Assessment of the potential for carbon sink enhancement in the overlapping ecological project areas of China DOI Creative Commons
XU Xiao-juan, Fusheng Jiao,

Dayi Lin

et al.

Frontiers in Plant Science, Journal Year: 2024, Volume and Issue: 15

Published: Nov. 26, 2024

Ecological engineering can significantly improve ecosystem carbon sequestration. However, few studies have projected the sink trends in regions where ecological projects overlap and not considered different climate change conditions land use scenarios. Using ensemble empirical mode decomposition method machine learning algorithms (enhanced boosted regression trees), aims of this study to elucidate stability sinks their driving mechanisms areas predict potential enhancement under varying human activity The findings revealed that: (1) clearly steadily increased five were implemented from 1982 2019. In contrast, did increase with two or three projects. (2) As number increased, impact activities on gradually decreased. eastern China, rapid economic development significant interference hindered growth sinks. western warming humidification trend climate, large-scale afforestation, other improved (3) overlapping exhibited greatest Compared SSP585 scenario, SSP126 was greater. Achieving neutrality requires major account for limitations imposed by climatic conditions. Instead isolated implementation single restoration measures, a comprehensive approach that uses synergistic effects combined strategies is recommended.

Language: Английский

Emergent constraints on historical and future global gross primary productivity DOI
Xin Chen, Tiexi Chen, Yi Liu

et al.

Global Change Biology, Journal Year: 2024, Volume and Issue: 30(8)

Published: Aug. 1, 2024

Terrestrial gross primary productivity (GPP) is the largest carbon flux in global cycle and plays a crucial role terrestrial sequestration. However, historical future GPP estimates still vary markedly. In this study, we reduced uncertainties by employing an innovative emergent constraint method on remote sensing-based datasets (RS-GPP), using ground-based of from towers as observational constraint. Using approach, 2001-2014 was estimated to be 126.8 ± 6.4 PgC year

Language: Английский

Citations

5
Estimation of Gross Primary Productivity Using Performance-Optimized Machine Learning Methods for the Forest Ecosystems in China DOI Open Access
Qin Na, Quan Lai, Gang Bao

et al.

Forests, Journal Year: 2025, Volume and Issue: 16(3), P. 518 - 518

Published: March 15, 2025

Gross primary productivity (GPP) quantifies the rate at which plants convert atmospheric carbon dioxide into organic matter through photosynthesis, playing a vital role in terrestrial cycle. Machine learning (ML) techniques excel handling spatiotemporally complex data, facilitating accurate spatial-scale inversion of forest GPP by integrating limited ground flux measurements with Remote Sensing (RS) observations. Enhancing ML algorithm performance for precise estimation is key research focus. This study introduces Random Grid Search Algorithm (RGSA) hyperparameters tuning to improve Forest (RF) and eXtreme Gradient Boosting (XGB) models across four major regions China. Model optimization progressed three stages: Unoptimized (UO) XGB model achieved R2 = 0.77 RMSE 1.42 g Cm−2 d−1; Hyperparameter Optimized (HO) using RGSA improved 5.19% (0.81) reduced 9.15% (1.29 d−1); Variable Combination (HVCO) selected variables (LAI, Temp, NR, VPD, NDVI) further enhanced 0.83 decreased 1.23 d−1. The optimized estimates exhibited high spatial consistency existing high-quality products like GOSIF GPP, GLASS FLUXCOM validating model’s reliability effectiveness. provides crucial insights improving accuracy optimizing methodologies ecosystems

Language: Английский

Citations

0
Climate change-related lessons learned from a long-term field experiment with maize DOI Creative Commons
Klára Pokovai, Hans-Peter Piepho, Jens Hartung

et al.

Agronomy for Sustainable Development, Journal Year: 2025, Volume and Issue: 45(2)

Published: March 27, 2025

Language: Английский

Citations

0
Addressing Challenges in Simulating Inter–Annual Variability of Gross Primary Production DOI Creative Commons
Ranit De, Shanning Bao, Sujan Koirala

et al.

Journal of Advances in Modeling Earth Systems, Journal Year: 2025, Volume and Issue: 17(5)

Published: April 28, 2025

Abstract A long‐standing challenge in studying the global carbon cycle has been understanding factors controlling inter–annual variation (IAV) of fluxes, and improving their representations existing biogeochemical models. Here, we compared an optimality‐based model a semi‐empirical light use efficiency to understand how current models can be improved simulate IAV gross primary production (GPP). Both simulated hourly GPP were parameterized for (a) each site–year, (b) site with additional constraint on (), (c) site, (d) plant–functional type, (e) globally. This was followed by forward runs using calibrated parameters, evaluations Nash–Sutcliffe (NSE) as model‐fitness measure at different temporal scales across 198 eddy‐covariance sites representing diverse climate–vegetation types. better (median normalized NSE: 0.83 0.85) than annual 0.54 0.63) most sites. Specifically, substantially from NSE −1.39 0.92 when drought stress explicitly included. Most variability performances due types parameterization strategies. The produced statistically simulations model, site–year yielded performance. Annual performance did not improve even . Furthermore, both underestimated peaks diurnal GPP, suggesting that predictions could produce Our findings reveal modeling deficiencies fluxes guide improvements further development.

Language: Английский

Citations

0
Disentangling the Influential Factors Driving NPP Decrease in Shandong Province: An Analysis from Time Series Evaluation Using MODIS and CASA Model DOI Creative Commons

Guangyu Lv,

Xuan Li, Lei Fang

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(11), P. 1966 - 1966

Published: May 30, 2024

Net Primary Productivity (NPP) is a critical metric for assessing terrestrial carbon sequestration and ecosystem health. While advancements in NPP modeling have enabled estimation at various scales, hidden anomalies within time series necessitate further investigation to understand the driving forces. This study focuses on Shandong Province, China, generating high-resolution (250 m) monthly product 2000–2019 using Carnegie–Ames–Stanford Approach (CASA) model, integrated with satellite remote sensing ground observations. We employed Seasonal Mann–Kendall (SMK) Test Breaks For Additive Season Trend (BFAST) algorithm differentiate between gradual declines abrupt losses, respectively. Beyond analyzing land use cover (LULC) transitions, we utilized Random Forest models elucidate influence of environmental factors changes. The findings revealed significant overall increase annual across area, moderate average 503.45 gC/(m2·a) during 2000–2019. Although 69.67% total area displayed substantial monotonic increase, 3.89% experienced 8.43% exhibited declines. Our analysis identified LULC primarily driven by urban expansion, as being responsible 55% loss areas 33% decline areas. effectively explained remaining areas, revealing that magnitude losses intensity were complex interplay factors. These varied vegetation types change types, explanatory variables related status climatic factors—particularly precipitation—having most prominent suggests intensified extreme events led diminishment Province. Nevertheless, positive growth trends observed some highlight potential enhancement through targeted management strategies.

Language: Английский

Citations

3
Causal inference reveals the dominant role of interannual variability of carbon sinks in complicated environmental-terrestrial ecosystems DOI
Chaoya Dang, Zhenfeng Shao, Peng Fu

et al.

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 311, P. 114300 - 114300

Published: July 2, 2024

Language: Английский

Citations

3
Better representation of vegetation phenology improves estimations of annual gross primary productivity DOI Creative Commons
Hanliang Gui, Qinchuan Xin, Xuewen Zhou

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: 82, P. 102767 - 102767

Published: Aug. 10, 2024

Carbon uptake by vegetation plays a vital role in the global carbon cycle. Annual gross primary productivity (AGPP) represents total amount of compounds produced photosynthesis over year and is crucial metric for quantifying uptake. Although some theories have been developed to explain spatiotemporal variation AGPP, they often overlook seasonal differences across phenological periods. This gap highlights need more reasonable representation phenology AGPP modelling. Therefore, we novel theoretical model that decomposes into detailed periods (green-up, maturation, senescence) investigated effects climatic factors on components AGPP. Compared with existing models, our considers length multiple periods, rather than just period. When evaluated against flux tower data, outperformed comparative model, higher determination coefficient lower root mean square error. The analysis also demonstrated can reproduce spatial temporal variations satellite-based In addition, identified distinct responses AGPP's factors: shortwave radiation predominantly affected component during senescence, air temperature green-up, vapor pressure deficit maturation. Our study proposes potential mechanism estimation importance accurately representing

Language: Английский

Citations

2
Coupled models of water and carbon cycles from leaf to global: A retrospective and a prospective DOI Creative Commons
Ying‐Ping Wang, Lu Zhang, Xu Liang

et al.

Agricultural and Forest Meteorology, Journal Year: 2024, Volume and Issue: 358, P. 110229 - 110229

Published: Sept. 13, 2024

Language: Английский

Citations

2
Spatiotemporal variability of near-surface CO2 and its affecting factors over Mongolia DOI Creative Commons

Terigelehu Te,

Hasi Bagan,

Meihui Che

et al.

Environmental Research, Journal Year: 2023, Volume and Issue: 236, P. 116796 - 116796

Published: July 29, 2023

We investigate the spatiotemporal variability of near-surface CO2 concentrations in Mongolia from 2010 to 2019 and factors affecting it over four climate zones based on Köppen-Geiger classification system, including arid desert (BWh), steppe (BSk), dry (Dw), polar frost (ET). Initially, we validate datasets obtained Greenhouse Gases Observing Satellite (GOSAT) using ground-based observations World Data Center for (WDCGG) found good agreement. The results showed that steadily increased 389.48 ppmv 409.72 2019, with an annual growth rate 2.24 ppmv/year. Spatially, southeastern Gobi region has highest average concentration, while northwestern Alpine Meadow exhibits most significant rate. Additionally, monthly seasonal variations were observed each zone, levels decreasing a minimum summer reaching maximum spring. Furthermore, our findings revealed negative correlation between vegetation parameters (NDVI, GPP, LAI) during when photosynthesis is at its peak, positive was spring autumn capacity carbon sequestration lower. Understanding different uptake may help improve estimates ecosystems such as deserts, steppes forests.

Language: Английский

Citations

6
Ecosystem responses dominate the trends of annual gross primary productivity over terrestrial ecosystems of China during 2000–2020 DOI
Xianjin Zhu, Guirui Yu, Zhi Chen

et al.

Agricultural and Forest Meteorology, Journal Year: 2023, Volume and Issue: 343, P. 109758 - 109758

Published: Oct. 18, 2023

Language: Английский

Citations

6