Detection, mapping, and interpretation of the main drivers of the Arctic GPP change from 2001 to 2019

Ma, Dujuan; Wu, Xiaodan; Yin, Gaofei; Li, Zheng; Wang, Jingping; Tang, Rongqi; Zeng, Qicheng; Mu, Cuicui

doi:10.1007/s00382-023-06935-z

Cited by 2 publications

(2 citation statements)

References 58 publications

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“…Therefore, the soil may be too wet due to higher Pre, causing plant roots to receive excessive soaking, which can limit plant growth and photosynthesis, ultimately reducing the NDVI. Previous studies have also shown a negative relationship between Pre and vegetation in other cold areas (Xu jie et al, 2019;Ma et al, 2023). Furthermore, Pre over the TP falls in the form of snowfall most of the time due to the special topography, and the snow cover can hinder the identification of vegetation by satellite remote sensing, which ultimately influences the NDVI.…”

Section: Analysis Of the Influencing Factors Of The Et Changesmentioning

confidence: 94%

Main influencing factors of terrestrial evapotranspiration for different land cover types over the Tibetan Plateau in 1982–2014

Li,

Pan,

Zhao

2024

Front. Environ. Sci.

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Introduction: Terrestrial evapotranspiration (ET) over the Tibetan Plateau (TP) has important implications for the global water cycle, climate change, and ecosystem, and its changes and driving factors have drawn increasing attention. Previous research studies have minimally quantified the effects and identified the pathways of the influencing factors on ET over different land surface types.Methods: In this study, we analyze the spatiotemporal distribution and variation of ET over the TP in 1982–2014 based on multiple datasets. Furthermore, the effects of each influencing factor on ET are quantified over different land surface types, and the major influencing factors and their affecting pathways are identified using structure equation modeling (SEM), which is a statistical method used to analyze relationships among multiple variables.Results: The results show that the climatology of ET decreases gradually from southeastern to northwestern TP, with the maximum spatial averaged value of 379.979 ± 0.417 mm a−1 for the fifth generation of European Reanalysis (ERA5) and the minimum of 249.899 ± 0.469 mm a−1 for the Global Land Data Assimilation System (GLDAS). The most significant differences among the ET datasets mainly occur in the summer. The annual ET averaged over the TP presents an increased trend from 1982 to 2014, as shown by all of the ET datasets. However, there are larger discrepancies in the spatial distribution of the increased trend for these datasets. The assessment result shows that the 0.05° land evapotranspiration dataset for the Qinghai–Tibet Plateau (LEDQTP) has the highest temporal correlation coefficient (0.80) and the smallest root-mean-square error (23.50 mm) compared to the observations. Based on LEDQTP, we find that precipitation is the main influencing factor of ET, which primarily affects ET through direct pathways in bare soil and grassland regions, with standardized estimates of 0.521 and 0.606, respectively. However, in meadow and shrub and forest regions, the primary factor influencing ET is air temperature, which is primarily affected by an indirect pathway through a vapor pressure deficit. Air temperature is also the controlling factor in sparse vegetation regions, but it affects ET through a direct pathway.Discussion: This study may provide some new useful information on the effects of climate change on ET in different land cover types over the TP.

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Section: Analysis Of the Influencing Factors Of The Et Changesmentioning

confidence: 94%

Main influencing factors of terrestrial evapotranspiration for different land cover types over the Tibetan Plateau in 1982–2014

Li,

Pan,

Zhao

2024

Front. Environ. Sci.

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“…Additionally, these effects differ during the growth and senescence stages of the vegetation in the high-latitude regions of the Northern Hemisphere [22]. Ma et al found that the monthly variation in GPP in the Arctic North is mainly determined by the spatial distribution of the meteorological factors and shows different time lags for different meteorological factors [23]. Feng et al analyzed the relationship between vegetation and precipitation in the Mara River Basin at a seasonal scale and found that the local vegetation lags behind the peak of precipitation by an average of 35.5 days [24].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Dynamics of Vegetation Productivity and Its Response to Meteorological Factors in China

Gong,

Ma,

Wang

et al. 2024

Atmosphere

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Climate is one of the key factors driving changes in vegetation, and the response of the vegetation to climate often occurs with a time delay. However, research on the cumulative lagged response of the vegetation to meteorological factors in large-scale regions is limited. Therefore, this study first evaluated the performance of the Gross Primary Productivity (GPP) products provided by Moderate Resolution Imaging Spectroradiometer (MODIS) and Penman–Monteith–Leuning (PML) over the past 20 years in China and then determined the lagged relationships between the GPP and major meteorological factors in different regions and land-use types in China based on a partial correlation analysis. The results indicate that (1) GPP_PML outperforms GPP_MODIS products in the regional context of China; (2) China’s regional GPP has shown a fluctuating upward trend over the past 20 years, with a stepwise increase in the multi-year average from the northwest inland to the southeast coastal regions, and a higher contribution from the southern regions than the northern ones; (3) unlike the recent upward trend in regional temperatures, both precipitation and radiation have decreased, with these two factors showing completely opposite multi-year trends in most regions; and (4) the proportion of regions with lagged effects of the GPP on meteorological factors is higher than those with cumulative effects in China. Among these, GPP exhibits a higher proportion of a 3-month lagged response to precipitation, which is particularly pronounced at altitudes between 500 and 2500 m and above 5500 m. the proportion of the areas with no lag cumulative response to temperature and radiation with GPP in China is the highest due to the influence of more barren land and grassland in the northwest interior. Simultaneously, grassland and barren land have a higher proportion of the non-lagged cumulative responses to temperature and precipitation. This study contributes to our understanding of vegetation dynamics in the context of global climate change and provides a theoretical foundation for regional ecological conservation and high-quality coordinated development.

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Detection, mapping, and interpretation of the main drivers of the Arctic GPP change from 2001 to 2019

Cited by 2 publications

References 58 publications

Main influencing factors of terrestrial evapotranspiration for different land cover types over the Tibetan Plateau in 1982–2014

Main influencing factors of terrestrial evapotranspiration for different land cover types over the Tibetan Plateau in 1982–2014

Spatiotemporal Dynamics of Vegetation Productivity and Its Response to Meteorological Factors in China

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