Eli Melaas, PhD
Postdoctoral Research Associate
Department of Earth and Environment
Boston University
Research Highlights
Phenology Modeling
Using FLUXNET Data to Improve Models of Springtime Vegetation Activity Onset in Forest Ecosystems
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Vegetation phenology is sensitive to climate change and variability, and is a first order control on the carbon budget of forest ecosystems. Robust representation of phenology is therefore needed to support model-based projections of how climate change will affect ecosystem function. A variety of models have been developed to predict species or site-specific phenology of trees. However, extension of these models to other sites or species has proven difficult. Using meteorological and eddy covariance data for 29 forest sites (encompassing 173 site-years), we evaluated the accuracy with which 11 different models were able to simulate, as a function of air temperature and photoperiod, spatial and temporal variability in the onset of spring photosynthetic activity. In parallel, we also evaluated the accuracy with which dynamics in remotely sensed vegetation indices from MODIS captured the timing of spring onset. To do this, we used a subset of sites in the FLUXNET La Thuile database located in evergreen needleleaf and deciduous broadleaf forests with distinct active and dormant seasons and where temperature is the primary driver of seasonality. As part of this analysis we evaluated predictions from refined versions of the 11 original models that include parameterizations for geographic variation in both thermal and photoperiod constraints on phenology. Results from cross-validation analysis show that the refined models predict the onset of spring photosynthetic activity with significantly higher accuracy than the original models. Estimates for the timing of spring onset from MODIS were highly correlated with the onset of photosynthesis derived from flux measurements, but were biased late for needleleaf sites. Our results demonstrate that simple phenology models can be used to predict the timing of spring photosynthetic onset both across sites and across years at individual sites. By extension, these models provide an improved basis for predicting how the phenology and carbon budgets of temperature-limited forest ecosystems may change in the coming decades.
Landsat Phenology
Detecting Interannual Variation in Deciduous Broadleaf Forest Phenology Using Landsat TM/ETM+ Data
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Observations of vegetation phenology provide valuable information regarding ecosystem responses to climate variability and change. Phenology is also a first-order control on terrestrial carbon and energy budgets, and remotely sensed observations of phenology are often used to parameterize seasonal vegetation dynamics in ecosystem models. Current land surface phenology products are only available at moderate spatial resolution and possess considerable uncertainty. Higher resolution products that resolve finer spatial detail are therefore needed. A need also exists for data sets and methods that link ground-based observations of phenology to moderate resolution land surface phenology products. Data from the Landsat TM and ETM + sensors have the potential to meet these needs, but have been largely unexplored by the phenology research community. In this paper we present a method for characterizing both long-term average and interannual dynamics in the phenology of temperate deciduous broadleaf forests using multi-decadal time series of Landsat TM/ETM + images. Results show that spring and autumn phenological transition dates estimated from Landsat data agree closely with in-situ measurements of phenology collected at the Harvard Forest in central Massachusetts, and that Landsat-derived estimates for the start and end of the growing season in Southern New England varied by as much as 4 weeks over the 30-year record of Landsat images. Application of this method over larger scales has the potential to provide valuable information related to landscape-scale patterns and long term dynamics in phenology, and for bridging the gap between in-situ phenological measurements collected at local scales and land surface phenology metrics derived from moderate spatial resolution of instruments such as MODIS and AVHRR.
Sunshine Duration
Blue Hill Observatory Sunshine - Assessment of Climate Signals in the Longest Continuous Meteorological Record in North America
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The Blue Hill Meteorological Observatory occupies a unique place in the history of the American Meteorological Society and the development of atmospheric science. Through its 129-yr history, the observatory has been operated by founder Abbott Lawrence Rotch (1861–1912), Harvard University, and the National Weather Service, and it is presently run by the nonprofit Blue Hill Observatory Science Center. While daily temperature and precipitation records are available through the National Climatic Data Center, they do not include the full record of sunshine duration data that were measured using a Campbell–Stokes sunshine recorder. We have recently digitized the observatory's original daily sunshine archives, and now present the first full collection and analysis of sunshine records extending from 1889 to the present. This dataset is unique and salient to modern climate research because the collection represents the earliest and longest continuous measurements of insolation outside of western Europe. This record provides an unprecedented glimpse into regional climate features as well as important links between global phenomena and regional climate. Analysis reveals long-term fluctuations of cloud cover and solar radiation, including signals of regional industrialization, global dimming, volcanic eruptions, and the 11-yr solar cycle. Shorter-period fluctuations include evidence of an intricate annual pattern of sunshine duration and correlations with the Arctic Oscillation, the North Atlantic Oscillation, and galactic cosmic rays.