Stochastic and analytical approaches for sediment accumulation in river reservoirs
Sediment accumulation in a river reservoir is studied by stochastic time series models and analytical approach. The first-order moving average process is found the best for the suspended sediment discharge time series of the Juniata River at Newport, Pennsylvania, USA. Synthetic suspended sediment d...
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| Veröffentlicht in: | Hydrological sciences journal 2020-04, Vol.65 (6), p.984-994 |
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| container_title | Hydrological sciences journal |
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| creator | Akar, Tanju Aksoy, Hafzullah |
| description | Sediment accumulation in a river reservoir is studied by stochastic time series models and analytical approach. The first-order moving average process is found the best for the suspended sediment discharge time series of the Juniata River at Newport, Pennsylvania, USA. Synthetic suspended sediment discharges are first generated with the chosen model after which analytical expressions are derived for the expected value and variance of sediment accumulation in the reservoir. The expected value and variance of the volume of sediment accumulation in the reservoir are calculated from a thousand synthetic time series each 38 years long and compared to the analytical approach. Stochastic and analytical approaches perfectly trace the observation in terms of the expected value and variability. Therefore, it is concluded that the expected value and variance of sediment accumulation in a reservoir could be estimated by analytical expressions without the cost of synthetic data generation mechanisms. |
| doi_str_mv | 10.1080/02626667.2020.1728474 |
| format | Article |
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The first-order moving average process is found the best for the suspended sediment discharge time series of the Juniata River at Newport, Pennsylvania, USA. Synthetic suspended sediment discharges are first generated with the chosen model after which analytical expressions are derived for the expected value and variance of sediment accumulation in the reservoir. The expected value and variance of the volume of sediment accumulation in the reservoir are calculated from a thousand synthetic time series each 38 years long and compared to the analytical approach. Stochastic and analytical approaches perfectly trace the observation in terms of the expected value and variability. 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The first-order moving average process is found the best for the suspended sediment discharge time series of the Juniata River at Newport, Pennsylvania, USA. Synthetic suspended sediment discharges are first generated with the chosen model after which analytical expressions are derived for the expected value and variance of sediment accumulation in the reservoir. The expected value and variance of the volume of sediment accumulation in the reservoir are calculated from a thousand synthetic time series each 38 years long and compared to the analytical approach. Stochastic and analytical approaches perfectly trace the observation in terms of the expected value and variability. Therefore, it is concluded that the expected value and variance of sediment accumulation in a reservoir could be estimated by analytical expressions without the cost of synthetic data generation mechanisms.</description><subject>Accumulation</subject><subject>Cost analysis</subject><subject>Discharge</subject><subject>Exact solutions</subject><subject>Expected values</subject><subject>Fluvial sediments</subject><subject>Geological time</subject><subject>Juniata River</subject><subject>Mathematical analysis</subject><subject>moving average model</subject><subject>Physical Sciences</subject><subject>Reservoirs</subject><subject>river reservoir</subject><subject>Rivers</subject><subject>Science & Technology</subject><subject>Sediment</subject><subject>Sediment discharge</subject><subject>Sediments</subject><subject>Stochasticity</subject><subject>storage volume</subject><subject>suspended sediment discharge</subject><subject>Suspended sediments</subject><subject>Time series</subject><subject>Water Resources</subject><issn>0262-6667</issn><issn>2150-3435</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>AOWDO</sourceid><recordid>eNqNkNtKxDAURYMoOF4-QSj4KNWTS9PMmzJ4A0Hw8hwy6SmTodOMSarM35s66qMYCOGEtQ-bRcgJhXMKCi6ASSalrM8ZsPxVMyVqsUMmjFZQcsGrXTIZmXKE9slBjEsALqaST8jTc_J2YWJytjB9k6_pNnkwXWHW6-CNXWAsWh-KiI1bYZ8KY-2wGjqTnO8L1xfBvWMoAkYM796FeET2WtNFPP5-D8nrzfXL7K58eLy9n109lJZzlUphG1FzhkrJRlrViAo4rayRNaUI0jIxN1TJCnkjphwQp_MpCIq1Eq01jeSH5HS7N9d8GzAmvfRDyP2jZgIAGBNMZKraUjb4GAO2eh3cyoSNpqBHffpHnx716W99Oae2uQ-c-zZah73F32xeX1GRq8N46MylLx0zP_QpR8_-H8305ZZ2fda8Mh8-dI1OZtP50AbTWxc1_7vrJ9Gcl6A</recordid><startdate>20200425</startdate><enddate>20200425</enddate><creator>Akar, Tanju</creator><creator>Aksoy, Hafzullah</creator><general>Taylor & Francis</general><general>Taylor & Francis Ltd</general><scope>AOWDO</scope><scope>BLEPL</scope><scope>DTL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7ST</scope><scope>7TG</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>KL.</scope><scope>L.G</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0001-5807-5660</orcidid></search><sort><creationdate>20200425</creationdate><title>Stochastic and analytical approaches for sediment accumulation in river reservoirs</title><author>Akar, Tanju ; Aksoy, Hafzullah</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c338t-4cd4732e886d6c8d450315ca6711e06c24ba1865e3d4930ee9b9041e784fcad63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Accumulation</topic><topic>Cost analysis</topic><topic>Discharge</topic><topic>Exact solutions</topic><topic>Expected values</topic><topic>Fluvial sediments</topic><topic>Geological time</topic><topic>Juniata River</topic><topic>Mathematical analysis</topic><topic>moving average model</topic><topic>Physical Sciences</topic><topic>Reservoirs</topic><topic>river reservoir</topic><topic>Rivers</topic><topic>Science & Technology</topic><topic>Sediment</topic><topic>Sediment discharge</topic><topic>Sediments</topic><topic>Stochasticity</topic><topic>storage volume</topic><topic>suspended sediment discharge</topic><topic>Suspended sediments</topic><topic>Time series</topic><topic>Water Resources</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Akar, Tanju</creatorcontrib><creatorcontrib>Aksoy, Hafzullah</creatorcontrib><collection>Web of Science - Science Citation Index Expanded - 2020</collection><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>CrossRef</collection><collection>Aqualine</collection><collection>Environment Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Environment Abstracts</collection><jtitle>Hydrological sciences journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Akar, Tanju</au><au>Aksoy, Hafzullah</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Stochastic and analytical approaches for sediment accumulation in river reservoirs</atitle><jtitle>Hydrological sciences journal</jtitle><stitle>HYDROLOG SCI J</stitle><date>2020-04-25</date><risdate>2020</risdate><volume>65</volume><issue>6</issue><spage>984</spage><epage>994</epage><pages>984-994</pages><issn>0262-6667</issn><eissn>2150-3435</eissn><abstract>Sediment accumulation in a river reservoir is studied by stochastic time series models and analytical approach. The first-order moving average process is found the best for the suspended sediment discharge time series of the Juniata River at Newport, Pennsylvania, USA. Synthetic suspended sediment discharges are first generated with the chosen model after which analytical expressions are derived for the expected value and variance of sediment accumulation in the reservoir. The expected value and variance of the volume of sediment accumulation in the reservoir are calculated from a thousand synthetic time series each 38 years long and compared to the analytical approach. Stochastic and analytical approaches perfectly trace the observation in terms of the expected value and variability. Therefore, it is concluded that the expected value and variance of sediment accumulation in a reservoir could be estimated by analytical expressions without the cost of synthetic data generation mechanisms.</abstract><cop>ABINGDON</cop><pub>Taylor & Francis</pub><doi>10.1080/02626667.2020.1728474</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0001-5807-5660</orcidid></addata></record> |
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| subjects | Accumulation Cost analysis Discharge Exact solutions Expected values Fluvial sediments Geological time Juniata River Mathematical analysis moving average model Physical Sciences Reservoirs river reservoir Rivers Science & Technology Sediment Sediment discharge Sediments Stochasticity storage volume suspended sediment discharge Suspended sediments Time series Water Resources |
| title | Stochastic and analytical approaches for sediment accumulation in river reservoirs |
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