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    Evaluating Seasonal Climate Forecasts from Dynamical Models over South America 

    Source: Journal of Hydrometeorology:;2023:;volume( 024 ):;issue: 004
    Author(s): Zhang, Jiaying; Guan, Kaiyu; Fu, Rong; Peng, Bin; Zhao, Siyu; Zhuang, Yizhou
    Publisher: American Meteorological Society
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    Evaluating Seasonal Climate Forecasts from Dynamical Models over South America 

    Source: Journal of Hydrometeorology:;2023:;volume( 024 ):;issue: 004:;page 801
    Author(s): Zhang, Jiaying; Guan, Kaiyu; Fu, Rong; Peng, Bin; Zhao, Siyu; Zhuang, Yizhou
    Publisher: American Meteorological Society
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    Recent Warming Has Resulted in Smaller Gains in Net Carbon Uptake in Northern High Latitudes 

    Source: Journal of Climate:;2019:;volume 032:;issue 018:;page 5849
    Author(s): Zhu, Peng; Zhuang, Qianlai; Welp, Lisa; Ciais, Philippe; Heimann, Martin; Peng, Bin; Li, Wenyu; Bernacchi, Carl; Roedenbeck, Christian; Keenan, Trevor F.
    Publisher: American Meteorological Society
    Abstract: AbstractCarbon balance of terrestrial ecosystems in the northern high latitudes (NHL) is sensitive to climate change. It remains uncertain whether current regional carbon uptake capacity can be sustained under future ...
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    Evaluation of Remotely Sensed Precipitation and Its Performance for Streamflow Simulations in Basins of the Southeast Tibetan Plateau 

    Source: Journal of Hydrometeorology:;2015:;Volume( 016 ):;issue: 006:;page 2577
    Author(s): Wang, Sheng; Liu, Suxia; Mo, Xingguo; Peng, Bin; Qiu, Jianxiu; Li, Mingxin; Liu, Changming; Wang, Zhonggen; Bauer-Gottwein, Peter
    Publisher: American Meteorological Society
    Abstract: our satellite-based precipitation products [TMPA real time (T-rt), its gauge-adjusted version (T-adj), Climate Prediction Center (CPC) morphing technique (CMORPH) real time (C-rt), and its gauge-adjusted version (C-adj)] ...
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    Data-Driven Shear Capacity Prediction of Reinforced Concrete Deep Beams with an Uncertainty-Aware Model 

    Source: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2025:;Volume ( 011 ):;issue: 001:;page 04024076-1
    Author(s): Xiang-Yu Wang; Peng-Bin Liang; Shi-Zhi Chen; Bi-Tao Wu
    Publisher: American Society of Civil Engineers
    Abstract: Data-driven approaches based on machine learning (ML) have become progressively popular for the shear capacity prediction of reinforced concrete (RC) deep beams because the fine-tuned ML models could mostly outperform the ...
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