5,995 research outputs found

    Environmental Monitoring Report January 2016-June 2017

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    https://digitalcommons.memphis.edu/govpubs-tn-dept-environment-conservation-environmental-monitoring-report/1006/thumbnail.jp

    Environmental Monitoring Report January through December 2013

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    https://digitalcommons.memphis.edu/govpubs-tn-dept-environment-conservation-environmental-monitoring-report/1010/thumbnail.jp

    Environmental Monitoring Report For Work Performed July 1, 2017 through June 30, 2018

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    https://digitalcommons.memphis.edu/govpubs-tn-dept-environment-conservation-environmental-monitoring-report/1005/thumbnail.jp

    Environmental Monitoring Report 2015

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    https://digitalcommons.memphis.edu/govpubs-tn-dept-environment-conservation-environmental-monitoring-report/1008/thumbnail.jp

    Evaluating the climate effects of mid-1800s deforestation in New England, USA, using a Weather, Research, and Forecasting (WRF) Model Multi-Physics Ensemble

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    The New England region of the northeastern United States has a land use history characterized by forest clearing for agriculture and other uses during European colonization and subsequent reforestation following widespread farm abandonment. Despite these broad changes, the potential influence on local and regional climate has received relatively little attention. This study investigated wintertime (December through March) climate impacts of reforestation in New England using a high-resolution (4 km) multiphysics ensemble of the Weather Research and Forecasting Model. In general, the conversion from mid-1800s cropland/grassland to forest led to warming, but results were sensitive to physics parameterizations. The 2-m maximum temperature (T2max) was most sensitive to choice of land surface model, 2-m minimum temperature (T2min) was sensitive to radiation scheme, and all ensemble members simulated precipitation poorly. Reforestation experiments suggest that conversion of mid-1800s cropland/grassland to present-day forest warmed T2max +0.5 to +3 K, with weaker warming during a warm, dry winter compared to a cold, snowy winter. Warmer T2max over forests was primarily the result of increased absorbed shortwave radiation and increased sensible heat flux compared to cropland/grassland. At night, T2min warmed +0.2 to +1.5 K where deciduous broadleaf forest replaced cropland/grassland, a result of decreased ground heat flux. By contrast, T2min of evergreen needleleaf forest cooled –0.5 to –2.1 K, primarily owing to increased ground heat flux and decreased sensible heat flux

    Enviornmental Monitoring Report January through December 2012

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    https://digitalcommons.memphis.edu/govpubs-tn-dept-environment-conservation-environmental-monitoring-report/1011/thumbnail.jp

    Research Report 2017 | 2018

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    Spatial and predictive foraging models for gray bats in northwest Georgia and a comparison of two acoustical bat survey techniques

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    The federally endangered gray bat (Myotis grisescens) is an obligate, year-round, cavedwelling species. Efforts to protect caves occupied by gray bats have proved beneficial to recovery. Specific gray bat foraging area characteristics also are critical to gray bat population recovery and maintenance. In summer 2000 and 2001, I determined gray bat presence/absence with Anabat II acoustic bat detectors in potential foraging areas near 2 bachelor colonies in northwest Georgia. In 2000, I systematically sampled 213 locations near water sources over an 8.3 x 9.4 km sampling grid that was established over a 5,100 km2 study area. Each sampling point was monitored for 20 minutes. A predictive landscape-level model was developed in a Geographic Information Systems (GIS). In summer 2001, to further refine gray bat spatial activity patterns on a macro-habitat level, I sampled 114 locations that were predicted on a landscape level as gray bat foraging areas based on data from 2000.;In 2001, I developed a macro-habitat foraging model based on 5,124 echolocation call sequences collected at 114 locations within gray bat foraging areas delineated by the GIS model. (Abstract shortened by UMI.)
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