scholarly journals Cumulative and time-lag effects of the main climate factors on natural vegetation across Siberia

2021 ◽  
Vol 133 ◽  
pp. 108446
Author(s):  
Shangyu Shi ◽  
Ping Wang ◽  
Yichi Zhang ◽  
Jingjie Yu
Keyword(s):  
Time Lag ◽  
Natural Vegetation ◽  
Climate Factors ◽  
Lag Effects

scholarly journals Warming enhanced the climate temporal effects on vegetation across Siberia

2021 ◽  
Author(s):  
Shangyu Shi ◽  
Ping Wang ◽  
Yichi Zhang ◽  
Jingjie Yu
Keyword(s):  
Solar Radiation ◽  
Northern Hemisphere ◽  
Land Surface ◽  
Vegetation Index ◽  
Time Lag ◽  
Climate Factors ◽  
Lag Effects ◽  
Temporal Effects ◽  
Cumulative Duration ◽  
Complex Ecosystem

Abstract Widespread climate warming and growing season greening have been observed across most of the Northern Hemisphere in recent decades. However, the response of greening to amplified warming remains unclear with regard to the temporal effects (cumulative and time-lag effects) in cold regions of the Northern Hemisphere. We therefore provide a comprehensive analysis of the relationship between the enhanced vegetation index (EVI) and climate factors (e.g., land surface temperature (LST), precipitation, and solar radiation) across Siberia, which is experiencing rapid warming and greening, during 2000–2016 by using a random forest regression model. We found that solar radiation was the dominant driving factor of vegetation greening in 55.95% of the study area (concentrated in southwest Siberia), followed by temperature (41.28%, concentrated in northeast Siberia) and precipitation (2.78%). Furthermore, the cumulative and time-lag effects increased the explanation of vegetation variation by climate factors, from 0.71 to 0.78, and were observed in more than 80% of the area of Siberia. The average cumulative duration was 3.61 ± 1.97 months, and the time-lag period was 1.51 ± 1.20 months, with the longest term found in deciduous broadleaf forests and the shortest term found in shrublands. Our results indicated that the vegetation activities were influenced by cumulative effects combined with time-lag effects. The temporal effects varied with land cover categories, and the complex ecosystem generally corresponded to long-term temporal effects, and vice versa. Hence, the tundra shrub converted to boreal forest caused by warming in Siberia enhanced the climate temporal effects.

scholarly journals Analysis of the Time-lag Effects of Climate Factors on Grassland Productivity in Inner Mongolia

2021 ◽  
pp. e01751
Author(s):  
Haixin Liu ◽  
Anbing Zhang ◽  
Chao Liu ◽  
Yuling Zhao ◽  
Anzhou Zhao ◽  
...  
Keyword(s):  
Inner Mongolia ◽  
Time Lag ◽  
Climate Factors ◽  
Lag Effects ◽  
Grassland Productivity

scholarly journals A Weighted-Time-Lag Method to Detect Lag Vegetation Response to Climate Variation: A Case Study in Loess Plateau, China, 1982–2013

2021 ◽  
Vol 13 (5) ◽  
pp. 923
Author(s):  
Qianqian Sun ◽  
Chao Liu ◽  
Tianyang Chen ◽  
Anbing Zhang
Keyword(s):  
Climate Change ◽  
Loess Plateau ◽  
Time Lag ◽  
New Method ◽  
Climate Factors ◽  
The Loess Plateau ◽  
Climate Conditions ◽  
Vegetation Growth ◽  
Lag Effect

Vegetation fluctuation is sensitive to climate change, and this response exhibits a time lag. Traditionally, scholars estimated this lag effect by considering the immediate prior lag (e.g., where vegetation in the current month is impacted by the climate in a certain prior month) or the lag accumulation (e.g., where vegetation in the current month is impacted by the last several months). The essence of these two methods is that vegetation growth is impacted by climate conditions in the prior period or several consecutive previous periods, which fails to consider the different impacts coming from each of those prior periods. Therefore, this study proposed a new approach, the weighted time-lag method, in detecting the lag effect of climate conditions coming from different prior periods. Essentially, the new method is a generalized extension of the lag-accumulation method. However, the new method detects how many prior periods need to be considered and, most importantly, the differentiated climate impact on vegetation growth in each of the determined prior periods. We tested the performance of the new method in the Loess Plateau by comparing various lag detection methods by using the linear model between the climate factors and the normalized difference vegetation index (NDVI). The case study confirmed four main findings: (1) the response of vegetation growth exhibits time lag to both precipitation and temperature; (2) there are apparent differences in the time lag effect detected by various methods, but the weighted time-lag method produced the highest determination coefficient (R2) in the linear model and provided the most specific lag pattern over the determined prior periods; (3) the vegetation growth is most sensitive to climate factors in the current month and the last month in the Loess Plateau but reflects a varied of responses to other prior months; and (4) the impact of temperature on vegetation growth is higher than that of precipitation. The new method provides a much more precise detection of the lag effect of climate change on vegetation growth and makes a smart decision about soil conservation and ecological restoration after severe climate events, such as long-lasting drought or flooding.

A Multi-Period Input DEA Model with Consistent Time Lag Effects

2019 ◽  
Vol 42 (3) ◽  
pp. 8-14
Author(s):  
Byungho Jeong ◽  
◽  
Yanshuang Zhang ◽  
Taehan Lee
Keyword(s):  
Time Lag ◽  
Dea Model ◽  
Lag Effects

scholarly journals Nonuniform Time-Lag Effects of Asymmetric Warming on Net Primary Productivity across Global Terrestrial Biomes

2018 ◽  
Vol 22 (8) ◽  
pp. 1-26 ◽  
Author(s):  
Youyue Wen ◽  
Xiaoping Liu ◽  
Guoming Du
Keyword(s):  
Partial Correlation ◽  
Net Primary Productivity ◽  
Net Primary Production ◽  
Time Lag ◽  
Correlation Coefficients ◽  
Terrestrial Ecosystem ◽  
Terrestrial Vegetation ◽  
Vegetation Growth ◽  
Lag Effects ◽  
Terrestrial Biomes

Abstract Climate warming exhibits asymmetric patterns over a diel time, with the trend of nighttime warming exceeding that of daytime warming, a phenomenon commonly known as asymmetric warming. Recently, increasing studies have documented the significant instantaneous impacts of asymmetric warming on terrestrial vegetation growth, but the indirect effects of asymmetric warming carrying over vegetation growth (referred to here as time-lag effects) remain unknown. Here, we quantitatively studied the time-lag effects (within 1 year) of asymmetric warming on global plant biomes by using terrestrial vegetation net primary production (NPP) derived by the Carnegie–Ames–Stanford Approach (CASA) model and accumulated daytime and nighttime temperature (ATmax and ATmin) from 1982 to 2013. Partial correlation and time-lag analyses were conducted at a monthly scale to obtain the partial correlation coefficients between NPP and ATmax/ATmin and the lagged durations of NPP responses to ATmax/ATmin. The results showed that (i) asymmetric warming has nonuniform time-lag effects on single plant biomes, and distinguishing correlations exist in different vegetation biomes’ associations to asymmetric warming; (ii) terrestrial biomes respond to ATmax (4.63 ± 3.92 months) with a shorter protracted duration than to ATmin (6.06 ± 4.27 months); (iii) forest biomes exhibit longer prolonged duration in responding to asymmetric warming than nonforest biomes do; (iv) mosses and lichens (Mosses), evergreen needleleaf forests (ENF), deciduous needleleaf forests (DNF), and mixed forests (MF) tend to positively correlate with ATmax, whereas the other biomes associate with ATmax with near-equal splits of positive and negative correlation; and (v) ATmin has a predominantly positive influence on terrestrial biomes, except for Mosses and DNF. This study provides a new perspective on terrestrial ecosystem responses to asymmetric warming and highlights the importance of including such nonuniform time-lag effects into currently used terrestrial ecosystem models during future investigations of vegetation–climate interactions.

Time-Lag Effects in Urban Environment Adaptation as a Warning System for Urban Growth Control: An Application to Flanders

1970 ◽  
Vol 2 (3) ◽  
pp. 303-322 ◽  
Author(s):  
M. van Naelten
Keyword(s):  
Factor Model ◽  
Time Lag ◽  
Growth Control ◽  
Warning System ◽  
Basic Point ◽  
Lag Effects ◽  
Environment Adaptation ◽  
Subsequent Measurement ◽  
Urban Growth Control ◽  
Urban Characteristics

In this paper we wish to discuss some aspects of a particular system approach in urban planning. An attempt has been made to explain the meaning of the first principal factor (Hotelling, 1933) in the verification of a set of supposed urban characteristics. The same factor model has been used in the subsequent measurement of the degree of urbanity in each municipal territory in Flanders. In mapping the results we have also attempted to verify some growth and communication theories for the Flanders case. Finally, the basic point of the paper is the detection of time-lag effects which create gaps between the slower development of more rigid environment elements, with which the planner is concerned, and the more quickly adapting elements—a time lag which could indicate urgent planning areas.

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