46 research outputs found

    On the Long-Term Hydroclimatic Sustainability of Perennial Bioenergy Crop Expansion over the United States

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    Large-scale cultivation of perennial bioenergy crops (e.g., miscanthus and switchgrass) offers unique opportunities to mitigate climate change through avoided fossil fuel use and associated greenhouse gas reduction. Although conversion of existing agriculturally intensive lands (e.g., maize and soy) to perennial bioenergy cropping systems has been shown to reduce near-surface temperatures, unintended consequences on natural water resources via depletion of soil moisture may offset these benefits. The hydroclimatic impacts associated with perennial bioenergy crop expansion over the contiguous United States are quantified using the Weather Research and Forecasting Model dynamically coupled to a land surface model (LSM). A suite of continuous (2000–09) medium-range resolution (20-km grid spacing) ensemble-based simulations is conducted using seasonally evolving biophysical representation of perennial bioenergy cropping systems within the LSM based on observational data. Deployment is carried out only over suitable abandoned and degraded farmlands to avoid competition with existing food cropping systems. Results show that near-surface cooling (locally, up to 5°C) is greatest during the growing season over portions of the central United States. For some regions, principal impacts are restricted to a reduction in near-surface temperature (e.g., eastern portions of the United States), whereas for other regions deployment leads to soil moisture reduction in excess of 0.15–0.2 m3 m−3 during the simulated 10-yr period (e.g., western Great Plains). This reduction (~25%–30% of available soil moisture) manifests as a progressively decreasing trend over time. The large-scale focus of this research demonstrates the long-term hydroclimatic sustainability of large-scale deployment of perennial bioenergy crops across the continental United States, revealing potential hot spots of suitable deployment and regions to avoid

    Integrating Cluster Analysis to the ARIMA Model for Forecasting Geosensor Data

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    Clustering geosensor data is a problem that has recently attracted a large amount of research. In this paper, we focus on clustering geophysical time series data measured by a geo-sensor network. Clusters are built by accounting for both spatial and temporal information of data. We use clusters to produce globally meaningful information from time series obtained by individual sensors. The cluster information is integrated to the ARIMA model, in order to yield accurate forecasting results. Experiments investigate the trade-off between accuracy and efficiency of the proposed algorithm
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