Bridging Spatial Scales Using Phenological Measurements To Improve Understanding of Springtime Atmosphere-Biosphere Interactions

BCS-0649380 Mark D Schwartz威斯康星大学密尔沃基分校提高大尺度通量估计的准确性是提高对全球气候变化和陆面过程之间相互作用的理解的关键部分。 目前的方法假定极小区域的估计数代表较大区域。 在春季，树种对气候变化高度敏感，叶片的刷新和随后的扩张（物候）的时间，反过来又对整个景观的低层大气能量-质量交换产生深远的影响。然而，在春季物候的时间在社区水平的空间变化还没有系统地测量和分析。 如果大，这些变化可能反映在春季叶片发育过程中，可能会促进系统错误的季节性通量相等或更大的幅度比那些在生长季节的高度植物生长的梯度。 因此，在空间上明确的方式收集的物候数据提供了相当大的机会，测量的通量测量准确的放大到更大的地区或缩小区域尺度的大气环流模型至关重要的水平空间变化。 在本项目中，将在群落水平上测量和分析春季物候数据的空间变异性，然后将其与微环境和遥感测量结果进行比较。 详细的空间集中的物候测量和空间分析，从这项研究中进行将奠定基础，明确地将物候变化与植物生理反应的未来工作。 本研究所产生的空间集中的物候措施将提供同步遥感研究正在进行的研究现场春季植物发育和生长的记录，包含更多的信息物种差异，空间变异性，和精确的事件发生的时间，通常记录在过去。 这些措施将为比较和验证从卫星获得的措施提供新的机会，并可能导致考虑在其他地点采用类似的物候监测计划。 总的来说，该项目的成果也将有助于更好地了解气候变化对生物圈的影响，这将增加对未来潜在变化的了解，并可能允许更好地规划社会影响。该项目将解决对气候学，植物生理生态学和遥感领域的发展具有重要意义的问题。物候（春季植物生长的开始）在空间和时间上的变化以前没有记录在一个大的区域，也没有结合支持测量。了解春季植物物候发育的空间格局（以及驱动它们的过程）可能是提高中分辨率遥感数据中水汽和二氧化碳流量的大面积估计所需的关键知识。这些结果将有助于更广泛地了解景观变异性、大气-生物圈相互作用，并产生必要的测量结果，以便将测量结果准确地扩大到更大的区域。

BCS-0649380Mark D SchwartzUniversity of Wisconsin-MilwaukeeEnhancing accuracy of flux estimates at large scales is a critical part of improving understanding of the interactions between global climate change and land surface processes. Current approaches assume that estimates of extremely small areas are representative of larger regions. The timing of leaf flush and subsequent expansion (phenology) during spring, which in tree species is highly sensitive to climate change, in turn has a profound impact on lower atmospheric energy-mass exchange across the landscape. However, spatial variations in the timing of spring phenology at the community level have not been systematically measured and analyzed. If large, these variations may reflect gradients in plant growth during spring leaf development that could foster systematic errors in seasonal fluxes of equal or greater magnitude than those during the height of the growing season. Thus, phenological data collected in a spatially explicit manner offer considerable opportunities for gauging landscape-level spatial variations crucial for accurate scaling-up of flux measurements to larger areas or downscaling regional-scale atmospheric circulation models. In this project, spatial variability of spring phenological data will be measured and analyzed at the community level, and then compared to the microenvironment and remote sensing measurements. The detailed spatially concentrated phenological measurements and spatial analyses that proceed from this study will lay the foundation for future work that explicitly links phenological variations with plant physiological responses. The spatially concentrated phenological measures produced by this study will provide concurrent remote sensing studies being conducted at the study site with a record of spring plant development and growth that contains vastly more information about species differences, spatial variability, and precise event timing that has typically been recorded in the past. These measures will present new opportunities for comparison and validation of the satellite-derived measures, and may lead to consideration of adoption of similar phenological monitoring schemes at additional sites. Overall, the results of this project will also contribute to better understanding of the impacts of climate change on the biosphere, which will increase knowledge of potential future changes, and may allow for better planning relative to societal impacts.This project will address issues that are significant for advancement across the fields of climatology, plant physiological ecology, and remote sensing. Phenological (onset of spring plant growth) variability in space and time has not been previously recorded over a large area nor combined with supporting measurements. Understanding spatial patterns of spring plant phenological development (and the processes that drive them) may be the key knowledge needed to improve larger area estimates of the flow of water vapor and carbon dioxide derived from moderate resolution remote sensing data. The results will contribute to broader understanding of landscape variability, atmosphere-biosphere interactions, and produce measurements that are necessary to accurately scale-up measurements to larger areas.