Literature DB >> 23176982

The importance of land cover change across urban-rural typologies for climate modeling.

Jason Vargo1, Dana Habeeb, Brian Stone.   

Abstract

Land cover changes affect local surface energy balances by changing the amount of solar energy reflected, the magnitude and duration over which absorbed energy is released as heat, and the amount of energy that is diverted to non-heating fluxes through evaporation. However, such local influences often are only crudely included in climate modeling exercises, if at all. A better understanding of local land conversion dynamics can serve to inform inputs for climate models and increase the role for land use planning in climate management policy. Here we present a new approach for projecting and incorporating metropolitan land cover change into mesoscale climate and other environmental assessment models. Our results demonstrate the relative contributions of different land development patterns to land cover change and conversion and suggest that regional growth management strategies serving to increase settlement densities over time can have a significant influence on the rate of deforestation per unit of population growth. Employing the approach presented herein, the impacts of land conversion on climate change and on parallel environmental systems and services, such as ground water recharge, habitat provision, and food production, may all be investigated more closely and managed through land use planning.
Copyright © 2012 Elsevier Ltd. All rights reserved.

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Year:  2012        PMID: 23176982     DOI: 10.1016/j.jenvman.2012.10.007

Source DB:  PubMed          Journal:  J Environ Manage        ISSN: 0301-4797            Impact factor:   6.789


  3 in total

1.  Conterminous United States land cover change patterns 2001-2016 from the 2016 National Land Cover Database.

Authors:  Collin Homer; Jon Dewitz; Suming Jin; George Xian; Catherine Costello; Patrick Danielson; Leila Gass; Michelle Funk; James Wickham; Stephen Stehman; Roger Auch; Kurt Riitters
Journal:  ISPRS J Photogramm Remote Sens       Date:  2020-04       Impact factor: 11.774

2.  Predictors of discordance between perceived and objective neighborhood data.

Authors:  Erin J Bailey; Kristen C Malecki; Corinne D Engelman; Matthew C Walsh; Andrew J Bersch; Ana P Martinez-Donate; Paul E Peppard; F Javier Nieto
Journal:  Ann Epidemiol       Date:  2013-12-28       Impact factor: 3.797

3.  Avoided heat-related mortality through climate adaptation strategies in three US cities.

Authors:  Brian Stone; Jason Vargo; Peng Liu; Dana Habeeb; Anthony DeLucia; Marcus Trail; Yongtao Hu; Armistead Russell
Journal:  PLoS One       Date:  2014-06-25       Impact factor: 3.240

  3 in total

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