Towards a general framework to assess scale dependency in environmental covariates.

建立一个评估环境协变量的尺度依赖性的通用框架。

• 批准号: NE/H000917/1
• 负责人: Ron Corstanje
• 金额: $ 4.28万
• 依托单位: CRANFIELD UNIVERSITY
• 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%2FH000917%2F1
• 关键词: Towards; general; framework; assess; scale

Our predictions of how the environment around may respond to climate change depend on scientific models of how the environment functions. Likewise predictions of how the climate is changing also depend on models of climate. These models all require vast amounts of input data (tend to be data hungry), which is expensive and time consuming to obtain. One of the solutions to this problem it is to use data that has been exhaustively collected over a geographical area or over a period of time, such as a radar image, and relate this data to the data that is needed, such as the amount of Carbon in the soil in an area. However, these relationships are often developed at a scale that is different to the scale at which they are needed. They made be developed in controlled circumstance in a laboratory, yet needed at national or global scales as inputs into these models. The form these relationships take at the laboratory may not hold at the coarser scales and this may therefore introduce error in the model prediction, a phenomenon known as scale dependency. The objective of this project is the lay the foundation of a diagnostic framework in which we can determine whether these scale dependencies exist and supply possible solutions. This framework will be very useful to assess the value of the current national datasets and will help with the design of future sampling and monitoring activities.

我们对周围环境如何应对气候变化的预测取决于环境如何运作的科学模型。同样，对气候变化的预测也依赖于气候模型。这些模型都需要大量的输入数据（往往是数据饥饿），这是昂贵的和耗时的获得。这个问题的解决方案之一是使用在一个地理区域或一段时间内详尽收集的数据，例如雷达图像，并将这些数据与所需的数据相关联，例如某个地区土壤中的碳含量。然而，这些关系的发展规模往往与需要它们的规模不同。它们可以在实验室的受控环境中开发，但在国家或全球范围内需要作为这些模型的投入。这些关系在实验室采取的形式可能在较粗的尺度上不成立，因此可能会在模型预测中引入误差，这种现象称为尺度依赖性。这个项目的目标是奠定一个诊断框架的基础，在这个框架中，我们可以确定这些尺度依赖性是否存在，并提供可能的解决方案。这一框架将非常有助于评估当前国家数据集的价值，并将有助于设计未来的采样和监测活动。