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

许多目前或预计的未来陆基可再生能源计划高度依赖于非常局部的气候条件，特别是在地形复杂的地区。例如，平均风速是评估风力发电场可行性的决定性因素，但在不大于典型发电场规模的距离上，平均风速变化很大。生物能源作物生产力的变化也发生在类似的空间尺度上。这种局部的气候变化将导致对陆地可再生能源（LBR）的景观响应的显着差异，如果没有更好的理解，可能会损害我们部署LBR以最大限度地提高环境和能源收益的能力。目前，气候预测模型的运行尺度比可再生能源应用所需的尺度要粗得多。所需的气候数据缩小尺度是使用各种经验技术实现的，其可靠性随着地形复杂性的增加而降低。在这一项目中，我们将利用数值天气预报领域的极高分辨率嵌套数值模型的新兴技术，开发一个微型气候模型，该模型将能够对局部气候进行预测，其尺度小于一公里。我们将在风电场和生物能源作物现场对新模型进行验证实验。该模型将被应用到的问题（一）预测的影响，一个风力发电场对土壤固碳的高地网站，从而解决问题的碳投资回报时间的风力发电场计划和（ii）预测当地的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。该项目将提供一个新的工具，用于在地方范围内预测气候影响，并将提供新的见解，以详细的物理，生物物理和地球化学过程，影响敏感（通常是高地）环境的复原力和适应性时，主办LBR。

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.