Effect of digital elevation model spatial resolution on depression storage
Surface water storage—including wetlands and other small waterbodies—has largely been disregarded in traditional hydrological models. In this paper, the grid resampling method is adopted to study the influence of the digital elevation model (DEM) grid resolution on depression storage (DS) considerin...
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description | Surface water storage—including wetlands and other small waterbodies—has largely been disregarded in traditional hydrological models. In this paper, the grid resampling method is adopted to study the influence of the digital elevation model (DEM) grid resolution on depression storage (DS) considering different rainfall return periods. It is observed that the DEM grid size highly affects DS, and the higher the grid resolution is, the larger the DS value. However, when the grid resolution reaches a certain value, the maximum DS value decreases. This suggests that a critical grid resolution value exists at which the water storage capacity of depressions is maximized, namely, 20 m in this work (except for the overall area simulation under infiltration). This phenomenon is further verified in two test cases with and without the infiltration process, that is, calculations of the local area and without infiltration area, respectively. This research may facilitate the accurate computation of the DS process, which is greatly affected by the grid resolution, thereby improving the reliability of hydrological models.
Depression storage (DS) is explored under different terrain grid resolution and rainfall return periods through the analysis of the overall study area and the local study area, with infiltration and without infiltration based resampling method. |
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Depression storage (DS) is explored under different terrain grid resolution and rainfall return periods through the analysis of the overall study area and the local study area, with infiltration and without infiltration based resampling method.</description><identifier>ISSN: 0885-6087</identifier><identifier>EISSN: 1099-1085</identifier><identifier>DOI: 10.1002/hyp.14381</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>Computation ; Depression storage ; digital elevation model ; Digital Elevation Models ; grid resampling ; grid resolution ; Hydrologic models ; hydrological models ; Hydrology ; Infiltration ; Rain ; Rainfall ; Resampling ; Resolution ; Spatial discrimination ; Spatial resolution ; Storage capacity ; Storage conditions ; Surface water ; Water storage ; Wetlands</subject><ispartof>Hydrological processes, 2021-10, Vol.35 (10), p.n/a</ispartof><rights>2021 John Wiley & Sons Ltd.</rights><rights>2021 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3981-ef36cda2e53ac5d26a4868d482c7b14fe41ac85a34eabe70919293a806fc194e3</citedby><cites>FETCH-LOGICAL-c3981-ef36cda2e53ac5d26a4868d482c7b14fe41ac85a34eabe70919293a806fc194e3</cites><orcidid>0000-0002-9097-3804 ; 0000-0002-1451-100X</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%2Fhyp.14381$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fhyp.14381$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids></links><search><creatorcontrib>Hou, Jingming</creatorcontrib><creatorcontrib>Li, Xinyi</creatorcontrib><creatorcontrib>Pan, Zhanpeng</creatorcontrib><creatorcontrib>Wang, Junhui</creatorcontrib><creatorcontrib>Wang, Ruike</creatorcontrib><title>Effect of digital elevation model spatial resolution on depression storage</title><title>Hydrological processes</title><description>Surface water storage—including wetlands and other small waterbodies—has largely been disregarded in traditional hydrological models. In this paper, the grid resampling method is adopted to study the influence of the digital elevation model (DEM) grid resolution on depression storage (DS) considering different rainfall return periods. It is observed that the DEM grid size highly affects DS, and the higher the grid resolution is, the larger the DS value. However, when the grid resolution reaches a certain value, the maximum DS value decreases. This suggests that a critical grid resolution value exists at which the water storage capacity of depressions is maximized, namely, 20 m in this work (except for the overall area simulation under infiltration). This phenomenon is further verified in two test cases with and without the infiltration process, that is, calculations of the local area and without infiltration area, respectively. This research may facilitate the accurate computation of the DS process, which is greatly affected by the grid resolution, thereby improving the reliability of hydrological models.
Depression storage (DS) is explored under different terrain grid resolution and rainfall return periods through the analysis of the overall study area and the local study area, with infiltration and without infiltration based resampling method.</description><subject>Computation</subject><subject>Depression storage</subject><subject>digital elevation model</subject><subject>Digital Elevation Models</subject><subject>grid resampling</subject><subject>grid resolution</subject><subject>Hydrologic models</subject><subject>hydrological models</subject><subject>Hydrology</subject><subject>Infiltration</subject><subject>Rain</subject><subject>Rainfall</subject><subject>Resampling</subject><subject>Resolution</subject><subject>Spatial discrimination</subject><subject>Spatial resolution</subject><subject>Storage capacity</subject><subject>Storage conditions</subject><subject>Surface water</subject><subject>Water storage</subject><subject>Wetlands</subject><issn>0885-6087</issn><issn>1099-1085</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp1kEFPwzAMhSMEEmNw4B9U4sShm9OmnXNE09hAk-AAB05RljqjU7eUpAPt35OtXJEsWe_5sy09xm45jDhANv48tCMucuRnbMBBypQDFudsAIhFWgJOLtlVCBsAEIAwYM8za8l0ibNJVa_rTjcJNfStu9rtkq2rqElCG1X0PQXX7E-DWBW10QhHFTrn9Zqu2YXVTaCbvz5k74-zt-kiXb7Mn6YPy9TkEnlKNi9NpTMqcm2KKiu1wBIrgZmZrLiwJLg2WOhckF7RBCSXmcw1QmkNl4LyIbvr77befe0pdGrj9n4XX6qswLJEWaCM1H1PGe9C8GRV6-ut9gfFQR2jUjEqdYoqsuOe_akbOvwPqsXHa7_xCzgOa1c</recordid><startdate>202110</startdate><enddate>202110</enddate><creator>Hou, Jingming</creator><creator>Li, Xinyi</creator><creator>Pan, Zhanpeng</creator><creator>Wang, Junhui</creator><creator>Wang, Ruike</creator><general>John Wiley & Sons, Inc</general><general>Wiley Subscription Services, Inc</general><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><orcidid>https://orcid.org/0000-0002-9097-3804</orcidid><orcidid>https://orcid.org/0000-0002-1451-100X</orcidid></search><sort><creationdate>202110</creationdate><title>Effect of digital elevation model spatial resolution on depression storage</title><author>Hou, Jingming ; Li, Xinyi ; Pan, Zhanpeng ; Wang, Junhui ; Wang, Ruike</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3981-ef36cda2e53ac5d26a4868d482c7b14fe41ac85a34eabe70919293a806fc194e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Computation</topic><topic>Depression storage</topic><topic>digital elevation model</topic><topic>Digital Elevation Models</topic><topic>grid resampling</topic><topic>grid resolution</topic><topic>Hydrologic models</topic><topic>hydrological models</topic><topic>Hydrology</topic><topic>Infiltration</topic><topic>Rain</topic><topic>Rainfall</topic><topic>Resampling</topic><topic>Resolution</topic><topic>Spatial discrimination</topic><topic>Spatial resolution</topic><topic>Storage capacity</topic><topic>Storage conditions</topic><topic>Surface water</topic><topic>Water storage</topic><topic>Wetlands</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hou, Jingming</creatorcontrib><creatorcontrib>Li, Xinyi</creatorcontrib><creatorcontrib>Pan, Zhanpeng</creatorcontrib><creatorcontrib>Wang, Junhui</creatorcontrib><creatorcontrib>Wang, Ruike</creatorcontrib><collection>CrossRef</collection><collection>Aqualine</collection><collection>Environment Abstracts</collection><collection>Meteorological & Geoastrophysical 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>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>Environment Abstracts</collection><jtitle>Hydrological processes</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hou, Jingming</au><au>Li, Xinyi</au><au>Pan, Zhanpeng</au><au>Wang, Junhui</au><au>Wang, Ruike</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Effect of digital elevation model spatial resolution on depression storage</atitle><jtitle>Hydrological processes</jtitle><date>2021-10</date><risdate>2021</risdate><volume>35</volume><issue>10</issue><epage>n/a</epage><issn>0885-6087</issn><eissn>1099-1085</eissn><abstract>Surface water storage—including wetlands and other small waterbodies—has largely been disregarded in traditional hydrological models. In this paper, the grid resampling method is adopted to study the influence of the digital elevation model (DEM) grid resolution on depression storage (DS) considering different rainfall return periods. It is observed that the DEM grid size highly affects DS, and the higher the grid resolution is, the larger the DS value. However, when the grid resolution reaches a certain value, the maximum DS value decreases. This suggests that a critical grid resolution value exists at which the water storage capacity of depressions is maximized, namely, 20 m in this work (except for the overall area simulation under infiltration). This phenomenon is further verified in two test cases with and without the infiltration process, that is, calculations of the local area and without infiltration area, respectively. This research may facilitate the accurate computation of the DS process, which is greatly affected by the grid resolution, thereby improving the reliability of hydrological models.
Depression storage (DS) is explored under different terrain grid resolution and rainfall return periods through the analysis of the overall study area and the local study area, with infiltration and without infiltration based resampling method.</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1002/hyp.14381</doi><tpages>21</tpages><orcidid>https://orcid.org/0000-0002-9097-3804</orcidid><orcidid>https://orcid.org/0000-0002-1451-100X</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Computation Depression storage digital elevation model Digital Elevation Models grid resampling grid resolution Hydrologic models hydrological models Hydrology Infiltration Rain Rainfall Resampling Resolution Spatial discrimination Spatial resolution Storage capacity Storage conditions Surface water Water storage Wetlands |
title | Effect of digital elevation model spatial resolution on depression storage |
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