Spatial-temporal variations of terrestrial evapotranspiration across China from 2000 to 2019

Fu, Jing; Gong, Yueqi; Wu, Zheng; Zou, Jun; Zhang, Meng; Zhang, Zhongbo; Qin, Jianxin; Liu, Jianxiong; Quan, Bin

doi:10.1016/j.scitotenv.2022.153951

Cited by 50 publications

(25 citation statements)

References 74 publications

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“…The excellent performance of the GLEAM in this study aligns with previous findings (Bai & Liu, 2018;Khan et al, 2018;. Moreover, the consistent increase in annual ET since 1982, as indicated by GLEAM, MPM-adj and MERRA2, is consistent with the conclusions of previous studies (T. Feng et al, 2018;J. Fu, Gong, et al, 2022;J.…”

Section: Discussionsupporting

confidence: 92%

“…However, the water balance ET exhibited a weak and negligible increasing trend from 1997 to 2013 (N. Ma et al., 2019; Mao & Wang, 2017). The MOD16 product showed a significant increase at a rate of 5.37 mm yr −1 between 2000 and 2019 (J. Fu, Gong, et al., 2022; J. Fu, Wang, et al., 2022). The upward trend is attributed to various factors such as vegetation changes (F. Zhao et al., 2022), multiple environmental factors including temperature, precipitation, and sunshine duration (T. Feng et al., 2018), land use/cover changes (G. Li et al., 2017), and combinations of these factors (S. Li et al., 2022; X. Li et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

“…The estimation and attribution of long‐term changes in global ET remain highly uncertain because of the substantial differences in input data, model structure, and parameterization across various approaches (Bai & Liu, 2018; M. Cao et al., 2021; Dong & Dai, 2017; Elnashar et al., 2021; J. Fu, Gong, et al., 2022; J. Fu, Wang, et al., 2022; Long et al., 2014; L. Yu et al., 2022; K. Zhang, Kimball, et al., 2016; Y. Zhang, Pena‐Arancibia, et al., 2016; W. Zhu et al., 2022). In the last two decades of the twentieth century, an increasing trend in global land ET was reported by Jung et al.…”

Section: Introductionmentioning

confidence: 99%

“…The MOD16 product showed a significant increase at a rate of 5.37 mm yr −1 between 2000 and 2019 (J. Fu, Gong, et al, 2022;J. Fu, Wang, et al, 2022).…”

mentioning

confidence: 99%

“…The estimation and attribution of long-term changes in global ET remain highly uncertain because of the substantial differences in input data, model structure, and parameterization across various approaches (Bai & Liu, 2018;M. Cao et al, 2021;Dong & Dai, 2017;Elnashar et al, 2021;J. Fu, Gong, et al, 2022;J.…”

mentioning

confidence: 99%

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Terrestrial Evapotranspiration Over China From 1982 to 2020: Consistency of Multiple Data Sets and Impact of Input Data

Mao,

Bai,

et al. 2024

JGR Atmospheres

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Due to limited direct measurements, regional or global terrestrial evapotranspiration (ET) is generally derived from a combination of meteorological and satellite observations. Although the inhomogeneity of the observed climate data has been widely reported, its impact on the calculated ET has not been adequately quantified. This study aimed to calculate ET using the modified Penman‐Monteith (MPM) model with raw and homogenized meteorological data. Additionally, we compared the calculated ET with those estimates from variable methods (water balance, satellite‐based, and reanalysis) in China and its six major river basins from 1982 to 2020. During the overlapping period of 1997–2018, ET calculated from raw input data decreased slightly at −0.39 mm yr−2 (p = 0.64) in China, whereas homogenized ET showed a significant increasing trend of 0.93 mm yr−2 (p = 0.02), with a better agreement with water balance ET (1.93 mm yr−2, p = 0). Global Land Evaporation Amsterdam Model (GLEAM) and Modern‐Era Retrospective Analysis for Research and Applications, version 2 (MERRA2) could reproduce the increasing trends with 2.08 mm yr−2 (p = 0) and 2.59 mm yr−2 (p = 0). The intercomparison of input variables (solar radiation, relative humidity, wind speed, precipitation, and air temperature) among ET products revealed substantial differences, which can account for the discrepancies in ET estimates. Homogenized ET, GLEAM and MERRA2 exhibited significant increasing trends in China and most river basins from 1982 to 2020. Our findings underscore the importance of utilizing homogenized input data for more accurate ET estimation.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The MOD16 product showed a significant increase at a rate of 5.37 mm yr −1 between 2000 and 2019 (J. Fu, Gong, et al, 2022;J. Fu, Wang, et al, 2022).…”

mentioning

confidence: 99%

mentioning

confidence: 99%

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Terrestrial Evapotranspiration Over China From 1982 to 2020: Consistency of Multiple Data Sets and Impact of Input Data

Mao,

Bai,

et al. 2024

JGR Atmospheres

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Monitoring long‐term vegetation dynamics over the Yangtze River Basin, China, using multi‐temporal remote sensing data

Fu,

Liu,

Qin

et al. 2024

Ecosphere

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Vegetation plays a crucial role in nature, with intricate interactions between it and the geographical environment. The Yangtze River Basin (YRB) refers to the third largest river basin globally and an essential ecological security barrier in China. Monitoring vegetation dynamics in the basin is of profound significance for addressing climate change, soil erosion, and biodiversity loss in the basin's ecosystems. Here, we investigate the spatiotemporal variations of vegetation at both the basin and land cover scales in the YRB from 2000 to 2020. We elucidate the determinants driving the changes and explore future normalized difference vegetation index (NDVI) trends. The results indicate that NDVI in the YRB increased at a rate of 0.0032 year−1 (p < 0.01) over the past 21 years, and it is anticipated to maintain an upward trend in the future. Regions in the upper and middle reaches of the YRB demonstrated higher NDVI, whereas regions in the headwater area and the lower reaches showed lower NDVI. Significant vegetation improvement was primarily concentrated in the central part of the basin, while noticeable vegetation degradation was observed in the eastern region. Temperature and wind speed were identified as the primary controlling factors affecting vegetation greenness. Global‐scale climate oscillations played a significant role in driving periodic variations in NDVI, with La Niña events tending to increase NDVI, while El Niño events hindered its rise. Land cover types were influenced by long‐term interactions between natural factors and human activities, although short‐term vegetation variations might be more affected by the latter. Our findings provide valuable insights into the mechanisms behind vegetation variability driven by multiple variables, and the strong vegetation carbon sink capacity advances the conservation and development of ecosystems.

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Stable or unstable? Landscape diversity and ecosystem stability across scales in the forest–grassland ecotone in northern China

Cai

et al. 2023

Landsc Ecol

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Spatial-temporal variations of terrestrial evapotranspiration across China from 2000 to 2019

Cited by 50 publications

References 74 publications

Terrestrial Evapotranspiration Over China From 1982 to 2020: Consistency of Multiple Data Sets and Impact of Input Data

Terrestrial Evapotranspiration Over China From 1982 to 2020: Consistency of Multiple Data Sets and Impact of Input Data

Monitoring long‐term vegetation dynamics over the Yangtze River Basin, China, using multi‐temporal remote sensing data

Stable or unstable? Landscape diversity and ecosystem stability across scales in the forest–grassland ecotone in northern China

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