scholarly journals Sensitivity of Vegetation on Alpine and Subalpine Timberline in Qinling Mountains to Temperature Change

Forests ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 1105
Author(s):  
Xinping Ma ◽  
Hongying Bai ◽  
Chenhui Deng ◽  
Tao Wu
Keyword(s):  
Climate Change ◽  
Temperature Change ◽  
Air Temperature ◽  
Early Warning ◽  
Temperature Response ◽  
Warning Signs ◽  
Qinling Mountains ◽  
Vegetation Types ◽  
Vegetation Activity ◽  
Taibai Mountain

Alpine timberline is a great place for monitoring climate change. The study of alpine and subalpine timberline in Qinling Mountains has led to early warning that reveals the response and adaptation of terrestrial vegetation ecosystem to climate change. Based on the remote sensing image classification method, the typical timberline area in Qinling Mountains was determined. Temperature and normalized difference vegetation index (NDVI) data were extracted from the typical timberline area based on spatial interpolation and NDVI data. The relationship between NDVI and temperature change and the critical temperature value affecting vegetation response in the timberline area in Qinling Mountains were analyzed. Correlation between NDVI and air temperature in the alpine and subalpine timberline areas of Qinling Mountains exhibited an upward trend, which implied that temperature promotes vegetation activity. A strong correlation between temperature and NDVI in typical timberline areas of Qinling Mountains, and a significant correlation between temperature and NDVI in the early growing season. A phenomenon of NDVI lagging behind air temperature was observed. Temperature response showed synchronization and hysteresis. The correlation between cumulative temperature and vegetation was similar between Taibai Mountain and Niubeiliang timberline, and the correlation between NDVI in April and cumulative temperature in the first 12 months was the strongest. Temperature threshold range of Taibai Mountain timberline played a dominant role in vegetation growth. Our results provide insights and basis for future studies of early warning signs of climate change, specifically between 0.34 and 1.34 °C. The threshold ranges of temperature response of different vegetation types vary. Compared with alpine shrub meadow, the threshold ranges of temperature effect of Coniferous forest and Larix chinensis Beissn. are smaller, implying that these vegetation types are more sensitive to temperature change.

scholarly journals Specific Drivers and Responses to Land Surface Phenology of Different Vegetation Types in the Qinling Mountains, Central China

2021 ◽  
Vol 13 (22) ◽  
pp. 4538
Author(s):  
Jiaqi Guo ◽  
Xiaohong Liu ◽  
Wensen Ge ◽  
Xiaofeng Ni ◽  
Wenyuan Ma ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Meteorological Factors ◽  
Growing Season ◽  
Central China ◽  
Shortwave Radiation ◽  
Qinling Mountains ◽  
Vegetation Types ◽  
Land Surface Phenology ◽  
Vegetation Growth

Land surface phenology (LSP), as a precise bio-indicator that responds to climate change, has received much attention in fields concerned with climate change and ecology. Yet, the dynamics of LSP changes in the Qinling Mountains (QMs)—A transition zone between warm-temperate and north subtropical climates with complex vegetation structure—under significant climatic environmental evolution are unclear. Here, we analyzed the spatiotemporal dynamics of LSP for different vegetation types in the QMs from 2001 to 2019 and quantified the degree of influence of meteorological factors (temperature, precipitation, and shortwave radiation), and soil (temperature and moisture), and biological factors (maximum of NDVI and middle date during the growing season) on LSP changes using random forest models. The results show that there is an advanced trend (0.15 days/year) for the start of the growing season (SOS), a delayed trend (0.24 days/year) for the end of the growing season (EOS), and an overall extended trend (0.39 days/year) for the length of the growing season (LOS) in the QMs over the past two decades. Advanced SOS and delayed EOS were the dominant patterns leading to a lengthened vegetation growing season, followed by a joint delay of SOS and EOS, and the latter was particularly common in shrub and evergreen broadleaved forests. The growth season length increased significantly in western QMs. Furthermore, we confirmed that meteorological factors are the main factors affecting the interannual variations in SOS and EOS, especially the meteorological factor of preseason mean shortwave radiation (SWP). The grass and crop are most influenced by SWP. The soil condition has, overall, a minor influence the regional LSP. This study highlighted the specificity of different vegetation growth in the QMs under warming, which should be considered in the accurate prediction of vegetation growth in the future.

scholarly journals Climate change, growing season water deficit and vegetation activity along the north–south transect of eastern China from 1982 through 2006

2012 ◽  
Vol 16 (10) ◽  
pp. 3835-3850 ◽  
Author(s):  
P. Sun ◽  
Z. Yu ◽  
S. Liu ◽  
X. Wei ◽  
J. Wang ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Deficit ◽  
Eastern China ◽  
Coniferous Forest ◽  
Growing Season ◽  
Future Climate Change ◽  
Vegetation Types ◽  
The North ◽  
Vegetation Activity ◽  
Energy Limitation

Abstract. Considerable work has been done to examine the relationship between environmental constraints and vegetation activities represented by the remote sensing-based normalized difference vegetation index (NDVI). However, the relationships along either environmental or vegetational gradients are rarely examined. The aim of this paper was to identify the vegetation types that are potentially susceptible to climate change through examining their interactions between vegetation activity and evaporative water deficit. We selected 12 major vegetation types along the north–south transect of eastern China (NSTEC), and tested their time trends in climate change, vegetation activity and water deficit during the period 1982–2006. The result showed significant warming trends accompanied by general precipitation decline in the majority of vegetation types. Despite that the whole transect increased atmospheric evaporative demand (ET0) during the study period, the actual evapotranspiration (ETa) showed divergent trends with ET0 in most vegetation types. Warming and water deficit exert counteracting controls on vegetation activity. Our study found insignificant greening trends in cold temperate coniferous forest (CTCF), temperate deciduous shrub (TDS), and three temperate herbaceous types including the meadow steppe (TMS), grass steppe (TGS) and grassland (TG), where warming exerted more effect on NDVI than offset by water deficit. The increasing growing season water deficit posed a limitation on the vegetation activity of temperate coniferous forest (TCF), mixed forest (TMF) and deciduous broad-leaved forest (TDBF). Differently, the growing season brownings in subtropical or tropical forests of coniferous (STCF), deciduous broad-leaved (SDBF), evergreen broad-leaved (SEBF) and subtropical grasslands (STG) were likely attributed to evaporative energy limitation. The growing season water deficit index (GWDI) has been formulated to assess ecohydrological equilibrium and thus indicating vegetation susceptibility to water deficit. The increasing GWDI trends in CTCF, TCF, TDS, TG, TGS and TMS indicated their rising susceptibility to future climate change.

scholarly journals Climate change, growing season water deficit and vegetation activity along the north-south transect of Eastern China from 1982 through 2006

2012 ◽  
Vol 9 (5) ◽  
pp. 6649-6688 ◽  
Author(s):  
P. Sun ◽  
Z. Yu ◽  
S. Liu ◽  
X. Wei ◽  
J. Wang ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Deficit ◽  
Eastern China ◽  
Coniferous Forest ◽  
Growing Season ◽  
Future Climate Change ◽  
Severe Drought ◽  
Vegetation Types ◽  
The North ◽  
Vegetation Activity

Abstract. Considerable work has been done to examine the relationship between environmental constraints and vegetation activities represented by the remote sensing-based Normalized Difference Vegetation Index (NDVI). However, the relationships along either environmental or vegetation type gradients are rarely examined. The aim of this paper was to identify the vegetation types that are potentially susceptible to climate change through examining the interaction between vegetation activity and water deficit. We selected 12 major vegetation types along the north-south transect of Eastern China (NSTEC), examined their time trends from 1982 to 2006 with respect to climate change, vegetation activity and water deficit. The results showed that all vegetation types experienced warming during the study period, and the majority of them experienced precipitation decline. Warming and growing season water deficit exert counteracting controls on vegetation activity. Our study found insignificant greening trends in the northernmost cold temperate coniferous forest (CTCF), three temperate herbaceous types including the meadow steppe (TMS), grass steppe (TGS) and grassland (TG), where the growing season warming exerted more than offset effect on vegetation activity (phenology) than growing season water deficit. For the three temperate forest including the coniferous (TCF), mixed (TMF) and deciduous-broadleaved (TDBF), growing season water deficit was the main constraint on vegetation activity. Differently, the growing season browning in subtropical or tropical forests of coniferous (STCF), deciduous-broadleaved (SDBF) and evergreen-broadleaved (SEBF) and subtropical grasslands (STG) were likely attributed to decline in sunshine duration due to increased summer cloudiness. Poor water status in TDS, TG, TMS and severe drought in TGS have been identified by using growing season water deficit index (GWDI), suggested these ecosystems were subjected to severe progressing drought that may create greening trend reversal in future. The emerging water deficit in CTCF, TCF and SDBF suggested their rising susceptibility to future climate change.

scholarly journals Response of the Energy Balance on the Margin of the Greenland Ice Sheet to Temperature Changes

1990 ◽  
Vol 36 (123) ◽  
pp. 217-221 ◽  
Author(s):  
Roger J. Braithwaite ◽  
Ole B. Olesen
Keyword(s):  
Heat Flux ◽  
Energy Balance ◽  
Air Temperature ◽  
Sensible Heat Flux ◽  
Ice Sheet ◽  
Temperature Response ◽  
Greenland Ice Sheet ◽  
Energy Balance Model ◽  
Balance Model ◽  
Sensible Heat

AbstractDaily ice ablation on two outlet glaciers from the Greenland ice sheet, Nordbogletscher (1979–83) and Qamanârssûp sermia (1980–86), is related to air temperature by a linear regression equation. Analysis of this ablation-temperature equation with the help of a simple energy-balance model shows that sensible-heat flux has the greatest temperature response and accounts for about one-half of the temperature response of ablation. Net radiation accounts for about one-quarter of the temperature response of ablation, and latent-heat flux and errors account for the remainder. The temperature response of sensible-heat flux at QQamanârssûp sermia is greater than at Nordbogletscher mainly due to higher average wind speeds. The association of high winds with high temperatures during Föhn events further increases sensible-heat flux. The energy-balance model shows that ablation from a snow surface is only about half that from an ice surface at the same air temperature.

scholarly journals Surface Water Temperature Predictions at a Mid-Latitude Reservoir under Long-Term Climate Change Impacts Using a Deep Neural Network Coupled with a Transfer Learning Approach

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1109
Author(s):  
Nobuaki Kimura ◽  
Kei Ishida ◽  
Daichi Baba
Keyword(s):  
Climate Change ◽  
Surface Water ◽  
Water Temperature ◽  
Transfer Learning ◽  
Air Temperature ◽  
Climate Models ◽  
Temporal Variations ◽  
Training Data ◽  
Surface Water Temperature

Long-term climate change may strongly affect the aquatic environment in mid-latitude water resources. In particular, it can be demonstrated that temporal variations in surface water temperature in a reservoir have strong responses to air temperature. We adopted deep neural networks (DNNs) to understand the long-term relationships between air temperature and surface water temperature, because DNNs can easily deal with nonlinear data, including uncertainties, that are obtained in complicated climate and aquatic systems. In general, DNNs cannot appropriately predict unexperienced data (i.e., out-of-range training data), such as future water temperature. To improve this limitation, our idea is to introduce a transfer learning (TL) approach. The observed data were used to train a DNN-based model. Continuous data (i.e., air temperature) ranging over 150 years to pre-training to climate change, which were obtained from climate models and include a downscaling model, were used to predict past and future surface water temperatures in the reservoir. The results showed that the DNN-based model with the TL approach was able to approximately predict based on the difference between past and future air temperatures. The model suggested that the occurrences in the highest water temperature increased, and the occurrences in the lowest water temperature decreased in the future predictions.

scholarly journals Projection of Hydro-Climatic Extreme Events under Climate Change in Yom and Nan River Basins, Thailand

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 665
Author(s):  
Chanchai Petpongpan ◽  
Chaiwat Ekkawatpanit ◽  
Supattra Visessri ◽  
Duangrudee Kositgittiwong
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Extreme Events ◽  
River Basins ◽  
Annual Rainfall ◽  
Extreme Floods ◽  
Extreme Flood ◽  
Rcp 8.5 ◽  
Near Future ◽  
Far Future

Due to a continuous increase in global temperature, the climate has been changing without sign of alleviation. An increase in the air temperature has caused changes in the hydrologic cycle, which have been followed by several emergencies of natural extreme events around the world. Thailand is one of the countries that has incurred a huge loss in assets and lives from the extreme flood and drought events, especially in the northern part. Therefore, the purpose of this study was to assess the hydrological regime in the Yom and Nan River basins, affected by climate change as well as the possibility of extreme floods and droughts. The hydrological processes of the study areas were generated via the physically-based hydrological model, namely the Soil and Water Assessment Tool (SWAT) model. The projected climate conditions were dependent on the outputs of the Global Climate Models (GCMs) as the Representative Concentration Pathways (RCPs) 2.6 and 8.5 between 2021 and 2095. Results show that the average air temperature, annual rainfall, and annual runoff will be significantly increased in the intermediate future (2046–2070) onwards, especially under RCP 8.5. According to the Flow Duration Curve and return period of peak discharge, there are fluctuating trends in the occurrence of extreme floods and drought events under RCP 2.6 from the future (2021–2045) to the far future (2071–2095). However, under RCP 8.5, the extreme flood and drought events seem to be more severe. The probability of extreme flood remains constant from the reference period to the near future, then rises dramatically in the intermediate and the far future. The intensity of extreme droughts will be increased in the near future and decreased in the intermediate future due to high annual rainfall, then tending to have an upward trend in the far future.

scholarly journals Heatwaves and Summer Urban Heat Islands: A Daily Cycle Approach to Unveil the Urban Thermal Signal Changes in Lisbon, Portugal

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 292 ◽  
Author(s):  
Ana Oliveira ◽  
António Lopes ◽  
Ezequiel Correia ◽  
Samuel Niza ◽  
Amílcar Soares
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Wind Direction ◽  
Urban Heat Islands ◽  
Daily Cycle ◽  
Local Climate ◽  
Thermal Signal ◽  
Air Temperatures ◽  
Local Climate Change ◽  
Urban Heat

Lisbon is a European Mediterranean city, greatly exposed to heatwaves (HW), according to recent trends and climate change prospects. Considering the Atlantic influence, air temperature observations from Lisbon’s mesoscale network are used to investigate the interactions between background weather and the urban thermal signal (UTS) in summer. Days are classified according to the prevailing regional wind direction, and hourly UTS is compared between HW and non-HW conditions. Northern-wind days predominate, revealing greater maximum air temperatures (up to 40 °C) and greater thermal amplitudes (approximately 10 °C), and account for 37 out of 49 HW days; southern-wind days have milder temperatures, and no HWs occur. Results show that the wind direction groups are significantly different. While southern-wind days have minor UTS variations, northern-wind days have a consistent UTS daily cycle: a diurnal urban cooling island (UCI) (often lower than –1.0 °C), a late afternoon peak urban heat island (UHI) (occasionally surpassing 4.0 °C), and a stable nocturnal UHI (1.5 °C median intensity). UHI/UCI intensities are not significantly different between HW and non-HW conditions, although the synoptic influence is noted. Results indicate that, in Lisbon, the UHI intensity does not increase during HW events, although it is significantly affected by wind. As such, local climate change adaptation strategies must be based on scenarios that account for the synergies between potential changes in regional air temperature and wind.

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.

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