Climate Change and Its Impacts on Terrestrial Ecosystems: Recent Advances and Future Directions

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Biosphere/Hydrosphere/Land–Atmosphere Interactions".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 16046

Special Issue Editors

Department of Ecology, School of Plant Protection, Yangzhou University, Yangzhou 225009, China
Interests: climate change; agro-meteorology; ecosystem carbon & water cycles; remote sensing of vegetation
Special Issues, Collections and Topics in MDPI journals
Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China
Interests: land–atmosphere interaction; desert carbon sinks; dust emission and transport; CO2 flux
Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
Interests: greenhouse gases fluxes over inland water bodies
Special Issues, Collections and Topics in MDPI journals
Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
Interests: land surface modelling; regional climate modelling; carbon cycle; terrestrial ecosystems

Special Issue Information

Dear Colleagues,

With the increasing concentration of greenhouse gases in the atmosphere, climate change is now an indisputable fact and poses diverse impacts on terrestrial ecosystems (e.g., cropland, forests, grassland, wetlands, lakes, and deserts). Over the past few decades, a number of studies have been conducted to acquire better knowledge of the spatiotemporal variation in climatic elements (e.g., temperature, precipitation, and radiation) and to quantify their impacts on the structure and function of terrestrial ecosystems. There are, however, large uncertainties in current research due to either the lack of observational data for some ecosystems or the mismatch of timescales between available data. Nowadays, multi-source data from ground-, sky-, or space-based observations and rapidly evolving methods (e.g., ecosystem monitoring networks, machine learning, cloud computing, numerical simulation, etc.) provide important methods through which we can understand the complex ecological responses to climate change. A large number of very important questions remain open and require new multifaceted studies.

For this Special Issue, we warmly invite scientists working in meteorology, climatology, ecology, geography, remote sensing and GIS, and environmental science to contribute novel theories, observations, and modeling studies on climate change and its impacts on terrestrial ecosystems across different time scales (historical to future) and spatial scales (regional to global). Contributions can include but are not limited to the following: regional climate change analysis using new observational data and statistical methods, the measurement and modeling of land surface–atmosphere interaction, assessments of the response of terrestrial ecosystem structure and function to climate change, climate change adaptation and mitigation in terrestrial ecosystems, etc.

Dr. Cheng Li
Dr. Fan Yang
Dr. Qitao Xiao
Dr. Yao Gao
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • regional climate change
  • land–atmosphere interactions
  • greenhouse gas emissions
  • agro-meteorology and plant production
  • climate and vegetation relationships
  • remote sensing and gis
  • machine learning and numerical modeling methods
  • climate change adaptation and mitigation in terrestrial ecosystems

Published Papers (9 papers)

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Editorial

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3 pages, 193 KiB  
Editorial
Climate Change and Its Impacts on Terrestrial Ecosystems: Recent Advances and Future Directions
by Cheng Li, Fan Yang, Qitao Xiao and Yao Gao
Atmosphere 2023, 14(7), 1176; https://doi.org/10.3390/atmos14071176 - 21 Jul 2023
Cited by 1 | Viewed by 1670
Abstract
With the increasing concentration of greenhouse gases in the atmosphere, climate change is now an indisputable fact and has strong impacts on various terrestrial ecosystems (e [...] Full article

Research

Jump to: Editorial

21 pages, 3992 KiB  
Article
Effects of Climate Change on Wheat Yield and Nitrogen Losses per Unit of Yield in the Middle and Lower Reaches of the Yangtze River in China
by Yanhui Zhou, Xinkai Zhu, Wenshan Guo and Chaonian Feng
Atmosphere 2023, 14(5), 824; https://doi.org/10.3390/atmos14050824 - 03 May 2023
Cited by 4 | Viewed by 1248
Abstract
Nitrogen fertilizer is one of the essential nutrients for wheat growth and development, and it plays an important role in increasing and stabilizing wheat yield. Future climate change will affect wheat growth, development, and yield, since climate change will also alter nitrogen cycles [...] Read more.
Nitrogen fertilizer is one of the essential nutrients for wheat growth and development, and it plays an important role in increasing and stabilizing wheat yield. Future climate change will affect wheat growth, development, and yield, since climate change will also alter nitrogen cycles in farmland. Therefore, further research is needed to understand the response of wheat yield and nitrogen losses to climate change during cultivation. In this study, we investigate the wheat-producing region in the middle and lower reaches of the Yangtze River in China, one of the leading wheat-producing areas, by employing a random forest model using wheat yield records from agricultural meteorological observation stations and spatial data on wheat yield, nitrogen application rate, and nitrogen losses. The model predicts winter wheat yield and nitrogen losses in the middle and lower reaches of the Yangtze River based on CMIP6 meteorological data and related environmental variables, under SSP126 and SSP585 emission scenarios. The results show that future climate change (temperature and precipitation changes) will decrease winter wheat yield by 2~4% and reduce total nitrogen losses by 0~5%, but in other areas, the total nitrogen losses will increase by 0~5% and the N leaching losses per unit of yield will increase by 0~10%. The results of this study can provide a theoretical basis and reference for optimizing nitrogen application rates, increasing yield, and reducing nitrogen losses in wheat cultivation under climate change conditions. Full article
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16 pages, 4178 KiB  
Article
Spatiotemporal Patterns and Characteristics of PM2.5 Pollution in the Yellow River Golden Triangle Demonstration Area
by Ning Jin, Liang He, Haixia Jia, Mingxing Qin, Dongyan Zhang, Cheng Wang, Xiaojian Li and Yanlin Li
Atmosphere 2023, 14(4), 733; https://doi.org/10.3390/atmos14040733 - 19 Apr 2023
Cited by 1 | Viewed by 946
Abstract
Improving air quality in the Yellow River Golden Triangle Demonstration Area (YRGTDA) is an important practice for ecological protection and high-quality development in the Yellow River Basin. Preventing and controlling PM2.5 pollution in this region will require a scientific understanding of the [...] Read more.
Improving air quality in the Yellow River Golden Triangle Demonstration Area (YRGTDA) is an important practice for ecological protection and high-quality development in the Yellow River Basin. Preventing and controlling PM2.5 pollution in this region will require a scientific understanding of the spatiotemporal patterns and characteristics of PM2.5 pollution. PM2.5 data from different sources were combined in this study (the annual average of PM2.5 concentrations were obtained from the Atmospheric Composition Analysis Group of Dalhousie University, and the daily PM2.5 concentration data were obtained from the China National Environmental Monitoring Centre). Then, the temporal variation of PM2.5 concentrations at annual, seasonal, and monthly scales, the spatial variation of PM2.5 concentrations, and the variation of PM2.5 pollution classes were analyzed. Results showed that: (1) at the annual scale, the PM2.5 concentrations showed a decreasing trend from 2000 to 2021 in the study area. The variation of PM2.5 concentrations were divided into two different stages. (2) At the seasonal scale, high PM2.5 concentrations occurred mainly in winter, low PM2.5 concentrations occurred in summer. At the monthly scale, PM2.5 concentrations showed a U-shaped variation pattern from January to December each year. (3) The hotspot analysis of the PM2.5 concentrations in the study area showed a cyclical variation pattern. (4) The PM2.5 concentrations exhibited a spatial pattern of high values in the central and low values in the northern and southern parts of YRGTDA. (5) The number of days for different PM2.5 pollution classes from 2015 to 2021 followed the order of Good > Excellent > Light pollution > Moderate pollution > Heavy pollution > Severe pollution in YRGTDA. The results of this study have great theoretical and practical significance because they reveal the spatiotemporal patterns and pollution characteristics of PM2.5 and will lead to the development of scientifically based measures to reasonably prevent and control pollution in YRGTDA. Full article
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13 pages, 3830 KiB  
Article
Physical Explanation for Paradoxical Climate Change in Semi-Arid Inland Eurasia Based on a Remodeled Precipitation Recycling Ratio and Clausius–Clapeyron Equation
by Xi-Yu Wang, Xin-Yue Bao, Yu Huang, Zhong-Wai Li, Jia-Hua Yong, Yong-Ping Wu, Guo-Lin Feng and Gui-Quan Sun
Atmosphere 2023, 14(2), 376; https://doi.org/10.3390/atmos14020376 - 14 Feb 2023
Cited by 1 | Viewed by 1612
Abstract
Under global warming, the climate in semi-arid inland Eurasia (SAIE) has changed in an opposite manner, thereby seriously impacting the local ecological environment. However, the key influencing factors and physical mechanism remain inconclusive. In this paper, we remodel the precipitation recycling ratio (PRR) [...] Read more.
Under global warming, the climate in semi-arid inland Eurasia (SAIE) has changed in an opposite manner, thereby seriously impacting the local ecological environment. However, the key influencing factors and physical mechanism remain inconclusive. In this paper, we remodel the precipitation recycling ratio (PRR) model to assess the contributions of moisture from different water vapor sources to local precipitation, analyze the characteristics of the PRR and precipitation in SAIE, and provide possible physical reasons based on the Clausius–Clapeyron equation. It is found that the PRR increased from 1970 to 2017 as the result of linear trend analysis, with obvious seasonality. Moreover, the component of precipitation contributed by locally evaporated moisture (Pl), and that contributed by advected moisture (Pa) as well as the total precipitation (P), increased during the past 48 years. In particular, the Pa, Pl, and P in autumn and winter all increased obviously during the past 20 years from the interdecadal change trend, as well as the PRR (Pl/P), which was opposite to the decrease in the total water vapor input I(Ω) in the horizontal direction. According to the Clausius–Clapeyron equation, one of the causes might be that global warming has accelerated the local water cycle and driven the increase in Pa, and the increase in atmospheric water holding capacity caused by global warming provides the power source. We suggest that the climate’s transformation from dry to wet in SAIE can only be temporary since SAIE is an inland area and the adjustment of atmospheric circulation did not lead to the increase in external water vapor. Full article
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14 pages, 1436 KiB  
Article
Dissolved Organic Carbon Dynamics Variability from Ponds Draining Different Landscapes in a Typical Agricultural Watershed
by Zhenjing Liu, Lu Sheng, Xinyue Zhang, Lijie Duan, Yuanhua Jiang and Qitao Xiao
Atmosphere 2023, 14(2), 363; https://doi.org/10.3390/atmos14020363 - 12 Feb 2023
Cited by 3 | Viewed by 1386
Abstract
Dissolved organic carbon (DOC) in inland waters (rivers, reservoirs, lakes, and small ponds) plays a significant role in the global carbon cycle and affects global climate change. In addition, DOC is also a vital indicator of the water environment due to its multiple [...] Read more.
Dissolved organic carbon (DOC) in inland waters (rivers, reservoirs, lakes, and small ponds) plays a significant role in the global carbon cycle and affects global climate change. In addition, DOC is also a vital indicator of the water environment due to its multiple physical, chemical, and ecological roles. Lakes and ponds of small sizes are abundant on a global and regional scale, and a large increase in ponds is expected with global agricultural land expansion. However, the DOC characteristics of ponds in agricultural watersheds are still unclear, posing a challenge to better understanding the carbon cycle of inland waters. In this study, we explored the DOC variability and their influencing factors in ponds draining different landscapes in a typical agricultural watershed to address the issue. The field measurements over a year showed the DOC concentration varied among ponds draining different landscapes. Specifically, the mean DOC concentrations in the natural pond, sewage pond, aquaculture pond, and irrigation pond were (6.17 ± 1.49) mg/L, (12.08 ± 2.92) mg/L, (9.36 ± 2.92) mg/L, and (8.91 ± 2.71) mg/L, respectively. Meanwhile, monthly measurements found the DOC varied across sampling dates. The DOC variability was positively correlated with nutrients, primary production, and precipitation, suggesting anthropogenic loadings, an internal production rate, and hydrological regime that regulated the substantial variability of DOC in these ponds at the watershed scale. Further, large pollutant discharge and high primary production led to peak DOC occurring in the sewage pond. Our results implied that more attention should be paid to ponds in agricultural watersheds to better understand the roles of inland waters in the global carbon cycle. Full article
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20 pages, 8197 KiB  
Article
Probabilistic Hotspot Prediction Model Based on Bayesian Inference Using Precipitation, Relative Dry Spells, ENSO and IOD
by Evi Ardiyani, Sri Nurdiati, Ardhasena Sopaheluwakan, Pandu Septiawan and Mohamad Khoirun Najib
Atmosphere 2023, 14(2), 286; https://doi.org/10.3390/atmos14020286 - 31 Jan 2023
Cited by 2 | Viewed by 1869
Abstract
Increasing global warming can potentially increase the intensity of ENSO and IOD extreme phenomena in the future, which could increase the potential for wildfires. This study aims to develop a hotspot prediction model in the Kalimantan region using climate indicators such as precipitation [...] Read more.
Increasing global warming can potentially increase the intensity of ENSO and IOD extreme phenomena in the future, which could increase the potential for wildfires. This study aims to develop a hotspot prediction model in the Kalimantan region using climate indicators such as precipitation and its derivatives, ENSO and IOD. The hotspot prediction model was developed using Principal Model Analysis (PMA) as the initial model basis. The overall model performance is evaluated using the concept of Cross-Validation. Furthermore, the model’s performance will be improved using the Bayesian Inference principle so that the average performance increases from 28.6% to 61.1% based on the model’s coefficient of determination (R2). The character of each year in the model development process is also evaluated using the concept of cross validation. Since the climate indicator we used was integrated with the ENSO and IOD index, model performance is strongly influenced by the ENSO and IOD phenomena. To obtain better performance when estimating future forest fires (related to El Niño and positive IOD), years with a high number of hotspots and coinciding with the occurrence of El Niño and IOD are better used as early model years (PMA). However, the model tends to overestimate the hotspot value, especially with a lower strength El Niño and positive IOD. Therefore, years with a low number of hotspots, as in normal years and La Niña, are better used in the model performance improvement stage (Bayesian Inference) to correct the overestimation. Full article
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14 pages, 6035 KiB  
Article
Assessment of Climatic Impact on Vegetation Spring Phenology in Northern China
by Zhaozhe Li, Yongping Wu, Ranghui Wang, Bo Liu, Zhonghua Qian and Cheng Li
Atmosphere 2023, 14(1), 117; https://doi.org/10.3390/atmos14010117 - 05 Jan 2023
Cited by 6 | Viewed by 1528
Abstract
Spring phenology is often considered the start of season (SOS) for vegetation, which can affect ecosystem photosynthesis, respiration, and evapotranspiration. However, the long-run variation of SOS remains unclear at the regional scale. In this research, the long-term variation of SOS in northern China [...] Read more.
Spring phenology is often considered the start of season (SOS) for vegetation, which can affect ecosystem photosynthesis, respiration, and evapotranspiration. However, the long-run variation of SOS remains unclear at the regional scale. In this research, the long-term variation of SOS in northern China was explored by using the updated normalized difference vegetation index and monthly climatic data during 1982–2014. Furthermore, the relative importance of climatic factors on SOS was analyzed through partial correlation and multivariate regression methods. The main results were as follows: (1) average SOS largely ranged between day 120 and 165 of the year and varied widely for different vegetation types; (2) SOS during 1982–2014 showed an advancing trend, but it appeared to be reversed after 1998; (3) preseason minimum temperature was a dominant factor controlling SOS in most pixels in northern China, followed by maximum temperature (Tmx). However, impacts of radiation and precipitation on the trend of SOS primarily depended on vegetation types; (4) impacts of climatic factors on SOS declined in the period after 1998, especially for Tmx. These findings provide important support for modeling vegetation phenology and growth in northern China. Full article
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27 pages, 42147 KiB  
Article
Optimal Solar Farm Site Selection in the George Town Conurbation Using GIS-Based Multi-Criteria Decision Making (MCDM) and NASA POWER Data
by Puteri Nur Atiqah Bandira, Mou Leong Tan, Su Yean Teh, Narimah Samat, Shazlyn Milleana Shaharudin, Mohd Amirul Mahamud, Fredolin Tangang, Liew Juneng, Jing Xiang Chung and Mohd Saiful Samsudin
Atmosphere 2022, 13(12), 2105; https://doi.org/10.3390/atmos13122105 - 15 Dec 2022
Cited by 6 | Viewed by 3418
Abstract
Many countries are committed to boosting renewable energy in their national energy mix by 2030 through the support and incentives for solar energy harnessing. However, the observed solar data limitation may result in ineffective decision making, regarding solar farm locations. Therefore, the aim [...] Read more.
Many countries are committed to boosting renewable energy in their national energy mix by 2030 through the support and incentives for solar energy harnessing. However, the observed solar data limitation may result in ineffective decision making, regarding solar farm locations. Therefore, the aim of this study is to utilise GIS-based multi criteria decision making (MCDM) and NASA POWER data to identify the optimal locations for solar farm installations, with the George Town Conurbation as a case study. Although NASA POWER is tailored for the application, at least, on the regional level, the information it provided on the solar radiation and the maximum and minimum temperatures are deemed useful for the initial solar mapping attempt at the local level, especially in the absence or lack of local data. The performance of the GIS-based MCDM model is categorized as good in identifying solar farms. There are no significant differences in the area under the curve (AUC) values between the map of the NASA POWER data and ground-measured data. This indicates the potential of using the NASA POWER data for generating the much-needed initial insights for the local optimal solar farm site selection. The stakeholders can benefit from the suitability map generated to effectively target the locations that have the highest potential to generate solar energy efficiently and sustainably. Full article
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12 pages, 2792 KiB  
Article
Dynamics of the Response of Vegetation Activity to Air Temperature Change in Temperate China
by Mingxing Qin, Ning Jin, Jie Zhao, Meichen Feng and Chao Wang
Atmosphere 2022, 13(10), 1574; https://doi.org/10.3390/atmos13101574 - 26 Sep 2022
Cited by 4 | Viewed by 1359
Abstract
Previous research has documented a tight positive relationship between vegetation activity and growing season air temperature in China’s temperate zone (TC). However, this relationship may change over time following alternations in other environmental factors. Using the linear regression analysis and the moving windows [...] Read more.
Previous research has documented a tight positive relationship between vegetation activity and growing season air temperature in China’s temperate zone (TC). However, this relationship may change over time following alternations in other environmental factors. Using the linear regression analysis and the moving windows based on partial correlation analysis method, the temporal variations of responses of vegetation NDVI to rising air temperature during 1982–2015 in the TC were examined. The results showed that the interannual partial correlation between NDVI and air temperature (RNDVI−T, include RNDVI−Tmean, RNDVI−Tmax, and RNDVI−Tmin, represents the partial correlation between NDVI and Tmean, Tmax, and Tmin, respectively) for the growing season (GS) in a 17−year moving window showed a significant decreasing trend during the last 34 years, mainly due to decreasing RNDVI−T in summer and autumn. The area with a significant decrease of RNDVI−Tmean, RNDVI−Tmax, and RNDVI−Tmin for the GS approximately accounted for 52.36%, 45.63%, and 49.98% of the TC, respectively. For the seasonal patterns of RNDVI−T, the regions with a significant downward trend in all seasons were higher than those with a significant upward trend. We also found a more significant and accelerating decrease of RNDVI−T for warm years compared to cold years, implying a decoupling or even a reverse correlation between NDVI and air temperature with continuous climate warming over the TC. Overall, our study provided evidence that the impact of Tmean, Tmax, and Tmin on vegetation activities exhibited a weakening trend and cautioned using results from interannual time scales to constrain the decadal response of vegetation growth to future global warming. Full article
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