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The first high spatial resolution multi-scale daily SPI and SPEI raster dataset for drought monitoring and evaluating over China from 1979 to 2018

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Big Earth Data 2023年第3期7卷 860-885页

作者： Rongrong Zhang Virgílio A.Bento Junyu Qi Feng Xu Jianjun Wu Jianxiu Qiu Jianwei Li Wei Shui Qianfeng Wang Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion and Disaster Protection/College of Environmental&Safety Engineering Fuzhou UniversityFuzhouChina Instituto Dom Luiz Faculdade de CiênciasUniversidade de LisboaLisboaPortugal Earth System Science Interdisciplinary Center University of MarylandCollege ParkMDUSA State Key Laboratory of Earth Surface Processes and Resource Ecology/Faculty of Geographical Science Beijing Normal UniversityBeijingChina Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation School of Geography and PlanningSun Yat-sen UniversityGuangzhouChina College of Physics and Information Engineering Fuzhou UniversityFuzhouChina Key Lab of Spatial Data Mining&Information Sharing Ministry of Education of ChinaFuzhouChina

Standardized Precipitation Index(SPI)and Standardized Precipitation Evapotranspiration Index(SPEI),traditionally derived at a monthly scale,are widely used drought *** overcome temporalresolution limitations,we have previously developed and published a well-validated daily SPI/SPEI in situ *** having a high temporal resolution,this in situ dataset presents low spatial resolution due to the scarcity of ***,based on the China Meteorological Forcing Dataset,which is composed of data from more than 1,000 ground-based observation sites and multiple remote sensing grid meteorological dataset,we present the first high spatiotemporal-resolution daily SPI/SPEI raster datasets over *** spans from 1979 to 2018,with a spatial resolution of 0.1°×0.1°,a temporal resolution of 1-day,and the timescales of 30-,90-,and *** show that the spatial distributions of drought event characteristics detected by the daily SPI/SPEI are consistent with the monthly SPI/*** correlation between the daily value of the 12-month scale and the monthly value of SPI/SPEI is the strongest,with linear correlation,Nash-Sutcliffe coefficient,and normalized root mean square error of 0.98,0.97,and 0.04,*** daily SPI/SPEI is shown to be more sensitive to flash drought than the monthly SPI/*** improved SPI/SPEI shows high accuracy and credibility,presenting enhanced results when compared to the monthly SPI/*** total data volume is up to 150 GB,compressed to 91 GB in Network Common Data Form(NetCDF).It can be available from Figshare(https://***/10.6084/***.c.5823533)and ScienceDB(https://***/10.57760/sciencedb.j00076.00103).

关键词： Drought index climate change climatic disaster generalized extreme value

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A Newly-Discovered GPD-GEV Relationship Together with Comparing Their Models of extreme Precipitation in Summer

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Advances in Atmospheric Sciences 2008年第3期25卷 507-516页

作者：丁裕国程炳岩江志红 National Key Laboratory of Meteorological Diseases of Jiangsu Nanjing University of Information Science and TechnologyNanjing 210044 Climate Center of Chongqing City Meteorological Bureau Chongqing 401147

It has been theoretically proven that at a high threshold an approximate expression for a quantile of GEV （generalized extreme values） distribution can be derived from GPD （generalized Pareto Distribution）. Afterwards, a quantile of extreme rainfall events in a certain return period is found using L-moment estimation and extreme rainfall events simulated by GPD and GEV, with all aspects of their results compared. Numerical simulations show that POT （Peaks Over Threshold）-based GPD is advantageous in its simple operation and subjected to practically no effect of the sample size of the primitive series, producing steady high-precision fittings in the whole field of values （including the high-end heavy tailed）. In comparison, BM （Block Maximum）-based GEV is limited, to some extent, to the probability and quantile simulation, thereby showing that GPD is an extension of GEV, the former being of greater utility and higher significance to climate research compared to the latter.

关键词： generalized Pareto Distribution generalized extreme value daily rainfall extreme precipitation rainstorm

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Integrating machine learning and extreme value theory for estimating crash frequency-by-severity via AI-based video analytics

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Communications in Transportation Research 2024年第1期4卷 382-392页

作者： Fizza Hussain Yuefeng Li Md Mazharul Haque School of Civil&Environment Engineering Queensland University of TechnologyBrisbane4000Australia School of Computer ScienceQueensland University of TechnologyBrisbane4000Australia

Traffic conflict techniques rely heavily on the proper identification of conflict extremes,which directly affects the prediction performance of extreme value *** sampling techniques,namely,block maxima and peak over threshold,form the core of these *** studies have demonstrated the inefficacy of extreme value models based on these sampling approaches,as their crash estimates are too imprecise,hindering their widespread practical ***,anomaly detection techniques for sampling conflict extremes have been used,but their application has been limited to estimating crash frequency without considering the crash severity *** address this research gap,this study proposes a hybrid model of machine learning and extreme value theory within a bivariate framework of traffic conflict measures to estimate crash frequency by severity *** particular,modified time-to-collision(MTTC)and expected post-collision change in velocity(Delta-V orΔV)have been proposed in the hybrid modeling framework to estimate rear-end crash frequency by severity ***-end conflicts were identified through artificial intelligence-based video analytics for three four-legged signalized intersections in Brisbane,Australia,using four days of ***-stationary bivariate hybrid generalized extreme value models with different anomaly detection/sampling techniques(isolation forest and minimum covariance determinant)were *** non-stationarity of traffic conflict extremes was handled by parameterizing model parameters,including location,scale,and both location and scale parameters *** results indicate that the bivariate hybrid models can estimate severe and non-severe crashes when compared with historical crash records,thereby demonstrating the viability of the proposed approach.A comparative analysis of two anomaly techniques reveals that the isolation forest model marginally outperforms the minimum covariance determinant ***,the modeling f

关键词： Bivariate models Traffic conflicts Crash severity Crash frequency generalized extreme value Anomaly detection techniques

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