Xiaoliang Lu

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have been extensively applied to global climate change. However, the noise impedes these data from being further analyzed and used. In this paper, a wavelet-based method is used to remove the contaminated data from time-series observations, which can effectively maintain the temporal pattern and approximate the " true " signals. The method is composed of(More)
The Midwest of the United States includes 12 states and accounts for about a quarter of the total United State land area. In recent years, there is an increasing interest in knowing the biomass potential and carbon balance over this region for the past and the future. In this study, we use the Terrestrial Ecosystem Model (TEM) to evaluate these quantities(More)
[1] Much progress has been made in methane modeling for the Arctic. However, there is still large uncertainty in emissions estimates due to the spatial variability in water table depth resulting from complex topographic gradients, and due to variations in methane production and oxidation due to complex freezing and thawing processes. Here we extended an(More)
a r t i c l e i n f o In this study, we used the remotely-sensed data from the Moderate Resolution Imaging Spectrometer (MODIS), meteorological and eddy flux data and an artificial neural networks (ANNs) technique to develop a daily evapotranspiration (ET) product for the period of 2004–2005 for the conterminous U.S. We then estimated and analyzed the(More)
Development of regional policies to reduce net emissions of carbon dioxide (CO2) would benefit from the quantification of the major components of the region's carbon balance--fossil fuel CO2 emissions and net fluxes between land ecosystems and the atmosphere. Through spatially detailed inventories of fossil fuel CO2 emissions and a terrestrial(More)
The MIT Joint Program on the Science and Policy of Global Change combines cutting-edge scientific research with independent policy analysis to provide a solid foundation for the public and private decisions needed to mitigate and adapt to unavoidable global environmental changes. Being data-driven, the Program uses extensive Earth system and economic data(More)