A workshop to assess and advance the prediction of land-surface response to climate and land-use changes

这项工作的中心是一个为期三天的讲习班，以评估了解和预测地表对气候和土地利用变化的反应方面的知识状况。该活动直接响应了2010年国家资源中心报告(边缘景观)中的主要主题，以及2011年出席人数众多的AGU市政厅会议参与者的行动呼吁。地貌界已经开发出概念和数学模型，研究地球表面将如何响应和反馈气候和土地利用的变化。然而，目前在预测地表对气候和土地利用变化的反应方面的知识状况还没有被综合和记录在一个地方。讲习班将在筹备阶段让基于网络的社区参与，并于2013年5月在亚利桑那州图森附近的生物圈2号召集20-25名参与者，以综合社区的投入，并编写一份关于科学状况的期刊论文和一份关于填补知识空白的战略的单独白皮书。讲习班的两个目标是：(1)评估和总结预测陆面对气候和土地利用变化的反应的知识状况；(2)制定一项利用陆面科学家群体的人才和资源填补知识空白的战略。通过这一基线评估，会议旨在为规划由地貌学家、水文学家、土壤学家、生态学家、大气科学家和其他人共同设计的后续会议奠定基础，以综合知识并讨论预测模型如何纳入所有相关学科的知识。

This work centers on a three-day workshop to assess the state of knowledge in understanding and predicting land-surface responses to climate and land-use changes. The activity responds directly to major themes in a 2010 NRC report (Landscapes on the Edge), as well as calls for action among participants at a well-attended AGU townhall meeting in 2011. The geomorphic community has developed conceptual and mathematical models for how Earth?s surface will respond to and feed back on changes in climate and land use. However, the current state of knowledge in predicting land-surface response to climate and land-use changes has not been synthesized and documented in one place.The workshop will involve web-based community engagement during its preparatory stages, and bring 20-25 participants to Biosphere 2 near Tucson, AZ, in May, 2013 to synthesize the community input and produce a journal paper on the state of the science plus a separate white paper on strategies for filling knowledge gaps. Two goals of the workshop are to (1) assess and summarize the state of knowledge in predicting land-surface response to climate and land-use changes, and (2) devise a strategy for filling knowledge gaps that leverages the talent and resources of the community of land surface scientists. Through this baseline assessment, the meeting aims to lay the foundations for planning a follow-on meeting co-designed by geomorphologists, hydrologists, soil scientists, ecologists, atmospheric scientists, and others to synthesize knowledge and discuss how predictive models can incorporate knowledge from all relevant disciplines.