Different responses of soil respiration to climate change in permafrost and non‐permafrost regions of the Tibetan plateau from 1979 to 2018

Pan, Yongjie; Li, Xia; Li, Suosuo; Li, Zhaoguo

doi:10.1002/joc.7639

Cited by 10 publications

(4 citation statements)

References 74 publications

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“…Additionally, the TP has permafrost, accounting for 75% of the permafrost in the mountainous regions of the Northern Hemisphere [79]. These cryosphere features are mainly distributed in the northern TP [80], which is consistent with the distribution of inconsistent changes in the NDVI and SPEI. Furthermore, the northern region of the TP showed a higher degree of warming than the southern part from 1982 to 1998 [68,81].…”

Section: Discussionsupporting

confidence: 72%

The Impacts of Drought Changes on Alpine Vegetation during the Growing Season over the Tibetan Plateau in 1982–2018

Li,

Pan

2024

Remote Sensing

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The Tibetan Plateau (TP) is a climate-sensitive and ecologically fragile area. Studying drought and its effects on vegetation over the TP is of great significance for ecological conservation. However, there were large uncertainties in previous studies on the drought characteristics and their impacts on alpine vegetation in this region. This study explored the drought changes and their impacts on alpine vegetation during the growing season over the TP in 1982–2018. The results showed that the TP has experienced a wetting trend in most regions of the TP. Correspondingly, the vegetation has become greener in most areas. The wetting and drying trend in the growing season changed around 1995. Before 1995, the TP experienced an overall drying trend with a spatial pattern of a drying trend in the northern regions and a wetting trend in the southern regions, while it showed an overall wetting trend after 1995, with a reversed spatial pattern to that before 1995. After 1995, wetting and drying trends affected the vegetation in 61% of the TP. However, before 1995, the NDVI presented an increasing trend in most areas of the TP under a drying trend. Therefore, a drying trend was not the primary factor affecting vegetation growth in this period. Instead, changes in the cryosphere induced by warming could be the main factor. In addition, the distribution of vegetation across the TP was primarily influenced by drought intensity, which had the greatest impact on sparse vegetation, followed by meadow and grassland. This study enhances our understanding of the impact of drought changes on alpine vegetation on the TP.

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Section: Discussionsupporting

confidence: 72%

The Impacts of Drought Changes on Alpine Vegetation during the Growing Season over the Tibetan Plateau in 1982–2018

Li,

Pan

2024

Remote Sensing

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“…In addition, the linear trend of ET was also calculated using NCL. SEM, a statistical technique widely used to test and assess causal relationships among climate system variables (Pan et al, 2022), was utilized to analyze the pathways of the influencing factors on ET. The direct and indirect impact factors were distinguished in the SEM analysis.…”

Section: Methodsmentioning

confidence: 99%

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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“…The dataset has a 3 h temporal resolution, 0.1 • spatial resolution, and spans the period 1979-2018. This forcing dataset has been evaluated in several studies in recent years, and the results indicate that it is very accurate and suitable for land surface process modeling [30,[39][40][41]. Since our simulation period in this study is 2000-2012, we only used data from the corresponding time period of this dataset.…”

Section: Forcing and Soil Datasetsmentioning

confidence: 99%

Impacts of Land Use/Land Cover Distributions on Permafrost Simulations on Tibetan Plateau

Pan,

Li,

Wang

et al. 2023

Remote Sensing

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The Tibetan Plateau (TP) is distributed with large areas of permafrost, which have received increasing attention as the climate warms. Accurately modeling the extent of permafrost and permafrost changes is now an important challenge for climate change research and climate modeling in this region. Uncertainty in land use and land cover (LULC), which is important information characterizing surface conditions, directly affects the accuracy of the simulation of permafrost changes in land surface models. In order to investigate the effect of LULC uncertainty on permafrost simulation, we conducted simulation experiments on the TP using the Community Land Model, version 5 (CLM5) with five high-resolution LULC products in this study. Firstly, we evaluated the simulation results using shallow soil temperature data and deep borehole data at several sites. The results show that the model performs well in simulating shallow soil temperatures and deep soil temperature profiles. The effect of different land use products on the shallow soil temperature and deep soil temperature contours is not obvious due to the small differences in land use products at these sites. Although there is little difference in the simulating results of different land use products when compared to the permafrost distribution map, the differences are noticeable for the simulation of the active layer. Land cover had a greater impact on soil temperature simulations in regions with greater land use inconsistency, such as at the junction of bare soil and grassland in the northwestern part of the TP, as well as in the southeast region with complex topography. The main way in which this effect occurs is that land cover affects the net surface radiation, which in turn causes differences in soil temperature simulations. In addition, we discuss other factors affecting permafrost simulation results and point out that increasing the model plant function types as well as carefully selecting LULC products is one of the most important ways to improve the simulation performance of land-surface models in permafrost regions.

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Different responses of soil respiration to climate change in permafrost and non‐permafrost regions of the Tibetan plateau from 1979 to 2018

Cited by 10 publications

References 74 publications

The Impacts of Drought Changes on Alpine Vegetation during the Growing Season over the Tibetan Plateau in 1982–2018

The Impacts of Drought Changes on Alpine Vegetation during the Growing Season over the Tibetan Plateau in 1982–2018

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

Impacts of Land Use/Land Cover Distributions on Permafrost Simulations on Tibetan Plateau

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