In order to improve root parameterization in land surface model, the sub-model for root in CERES-Maize was coupled in the SSiB2 after calibrating of maize parameters in SSiB2. The effects of two improved root parameterization schemes on simulated results of land surface flux were analyzed. Results indicated that simulation accuracy of land surface flux was enhanced when the root module provided root depth only with the SSiB2 model (scheme I). Correlation coefficients between observed and simulated values of latent flux and sensible flux increased during the whole growing season, and RMSE of linear fitting decreased. Simulation accuracy of CO2 flux was also enhanced from 121 days after sowing to mature period. On the other hand, simulation accuracy of the flux was enhanced when the root module provided root depth and root length density simultaneously for the SSiB2 model (scheme II). Compared with the scheme I, the scheme II was more comprehensive, while its simulation accuracy of land surface flux decreased. The improved root parameterization in the SSiB2 model was better than the original one, which made simulated accuracy of land-atmospheric flux improved. The scheme II overestimated root relative growth in the surface layer soil, so its simulated accuracy was lower than that of the scheme I.
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Sci Rep
January 2025
Hubei Key Laboratory of Biologic Resources Protection and Utilization, Hubei Minzu University, Enshi, 445000, Hubei Province, China.
As a key food production base, land use changes in the Jianghan Plain (JHP) significantly affect the surface landscape structure and ecological risks, posing challenges to food security. Assessing the ecological risk of the JHP, identifying its drivers, and predicting the risk trends under different scenarios can provide strategic support for ecological risk management and safeguarding food security in the JHP. In this study, the landscape ecological risk (LER) index was constructed by integrating landscape indices from 2000 to 2020, firstly analyzing its spatiotemporal characteristics, subsequently identifying the key influencing factors by using the GeoDetector model, and finally, simulating the risk changes under the four scenarios by using the Markov-PLUS model.
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January 2025
Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China. Electronic address:
Microplastics (MPs) have become pervasive pollutants in terrestrial ecosystems, raising significant ecological risks and human health concerns. Despite growing attention, a comprehensive understanding of their quantification, sources, emissions, transport, degradation, and accumulation in soils remains incomplete. This review synthesizes the current knowledge on the anthropogenic activities contributing to soil MP contamination, both intentional and unintentional behaviors, spanning sectors including agriculture, domestic activities, transportation, construction, and industry.
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January 2025
Land and Food Systems, University of British Columbia, Vancouver, Canada; Faculty of Agricultural, Life and Environmental Sciences, University of Alberta, Edmonton, Canada. Electronic address:
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Research Institute for Environmental Innovation (Binhai, Tianjin), Tianjin 300450, PR China. Electronic address:
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January 2025
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