Regional and Local Scale Modeling of Stream Temperatures and Spatio-Temporal Variation in Thermal Sensitivities
Understanding variation in stream thermal regimes becomes increasingly important as the climate changes and aquatic biota approach their thermal limits. We used data from paired air and water temperature loggers to develop region-scale and stream-specific models of average daily water temperature an...
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description | Understanding variation in stream thermal regimes becomes increasingly important as the climate changes and aquatic biota approach their thermal limits. We used data from paired air and water temperature loggers to develop region-scale and stream-specific models of average daily water temperature and to explore thermal sensitivities, the slopes of air–water temperature regressions, of mostly forested streams across Maryland, USA. The region-scale stream temperature model explained nearly 90 % of the variation (root mean square error = 0.957 °C), with the mostly flat coastal plain streams having significantly higher thermal sensitivities than the steeper highlands streams with piedmont streams intermediate. Model R ² for stream-specific models was positively related to a stream’s thermal sensitivity. Both the regional and the stream-specific air–water temperature regression models benefited from including mean daily discharge from regional gaging stations, but the degree of improvement declined as a stream’s thermal sensitivity increased. Although catchment size had no relationship to thermal sensitivity, steeper streams or those with greater amounts of forest in their upstream watershed were less thermally sensitive. The subset of streams with three or more summers of temperature data exhibited a wide range of annual variation in thermal sensitivity at a site, with the variation not attributable to discharge, precipitation patterns, or physical attributes of streams or their watersheds. Our findings are a useful starting point to better understand patterns in stream thermal regimes. However, a more spatially and temporally comprehensive monitoring network should increase understanding of stream temperature variation and its controls as climatic patterns change. |
doi_str_mv | 10.1007/s00267-014-0272-4 |
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We used data from paired air and water temperature loggers to develop region-scale and stream-specific models of average daily water temperature and to explore thermal sensitivities, the slopes of air–water temperature regressions, of mostly forested streams across Maryland, USA. The region-scale stream temperature model explained nearly 90 % of the variation (root mean square error = 0.957 °C), with the mostly flat coastal plain streams having significantly higher thermal sensitivities than the steeper highlands streams with piedmont streams intermediate. Model R ² for stream-specific models was positively related to a stream’s thermal sensitivity. Both the regional and the stream-specific air–water temperature regression models benefited from including mean daily discharge from regional gaging stations, but the degree of improvement declined as a stream’s thermal sensitivity increased. Although catchment size had no relationship to thermal sensitivity, steeper streams or those with greater amounts of forest in their upstream watershed were less thermally sensitive. The subset of streams with three or more summers of temperature data exhibited a wide range of annual variation in thermal sensitivity at a site, with the variation not attributable to discharge, precipitation patterns, or physical attributes of streams or their watersheds. Our findings are a useful starting point to better understand patterns in stream thermal regimes. 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We used data from paired air and water temperature loggers to develop region-scale and stream-specific models of average daily water temperature and to explore thermal sensitivities, the slopes of air–water temperature regressions, of mostly forested streams across Maryland, USA. The region-scale stream temperature model explained nearly 90 % of the variation (root mean square error = 0.957 °C), with the mostly flat coastal plain streams having significantly higher thermal sensitivities than the steeper highlands streams with piedmont streams intermediate. Model R ² for stream-specific models was positively related to a stream’s thermal sensitivity. Both the regional and the stream-specific air–water temperature regression models benefited from including mean daily discharge from regional gaging stations, but the degree of improvement declined as a stream’s thermal sensitivity increased. Although catchment size had no relationship to thermal sensitivity, steeper streams or those with greater amounts of forest in their upstream watershed were less thermally sensitive. The subset of streams with three or more summers of temperature data exhibited a wide range of annual variation in thermal sensitivity at a site, with the variation not attributable to discharge, precipitation patterns, or physical attributes of streams or their watersheds. Our findings are a useful starting point to better understand patterns in stream thermal regimes. However, a more spatially and temporally comprehensive monitoring network should increase understanding of stream temperature variation and its controls as climatic patterns change.</description><subject>air</subject><subject>Air temperature</subject><subject>Analysis of Variance</subject><subject>Annual variations</subject><subject>Aquatic animals</subject><subject>Aquatic Pollution</subject><subject>Atmospheric Protection/Air Quality Control/Air Pollution</subject><subject>Biota</subject><subject>Climate Change</subject><subject>Climate science</subject><subject>Coastal plains</subject><subject>Coastal streams</subject><subject>Creeks & streams</subject><subject>Discharge</subject><subject>Discharge measurement</subject><subject>Earth and Environmental Science</subject><subject>Ecology</subject><subject>Environment</subject><subject>Environmental Management</subject><subject>Forestry Management</subject><subject>forests</subject><subject>Freshwater</subject><subject>Gaging stations</subject><subject>Geography</subject><subject>Groundwater</subject><subject>highlands</subject><subject>Maryland</subject><subject>Models, Theoretical</subject><subject>monitoring</subject><subject>Nature Conservation</subject><subject>Networks</subject><subject>piedmont</subject><subject>Regional</subject><subject>Regression Analysis</subject><subject>Rivers - chemistry</subject><subject>Slopes</subject><subject>Stream discharge</subject><subject>Streams</subject><subject>Temperature</subject><subject>Thermal energy</subject><subject>Trees</subject><subject>Waste Water Technology</subject><subject>Water Management</subject><subject>Water Pollution Control</subject><subject>Water temperature</subject><subject>Watershed management</subject><subject>Watersheds</subject><issn>0364-152X</issn><issn>1432-1009</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqFkU9v1DAQxS1ERZeFD8AFInHh4nb8J7ZzRFWBSosqdbeIm-VNJourJF7sBIlvX4eUCnGAg-3RzO-9kfwIecXgjAHo8wTAlabAJAWuOZVPyIpJwWmeVk_JCoSSlJX86yl5ntIdAAhjymfklEstwTC9IuEGDz4Mrivc0BSbUOdqmy8sPocGOz8citAW2zGi64sd9keMbpwipl_89uhGH-jcDzErv7jo585Q-KHYfcPYz3Y4JD_6H_lgekFOWtclfPnwrsnth8vdxSe6uf54dfF-Q-uSm5GWlW4rVjUKpahz1bhKq8rshRHKOaYVYF0pp_a8FAqE40o22ArcM66MbhuxJu8W32MM3ydMo-19qrHr3IBhSpYpyQVTUKn_o6WEzBpuMvr2L_QuTDH_3kwJKaURGV0TtlB1DClFbO0x-t7Fn5aBnYOzS3A2B2fn4KzMmtcPztO-x-ZR8TupDPAFSHk0HDD-sfofrm8WUeuCdYfok73d8gwAMJACjLgHVmCrOQ</recordid><startdate>20140701</startdate><enddate>20140701</enddate><creator>Hilderbrand, Robert H</creator><creator>Kashiwagi, Michael T</creator><creator>Prochaska, Anthony P</creator><general>Springer-Verlag</general><general>Springer US</general><general>Springer Nature B.V</general><scope>FBQ</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QL</scope><scope>7RV</scope><scope>7SN</scope><scope>7ST</scope><scope>7T7</scope><scope>7U9</scope><scope>7WY</scope><scope>7WZ</scope><scope>7X7</scope><scope>7XB</scope><scope>87Z</scope><scope>88E</scope><scope>88I</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FL</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FRNLG</scope><scope>FYUFA</scope><scope>F~G</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K60</scope><scope>K6~</scope><scope>K9.</scope><scope>L.-</scope><scope>M0C</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M2P</scope><scope>M7N</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>P64</scope><scope>PATMY</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>SOI</scope><scope>7QH</scope><scope>7TG</scope><scope>7U6</scope><scope>7UA</scope><scope>F1W</scope><scope>H97</scope><scope>KL.</scope><scope>L.G</scope><scope>7SU</scope><scope>KR7</scope></search><sort><creationdate>20140701</creationdate><title>Regional and Local Scale Modeling of Stream Temperatures and Spatio-Temporal Variation in Thermal Sensitivities</title><author>Hilderbrand, Robert H ; Kashiwagi, Michael T ; Prochaska, Anthony P</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c528t-597f919d6e43cf91da97698b3836aa1760ec96a6b253603a264def3eb12687fd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>air</topic><topic>Air temperature</topic><topic>Analysis of Variance</topic><topic>Annual variations</topic><topic>Aquatic animals</topic><topic>Aquatic Pollution</topic><topic>Atmospheric Protection/Air Quality Control/Air Pollution</topic><topic>Biota</topic><topic>Climate Change</topic><topic>Climate science</topic><topic>Coastal plains</topic><topic>Coastal streams</topic><topic>Creeks & streams</topic><topic>Discharge</topic><topic>Discharge measurement</topic><topic>Earth and Environmental Science</topic><topic>Ecology</topic><topic>Environment</topic><topic>Environmental Management</topic><topic>Forestry Management</topic><topic>forests</topic><topic>Freshwater</topic><topic>Gaging stations</topic><topic>Geography</topic><topic>Groundwater</topic><topic>highlands</topic><topic>Maryland</topic><topic>Models, Theoretical</topic><topic>monitoring</topic><topic>Nature Conservation</topic><topic>Networks</topic><topic>piedmont</topic><topic>Regional</topic><topic>Regression Analysis</topic><topic>Rivers - chemistry</topic><topic>Slopes</topic><topic>Stream discharge</topic><topic>Streams</topic><topic>Temperature</topic><topic>Thermal energy</topic><topic>Trees</topic><topic>Waste Water Technology</topic><topic>Water Management</topic><topic>Water Pollution Control</topic><topic>Water temperature</topic><topic>Watershed management</topic><topic>Watersheds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hilderbrand, Robert H</creatorcontrib><creatorcontrib>Kashiwagi, Michael T</creatorcontrib><creatorcontrib>Prochaska, Anthony P</creatorcontrib><collection>AGRIS</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Environment Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Virology and AIDS Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Business Premium Collection (Alumni)</collection><collection>Health Research Premium Collection</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Global</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Science Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>Environment Abstracts</collection><collection>Aqualine</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Sustainability Science Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 3: Aquatic Pollution & Environmental Quality</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Environmental Engineering Abstracts</collection><collection>Civil Engineering Abstracts</collection><jtitle>Environmental management (New York)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hilderbrand, Robert H</au><au>Kashiwagi, Michael T</au><au>Prochaska, Anthony P</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Regional and Local Scale Modeling of Stream Temperatures and Spatio-Temporal Variation in Thermal Sensitivities</atitle><jtitle>Environmental management (New York)</jtitle><stitle>Environmental Management</stitle><addtitle>Environ Manage</addtitle><date>2014-07-01</date><risdate>2014</risdate><volume>54</volume><issue>1</issue><spage>14</spage><epage>22</epage><pages>14-22</pages><issn>0364-152X</issn><eissn>1432-1009</eissn><abstract>Understanding variation in stream thermal regimes becomes increasingly important as the climate changes and aquatic biota approach their thermal limits. We used data from paired air and water temperature loggers to develop region-scale and stream-specific models of average daily water temperature and to explore thermal sensitivities, the slopes of air–water temperature regressions, of mostly forested streams across Maryland, USA. The region-scale stream temperature model explained nearly 90 % of the variation (root mean square error = 0.957 °C), with the mostly flat coastal plain streams having significantly higher thermal sensitivities than the steeper highlands streams with piedmont streams intermediate. Model R ² for stream-specific models was positively related to a stream’s thermal sensitivity. Both the regional and the stream-specific air–water temperature regression models benefited from including mean daily discharge from regional gaging stations, but the degree of improvement declined as a stream’s thermal sensitivity increased. Although catchment size had no relationship to thermal sensitivity, steeper streams or those with greater amounts of forest in their upstream watershed were less thermally sensitive. The subset of streams with three or more summers of temperature data exhibited a wide range of annual variation in thermal sensitivity at a site, with the variation not attributable to discharge, precipitation patterns, or physical attributes of streams or their watersheds. Our findings are a useful starting point to better understand patterns in stream thermal regimes. However, a more spatially and temporally comprehensive monitoring network should increase understanding of stream temperature variation and its controls as climatic patterns change.</abstract><cop>New York</cop><pub>Springer-Verlag</pub><pmid>24740817</pmid><doi>10.1007/s00267-014-0272-4</doi><tpages>9</tpages></addata></record> |
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subjects | air Air temperature Analysis of Variance Annual variations Aquatic animals Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution Biota Climate Change Climate science Coastal plains Coastal streams Creeks & streams Discharge Discharge measurement Earth and Environmental Science Ecology Environment Environmental Management Forestry Management forests Freshwater Gaging stations Geography Groundwater highlands Maryland Models, Theoretical monitoring Nature Conservation Networks piedmont Regional Regression Analysis Rivers - chemistry Slopes Stream discharge Streams Temperature Thermal energy Trees Waste Water Technology Water Management Water Pollution Control Water temperature Watershed management Watersheds |
title | Regional and Local Scale Modeling of Stream Temperatures and Spatio-Temporal Variation in Thermal Sensitivities |
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