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

时空气候变化对环境承载的陆基可再生能源的影响：微气候

基本信息

批准号：
NE/H010351/1
负责人：
Susan Waldron
金额：
$ 35.99万
依托单位：
University of Glasgow
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2010
资助国家：
英国
起止时间：
2010 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FH010351%2F1
关键词：
Impact Spatio Climatic Variability Environment

项目摘要

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.

许多当前或预计的未来土地可再生能源方案高度依赖于非常局部的气候条件，尤其是在复杂地形地区。例如，平均风速（这是评估风电场生存能力的决定因素）的变化，距离不超过典型农场的大小。生物能源作物生产率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在托管陆基可再生能源（LBR）中景观的反应显着差异，而无需更好的理解可能会损害我们部署LBR以最大程度地提高环境和能源增长的能力。目前，气候预测模型的运行量比可再生能源应用所需的尺寸要少得多。使用多种经验技术实现了气候数据所需的缩减，其可靠性随着地形的复杂性的增加而降低。在这个项目中，我们将使用从数值天气预测领域采取的非常高分辨率的嵌套数值建模的新出现技术来开发微气候模型，该模型将能够使气候预测在本地降低到小于一公里的尺度。我们将对风电场和生物能源作物网站的新模型进行验证实验。该模型将应用于（i）预测风电场对土壤碳固相位的影响的问题，从而解决了风电场方案的碳投资回报时间和（ii）预测生物能源作物的局部产量变化的问题。将使用计算流体动力学模型（CFD）进行风力涡轮机对彼此和空地交换的影响的极高分辨率数值建模。该项目将为当地规模提供气候影响预测的新工具，并将为托管LBR时敏感（通常是高地）环境的弹性和适应性的详细物理，生物形态和地球化学过程提供新的见解。