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Article: A new method for estimation of spatially distributed rainfall through merging satellite observations, raingauge records, and terrain digital elevation model data

TitleA new method for estimation of spatially distributed rainfall through merging satellite observations, raingauge records, and terrain digital elevation model data
Authors
KeywordsData merging
Elevation
Rainfall
Raingauge record
Satellite observation
Spatial distribution
Issue Date2017
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/wps/find/journaldescription.cws_home/711617/description#description
Citation
Journal of Hydro-Environment Research, 2017 How to Cite?
AbstractThis study develops a new method to estimate spatially distributed rainfall through merging the satellite observation, the raingauge record, and the terrain digital elevation model (DEM) data, including the following four steps: (1) to select a suitable satellite observation dataset, (2) to downscale the selected satellite observation dataset with the DEM data, (3) to determine the weighted differences between the raingauge record and the downscaled satellite observation dataset, and (4) to calculate the spatially distributed rainfall through merging the downscaled satellite observation dataset and the weighted differences. The rainstorm occurred on 21 July 2012 in Beijing, China, was considered as a case study to validate the method. Three satellite observation datasets (i.e., TMPA 3B41RT, 3B42RT and CMORPH) were compared with the related raingauge record. Using the new method, this study generated the spatially distributed rainfall data, which were further compared with the three rainfall datasets, i.e., two original rainfall datasets (the selected satellite observation dataset and the raingauge record) and one merged rainfall dataset without consideration of topographic influence. The result revealed that the merged spatially distributed rainfall data is a more rational representation of the actual rainfall than the three other datasets. Furthermore, using this data merging method and a hydrological model, the Digital Yellow River Integrated Model (DYRIM), this study simulated the streamflow process at the Dashi River basin in the southwest of Beijing and the Qingjian River basin in the middle Yellow River. The simulation results showed that the spatially distributed rainfall data could have better performance than those three other datasets, especially for the peak flow simulation. Overall, it is concluded that this data merging method can enhance our capability in estimating the spatial distribution of rainfall. © 2017 International Association for Hydro-environment Engineering and Research, Asia Pacific Division.
Persistent Identifierhttp://hdl.handle.net/10722/249982
ISSN
2017 Impact Factor: 2.087
2015 SCImago Journal Rankings: 0.641

 

DC FieldValueLanguage
dc.contributor.authorShi, H-
dc.contributor.authorChen, J-
dc.contributor.authorLi, T-
dc.contributor.authorWang, G-
dc.date.accessioned2017-12-20T09:18:57Z-
dc.date.available2017-12-20T09:18:57Z-
dc.date.issued2017-
dc.identifier.citationJournal of Hydro-Environment Research, 2017-
dc.identifier.issn1570-6443-
dc.identifier.urihttp://hdl.handle.net/10722/249982-
dc.description.abstractThis study develops a new method to estimate spatially distributed rainfall through merging the satellite observation, the raingauge record, and the terrain digital elevation model (DEM) data, including the following four steps: (1) to select a suitable satellite observation dataset, (2) to downscale the selected satellite observation dataset with the DEM data, (3) to determine the weighted differences between the raingauge record and the downscaled satellite observation dataset, and (4) to calculate the spatially distributed rainfall through merging the downscaled satellite observation dataset and the weighted differences. The rainstorm occurred on 21 July 2012 in Beijing, China, was considered as a case study to validate the method. Three satellite observation datasets (i.e., TMPA 3B41RT, 3B42RT and CMORPH) were compared with the related raingauge record. Using the new method, this study generated the spatially distributed rainfall data, which were further compared with the three rainfall datasets, i.e., two original rainfall datasets (the selected satellite observation dataset and the raingauge record) and one merged rainfall dataset without consideration of topographic influence. The result revealed that the merged spatially distributed rainfall data is a more rational representation of the actual rainfall than the three other datasets. Furthermore, using this data merging method and a hydrological model, the Digital Yellow River Integrated Model (DYRIM), this study simulated the streamflow process at the Dashi River basin in the southwest of Beijing and the Qingjian River basin in the middle Yellow River. The simulation results showed that the spatially distributed rainfall data could have better performance than those three other datasets, especially for the peak flow simulation. Overall, it is concluded that this data merging method can enhance our capability in estimating the spatial distribution of rainfall. © 2017 International Association for Hydro-environment Engineering and Research, Asia Pacific Division.-
dc.languageeng-
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/wps/find/journaldescription.cws_home/711617/description#description-
dc.relation.ispartofJournal of Hydro-Environment Research-
dc.subjectData merging-
dc.subjectElevation-
dc.subjectRainfall-
dc.subjectRaingauge record-
dc.subjectSatellite observation-
dc.subjectSpatial distribution-
dc.titleA new method for estimation of spatially distributed rainfall through merging satellite observations, raingauge records, and terrain digital elevation model data-
dc.typeArticle-
dc.identifier.emailShi, H: shy2004@hku.hk-
dc.identifier.emailChen, J: jichen@hku.hk-
dc.identifier.authorityChen, J=rp00098-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.jher.2017.10.006-
dc.identifier.scopuseid_2-s2.0-85032936401-
dc.identifier.hkuros283620-
dc.publisher.placeNetherlands-

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