Assessing the effects of climate and human activity on vegetation change in Northern China

Environmental Research, Journal Year: 2024, Volume and Issue: 247, P. 118233 - 118233

Published: Jan. 21, 2024

Language: Английский

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Soil erosion prediction and spatiotemporal heterogeneity in driving effects of precipitation and vegetation on the northern slope of Tianshan Mountain

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 459, P. 142561 - 142561

Published: May 13, 2024

Language: Английский

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Identification and Evaluation of Key Environmental Drivers Based on the Ten-Year Evolutionary Characteristics of Global Grassland Gpp

Published: Jan. 1, 2025

As the largest terrestrial ecosystem globally, grasslands and their Gross Primary Productivity (GPP) play a critical role in global carbon cycle, influenced by environmental changes human activities. This study classifies into multiple types, uses trend analysis to investigate temporal spatial of GPP for various grassland types from 2010 2020, extracts approximately 940,000 pixel data identify evaluate factors using best prediction model PLS-PM structural equation model. The results indicate that shows an increasing trend, concentrated mid- low-latitude regions, with differences between hemispheres. Woody Savannas have highest mean GPP, while Grasslands lowest. At low altitudes, peaks, reaching maximum elevations at 4580 m 4950 m, respectively, persist higher altitudes lowest GPP. Climate soil hydrology contributed most significantly accounting 62.11%-77.95%, showing contribution (71.63%). Within climate factors, actual evapotranspiration, volumetric water layer, fraction photosynthetically active radiation, temperature had significant positive impacts on CO2 concentration activities smaller direct contributions, primarily influencing indirectly. Topographic least. These findings reveal dominant highlight differing growth trends among providing insights responses change

Language: Английский

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Application of a Random Forest Method to Estimate the Water Use Efficiency on the Qinghai Tibetan Plateau During the 1982–2018 Growing Season

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(3), P. 527 - 527

Published: Feb. 4, 2025

Water use efficiency (WUE) reflects the quantitative relationship between vegetation gross primary productivity (GPP) and surface evapotranspiration (ET), serving as a crucial indicator for assessing coupling of carbon water cycles in ecosystems. As sensitive region to climate change, Qinghai Tibetan Plateau’s WUE dynamics are significant scientific interest understanding interactions forecasting future trends. However, due scarcity observational data unique environmental conditions plateau, existing studies show substantial errors GPP simulation accuracy considerable discrepancies ET outputs from different models, leading uncertainties current estimates. This study addresses these gaps by first employing machine learning approach (random forest) integrate observed flux with multi-source information, developing predictive model capable accurately simulating Plateau (QTP). The random forest results, RF_GPP (R2 = 0.611, RMSE 69.162 gC·m−2·month−1), is higher than that multiple linear regression model, regGPP 0.429, 86.578 significantly better GLASS product, GLASS_GPP 0.360, 91.764 gC·m−2·month−1). Subsequently, based on data, we quantitatively evaluate products various models construct integrates products. REG_ET, obtained integrating five using 0.601, 21.04 mm·month−1), product derived through mean processing, MEAN_ET 0.591, 25.641 mm·month−1). Finally, optimized calculate during growing season 1982 2018 analyze its spatiotemporal evolution. In this study, were observation thereby enhancing estimation WUE. On basis, interannual variation was analyzed, providing foundation studying QTP ecosystems supporting formulation policies ecological construction resource management future.

Language: Английский

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Exploring the Spatiotemporal Alterations in China’s GPP Based on the DTEC Model

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(8), P. 1361 - 1361

Published: April 12, 2024

Gross primary productivity (GPP) is a reliable measure of the carbon sink potential terrestrial ecosystems and an essential element cycle research. This study employs diffuse fraction-based two-leaf light-use efficiency (DTEC) model to imitate China’s monthly GPP from 2001 2020. We studied trend GPP, investigated its relationship with climatic factors, separated contributions climate change human activities. The findings showed that DTEC was widely applicable in China. During period, average increased significantly, by 9.77 g C m−2 yr−1 (p < 0.001). detrimental effect aerosol optical depth (AOD) on more widespread than total precipitation, temperature, solar radiation. Areas benefited AOD, such as Northwest China, experienced significant increases GPP. Climate activities had positive influence during accounting for 28% 72% increase, respectively. Human activities, particularly ecological restoration projects adoption advanced agricultural technologies, played role growth. afforestation plan notable, increasing areas at rate greater 10 yr−1. research provides theoretical foundation long-term management helps develop adaptive tactics.

Language: Английский

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Evolution and Mechanism Analysis of Terrestrial Ecosystems in China with Respect to Gross Primary Productivity

Land, Journal Year: 2024, Volume and Issue: 13(9), P. 1346 - 1346

Published: Aug. 24, 2024

The gross primary productivity (GPP) of vegetation stores atmospheric carbon dioxide as organic compounds through photosynthesis. Its spatial heterogeneity is primarily influenced by the uptake period (CUP) and maximum photosynthetic (GPPmax). Grassland, cropland, forest are crucial components China’s terrestrial ecosystems strongly seasonal climate. However, it remains unclear whether evolutionary characteristics GPP attributable to physiology or phenology. In this study, ecosystem models remote sensing observations multi-source data were utilized quantitatively analyze spatio-temporal dynamics from 1982 2018. We found that exhibited a significant upward trend in most areas over past four decades. Over 60% Chinese grassland 50% its cropland positive growth trend. average annual rates 0.23 3.16 g C m−2 year−1 for grassland, 0.40 7.32 0.67 7.81 forest. GPPmax also indicated overall rate was above 1 regions China. pattern closely mirrored GPP, although local remain uncertain. partial correlation analysis results controlled interannual changes This particularly evident where more than 99% variation GPPmax. context rapid global change, our study provides an accurate assessment long-term factors regulate variability across ecosystems. helpful estimating predicting budget

Language: Английский

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Modeling adaptation strategies to climate change in prospect of agriculture

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 283 - 305

Published: Oct. 11, 2024

Language: Английский

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