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Early flood warning in the Itajaí-Açu River basin using numerical weather forecasting and hydrological modeling

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Abstract

In recent decades, population growth associated with unplanned urban occupation has increased the vulnerability of the Brazilian population to natural disasters. In susceptible regions, early flood forecasting is essential for risk management. Still, in Brazil, most flood forecast and warning systems are based either on simplified models of flood wave propagation through the drainage network or on stochastic models. This paper presents a methodology for flood forecasting aiming to an operational warning system that proposes to increase the lead time of a warning through the use of an ensemble of meteorological forecasts. The chosen configuration was chosen so it would be feasible for an operational flood forecast and risk management. The methodology was applied to the flood forecast for the Itajaí-Açu River basin, a region which comprises a drainage area of approximately 15,500 km2 in the state of Santa Catarina, Brazil, historically affected by floods. Ensemble weather forecasts were used as input to the MHD-INPE hydrological model, and the performance of the methodology was assessed through statistical indicators. Results suggest that flood warnings can be issued up to 48 h in advance, with a low rate of false warnings. Streamflow forecasting through the use of hydrological ensemble prediction systems is still scarce in Brazil. To the best of our knowledge, this is the first time this methodology aiming to an operational flood risk management system has been tested in Brazil.

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References

  • Abreu ES, Rosim S, Rennó CD, Ricardo J, Oliveira DF, Jardim AC, Ortiz JDO, Dutra LV (2012) TERRAHIDRO–a distributed hydrological system to delimit large basins. In: Geoscience and remote sensing symposium (IGARSS), 2012 IEEE international. Munich, pp 546–549

  • Alfieri L, Burek P, Dutra E, Krzeminski B, Muraro D, Thielen J, Pappenberger F (2013) GloFAS-global ensemble streamflow forecasting and flood early warning. Hydrol Earth Syst Sci 17:1161–1175. doi:10.5194/hess-17-1161-2013

    Article  Google Scholar 

  • Alfieri L, Pappenberger F, Wetterhall F, Haiden T, Richardson D, Salamon P (2014) Evaluation of ensemble streamflow predictions in Europe. J Hydrol 517:913–922. doi:10.1016/j.jhydrol.2014.06.035

    Article  Google Scholar 

  • Alfieri L, Burek P, Feyen L, Forzieri G (2015) Global warming increases the frequency of river floods in Europe. Hydrol Earth Syst Sci 19:2247–2260. doi:10.5194/hess-19-2247-2015

    Article  Google Scholar 

  • ANA (2015) Sistema de informações hidrológicas Hidroweb. http://hidroweb.ana.gov.br/. Accessed 20 May 2005

  • Bao HJ, Zhao LN, He Y, Li ZJ, Wetterhall F, Cloke HL, Pappenberger F, Manful D (2011) Coupling ensemble weather predictions based on TIGGE database with Grid-Xinanjiang model for flood forecast. Adv Geosci 29:61–67. doi:10.5194/adgeo-29-61-2011

    Article  Google Scholar 

  • Bartholmes J, Todini E (2005) Coupling meteorological and hydrological models for flood forecasting. Hydrol Earth Syst Sci 9:333–346. doi:10.5194/hess-9-333-2005

    Article  Google Scholar 

  • Bartholmes JC, Thielen J, Ramos MH, Gentilini S (2009) The European Flood Alert System EFAS—Part 2: statistical skill assessment of probabilistic and deterministic operational forecasts. Hydrol Earth Syst Sci 13:141–153. doi:10.5194/hessd-5-289-2008

    Article  Google Scholar 

  • Beven KJ, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology. Hydrol Sci Bull 24:43–69. doi:10.1080/02626667909491834

    Article  Google Scholar 

  • Bezada M (2009) Natural hazards and human-induced disasters triggered by intense and episodic tropical rains in the Venezuelan Mountains. Dev Earth Surf Process 13:115–129

    Article  Google Scholar 

  • Black TL (1994) The new NMC mesoscale Eta model: description and forecast examples. Weather Forecast 9:265–278. doi:10.1175/1520-0434(1994)009<0265:TNNMEM>2.0.CO;2

    Article  Google Scholar 

  • Bravo JM, Paz AR, Collischonn W, Uvo CB, Pedrollo OC, Chou SC (2009) Incorporating forecasts of rainfall in two hydrologic models used for medium-range streamflow forecasting. J Hydrol Eng 14:435–445. doi:10.1061/(ASCE)HE.1943-5584.0000014

    Article  Google Scholar 

  • Brier GW (1950) Verification of forecasts expersses in terms of probaility. Mon Weather Rev 78:1–3

    Article  Google Scholar 

  • Castilho AS, Oliveira LM (2001) Previsão Hidrológica de vazões para a cidade de Governador Valadares utilizando modelo linear de propagação. In: ABRH (ed) XIV Simpósio Brasileiro de Recursos Hídricos. Aracaju

  • CEMADEN (2015) Projeto Pluviômetros automáticos. In: CEMADEN. http://www.cemaden.gov.br/pluviometrosautomaticos/. Accessed 20 May 2007

  • CEPED/UFSC (2013a) Atlas Brasileiro de desastres naturais 1991 a 2012. Vol. Brasil., 2nd edn. CEPED/UFSC, Florianópolis

  • CEPED/UFSC (2013b) Atlas Brasileiro de desastres naturais 1991 a 2012. Vol. Santa Catarina. In: 2nd edn. CEPED/UFSC, Florianópolis, p 168

  • Chou SC (1996) Modelo regional Eta. Climanálise ISSN 0103-0019 Edição Esp:203–207

  • Chou SC, Nunes AMB (2000) Extended range forecasts over South America using the Eta regional. J Geophys Res 105:10147–10160

    Article  Google Scholar 

  • Cloke HL, Pappenberger F (2009) Ensemble flood forecasting: a review. J Hydrol 375:613–626. doi:10.1016/j.jhydrol.2009.06.005

    Article  Google Scholar 

  • Collischonn W, Haas R, Andreolli I, Tucci CEM (2005) Forecasting river Uruguay flow using rainfall forecasts from a regional weather-prediction model. J Hydrol 305:87–98. doi:10.1016/j.jhydrol.2004.08.028

    Article  Google Scholar 

  • Collischonn W, Morelli Tucci CE, Clarke RT, Chou SC, Guilhon LG, Cataldi M, Allasia D (2007) Medium-range reservoir inflow predictions based on quantitative precipitation forecasts. J Hydrol 344:112–122. doi:10.1016/j.jhydrol.2007.06.025

    Article  Google Scholar 

  • Cordero A, Momo MR, Severo DL (2011) Previsão de cheia em tempo atual, com um modelo ARMAX, para a cidade de Rio do Sul—SC. In: XIX Simpósio Brasileiro de Recursos Hídricos. Maceió, pp 1–13

  • Daupras F, Antoine JM, Becerra S, Peltier A (2015) Analysis of the robustness of the French flood warning system: a study based on the 2009 flood of the Garonne River. Nat Hazards. doi:10.1007/s11069-014-1318-x

    Google Scholar 

  • Demargne J, Wu L, Regonda SK, Brown JD, Lee H, He M, Seo DJ, Hartman R, Herr HD, Fresch M, Schaake J, Zhu Y (2014) The science of NOAA’s operational hydrologic ensemble forecast service. Bull Am Meteorol Soc 95:79–98. doi:10.1175/BAMS-D-12-00081.1

    Article  Google Scholar 

  • Dodson R, Marks D (1997) Daily air temperature interpolated at high spatial resolution over a large mountainous region. Clim Res 8:1–20. doi:10.3354/cr008001

    Article  Google Scholar 

  • Duan Q, Sorooshian S, Gupta HV (1992) Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resour Res 28:1015–1031. doi:10.1029/91WR02985

    Article  Google Scholar 

  • Duan Q, Sorooshian S, Gupta VK (1994) Optimal use of the SCE-UA global optimization method for calibrating watershed models. J Hydrol 158:265–284

    Article  Google Scholar 

  • EMBRAPA (2004) Solos do Estado de Santa Catarina

  • Epstein ES (1969) A scoring system for probability forecasts of ranked categories. J Appl Meteorol 8:985–987. doi:10.1175/1520-0450(1969)008<0985:ASSFPF>2.0.CO;2

    Article  Google Scholar 

  • Fan FM, Collischonn W, Meller A, Botelho LCM (2014) Ensemble streamflow forecasting experiments in a tropical basin: the São Francisco river case study. J Hydrol 519:2906–2919. doi:10.1016/j.jhydrol.2014.04.038

    Article  Google Scholar 

  • Fan FM, Collischonn W, Quiroz KJ, Sorribas MV, Buarque DC, Siqueira VA (2015) Flood forecasting on the Tocantins River using ensemble rainfall forecasts and real-time satellite rainfall estimates. J Flood Risk Manag. doi:10.1111/jfr3.12177

    Google Scholar 

  • Farr TG, Rosen PA, 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 DE (2007) The shuttle radar topography mission. Rev Geophys 45:1–43. doi:10.1029/2005RG000183

    Article  Google Scholar 

  • Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27:861–874. doi:10.1016/j.patrec.2005.10.010

    Article  Google Scholar 

  • Fernández Bou AS, De Sá RV, Cataldi M (2015) Flood forecasting in the upper Uruguay River basin. Nat Hazards. doi:10.1007/s11069-015-1903-7

    Google Scholar 

  • Ferrier BS, Jin Y, Lin Y, Black T, Rogers E, DiMego G (2002) Implementation of a new grid-scale cloud and precipitation scheme in the NCEP Eta model. In: Conference on weather analysis and forecasting, pp 280–283

  • GEOAMBIENTE Sensoriamento Remoto Ltda (2008) Projeto de proteção da mata atlântica em Santa Catarina (PPMA/SC). Relatório Técnico do Mapeamento Temático Geral do Estado de SC. GEOAMBIENTE, São José dos Campos

    Google Scholar 

  • Gomes Soares D, Ghizoni Teive RC (2015) Previsão de Cheias do Rio Itajaí-Açu Utilizando Redes Neurais Artificiais. In: Anais do Computer on the Beach 2015, pp 308–317

  • Gonçalves AS, Caram RDO, Scofield GB, Duarte AG, Tomasella J (2014a) Previsões preliminares de desastres hidrológicos na bacia do Rio Doce. In: Anais do XII Simpósio de Recursos Hídricos do Nordeste. Natal, pp 1–10

  • Gonçalves AS, Tomasella J, Rodriguez DA (2014b) Estudos dos efeitos das mudanças globais na bacia do rio Madeira. In: Anais do XII Simpósio de Recursos Hídricos do Nordeste. Natal, p 109

  • Groisman PY, Knight RW, Easterling DR, Karl TR, Hegerl GC, Razuvaev VN (2005) Trends in intense precipitation in the climate record. J Clim 18:1326–1350. doi:10.1175/JCLI3339.1

    Article  Google Scholar 

  • Harris A, Hossain F (2008) Investigating the optimal configuration of conceptual hydrologie models for satellite-rainfall-based flood prediction. IEEE Geosci Remote Sens Lett 5:532–536. doi:10.1109/LGRS.2008.922551

    Article  Google Scholar 

  • Hashino T, Bradley AA, Schwartz SS (2007) Evaluation of bias-correction methods for ensemble streamflow volume forecasts. Hydrol Earth Syst Sci 11:939–950. doi:10.5194/hess-11-939-2007

    Article  Google Scholar 

  • INMET (2009) Normais climatológicas do Brasil 1961–1990. http://www.inmet.gov.br/portal/index.php?r=clima/normaisclimatologicas. Accessed 1 Feb 2015

  • INMET (2015) BDMEP—Banco de Dados Meteorológicos para Ensino e Pesquisa. http://www.inmet.gov.br/portal/index.php?r=bdmep/bdmep. Accessed 1 Feb 2015

  • INPE/CPTEC (2013) Boletim de Monitoramento e Análise Climática. Climanálise ISSN 0103-0019 28:44

  • IPCC (2013) The physical science basis. Working group i contribution to the fifth assessment report of the intergovernmental panel on climate change

  • Janjić ZI (1994) The step-mountain eta coordinate model: further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon Weather Rev 122:927–945

    Article  Google Scholar 

  • Kain JS (2004) The Kain-Fritsch convective parameterization: an update. J Appl Meteorol 43:170–181

    Article  Google Scholar 

  • Kain JS, Fritsch JM (1990) A one-dimensional entraining/detraining plume model and its application in convective parameterization. J Atmos Sci 47:2784–2802

    Article  Google Scholar 

  • Kain JS, Fritsch JM (1993) Convective parameterization for mesoscale models: the Kain-Fritsch scheme. In: The representation of cumulus convection in numerical models. Springer, pp 165–170

  • Kang T-H, Kim Y-O, Hong I-P (2010) Comparison of pre- and post-processors for ensemble streamflow prediction. Atmos Sci Lett 11:153–159. doi:10.1002/asl.276

    Article  Google Scholar 

  • Lu G, Wu Z, Wen L, Lin CA, Zhang J, Yang Y (2008) Real-time flood forecast and flood alert map over the Huaihe River Basin in China using a coupled hydro-meteorological modeling system. Sci China Ser E Technol Sci 51:1049–1063. doi:10.1007/s11431-008-0093-x

    Article  Google Scholar 

  • McEnery J, Ingram J, Duan Q, Adams T, Anderson L (2005) NOAA’s advanced hydrologic prediction service: building pathways for better science in water forecasting. Bull Am Meteorol Soc 86:375–385. doi:10.1175/BAMS-86-3-375

    Article  Google Scholar 

  • Meller A, Collischonn W, Fan FM, Buarque DC, DePaiva RCD, da Silva Dias PL, Moreira DS (2014) Previsão de cheias por conjunto em curto prazo. Rev Bras Recur Hídricos 19:33–49

    Google Scholar 

  • Mesinger F, Black TL (1992) On the impact on forecast accuracy of the step-mountain (eta) versus sigma coordinate. Meteorol Atmos Phys 50:47–60

    Article  Google Scholar 

  • Mohor GS, Rodriguez DA, Tomasella J, Siqueira Júnior JL (2015) Exploratory analyses for the assessment of climate change impacts on the energy production in an Amazon run-of-river hydropower plant. J Hydrol Reg Stud 4:41–59. doi:10.1016/j.ejrh.2015.04.003

    Article  Google Scholar 

  • Nóbrega MT, Collischonn W, Tucci CEM, Paz AR (2011) Uncertainty in climate change impacts on water resources in the Rio Grande Basin, Brazil. Hydrol Earth Syst Sci 15:585–595. doi:10.5194/hess-15-585-2011

    Article  Google Scholar 

  • Olsson J, Lindström G (2008) Evaluation and calibration of operational hydrological ensemble forecasts in Sweden. J Hydrol 350:14–24. doi:10.1016/j.jhydrol.2007.11.010

    Article  Google Scholar 

  • Pappenberger F, Beven KJ, Hunter N, Bates P, Gouweleeuw B, Thielen J, de Roo A (2005) Cascading model uncertainty from medium range weather forecasts (10 days) through a rainfall-runoff model to flood inundation predictions within the European Flood Forecasting System (EFFS). Hydrol Earth Syst Sci 9:381–393. doi:10.5194/hess-9-381-2005

    Article  Google Scholar 

  • Paz AR, Collischonn W (2007) River reach length and slope estimates for large-scale hydrological models based on a relatively high-resolution digital elevation model. J Hydrol 343:127–139. doi:10.1016/j.jhydrol.2007.06.006

    Article  Google Scholar 

  • Pedrollo M, Germano A, Sotério P, Rodrigues É, Maduell JC (2011) Alerta hidrológico da bacia do rio Caí: concepção e implantação do sistema. In: XIX Simpósio Brasileiro de Recursos Hídricos. Maceió, pp 1–14

  • Peel MC, Finlayson BL, McMahon TA (2007) Updated world map of the Köppen–Geiger climate classification. Hydrol Earth Syst Sci 11:1633–1644. doi:10.5194/hess-11-1633-2007

    Article  Google Scholar 

  • Rodriguez DA (2011) Impactos dos padrões espaciais da vegetação nas variáveis atmosférica e terrestre do ciclo hidrológico, em bacia de floresta amazônica. Tesis de doutorado. Instituto Nacional de Pesquisas Espaciais

  • Rodriguez DA, Tomasella J (2015) On the ability of large-scale hydrological models to simulate land use and land cover change impacts in Amazonian basins. Hydrol Sci J. doi:10.1080/02626667.2015.1051979

    Google Scholar 

  • Romang H, Zappa M, Hilker N, Gerber M, Dufour F, Frede V, Bérod D, Oplatka M, Hegg C, Rhyner J (2011) IFKIS-Hydro: an early warning and information system for floods and debris flows. Nat Hazards 56:509–527. doi:10.1007/s11069-010-9507-8

    Article  Google Scholar 

  • Rosim S, Freitas OJR, Copertino JA, Namikawa LM, Rennó CD (2013) TerraHidro: a distributed hydrology modelling system with high quality drainage extraction. In: GEOProcessing 2013, the fifth international conference on advanced geographic information systems, applications, and services, pp 161–167

  • Sangati M, Borga M (2009) Influence of rainfall spatial resolution on flash flood modelling. Nat Hazards Earth Syst Sci 9:575–584. doi:10.5194/nhess-9-575-2009

    Article  Google Scholar 

  • SAR (2005) Inventário Florístico Florestal de Santa Catarina. Relatório do Projeto Piloto, Florianópolis

    Google Scholar 

  • Schaake J, Franz K, Bradley A, Buizza R (2006) The hydrologic ensemble prediction experiment (HEPEX). Hydrol Earth Syst Sci Discuss 3:3321–3332. doi:10.5194/hessd-3-3321-2006

    Article  Google Scholar 

  • Siqueira-Júnior JL, Tomasella J, Rodriguez DA (2015) Impacts of future climatic and land cover changes on the hydrological regime of the Madeira River basin. Clim Change. doi:10.1007/s10584-015-1338-x

    Google Scholar 

  • Stillwell HD (1992) Natural hazards and disasters in Latin America. Nat Hazards 6:131–159. doi:10.1016/S0928-2025(08)10006-2

    Article  Google Scholar 

  • Swets JA, Dawes RM, Monahan J (2000) Better decisions through science. Sci Am 283:82–87

    Article  Google Scholar 

  • Thielen J, Bartholmes J, Ramos M-H, de Roo A (2009) The European flood alert system—Part 1: concept and development. Hydrol Earth Syst Sci 13:125–140. doi:10.5194/hess-13-125-2009

    Article  Google Scholar 

  • Thiemig V, Pappenberger F, Thielen J, Gadain H, de Roo A, Bodis K, Del Medico M, Muthusi F (2010) Ensemble flood forecasting in Africa: a feasibility study in the Juba-Shabelle river basin. Atmos Sci Lett 11:123–131. doi:10.1002/asl.266

    Article  Google Scholar 

  • Werner M, Cranston M, Harrison T, Whitfield D, Schellekens J (2009) Recent developments in operational flood forecasting in England, Wales and Scotland. Meteorol Appl 16:13–22. doi:10.1002/met.124

    Article  Google Scholar 

  • Wilks DS (2006) Statistical methods in the atmospheric sciences, 2nd edn. Elsevier, Amsterdam

    Google Scholar 

  • Wood AW, Lettenmaier DP (2006) A test bed for new seasonal hydrologic forecasting approaches in the Western United States. Bull Am Meteorol Soc 87:1699–1712. doi:10.1175/BAMS-87-12-1699

    Article  Google Scholar 

  • Wood AW, Schaake JC (2008) Correcting errors in streamflow forecast ensemble mean and spread. J Hydrometeorol 9:132–148. doi:10.1175/2007JHM862.1

    Article  Google Scholar 

  • Wood AW, Maurer EP, Kumar A, Lettenmaier DP (2002) Long-range experimental hydrologic forecasting for the eastern United States. J Geophys Res D Atmos 107:1–15. doi:10.1029/2001JD000659

    Google Scholar 

  • Younis J, Anquetin S, Thielen J (2008) The benefit of high-resolution operational weather forecasts for flash flood warning. Hydrol Earth Syst Sci 12:1039–1051. doi:10.5194/hess-12-1039-2008

    Article  Google Scholar 

  • Zhao RJ (1992) The Xinanjiang model applied in China. J Hydrol 135:371–381

    Article  Google Scholar 

  • Zhao Q, Carr FH (1997) A prognostic cloud scheme for operational NWP models. Mon Weather Rev 125:1931–1953. doi:10.1175/1520-0493(1997)125<1931:APCSFO>2.0.CO;2

    Article  Google Scholar 

  • Zhao RJ, Liu XR, Singh VP (1995) The Xinanjiang model. Comput Model Watershed Hydrol 215–232

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The authors thank the support to this work received from the Coordination for the Improvement of Higher Education Personnel (CAPES) through a Doctoral candidate scholarship.

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Casagrande, L., Tomasella, J., dos Santos Alvalá, R.C. et al. Early flood warning in the Itajaí-Açu River basin using numerical weather forecasting and hydrological modeling. Nat Hazards 88, 741–757 (2017). https://doi.org/10.1007/s11069-017-2889-0

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