A resilience-based analysis on the spatial heterogeneity of vegetation restoration and its affecting factors in the construction of eco-cities: a case study of Shangluo, Shaanxi

2015 ◽  
Vol 35 (13) ◽  
Author(s):  
温晓金 WEN Xiaojin ◽  
刘焱序 LIU Yanxu ◽  
杨新军 YANG Xinjun
Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1296 ◽  
Author(s):  
Huiying Ren ◽  
Z. Jason Hou ◽  
Mark Wigmosta ◽  
Ying Liu ◽  
L. Ruby Leung

Changes in extreme precipitation events may require revisions of civil engineering standards to prevent water infrastructures from performing below the designated guidelines. Climate change may invalidate the intensity-duration-frequency (IDF) computation that is based on the assumption of data stationarity. Efforts in evaluating non-stationarity in the annual maxima series are inadequate, mostly due to the lack of long data records and convenient methods for detecting trends in the higher moments. In this study, using downscaled high resolution climate simulations of the historical and future periods under different carbon emission scenarios, we tested two solutions to obtain reliable IDFs under non-stationarity: (1) identify quasi-stationary time windows from the time series of interest to compute the IDF curves using data for the corresponding time windows; (2) introduce a parameter representing the trend in the means of the extreme value distributions. Focusing on a mountainous site, the Walker Watershed, the spatial heterogeneity and variability of IDFs or extremes are evaluated, particularly in terms of the terrain and elevation impacts. We compared observations-based IDFs that use the stationarity assumption with the two approaches that consider non-stationarity. The IDFs directly estimated based on the traditional stationarity assumption may underestimate the 100-year 24-h events by 10% to 60% towards the end of the century at most grids, resulting in significant under-designing of the engineering infrastructure at the study site. Strong spatial heterogeneity and variability in the IDF estimates suggest a preference for using high resolution simulation data for the reliable estimation of exceedance probability over data from sparsely distributed weather stations. Discrepancies among the three IDFs analyses due to non-stationarity are comparable to the spatial variability of the IDFs, underscoring a need to use an ensemble of non-stationary approaches to achieve unbiased and comprehensive IDF estimates.


2022 ◽  
Vol 17 (s1) ◽  
Author(s):  
Michał Paweł Michalak ◽  
Jack Cordes ◽  
Agnieszka Kulawik ◽  
Sławomir Sitek ◽  
Sławomir Pytel ◽  
...  

Spatiotemporal modelling of infectious diseases such as coronavirus disease 2019 (COVID-19) involves using a variety of epidemiological metrics such as regional proportion of cases and/or regional positivity rates. Although observing changes of these indices over time is critical to estimate the regional disease burden, the dynamical properties of these measures, as well as crossrelationships, are usually not systematically given or explained. Here we provide a spatiotemporal framework composed of six commonly used and newly constructed epidemiological metrics and conduct a case study evaluation. We introduce a refined risk estimate that is biased neither by variation in population size nor by the spatial heterogeneity of testing. In particular, the proposed methodology would be useful for unbiased identification of time periods with elevated COVID-19 risk without sensitivity to spatial heterogeneity of neither population nor testing coverage.We offer a case study in Poland that shows improvement over the bias of currently used methods. Our results also provide insights regarding regional prioritisation of testing and the consequences of potential synchronisation of epidemics between regions. The approach should apply to other infectious diseases and other geographical areas.


2018 ◽  
Vol 11 (2) ◽  
pp. 574-592 ◽  
Author(s):  
Elham Amini ◽  
Mohammad Baniasadi ◽  
Hossein Vahidi ◽  
Hossein Nematollahi ◽  
Mehrdad Khatami ◽  
...  

2015 ◽  
Vol 35 (3) ◽  
Author(s):  
刘二佳 LIU Erjia ◽  
张晓萍 ZHANG Xiaoping ◽  
谢名礼 XIE Mingli ◽  
陈妮 CHEN Ni ◽  
张亭亭 ZHANG Tingting ◽  
...  

2018 ◽  
Vol 38 (13) ◽  
Author(s):  
蔺琛 LIN Chen ◽  
龚明昊 GONG Minghao ◽  
刘洋 LIU Yang ◽  
潘旭 PAN Xu ◽  
朴正吉 PIAO Zhengji

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