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Qing Zhu
Lawrence Berkeley National LaboratoryMeeting roles in:
Observation-Constrained and Causal Machine Learning Projection of Global Wetland CH₄ Emissions in the 21st Century
Temporal Variability in the Causal Dynamics of Methanogenesis and Methanotrophy in a High-Latitude Fen Peatland
How non-growing season plant nitrogen uptake affects northern temperate forest growth: modeling and field measurements
Informing Land Surface Model Performance Through Functional Relationship Benchmarks: A Case Study of High-Latitude Respiration Sensitivity to Temperature
Carbon-Nutrient Interactions Control Arctic Vegetation Dynamics
Advancing Process-Based Modeling of Wetland Methane Emissions with Data-Driven Parameterization in ELM-Wet
Weaker CMIP6 Carbon Sink Projections and Enhanced Confidence Level Constrained by Artificial Intelligence
Emerging Machine Learning Approaches for Process Understanding and Predictions in Ecosystem Sciences I Poster
Environmental and forest management changes jointly sustain natural lands in the 21st century
Fire-Induced Reorganization of Land–Atmosphere Coupling Across Ecosystems and Regions
AI Enabled Model-Data-Knowledge Integration for Ecosystem Predictability
Emerging Machine Learning Approaches for Process Understanding and Predictions in Ecosystem Sciences II Oral
Next-Generation Modeling of Global Natural Methane Fluxes: Integrating Multi-scale Observations with Knowledge-Guided Machine Learning
Physically Interpretable Machine Learning for Wildfire Prediction in the Western US
Spatial and Temporal Distribution of Global Wetland Methane Emissions During 2019–2020 Estimated from Satellite Observations
Minimal sampling, maximum insight: A deep learning BRIDGE framework for regional carbon assessment
Soil moisture controls over carbon sequestration and greenhouse gas emissions
Data-driven upscaling of global wetland methane emissions
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