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Analyzing Hydro-Climatic Data to Improve Hydrological Understanding in Rural Rio de Janeiro, Southeast Brazil

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Strategies and Tools for a Sustainable Rural Rio de Janeiro

Abstract

The rural area of Rio de Janeiro (RJ) state has experienced increased pressure on water resources, due to an increasing population linked with the growth of the industrial and agricultural sectors. High interannual variability of rainfall causes frequent extreme events leading to droughts, floods, and landslides. Therefore, it is crucial to understand how climate affects the interaction between the timing of extreme rainfall events, hydrological processes, vegetation growth, soil cover, and soil erosion. Ecohydrological modeling can contribute to a better understanding of spatial–temporal process dynamics to develop adaptation strategies. However, prior to modeling, it is crucial to evaluate the reliability of the climate and hydrological data. This study aims to homogenize the climatic data and to analyze the hydro-climatic time series needed for further hydrological studies (e.g., ecohydrological modeling) and to contribute to a better understanding of long-term hydro-climatic patterns in a mesoscale watershed, the Muriaé River Basin. The analyses include homogeneity assessment, statistical analyses, and trend detection for a time period of over 50 years. The assessment provides important insights into long-term hydro-climatic patterns, such as an increase of the annual mean temperature, a decrease of the annual relative humidity, and an increase of the frequency of intense rainfall events.

Resumo (Português) Análise de Dados Hidro-climáticos para Melhorar a Compreensão Hidrológica na Área Rural do Rio de Janeiro, Sudeste do Brasil

A área rural do Estado do Rio de Janeiro (RJ) vem sofrendo uma crescente pressão sobre os recursos hídricos, devido ao aumento da população ligada ao crescimento dos sectores industrial e agrícola. A elevada variabilidade interanual da precipita ção causa eventos extremos frequentes que originam períodos de seca, inundações e deslizamentos de terra. Portanto, é crucial entender como o clima afeta a interação entre o momento de ocorrência de eventos extremos de precipitação, e os processos hidrológicos, o crescimento da vegetação, a cobertura do solo e a erosão do solo. A modelação eco-hidrológica pode contribuir para melhorar a compreensão da dinâmica dos processos espaço-temporais para desenvolver estratégias de adaptação. No entanto, antes da modelação, é crucial avaliar a confiabilidade dos dados de clima e hidrológicos. Este estudo tem como objetivo homogeneizar os dados climáticos e analisar as séries temporais hidro-climáticas necessárias para posteriores estudos hidrológicos (por exemplo, modelação eco-hidrológica) e contribuir para melhorar a compreensão dos padrões hidro-climáticos de longo prazo numa bacia hidrográfica de meso-escala, a bacia do rio Muriaé. As análises incluem avaliação de homogeneidade, análises estatísticas e detecção de tendências para um período de tempo de mais de 50 anos. Estas fornecem informações importantes sobre padrões hidro-climáticos de longo prazo, tais como o aumento da temperatura média anual, a diminuição da umidade relativa anual e o aumento da frequência de eventos intensos de precipitação.

Resumen (Español) Análisis de Datos Hidro-climaticos para Mejorar el Entendimiento Hidrológico en el Area Rural de Rio de Janeiro, Sureste de Brasil

La zona rural del estado de Río de Janeiro (RJ) ha experimentado una gran presión sobre los recursos hídricos, debido a la creciente población vinculada al incremento de los sectores industrial y agrícola. La alta variabilidad interanual de las precipitaciones causa frecuentes eventos extremos que provocan sequías, inundaciones y deslizamientos de tierra. Por lo tanto, es crucial entender cómo el clima afecta a la interacción entre el momento de precipitaciones extremas, los procesos hidrológicos, el crecimiento vegetativo, la cubierta del suelo y la erosión del suelo. El modelaje eco-hidrológico puede contribuir a una mejor comprensión de la dinámica de procesos espacio-temporales para desarrollar estrategias de adaptación. Sin embargo, antes de aplicar el modelo, es crucial evaluar la fiabilidad de los datos climáticos e hidrológicos. Este estudio tiene como objetivo homogeneizar los datos climáticos y analizar las series de tiempo hidro-climáticas necesarias para realizar estudios hidrológicos adicionales (por ejemplo, modelaje eco-hidrológico) y contribuir a una mejor comprensión de los patrones hidro-climáticos a largo plazo en una cuenca de meso escala, la cuenca del río Muriaé. Los análisis incluyen la evaluación de homogeneidad, análisis estadísticos y detección de tendencias para un período de tiempo de más de 50 años. La evaluación proporciona información importante sobre los patrones hidro-climáticos a largo plazo, como un aumento de la temperatura media anual, una disminución de la humedad relativa anual y un aumento de la frecuencia de eventos de lluvia intensa.

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References

  • Aguilar E, Auer I, Brunet M et al (2003) Guidelines on climate metadata and homogenization. WMO-TD World Meteorological Organization, WMO/TD No. 1186

    Google Scholar 

  • Alexandersson H (1986) A homogeneity test applied to precipitation data. J Climatol 6:661–675

    Article  Google Scholar 

  • Allen RG, Pereira LS, Raes D et al (1998) Crop evapotranspiration: guidelines for computing crop water requirements. Irrigation and Drainage Paper No. 56, Food and Agriculture Organization of the United Nations, Rome, Italy

    Google Scholar 

  • Alvares CA, Stape JL, Sentelhas PC et al (2014) Köppen’s climate classification map for Brazil. Meteorol Z 22(6):711–728

    Article  Google Scholar 

  • ANA (2012) Orientações para consistência de dados fluviométricos/Agência Nacional de Águas; Superintendência de Gestão da Rede Hidrometeorológica. ANA, SGH, Brasília. 19 p

    Google Scholar 

  • ANA (2015) Agência Nacional de Águas, HidroWeb, Sistema de Informações Hidrológicas. http://hidroweb.ana.gov.br/. Accessed 14 Nov 2015

  • ANA (2017) Software Hidro 1.3 (Sistema de informações hidrológicas) – 3. Inventário pluviométrico/fluviométrico atualizado. http://hidroweb.ana.gov.br/HidroWeb.asp?TocItem=6010. Accessed 14 Jan 2017

  • André RGB, Marques VS, Pinheiro FMA et al (2008) Identificação de regiões pluviometricamente homogêneas no estado do Rio de Janeiro, utilizando-se valores mensais. Rev Bras Meteorol 23(4):501–509

    Article  Google Scholar 

  • Ångström A (1924) Solar and terrestrial radiation. Q J R Meteorol Soc 50:121–125

    Article  Google Scholar 

  • Ávila MW, Hora MAGM, Ávila CR et al (2016) Gestão qualitativa dos recursos hídricos. Proposta metodológica para o planejamento de uma rede de estações para monitoramento da qualidade de águas superficiais. Estudo de caso: bacia hidrográfica do Rio Muriaé RBRH 21(2):401–415

    Google Scholar 

  • Bergamaschi H (1999) Estratégias para reduzir riscos por estiagens. Seminário PFA/UFRGS

    Google Scholar 

  • Begert M, Schlegel T, Kirchhofer W (2005) Homogeneous temperature and precipitation series of Switzerland from 1864 to 2000. Int J Climatol 25:65–80

    Article  Google Scholar 

  • Brasil (2011) Law n. 12.527, 18 November 2011. Regulates the constitutional right of access to public information. Law on access to information. Presidência da República, Brasília. http://www.planalto.gov.br/ccivil_03/_Ato2011-2014/2011/Lei/L12527.htm. Accessed 06 Jun 2015

  • Brunini O, Pinto HS (1997) As oscilações climáticas e agricultura. Laranja & Cia 47:6–7

    Google Scholar 

  • Carvalho JRP, Assad ED, Fortes de Oliveira A et al (2014) Annual maximum daily rainfall trends in the Midwest, southeast and southern Brazil in the last 71 years. Weather Clim Extrem 5–6:7–15

    Article  Google Scholar 

  • Climpag (2017) Climate impact on agriculture, FAO – Food and Agriculture Organization of the United Nations. http://www.fao.org/nr/climpag/climate/index_en.asp/. Accessed 29 May 2017

  • Colodro G, Carvalho MP, Roque CG et al (2002) Erosividade da chuva: distribuição e correlação com a precipitação pluviométrica de Teodoro Sampaio (SP). Rev Bras Ciênc Solo 26:809–818

    Article  Google Scholar 

  • Colombo AF, Joly CA (2010) Brazilian Atlantic Forest lato sensu: the most ancient Brazilian forest, and a biodiversity hotspot, is highly threatened by climate change. Braz J Biol 70:697–708

    Article  CAS  Google Scholar 

  • CPTEC (2012) Centro de previsão de tempo e estudos climáticos. El Niño e La Niña. http://enos.cptec.inpe.br. Accessed 01 Jun 2016

  • de la Casa A, Nasello OB (2012) Low frequency oscillation of rainfall in Córdoba, Argentina, and its relation with solar cycles and cosmic rays. Atmos Res 113:140–146

    Article  Google Scholar 

  • Dickey DA, Fuller WA (1979) Distribution of the estimators for autoregressive time series with unit root. J Am Stat Assoc 74:427–431

    Google Scholar 

  • Dickey DA, Fuller WA (1981) Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica 49:1057–1072

    Article  Google Scholar 

  • Fenton JD, Keller RJ (2001) The calculation of streamflow from measurements of stage. Technical Report 01/6. Cooperative Research Centre for Catchment Hydrology, Melbourne, Australia

    Google Scholar 

  • González-Rouco JF, Jiménez JL, Quesada V et al (2001) Quality control and homogeneity of precipitation data in the Southwest of Europe. J Clim 14:964–978

    Article  Google Scholar 

  • Groisman PY, Knight RW, Easterling DR et al (2004) Trends in intense precipitation in the climate record. J Clim 18:1326–1350

    Article  Google Scholar 

  • Hanssen-Bauer I, Førland E (1994) Homogenizing long Norwegian precipitation series. J Clim 7:1001–1013

    Article  Google Scholar 

  • Hawtree D, Nunes JP, Keizer JJ et al (2014) Time-series analysis of the long-term hydrologic impacts of afforestation in the Águeda watershed of North-Central Portugal. Hydrol Earth Syst Sci Discuss 11:12223–12256

    Article  Google Scholar 

  • Haylock MR, Peterson TC, Alves LM et al (2006) Trends in total and extreme South American rainfall 1960-2000 and links with sea surface temperature. J Clim 19:1490–1512

    Article  Google Scholar 

  • Helsel DR, Hirsch RM (eds) (2002) Statistical methods in water resources. Elsevier, New York

    Google Scholar 

  • Helsel DR, Mueller DK, Slack JR (2006) Computer program for the Kendall family of trend tests. U.S. Geological Survey Scientific Investigations Report 2005–5275, p 4

    Google Scholar 

  • Hosking JRM, Wallis JR (1997) Regional frequency analysis – an approach based on L-moments. Cambridge University Press, New York

    Book  Google Scholar 

  • INMET (2014) BDMEP – Banco de Dados Meteorológicos para Ensino e Pesquisa. INMET – Instituto Nacional de Meteorologia. http://www.inmet.gov.br/portal/index.php?r=bdmep/bdmep. Accessed 16 Jun 2014

  • INMET (2016) Instituto Nacional de Meteorologia (National Meteorological Institute)/Seção de Armazenamento de Dados Meteorológicos (Brazilian Environmental Data System) – INMET/SADMET. http://www.inmet.gov.br/portal/index.php?r=home/page&page=central_servicos

  • INMET (2017) Normais Climatológicas do Brasil/1961–1990. http://www.inmet.gov.br/portal/index.php?r=clima/normaisclimatologicas. Accessed 29 May 2017

  • Kendall MG (1975) Rank correlation methods, 4th edn. Charles Griffin, London

    Google Scholar 

  • Kuwajima JI (2013) Application of SWAT in a Brazilian watershed with inconsistent hydrological data. SWAT Conference 2013, Toulouse

    Google Scholar 

  • Künne A, Kralisch S, Santos JM, Flügel W-A (2018) Ecohydrological modeling and scenario impact assessment in Rural Rio de Janeiro. In: Nehren U, Schlüter S, Raedig C, Sattler D, Hissa H (eds) Strategies and tools for a sustainable rural Rio de Janeiro. Springer International Publishing, Cham

    Google Scholar 

  • Lange W, Sandholz S, Viezzer J et al (2018) Ecosystem-based approaches for disaster risk reduction and climate change adaptation in Rio de Janeiro state. In: Nehren U, Schlüter S, Raedig C, Sattler D, Hissa H (eds) Strategies and tools for a sustainable rural Rio de Janeiro. Springer International Publishing, Cham

    Google Scholar 

  • Lombardi Neto F, Moldenhauer WC (1992) Erosividade da chuva: sua distribuição e relação com as perdas de solo em Campinas. Bragrantia 51:189–196

    Article  Google Scholar 

  • Mann HB (1945) Non-parametric tests against trend. Econometrica 13:163–171

    Article  Google Scholar 

  • Marengo J (2001) Mudanças climáticas globais e regionais: Avaliação do clima atual do Brasil e projeções de cenários climáticos do futuro. Rev Bras Meteor 16:1–18

    Google Scholar 

  • Marengo J (2003) Condições climáticas e recursos hídricos no Norte Brasileiro. In: Tucciand CE, Braga B (eds) Clima e recursos hídricos no Brasil. Associação Brasileira de Recursos Hídricos FBMC/ANA, Brazil, p 117–161

    Google Scholar 

  • Marengo JA, Camargo CC (2008) Surface air temperature trends in southern Brazil for 1960–2002. Int J Climatol 28:893–904

    Article  Google Scholar 

  • Marengo JA, Jones R, Alves LM et al (2009) Future change of temperature and precipitation extremes in South America as derived from the PRECIS regional climate modeling system. Int J Climatol 29:2241–2255

    Article  Google Scholar 

  • Martirani LA, Peres IK (2016) Water crisis in São Paulo: news coverage, public perception and the right to information. Ambiente Soc 1:1–20

    Google Scholar 

  • Mello CR, de Sá MAC, Curi N et al (2007) Erosividade mensal e anual da chuva no Estado de Minas Gerais. Pesq Agropec Bras 42:537–545

    Article  Google Scholar 

  • Merten GH, Minella JPG (2013) The expansion of Brazilian agriculture: soil erosion scenarios. Int Soil Water Conserv Res 1:37–48

    Article  Google Scholar 

  • Nehren U, Kirchner A, Sattler D et al (2013) Impact of natural climate change and historical land use on landscape development in the Atlantic Forest of Rio de Janeiro, Brazil. Ann Braz Acad Sci 85(2):497–518

    Article  Google Scholar 

  • Nehren U, Kirchner A, Lange W et al (2018) Natural hazards and climate change impacts in the state of Rio de Janeiro: a landscape historical analysis. In: Nehren U, Schlüter S, Raedig C, Sattler D, Hissa H (eds) Strategies and tools for a sustainable rural Rio de Janeiro. Springer International Publishing, Cham

    Google Scholar 

  • Oliveira VPS, Zanetti SS, Pruski FF (2005) Climabr parte I: Modelo para a geração de series sintéticas de precipitação. Rev Bras Eng Agríc Ambient 9(3):348–355

    Article  Google Scholar 

  • Oliveira LFC, Antonini JCA, Griebeler NP (2008a) Estimativas de chuvas intensas para o estado de Góias. Eng Agríc 28(1):22–33

    Article  Google Scholar 

  • Oliveira LFC, Antonini JCA, Fioreze AP et al (2008b) Métodos de estimative de precipitação máxima para o Estado de Góias. Rev Bras Eng Agríc Ambient 12(6):620–625

    Article  Google Scholar 

  • Oliveira LFC, Fioreze AP, Medeiros AMM et al (2010) Comparação de metodologias de preenchimento de falhas históricas de precipitação pluvial annual. Rev Bras Eng Agríc Ambient 14(11):1186–1192

    Article  Google Scholar 

  • ONS – Operador Nacional do Sistema Eléctrico (2008) Relatório de Análise dos Dados Pluviométricos da Bacia do rio Paraíba do Sul e Ribeirão das Lajes. Contrato n° GPD-CT-185/06-2, Consórcio Enerconsult-Hidrosistem-Internave

    Google Scholar 

  • Pachauri RK, Allen MR, Barros VR et al (2014) Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change / R. Pachauri and L. Meyer (editors), Geneva, Switzerland, IPCC, 151 p., ISBN: 978-92-9169-143-2

    Google Scholar 

  • Prescott J (1940) Evaporation from a water surface in relation to solar radiation. Trans R Soc South Aust 64:114–125

    Google Scholar 

  • RBIS–INTECRAL (2014) River Basin Information System – Integrated Eco Technologies and Services for a Sustainable Rural Rio de Janeiro. http://leutra.geogr.uni-jena.de/intecralRBIS. Accessed 14 Jul 2014

  • Ribeiro MC, Metzger JP, Martensen AC et al (2009) The Brazilian Atlantic Forest: how much is left, and how is the remaining forest distributed? Implications for conservation. Biol Conserv 142:1141–1153

    Article  Google Scholar 

  • Rosmann T, Dominguez E, Chavarro J (2016) Comparing trends in hydrometeorological average and extreme data sets around the world at different time scales. J Hydrol Reg Stud 5:200–212

    Article  Google Scholar 

  • Roy SS, Balling RC (2004) Trends in extreme daily rainfall indices in India. Int J Climatol 24:457–466

    Article  Google Scholar 

  • SACE-Muriaé (2016) Sistema de Alerta de Eventos Críticos – Bacia do Rio Muriaé. http://www.cprm.gov.br/sace/index_bacias_monitoradas.php?getbacia=bmuriae#. Accessed 15 Feb 2016

  • Sansigolo C, Rodriguez R, Etchichury P (1992) Tendências nas temperaturas médias do Brasil, vol 1. Congresso Brasileiro de Meteorologia, São Paulo, Brazil, pp 367–371

    Google Scholar 

  • Sattler D, Raedig C, Hebner A, Wesenberg J (2018) Use of native plant species for ecological restoration and rehabilitation measures in Southeast Brazil. In: Nehren U, Schlüter S, Raedig C, Sattler D, Hissa H (eds) Strategies and tools for a sustainable rural Rio de Janeiro. Springer International Publishing, Cham

    Google Scholar 

  • Searcy JK, Hardison CH (1960) Double-mass curves. In: Manual of hydrology: part 1. General surface water techniques. United States Government Printing Offices, Washington, DC

    Google Scholar 

  • Searcy JK (1959) Flow-duration curves. In: Manual of hydrology. Water supply paper 1542-A. United States Geological Survey, Washington, DC

    Google Scholar 

  • Seliger R, Sattler D, Soares da Silva A et al (2018) Rehabilitation of degraded sloped pastures – lessons learned in Itaocara. In: Nehren U, Schlüter S, Raedig C, Sattler D, Hissa H (eds) Strategies and tools for a sustainable rural Rio de Janeiro. Springer International Publishing, Cham

    Google Scholar 

  • Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63:1379–1389

    Article  Google Scholar 

  • Shahid S, Harun SB, Katimon A (2012) Changes in diurnal temperature range in Bangladesh during the time period 1961–2008. Atmos Res 118:260–270

    Article  Google Scholar 

  • Silva AM, Oliveira PM, Mello CR et al (2006) Vazões mínimas e de referência para outorga na região do Alto Rio Grande, Minas Gerais. Rev Bras Eng Agríc Ambient 10:374–380

    Article  Google Scholar 

  • SINDA (2015) Sistema Integrado de Dados Ambientais (Integrated Data Environmental System)/Instituto Nacional de Pesquisas Espaciais (National Institute for Spatial Research) – SINDA/INPE. http://sinda.crn2.inpe.br/PCD/SITE/novo/site/historico/index.php. Accessed 15 Mar 2015

  • Stone DA, Weaver AJ, Zwiers FW (2000) Trends in Canadian precipitation intensity. Atmos Ocean 38:321–347

    Article  Google Scholar 

  • Subash N, Singha SS, Priya N (2011a) Extreme rainfall indices and its impact on rice productivity — a case study over sub-humid climatic environment. Atmos Res 98:1373–1387

    Google Scholar 

  • Subash N, Singha SS, Priya N (2011b) Variability of rainfall and effective onset and length of the monsoon season over a sub-humid climatic environment. Atmos Res 99:479–487

    Article  Google Scholar 

  • Vincent LA, Peterson TC, Barros VR et al (2005) Observed trends in indices of daily temperature extremes in South America 1960–2000. J Clim 18:5011–5023q

    Article  Google Scholar 

  • WMO (2011) Guide to climatological practices, 3rd edn. WMO-N°. 100, Geneva

    Google Scholar 

  • Yue S, Wang CY (2004) The Mann-Kendall test modified by effective sample size to detect trend in serially correlated hydrological series. Water Resour Manag 18:201–218

    Article  Google Scholar 

  • Zeng C, Deng X, Dong J et al (2016) Urbanization and sustainability: comparison of the processes in “BIC” countries. Sustainability 8:400

    Article  Google Scholar 

  • Zhai PM, Sun A, Ren F et al (1999) Changes of climate extremes in China. Clim Chang 42:203–218

    Article  Google Scholar 

  • Zhai PM, Zhang XB, Wan H et al (2005) Trends in total precipitation and frequency of daily precipitation extremes over China. J Clim 18:1096–1108

    Article  Google Scholar 

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Acknowledgments

This work was supported by the German Ministry of Education and Research under the project INTECRAL, grant number033L162K.

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Santos, J.M., Künne, A., Kralisch, S., Fink, M., Brenning, A. (2019). Analyzing Hydro-Climatic Data to Improve Hydrological Understanding in Rural Rio de Janeiro, Southeast Brazil. In: Nehren, U., Schlϋter, S., Raedig, C., Sattler, D., Hissa, H. (eds) Strategies and Tools for a Sustainable Rural Rio de Janeiro. Springer Series on Environmental Management. Springer, Cham. https://doi.org/10.1007/978-3-319-89644-1_16

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