Vote for your favorite mineral in #MinCup25! - Paddlewheelite vs. Mannardite
It's a battle of the tiny as bright green #paddlewheelite goes up against jet black #mannardite. Both are small yet mighty minerals, but only one can win!
Log InRegister
Quick Links : The Mindat ManualThe Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
Home PageAbout MindatThe Mindat ManualHistory of MindatCopyright StatusWho We AreContact UsAdvertise on Mindat
Donate to MindatCorporate SponsorshipSponsor a PageSponsored PagesMindat AdvertisersAdvertise on Mindat
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
Minerals by PropertiesMinerals by ChemistryAdvanced Locality SearchRandom MineralRandom LocalitySearch by minIDLocalities Near MeSearch ArticlesSearch GlossaryMore Search Options
Search For:
Mineral Name:
Locality Name:
Keyword(s):
 
The Mindat ManualAdd a New PhotoRate PhotosLocality Edit ReportCoordinate Completion ReportAdd Glossary Item
Mining CompaniesStatisticsUsersMineral MuseumsClubs & OrganizationsMineral Shows & EventsThe Mindat DirectoryDevice SettingsThe Mineral Quiz
Photo SearchPhoto GalleriesSearch by ColorNew Photos TodayNew Photos YesterdayMembers' Photo GalleriesPast Photo of the Day GalleryPhotography

Leitão, J.P., de Sousa, L.M. (2018) Towards the optimal fusion of high-resolution Digital Elevation Models for detailed urban flood assessment. Journal of Hydrology, 561. 651-661 doi:10.1016/j.jhydrol.2018.04.043

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleTowards the optimal fusion of high-resolution Digital Elevation Models for detailed urban flood assessment
JournalJournal of Hydrology
AuthorsLeitão, J.P.Author
de Sousa, L.M.Author
Year2018 (June)Volume561
PublisherElsevier BV
DOIdoi:10.1016/j.jhydrol.2018.04.043Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID7245844Long-form Identifiermindat:1:5:7245844:8
GUID0
Full ReferenceLeitão, J.P., de Sousa, L.M. (2018) Towards the optimal fusion of high-resolution Digital Elevation Models for detailed urban flood assessment. Journal of Hydrology, 561. 651-661 doi:10.1016/j.jhydrol.2018.04.043
Plain TextLeitão, J.P., de Sousa, L.M. (2018) Towards the optimal fusion of high-resolution Digital Elevation Models for detailed urban flood assessment. Journal of Hydrology, 561. 651-661 doi:10.1016/j.jhydrol.2018.04.043
In(2018) Journal of Hydrology Vol. 561. Elsevier BV

See Also

These are possibly similar items as determined by title/reference text matching only.

Leitão, J.P., Carbajal, J.P., Rieckermann, J., Simões, N.E., Sá Marques, A., de Sousa, L.M. (2018) Identifying the best locations to install flow control devices in sewer networks to enable in-sewer storage. Journal of Hydrology, 556. 371-383 doi:10.1016/j.jhydrol.2017.11.020
Muthusamy, Manoranjan, Casado, Mónica Rivas, Butler, David, Leinster, Paul (2021) Understanding the effects of Digital Elevation Model resolution in urban fluvial flood modelling. Journal of Hydrology, 596. 126088pp. doi:10.1016/j.jhydrol.2021.126088
Zandsalimi, Zanko, Barbosa, Sergio A., Alemazkoor, Negin, Goodall, Jonathan L., Shafiee-Jood, Majid (2025) Deep learning-based downscaling of global digital elevation models for enhanced urban flood modeling. Journal of Hydrology, 653. doi:10.1016/j.jhydrol.2025.132687
Paz, Adriano Rolim, Collischonn, Walter (2007) River reach length and slope estimates for large-scale hydrological models based on a relatively high-resolution digital elevation model. Journal of Hydrology, 343 (3). 127-139 doi:10.1016/j.jhydrol.2007.06.006
Xian, Siyuan, Lin, Ning, Kunreuther, Howard (2017) Optimal house elevation for reducing flood-related losses. Journal of Hydrology, 548. 63-74 doi:10.1016/j.jhydrol.2017.02.057
Zhang, Lin, Qin, Huapeng, Mao, Junqi, Cao, Xiaoyan, Fu, Guangtao (2023) High temporal resolution urban flood prediction using attention-based LSTM models. Journal of Hydrology, 620. 129499 doi:10.1016/j.jhydrol.2023.129499
Leitão, J.P., Prodanović, D., Maksimović, Č. (2016) Improving merge methods for grid-based digital elevation models. Computers & Geosciences, 88. 115-131 doi:10.1016/j.cageo.2016.01.001
Getirana, Augusto, Mandarino, Felipe, Ney de Montezuma, Patricia, Kirschbaum, Dalia (2023) An urban drainage scheme for large-scale flood models. Journal of Hydrology, 627. 130410 doi:10.1016/j.jhydrol.2023.130410
Farfán-Durán, Juan F.; Montalvo, Carlos; Cea, Luis; Leitão, João P. (2025) Integrating net rainfall calculation in deep learning-based surrogate modeling frameworks for 2D flood prediction. Journal of Hydrology, 661. doi:10.1016/j.jhydrol.2025.133632
Leitão, João P., Moy de Vitry, Matthew, Scheidegger, Andreas, Rieckermann, Jörg (2016) Assessing the quality of digital elevation models obtained from mini unmanned aerial vehicles for overland flow modelling in urban areas. Hydrology and Earth System Sciences, 20 (4) 1637-1653 doi:10.5194/hess-20-1637-2016
Leandro, J., Schumann, A., Pfister, A. (2016) A step towards considering the spatial heterogeneity of urban key features in urban hydrology flood modelling. Journal of Hydrology, 535. 356-365 doi:10.1016/j.jhydrol.2016.01.060
 
and/or  
Mindat.org is an outreach project of the Hudson Institute of Mineralogy, a 501(c)(3) not-for-profit organization.
Copyright © mindat.org and the Hudson Institute of Mineralogy 1993-2025, except where stated. Most political location boundaries are © OpenStreetMap contributors. Mindat.org relies on the contributions of thousands of members and supporters. Founded in 2000 by Jolyon Ralph.
To cite: Ralph, J., Von Bargen, D., Martynov, P., Zhang, J., Que, X., Prabhu, A., Morrison, S. M., Li, W., Chen, W., & Ma, X. (2025). Mindat.org: The open access mineralogy database to accelerate data-intensive geoscience research. American Mineralogist, 110(6), 833–844. doi:10.2138/am-2024-9486.
Privacy Policy - Terms & Conditions - Contact Us / DMCA issues - Report a bug/vulnerability Current server date and time: September 16, 2025 07:43:55
Go to top of page