Institute of Natural Disaster Research, School of Environment, Northeast Normal University, Changchun, 130024, China.
State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, Northeast Normal University, Changchun, 130024, China.
Sci Rep. 2020 Nov 17;10(1):19961. doi: 10.1038/s41598-020-76191-2.
Grassland fire dynamics are subject to myriad climatic, biological, and anthropogenic drivers, thresholds, and feedbacks and therefore do not conform to assumptions of statistical stationarity. The presence of non-stationarity in time series data leads to ambiguous results that can misinform regional-level fire management strategies. This study employs non-stationarity in time series data among multiple variables and multiple intensities using dynamic simulations of autoregressive distributed lag models to elucidate key drivers of climate and ecological change on burned grasslands in Xilingol, China. We used unit root methods to select appropriate estimation methods for further analysis. Using the model estimations, we developed scenarios emulating the effects of instantaneous changes (i.e., shocks) of some significant variables on climate and ecological change. Changes in mean monthly wind speed and maximum temperature produce complex responses on area burned, directly, and through feedback relationships. Our framework addresses interactions among multiple drivers to explain fire and ecosystem responses in grasslands, and how these may be understood and prioritized in different empirical contexts needed to formulate effective fire management policies.
草原火动态受到无数气候、生物和人为驱动因素、阈值和反馈的影响，因此不符合统计平稳性的假设。时间序列数据中存在非平稳性会导致结果模糊，从而误导区域火灾管理策略。本研究采用自回归分布滞后模型的动态模拟，研究了多种变量和多种强度的时间序列数据中的非平稳性，以阐明中国锡林郭勒草原火烧迹地气候和生态变化的关键驱动因素。我们使用单位根方法为进一步分析选择适当的估计方法。使用模型估计，我们开发了模拟某些重要变量的瞬时变化（即冲击）对气候和生态变化影响的情景。平均月风速和最高温度的变化对火烧面积直接和通过反馈关系产生复杂的响应。我们的框架解决了多个驱动因素之间的相互作用，以解释草原火灾和生态系统的响应，以及如何在制定有效的火灾管理政策所需的不同经验背景下理解和优先考虑这些响应。
