Projecting Future Vegetation Change for Northeast China Using CMIP6 Model

Yuan, Wei; Wu, Shuangye; Hou, Shugui; Xu, Zhiwei; Pang, Hongxi; Lu, Huayu

doi:10.3390/rs13173531

Cited by 12 publications

(8 citation statements)

References 71 publications

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“…These projected changes in the LAI in the present study agree with the estimates for 2021–2050 using geographically weighted regression based on a regional simulation driven by 5 CMIP5 projections (Gao et al., 2017; Shi et al., 2018). The results from multiple linear regression with 11 CMIP6 projections similarly reported that the northeast is likely to become warmer and wetter, and hence have an increase in regional LAI (Yuan et al., 2021). Likewise, throughout the twenty‐first century, vegetation in China has a greening trend according to another study with 12 CMIP5 models (Zhou et al., 2020).…”

Section: Discussionmentioning

confidence: 91%

Ecological Response to Climate Change Across China From Combined Soil Temperature and Moisture Changes

et al. 2022

Earth and Space Science

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A coupled soil temperature (ST) and moisture (SM) balance reflects a synthetic climate regime, having huge ecological impacts. This paper used ST and SM data from the European Center for Medium‐Range Weather Forecasts climate reanalysis‐Land and the Coupled Model Intercomparison Project Phase 6 and leaf area index (LAI) data from the Global Land Surface Satellite Product Suite. The focus was on understanding joint ST‐SM changes and the resulting ecological response across China. The results show that during 2000–2020, 24.5% of the land area in China experienced a warming‐drying trend resulting in a 9.7% LAI decrease, while 6.4% of the area experienced a warming‐wetting trend leading to an 8.6% LAI increase. During 2015–2100, 30.6% of the land area in China will be warmer and drier, while 55.2% of the area will be warmer but wetter across three shared socioeconomic pathways (SSP126, 245, and 585). Superimposed on the long‐term trends, there are also significant spatiotemporal variabilities in ST and SM on annual to decadal timescales. The LAI also showed substantial short‐term fluctuations in both typical regions and ecosystems despite consistent long‐term increases. Our findings suggest that ecosystems could be impaired on annual to decadal scales by adverse soil conditions in the twenty‐first century, but in terms of long‐term trends, ecosystems may be resilient partly because of the compensating effects of global warming and regional hydrological changes. Impact studies should thus focus more on annual to decadal soil‐ecosystem anomalous events.

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Section: Discussionmentioning

confidence: 91%

Ecological Response to Climate Change Across China From Combined Soil Temperature and Moisture Changes

et al. 2022

Earth and Space Science

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“…Climate data can be integrated with vegetation data in a statistical model to predict future changes in vegetation dynamics. This approach has shown promise in predicting future leaf area index (LAI) trends [51, 52], NDVI trends [53], and short-term prediction of NDVI as an early warning system for disasters such as famine and epidemic diseases [54], and the risk of Rift Valley fever (RVF) [55, 56]. These methods help our understanding of ecological dynamics and facilitate informed decision-making for sustainable resource management.…”

Section: Introductionmentioning

confidence: 99%

Modelling resource-driven movements of livestock herds to predict the impact of climate change on network dynamics

Sulaimon,

Ekwem,

Finch

et al. 2024

Preprint

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In East Africa, climate change is likely to profoundly impact livestock management and the potential spread of infectious diseases. Here, we developed a network model to describe livestock movements to grazing and watering sites, fitted it to data from the Serengeti district of Tanzania, and used it to explore how projected changes in resource availability due to climate change could impact future network structures and therefore infectious disease risks, using 2050 and 2080 as exemplar scenarios. Our modelled networks show increased connections between villages in grazing and watering networks, with connectivity increasing further in the future in correspondence with changes in vegetation and water availability. Our analyses show that targeted interventions to efficiently control regional disease spread may become more difficult, as village connectivity increases and disease vulnerability becomes more evenly distributed. This analysis also provides proof of principle for a novel approach applicable to agropastoral settings across many developing countries, where livestock trade plays a crucial role in maintaining local livelihoods but also in spreading disease.

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“…Zhao et al investigated the impact of climate change on vegetation LAI in a global study [7]. Yuan et al studied vegetation phenological changes in Northeast China based on LAI [8]. These studies highlight the importance of LAI in understanding vegetation dynamics and its response to climate change.…”

Section: Introductionmentioning

confidence: 99%

Projected Climate Change Effects on Global Vegetation Growth: A Machine Learning Approach

Nguyen,

Seeboonruang,

Chen

2023

Environments

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In this study, a machine learning model was used to investigate the potential consequences of climate change on vegetation growth. The methodology involved analyzing the historical Normalized Difference Vegetation Index (NDVI) data and future climate projections under four Shared Socioeconomic Pathways (SSPs). Data from the Global Inventory Monitoring and Modeling System (GIMMS) dataset for the period 1981–2000 were used to train the machine learning model, while CMIP6 (Coupled Model Intercomparison Project Phase 6) global climate projections from 2021–2100 were employed to predict future NDVI values under different SSPs. The study results revealed that the global mean NDVI is projected to experience a significant increase from the period 1981–2000 to the period 2021–2040. Following this, the mean NDVI slightly increases under SSP126 and SSP245 while decreasing substantially under SSP370 and SSP585. In the near-term span of 2021–2040, the average NDVI value of SSP585 slightly exceeds that of SSP245 and SSP370, suggesting a positive vegetation development in response to a more pronounced temperature increase in the near term. However, if the trajectory of SSP585 persists, the mean NDVI will commence a decline over the subsequent three periods (2041–2060, 2061–2080, and 2080–2100) with a faster speed than that of SSP370. This decline is attributed to the adverse effects of a rapid temperature rise on vegetation. Based on the examination of individual continents, it is projected that the NDVI values in Africa, South America, and Oceania will decline over time, except under the scenario SSP126 during 2081–2100. On the other hand, the NDVI values in North America and Europe are anticipated to increase, with the exception of the scenario SSP585 during 2081–2100. Additionally, Asia is expected to follow an increasing trend, except under the scenario SSP126 during 2081–2100. In the larger scope, our research findings carry substantial implications for biodiversity preservation, greenhouse gas emission reduction, and efficient environmental management. The utilization of machine learning technology holds the potential to accurately predict future changes in vegetation growth and pinpoint areas where intervention is imperative.

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Projecting Future Vegetation Change for Northeast China Using CMIP6 Model

Cited by 12 publications

References 71 publications

Ecological Response to Climate Change Across China From Combined Soil Temperature and Moisture Changes

Ecological Response to Climate Change Across China From Combined Soil Temperature and Moisture Changes

Modelling resource-driven movements of livestock herds to predict the impact of climate change on network dynamics

Projected Climate Change Effects on Global Vegetation Growth: A Machine Learning Approach

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