Context

Accurately predicting microclimate is considered a high priority for understanding organismal responses to climate change at biologically relevant scales. However, approaches to developing robust microclimate datasets and understanding of the biophysical processes altering microclimatic regimes are limited.

Objectives

We developed and evaluated an approach for predicting microclimatic temperatures in montane forests that incorporates the influence of complex vegetation structure and landscape physiography. Additionally, we determined spatiotemporal mismatches between free-air and microclimatic temperatures to highlight the location, phenology, and magnitude of differences in predicted temperature.

Methods

We combined temperature datalogger measurements with LiDAR-derived vegetation and GIS-derived landscape physiographic characteristics to downscale free-air temperatures to microclimatic (3 m² spatial resolution) temperatures in the Great Smoky Mountains. We assessed the contribution of forest vegetation layers in altering microclimatic temperatures and model accuracy, and compared coarse-grain temperature maps with microclimatic temperature maps.

Results

Understory vegetation structure contributes to microclimatic buffering of near-surface, forest temperatures and enhances the accuracy of maximum temperature predictions during the growing season by altering the effects of solar insolation and topographic convergence index on microclimatic temperatures. Elevation and solar insolation covaried with spatiotemporal mismatches between free-air and microclimatic temperatures, suggesting that these landscape physiographic characteristics may contribute to deviations between macro- and micro-scale temperature.

Conclusions

Our findings demonstrate the importance of including complex vegetation characteristics and biophysical interactions as climate forcing factors in microclimate modeling. We also demonstrate the plausibility of accurately predicting microclimatic temperatures over broad extents, an important step in predicting potential organismal responses to climate change.

中文翻译：

---

在温带落叶林中，林下植被有助于微气候缓冲近地表温度

语境

准确预测小气候被认为是在生物学上相关的尺度上理解生物对气候变化的反应的高度优先事项。然而，开发健壮的微气候数据集和了解改变微气候制度的生物物理过程的方法是有限的。

目标

我们开发并评估了一种预测山地森林微气候温度的方法，该方法结合了复杂植被结构和景观生理的影响。此外，我们确定了自由空气温度和微气候温度之间的时空失配，以突出显示预测温度的位置，物候和差异幅度。

方法

我们将温度数据记录仪的测量结果与LiDAR派生的植被和GIS派生的景观地貌特征相结合，将大风山的自由空气温度降低到微气候（3 m ²空间分辨率）。我们评估了森林植被层在改变微气候温度和模型准确性方面的贡献，并将粗粮温度图与微气候温度图进行了比较。

结果

地下植被结构通过改变日照和地形收敛指数对微气候温度的影响，有助于微气候缓冲近地表温度，森林温度，并提高生长期最高温度预测的准确性。海拔高度和日照强度与自由空气温度和微气候温度之间的时空失配有关，表明这些景观的生理特征可能会导致宏观温度和微观温度之间的偏差。

结论

我们的发现表明，在微气候模拟中将复杂的植被特征和生物物理相互作用作为气候强迫因素的重要性。我们还证明了在广泛范围内准确预测微气候温度的合理性，这是预测潜在生物对气候变化的重要一步。