2016
DOI: 10.1016/j.atmosres.2016.07.014
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Nonstationary modeling of extreme precipitation in China

Abstract: The statistical methods based on extreme value theory have been traditionally used in meteorology and hydrology for a long time. Due to climate change and variability, the hypothesis of stationarity in meteorological or hydrological time series was usually not satisfied. In this paper, a nonstationary extreme value analysis was conducted for annual maximum daily precipitation (AMP) at 631 meteorological stations over China for the period 1951-2013. Stationarity of all 631 AMP time series was firstly tested usi… Show more

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Cited by 68 publications
(32 citation statements)
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“…Dynamical downscaling (the first step in our methodology) joint with non-stationary multivariate statistics (second step) provides an affordable solution, without requiring prohibitive computational cost. Once the statistical model is fitted with process-based model outputs and after establishing relationships between the loads and related climate indices [21,97,98], uncertainty can be bounded by comparing climate indices from several publicly available GCMs (fourth step).…”
Section: Applicability Of the Resultsmentioning
confidence: 99%
“…Dynamical downscaling (the first step in our methodology) joint with non-stationary multivariate statistics (second step) provides an affordable solution, without requiring prohibitive computational cost. Once the statistical model is fitted with process-based model outputs and after establishing relationships between the loads and related climate indices [21,97,98], uncertainty can be bounded by comparing climate indices from several publicly available GCMs (fourth step).…”
Section: Applicability Of the Resultsmentioning
confidence: 99%
“…The nonparametric Mann-Kendall (MK) test was used to characterize the trends of extreme precipitation intensity in this study. MK method was widely used to analyze the monotonically increasing or decreasing trends (Gao, Mo, & Wu, 2016;Gu et al, 2017a;Villarini et al, 2012;Xiao et al, 2017). However, that results of the MK test are affected by the serial correlation of time series (Xiao et al, 2017;Yang et al, 2017;Yue et al, 2002).…”
Section: Trend Analysismentioning
confidence: 99%
“…Defining Q as the annual minimum daily flow, the stationarity of the time series data Q can then be tested by the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test method [47]. The time series Q t is assumed to be the sum of a deterministic trend β t , random walk γ t , and stationary error ε t at time t in the following linear regression model [48].…”
Section: Nonstationary and Trend Test Methodsmentioning
confidence: 99%