scholarly journals Temporal and spatial variation of maximum wind speed days during the past 20 years in major cities of Xinjiang

2018 ◽  
Author(s):  
Aliya Baidourela ◽  
Zhen Jing ◽  
Kahaer Zhayimu ◽  
Adili Abulaiti ◽  
Hakezi Ubuli

2014 ◽  
Vol 33 (11) ◽  
pp. 48-54 ◽  
Author(s):  
Shuzong Han ◽  
Huirong Zhang ◽  
Yunxia Zheng


2017 ◽  
Vol 8 (3) ◽  
pp. 529-545 ◽  
Author(s):  
Ari Venäläinen ◽  
Mikko Laapas ◽  
Pentti Pirinen ◽  
Matti Horttanainen ◽  
Reijo Hyvönen ◽  
...  

Abstract. The bioeconomy has an increasing role to play in climate change mitigation and the sustainable development of national economies. In Finland, a forested country, over 50 % of the current bioeconomy relies on the sustainable management and utilization of forest resources. Wind storms are a major risk that forests are exposed to and high-spatial-resolution analysis of the most vulnerable locations can produce risk assessment of forest management planning. In this paper, we examine the feasibility of the wind multiplier approach for downscaling of maximum wind speed, using 20 m spatial resolution CORINE land-use dataset and high-resolution digital elevation data. A coarse spatial resolution estimate of the 10-year return level of maximum wind speed was obtained from the ERA-Interim reanalyzed data. Using a geospatial re-mapping technique the data were downscaled to 26 meteorological station locations to represent very diverse environments. Applying a comparison, we find that the downscaled 10-year return levels represent 66 % of the observed variation among the stations examined. In addition, the spatial variation in wind-multiplier-downscaled 10-year return level wind was compared with the WAsP model-simulated wind. The heterogeneous test area was situated in northern Finland, and it was found that the major features of the spatial variation were similar, but in some locations, there were relatively large differences. The results indicate that the wind multiplier method offers a pragmatic and computationally feasible tool for identifying at a high spatial resolution those locations with the highest forest wind damage risks. It can also be used to provide the necessary wind climate information for wind damage risk model calculations, thus making it possible to estimate the probability of predicted threshold wind speeds for wind damage and consequently the probability (and amount) of wind damage for certain forest stand configurations.





Author(s):  
Masataka YAMAGUCHI ◽  
Kunimitsu INOUCHI ◽  
Yoshihiro UTSUNOMIYA ◽  
Hirokazu NONAKA ◽  
Yoshio HATADA ◽  
...  


Author(s):  
Masafumi KIMIZUKA ◽  
Tomotsuka TAKAYAMA ◽  
Hiroyasu KAWAI ◽  
Masafumi MIYATA ◽  
Katsuya HIRAYAMA ◽  
...  


Paleobiology ◽  
2021 ◽  
Vol 47 (2) ◽  
pp. 171-177
Author(s):  
James C. Lamsdell ◽  
Curtis R. Congreve

The burgeoning field of phylogenetic paleoecology (Lamsdell et al. 2017) represents a synthesis of the related but differently focused fields of macroecology (Brown 1995) and macroevolution (Stanley 1975). Through a combination of the data and methods of both disciplines, phylogenetic paleoecology leverages phylogenetic theory and quantitative paleoecology to explain the temporal and spatial variation in species diversity, distribution, and disparity. Phylogenetic paleoecology is ideally situated to elucidate many fundamental issues in evolutionary biology, including the generation of new phenotypes and occupation of previously unexploited environments; the nature of relationships among character change, ecology, and evolutionary rates; determinants of the geographic distribution of species and clades; and the underlying phylogenetic signal of ecological selectivity in extinctions and radiations. This is because phylogenetic paleoecology explicitly recognizes and incorporates the quasi-independent nature of evolutionary and ecological data as expressed in the dual biological hierarchies (Eldredge and Salthe 1984; Congreve et al. 2018; Fig. 1), incorporating both as covarying factors rather than focusing on one and treating the other as error within the dataset.



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