Climate change and its effects on grassland productivity and carrying capacity of livestock in the main grasslands of China

2012 ◽  
Vol 34 (4) ◽  
pp. 341 ◽  
Author(s):  
S. Qian ◽  
L. Y. Wang ◽  
X. F. Gong
Keyword(s):  
Climate Change ◽  
Carrying Capacity ◽  
Meteorological Data ◽  
Growing Season ◽  
Average Decrease ◽  
South West ◽  
Rate Of Increase ◽  
Humidity Index ◽  
Grassland Productivity ◽  
Increase In Productivity

Climate change and its effects on grassland productivity and potential carrying capacity of livestock were systemically studied using AVIM-GRASS and other models, using daily meteorological data for the period from 1961 to 2007 for 70% (275 million ha) of grasslands across China. The results showed an overall trend for increasing temperatures per year and in the length of the grass-growing season from April to September. Sunlight hours decreased in most places. Precipitation and a humidity index had decreased in the northern grasslands of China, where the climate has become warmer and drier, and had increased in the western grasslands, where the climate has become warmer and wetter. Changes to a warmer and drier climate in the more productive northern grasslands resulted in a decrease in annual available herbage production and the carrying capacity of livestock. The greatest reductions in productivity have been in middle and east Inner Mongolia and south-east Gansu. Where there had been a trend for a warmer and wetter climate in western grassland areas, the trend in available herbage production and carrying capacity of livestock has been for a small increase or none at all. The largest rate of increase in productivity was in south-west Xinjiang and east Xizang. Annual available herbage production and carrying capacity of livestock decreased in north and east Xinjiang and south Qinghai where there was very little increase in precipitation. Overall, climate change has resulted in an average decrease in annual available herbage production and carrying capacity of livestock in most of the main grassland areas in China from 1961 to 2007.

scholarly journals The Impact of Climate Change on the Surface Albedo over the Qinghai-Tibet Plateau

2021 ◽  
Vol 13 (12) ◽  
pp. 2336
Author(s):  
Chaonan Chen ◽  
Li Tian ◽  
Lianqi Zhu ◽  
Yuanke Zhou
Keyword(s):  
Climate Change ◽  
Solar Radiation ◽  
Land Surface ◽  
Meteorological Data ◽  
Growing Season ◽  
High Elevation ◽  
Tibet Plateau ◽  
Decrease Rate ◽  
The Impact ◽  
Qinghai Tibet Plateau

Albedo is a characterization of the Earth’s surface ability to reflect solar radiation, and control the amount of solar radiation absorbed by the land surface. Within the context of global warming, the temporal and spatial changes of the albedo and its response to climate factors remain unclear. Based on MCD43A3 (V005) albedo and meteorological data (i.e., temperature and precipitation), we analyzed the spatiotemporal variations of albedo (2000–2016) and its responses to climate change during the growing season on the Qinghai-Tibet Plateau (QTP). The results indicated an overall downward trend in the annual albedo during the growing season, the decrease rate was 0.25%/decade, and the monthly albedo showed a similar trend, especially in May, when the decrease rate was 0.53%/decade. The changes also showed regional variations, such as for the annual albedo, the areas with significant decrease and increase in albedo were 181.52 × 103 km2 (13.10%) and 48.82 × 103 km2 (3.52%), respectively, and the intensity of albedo changes in low-elevation areas was more pronounced than in high-elevation areas. In addition, the annual albedo-temperature/precipitation relationships clearly differed at different elevations. The albedo below 2000 m and at 5000–6000 m was mainly negatively correlated with temperature, while at 2000–4000 m it was mainly negatively correlated with precipitation. The contemporaneous temperature could negatively impact the monthly albedo in significant ways at the beginning of the growing season (May and June), whereas in the middle of the growing season (July and August), the albedo was mainly negatively correlated with precipitation, and at the end of the growing season (September), the albedo showed a weak correlation with temperature/precipitation.

The Analysis of Vegetation Phenology and Climate Change in the North-South Transect of Eastern China

2014 ◽  
Vol 1010-1012 ◽  
pp. 1230-1233 ◽  
Author(s):  
Zhi Wang ◽  
Shi Rong Liu
Keyword(s):  
Climate Change ◽  
Spatial Pattern ◽  
Meteorological Data ◽  
Eastern China ◽  
Terrestrial Ecosystems ◽  
Growing Season ◽  
Distribution Patterns ◽  
Observation Data ◽  
The North ◽  
Temperate Desert

In order to explore additional distribution patterns of global change to terrestrial ecosystems, phenology refers to seasonal biological life stages driven by environmental factors, and is considered to be a sensitive and precise indicator of climate change. Therefore, the author developed a ‘bottom-up’ method for first determining the phenological growing season at sample stations, and based on NOAA/AVVHRR, meteorological data, ground phonology observation data, vegetation category data, and so on. The author built a Logistic fitting model on cumulative frequency of NDVI to determine length of greenness period since 1982, then analyzed correlation between NDVI and precipitation, primarily revealed the dynamic mechanism of climate on vegetation. The spatial pattern of average turning green and wilting dates of the growing season correlated significantly with the spatial pattern of average temperatures in spring and winter across the north south transect of eastern China during 1982 to 2003; the growing season extended on average by 5 to 8 days . Temperate desert regions had the trend of increase of desertification.

Prediction of the livestock carrying capacity using neural network in the meadow steppe

2019 ◽  
Vol 41 (1) ◽  
pp. 65 ◽  
Author(s):  
T. S. Wu ◽  
H. P. Fu ◽  
G. Jin ◽  
H. F. Wu ◽  
H. M. Bai
Keyword(s):  
Neural Network ◽  
Carrying Capacity ◽  
Vegetation Index ◽  
Meteorological Data ◽  
Back Propagation ◽  
Remote Sensing Data ◽  
Growing Season ◽  
Back Propagation Neural Network ◽  
Meadow Steppe ◽  
Normalised Difference Vegetation Index

In order to predict the livestock carrying capacity in meadow steppe, a method using back propagation neural network is proposed based on the meteorological data and the remote-sensing data of Normalised Difference Vegetation Index. In the proposed method, back propagation neural network was first utilised to build a behavioural model to forecast precipitation during the grass-growing season (June–July–August) from 1961 to 2015. Second, the relationship between precipitation and Normalised Difference Vegetation Index during the grass-growing season from 2000 to 2015 was modelled with the help of back propagation neural network. The prediction results demonstrate that the proposed back propagation neural network-based model is effective in the forecast of precipitation and Normalised Difference Vegetation Index. Thus, an accurate prediction of livestock carrying capacity is achieved based on the proposed back propagation neural network-based model. In short, this work can be used to improve the utilisation of grassland and prevent the occurrence of vegetation degradation by overgrazing in drought years for arid and semiarid grasslands.

scholarly journals Comparison of the Radial Growth Response of Picea crassifolia to Climate Change in Different Regions of the Central and Eastern Qilian Mountains

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1015
Author(s):  
Xuan Wu ◽  
Liang Jiao ◽  
Dashi Du ◽  
Changliang Qi ◽  
Ruhong Xue
Keyword(s):  
Climate Change ◽  
Tree Growth ◽  
Central Region ◽  
Radial Growth ◽  
Growing Season ◽  
Qilian Mountains ◽  
Growth Patterns ◽  
Total Precipitation ◽  
Picea Crassifolia ◽  
The Qilian Mountains

It is important to explore the responses of radial tree growth in different regions to understand growth patterns and to enhance forest management and protection with climate change. We constructed tree ring width chronologies of Picea crassifolia from different regions of the Qilian Mountains of northwest China. We used Pearson correlation and moving correlation to analyze the main climate factors limiting radial growth of trees and the temporal stability of the growth–climate relationship, while spatial correlation is the result of further testing the first two terms in space. The conclusions were as follows: (1) Radial growth had different trends, showing an increasing followed by a decreasing trend in the central region, a continuously increasing trend in the eastern region, and a gradually decreasing trend in the isolated mountain. (2) Radial tree growth in the central region and isolated mountains was constrained by drought stress, and tree growth in the central region was significantly negatively correlated with growing season temperature. Isolated mountains showed a significant negative correlation with mean minimum of growing season and a significant positive correlation with total precipitation. (3) Temporal dynamic responses of radial growth in the central region to the temperatures and SPEI (the standardized precipitation evapotranspiration index) in the growing season were unstable, the isolated mountains to total precipitation was unstable, and that to SPEI was stable. The results of this study suggest that scientific management and maintenance plans of the forest ecosystem should be developed according to the response and growth patterns of the Qinghai spruce to climate change in different regions of the Qilian Mountains.

scholarly journals Characterizing Growing Season Length of Subtropical Coniferous Forests with a Phenological Model

Forests ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 95
Author(s):  
Yuan Gong ◽  
Christina L. Staudhammer ◽  
Susanne Wiesner ◽  
Gregory Starr ◽  
Yinlong Zhang
Keyword(s):  
Climate Change ◽  
Soil Water ◽  
Prescribed Fire ◽  
Water Availability ◽  
Longleaf Pine ◽  
Growing Season ◽  
Soil Water Availability ◽  
Future Climate Change ◽  
Short Term ◽  
Phenological Model

Understanding plant phenological change is of great concern in the context of global climate change. Phenological models can aid in understanding and predicting growing season changes and can be parameterized with gross primary production (GPP) estimated using the eddy covariance (EC) technique. This study used nine years of EC-derived GPP data from three mature subtropical longleaf pine forests in the southeastern United States with differing soil water holding capacity in combination with site-specific micrometeorological data to parameterize a photosynthesis-based phenological model. We evaluated how weather conditions and prescribed fire led to variation in the ecosystem phenological processes. The results suggest that soil water availability had an effect on phenology, and greater soil water availability was associated with a longer growing season (LOS). We also observed that prescribed fire, a common forest management activity in the region, had a limited impact on phenological processes. Dormant season fire had no significant effect on phenological processes by site, but we observed differences in the start of the growing season (SOS) between fire and non-fire years. Fire delayed SOS by 10 d ± 5 d (SE), and this effect was greater with higher soil water availability, extending SOS by 18 d on average. Fire was also associated with increased sensitivity of spring phenology to radiation and air temperature. We found that interannual climate change and periodic weather anomalies (flood, short-term drought, and long-term drought), controlled annual ecosystem phenological processes more than prescribed fire. When water availability increased following short-term summer drought, the growing season was extended. With future climate change, subtropical areas of the Southeastern US are expected to experience more frequent short-term droughts, which could shorten the region’s growing season and lead to a reduction in the longleaf pine ecosystem’s carbon sequestration capacity.

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