Articles | Volume 28, issue 14
https://doi.org/10.5194/hess-28-3367-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-3367-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Regionalization of GR4J model parameters for river flow prediction in Paraná, Brazil
Louise Akemi Kuana
Programa de Pós-Graduação em Engenharia Ambiental, Universidade Federal do Paraná, Curitiba, Brazil
Arlan Scortegagna Almeida
Sistema de Tecnologia e Monitoramento Ambiental do Paraná (Simepar), Curitiba, Brazil
Emílio Graciliano Ferreira Mercuri
CORRESPONDING AUTHOR
Departamento de Engenharia Ambiental, Universidade Federal do Paraná, Curitiba, Brazil
Institute of Forestry and Engineering, Estonian University of Life Sciences, Tartu, Estonia
Steffen Manfred Noe
Institute of Forestry and Engineering, Estonian University of Life Sciences, Tartu, Estonia
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Cited articles
AGUASPARANÁ: Manual técnico de outorgas, i Edn., Estado do Paraná, https://www.iat.pr.gov.br/sites/agua-terra/arquivos_restritos/files/documento/2020-10/manual_outorgas_suderhsa_2006.pdf (last access: 17 July 2024), 2010. a
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration: guidelines for computing crop water requirements, Food and Agriculture Organization of the United Nations, Rome, ISBN 9251042195, 1998. a
Almagro, A., Oliveira, P. T. S., Neto, A. A. M., Roy, T., and Troch, P.: CABra: a novel large-sample dataset for Brazilian catchments, Hydrol. Earth Syst. Sci., 25, 3105–3135, https://doi.org/10.5194/hess-25-3105-2021, 2021. a
Arsenault, R., Breton-Dufour, M., Poulin, A., Dallaire, G., and Romero-Lopez, R.: Streamflow prediction in ungauged basins: analysis of regionalization methods in a hydrologically heterogeneous region of Mexico, Hydrolog. Sci. J., 64, 1297–1311, https://doi.org/10.1080/02626667.2019.1639716, 2019. a, b, c, d
Ayzel, G., Varentsova, N., Erina, O., Sokolov, D., Kurochkina, L., and Moreydo, V.: OpenForecast: The First Open-Source Operational Runoff Forecasting System in Russia, Water, 11, 1546, https://doi.org/10.3390/w11081546, 2019. a
Barbieri, G. M. L., Costa, A. B. F., Olivieira, C., Jusevicius, M., and D'Ávila, V. C.: Atlas Solarimétrico Do Estado Do Paraná, Manuscrito não publicado, https://solar.copel.com/solar/atlas-solarimetrico-copel.pdf (last access: 17 July 2024), 2017. a
Bartiko, D., Oliveira, D., Bonumá, N., and Chaffe, P.: Spatial and seasonal patterns of flood change across Brazil, Hydrolog. Sci. J., 64, 1071–1079, 2019. a
Bazzo, J. P. V. and Almeida, R. C. d.: Regionalização de Vazões com o Emprego de Redes Neurais Artificiais RBF, in: I Simpósio de Métodos Numéricos em Engenharia, 30 November 2016, Curitiba, 2016. a
Blöschl, G., Sivapalan, M., Wagener, T., Viglione, A., and Savenije, H.: Runoff Prediction in Ungauged Basins, Cambridge University Press, https://doi.org/10.1017/cbo9781139235761, 2013. a, b, c
Boutsidis, C., Zouzias, A., Mahoney, M. W., and Drineas, P.: Randomized Dimensionality Reduction for k-Means Clustering, IEEE T. Inf. Theory, 61, 1045–1062, 2014. a
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, 2001. a
Burn, D. H., Zrinji, Z., and Kowalchuk, M.: Regionalization of catchments for regional flood frequency analysis, J. Hydrol. Eng., 2, 76–82, 1997. a
Burnash, R. J. C.: The NWS River Forecast System-catchment modeling, in: Computer models of watershed hydrology, 311–366, https://www.cabidigitallibrary.org/doi/full/10.5555/19961904770 (last access: 1 February 2020), 1995. a
Burt, T. P. and McDonnell, J. J.: Whither field hydrology? The need for discovery science and outrageous hydrological hypotheses, Water Resour. Res., 51, 5919–5928, 2015. a
Calvetti, L., Beneti, C., Neundorf, R. L. A., Inouye, R. T., dos Santos, T. N., Gomes, A. M., Herdies, D. L., and de Gonçalves, L. G. G.: Quantitative Precipitation Estimation Integrated by Poisson's Equation Using Radar Mosaic, Satellite, and Rain Gauge Network, J. Hydrol. Eng., 22, E5016003, https://doi.org/10.1061/(asce)he.1943-5584.0001432, 2017. a
Carneiro, L., Ostroski, A., and Mercuri, E. G. F.: Trophic state index for heavily impacted watersheds: modeling the influence of diffuse pollution in water bodies, Hydrolog. Sci. J., 65, 2548–2560, 2020. a
Chagas, V. B. P., Chaffe, P. L. B., Addor, N., Fan, F. M., Fleischmann, A. S., Paiva, R. C. D., and Siqueira, V. A.: CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil, Earth Syst. Sci. Data, 12, 2075–2096, https://doi.org/10.5194/essd-12-2075-2020, 2020. a
Cunha, A. P. M. A., Zeri, M., Leal, K. D., Costa, L., Cuartas, L. A., Marengo, J. A., Tomasella, J., Vieira, R. M., Barbosa, A. A., Cunningham, C., Garcia, J. V. C., Broedel, E., Alvalá, R., and Ribeiro-Neto, G.: Extreme drought events over Brazil from 2011 to 2019, Atmosphere, 10, 642, https://doi.org/10.3390/atmos10110642, 2019. a
Daggupati, P., Pai, N., Ale, S., Douglas-Mankin, K. R., andJ. Jeong, R. W. Z., Parajuli, P. B., Saraswat, D., and Youssef, M. A.: A Recommended Calibration and Validation Strategy for Hydrologic and Water Quality Models, Am. Soc. Agricult. Biol. Eng., 58, 1705–1719, https://doi.org/10.13031/trans.58.10712, 2015. a
Embrapa: Mapa de solos do estado do Paraná, http://geoinfo.cnps.embrapa.br/layers/geonode:parana_solos_20201105, (last access: 5 July 2021), 2020. a
Guo, Y., Zhang, Y., Zhang, L., and Wang, Z.: Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review, Wires Water, 8, e1487, https://doi.org/10.1002/wat2.1487, 2020. a, b
He, Y., Bárdossy, A., and Zehe, E.: A review of regionalisation for continuous streamflow simulation, Hydrol. Earth Syst. Sci., 15, 3539–3553, https://doi.org/10.5194/hess-15-3539-2011, 2011. a
Hengl, T., Heuvelink, G. B. M., and Rossiter, G. D.: About regression-kriging: From equations to case studies, Comput. Geosci., 33, 1301–1315, 2007. a
Hirata, R. and Foster, S.: The Guarani Aquifer System – from regional reserves to local use, Q. J. Eng. Geol. Hydrogeol., 54, qjegh2020-091, https://doi.org/10.1144/qjegh2020-091, 2021. a
Hrachowitz, M., Savenije, H. H. G., Blöschl, G., McDonnell, J. J., Sivapalan, M., Pomeroy, J. W., Arheimer, B., Blume, T., Clark, M. P., Ehret, U., Fenicia, F., Freer, J. E., Gelfan, A., Gupta, H. V., Hughes, D. A., Hut, R. W., Montanari, A., Pande, S., Tetzlaff, D., Troch, P. A., Uhlenbrook, S., Wagener, T., Winsemius, H. C., Woods, R. A., Zehe, E., and Cudennec, C.: A decade of Predictions in Ungauged Basins (PUB) – a review, Hydrolog. Sci. J., 58, 1–58, 2013. a
IAT: Mapas e Dados Espaciais, http://www.iat.pr.gov.br/Pagina/Mapas-e-Dados-Espaciais (last access: 5 July 2021), 2020. a
Juliani, B. H. T., de Campos, A. L., Almeida, A. S., and Leite, E. A.: Estatísticas meteorológicas da seca de 2020 no estado do Paraná, in: Anais do II END – Encontro Nacional de Desastres da ABRHidro, ABRHidro, https://anais.abrhidro.org.br/job.php?Job=7358 (last access: 17 July 2024), 2020. a
Kaviski, E., Rohn, M. d. C., and Mazer, W.: Projeto HG-171: Consistência e regionalização de dados hidrológicos, Centro de Hidráulica e Hidrologia Prof. Parigot de Souza, 2002. a
Ketchen Junior, D. J. and Shook, C. L.: The application of cluster analysis in strategic management research: an analysis and critique, Strat. Manage. J., 17, 441–458, 1996. a
Krause, P., Boyle, D. P., and Bäse, F.: Comparison of different efficiency criteria for hydrological model assessment, Adv. Geosci., 5, 89–97, https://doi.org/10.5194/adgeo-5-89-2005, 2005. a
Kuentz, A., Arheimer, B., Hundecha, Y., and Wagener, T.: Understanding hydrologic variability across Europe through catchment classification, Hydrol. Earth Syst. Sci., 21, 2863–2879, https://doi.org/10.5194/hess-21-2863-2017, 2017. a
Llabrés-Brustenga, A., Rius, A., Rodríguez-Sol, R., Casas-Castillo, M. C., and Redaño, A.: Quality control process of the daily rainfall series available in Catalonia from 1855 to the present, Theor. Appl. Climatol., 137, 2715–2729, 2019. a
Matallo Junior, H.: Indicadores de desertificação: histórico e perspectivas, Edições UNESCO Brasil, Brasília, DF, Brasil, ISBN 8587853279, 2001. a
Melo, D., Ramos, G., Ferreira, G., Schwamback, D., Siqueira, J., Duarte-Carvajalino, J., Jhunior, H., Nóbrega, J., Morita, A., Almeida, C., Coutinho, J., Leite, C., Guedes, A., Coelho, V. H., Anache, J., Pelinson, N., Rosalem, L., Calixto, K. G., and Wendland, E.: The big picture of field hydrology studies in Brazil, Hydrolog. Sci. J., 65, 1262–1280, 2020. a
Melo, D. D. C. D., Scanlon, B. R., Zhang, Z., Wendland, E., and Yin, L.: Reservoir storage and hydrologic responses to droughts in the Paraná River basin, south-eastern Brazil, Hydrol. Earth Syst. Sci., 20, 4673–4688, https://doi.org/10.5194/hess-20-4673-2016, 2016. a
Mohamed, S., Ludovic, O., and Ribstein, P.: Random Forest Ability in Regionalizing Hourly Hydrological Model Parameters, Water, 11, 8, https://doi.org/10.3390/w11081540, 2019. a
Muleta, M. K.: Model Performance Sensitivity to Objective Function during Automated Calibrations, J. Hydrol. Eng., 17, 756–767, https://doi.org/10.1061/(asce)he.1943-5584.0000497, 2012. a
Musy, A., Hingray, B., and Picouet, C.: Hydrology: a science for engineers, CRC Press, https://doi.org/10.1201/b17169, 2014. a
Neto, W. M. P., Vieira, F. R., and Matosinhos, C. C.: Avaliação da perfomance dos modelos GR4J, GR5J e GR6J na bacia hidrográfica do ribeirão São João, Minas Gerais, in: Base de Conhecimentos Gerados na Engenharia Ambiental e Sanitária 3, Atena, https://doi.org/10.22533/at.ed.74521080423, 2021. a
Oudin, L., Andréassian, V., Perrin, C., Michel, C., and Moine, N. L.: Spatial proximity, physical similarity, regression and ungaged catchments: A comparison of regionalization approaches based on 913 French catchments, Water Resour. Res., 44, W03413, https://doi.org/10.1029/2007wr006240, 2008. a, b, c, d
Oudin, L., Kay, A., Andréassian, V., and Perrin, C.: Are seemingly physically similar catchments truly hydrologically similar?, Water Resour. Res., 46, W11558, https://doi.org/10.1029/2009wr008887, 2010. a, b, c
Pagano, T., Hapuarachchi, P., and Wang, Q. J.: Continuous rainfall-runoff model comparison and short-term daily streamflow forecast skill evaluation, Tech. Rep., CSIRO, EP103545, https://doi.org/10.4225/08/58542C672DD2C, 2010. a
Parajka, J., Merz, R., and Blöschl, G.: A comparison of regionalisation methods for catchment model parameters, Hydrol. Earth Syst. Sci., 9, 157–171, https://doi.org/10.5194/hess-9-157-2005, 2005. a, b
Parajka, J., Kohnová, S., Bálint, G., Barbuc, M., Borga, M., Claps, P., Cheval, S., Dumitrescu, A., Gaume, E., Hlavčová, K., Merz, R., Pfaundler, M., Stancalie, G., Szolgay, J., and Blöschl, G.: Seasonal characteristics of flood regimes across the Alpine–Carpathian range, J. Hydrol., 394, 78–89, 2010. a
Perrin, C., Michel, C., and Andréassian, V.: Improvement of a parsimonious model for streamflow simulation, J. Hydrol., 279, 275–289, 2003. a
Pettitt, A. N.: A Non-Parametric Approach to the Change-Point Problem, Appl. Stat., 28, 126, https://doi.org/10.2307/2346729, 1979. a
Pfafstetter, O.: Classificação de bacias hidrográficas, manuscrito não publicado, DNOS – Departamento Nacional de Obras de Saneamento, 1989. a
Pugliese, A., Castellarin, A., and Brath, A.: Geostatistical prediction of flow–duration curves in an index-flow framework, Hydrol. Earth Syst. Sci., 18, 3801–3816, https://doi.org/10.5194/hess-18-3801-2014, 2014. a
Pushpalatha, R., Perrin, C., Le Moine, N., Mathevet, T., and Andréassian, V.: A downward structural sensitivity analysis of hydrological models to improve low-flow simulation, J. Hydrol., 411, 66–76, 2011. a
Razavi, T. and Coulibaly, P.: Streamflow Prediction in Ungauged Basins: Review of Regionalization Methods, J. Hydrol. Eng., 18, 958–975, https://doi.org/10.1061/(asce)he.1943-5584.0000690, 2013. a
Rousseeuw, P. J.: Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, J. Comput. Appl. Math., 20, 53–65, https://doi.org/10.1016/0377-0427(87)90125-7, 1987. a
Shin, M.-J. and Kim, C.-S.: Assessment of the suitability of rainfall–runoff models by coupling performance statistics and sensitivity analysis, Hydrol. Res., 48, 1192–1213, https://doi.org/10.2166/nh.2016.129, 2016. a
Soil Conservation Service: National engineering handbook, in: Chap. Seção 4, Hydrology, Department ofAgriculture, Washington, p. 762, https://books.google.com.br/books?id=sjOEf-5zjXgC (last access: 1 December 2023), 1972. a
Sousa, F. M. L., Neto, V. S. C., Pacheco, W. E., and Barbosa, S. A.: Sistema Nacional De Informações Sobre Recursos Hídricos: Sistematização Conceitual E Modelagem Funcional, in: Anais do XVIII Simpósio Brasileiro de Recursos Hídricos, Associação Brasileira de Recursos Hídricos, Campo Grande, https://anais.abrhidro.org.br/job.php?Job=10334 (last access: 1 December 2023), 2009. a
Souza, C. M., Shimbo, J. Z., Rosa, M. R., Parente, L. L., Alencar, A. A., Rudorff, B. F. T., Hasenack, H., Matsumoto, M., Ferreira, L. G., Souza-Filho, P. W. M., de Oliveira, S. W., Rocha, W. F., Fonseca, A. V., Marques, C. B., Diniz, C. G., Costa, D., Monteiro, D., Rosa, E. R., Vélez-Martin, E., Weber, E. J., Lenti, F. E. B., Paternost, F. F., Pareyn, F. G. C., Siqueira, J. V., Viera, J. L., Neto, L. C. F., Saraiva, M. M., Sales, M. H., Salgado, M. P. G., Vasconcelos, R., Galano, S., Mesquita, V. V., and Azevedo, T.: Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine, Remote Sens., 12, 2735, https://doi.org/10.3390/rs12172735, 2020. a
Storn, R. and Price, K.: Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces, J. Global Optimiz., 11, 341–359, https://doi.org/10.1023/a:1008202821328, 1997. a
Viviroli, D., Mittelbach, H., Gurtz, J., and Weingartner, R.: Continuous simulation for flood estimation in ungauged mesoscale catchments of Switzerland – Part II: Parameter regionalisation and flood estimation results, J. Hydrol., 377, 208–225, https://doi.org/10.1016/j.jhydrol.2009.08.022, 2009. a
Wilks, D. S.: Statistical Methods in the Atmospheric Sciences, Academic Press, ISBN 0123850223, https://www.ebook.de/de/product/14751307/daniel_s_wilks_statistical_methods_in_the_atmospheric_sciences_100.html (last access: 1 December 2023), 2011. a
Short summary
The authors compared regionalization methods for river flow prediction in 126 catchments from the south of Brazil, a region with humid subtropical and hot temperate climate. The regionalization method based on physiographic–climatic similarity had the best performance for predicting daily and Q95 reference flow. We showed that basins without flow monitoring can have a good approximation of streamflow using machine learning and physiographic–climatic information as inputs.
The authors compared regionalization methods for river flow prediction in 126 catchments from...