Global impacts of vegetation clumping on regulating land surface heat fluxes

Li, Fa; Hao, Dalei; Zhu, Qing; Yuan, Kunxiaojia; Braghiere, Renato K.; He, Liming; Luo, Xiangzhong; Wei, Shanshan; Riley, William J.; Zeng, Yelu; Chen, Min

doi:10.1016/j.agrformet.2023.109820

Agricultural and Forest Meteorology

2024

DOI: 10.1016/j.agrformet.2023.109820

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Global impacts of vegetation clumping on regulating land surface heat fluxes

Fa Li,

Dalei Hao,

Qing Zhu

et al.

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Cited by 3 publications

References 70 publications

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Seasonal and vertical variation in canopy structure and leaf spectral properties determine the canopy reflectance of a rice field

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Mõttus,

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Agricultural and Forest Meteorology

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Seasonal and vertical variation in canopy structure and leaf spectral properties determine the canopy reflectance of a rice field

Liu,

Mõttus,

Gastellu-Etchegorry

et al. 2024

Agricultural and Forest Meteorology

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Observational study on the surface radiation budget and energy balance closure of grassland in the eastern arid region of Northwest China

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et al. 2024

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Projecting Large Fires in the Western US With an Interpretable and Accurate Hybrid Machine Learning Method

Li,

Zhu,

Yuan

et al. 2024

Earth's Future

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More frequent and widespread large fires are occurring in the western United States (US), yet reliable methods for predicting these fires, particularly with extended lead times and a high spatial resolution, remain challenging. In this study, we proposed an interpretable and accurate hybrid machine learning (ML) model, that explicitly represented the controls of fuel flammability, fuel availability, and human suppression effects on fires. The model demonstrated notable accuracy with a F1‐score of 0.846 ± 0.012, surpassing process‐driven fire danger indices and four commonly used ML models by up to 40% and 9%, respectively. More importantly, the ML model showed remarkably higher interpretability relative to other ML models. Specifically, by demystifying the “black box” of each ML model using the explainable AI techniques, we identified substantial structural differences across ML fire models, even among those with similar accuracy. The relationships between fires and their drivers, identified by our model, were aligned closer with established fire physical principles. The ML structural discrepancy led to diverse fire predictions and our model predictions exhibited greater consistency with actual fire occurrence. With the highly interpretable and accurate model, we revealed the strong compound effects from multiple climate variables related to evaporative demand, energy release component, temperature, and wind speed, on the dynamics of large fires and megafires in the western US. Our findings highlight the importance of assessing the structural integrity of models in addition to their accuracy. They also underscore the critical need to address the rise in compound climate extremes linked to large wildfires.

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Global impacts of vegetation clumping on regulating land surface heat fluxes

Cited by 3 publications

References 70 publications

Seasonal and vertical variation in canopy structure and leaf spectral properties determine the canopy reflectance of a rice field

Seasonal and vertical variation in canopy structure and leaf spectral properties determine the canopy reflectance of a rice field

Observational study on the surface radiation budget and energy balance closure of grassland in the eastern arid region of Northwest China

Projecting Large Fires in the Western US With an Interpretable and Accurate Hybrid Machine Learning Method

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