Impact of Spatio-Climatic Variability on Environment-Hosted Land-based Renewables: Microclimates

许多目前或预计未来的陆上可再生能源计划高度依赖于非常局部性的气候条件，特别是在地形复杂的地区。例如，平均风速是评估风电场生存能力的决定因素，在不超过典型风电场大小的距离上变化很大。生物能源作物生产力的变化也在类似的空间尺度上发生。这种局部性的气候变化将导致在拥有陆上可再生能源(LBR)时景观的显著差异，如果没有更好的了解，可能会影响我们部署LBR以最大化环境和能源收益的能力。目前，气候预测模型的运行规模比可再生能源应用所需的规模要大得多。气候数据所需的缩小尺度是使用各种经验技术实现的，这些技术的可靠性随着地形复杂性的增加而降低。在这个项目中，我们将使用来自数值天气预报领域的新出现的高分辨率嵌套数值模拟技术来开发一个微气候模式，该模式将能够对当地的气候预测进行不到一公里的尺度。我们将在风电场和生物能源作物基地对新模型进行验证实验。该模型将被应用于(I)预测风电场对旱地土壤固碳的影响，从而解决风电场方案的碳回收时间问题，以及(Ii)预测当地生物能源作物的产量变化。将使用计算流体动力学模型(CFD)对风力涡轮机之间的相互影响以及对空气-陆地交换的影响进行极高分辨率的数值模拟。该项目将为地方规模的气候影响预测提供新的工具，并将提供新的见解，以了解在托管LBR时影响敏感(通常是高地)环境的复原力和适应能力的详细物理、生物物理和地球化学过程。

Many current or projected future land-based renewable energy schemes are highly dependent on very localised climatic conditions, especially in regions of complex terrain. For example, mean wind speed, which is the determining factor in assessing the viability of wind farms, varies considerably over distances no greater than the size of a typical farm. Variations in the productivity of bio-energy crops also occur on similar spatial scales. This localised climatic variation will lead to significant differences in response of the landscape in hosting land-based renewables (LBR) and without better understanding could compromise our ability to deploy LBR to maximise environmental and energy gains. Currently climate prediction models operate at much coarser scales than are required for renewable energy applications. The required downscaling of climate data is achieved using a variety of empirical techniques, the reliability of which decreases as the complexity of the terrain increases. In this project, we will use newly emerging techniques of very high resolution nested numerical modelling, taken from the field of numerical weather prediction, to develop a micro-climate model, which will be able to make climate predictions locally down to scales of less than one kilometre. We will conduct validation experiments for the new model at wind farm and bio-energy crop sites. The model will be applied to the problems of (i) predicting the effect of a wind farm on soil carbon sequestration on an upland site, thus addressing the question of carbon payback time for wind farm schemes and (ii) for predicting local yield variations of bio-energy crops. Extremely high resolution numerical modelling of the effect of wind turbines on each other and on the air-land exchanges will be undertaken using a computational fluid dynamics model (CFD). The project will provide a new tool for climate impact prediction at the local scale and will provide new insight into the detailed physical, bio-physical and geochemical processes affecting the resilience and adaptation of sensitive (often upland) environments when hosting LBR.