Spatiotemporal Variability of Extreme Summer Precipitation over the Yangtze River Basin and the Associations with Climate Patterns

Su, Zhenkuan; Hao, Zhenchun; Yuan, Feifei; Chen, Xi; Cao, Qing

doi:10.3390/w9110873

Cited by 26 publications

(15 citation statements)

References 39 publications

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“…This has meant that the applications of AA and ADA have spread to many different fields, such as astrophysics (Chan et al, 2003), biology (D'Esposito et al, 2012), climate (Steinschneider and Lall, 2015;Su et al, 2017), developmental psychology (Ragozini et al, 2017), e-learning (Theodosiou et al, 2013), genetics (Thøgersen et al, 2013), human development (Epifanio, 2016), industrial engineering (Epifanio et al, 2013(Epifanio et al, , 2018Millán-Roures et al, 2018), machine learning (Mørup and Hansen, 2012;Seth and Eugster, 2016a,b;Ragozini and D'Esposito, 2015), market research (Li et al, 2003;Porzio et al, 2008;Midgley and Venaik, 2013), multi-document summarization Kononenko, 2013, 2014), nanotechnology (Fernandez and Barnard, 2015), neuroscience (Tsanousa et al, 2015;Hinrich et al, 2016) and sports (Eugster, 2012;Vinué and Epifanio, 2017).…”

Section: Archetypal Analysismentioning

confidence: 99%

Archetypal Analysis With Missing Data: See All Samples by Looking at a Few Based on Extreme Profiles

Epifanio

Ibáñez

Simó

2019

The American Statistician

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In this paper we propose several methodologies for handling missing or incomplete data in Archetype analysis (AA) and Archetypoid analysis (ADA). AA seeks to find archetypes, which are convex combinations of data points, and to approximate the samples as mixtures of those archetypes. In ADA, the representative archetypal data belong to the sample, i.e. they are actual data points. With the proposed procedures, missing data are not discarded or previously filled by imputation and the theoretical properties regarding location of archetypes are guaranteed, unlike the previous approaches. The new procedures adapt the AA algorithm either by considering the missing values in the computation of the solution or by skipping them. In the first case, the solutions of previous approaches are modified in order to fulfill the theory and a new procedure is proposed, where the missing values are updated by the fitted values. In this second case, the procedure is based on the estimation of dissimilarities between samples and the projection of these dissimilarities in a new space, where AA or ADA is applied, and those results are used to provide a solution in the original space. A comparative analysis is carried out in a simulation study, with favorable results. The methodology is also applied to two real data sets: a well-known climate data set and a global development data set. We illustrate how these unsupervised methodologies allow complex data to be understood, even by non-experts.

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Section: Archetypal Analysismentioning

confidence: 99%

Archetypal Analysis With Missing Data: See All Samples by Looking at a Few Based on Extreme Profiles

Epifanio

Ibáñez

Simó

2019

The American Statistician

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“…Currently, researches on extreme climate events mainly focus on studying the spatiotemporal changes in temperature extremes, conducting analyses and discussing the various influencing factors involved in extreme temperatures [24][25][26]. Studies have examined extreme temperature changes in parts of China and throughout China, and it has been shown that these changes in extreme temperature are closely related to climate warming and atmospheric circulation based on the result of univariate linear regression and correlation analysis [27][28][29]. These analyses, however, ignored the spatial and temporal effects on the temperature extremes and failed to sufficiently reveal the spatiotemporal heterogeneity in temperature extremes.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Study of Spatiotemporal Variations in Temperature Extremes across China during 1960–2018

Zhao

Chen

2021

Sustainability

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Understanding the changing patterns of extreme temperatures is important for taking measures to reduce their associated negative impacts. Based on daily temperature data derived from 2272 meteorological stations in China, the spatiotemporal variations in temperature extremes were examined with respect to covariates by means of the Mann–Kendall test and a spatiotemporal model during 1960–2018. The results indicated that the temporal changes in cold extremes showed decreasing trends and warm extremes experienced increasing trends across almost all of China, with mean change rates of −3.9 days, −1.8 days, 3.7 days and 2.3 days per decade for TN10p, TX10p, TN90p and TX90p, respectively. Nighttime warming/cooling was higher than daytime warming/cooling, which indicated that trends in minimum temperature extremes are more rapid than trends in maximum temperature extremes. In addition, the temporal effect on the temperature extremes varied throughout the year, with significant increasing trends in the temporal heterogeneity of warm extremes occurring during 1992–2018. The areas with strong spatial heterogeneity of cool nights mainly included northeastern and central China, and the spatial variation on cool days was more prominent in northern China. For warm nights, the areas showing high spatial heterogeneity were mainly located in the northwestern part of China, while areas for warm days were distributed in northern China. Our results provide meaningful information for a deeper understanding of the spatiotemporal variations in temperature extremes across mainland China.

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“…Because of increased human activity and drastic climate change, water resources in many regions of China are facing severe challenges, especially in the middle and lower reaches of the Yangtze River, where there are many different types and degrees of flood risk [7]. The high frequency and wide range of floods seriously threaten the safety of people and property [8,9].…”

Section: Introductionmentioning

confidence: 99%

Research on the Allocation of Flood Drainage Rights of the Sunan Canal Based on a Bi-level Multi-Objective Programming Model

Zhang

Shen

Sun

et al. 2019

Water

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To reduce flood disasters and optimize of the comprehensive benefit of the water basin, the allocation of regional flood drainage rights is of great significance. Using the “top-down” allocation mode, we consider the influence of the social, economic, and ecological environments, flood drainage demand and efficiency, and other factors on the allocation of flood drainage rights. A bi-level multi-objective programming model from the perspective of fairness and efficiency is established for the allocation. The Sunan Canal is taken as a typical case study. The model is solved by the multi-objective optimal allocation method and the master–slave hierarchical interactive iteration algorithm. After three iterations of the initial solution, the allocation of flood drainage rights in six flood control regions finally reach an effective state. The results of the model were compared with results based on historical allocation principles, showing that the bi-level multi-objective programming model, based on the principles of fairness and efficiency, is more in line with the current social and economic development of the canal. In view of the institutional background of water resources management in China and the flood drainage pressure faced by various regions, the allocation of flood drainage rights should be comprehensively considered in combination with various factors, and the market mechanism should be utilized to optimize the allocation.

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Spatiotemporal Variability of Extreme Summer Precipitation over the Yangtze River Basin and the Associations with Climate Patterns

Cited by 26 publications

References 39 publications

Archetypal Analysis With Missing Data: See All Samples by Looking at a Few Based on Extreme Profiles

Archetypal Analysis With Missing Data: See All Samples by Looking at a Few Based on Extreme Profiles

A Comprehensive Study of Spatiotemporal Variations in Temperature Extremes across China during 1960–2018

Research on the Allocation of Flood Drainage Rights of the Sunan Canal Based on a Bi-level Multi-Objective Programming Model

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