Journal: Region - Water Conservancy DOI: 10.32629/rwc.v9i2.5489
Abstract
The most severe extreme climatecrisis event in Brazil, the floods and inundations that struck the state of Rio Grande do Sul (RGS) in May 2024, has highlighted the urgent need to rethink urban infrastructure and research priorities within contexts marked by limited access to knowledge and disorganized territorial planning. The main objective of this study is to analyse the relationship between flood management and the implementation of structural and nonstructural measures. Through a systematic literature review, solutions based on hydrodynamic models with application potential for preventing and mitigating floods in urban environments are identified. Likewise, a critical evaluation is carried out of the strategies deployed during the major flood in Rio Grande do Sul, and more effective and sustainable alternatives are proposed. Rio Grande do Sul also faces culturalrelated problems with cascade effects: serious separatist attempts to break away from the rest of Brazil, together with persistent manifestations of machismo and racism that have caused multiple fatalities rooted in notions of superiority, all of which exert an impact on crisis management. Drawing on this perspective, the following research question is formulated: What role does culture play in knowledge management for floods and drainage, particularly with regard to the use of hydrodynamic models? This question motivates the proposal of an integrated model of Cultural Intelligence, Knowledge Management and Social Participation, intended to optimize the design and implementation of floodcontrol plans. Initially, following a comparative analysis of the CRESTv2.1, HECRAS, MIKE 21 and cGANFlood models, the Hydropol2D forecasting model was considered for application. Nevertheless, significant limitations were identified, such as its failure to account for the effects of agricultural activity on hydrological dynamics. Consequently, it is suggested that Hydropol2D be replaced by the SWAT+ (Soil and Water Assessment Tool) model combined with the groundwater module (GWFlow). This enables more accurate simulation of surface and subsurface hydrological processes. This integrated tool not only provides enhanced capacity for assessing the impacts of landuse patterns and agricultural practices but also improves accuracy when modelling urbandrainage scenarios.
Keywords
cultural change; knowledge management, hydrodynamic models; organizational intelligence; SWAT+ GWFlow; urban planning
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Disponivel em https://www.jornaldocomercio.com/cadernos/empresas-enegocios/2024/05/1155968-emergencia-climatica-traz-necessidade-de-mudanca-em-parametros-de-risco-de- desastres.html
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[3] Bao, Zhenxin, et al. "Comparison of regionalization approaches based on regression and similarity for predictions in ungauged catchments under multiple hydro-climatic conditions." Journal of Hydrology 466 (2012): 37-46.
[4] Brunner, G. W. HEC-RAS river analysis system, 2D modeling users' manual. U.S. Army Corps of Engineer, Institute for Water Resource, Hydrologic Engineering Center. 2016.
[5] Choo, C.W. The Knowing Organisation, Oxford University Press, New York, NY. 1998.
[6] Davenport, T.H. and Prusak, L. Working Knowledge, 2nd ed., Harvard Business School Press, Boston, MA. 2000.
[7] Cutter, S., B. Boruff y L. Shirley. "Social vulnerability to environmental hazards", Social Science Quarterly, 2003. vol. 84, N° 2
[8] De Angelis, C. T. Um modelo e Plano de Emergência Padronizado para as inundações. Jornal do Comércio. 2024. Dispononível em https://www.jornaldocomercio.com/opiniao/2024/07/1165074-um-modelo-e-plano-de-emergencia- padronizado-para-as-inundacoes.html
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[10] De Angelis, C. T. Gomes Júnior, M. N. Uma sugestão de modelo hidrodinâmico para prever e gerir inundações . Jornal do Comércio. 2024. Disponível em https://www.jornaldocomercio.com/opiniao/2024/07/1160994-uma- sugestao-de-modelo-hidrodinamico-para-prever-e-gerir-inundacoes.html
[11] Do Lago, Cesar & Brasil, José & Nóbrega, Marcus & Mendiondo, Eduardo & Giacomoni, Marcio. Improving pluvial flood mapping resolution of large coarse models with deep learning. Hydrological Sciences Journal, 2024. 69(5), 607-621. https://doi.org/10.1080/02626667.2024.2329268
[12] Fassoni-Andrade, A. C. Paiva, R. C. Rudorff, C. M. Barbosa, C.C. Leão, E. M. High-resolution mapping of floodplain topography from space: A case study in the Amazon, Remote Sensing of Environment,Volume 251, 2020.
[13] Fassoni-Andrade, A. C. Durand, F. Azevedo,A. Bertin, X. Santos, L.G. Khan, J. U. Testut, Moreira, D. M. Seasonal to interannual variability of the tide in the Amazon estuary, Continental Shelf Research,Volume 255, 2023.
[14] Getirana, A., Boone, A., Yamazaki, D., Decharme, B., Papa, F., & Mognard, N. The hydrological modeling and analysis platform (HyMAP): Evaluation in the Amazon basin. Journal of Hydrometeorology, 2012. 13, 1641-1665
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[21] Li, G. Zhu, H. Jian, H. Zha, W. JWang, J. Shu, Z. Yao, S. Han, H. A combined hydrodynamic model and deep learning method to predict water level in ungauged rivers, Journal of Hydrology, Volume 625, Part A,2023.
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Disponivel em
https://www.ufrgs.br/iph/wpcontent/uploads/2024/05/CriteriosAdaptacaoMudancaClimaticaChuvasCheiasExtremasSul.pdf
[26] Rennó, C.D.; Soares, J. V. Modelos hidrológicos para gestão ambiental. Cursos INPE. 2022. Disponível em:
[27] Rosman, P. C.C. Um Sistema Computacional de Hidrodinâmica Ambiental–Capítulo 1 (pp 1-161) do livro Métodos Numéricos em Recursos Hídricos, Vol. 5. Editora ABRH e Fundação COPPETEC. 2001.
[28] Rothberg, H. N. Erickson, G. S. "From Knowledge to Intelligence: Creating Competitive Advantage in the Next Economy." 2004.
[29] Schein, Edgar H. Organizational Culture and Leadership. San Francisco: Jossey-Bass Publishers. 1985.
[30] Stokes Oceanografia. Estudos sobre modelos hidrodinâmicos. 2023.
Disponível em http://stokesoceanografia.com.br/2020/08/07/modelos-hidrodinamicos1/
[31] Yang, Linhan, et al. "Effects of the Three Gorges Dam on the downstream streamflow based on a large-scale hydrological and hydrodynamics coupled model." Journal of Hydrology: Regional Studies 40 (2022): 101039.
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