Scale dependency of trade-offs/synergies analysis of ecosystem services based on Bayesian Belief Networks: A case of the Yellow River Basin

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 375, P. 124410 - 124410

Published: Feb. 1, 2025

Language: Английский

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Sub-seasonal soil moisture anomaly forecasting using combinations of deep learning, based on the reanalysis soil moisture records

Agricultural Water Management, Journal Year: 2024, Volume and Issue: 295, P. 108772 - 108772

Published: March 13, 2024

Sub-seasonal drought forecasting is crucial for early warning in estimating agricultural production and optimizing irrigation management, as skills are relatively weak during this period. Soil moisture exhibits stronger persistence compared to other climate system quantities, which makes it especially influential shaping land-atmosphere feedback, thus supplying a unique insight into forecasting. Relying on the soil memory, study investigates combination of multiple deep-learning modules sub-seasonal indices hindcast Huai River basin China, using long-term ERA5-Land records with noise-assisted data analysis tool. The inter-compared models include hybrid model committee machine framework. results show that performance framework can be improved help series decomposition skill not impaired lead time increases. Overall, highlights potential combining memory improve

Language: Английский

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Global Greening Major Contributed by Climate Change With More Than Two Times Rate Against the History Period During the 21th Century

Global Change Biology, Journal Year: 2025, Volume and Issue: 31(3)

Published: March 1, 2025

ABSTRACT Future variations of global vegetation are paramount importance for the socio‐ecological systems. However, up to now, it is still difficult develop an approach project considering spatial heterogeneities from vegetation, climate factors, and models. Therefore, this study first proposes a novel model framework named GGMAOC (grid‐by‐grid; multi‐algorithms; optimal combination) construct using six algorithms (i.e., LR: linear regression; SVR: support vector RF: random forest; CNN: convolutional neural network; LSTM: long short‐term memory; transformer) based on five climatic factors Tmp: temperature; Pre: precipitation; ET: evapotranspiration, SM: soil moisture, CO 2 ). The employed future changes in leaf area index (LAI) four sub‐regions: high‐latitude northern hemisphere (NH), mid‐latitude NH, tropics, southern hemisphere. Our results indicate that LAI will continue increase, with greening rate expanding 2.25 times NH by 2100 against 1982–2014 period. Moreover, RF shows strong applicability In study, we introduce innovative GGMAOC, which provides new scheme environmental geoscientific research.

Language: Английский

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Disentangling the impact of climate change, human activities, vegetation dynamics and atmospheric CO2 concentration on soil water use efficiency in global karst landscapes

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 932, P. 172865 - 172865

Published: April 29, 2024

Language: Английский

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Nonlinear Spatiotemporal Variability of Gross Primary Production in China's Terrestrial Ecosystems Under Water Energy Constraints

Environmental Research, Journal Year: 2025, Volume and Issue: unknown, P. 120919 - 120919

Published: Jan. 1, 2025

Language: Английский

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Nonlinear influences of climatic, vegetative, geographic and soil factors on soil water use efficiency of global karst landscapes: Insights from explainable machine learning

The Science of The Total Environment, Journal Year: 2025, Volume and Issue: 965, P. 178672 - 178672

Published: Feb. 1, 2025

Language: Английский

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Optimizing the ratios of ridge-furrow mulching patterns and urea types improve the resource use efficiency and yield of broomcorn millet on the Loess Plateau of China

Agricultural Water Management, Journal Year: 2025, Volume and Issue: 312, P. 109415 - 109415

Published: March 6, 2025

Language: Английский

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An Evaluation of Future Climate Change Impacts on Key Elements of the Water–Carbon Cycle Using a Physics-Based Ecohydrological Model in Sanchuan River Basin, Loess Plateau

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(19), P. 3581 - 3581

Published: Sept. 26, 2024

The cycle of carbon and water in ecosystems is likely to be significantly impacted by future climate change, especially semiarid regions. While a considerable number investigations have scrutinized the repercussions impending climatic transformations on either or cycles, there scarcity studies delving into effects change coupled water–carbon process its interrelationships. Based this, Sanchuan River Basin, an ecologically fragile region Loess Plateau, was chosen as research area. General circulation model-projected scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) ecohydrological model were integrated predict (2021–2100) changes actual evapotranspiration (ET), surface runoff (Rs), net primary productivity (NPP), soil organic (SOC). results indicated that under impacts warming humidification, ET, Rs, NPP will increase 0.17–6.88%, 1.08–42.04%, 2.18–10.14%, respectively, while SOC decrease 3.38–10.39% basin. A path analysis showed precipitation temperature had significant ET NPP, Rs more sensitive precipitation, impact SOC. Furthermore, all average ET-NPP correlation coefficient greater than 0.6, showing basin’s tightly coupled. However, SSP5-8.5, Rs-NPP decreased from −0.35 near-future period −0.44 far-future period, which may indicate positive effect increased would barely offset negative large increases. As foundation for achieving sustainable resource management ecosystem preservation policies, this study can utilized build adaptation methods manage change.

Language: Английский

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Spatio-temporal dynamics of net primary productivity and the economic value of Spartina alterniflora in the coastal regions of China

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 953, P. 176099 - 176099

Published: Sept. 10, 2024

Language: Английский

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