Emergent relationships with respect to burned area in global satellite observations and fire-enabled vegetation models
Emergent relationships with respect to burned area in global satellite observations and fire-enabled vegetation models
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DOI：
10.5194/bg-16-57-2019
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发表时间：
2019-01-11
期刊：
影响因子：
4.9
通讯作者：
Arneth, Almut
中科院分区：
文献类型：
--
作者：
Forkel, Matthias;Andela, Niels;Arneth, Almut
最近的气候变化增加了许多地区的火灾易发天气条件，并可能影响火灾的发生，这可能会影响生态系统功能、生物地球化学循环和社会。由于火灾发生和烧毁面积控制的复杂性，预测未来火灾影响可能如何变化是很困难的。在这里，我们的目标是评估基于过程的火灾驱动的动态全球植被模型（DGVM）如何表示控制因素和烧毁面积之间的关系。我们开发了一种面向模式的模型评估方法，使用随机森林 (RF) 算法来识别气候、植被、社会经济预测变量与烧毁面积之间的新兴关系。我们将这种方法应用于 2005 年至 2011 年期间的每月燃烧面积时间序列，这些时间序列来自卫星观测和“火灾建模比对项目”(FireMIP) 的 DGVM，这些数据使用通用协议和强制数据集运行。卫星衍生的关系表明对气候变量（例如最高温度、潮湿天数）、植被特性（例如植被类型、上季植物生产力和叶面积、木质凋落物）和社会经济变量（例如人口密度）具有很强的敏感性。 DGVM 广泛再现了与气候变量的关系，对于某些模型，还再现了与人口密度的关系。有趣的是，卫星响应显示，在大多数易发生火灾的生态系统中，随着前季叶面积指数和植物生产力的增加，烧毁面积大幅增加，而大多数 DGVM 在很大程度上低估了这一点。因此，我们的面向模式的模型评估方法使我们能够诊断出植被对火灾的影响是火驱动的动态全球植被模型准确模拟火灾在全球环境变化下的作用的能力的主要缺陷。
Recent climate changes have increased fire-prone weather conditions in many regions and have likely affected fire occurrence, which might impact ecosystem functioning, biogeochemical cycles, and society. Prediction of how fire impacts may change in the future is difficult because of the complexity of the controls on fire occurrence and burned area. Here we aim to assess how process-based firee-nabled dynamic global vegetation models (DGVMs) represent relationships between controlling factors and burned area. We developed a pattern-oriented model evaluation approach using the random forest (RF) algorithm to identify emergent relationships between climate, vegetation, and socio-economic predictor variables and burned area. We applied this approach to monthly burned area time series for the period from 2005 to 2011 from satellite observations and from DGVMs from the "Fire Modeling Intercomparison Project" (FireMIP) that were run using a common protocol and forcing data sets. The satellite-derived relationships indicate strong sensitivity to climate variables (e.g. maximum temperature, number of wet days), vegetation properties (e.g. vegetation type, previous-season plant productivity and leaf area, woody litter), and to socio-economic variables (e.g. human population density). DGVMs broadly reproduce the relationships with climate variables and, for some models, with population density. Interestingly, satellite-derived responses show a strong increase in burned area with an increase in previous-season leaf area index and plant productivity in most fire-prone ecosystems, which was largely underestimated by most DGVMs. Hence, our pattern-oriented model evaluation approach allowed us to diagnose that veg-etation effects on fire are a main deficiency regarding fireenabled dynamic global vegetation models' ability to accurately simulate the role of fire under global environmental change.