ORCHIDEE-MICT-BIOENERGY: an attempt to represent the production of lignocellulosic crops for bioenergy in a global vegetation model
ORCHIDEE-MICT-BIOENERGY: an attempt to represent the production of lignocellulosic crops for bioenergy in a global vegetation model
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DOI：
10.5194/gmd-11-2249-2018
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发表时间：
2018-06-15
影响因子：
5.1
通讯作者：
Jornet-Puig, Albert
中科院分区：
文献类型：
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作者：
Li, Wei;Yue, Chao;Jornet-Puig, Albert
用于木质纤维素生物质的生物能源作物种植对于未来的气候减缓越来越重要，并且在综合评估模型（IAM）中大规模假设，该模型开发了未来土地利用变化情景，该情景与足够的粮食生产和深度脱碳的双重约束相一致，以实现低气候变暖目标。在大多数全球植被模型中，没有生产木质纤维素生物质的作物的具体表示，导致生物质产量和其他碳输出的模拟偏差，进而导致未来生物能源生产。在这里，我们介绍了四个新的植物功能类型（PFT），代表四个主要的木质纤维素生物能源作物，桉树，白杨和杨柳，芒草，柳枝稷，在全球过程为基础的植被模型ORCHIDEE。光合作用，碳分配和物候的新参数化建议的基础上汇编现场测量。在模型中还增加了一个特定的收获模块，以模拟基于其年龄动态的生物能源树PFT的旋转。由此产生的ORCHIDEE-MICT-BIOENERGY模型应用于296个地点，在这些地点，收获的生物质的实地测量可用于不同的生物能源作物。新模型基本上可以再现全球生物能源作物产量观测数据。与基于网格的模拟与点尺度测量和缺乏施肥和施肥管理实践的模型结果的偏差进行了讨论。这项研究揭示了正确代表生物能源作物模拟其产量的重要性。这里提出的生物能源作物的参数化是通用的，足以适用于其他全球植被模型。
Bioenergy crop cultivation for lignocellulosic biomass is increasingly important for future climate mitigation, and it is assumed on large scales in integrated assessment models (IAMs) that develop future land use change scenarios consistent with the dual constraint of sufficient food production and deep decarbonization for low climate-warming targets. In most global vegetation models, there is no specific representation of crops producing lignocellulosic biomass, resulting in simulation biases of biomass yields and other carbon outputs, and in turn of future bioenergy production. Here, we introduced four new plant functional types (PFTs) to represent four major lignocellulosic bioenergy crops, eucalypt, poplar and willow, Miscanthus, and switchgrass, in the global process-based vegetation model ORCHIDEE. New parameterizations of photosynthesis, carbon allocation, and phenology are proposed based on a compilation of field measurements. A specific harvest module is further added to the model to simulate the rotation of bioenergy tree PFTs based on their age dynamics. The resulting ORCHIDEE-MICT-BIOENERGY model is applied at 296 locations where field measurements of harvested biomass are available for different bioenergy crops. The new model can generally reproduce the global bioenergy crop yield observations. Biases in the model results related to grid- based simulations versus the point-scale measurements and the lack of fertilization and fertilization management practices in the model are discussed. This study sheds light on the importance of properly representing bioenergy crops for simulating their yields. The parameterizations of bioenergy crops presented here are generic enough to be applicable in other global vegetation models.