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Optimal Temporal Resolution of Rainfall for Urban Applications and Uncertainty Propagation
The optimal temporal resolution for rainfall applications in urban hydrological models depends on different factors. Accumulations are often used to reduce uncertainty, while a sufficiently fine resolution is needed to capture the variability of the urban hydrological processes. Merging radar and ra...
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Published in: | Water (Basel) 2017-10, Vol.9 (10), p.762 |
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description | The optimal temporal resolution for rainfall applications in urban hydrological models depends on different factors. Accumulations are often used to reduce uncertainty, while a sufficiently fine resolution is needed to capture the variability of the urban hydrological processes. Merging radar and rain gauge rainfall is recognized to improve the estimation accuracy. This work explores the possibility to merge radar and rain gauge rainfall at coarser temporal resolutions to reduce uncertainty, and to downscale the results. A case study in the UK is used to cross-validate the methodology. Rainfall estimates merged and downscaled at different resolutions are compared. As expected, coarser resolutions tend to reduce uncertainty in terms of rainfall estimation. Additionally, an example of urban application in Twenterand, the Netherlands, is presented. The rainfall data from four rain gauge networks are merged with radar composites and used in an InfoWorks model reproducing the urban drainage system of Vroomshoop, a village in Twenterand. Fourteen combinations of accumulation and downscaling resolutions are tested in the InfoWorks model and the optimal is selected comparing the results to water level observations. The uncertainty is propagated in the InfoWorks model with ensembles. The results show that the uncertainty estimated by the ensemble spread is proportional to the rainfall intensity and dependent on the relative position between rainfall cells and measurement points. |
doi_str_mv | 10.3390/w9100762 |
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Fourteen combinations of accumulation and downscaling resolutions are tested in the InfoWorks model and the optimal is selected comparing the results to water level observations. The uncertainty is propagated in the InfoWorks model with ensembles. The results show that the uncertainty estimated by the ensemble spread is proportional to the rainfall intensity and dependent on the relative position between rainfall cells and measurement points.</description><identifier>ISSN: 2073-4441</identifier><identifier>EISSN: 2073-4441</identifier><identifier>DOI: 10.3390/w9100762</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Case studies ; Comparative analysis ; Data processing ; Hydrologic models ; Hydrology ; Position measurement ; Radar ; Rain ; Rain and rainfall ; Rainfall ; Rainfall intensity ; Technology application ; Temporal resolution ; Uncertainty ; Urban areas ; Urban drainage ; Water levels</subject><ispartof>Water (Basel), 2017-10, Vol.9 (10), p.762</ispartof><rights>COPYRIGHT 2017 MDPI AG</rights><rights>Copyright MDPI AG 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c328t-84deb92cc74d18f7582db30de31369dcacaf055bf87b95abff9b80212c5a30e63</citedby><cites>FETCH-LOGICAL-c328t-84deb92cc74d18f7582db30de31369dcacaf055bf87b95abff9b80212c5a30e63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/1965585309/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1965585309?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,25752,27923,27924,37011,44589,74997</link.rule.ids></links><search><creatorcontrib>Cecinati, Francesca</creatorcontrib><creatorcontrib>de Niet, Arie</creatorcontrib><creatorcontrib>Sawicka, Kasia</creatorcontrib><creatorcontrib>Rico-Ramirez, Miguel</creatorcontrib><title>Optimal Temporal Resolution of Rainfall for Urban Applications and Uncertainty Propagation</title><title>Water (Basel)</title><description>The optimal temporal resolution for rainfall applications in urban hydrological models depends on different factors. 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Fourteen combinations of accumulation and downscaling resolutions are tested in the InfoWorks model and the optimal is selected comparing the results to water level observations. The uncertainty is propagated in the InfoWorks model with ensembles. The results show that the uncertainty estimated by the ensemble spread is proportional to the rainfall intensity and dependent on the relative position between rainfall cells and measurement points.</description><subject>Case studies</subject><subject>Comparative analysis</subject><subject>Data processing</subject><subject>Hydrologic models</subject><subject>Hydrology</subject><subject>Position measurement</subject><subject>Radar</subject><subject>Rain</subject><subject>Rain and rainfall</subject><subject>Rainfall</subject><subject>Rainfall intensity</subject><subject>Technology application</subject><subject>Temporal resolution</subject><subject>Uncertainty</subject><subject>Urban areas</subject><subject>Urban drainage</subject><subject>Water levels</subject><issn>2073-4441</issn><issn>2073-4441</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNpNUE1LAzEQDaJgqQV_QsCLl6352GySYylqhUKltBcvSzablC3bJCZbpP_e1Co4l3nMvPeGeQDcYzSlVKKnL4kR4hW5AiOCOC3KssTX__AtmKS0R7lKKQRDI_CxCkN3UD3cmEPwMYO1Sb4_Dp130Fu4Vp2zqu-h9RFuY6McnIXQd1qdGQkq18Kt0yYOmTic4Hv0Qe1-lnfgJiuTmfz2Mdi-PG_mi2K5en2bz5aFpkQMhShb00iiNS9bLCxngrQNRa2hmFay1UorixhrrOCNZKqxVjYCEUw0UxSZio7Bw8U3RP95NGmo9_4YXT5ZY1kxJhhFMrOmF9ZO9abOT_khqrN5aw6d9s7YLs9nXHBelVySLHi8CHT0KUVj6xBzUvFUY1Sf067_0qbfzNVx7Q</recordid><startdate>20171004</startdate><enddate>20171004</enddate><creator>Cecinati, Francesca</creator><creator>de Niet, Arie</creator><creator>Sawicka, Kasia</creator><creator>Rico-Ramirez, Miguel</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20171004</creationdate><title>Optimal Temporal Resolution of Rainfall for Urban Applications and Uncertainty Propagation</title><author>Cecinati, Francesca ; de Niet, Arie ; Sawicka, Kasia ; Rico-Ramirez, Miguel</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c328t-84deb92cc74d18f7582db30de31369dcacaf055bf87b95abff9b80212c5a30e63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Case studies</topic><topic>Comparative analysis</topic><topic>Data processing</topic><topic>Hydrologic models</topic><topic>Hydrology</topic><topic>Position measurement</topic><topic>Radar</topic><topic>Rain</topic><topic>Rain and rainfall</topic><topic>Rainfall</topic><topic>Rainfall intensity</topic><topic>Technology application</topic><topic>Temporal resolution</topic><topic>Uncertainty</topic><topic>Urban areas</topic><topic>Urban drainage</topic><topic>Water levels</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cecinati, Francesca</creatorcontrib><creatorcontrib>de Niet, Arie</creatorcontrib><creatorcontrib>Sawicka, Kasia</creatorcontrib><creatorcontrib>Rico-Ramirez, Miguel</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Water (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cecinati, Francesca</au><au>de Niet, Arie</au><au>Sawicka, Kasia</au><au>Rico-Ramirez, Miguel</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimal Temporal Resolution of Rainfall for Urban Applications and Uncertainty Propagation</atitle><jtitle>Water (Basel)</jtitle><date>2017-10-04</date><risdate>2017</risdate><volume>9</volume><issue>10</issue><spage>762</spage><pages>762-</pages><issn>2073-4441</issn><eissn>2073-4441</eissn><abstract>The optimal temporal resolution for rainfall applications in urban hydrological models depends on different factors. Accumulations are often used to reduce uncertainty, while a sufficiently fine resolution is needed to capture the variability of the urban hydrological processes. Merging radar and rain gauge rainfall is recognized to improve the estimation accuracy. This work explores the possibility to merge radar and rain gauge rainfall at coarser temporal resolutions to reduce uncertainty, and to downscale the results. A case study in the UK is used to cross-validate the methodology. Rainfall estimates merged and downscaled at different resolutions are compared. As expected, coarser resolutions tend to reduce uncertainty in terms of rainfall estimation. Additionally, an example of urban application in Twenterand, the Netherlands, is presented. The rainfall data from four rain gauge networks are merged with radar composites and used in an InfoWorks model reproducing the urban drainage system of Vroomshoop, a village in Twenterand. Fourteen combinations of accumulation and downscaling resolutions are tested in the InfoWorks model and the optimal is selected comparing the results to water level observations. The uncertainty is propagated in the InfoWorks model with ensembles. The results show that the uncertainty estimated by the ensemble spread is proportional to the rainfall intensity and dependent on the relative position between rainfall cells and measurement points.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/w9100762</doi><oa>free_for_read</oa></addata></record> |
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subjects | Case studies Comparative analysis Data processing Hydrologic models Hydrology Position measurement Radar Rain Rain and rainfall Rainfall Rainfall intensity Technology application Temporal resolution Uncertainty Urban areas Urban drainage Water levels |
title | Optimal Temporal Resolution of Rainfall for Urban Applications and Uncertainty Propagation |
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