Benchmark estimates for aboveground litterfall data derived from ecosystem models

Li, Shihua; Yuan, Wenping; Ciais, Philippe; Viovy, Nicolas; Ito, Akihiko; Jia, Bingrui; Zhu, Dan

doi:10.1088/1748-9326/ab2ee4

Cited by 25 publications

(16 citation statements)

References 44 publications

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“…The results not only provide a reference for assessing forest management and ecosystem services; they also serve as a benchmark to improve evaluation models for ecosystem services related to water retention. Extending regional models to the global scale results in large errors, making it necessary to use site observation data to benchmark the models in the future 36 . In other words, the canopy, litter, and soil data for a large number of observation sites can be simulated on a global scale based on machine learning to generate global products for model correction 36 .…”

Section: Discussionmentioning

confidence: 99%

“…Extending regional models to the global scale results in large errors, making it necessary to use site observation data to benchmark the models in the future 36 . In other words, the canopy, litter, and soil data for a large number of observation sites can be simulated on a global scale based on machine learning to generate global products for model correction 36 . Comparisons of the model results with site observation data can be used to reduce the uncertainty of the model 37 .…”

Section: Discussionmentioning

confidence: 99%

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Global water retention in forest canopy, litter, and soil layers and its controlling factors

Liu

Shi

Tao

et al. 2021

Preprint

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Forest ecosystems play a vital role in the earth’s hydrological process, and precipitation intercepted by forests accounts for more than a quarter of the water in the terrestrial hydrologic cycle. However, water retention in the three layers (canopy, litter, and soil) of forest ecosystems has not yet been thoroughly investigated on a global scale. Here, we investigate the global pattern of forest water retention capacity (WRC) and its controlling environmental factors based on 982 observations of 21 controlling factors in the three forest layers, mainly from 1990 to 2018. The results show that global WRC varies among the different forest types and climatic zones with a mean of 456.71 mm, while the average total water storage is 22,662.47 km3 in forest ecosystems. Climatic variables are the leading factors contributing to the variations in forest WRC, followed by forest structure factors, soil properties, terrain factors, and litter factors. This study advances our understanding of the mechanisms underlying large-scale variations in forest WRC in different climate zones and forest types. The findings demonstrate that controlling factors should be considered when developing policy for regions with important ecological functions. They also provide a benchmark to improve ecohydrological models for simulating global WRC.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Global water retention in forest canopy, litter, and soil layers and its controlling factors

Liu

Shi

Tao

et al. 2021

Preprint

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“…Data were extracted from the main text, tables and appendices, or digitized from figures using Engauge Digitizer version 12.1 (Free Software Foundation, Inc.). Where data were not provided, we obtained the six climatic variables considered from the WorldClim v.2 (Fick & Hijmans, 2017) database and the litter mass from the LitterfallFlux (Li et al, 2019) database.…”

Section: Data Compilationmentioning

confidence: 99%

Litter facilitates plant development but restricts seedling establishment during vegetation regeneration

Zhang

Heděnec

et al. 2022

Functional Ecology

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1. Rapid vegetation regeneration and increased litter production have been predicted under the global greening scenario, but the overall relationship between litter production and vegetation regeneration has not been well addressed. 2. To bridge this knowledge gap, we quantified the responses of the seed stage, seedling stage, plant development stage and vegetation community to litter using 3193 paired observations at the global scale based on the effect size across different categories. 3. Overall, litter significantly decreased seedling establishment and density by 28.4% and 27.7%, increased plant height by 17.4% and decreased species richness by 15.4%. Seed germination at the seed stage was not directly controlled by litter but was positively regulated by changes in soil moisture from litter. In contrast, litter type and litter mass displayed negative effects on the seedling stage, but litter promoted seedling survival with increasing elevation. Moreover, litter composition and plant type dominated the effects of litter on the plant development stage, showing that the increase in litter production might favour the development of broadleaved trees rather than other plants. 4. The present results suggest that litter could restrict the stage from seed to seedling but facilitate plant development after the seedling establishment and slightly affect the vegetation community except for species richness. These results provide deep insight into the relationships between litter production and vegetation regeneration at different stages, which are important for developing models accounting for vegetation regeneration responses to ongoing global greening.

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“…Predicting vegetation growth remains challenging [10]. While process-based ecosystem models play an important role in predicting vegetation growth [4], multiple ecosystem processes impact vegetation growth, and the current process-based models fail to accurately reproduce these critical ecosystem processes [11,12]. An accurate simulation of vegetation growth requires a more realistic representation of multiple processes, such as plant photosynthesis, respiration, and carbon allocation.…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning methods, which are independent of ecosystem process mechanisms, are an alternative means of predicting ecosystem structure and function [12,14,15]. Several approaches, including artificial neural networks, regression trees, support vector regression, and random forest, have been widely employed to predict vegetation growth [15].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Method for Predicting Vegetation Indices in China

Yuan

Dong

2021

Remote Sensing

Self Cite

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To forecast the terrestrial carbon cycle and monitor food security, vegetation growth must be accurately predicted; however, current process-based ecosystem and crop-growth models are limited in their effectiveness. This study developed a machine learning model using the extreme gradient boosting method to predict vegetation growth throughout the growing season in China from 2001 to 2018. The model used satellite-derived vegetation data for the first month of each growing season, CO2 concentration, and several meteorological factors as data sources for the explanatory variables. Results showed that the model could reproduce the spatiotemporal distribution of vegetation growth as represented by the satellite-derived normalized difference vegetation index (NDVI). The predictive error for the growing season NDVI was less than 5% for more than 98% of vegetated areas in China; the model represented seasonal variations in NDVI well. The coefficient of determination (R2) between the monthly observed and predicted NDVI was 0.83, and more than 69% of vegetated areas had an R2 > 0.8. The effectiveness of the model was examined for a severe drought year (2009), and results showed that the model could reproduce the spatiotemporal distribution of NDVI even under extreme conditions. This model provides an alternative method for predicting vegetation growth and has great potential for monitoring vegetation dynamics and crop growth.

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Benchmark estimates for aboveground litterfall data derived from ecosystem models

Cited by 25 publications

References 44 publications

Global water retention in forest canopy, litter, and soil layers and its controlling factors

Global water retention in forest canopy, litter, and soil layers and its controlling factors

Litter facilitates plant development but restricts seedling establishment during vegetation regeneration

A Machine Learning Method for Predicting Vegetation Indices in China

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