Modeling the influence of hypsometry, vegetation, and storm energy on snowmelt contributions to basins during rain-on-snow floods
Point observations and previous basin modeling efforts have suggested that snowmelt may be a significant input of water for runoff during extreme rain‐on‐snow floods within western U.S. basins. Quantifying snowmelt input over entire basins is difficult given sparse observations of snowmelt. In order...
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description | Point observations and previous basin modeling efforts have suggested that snowmelt may be a significant input of water for runoff during extreme rain‐on‐snow floods within western U.S. basins. Quantifying snowmelt input over entire basins is difficult given sparse observations of snowmelt. In order to provide a range of snowmelt contributions for water managers, a physically based snow model coupled with an idealized basin representation was evaluated in point simulations and used to quantify the maximum basin‐wide input from snowmelt volume during flood events. Maximum snowmelt basin contributions and uncertainty ranges were estimated as 29% (11–47%), 29% (8–37%), and 7% (2–24%) of total rain plus snowmelt input, within the Snoqualmie, East North Fork Feather, and Upper San Joaquin basins, respectively, during historic flooding events between 1980 and 2008. The idealized basin representation revealed that both hypsometry and forest cover of a basin had similar magnitude of impacts on the basin‐wide snowmelt totals. However, the characteristics of a given storm (antecedent SWE and available energy for melt) controlled how much hypsometry and forest cover impacted basin‐wide snowmelt. These results indicate that for watershed managers, flood forecasting efforts should prioritize rainfall prediction first, but cannot neglect snowmelt contributions in some cases. Efforts to reduce the uncertainty in the above snowmelt simulations should focus on improving the meteorological forcing data (especially air temperature and wind speed) in complex terrain.
Key Points:
Simulated snowmelt spanned 0–29% of basin input during floods, with rainfall making up the majority
Storm variability influences how basin characteristics control basin melt
Snowmelt magnitude was invariant with rainfall amount |
doi_str_mv | 10.1002/2014WR016576 |
format | Article |
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Key Points:
Simulated snowmelt spanned 0–29% of basin input during floods, with rainfall making up the majority
Storm variability influences how basin characteristics control basin melt
Snowmelt magnitude was invariant with rainfall amount</description><identifier>ISSN: 0043-1397</identifier><identifier>EISSN: 1944-7973</identifier><identifier>DOI: 10.1002/2014WR016576</identifier><language>eng</language><publisher>Washington: Blackwell Publishing Ltd</publisher><subject>Air temperature ; Atmospheric forcing ; Atmospheric precipitations ; Basins ; Computer simulation ; Extreme weather ; Flood forecasting ; Flood management ; Flood predictions ; Flooding ; Floods ; Forests ; Freshwater ; Historic floods ; Hypsometry ; idealized model ; Modelling ; Rain ; rain-on-snow ; Rainfall ; Rainfall forecasting ; Representations ; River discharge ; Runoff ; Snow ; Snowmelt ; Storms ; surface energy balance ; Uncertainty ; Water management ; Water temperature ; Watershed management ; Wind speed</subject><ispartof>Water resources research, 2015-10, Vol.51 (10), p.8551-8569</ispartof><rights>2015. American Geophysical Union. All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5803-3d287492e3302bd449a9964bdd5438d0f783dd74ffbc4972a541a4b2665fd2a23</citedby><cites>FETCH-LOGICAL-c5803-3d287492e3302bd449a9964bdd5438d0f783dd74ffbc4972a541a4b2665fd2a23</cites><orcidid>0000-0001-9894-6213</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2F2014WR016576$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2F2014WR016576$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,11514,27924,27925,45574,45575,46468,46892</link.rule.ids></links><search><creatorcontrib>Wayand, Nicholas E.</creatorcontrib><creatorcontrib>Lundquist, Jessica D.</creatorcontrib><creatorcontrib>Clark, Martyn P.</creatorcontrib><title>Modeling the influence of hypsometry, vegetation, and storm energy on snowmelt contributions to basins during rain-on-snow floods</title><title>Water resources research</title><addtitle>Water Resour. Res</addtitle><description>Point observations and previous basin modeling efforts have suggested that snowmelt may be a significant input of water for runoff during extreme rain‐on‐snow floods within western U.S. basins. Quantifying snowmelt input over entire basins is difficult given sparse observations of snowmelt. In order to provide a range of snowmelt contributions for water managers, a physically based snow model coupled with an idealized basin representation was evaluated in point simulations and used to quantify the maximum basin‐wide input from snowmelt volume during flood events. Maximum snowmelt basin contributions and uncertainty ranges were estimated as 29% (11–47%), 29% (8–37%), and 7% (2–24%) of total rain plus snowmelt input, within the Snoqualmie, East North Fork Feather, and Upper San Joaquin basins, respectively, during historic flooding events between 1980 and 2008. The idealized basin representation revealed that both hypsometry and forest cover of a basin had similar magnitude of impacts on the basin‐wide snowmelt totals. However, the characteristics of a given storm (antecedent SWE and available energy for melt) controlled how much hypsometry and forest cover impacted basin‐wide snowmelt. These results indicate that for watershed managers, flood forecasting efforts should prioritize rainfall prediction first, but cannot neglect snowmelt contributions in some cases. Efforts to reduce the uncertainty in the above snowmelt simulations should focus on improving the meteorological forcing data (especially air temperature and wind speed) in complex terrain.
Key Points:
Simulated snowmelt spanned 0–29% of basin input during floods, with rainfall making up the majority
Storm variability influences how basin characteristics control basin melt
Snowmelt magnitude was invariant with rainfall amount</description><subject>Air temperature</subject><subject>Atmospheric forcing</subject><subject>Atmospheric precipitations</subject><subject>Basins</subject><subject>Computer simulation</subject><subject>Extreme weather</subject><subject>Flood forecasting</subject><subject>Flood management</subject><subject>Flood predictions</subject><subject>Flooding</subject><subject>Floods</subject><subject>Forests</subject><subject>Freshwater</subject><subject>Historic floods</subject><subject>Hypsometry</subject><subject>idealized model</subject><subject>Modelling</subject><subject>Rain</subject><subject>rain-on-snow</subject><subject>Rainfall</subject><subject>Rainfall forecasting</subject><subject>Representations</subject><subject>River discharge</subject><subject>Runoff</subject><subject>Snow</subject><subject>Snowmelt</subject><subject>Storms</subject><subject>surface energy balance</subject><subject>Uncertainty</subject><subject>Water management</subject><subject>Water temperature</subject><subject>Watershed management</subject><subject>Wind speed</subject><issn>0043-1397</issn><issn>1944-7973</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNp90c1u1DAUBWALgcRQ2PEAltiwmID_PV6iAVqgFGkEmqXlxPbUJbGntkPJkjcn0SCEWHRlL75zz5UuAM8xeoURIq8Jwmy_Q1hwKR6AFVaMNVJJ-hCsEGK0wVTJx-BJKTdollzIFfj1OVnXh3iA9drBEH0_utg5mDy8no4lDa7maQ1_uIOrpoYU19BEC0tNeYAuunyYYIqwxHQ3uL7CLsWaQzsutMCaYGtKmH92zEtJNiE2KTaLh75PyZan4JE3fXHP_rxn4Nv7d1-3F83ll_MP2zeXTcc3iDbUko1kijhKEWktY8ooJVhrLWd0Y5GXG2qtZN63HVOSGM6wYS0RgntLDKFn4OVp7jGn29GVqodQOtf3Jro0Fo2lQorPXQt98R-9SWOO83aaIEW4oPMS9yksuZSMCr7MWp9Ul1Mp2Xl9zGEwedIY6eVq-t-rzZye-F3o3XSv1fvddkfw3DOnmlMqlOp-_k2Z_F0LSSXX-6tzfcGu8Nvdp4-a0N_jsqg3</recordid><startdate>201510</startdate><enddate>201510</enddate><creator>Wayand, Nicholas E.</creator><creator>Lundquist, Jessica D.</creator><creator>Clark, Martyn P.</creator><general>Blackwell Publishing Ltd</general><general>John Wiley & Sons, Inc</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7QL</scope><scope>7T7</scope><scope>7TG</scope><scope>7U9</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H94</scope><scope>H96</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>M7N</scope><scope>P64</scope><orcidid>https://orcid.org/0000-0001-9894-6213</orcidid></search><sort><creationdate>201510</creationdate><title>Modeling the influence of hypsometry, vegetation, and storm energy on snowmelt contributions to basins during rain-on-snow floods</title><author>Wayand, Nicholas E. ; Lundquist, Jessica D. ; Clark, Martyn P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5803-3d287492e3302bd449a9964bdd5438d0f783dd74ffbc4972a541a4b2665fd2a23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Air temperature</topic><topic>Atmospheric forcing</topic><topic>Atmospheric precipitations</topic><topic>Basins</topic><topic>Computer simulation</topic><topic>Extreme weather</topic><topic>Flood forecasting</topic><topic>Flood management</topic><topic>Flood predictions</topic><topic>Flooding</topic><topic>Floods</topic><topic>Forests</topic><topic>Freshwater</topic><topic>Historic floods</topic><topic>Hypsometry</topic><topic>idealized model</topic><topic>Modelling</topic><topic>Rain</topic><topic>rain-on-snow</topic><topic>Rainfall</topic><topic>Rainfall forecasting</topic><topic>Representations</topic><topic>River discharge</topic><topic>Runoff</topic><topic>Snow</topic><topic>Snowmelt</topic><topic>Storms</topic><topic>surface energy balance</topic><topic>Uncertainty</topic><topic>Water management</topic><topic>Water temperature</topic><topic>Watershed management</topic><topic>Wind speed</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wayand, Nicholas E.</creatorcontrib><creatorcontrib>Lundquist, Jessica D.</creatorcontrib><creatorcontrib>Clark, Martyn P.</creatorcontrib><collection>Istex</collection><collection>CrossRef</collection><collection>Aqualine</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Water resources research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wayand, Nicholas E.</au><au>Lundquist, Jessica D.</au><au>Clark, Martyn P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Modeling the influence of hypsometry, vegetation, and storm energy on snowmelt contributions to basins during rain-on-snow floods</atitle><jtitle>Water resources research</jtitle><addtitle>Water Resour. Res</addtitle><date>2015-10</date><risdate>2015</risdate><volume>51</volume><issue>10</issue><spage>8551</spage><epage>8569</epage><pages>8551-8569</pages><issn>0043-1397</issn><eissn>1944-7973</eissn><abstract>Point observations and previous basin modeling efforts have suggested that snowmelt may be a significant input of water for runoff during extreme rain‐on‐snow floods within western U.S. basins. Quantifying snowmelt input over entire basins is difficult given sparse observations of snowmelt. In order to provide a range of snowmelt contributions for water managers, a physically based snow model coupled with an idealized basin representation was evaluated in point simulations and used to quantify the maximum basin‐wide input from snowmelt volume during flood events. Maximum snowmelt basin contributions and uncertainty ranges were estimated as 29% (11–47%), 29% (8–37%), and 7% (2–24%) of total rain plus snowmelt input, within the Snoqualmie, East North Fork Feather, and Upper San Joaquin basins, respectively, during historic flooding events between 1980 and 2008. The idealized basin representation revealed that both hypsometry and forest cover of a basin had similar magnitude of impacts on the basin‐wide snowmelt totals. However, the characteristics of a given storm (antecedent SWE and available energy for melt) controlled how much hypsometry and forest cover impacted basin‐wide snowmelt. These results indicate that for watershed managers, flood forecasting efforts should prioritize rainfall prediction first, but cannot neglect snowmelt contributions in some cases. Efforts to reduce the uncertainty in the above snowmelt simulations should focus on improving the meteorological forcing data (especially air temperature and wind speed) in complex terrain.
Key Points:
Simulated snowmelt spanned 0–29% of basin input during floods, with rainfall making up the majority
Storm variability influences how basin characteristics control basin melt
Snowmelt magnitude was invariant with rainfall amount</abstract><cop>Washington</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1002/2014WR016576</doi><tpages>19</tpages><orcidid>https://orcid.org/0000-0001-9894-6213</orcidid><oa>free_for_read</oa></addata></record> |
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language | eng |
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source | Wiley Online Library Journals Frontfile Complete; Wiley-Blackwell AGU Digital Library; EZB-FREE-00999 freely available EZB journals |
subjects | Air temperature Atmospheric forcing Atmospheric precipitations Basins Computer simulation Extreme weather Flood forecasting Flood management Flood predictions Flooding Floods Forests Freshwater Historic floods Hypsometry idealized model Modelling Rain rain-on-snow Rainfall Rainfall forecasting Representations River discharge Runoff Snow Snowmelt Storms surface energy balance Uncertainty Water management Water temperature Watershed management Wind speed |
title | Modeling the influence of hypsometry, vegetation, and storm energy on snowmelt contributions to basins during rain-on-snow floods |
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