Assessing Urban Landscape Variables’ Contributions to Microclimates
The well-known urban heat island (UHI) effect recognizes prevailing patterns of warmer urban temperatures relative to surrounding rural landscapes. Although UHIs are often visualized as single features, internal variations within urban landscapes create distinctive microclimates. Evaluating intraurb...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2016/8736263 |
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| author | Tammy E. Parece Jie Li James B. Campbell David Carroll |
| author_facet | Tammy E. Parece Jie Li James B. Campbell David Carroll |
| author_sort | Tammy E. Parece |
| collection | DOAJ |
| description | The well-known urban heat island (UHI) effect recognizes prevailing patterns of warmer urban temperatures relative to surrounding rural landscapes. Although UHIs are often visualized as single features, internal variations within urban landscapes create distinctive microclimates. Evaluating intraurban microclimate variability presents an opportunity to assess spatial dimensions of urban environments and identify locations that heat or cool faster than other locales. Our study employs mobile weather units and fixed weather stations to collect air temperatures across Roanoke, Virginia, USA, on selected dates over a two-year interval. Using this temperature data, together with six landscape variables, we interpolated (using Kriging and Random Forest) air temperatures across the city for each collection period. Our results estimated temperatures with small mean square errors (ranging from 0.03 to 0.14); landscape metrics explained between 60 and 91% of temperature variations (higher when the previous day’s average temperatures were included as a variable). For all days, similar spatial patterns appeared for cooler and warmer areas in mornings, with distinctive patterns as landscapes warmed during the day and over successive days. Our results revealed that the most potent landscape variables vary according to season and time of day. Our analysis contributes new dimensions and new levels of spatial and temporal detail to urban microclimate research. |
| format | Article |
| id | doaj-art-72934a308a914243b53c3c5894f906b1 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-72934a308a914243b53c3c5894f906b12025-02-03T06:44:26ZengWileyAdvances in Meteorology1687-93091687-93172016-01-01201610.1155/2016/87362638736263Assessing Urban Landscape Variables’ Contributions to MicroclimatesTammy E. Parece0Jie Li1James B. Campbell2David Carroll3Department of Geography, Virginia Polytechnic Institute and State University, 220 Stanger Street, Blacksburg, VA 24061, USADepartment of Statistics, Virginia Polytechnic Institute and State University, 410 Hutcheson Hall, 250 Drillfield Drive, Blacksburg, VA 24061, USADepartment of Geography, Virginia Polytechnic Institute and State University, 220 Stanger Street, Blacksburg, VA 24061, USADepartment of Geography, Virginia Polytechnic Institute and State University, 220 Stanger Street, Blacksburg, VA 24061, USAThe well-known urban heat island (UHI) effect recognizes prevailing patterns of warmer urban temperatures relative to surrounding rural landscapes. Although UHIs are often visualized as single features, internal variations within urban landscapes create distinctive microclimates. Evaluating intraurban microclimate variability presents an opportunity to assess spatial dimensions of urban environments and identify locations that heat or cool faster than other locales. Our study employs mobile weather units and fixed weather stations to collect air temperatures across Roanoke, Virginia, USA, on selected dates over a two-year interval. Using this temperature data, together with six landscape variables, we interpolated (using Kriging and Random Forest) air temperatures across the city for each collection period. Our results estimated temperatures with small mean square errors (ranging from 0.03 to 0.14); landscape metrics explained between 60 and 91% of temperature variations (higher when the previous day’s average temperatures were included as a variable). For all days, similar spatial patterns appeared for cooler and warmer areas in mornings, with distinctive patterns as landscapes warmed during the day and over successive days. Our results revealed that the most potent landscape variables vary according to season and time of day. Our analysis contributes new dimensions and new levels of spatial and temporal detail to urban microclimate research.http://dx.doi.org/10.1155/2016/8736263 |
| spellingShingle | Tammy E. Parece Jie Li James B. Campbell David Carroll Assessing Urban Landscape Variables’ Contributions to Microclimates Advances in Meteorology |
| title | Assessing Urban Landscape Variables’ Contributions to Microclimates |
| title_full | Assessing Urban Landscape Variables’ Contributions to Microclimates |
| title_fullStr | Assessing Urban Landscape Variables’ Contributions to Microclimates |
| title_full_unstemmed | Assessing Urban Landscape Variables’ Contributions to Microclimates |
| title_short | Assessing Urban Landscape Variables’ Contributions to Microclimates |
| title_sort | assessing urban landscape variables contributions to microclimates |
| url | http://dx.doi.org/10.1155/2016/8736263 |
| work_keys_str_mv | AT tammyeparece assessingurbanlandscapevariablescontributionstomicroclimates AT jieli assessingurbanlandscapevariablescontributionstomicroclimates AT jamesbcampbell assessingurbanlandscapevariablescontributionstomicroclimates AT davidcarroll assessingurbanlandscapevariablescontributionstomicroclimates |