Extreme Value Distribution ◽  
Location Parameter ◽  
Generalized Extreme Value Distribution ◽  
Regional Frequency Analysis ◽  
Time Varying ◽  
Shape Parameters ◽  
Index Flood ◽  
Nonstationary Data ◽  
Time Invariant ◽  
Regional Frequency

Abstract. Regional frequency analysis is widely used to estimate more reliable quantiles of extreme hydro-meteorological events. The stationarity of data is required for its application. This assumption tends to be violated due to climate change. In this paper, four nonstationary index flood models were used to analyze the nonstationary regional data. Monte Carlo simulation was used to evaluate the performances of these models for the generalized extreme value distribution with linearly time varying location parameter and constant scale and shape parameters. As a results, it was found that the index flood model with time-invariant index flood and time-variant growth curve could yield more statistically efficient quantile when record is long enough to show significant nonstationarity.

Evaluation of the reliability of the high-resolution WRF fire danger forecasts in Poland

2021 ◽  
Author(s):  
Marta Gruszczynska ◽  
Alan Mandal ◽  
Grzegorz Nykiel ◽  
Tomasz Strzyzewski ◽  
Weronika Wronska ◽  
...  
Keyword(s):  
High Resolution ◽  
Forest Fires ◽  
Human Life ◽  
Meteorological Data ◽  
Fire Service ◽  
Fire Danger ◽  
Economic Losses ◽  
Fire Weather ◽  
Weather Index ◽  
Fire Weather Index

<p>Fires negatively affect the composition and structure of fauna and flora, as well as the quality of air, soils and water. They cause economic losses and pose a risk to human life. Poland is at the forefront of European countries in terms of forest fires. Therefore, Institute of Meteorology and Water Management - National Research Institute (IMWM-NIR) implemented fire danger forecast system based on high-resolution (2.5 km) Weather Research and Forecast (WRF) model. Forecasted meteorological data are used to calculate parameters of Canadian Forest Fire Weather Index (FWI) System: Fire Weather Index (FWI), Initial Spread Index (ISI), Buildup Index (BUI), Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), and Drought Code (DC). Each parameter is presented in one of the classes corresponding to the fire danger – from low to extreme. In this way, a daily 24- and 48-hour fire danger forecasts are generated for the whole area of Poland and presented on IMWM-NIR meteorological website (meteo.imgw.pl).</p><p>In this presentation we show analyses of reliability of implemented FWI system. For this purpose, data reprocessing from March to September 2019 were made. Also data on fires occurrence on forest lands: time of occurrence, characteristics and location, from the resources of the State Fire Service were collected. Finally, for the selected period, we obtained a dataset of about 8 thousand events for which we assigned values of FWI parameters. Generally, based on our analysis, correlation between number of fires and averaged value of FWI amounted over 0.8. We found out, the correlation coefficient calculated for regions differ. The correlation is higher in central and northern Poland compared to the eastern part of the country, which also correspond to the number of fires. This may be related to the different forest structure - there is a higher proportion of broadleaf forests in the east. The comparison of 24- and 48-hour forecasts showed that they have similar reliability.</p>

Spatial effects on extreme precipitation in the coastal areas of southeast China

2020 ◽  
Author(s):  
Weikang Qian ◽  
Xun Sun
Keyword(s):  
Extreme Precipitation ◽  
Extreme Value Distribution ◽  
Coastal Areas ◽  
Generalized Extreme Value Distribution ◽  
Precipitation Event ◽  
Human Society ◽  
Spatial Effects ◽  
Southeast China ◽  
Spatial Factors ◽  
Daily Precipitation Data

<p>Extreme precipitation event, along with its secondary disasters, is one of the largest natural hazards leading to massive loss in human society. In the coastal areas of southeast china, tropical cyclones (TC) frequently visit the region with intensive precipitation in summer and autumn. Besides TC induced extreme precipitation, convectional precipitation is an alternative reason of extreme precipitation. This study investigated the spatial effects of the extreme precipitation during the raining season for both TC induced and non-TC induced extreme precipitation. The seasonal maximum daily precipitation data through 94 stations in southeast coastal areas of China from 1964 to 2013 were used. We developed a hierarchical Bayesian model with generalized extreme value distribution (GEV) to quantitatively assess the effects of spatial factors on the extreme precipitation. TC induced and non-TC induced extreme precipitation are modelled separately. It was found that the spatial factors that affect the TC induced and non-TC induced extreme precipitation are clearly different. For the TC induced extreme precipitation, the distance to the coastline has been found to be a significant spatial covariate that affects both the location and scale parameter of GEV across the whole areas, while spatial factors are diverse in different locations for non-TC induced extreme precipitation.</p>

scholarly journals Mapping snow depth return levels: smooth spatial modeling versus station interpolation

2010 ◽  
Vol 14 (12) ◽  
pp. 2527-2544 ◽  
Author(s):  
J. Blanchet ◽  
M. Lehning
Keyword(s):  
Snow Depth ◽  
Extreme Value Distribution ◽  
Value Theory ◽  
Extreme Value ◽  
Generalized Extreme Value Distribution ◽  
Return Level ◽  
Distribution Fitting ◽  
Regional Variability ◽  
Gev Distribution ◽  
Return Levels

Abstract. For adequate risk management in mountainous countries, hazard maps for extreme snow events are needed. This requires the computation of spatial estimates of return levels. In this article we use recent developments in extreme value theory and compare two main approaches for mapping snow depth return levels from in situ measurements. The first one is based on the spatial interpolation of pointwise extremal distributions (the so-called Generalized Extreme Value distribution, GEV henceforth) computed at station locations. The second one is new and based on the direct estimation of a spatially smooth GEV distribution with the joint use of all stations. We compare and validate the different approaches for modeling annual maximum snow depth measured at 100 sites in Switzerland during winters 1965–1966 to 2007–2008. The results show a better performance of the smooth GEV distribution fitting, in particular where the station network is sparser. Smooth return level maps can be computed from the fitted model without any further interpolation. Their regional variability can be revealed by removing the altitudinal dependent covariates in the model. We show how return levels and their regional variability are linked to the main climatological patterns of Switzerland.

scholarly journals Bayesian Inference in Extremes Using the Four-Parameter Kappa Distribution

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2180
Author(s):  
Palakorn Seenoi ◽  
Piyapatr Busababodhin ◽  
Jeong-Soo Park
Keyword(s):  
Bayesian Inference ◽  
Goodness Of Fit ◽  
Extreme Value Distribution ◽  
Location Parameter ◽  
Small Sample ◽  
Generalized Extreme Value Distribution ◽  
Return Level ◽  
Kappa Distribution ◽  
Annual Maximum ◽  
Bayesian Estimators

Maximum likelihood estimation (MLE) of the four-parameter kappa distribution (K4D) is known to be occasionally unstable for small sample sizes and to be very sensitive to outliers. To overcome this problem, this study proposes Bayesian analysis of the K4D. Bayesian estimators are obtained by virtue of a posterior distribution using the random walk Metropolis–Hastings algorithm. Five different priors are considered. The properties of the Bayesian estimators are verified in a simulation study. The empirical Bayesian method turns out to work well. Our approach is then compared to the MLE and the method of the L-moments estimator by calculating the 20-year return level, the confidence interval, and various goodness-of-fit measures. It is also compared to modeling using the generalized extreme value distribution. We illustrate the usefulness of our approach in an application to the annual maximum wind speeds in Udon Thani, Thailand, and to the annual maximum sea-levels in Fremantle, Australia. In the latter example, non-stationarity is modeled through a trend in time on the location parameter. We conclude that Bayesian inference for K4D may be substantially useful for modeling extreme events.

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