Exploring the Dynamic Impact of Extreme Climate Events on Vegetation Productivity under Climate Change

Xu, Hanqing; Tan, Jinfeng; Li, Chunlan; Niu, Yiying; Wang, Jun

doi:10.3390/f14040744

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…Frost days can damage crops, which results in a reduction in agricultural production 46 . Daily temperature range (DTR) provides insights into temperature fluctuations and variations that crops may experience during their growth cycle 47 . Ice days (ID) are decreasing, while summer days (SU) are increasing throughout Canada 48 .…”

Section: Methodsmentioning

confidence: 99%

Future projections of temperature-related indices in Prince Edward Island using ensemble average of three CMIP6 models

Maqsood,

Wang,

Farooque

et al. 2024

Sci Rep

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Prince Edward Island (PEI) is an agricultural province heavily relying on rainfed agriculture. The island has already experienced significant impacts from climate change. Accurate projections of PEI temperature extreme indices are required to mitigate and adapt to the changing climate conditions. This study aims to develop ensemble projections using Coupled Model Intercomparison Project Phase 6 (CMIP6) global circulation models (GCMs) to analyze temperature extremes on PEI. In this study, the ECMWF ERA5 reanalysis dataset was chosen for stepwise cluster analysis (SCA) due to its high accuracy. Three CMIP6 (NorESM2-MM, MPI-ESM1.2-HR, and CanESM5) GCMs, along with their ensemble average, were utilized in the SCA model to project future changes in daily maximum temperature (Tmax) and minimum temperature (Tmin) at four meteorological stations on PEI (East Point, Charlottetown, Summerside, and North Cape) under two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5). These GCMs were selected based on their low, medium, and high Equilibrium Climate Sensitivity. The bias-corrected results for the future period of Tmax and Tmin showed that the GCM-specific changes in the ECS also impact the regional scale. Additionally, several temperature extreme indices, including the daily temperature range (DTR), summer days (SU), growing degree days (GDD), growing season length (GSL), ice days (ID), and frost days (FD), were analyzed for two future periods: FP1(202–2050) and FP2 (2051–2075). The results indicate that DTR, SU, GDD, and GSL are expected to increase, while ID and FD are projected to decrease during FP1 and FP2 under both scenarios. The future projected mean monthly changes in Tmax, Tmin, and the selected temperature extreme indices highlight warmer future periods and an increase in agriculture-related indices such as GDD and GSL. Specifically, July, August, and September are expected to experience even higher temperatures in the future. As the climate becomes warmer, cold extreme events are projected to be shorter in duration but more intense in terms of their impact. The largest increments/decrements for Tmax, Tmin, and their relevant indices were observed during FP2 under SSP5-8.5. The outcomes of this study provide valuable insights for agricultural development, water resource management, and the formulation of effective mitigation strategies to address the impacts of climate change on PEI.

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Section: Methodsmentioning

confidence: 99%

Future projections of temperature-related indices in Prince Edward Island using ensemble average of three CMIP6 models

Maqsood,

Wang,

Farooque

et al. 2024

Sci Rep

View full text Add to dashboard Cite

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Towards understanding climate change impacts: monitoring the vegetation dynamics of terrestrial national parks in Indonesia

Ramdani,

Setiani,

Sianturi

2024

Sci Rep

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Monitoring vegetation dynamics in terrestrial national parks (TNPs) is crucial for ensuring sustainable environmental management and mitigating the potential negative impacts of short- and long-term disturbances understanding the effect of climate change within natural and protected areas. This study aims to monitor the vegetation dynamics of TNPs in Indonesia by first categorizing them into the regions of Sumatra, Jawa, Kalimantan, Sulawesi, and Eastern Indonesia and then applying ready-to-use MODIS EVI time-series imageries (MOD13Q1) taken from 2000 to 2022 on the GEE cloud-computing platform. Specifically, this research investigates the greening and browning fraction trends using Sen's slope, considers seasonality by analyzing the maximum and minimum EVI values, and assesses anomalous years by comparing the annual time series and long-term median EVI value. The findings reveal significantly increasing greening trends in most TNPs, except Danau Sentarum, from 2000 to 2022. The seasonality analysis shows that most TNPs exhibit peak and trough greenness at the end of the rainy and dry seasons, respectively, as the vegetation response to precipitation increases and decreases. Anomalies in seasonality that is affected by climate change was detected in all of the regions. To increase TNPs resilience, suggested measures include active reforestation and implementation of Assisted Natural Regeneration, strengthen the enforcement of fundamental managerial task, and forest fire management.

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Impact of Extreme Climate Indices on Vegetation Dynamics in the Qinghai–Tibet Plateau: A Comprehensive Analysis Utilizing Long-Term Dataset

Duan,

Huang,

Liu

et al. 2024

IJGI

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The Qinghai–Tibet Plateau (QTP) is crucial for global climate regulation and ecological equilibrium. However, the phenomenon of global climate warming has increased the frequency of extreme weather events on the QTP, exerting substantial effects on both regional and global ecological systems. This study utilized long-term series NDVI and extreme climate indices to comprehensively evaluate the impact of extreme climatic changes on diverse vegetation types within the QTP. A variety of analytical methodologies, including trend analysis, a Mann–Kendall test, correlation analysis, and random forest importance ranking, were employed in this study. These methodologies were applied to investigate the distribution patterns and variation trends of diverse vegetation types and extreme climate indices. This comprehensive approach facilitated a detailed analysis of the responses of different vegetation types to interannual variability under extreme climatic conditions and enabled the assessment of the impact of extreme climate indices on these vegetation types. The findings have the following implications: (1) Except for forests, the annual NDVI for overall vegetation, meadows, steppes, deserts, and alpine vegetation in the QTP exhibits a significant upward trend (p < 0.01). Notably, meadows and deserts demonstrate the highest growth rates at 0.007/10y, whereas the annual NDVI of forests is not statistically significant (p > 0.05). Substantial increases in vegetation were predominantly detected in the central and northeastern regions of the QTP, while significant decreases were mostly observed in the southeastern and western regions. The area exhibiting significant vegetation increase (38.71%) considerably surpasses that of the area with a significant decrease (14.24%). (2) There was a statistically significant reduction (p < 0.05) in the number of days associated with extreme cold temperature indices, including CSDI, DTR, FD, ID, TN10p, and TX10p. In contrast, indices related to extremely warm temperatures, such as GSL, WSDI, SU25, TN90p, TNn, TNx, TX90p, and TXx, exhibited a statistically significant increase (p < 0.01). The pronounced rise in minimum temperatures, reflected by fewer cold days, has notably contributed to climate warming. Although extreme precipitation events have become less frequent, their intensity has increased. Notable spatial variations in extreme precipitation were observed, although no consistent changing pattern emerged. (3) The annual NDVI for non-forest vegetation types showed a significant negative correlation with most extreme cold temperature indices and a significant positive correlation with extreme warm temperature indices. A significant positive correlation (p < 0.05) between annual NDVI and extreme precipitation indices is found only in steppe and desert ecosystems, with no such correlation observed in other vegetation types. Both correlation analysis and random forest methodologies underscore the impact of extreme climate indices on vegetation variations, with the random forest model exhibiting superior capability in capturing nonlinear relationships. In conclusion, global climate change is projected to result in a heightened frequency of extreme warm events. Although these conditions might temporarily enhance vegetation growth, they are also associated with numerous detrimental impacts. Therefore, it is imperative to enhance awareness and take proactive measures for early warning and prevention.

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Exploring the Dynamic Impact of Extreme Climate Events on Vegetation Productivity under Climate Change

Cited by 3 publications

References 32 publications

Future projections of temperature-related indices in Prince Edward Island using ensemble average of three CMIP6 models

Future projections of temperature-related indices in Prince Edward Island using ensemble average of three CMIP6 models

Towards understanding climate change impacts: monitoring the vegetation dynamics of terrestrial national parks in Indonesia

Impact of Extreme Climate Indices on Vegetation Dynamics in the Qinghai–Tibet Plateau: A Comprehensive Analysis Utilizing Long-Term Dataset

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