scholarly journals Climate change increases risk of fusarium ear blight on wheat in central China

2014 ◽  
Vol 164 (3) ◽  
pp. 384-395 ◽  
Author(s):  
X. Zhang ◽  
J. Halder ◽  
R.P. White ◽  
D.J. Hughes ◽  
Z. Ye ◽  
...  
Keyword(s):  
Climate Change ◽  
Central China ◽  
Ear Blight

scholarly journals Impact of Climate Change on Potential Distribution of Chinese White Pine Beetle Dendroctonus armandi in China

Forests ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 544
Author(s):  
Hang Ning ◽  
Ming Tang ◽  
Hui Chen
Keyword(s):  
Climate Change ◽  
Potential Distribution ◽  
Greenhouse Gas Emission ◽  
Central China ◽  
Qinling Mountains ◽  
Climate Scenarios ◽  
Impact Of Climate Change ◽  
Precipitation Seasonality ◽  
Suitable Area ◽  
The Impact

Dendroctonus armandi (Coleoptera: Curculionidae: Scolytidae) is a bark beetle native to China and is the most destructive forest pest in the Pinus armandii woodlands of central China. Due to ongoing climate warming, D. armandi outbreaks have become more frequent and severe. Here, we used Maxent to model its current and future potential distribution in China. Minimum temperature of the coldest month and precipitation seasonality are the two major factors constraining the current distribution of D. armandi. Currently, the suitable area of D. armandi falls within the Qinling Mountains and Daba Mountains. The total suitable area is 15.83 × 104 km2. Under future climate scenarios, the total suitable area is projected to increase slightly, while remaining within the Qinling Mountains and Daba Mountains. Among the climate scenarios, the distribution expanded the most under the maximum greenhouse gas emission scenario (representative concentration pathway (RCP) 8.5). Under all assumptions, the highly suitable area is expected to increase over time; the increase will occur in southern Shaanxi, northwest Hubei, and northeast Sichuan Provinces. By the 2050s, the highly suitable area is projected to increase by 0.82 × 104 km2. By the 2050s, the suitable climatic niche for D. armandi will increase along the Qinling Mountains and Daba Mountains, posing a major challenge for forest managers. Our findings provide information that can be used to monitor D. armandi populations, host health, and the impact of climate change, shedding light on the effectiveness of management responses.

scholarly journals Observed Changes of Rain-Season Precipitation in China from 1960 to 2018

2021 ◽  
Vol 18 (19) ◽  
pp. 10031
Author(s):  
Yanyu Zhang ◽  
Shuying Zang ◽  
Xiangjin Shen ◽  
Gaohua Fan
Keyword(s):  
Climate Change ◽  
Global Climate ◽  
Regional Climate ◽  
Precipitation Amount ◽  
Heavy Precipitation ◽  
Central China ◽  
The Tibetan Plateau ◽  
Natural Ecosystems ◽  
Daily Precipitation Data ◽  
Rain Season

Precipitation during the main rain season is important for natural ecosystems and human activities. In this study, according to daily precipitation data from 515 weather stations in China, we analyzed the spatiotemporal variation of rain-season (May–September) precipitation in China from 1960 to 2018. The results showed that rain-season precipitation decreased over China from 1960 to 2018. Rain-season heavy (25 ≤ p < 50 mm/day) and very heavy (p ≥ 50 mm/day) precipitation showed increasing trends, while rain-season moderate (10 ≤ p < 25 mm/day) and light (0.1 ≤ p < 10 mm/day) precipitation showed decreasing trends from 1960 to 2018. The temporal changes of precipitation indicated that rain-season light and moderate precipitation displayed downward trends in China from 1980 to 2010 and rain-season heavy and very heavy precipitation showed fluctuant variation from 1960 to 2018. Changes of rain-season precipitation showed clear regional differences. Northwest China and the Tibetan Plateau showed the largest positive trends of precipitation amount and days. In contrast, negative trends were found for almost all precipitation grades in North China Plain, Northeast China, and North Central China. Changes toward drier conditions in these regions probably had a severe impact on agricultural production. In East China, Southeast China and Southwest China, heavy and very heavy precipitation had increased while light and moderate precipitation had decreased. This result implied an increasing risk of flood and mudslides in these regions. The advance in understanding of precipitation change in China will contribute to exactly predict the regional climate change under the background of global climate change.

scholarly journals Forest Phenology Dynamics to Climate Change and Topography in a Geographic and Climate Transition Zone: The Qinling Mountains in Central China

Forests ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 1007 ◽  
Author(s):  
Haoming Xia ◽  
Yaochen Qin ◽  
Gary Feng ◽  
Qingmin Meng ◽  
Yaoping Cui ◽  
...  
Keyword(s):  
Climate Change ◽  
Vegetation Index ◽  
Climate Change Impacts ◽  
Growing Season ◽  
Biological Indicators ◽  
Central China ◽  
Qinling Mountains ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Climate Transition

Forest ecosystems in an ecotone and their dynamics to climate change are growing ecological and environmental concerns. Phenology is one of the most critical biological indicators of climate change impacts on forest dynamics. In this study, we estimated and visualized the spatiotemporal patterns of forest phenology from 2001 to 2017 in the Qinling Mountains (QMs) based on the enhanced vegetation index (EVI) from MODerate-resolution Imaging Spectroradiometer (MODIS). We further analyzed this data to reveal the impacts of climate change and topography on the start of the growing season (SOS), end of the growing season (EOS), and the length of growing season (LOS). Our results showed that forest phenology metrics were very sensitive to changes in elevation, with a 2.4 days delayed SOS, 1.4 days advanced EOS, and 3.8 days shortened LOS for every 100 m increase in altitude. During the study period, on average, SOS advanced by 0.13 days year−1, EOS was delayed by 0.22 days year−1, and LOS increased by 0.35 day year−1. The phenological advanced and delayed speed across different elevation is not consistent. The speed of elevation-induced advanced SOS increased slightly with elevation, and the speed of elevation-induced delayed EOS shift reached a maximum value of 1500 m from 2001 to 2017. The sensitivity of SOS and EOS to preseason temperature displays that an increase of 1 °C in the regionally averaged preseason temperature would advance the average SOS by 1.23 days and delay the average EOS by 0.72 days, respectively. This study improved our understanding of the recent variability of forest phenology in mountain ecotones and explored the correlation between forest phenology and climate variables in the context of the ongoing climate warming.

scholarly journals Synoptic meteorological modes of variability for fine particulate matter (PM<sub>2.5</sub>) air quality in major metropolitan regions of China

2018 ◽  
Vol 18 (9) ◽  
pp. 6733-6748 ◽  
Author(s):  
Danny M. Leung ◽  
Amos P. K. Tai ◽  
Loretta J. Mickley ◽  
Jonathan M. Moch ◽  
Aaron van Donkelaar ◽  
...  
Keyword(s):  
Climate Change ◽  
Relative Humidity ◽  
Negative Correlation ◽  
Southern China ◽  
Central China ◽  
Meteorological Variables ◽  
Strong Correlations ◽  
Siberian High ◽  
Synoptic Weather ◽  
Bth Region

Abstract. In his study, we use a combination of multivariate statistical methods to understand the relationships of PM2.5 with local meteorology and synoptic weather patterns in different regions of China across various timescales. Using June 2014 to May 2017 daily total PM2.5 observations from ∼ 1500 monitors, all deseasonalized and detrended to focus on synoptic-scale variations, we find strong correlations of daily PM2.5 with all selected meteorological variables (e.g., positive correlation with temperature but negative correlation with sea-level pressure throughout China; positive and negative correlation with relative humidity in northern and southern China, respectively). The spatial patterns suggest that the apparent correlations with individual meteorological variables may arise from common association with synoptic systems. Based on a principal component analysis of 1998–2017 meteorological data to diagnose distinct meteorological modes that dominate synoptic weather in four major regions of China, we find strong correlations of PM2.5 with several synoptic modes that explain 10 to 40 % of daily PM2.5 variability. These modes include monsoonal flows and cold frontal passages in northern and central China associated with the Siberian High, onshore flows in eastern China, and frontal rainstorms in southern China. Using the Beijing–Tianjin–Hebei (BTH) region as a case study, we further find strong interannual correlations of regionally averaged satellite-derived annual mean PM2.5 with annual mean relative humidity (RH; positive) and springtime fluctuation frequency of the Siberian High (negative). We apply the resulting PM2.5-to-climate sensitivities to the Intergovernmental Panel on Climate Change (IPCC) Coupled Model Intercomparison Project Phase 5 (CMIP5) climate projections to predict future PM2.5 by the 2050s due to climate change, and find a modest decrease of ∼ 0.5 µg m−3 in annual mean PM2.5 in the BTH region due to more frequent cold frontal ventilation under the RCP8.5 future, representing a small “climate benefit”, but the RH-induced PM2.5 change is inconclusive due to the large inter-model differences in RH projections.

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