近日，内蒙古师范大学红英及其团队通过耦合机器学习和日温指标模拟了北方森林的春季物候。相关论文于2024年12月17日发表于《中国地理科学》杂志。

对于植被春季物候，日间温度在提供热量积累和降温需求方面的作用是不同的。虽然以往的研究已经建立了叶始期与日温之间的相关性，但目前基于日温指标建立春季物候模型的研究仍然有限。

在该项研究中，研究人员证实了北方森林生长季节开始（SOS）对日温和平均温度的敏感性。研究人员采用K-最近邻回归（KNR-T_DN）模型、随机森林回归（RFR-T_DN）模型、极端梯度增强（XGB-T_DN）模型和由日间温度指标驱动的光梯度增强机器模型（LightGBM-T_DN），对1982—2015年期间的SOS进行了估算，并基于耦合模式比较项目第6阶段（CMIP6）气候情景数据集，对2015—2100年的SOS进行了预估。

结果显示，北方森林SOS对白天温度的敏感性大于平均温度和夜间温度。与KNR-T_DN模型、RFR-T_DN模型和XGB-T_DN模型相比，LightGBM-T_DN模型在所有植被类型中表现最佳，RMSE和偏差最低。通过引入日温指标代替单纯依靠平均温度指标模拟春季物候，提高了模型的准确性。

此外，季前日积温、日积温和积雪结束日期是研究区域SOS模拟的重要驱动因素。基于LightGBM-T_DN模型的模拟结果显示，在未来气候情景下，SOS呈现出先上升后稳定的趋势。研究结果强调了日间温度指标作为替代平均温度指标驱动春季物候模型的潜力，为春季物候模拟提供了一种有前景的新方法。

附：英文原文

Title: Modeling of Spring Phenology of Boreal Forest by Coupling Machine Learning and Diurnal Temperature Indicators

Author: Deng, Guorong, Zhang, Hongyan, Hong, Ying, Guo, Xiaoyi, Yi, Zhihua, Biniyaz, Ehsan

Issue&Volume: 2024-12-17

Abstract: The roles of diurnal temperature in providing heat accumulation and chilling requirements for vegetation spring phenology differ. Although previous studies have established a stronger correlation between leaf onset and diurnal temperature than between leaf onset and average temperature, current research on modeling spring phenology based on diurnal temperature indicators remains limited. In this study, we confirmed the start of the growing season (SOS) sensitivity to diurnal temperature and average temperature in boreal forest. The estimation of SOS was carried out by employing K-Nearest Neighbor Regression (KNR-T_DN) model, Random Forest Regression (RFR-T_DN) model, eXtreme Gradient Boosting (XGB-T_DN) model and Light Gradient Boosting Machine model (LightGBM-T_DN) driven by diurnal temperature indicators during 1982–2015, and the SOS was projected from 2015 to 2100 based on the Coupled Model Intercomparison Project Phase 6 (CMIP6) climate scenario datasets. The sensitivity of boreal forest SOS to daytime temperature is greater than that to average temperature and nighttime temperature. The LightGBM-T_DN model perform best across all vegetation types, exhibiting the lowest RMSE and bias compared to the KNR-T_DN model, RFR-T_DN model and XGB-T_DN model. By incorporating diurnal temperature indicators instead of relying only on average temperature indicators to simulate spring phenology, an improvement in the accuracy of the model is achieved. Furthermore, the preseason accumulated daytime temperature, daytime temperature and snow cover end date emerged as significant drivers of the SOS simulation in the study area. The simulation results based on LightGBM-T_DN model exhibit a trend of advancing SOS followed by stabilization under future climate scenarios. This study underscores the potential of diurnal temperature indicators as a viable alternative to average temperature indicators in driving spring phenology models, offering a promising new method for simulating spring phenology.

DOI: 10.1007/s11769-024-1478-x

Source: https://link.springer.com/article/10.1007/s11769-024-1478-x