Concepts in modelling N2O emissions from land use
Concepts in modelling N2O emissions from land use
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DOI：
10.1007/s11104-007-9485-0
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发表时间：
2008-08-01
期刊：
影响因子：
4.9
通讯作者：
Baldock, Jeff
中科院分区：
文献类型：
--
作者：
Farquharson, Ryan;Baldock, Jeff
模拟土壤中的一氧化二氮（N2 O）排放具有挑战性，因为涉及多个生物过程，每个过程对各种环境和土壤因素的反应不同。土壤含水量、有机碳、温度和pH值经常用于预测N2 O排放的模型，但对于这些因素中的每一个，都有尚未完全理解的概念。虽然普遍的土壤水分模型的措施，水填充的孔隙空间的基础上的功能，在不同的土壤容重的应用并不理想。气体和溶质在土壤中的扩散受空气和水的体积分数控制。在不同容重的土壤中，这两个术语在恒定的充满水的孔隙空间中变化。土壤有机碳以两种方式影响N2 O排放：作为微生物的能量来源，以及驱动生物需氧量和在土壤中创建厌氧区。土壤温度通过影响微生物和酶的活性来影响N2 O的排放。已经提出了各种温度响应函数。优选的响应函数应包含最佳温度，该最佳温度可根据气候条件而变化，以说明微生物的适应性。土壤pH值对硝化和反硝化的速率和产物比有直接和间接的影响。在建模中需要考虑pH最适和微生物适应的概念。方法问题，如微与散装土壤测量和分配N2 O通量的各种N转化过程仍然是一个障碍，表征pH值和其他因素对N2 O排放量的影响。使用回归分析量化N2 O排放量对单个因素的响应，需要通过实验控制所有其他因素。边界线分析提供了一种定义对单个输入变量的响应的方法，其中其他影响变量不受控制。这样的分析可以帮助定义的形状和大小的响应函数被纳入过程模拟模型。过程/机理模拟模型提供了比经验模型更大的可转移性，但在开发模型结构时，仔细考虑时间和空间尺度以及运行这些模型的数据的可用性至关重要。
Modelling nitrous oxide (N2O) emissions from soil is challenging because multiple biological processes are involved that each respond differently to various environmental and soil factors. Soil water content, organic carbon, temperature and pH are often used in models that predict N2O emissions, yet for each of these factors there are concepts that are not fully understood. Though a ubiquitous measure of soil water for models, the application of functions based on water filled pore space across soils that vary in bulk density is not ideal. Diffusion of gases and solutes in soil are controlled by the volume fractions of air and water present. Across soils with different bulk densities, both of these terms vary at constant water filled pore space. Soil organic carbon influences N2O emissions in two ways: as a source of energy for denitrifiers and also by driving biological oxygen demand and the creation of anaerobic zones in the soil. Soil temperature influences N2O emissions through its effect on the activity of microorganisms and enzymes. A variety of temperature response functions have been proposed. The preferred response function should contain a temperature optimum that can be varied in response to climatic conditions to account for microbial adaptation. Soil pH can have direct and indirect influences on rates and product ratios of nitrification and denitrification. The concepts of pH optima and microbial adaptation need to be considered in modelling. Methodological issues such as microsite versus bulk soil measurements and apportioning N2O fluxes to the various N transformation processes remain an impediment to characterising the influence of pH and other factors on N2O emissions. Quantifying the response of N2O emissions to individual factors using regression analysis requires all other factors to be controlled experimentally. Boundary line analysis provides a way of defining the response to a single input variable where other influencing variables are not controlled. Such analyses can aid in the definition of the shape and magnitude of response functions to be incorporated into process simulation models. Process/mechanistic simulation models offer a greater transferability than empirical models but careful consideration of temporal and spatial scale and the availability of data to run these models is critical in developing model structure.