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

BCS-0649380Mark 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改善对全球气候变化和陆地表面过程之间相互作用的理解的关键部分。目前的方法假定对极小面积的估计代表较大的区域。树种对气候变化高度敏感，春季的树叶盛开和随后的扩展(物候期)的时机反过来又对整个景观中较低的大气能量-质量交换产生深远影响。然而，在群落水平上春季物候时间的空间差异还没有得到系统的测量和分析。如果变化很大，这些变化可能反映了春季叶片发育期间植物生长的梯度，这可能会导致季节性通量的系统误差，其幅度等于或大于生长季节的高峰时期。因此，以空间明确的方式收集的物候数据为测量地貌一级的空间变化提供了相当大的机会，这对于将通量测量准确扩大到更大的区域或缩小区域尺度的大气环流模型至关重要。在这个项目中，春季物候数据的空间变异性将在群落水平上进行测量和分析，然后与微环境和遥感测量进行比较。从这项研究出发的详细的空间集中物候测量和空间分析将为未来明确将物候变化与植物生理反应联系起来的工作奠定基础。这项研究产生的空间集中的物候测量将为研究地点同时进行的遥感研究提供春季植物发育和生长的记录，其中包含更多关于物种差异、空间变异性和准确的事件时间的信息，这些信息通常在过去被记录下来。这些措施将为比较和验证卫星衍生措施提供新的机会，并可能导致考虑在更多地点采用类似的物候监测计划。总体而言，该项目的成果还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与社会影响相关的问题。该项目将解决在气候学、植物生理生态学和遥感领域取得进展的重大问题。以前没有记录到大范围的物候(春季植物生长开始)在空间和时间上的变异性，也没有结合辅助测量进行记录。了解春季植物物候发育的空间模式(以及驱动这些模式的过程)可能是改进从中等分辨率遥感数据得出的更大面积的水蒸气和二氧化碳流动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化、大气-生物圈相互作用，并产生准确扩大到更大区域的测量所必需的测量结果。

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.