Improved spatial delineation of streambed properties and water fluxes using distributed temperature sensing
A new method was developed for analysing and delineating streambed water fluxes, flow conditions and hydraulic properties using coiled fibre‐optic distributed temperature sensing or closely spaced discrete temperature sensors. This method allows for a thorough treatment of the spatial information em...
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Veröffentlicht in: | Hydrological processes 2016-07, Vol.30 (15), p.2686-2702 |
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description | A new method was developed for analysing and delineating streambed water fluxes, flow conditions and hydraulic properties using coiled fibre‐optic distributed temperature sensing or closely spaced discrete temperature sensors. This method allows for a thorough treatment of the spatial information embedded in temperature data by creating a matrix visualization of all possible sensor pairs. Application of the method to a 5‐day field dataset reveals the complexity of shallow streambed thermal regimes. To understand how velocity estimates are affected by violations of assumptions of one‐dimensional, saturated, homogeneous flow and to aid in the interpretation of field observations, the method was also applied to temperature data generated by numerical models of common field conditions: horizontal layering, presence of lateral flow and variable streambed saturation. The results show that each condition creates a distinct signature visible in the triangular matrices. The matrices are used to perform a comparison of the behaviour of one‐dimensional analytical heat‐tracing models. The results show that the amplitude ratio‐based method of velocity calculation leads to the most reliable estimates. The minimum sensor spacing required to obtain reliable velocity estimates with discrete sensors is also investigated using field data. The developed method will aid future heat‐tracing studies by providing a technique for visualizing and comparing results from fibre‐optic distributed temperature sensing installations and testing the robustness of analytical heat‐tracing models. Copyright © 2016 John Wiley & Sons, Ltd. |
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S. ; Roshan, Hamid ; Rau, Gabriel C. ; Andersen, Martin S. ; Acworth, R. Ian</creator><creatorcontrib>Halloran, Landon J. S. ; Roshan, Hamid ; Rau, Gabriel C. ; Andersen, Martin S. ; Acworth, R. Ian</creatorcontrib><description>A new method was developed for analysing and delineating streambed water fluxes, flow conditions and hydraulic properties using coiled fibre‐optic distributed temperature sensing or closely spaced discrete temperature sensors. This method allows for a thorough treatment of the spatial information embedded in temperature data by creating a matrix visualization of all possible sensor pairs. Application of the method to a 5‐day field dataset reveals the complexity of shallow streambed thermal regimes. To understand how velocity estimates are affected by violations of assumptions of one‐dimensional, saturated, homogeneous flow and to aid in the interpretation of field observations, the method was also applied to temperature data generated by numerical models of common field conditions: horizontal layering, presence of lateral flow and variable streambed saturation. The results show that each condition creates a distinct signature visible in the triangular matrices. The matrices are used to perform a comparison of the behaviour of one‐dimensional analytical heat‐tracing models. The results show that the amplitude ratio‐based method of velocity calculation leads to the most reliable estimates. The minimum sensor spacing required to obtain reliable velocity estimates with discrete sensors is also investigated using field data. The developed method will aid future heat‐tracing studies by providing a technique for visualizing and comparing results from fibre‐optic distributed temperature sensing installations and testing the robustness of analytical heat‐tracing models. Copyright © 2016 John Wiley & Sons, Ltd.</description><identifier>ISSN: 0885-6087</identifier><identifier>EISSN: 1099-1085</identifier><identifier>DOI: 10.1002/hyp.10806</identifier><language>eng</language><publisher>Chichester: Blackwell Publishing Ltd</publisher><subject>Amplitude ; Complexity ; Data processing ; Delineation ; Detection ; Dimensional analysis ; distributed temperature sensing ; Estimates ; Fiber optics ; Flow ; Fluxes ; Heat ; heat as a tracer ; Hydraulic properties ; Layering ; Mathematical analysis ; Mathematical models ; Matrices (mathematics) ; Methods ; Numerical models ; Optical fibers ; Properties ; Robustness (mathematics) ; Saturation ; Sensors ; Spatial analysis ; Spatial distribution ; Streambeds ; surface water-groundwater interaction ; Temperature ; Temperature data ; Temperature effects ; Temperature sensors ; Testing ; time-series analysis ; Velocity ; Visualization ; Water</subject><ispartof>Hydrological processes, 2016-07, Vol.30 (15), p.2686-2702</ispartof><rights>Copyright © 2016 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4956-4576c72d5deb9b045baf25f24fae7314d91ffd44068652f9e27e19922650e7ca3</citedby><cites>FETCH-LOGICAL-c4956-4576c72d5deb9b045baf25f24fae7314d91ffd44068652f9e27e19922650e7ca3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fhyp.10806$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fhyp.10806$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids></links><search><creatorcontrib>Halloran, Landon J. S.</creatorcontrib><creatorcontrib>Roshan, Hamid</creatorcontrib><creatorcontrib>Rau, Gabriel C.</creatorcontrib><creatorcontrib>Andersen, Martin S.</creatorcontrib><creatorcontrib>Acworth, R. Ian</creatorcontrib><title>Improved spatial delineation of streambed properties and water fluxes using distributed temperature sensing</title><title>Hydrological processes</title><addtitle>Hydrol. Process</addtitle><description>A new method was developed for analysing and delineating streambed water fluxes, flow conditions and hydraulic properties using coiled fibre‐optic distributed temperature sensing or closely spaced discrete temperature sensors. This method allows for a thorough treatment of the spatial information embedded in temperature data by creating a matrix visualization of all possible sensor pairs. Application of the method to a 5‐day field dataset reveals the complexity of shallow streambed thermal regimes. To understand how velocity estimates are affected by violations of assumptions of one‐dimensional, saturated, homogeneous flow and to aid in the interpretation of field observations, the method was also applied to temperature data generated by numerical models of common field conditions: horizontal layering, presence of lateral flow and variable streambed saturation. The results show that each condition creates a distinct signature visible in the triangular matrices. The matrices are used to perform a comparison of the behaviour of one‐dimensional analytical heat‐tracing models. The results show that the amplitude ratio‐based method of velocity calculation leads to the most reliable estimates. The minimum sensor spacing required to obtain reliable velocity estimates with discrete sensors is also investigated using field data. The developed method will aid future heat‐tracing studies by providing a technique for visualizing and comparing results from fibre‐optic distributed temperature sensing installations and testing the robustness of analytical heat‐tracing models. Copyright © 2016 John Wiley & Sons, Ltd.</description><subject>Amplitude</subject><subject>Complexity</subject><subject>Data processing</subject><subject>Delineation</subject><subject>Detection</subject><subject>Dimensional analysis</subject><subject>distributed temperature sensing</subject><subject>Estimates</subject><subject>Fiber optics</subject><subject>Flow</subject><subject>Fluxes</subject><subject>Heat</subject><subject>heat as a tracer</subject><subject>Hydraulic properties</subject><subject>Layering</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Matrices (mathematics)</subject><subject>Methods</subject><subject>Numerical models</subject><subject>Optical fibers</subject><subject>Properties</subject><subject>Robustness (mathematics)</subject><subject>Saturation</subject><subject>Sensors</subject><subject>Spatial analysis</subject><subject>Spatial distribution</subject><subject>Streambeds</subject><subject>surface water-groundwater interaction</subject><subject>Temperature</subject><subject>Temperature data</subject><subject>Temperature effects</subject><subject>Temperature sensors</subject><subject>Testing</subject><subject>time-series analysis</subject><subject>Velocity</subject><subject>Visualization</subject><subject>Water</subject><issn>0885-6087</issn><issn>1099-1085</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNqN0U1vFSEUBmBiNPFaXfgPSNzoYiwwfC5No_c2aaqxGuOKMDMHpZ0vYcb2_ntPe9WFicYVB3gOAV5CnnL2kjMmjr_uZyws0_fIhjPnKpyo-2TDrFWVZtY8JI9KuWSMSVQbcnU6zHn6Dh0tc1hS6GkHfRoB62mkU6RlyRCGBgG6GfKSoNAwdvQ6LJBp7NcbXFhLGr_QLqFOzbqgXmBAHZY1Ay0w3u4_Jg9i6As8-TkekY9vXn842VVnb7enJ6_OqlY6pSupjG6N6FQHjWuYVE2IQkUhYwBTc9k5HmMnJdNWKxEdCAPcOSG0YmDaUB-R54dz8cbfViiLH1Jpoe_DCNNaPLdCKcSG_QdlVgtRS4n02R_0clrziA_x3DGlJX53_U9lWc2NFVygenFQbZ5KyRD9nNMQ8t5z5m9z9Jijv8sR7fHBXqce9n-Hfvf53a-O6tCBccDN746Qr7w2tVH-0_nWy93F7mJrnH9f_wDwq64x</recordid><startdate>20160715</startdate><enddate>20160715</enddate><creator>Halloran, Landon J. S.</creator><creator>Roshan, Hamid</creator><creator>Rau, Gabriel C.</creator><creator>Andersen, Martin S.</creator><creator>Acworth, R. Ian</creator><general>Blackwell Publishing Ltd</general><general>Wiley Subscription Services, Inc</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7ST</scope><scope>7TG</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H96</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>SOI</scope></search><sort><creationdate>20160715</creationdate><title>Improved spatial delineation of streambed properties and water fluxes using distributed temperature sensing</title><author>Halloran, Landon J. S. ; Roshan, Hamid ; Rau, Gabriel C. ; Andersen, Martin S. ; Acworth, R. Ian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4956-4576c72d5deb9b045baf25f24fae7314d91ffd44068652f9e27e19922650e7ca3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Amplitude</topic><topic>Complexity</topic><topic>Data processing</topic><topic>Delineation</topic><topic>Detection</topic><topic>Dimensional analysis</topic><topic>distributed temperature sensing</topic><topic>Estimates</topic><topic>Fiber optics</topic><topic>Flow</topic><topic>Fluxes</topic><topic>Heat</topic><topic>heat as a tracer</topic><topic>Hydraulic properties</topic><topic>Layering</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>Matrices (mathematics)</topic><topic>Methods</topic><topic>Numerical models</topic><topic>Optical fibers</topic><topic>Properties</topic><topic>Robustness (mathematics)</topic><topic>Saturation</topic><topic>Sensors</topic><topic>Spatial analysis</topic><topic>Spatial distribution</topic><topic>Streambeds</topic><topic>surface water-groundwater interaction</topic><topic>Temperature</topic><topic>Temperature data</topic><topic>Temperature effects</topic><topic>Temperature sensors</topic><topic>Testing</topic><topic>time-series analysis</topic><topic>Velocity</topic><topic>Visualization</topic><topic>Water</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Halloran, Landon J. 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S.</au><au>Roshan, Hamid</au><au>Rau, Gabriel C.</au><au>Andersen, Martin S.</au><au>Acworth, R. Ian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Improved spatial delineation of streambed properties and water fluxes using distributed temperature sensing</atitle><jtitle>Hydrological processes</jtitle><addtitle>Hydrol. Process</addtitle><date>2016-07-15</date><risdate>2016</risdate><volume>30</volume><issue>15</issue><spage>2686</spage><epage>2702</epage><pages>2686-2702</pages><issn>0885-6087</issn><eissn>1099-1085</eissn><abstract>A new method was developed for analysing and delineating streambed water fluxes, flow conditions and hydraulic properties using coiled fibre‐optic distributed temperature sensing or closely spaced discrete temperature sensors. This method allows for a thorough treatment of the spatial information embedded in temperature data by creating a matrix visualization of all possible sensor pairs. Application of the method to a 5‐day field dataset reveals the complexity of shallow streambed thermal regimes. To understand how velocity estimates are affected by violations of assumptions of one‐dimensional, saturated, homogeneous flow and to aid in the interpretation of field observations, the method was also applied to temperature data generated by numerical models of common field conditions: horizontal layering, presence of lateral flow and variable streambed saturation. The results show that each condition creates a distinct signature visible in the triangular matrices. The matrices are used to perform a comparison of the behaviour of one‐dimensional analytical heat‐tracing models. The results show that the amplitude ratio‐based method of velocity calculation leads to the most reliable estimates. The minimum sensor spacing required to obtain reliable velocity estimates with discrete sensors is also investigated using field data. The developed method will aid future heat‐tracing studies by providing a technique for visualizing and comparing results from fibre‐optic distributed temperature sensing installations and testing the robustness of analytical heat‐tracing models. Copyright © 2016 John Wiley & Sons, Ltd.</abstract><cop>Chichester</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1002/hyp.10806</doi><tpages>17</tpages></addata></record> |
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subjects | Amplitude Complexity Data processing Delineation Detection Dimensional analysis distributed temperature sensing Estimates Fiber optics Flow Fluxes Heat heat as a tracer Hydraulic properties Layering Mathematical analysis Mathematical models Matrices (mathematics) Methods Numerical models Optical fibers Properties Robustness (mathematics) Saturation Sensors Spatial analysis Spatial distribution Streambeds surface water-groundwater interaction Temperature Temperature data Temperature effects Temperature sensors Testing time-series analysis Velocity Visualization Water |
title | Improved spatial delineation of streambed properties and water fluxes using distributed temperature sensing |
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