Use of HAND terrain descriptor for estimating flood-prone areas in river basins

Authors

DOI:

https://doi.org/10.5327/Z21769478892

Keywords:

geomatics; digital elevation model; floods.

Abstract

The flood hazard mapping in a river basin is crucial for flooding risk management, mitigation strategies, and flood forecasting and warning systems, among other benefits. One approach for this mapping is based on the HAND (Height Above Nearest Drainage) terrain descriptor, directly derived from the Digital Elevation Model (DEM), in which each pixel represents the elevation difference of this point in relation to the river drainage network to which it is connected. Considering the Mamanguape river basin (3,522.7 km²; state of Paraíba, Brazil) as the study location, the present research applied this method and verified it as for five aspects: consideration of a spatially variable minimum drainage area for denoting the river drainage initiation; the impact of considering a depressionless DEM; evaluation of hydrostatic condition; effect of incorporating an existing river vector network; and comparative analysis of basin morphology regarding longitudinal river profiles. According to the results, adopting a uniform minimum drainage area for the river network initiation is a simplification that should be avoided, using a spatially variable approach, which influences the amount and spatial distribution of flooded areas. Additionally, considering the depressionless DEM leads to higher values of HAND and to a smaller flooded area (difference ranging between 3% and 99%), when compared with the use of DEM with depression, despite 3.1% of the pixels representing depressions. The use of the depressionless DEM is recommended, whereas the DEM pre-processing by incorporating a vector network (stream burning) generates dubious results regarding the relation between HAND and the morphological pattern presented in the DEM. Moreover, the estimation of flooded areas based on HAND does not guarantee the hydrostatic condition, but this disagreement comprises a negligible area for practical purposes.

Downloads

Download data is not yet available.

References

Aagisa, V., 2004. Relatório sobre a elaboração do mapa de inundações: Bacia do Rio Mamanguape. João Pessoa.

Afshari, S.; Tavakoly, A.A.; Rajib, M.A.; Zheng, X.; Follum, M.L.; Omranian, E.; Fekete, B.M., 2018. Comparison of new generation low-complexity flood inundation mapping tools with a hydrodynamic model. Journal of Hydrology, v. 556, 539-556. https://doi.org/10.1016/j.jhydrol.2017.11.036.

Alfieri, L.; Bisselink, B.; Dottori, F.; Naumann, G.; De Roo, A.; Salamon, P.; Wyser, K.; Feyen, L., 2017. Global projections of river flood risk in a warmer world. Earth's Future, v. 5, (2), 171-182. https://doi.org/10.1002/2016EF000485.

Ali, S.A.; Parvin, F.; Pham, Q.B.; Vojtek, M.; Vojteková, J.; Costache, R.; Linh, N.T.T.; Nguyen, H.Q.; Ahmad, A.; Ghorbani, M.A., 2020. GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, naïve Bayes tree, bivariate statistics, and logistic regression: A case of Topľa basin, Slovakia. Ecological Indicators, v. 117, 106620. https://doi.org/10.1016/j.ecolind.2020.106620.

Arabameri, A.; Saha, S.; Chen, W.; Roy, J.; Pradhan, B.; Bui, D.T., 2020. Flash flood susceptibility modelling using functional tree and hybrid ensemble techniques. Journal of Hydrology, v. 587, 125007. https://doi.org/10.1016/j.jhydrol.2020.125007.

Barbosa, F.A.R., 2006. Medidas de proteção e controle de inundações urbanas na bacia do rio Mamanguape/PB. Master Thesis, Mestrado em Engenharia Urbana, Universidade Federal da Paraíba, João Pessoa. Retrieved 2020-06-24, from www.repositorio.ufpb.br

Barnes, R.; Lehman, C.; Mulla, D., 2014. An efficient assignment of drainage direction over flat surfaces in raster digital elevation models. Computers & Geosciences, v. 62, 128-135. http://dx.doi.org/10.1016/j.cageo.2013.01.009.

Benini, R.M.; Mendiondo, E.M., 2015. Urbanização e Impactos no Ciclo Hidrológico na Bacia do Mineirinho. Floresta e Ambiente, v. 22, (2), 211-222. https://doi.org/10.1590/2179-8087.103114.

Bork, C.; Castro, A.; Leandro, D.; Corrêa, L.; Siqueira, T., 2017. Índices de precipitação extrema para os períodos atual (1961-1990) e futuro (2011-2100) na bacia do rio Taquari-Antas, RS. Brazilian Journal of Environmental Sciences (Online), (46), 29-45. https://doi.org/10.5327/Z2176-947820170233.

Bravo, J.M.; Allasia, D.; Paz, A.R.; Collischonn, W.; Tucci, C.E.M., 2012. Coupled Hydrologic-Hydraulic Modelling of the Upper Paraguay River Basin. Journal of Hydrologic Engineering, v. 17, (5), 635-646. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000494.

Buarque, D.C.; Fan, F.M.; Paz, A.R.; Collischonn, W., 2009. Comparação de Métodos para Definir Direções de Escoamento a partir de Modelos Digitais de Elevação. Revista Brasileira de Recursos Hídricos, v. 14, (2), 91-103. https://doi.org/10.21168/rbrh.v14n2.p91-103.

Caldas, A.M.; Pissarra, T.C.T.; Costa, R.C.A.; Rolim Neto, F.C.; Zanata, M.; Parahyba, R.D.B.V.; Fernandes, L.F.S.; Pacheco, F.A.L., 2018. Flood vulnerability, environmental land use conflicts, and conservation of soil and water: A study in the Batatais SP municipality, Brazil. Water, v. 10, (10), 1357. https://doi.org/10.3390/w10101357.

Centro de Estudos e Pesquisas em Engenharia e Defesa Civil/Universidade Federal de Santa Catarina (CEPED/UFSC), 2013. Atlas brasileiro de desastres naturais: 1991 a 2012. 2. ed. Centro Universitário de Estudos e Pesquisas sobre Desastres, Florianópolis. Available from: https://www.ceped.ufsc.br. Access on June 20, 2020.

Cirilo, J.A.; Alves, F.H.B.; Silva, L.A.C.; Campos, J.H.A.L., 2014. Suporte de informações georreferenciadas de alta resolução para implantação de infraestrutura e planejamento territorial. Revista Brasileira de Geografia Física, v. 7, (4), 755-763. https://doi.org/10.26848/rbgf.v7.4.p755-763.

Clement, M.; Kilsby, C.; Moore, P., 2018. Multi‐temporal synthetic aperture radar flood mapping using change detection. Journal of Flood Risk Management, v. 11, (2), 152-168. https://doi.org/10.1111/jfr3.12303.

Cuartas, L.A.; Tomasella, J.; Nobre, A.D.; Nobre, C.A.; Hodnett, M.G.; Waterloo, M.J.; Oliveira, S.M.; Von Randow, R.C.; Trancoso, R.; Ferreira, M., 2012. Distributed hydrological modeling of a micro-scale rainforest watershed in Amazonia: Model evaluation and advances in calibration using the new HAND terrain model. Journal of Hydrology, v. 462-463, 15-27. https://doi.org/10.1016/j.jhydrol.2011.12.047.

Degiorgis, M.; Gnecco, G.; Gorni, S.; Roth, G.; Sanguineti, M.; Taramasso, A.C., 2012. Classifiers for the detection of flood-prone areas using remote sensed elevation data. Journal of Hydrology, v. 470-471, 302-315. https://doi.org/10.1016/j.jhydrol.2012.09.006.

Fan, M.F.; Collischonn, W.; Sorribas, M.V.; Pontes, P.R.M., 2013. Sobre o início da rede de drenagem definida a partir dos modelos digitais de elevação. Revista Brasileira de Recursos Hídricos, v. 18, (3), 241-257. https://doi.org/10.21168/rbrh.v18n3.p241-257.

Farr, T.G.; Rosen, P.A.; Caro, E; Crippen, R.; Duren, R.; Hensley, S.; Kobrick, M.; Paller, M.; Rodriguez, E.; Roth, L.; Seal, D.; Shaffer, S.; Shimada, J.; Umland, J.; Werner, M.; Oskin, M.; Burbank, D.; Alsdorf, D., 2007. The Shuttle Radar Topography Mission. Reviews of Geophysics, v. 45, (2). https://doi.org/10.1029/2005RG000183.

Fernandes, R.; Valverde, M., 2017. Análise da resiliência aos extremos climáticos de chuva: estudo preliminar na região de Mauá no ABC Paulista – São Paulo. Brazilian Journal of Environmental Sciences (Online), (44), 1-17. https://doi.org/10.5327/Z2176-947820170183.

Folha de S.Paulo, 2004. Barragem rompe e inunda cidades da PB - Em Alagoa Grande e Mulungu ao menos três pessoas morreram e outras 1.600 estão desabrigadas, segundo a Defesa Civil. Available from: https://www1.folha.uol.com.br/fsp/cotidian/ff1906200430.htm. Access on June 20, 2020.

Garousi-Nejad, I.; Tarboton, D.G.; Aboutalebi, M.; Torres-Rua, A.F., 2019. Terrain Analysis Enhancements to the Height Above Nearest Drainage Flood Inundation Mapping Method. Water Resources Research, v. 55, (10), 7983-8009. http://dx.doi.org/10.1029/2019WR024837.

Gharari, S.; Hrachowitz, M.; Fenicia, F.; Savenije, H.H.G., 2011. Hydrological landscape classification: investigating the performance of HAND based landscape classifications in a central European meso-scale catchment. Hydrology and Earth System Sciences, v. 15, (11), 3275-3291. https://doi.org/10.5194/hess-15-3275-2011.

Goerl, R.F.; Michel, G.P.; Kobiyama, M., 2017. Mapeamento de áreas susceptíveis a inundação com o modelo HAND e análise do seu desempenho em diferentes resoluções espaciais. Revista Brasileira de Cartografia, v. 69, (1), 61-69. Available from: http://www.seer.ufu.br/index.php/revistabrasileiracartografia/article/view/44032. Access on April 11, 2020.

Governo do Estado da Paraíba. Agência Executiva de Gestão das Águas do Estado da Paraíba (AESA), 2006. Plano Estadual de Recursos Hídricos – PERH. Available from: http://www.aesa.pb.gov.br/aesa-website/wp-content/uploads/2020/03/PERH-Resumo-Executivo.pdf. Access on June 20, 2020

Hawker, L.; Bates, P.; Neal, J.; Rougier, J., 2018. Perspectives on Digital Elevation Model (DEM) simulation for flood modeling in the absence of a high-accuracy open access global DEM. Frontiers in Earth Science, v. 6, 1-9. https://doi.org/10.3389/feart.2018.00233.

Hdeib, R.; Abdallah, C.; Colin, F.; Brocca, L.; Moussa, R., 2018. Constraining coupled hydrological-hydraulic flood model by past storm events and post-event measurements in data-sparse regions. Journal of Hydrology, v. 565, 160-176. https://doi.org/10.1016/j.jhydrol.2018.08.008.

Heinzlef, C.; Becue, V.; Serre, D., 2020. A spatial decision support system for enhancing resilience to floods: bridging resilience modelling and geovisualization techniques. Natural Hazards and Earth System Sciences, v. 20, (4), 1049-1068. https://doi.org/10.5194/nhess-20-1049-2020.

Jenson, S.; Domingue, J., 1988. Extracting topographic sctruture from digital elevation data for Geographic Information System analysis. Photogrammetric Engineering and Remote Sensing, v. 54, (11), 1593-1600. Available from: https://pubs.er.usgs.gov/publication/70142175. Access on August 16, 2020.

Jones, R., 2002. Algorithms for using a DEM for mapping catchment areas of stream sediment samples. Computers and Geosciences, v. 28, (9), 1051-1060. https://doi.org/10.1016/S0098-3004(02)00022-5.

Krieger, G.; Moreira, A.; Fiedler, H.; Hajnsek, I.; Werner, M.; Younis, M., Zink, M., 2007. TANDEM-X: A satellite formation for high-resolution SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, v. 45, (11), 3317-3341. https://doi.org/10.1109/TGRS.2007.900693.

Landuyt, L.; Van Wesemael, A.; Schumann, G.J.; Hostache, R.; Verhoest, N.E.C.; Van Coillie, F.M.B., 2019. Flood Mapping Based on Synthetic Aperture Radar: An Assessment of Established Approaches. IEEE Transactions on Geoscience and Remote Sensing, v. 57, (2), 722-739. https://doi.org/10.1109/TGRS.2018.2860054.

Li, J.; Li, T.; Zhang, L.; Sivakumar, B.; Fu, X.; Huang, Y.; Bai, R., 2020. A D8-compatible high-efficient channel head recognition method. Environmental Modelling and Software, v. 125, 104624. https://doi.org/10.1016/j.envsoft.2020.104624.

Lin, Q.; Leandro, J.; Wu, W.; Bhola, P.; Disse, M., 2020. Prediction of maximum flood inundation extents with resilient backpropagation neural network: Case study of Kulmbach. Frontiers in Earth Science, v. 8, 1-8. https://doi.org/10.3389/feart.2020.00332.

Lindsay, J.B., 2016. The practice of DEM stream burning revisited. Earth Surface Processes and Landforms, v. 41, (5), 658-668. https://doi.org/10.1002/esp.3888.

Lira, F.; Cardoso, A., 2018. Estudo de tendência de vazões de rios das principais bacias hidrográficas brasileiras. Brazilian Journal of Environmental Sciences (Online), (48), 21-37. https://doi.org/10.5327/Z2176-947820180273.

Liu, Y.Y.; Maidment, D.R.; Tarboton, D.G.; Zheng, X.; Wang, S., 2018. A CyberGIS Integration and Computation Framework for High-Resolution Continental-Scale Flood Inundation Mapping. Journal of the American Water Resources Association, v. 54, (4), 770-784. https://doi.org/10.1111/1752-1688.12660.

Mark, D.M., 1984. Automated detection of drainage networks from digital elevation model. Cartographica, v. 21, (2-3), 168-178. https://doi.org/10.3138/10LM-4435-6310-251R.

Marques, A.L.; Silva, J.B.; Silva, D.G., 2015. Compartimentação geológico-geomorfológica da bacia hidrográfica do rio Mamanguape-PB utilizando modelagem espacial. In: Anais XVII Simpósio Brasileiro de Sensoriamento Remoto, João Pessoa-PB, Brasil, 346-353. Available from: http://marte2.sid.inpe.br/rep/sid.inpe.br/marte2/2015/06.15.14.08.06. Access on August 16, 2020.

McGrath, H.; Bourgon, J.F.; Proulx-Bourque, J.S.; Nastev, M.; Abo El Ezz, A., 2018. A comparison of simplified conceptual models for rapid web-based flood inundation mapping. Natural Hazards, v. 93, 905-920. https://doi.org/10.1007/s11069-018-3331-y.

Mengue, V.P.; Scottá, F.C.; Silva, T.S.; Farina, F., 2016. Utilização do Modelo HAND para mapeamento das áreas mais suscetíveis à inundação no Rio Uruguai. Pesquisas em Geociências, v. 43, (1), 41-53. https://doi.org/10.22456/1807-9806.78191.

Meyer, V.; Becker, N.; Markantonis, W.; Schwarze, R.; van den Bergh, J. C. J. M.; Bouwer, L. M.; Bubeck, P.; Ciavola, P.; Genovese, E.; Green, C.; Hallegatte, S.; Kreibich, H.; Lequeux, Q.; Logar, I.; Papyrakis, E.; Pfurtscheller, C.; Poussin, J.; Przyluski, V.; Thieken, A. H.; Viavattene, C., 2013. Review article: Assessing the costs of natural hazards – state of the art and knowledge gaps. Natural Hazards and Earth System Sciences, v. 13, (5), 1351-1373. https://doi.org/10.5194/nhess-13-1351-2013.

Momo, M.R.; Pinheiro, A.; Severo, D.L.; Cuartas, L.A.; Nobre, A.D., 2016. Desempenho do modelo HAND no mapeamento de áreas suscetíveis à inundação usando dados de alta resolução espacial. Revista Brasileira de Recursos Hídricos, v. 21, (1), 200-208. http://dx.doi.org/10.21168/rbrh.v21n1.p200-208.

Morelli, S.; Battistini, A.; Catani, F., 2014. Rapid assessment of flood susceptibility in urbanized rivers using digital terrain data: Application to the Arno river case study (Firenze, northern Italy). Applied Geography, v. 54, 35-53. http://dx.doi.org/10.1016/j.apgeog.2014.06.032.

Niipele, J.N.; Chen, J., 2019. The usefulness of Alos-Palsar DEM data for drainage extraction in semi-arid environments in The Iishana sub-basin. Journal of Hydrology: Regional Studies, v. 21, 57-67. https://doi.org/10.1016/j.ejrh.2018.11.003.

Nobre, A.D.; Cuartas, L.A.; Hodnett, M.; Rennó, C.D.; Rodrigues, G.; Silveira, A.; Saleska, S., 2011. Height Above the Nearest Drainage–a hydrologically relevant new terrain model. Journal of Hydrology, v. 404, (1-2), 13-29. https://doi.org/10.1016/j.jhydrol.2011.03.051.

Nobre, A.D.; Cuartas, L.A.; Momo, M.R.; Severo, D.L.; Pinheiro, A.; Nobre, C.A., 2016. HAND contour: a new proxy predictor of inundation extent. Hydrological Processes, v. 30, (2), 320-333. https://doi.org/10.1002/hyp.10581.

O’Loughlin, F.E.; Paiva, R.C.D.; Durand, M.; Alsdorf, D.E.; Bates, P.D., 2016. A multi-sensor approach towards a global vegetation corrected SRTM DEM product. Remote Sensing of Environment, v. 182, 49-59. https://doi.org/10.1016/j.rse.2016.04.018.

Paprotny, D.; Sebastian, A.; Morales-Nápoles, O.; Jonkman, S.N., 2018. Trends in flood losses in Europe over the past 150 years. Nature Communications, v. 9, 1985. https://doi.org/10.1038/s41467-018-04253-1.

Paul, G.C.; Saha, S.; Hembram, T.K., 2019. Application of the GIS-Based Probabilistic Models for mapping the flood susceptibility in Bansloi sub-basin of Ganga-Bhagirathi River and their comparison. Remote Sensing in Earth Systems Sciences, v. 2, 120-146. https://doi.org/10.1007/s41976-019-00018-6.

Paz, A.R.; Collischonn, W., 2008. Derivação de rede de drenagem a partir de dados do SRTM. Revista Geográfica Acadêmica, 2, (2), 84-95. Available from: https://go.gale.com/ps/i.do?id=GALE%7CA186470659&sid=googleScholar&v=2.1&it=r&linkaccess=abs&issn=16787226&p=AONE&sw=w&userGroupName=anon%7E6ac0a33e. Access on July 20, 2020.

Paz, A.R.; Collischonn, W.; Tucci, C.E.M.; Padovani, C.R., 2011. Large-scale modelling of channel flow and floodplain inundation dynamics and its application to the Pantanal (Brazil). Hydrological Process, v. 25, (9), 1498-1516. https://doi.org/10.1002/hyp.7926.

Pontes, P.R.M.; Fan, F.M.; Fleischmann, A.S.; Paiva, R.C.D.; Buarque, D.C.; Siqueira, V.A.; Jardim, P.F.; Sorribas, M.V.; Collischonn, W., 2017. MGB-IPH model for hydrological and hydraulic simulation of large floodplain river systems coupled with open source GIS. Environmental Modelling & Software, v. 94, 1-20. https://doi.org/10.1016/j.envsoft.2017.03.029.

Prakash, M.; Cohen, R.; Hilton, J.; Khan, S.H., 2020. An evidence based approach to evaluating flood adaptation effectiveness including climate change considerations for coastal cities: City of Port Phillip, Victoria, Australia. Journal of Flood Risk Management, v. 13, (Suppl. 1), e12556. https://doi.org/10.1111/jfr3.12556.

Rahmati, O.; Kornejady, A.; Samadi, M.; Nobre, A.D.; Melesse, A.M., 2018. Development of an automated GIS tool for reproducing the HAND terrain model. Environmental Modeling & Software, v. 102, 1-12. https://doi.org/10.1016/j.envsoft.2018.01.004.

Rennó, C.D.; Nobre, A.D.; Cuartas, L.A.; Soares, J.V.; Hodnett, M.G.; Tomasella, J.; Waterloo, M.J., 2008. HAND, a new terrain descriptor using SRTM-DEM: Mapping terra-firme rainforest environments in Amazonia. Remote Sensing of Environment, v. 112, (9), 3469-3481. https://doi.org/10.1016/j.rse.2008.03.018.

Robinson, N.; Regetz, J.; Guralnick, R.P., 2014. EarthEnv-DEM90: A nearly-global, void-free, multi-scale smoothed, 90m digital elevation model from fused ASTER and SRTM data. ISPRS Journal of Photogrammetry and Remote Sensing, v. 87, 57-67. https://doi.org/10.1016/j.isprsjprs.2013.11.002.

Rodda, H.J.E., 2005. The Development and Application of a Flood Risk Model for the Czech Republic. Natural Hazards, v. 36, 207-220. https://doi.org/10.1007/s11069-004-4549-4.

Rodrigues, I.A.; Antunes, L.R.; Rodovalho, R.B., 2005. Perfis Social, Econômico e Ecológico da Área de Influência da APA da Barra do Rio Mamanguape (PB) - Bases para a classificação e seleção de estabelecimentos rurais para Gestão Ambiental. In: Rodrigues, G.S.; Buschinelli, C.C.A.; Rodrigues, I.A.; Neves, M.C.M. (Eds.), Avaliação de Impactos Ambientais para Gestão da APA da Barra do Rio Mamanguape/PB. EMBRAPA, São Paulo, pp. 40.

Santos, E.C.A.; Araújo, L.E.; Marcelino, A.S., 2015. Análise climática da Bacia Hidrográfica do Rio Mamanguape. Revista Brasileira de Engenharia Agrícola e Ambiental, v. 19, (1). http://dx.doi.org/10.1590/1807-1929/agriambi.v19n1p9-14.

Sedgewick, R. (1992). Algorithms in C++. Addison-Wesley, Reading.

Siqueira, V.A.; Fleischmann, A.; Jardim, P.F.; Fan, F.M.; Collischonn, W., 2016. IPH-Hydro Tools: a GIS coupled tool for watershed topology acquisition in an opensource environment. Revista Brasileira de Recursos Hídricos, v. 21, (1), 274-287. http://dx.doi.org/10.21168/rbrh.v21n1.p274-287.

Speckhann, G.A.; Chaffe, P.L.B.; Goerl, R.F.; Abreu, J.J.; Flores, J.A.A., 2018. Flood hazard mapping in Southern Brazil: a combination of flow frequency analysis and the HAND model. Hydrological Sciences Journal, v. 63, (1), 87-100. https://doi.org/10.1080/02626667.2017.1409896.

Tadono, T.; Takaku, J.; Tsutsui, K.; Oda, F.; Nagai, H., 2015. Status of “ALOS World 3D (AW3D)” global DSM generation. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milão, 3822-3825. https://doi.org/10.1109/IGARSS.2015.7326657.

Tehrany, M.S.; Pradhan, B.; Mansor, S.; Ahmad, N., 2015. Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena, v. 125, 91-101. http://dx.doi.org/10.1016/j.catena.2014.10.017.

Van Zyl, J.J., 2001. The Shuttle Radar Topography Mission (SRTM): a breakthrough in remote sensing of topography. Acta Astronautica, v. 48, (5-12), 559-565. https://doi.org/10.1016/S0094-5765(01)00020-0.

Wu, T.; Li, J.; Li, T.; Sivakumar, B.; Zhang, G.; Wang, G., 2019. High-efficient extraction of drainage networks from digital elevation models constrained by enhanced flow enforcement from known river maps. Gemorphology, v. 340, 184-201. https://doi.org/10.1016/j.geomorph.2019.04.022.

Yamazaki, D.; Ikeshima, D.; Tawatari, R.; Yamaguchi, T.; O’Loughlin, F.; Neal, J.C.; Sampson, C.C.; Kanae, S.; Bates, P.D., 2017. A high-accuracy map of global terrain elevations. Geophysical Research Letters, v. 44, (11), 5844-5853. https://doi.org/10.1002/2017GL072874.

Zambrano, F.C.; Kobiyama, M.; Pereira, M.A.F.; Michel, G.P.; Fan, F.M., 2020. Influence of different sources of topographic data on flood mapping: urban area São Vendelino municipality, southern Brazil. Revista Brasileira de Recursos Hídricos, v. 25, e40. https://doi.org/10.1590/2318-0331.252020190108.

Zheng, X.; Maidment, D.R.; Tarboton, D.G.; Liu, Y.Y.; Passalacqua, P., 2018a. GeoFlood: Large-scale flood inundation mapping based on high-resolution terrain analysis. Water Resources Research, v. 54, (12), 10013-10033. https://doi.org/10.1029/2018WR023457.

Zheng, X.; Tarboton, D.G.; Maidment, D.R.; Liu, Y.Y.; Passalacqua, P., 2018b. River Channel Geometry and Rating Curve Estimation Using Height above the Nearest Drainage. Journal of the American Water Resources Association, v. 54, (4), 785-806. https://doi.org/10.1111/1752-1688.12661.

Downloads

Published

2021-08-17

How to Cite

Dantas, A. A. R., & Paz, A. R. (2021). Use of HAND terrain descriptor for estimating flood-prone areas in river basins. Revista Brasileira De Ciências Ambientais (RBCIAMB), 56(3), 501–516. https://doi.org/10.5327/Z21769478892