scholarly journals Has fire policy decreased the return period of the largest wildfire events in France? A Bayesian assessment based on extreme value theory

2018 ◽  
Author(s):  
Guillaume Evin ◽  
Thomas Curt ◽  
Nicolas Eckert
Keyword(s):  
Return Period ◽  
Extreme Value Theory ◽  
Fire Suppression ◽  
Value Theory ◽  
Extreme Value ◽  
Ecological Impacts ◽  
Return Level ◽  
Burned Area ◽  
Return Periods ◽  
Fire Policy

Abstract. Very large wildfires have high human, economic and ecological impacts so that robust evaluation of their return period is crucial. Preventing such events is a major objective of the new fire policy set up in France in 1994, which is oriented towards fast and massive fire suppression. Whereas this policy is probably efficient for reducing the mean burned area (BA), its effect on the largest fires is still unknown. In this study, we make use of statistical Extreme Value Theory (EVT) to compute return periods of very large BA in southern France, for two distinct periods (1973 to 1994, and 1995 to 2016) and for three pyroclimatic regions characterized by specific fire activities. Bayesian inference and related predictive simulations are used to fairly evaluate related uncertainties. Results demonstrate that the BA corresponding to a return period of 5 years has actually significantly decreased, but that this is not the case for large return periods (e.g. 50 years). For example, in the most fire-prone region, which includes Corsica and Provence, the median 5-year return level decreased from 5,000 ha. to 2,400 ha., while the median 50-year return level decreased only from 17,800 ha. to 12,500 ha. This finding is coherent with the recent occurrence of conflagrations of large and intense fires clearly far beyond the suppression capacity of firemen. These fires may belong to a new generation of fires promoted by long-term fuel accumulation, urbanization into the wildland, and ongoing climate change. These findings may help adapting the operational system of fire prevention and suppression to ongoing changes. Also, the proposed methodology may be useful for other case studies worldwide.

scholarly journals Has fire policy decreased the return period of the largest wildfire events in France? A Bayesian assessment based on extreme value theory

2018 ◽  
Vol 18 (10) ◽  
pp. 2641-2651 ◽  
Author(s):  
Guillaume Evin ◽  
Thomas Curt ◽  
Nicolas Eckert
Keyword(s):  
Return Period ◽  
Extreme Value Theory ◽  
Fire Suppression ◽  
Value Theory ◽  
Extreme Value ◽  
Ecological Impacts ◽  
Return Level ◽  
Burned Area ◽  
Return Periods ◽  
Fire Policy

Abstract. Very large wildfires have high human, economic, and ecological impacts so that robust evaluation of their return period is crucial. Preventing such events is a major objective of the new fire policy set up in France in 1994, which is oriented towards fast and massive fire suppression. Whereas this policy is probably efficient for reducing the mean burned area (BA), its effect on the largest fires is still unknown. In this study, we make use of statistical extreme value theory (EVT) to compute return periods of very large BAs in southern France, for two distinct periods (1973 to 1994 and 1995 to 2016) and for three pyroclimatic regions characterized by specific fire activities. Bayesian inference and related predictive simulations are used to fairly evaluate related uncertainties. Results demonstrate that the BA corresponding to a return period of 5 years has actually significantly decreased, but that this is not the case for large return periods (e.g., 50 years). For example, in the most fire-prone region, which includes Corsica and Provence, the median 5-year return level decreased from 5000 to 2400 ha, while the median 50-year return level decreased only from 17 800 to 12 500 ha. This finding is coherent with the recent occurrence of conflagrations of large and intense fires clearly far beyond the suppression capacity of firemen. These fires may belong to a new generation of fires promoted by long-term fuel accumulation, urbanization into the wildland, and ongoing climate change. These findings may help adapt the operational system of fire prevention and suppression to ongoing changes. Also, the proposed methodology may be useful for other case studies worldwide.

scholarly journals Application of Extreme Value Theory in Predicting Climate Change Induced Extreme Rainfall in Kenya

2019 ◽  
Vol 8 (4) ◽  
pp. 85
Author(s):  
Faithful C. Onwuegbuche ◽  
Alpha B. Kenyatta ◽  
Steeven B. Affognon ◽  
Exavery P. Enock ◽  
Mary O. Akinade
Keyword(s):  
Climate Change ◽  
Extreme Value Theory ◽  
Extreme Rainfall ◽  
Value Theory ◽  
Extreme Value ◽  
Generalized Extreme Value Distribution ◽  
Return Level ◽  
Return Periods ◽  
Optimal Model ◽  
Return Levels

Climate change has brought about unprecedented new weather patterns, one of which is changes in extreme rainfall. In Kenya, heavy rains and severe flash floods have left people dead and displaced hundreds from their settlements. In order to build a resilient society and achieve sustainable development, it is paramount that adequate inference about extreme rainfall be made. To this end, this research modelled and predicted extreme rainfall events in Kenya using Extreme Value Theory for rainfall data from 1901-2016. Maximum Likelihood Estimation was used to estimate the model parameters and block maxima approach was used to fit the Generalized Extreme Value Distribution (GEVD) while the Peak Over Threshold method was used to fit the Generalized Pareto Distribution (GPD). The Gumbel distribution was found to be the optimal model from the GEVD while the Exponential distribution gave the optimal model over the threshold value. Furthermore, prediction for the return periods of 10, 20, 50 and 100 years were made using the return level estimates and their corresponding confidence intervals were presented. It was found that increase in return periods leads to a corresponding increase in return levels. However, the GPD gave higher return levels for 10 and 20 years compared to GEVD. While, for higher return periods 50 and 100 years, the GEVD gave higher return levels compared to the GPD. Model diagnostics using probability, density, quantile and return level plots indicated that the models provided were a good fit for the data.

scholarly journals Return Period Evaluation of the Largest Possible Earthquake Magnitudes in Mainland China Based on Extreme Value Theory

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3519
Author(s):  
Yanbing Bai ◽  
Ning Ma ◽  
Shengwang Meng
Keyword(s):  
Earthquake Engineering ◽  
Return Period ◽  
Extreme Value Theory ◽  
Threshold Model ◽  
Mainland China ◽  
Value Theory ◽  
Extreme Value ◽  
Geographical Information ◽  
Geographical Regions ◽  
The Right

The largest possible earthquake magnitude based on geographical characteristics for a selected return period is required in earthquake engineering, disaster management, and insurance. Ground-based observations combined with statistical analyses may offer new insights into earthquake prediction. In this study, to investigate the seismic characteristics of different geographical regions in detail, clustering was used to provide earthquake zoning for Mainland China based on the geographical features of earthquake events. In combination with geospatial methods, statistical extreme value models and the right-truncated Gutenberg–Richter model were used to analyze the earthquake magnitudes of Mainland China under both clustering and non-clustering. The results demonstrate that the right-truncated peaks-over-threshold model is the relatively optimal statistical model compared with classical extreme value theory models, the estimated return level of which is very close to that of the geographical-based right-truncated Gutenberg–Richter model. Such statistical models can provide a quantitative analysis of the probability of future earthquake risks in China, and geographical information can be integrated to locate the earthquake risk accurately.

scholarly journals Risk Analysis of Gold Prices in Pakistan Using Extreme Value Theory

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Ghulam Raza Khan ◽  
Alanazi Talal Abdulrahman ◽  
Osama Alamri ◽  
Zahid Iqbal ◽  
Maqsood Ahmad
Keyword(s):  
Extreme Value Theory ◽  
Value At Risk ◽  
Risk Measures ◽  
Value Theory ◽  
Extreme Value ◽  
Return Level ◽  
Extreme Stress ◽  
Daily Data ◽  
Peak Over Threshold ◽  
The Impact

Extreme value theory (EVT) is useful for modeling the impact of crashes or situations of extreme stress on investor portfolios. EVT is mostly utilized in financial modeling, risk management, insurance, and hydrology. The price of gold fluctuates considerably over time, and this introduces a risk on its own. The goal of this study is to analyze the risk of gold investment by applying the EVT to historical daily data for extreme daily losses and gains in the price of gold. We used daily gold prices in the Pakistan Bullion Market from August 1, 2011 to July 30, 2021. This paper covers two methods such as Block Maxima (BM) and Peak Over Threshold (POT) modeling. The risk measures which are adopted in this paper are Value at Risk (VaR) and Expected Shortfall (ES). The point and interval estimates of VaR and ES are obtained by fitting the Generalized Pareto (GPA) distribution. Moreover, in this paper, return-level forecasting is also included for the next 5 and 10 years by analyzing the Generalized Extreme Value (GEV) distribution.

Frequentist and Bayesian extreme value analysis on the wildfire events in Greece

2020 ◽  
Author(s):  
Nikos Koutsias ◽  
Frank A. Coutelieris
Keyword(s):  
Extreme Value Theory ◽  
Extreme Values ◽  
Value Theory ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Burned Area ◽  
Accurate Assessment ◽  
Peaks Over Threshold ◽  
Bayesian Approaches ◽  
Value Analysis

<p>A statistical analysis on the wildfire events, that have taken place in Greece during the period 1985-2007, for the assessment of the extremes has been performed. The total burned area of each fire was considered here as a key variable to express the significance of a given event. The data have been analyzed through the extreme value theory, which has been in general proved a powerful tool for the accurate assessment of the return period of extreme events. Both frequentist and Bayesian approaches have been used for comparison and evaluation purposes. Precisely, the Generalized Extreme Value (GEV) distribution along with Peaks over Threshold (POT) have been compared with the Bayesian Extreme Value modelling. Furthermore, the correlation of the burned area with the potential extreme values for other key parameters (e.g. wind, temperature, humidity, etc.) has been also investigated.</p>

Estimated earthquake probabilities in the north circum-Pacific area

1972 ◽  
Vol 62 (6) ◽  
pp. 1397-1410
Author(s):  
A. F. Shakal ◽  
D. E. Willis
Keyword(s):  
Extreme Value Theory ◽  
Probability Model ◽  
Value Theory ◽  
Extreme Value ◽  
Return Periods ◽  
The North ◽  
Aleutian Arc ◽  
Large Earthquakes ◽  
Earthquake Data ◽  
Pacific Area

abstract Gumbel's extreme value theory is applied to the estimation of probabilities of occurrence and return periods for large earthquakes in the north circum-Pacific area, using earthquake data from 1930 through 1971. The probability model of Epstein and Lomnitz is discussed with reference to Gumbel's extreme value theory. Estimated probabilities and expected extremes within individual tectonic blocks are calculated and compared. The area of the Aleutian Arc between 155°W and 167°W is found to have about an 80 per cent probability of an Ms ≧ 8 earthquake by 1980.

scholarly journals Vertical structure of extreme currents in the Faroe-Bank Channel

2005 ◽  
Vol 23 (6) ◽  
pp. 1977-1986 ◽  
Author(s):  
C. Carollo ◽  
I. Astin ◽  
J. Graff
Keyword(s):  
Extreme Value Theory ◽  
General Circulation ◽  
Regional Scale ◽  
Generalized Pareto Distribution ◽  
Value Theory ◽  
Extreme Value ◽  
Horizontal Resolution ◽  
Return Level ◽  
Near Surface ◽  
Deep Waters

Abstract. Extreme currents are studied with the aim of understanding their vertical and spatial structures in the Faroe-Bank Channel. Acoustic Doppler Current Profiler time series recorded in 3 deployments in this channel were investigated. To understand the main features of extreme events, the measurements were separated into their components through filtering and tidal analysis before applying the extreme value theory to the surge component. The Generalized Extreme Value (GEV) distribution and the Generalized Pareto Distribution (GPD) were used to study the variation of surge extremes from near-surface to deep waters. It was found that this component alone is not able to explain the extremes measured in total currents, particularly below 500 m. Here the mean residual flow enhanced by tidal rectification was found to be the component feature dominating extremes. Therefore, it must be taken into consideration when applying the extreme value theory, not to underestimate the return level for total currents. Return value speeds up to 250 cm s–1 for 50/250 years return period were found for deep waters, where the flow is constrained by the topography at bearings near 300/330° It is also found that the UK Meteorological Office FOAM model is unable to reproduce either the magnitude or the form for the extremes, perhaps due to its coarse vertical and horizontal resolution, and is thus not suitable to model extremes on a regional scale. Keywords. Oceanography: Physical (Currents; General circulation; General or miscellaneous)

scholarly journals Extreme Value Theory in Application to Delivery Delays

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 788
Author(s):  
Marcin Fałdziński ◽  
Magdalena Osińska ◽  
Wojciech Zalewski
Keyword(s):  
Maximum Likelihood ◽  
Extreme Value Theory ◽  
Maximum Likelihood Method ◽  
Rare Events ◽  
Value Theory ◽  
Extreme Value ◽  
Small Sample ◽  
Generalized Extreme Value Distribution ◽  
Return Level ◽  
Likelihood Method

This paper uses the Extreme Value Theory (EVT) to model the rare events that appear as delivery delays in road transport. Transport delivery delays occur stochastically. Therefore, modeling such events should be done using appropriate tools due to the economic consequences of these extreme events. Additionally, we provide the estimates of the extremal index and the return level with the confidence interval to describe the clustering behavior of rare events in deliveries. The Generalized Extreme Value Distribution (GEV) parameters are estimated using the maximum likelihood method and the penalized maximum likelihood method for better small-sample properties. The findings demonstrate the advantages of EVT-based prediction and its readiness for application.

scholarly journals Chinese Sunspot Drawings and Their Digitizations-(VI) Extreme Value Theory Applied to the Sunspot Number Series from the Purple Mountain Observatory

Atmosphere ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1176
Author(s):  
Yan-Qing Chen ◽  
Sheng Zheng ◽  
Yan-Shan Xiao ◽  
Shu-Guang Zeng ◽  
Tuan-Hui Zhou ◽  
...  
Keyword(s):  
Sunspot Number ◽  
Extreme Value Theory ◽  
Extreme Values ◽  
Threshold Value ◽  
Value Theory ◽  
Extreme Value ◽  
Return Level ◽  
Block Maxima ◽  
First Time

Based on the daily sunspot number (SN) data (1954–2011) from the Purple Mountain Observatory, the extreme value theory (EVT) is employed for the research of the long-term solar activity. It is the first time that the EVT is applied on the Chinese SN. Two methods are used for the research of the extreme events with EVT. One method is the block maxima (BM) approach, which picks the maximum SN value of each block. Another one is the peaks-over-threshold (POT) approach. After a declustering process, a threshold value (here it is 300) is set to pick the extreme values. The negative shape parameters are obtained by the two methods, respectively, indicating that there is an upper bound for the extreme SN value. Only one value of the N-year return level (RL) is estimated: N = 19 years. For N = 19 years, the RL values of SN obtained by two methods are similar with each other. The RL values are found to be 420 for the POT method and the BM method. Here, the trend of 25th solar cycle is predicted to be stronger, indicating that the length of meridional forms of atmospheric circulation will be increased.

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