Watershed fragility Assessment: a Methodological Approach of Siltation and Pollution Vulnerability on a Rural Watershed in Ibiúna (Southeastern Brazilian Region)

Page: [210 - 219] Pages: 10

  • * (Excluding Mailing and Handling)

Abstract

Background: The São Paulo State area has been facing a water crisis that caused water shortage in many cities, and a series of socioeconomic problems as an outcome. Water supply massive land-use alteration throughout São Paulo State river basins, coupled with climate change effects might produce severe damage to the region if preventive actions are not enforced in time.

Objective: This study aims to apply an adapted methodology of fragility analysis to a tributary of the Itupararanga reservoir (Brazil) using a hydrological modeling tool. Based on the determination of the flow and drainage system direction and object-based image analysis, a final map of the fragility will be constructed.

Methods: This paper presents a fragility assessment methodology on a local scale using a rural watershed of the study area. This approach uses object-based classification of topography data from Shuttle Radar Topography Mission to extract the most fragile territorial units of the watershed area, in terms of pollution and siltation contribution risk, combined with land cover classification.

Results: The study area exhibits very high and medium fragility areas related to water contamination and siltation risk that can be seen as priority areas for land cover management and monitoring, although most of the basin area was classified as very low fragility.

Conclusion: The methodology applications have great potential uses in territorial planning, protected areas and restoration priorities delimitation, ecological-economic zoning, hazard evaluation and mitigation, erosional processes and water protection and management at both local and regional scale studies.

Keywords: Environmental fragility, land cover, physiographic segmentation, SRTM, swater, crisis.

Graphical Abstract

[1]
Forney W, Richards L, Adams K, et al. Land cover change and effects on water quality and ecosystem health in the lake Tahoe Basin, Nevada and California. In: US Geological Survey Open-File Report, 2001; p. 01-418.
[2]
ANAConjuntura dos recursos hídricos no Brasil: informe 2012.
[3]
Cruz BB, Manfré LA, Ricci D, et al. Environmental fragility framework for water supply systems: A case study in the Paulista Macro Metropolis area (SE Brazil). Environ Earth Sci 2017; 76: 441-71.
[http://dx.doi.org/10.1007/s12665-017-6778-3]
[4]
Macedo DR, Hughes RM, Kaufmann PR, Callisto M. Development and validation of an Environmental Fragility Index (EFI) for the neotropical savannah biome. Sci Total Environ 2018; 635: 1267-79.
[http://dx.doi.org/10.1016/j.scitotenv.2018.04.216] [PMID: 29710580]
[5]
Corrêa CVS, Reis FAGV, Giordano LC, et al. Geo-environmental zoning using physiographic compartmentalization: A proposal for supporting sustainable decision-making. An Acad Bras Cienc 2017; 89(3): 1503-30.
[http://dx.doi.org/10.1590/0001-3765201720160915] [PMID: 28793008]
[6]
Gao Y, Zhang H. The study of ecological environment fragility based on remote sensing and GIS. J Indian Soc Remote 2018; 46(5): 793-9.
[http://dx.doi.org/10.1007/s12524-018-0759-1]
[7]
Celestino EF, Celestino LF, Silva JFM, et al. Environmental assessment in neotropical watersheds: A multi-factorial approach. Sustainability 2019; 11(2): 490-507.
[http://dx.doi.org/10.3390/su11020490]
[8]
Ross JL. Análise empírica da fragilidade dos ambientes naturais antropizadosRevista do departamento de geografia 1994; 8: 63-74.
[http://dx.doi.org/10.7154/RDG.1994.0008.0006]
[9]
Tricart J. Ecodinâmica. Rio de Janeiro, Brasil: IBGE 1977.
[10]
Manfré LA, Silva AM, Urban RC, Rodgers J. Environmental fragility evaluation and guidelines for environmental zoning: A study case on Ibiúna (the Southeastern Brazilian region). Environ Earth Sci 2013; 69(3): 947-57.
[http://dx.doi.org/10.1007/s12665-012-1979-2]
[11]
Faggiano L, de Zwart D, García-Berthou E, Lek S, Gevrey M. Patterning ecological risk of pesticide contamination at the river Basin scale. Sci Total Environ 2010; 408(11): 2319-26.
[http://dx.doi.org/10.1016/j.scitotenv.2010.02.002] [PMID: 20206965]
[12]
Carneiro FF, Augusto LGS, Rigotto RM, et al. Dossiê ABRASCO: um alerta sobre os impactos dos agrotóxicos na saúde - Rio de Janeiro: EPSJV. São Paulo: Expressão Popular 2015.
[13]
Ross JLS. Recursos Hídricos e as Bacias Hidrográficas: âncoras do planejamento e gestão ambientalRevista do Departamento de Geografia - FFLCH-USP 1998; 12: 89-121
[14]
Comitê de bacias hidrográficas. Nossas Águas. Comitê de Bacias Hidrográficas dos rios Sorocaba e Médio Tietê, Sorocaba, 2006.
[15]
Manfré LA, da Silva AM, Urban RC. Atributos de qualidade de solos sob dois diferentes tipos de manejo no município de Ibiúna/SP, Brazil. Interciencia 2011; 36(10): 757-63.
[16]
SIGRH - Sistema de Informação para o Gerenciamento de Recursos Hídricos. 2018. http://www.sigrh.sp.gov.br/ Accessed in: 20/08/2019.
[17]
Companhia Ambiental do Estado – CETESB. Qualidade das águas superficiais no estado de São Paulo2014. São Paulo: CETESB. Série Relatórios /CETESB, ISSN 0103-4103).2015.
[18]
Brasil. Topodata: banco de dados geomorfométricos do Brasil. 2008. INPE, São José dos Campos, Brazil.
[19]
de Morisson Valeriano M, de Fátima Rossetti D. Topodata: Brazilian full coverage refinement of SRTM data. Appl Geogr 2012; 32(2): 300-9.
[http://dx.doi.org/10.1016/j.apgeog.2011.05.004]
[20]
Chinnayakanahalli K, Kroeber C, Hill RA, Tarboton DG, Olson JR, Hawkins CP. The Multi-Watershed Delineation Tool: GIS Software in support of regional watershed analyses. Utah State University: Logan, Utah 2006.
[21]
Kemper JT, Macdonald SE. Directional change in upland tundra plant communities 20-30 years after seismic exploration in the Canadian low-arctic. J Veg Sci 2009; 20(3): 557-67.
[http://dx.doi.org/10.1111/j.1654-1103.2009.01069.x]
[22]
Sharma L, Pandey PC, Nathawat MS. Assessment of land consumption rate with urban dynamics change using geospatial techniques. J Land Use Sci 2012; 7(2): 135-48.
[http://dx.doi.org/10.1080/1747423X.2010.537790]
[23]
Brookes IA. Spatially variable sedimentary responses to orbitally driven pluvial climate during Marine Oxygen Isotope Stage 5.1, Dakhla Oasis region, Egypt. Quat Res 2010; 74(2): 252-64.
[http://dx.doi.org/10.1016/j.yqres.2010.05.001]
[24]
Partridge TC, Dollar ESJ, Moolman J, Dollar LH. The geomorphic provinces of South Africa, Lesotho and Swaziland: a physiographic subdivision for earth and environmental scientists. Trans R Soc S Afr 2010; 65(1): 1-47.
[http://dx.doi.org/10.1080/00359191003652033]
[25]
Definiens AG. Definiens, AG. eCognition Developer 8.0. 1 Reference Book. 2011. URL: http://www. definiens. com
[26]
Drăguţ L, Eisank C. Automated object-based classification of topography from SRTM data. Geomorphology 2012; 141-142(4): 21-33.
[http://dx.doi.org/10.1016/j.geomorph.2011.12.001] [PMID: 22485060]
[27]
Manfré LA. Identificação e mapeamento de áreas de deslizamentos associadas a rodovias utilizando imagens de sensoriamento remote 2015.
[28]
Martinelli M. Compartimentos do Relevo-IBGE 2000. Atlas do Estado de São Paulo 2000.
[29]
Florinsky IV. The Dokuchaev hypothesis as a basis for predictive digital soil mapping (on the 125th anniversary of its publication). Eurasian Soil Sci 2012; 45(4): 445-51.
[http://dx.doi.org/10.1134/S1064229312040047]
[30]
Blaschke T, Johansen K, Tiede D. Object-based image analysis for vegetation mapping and monitoring Advances in Environmental Remote Sensing: Sensors, Algorithms and Applications. United States: CRC Press Taylor & Francis Group 2011; pp. 241-71.
[http://dx.doi.org/10.1201/b10599-13]
[31]
Bossard M, Feranec J, Otahel J. CORINE land cover technical guide-Addendum. Copenhagen: EEA 2000.
[32]
Beltrame AMK, Quintanilha JA. Aplicação do programa CORINE e classificação baseada em objetos para mapeamento da cobertura do solo de faixa de domínioXIV SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO. Natal, RN: Anais 2009; pp. 6781-8.
[33]
Congalton RG. A review of assessing the accuracy of classifications of remote sensed data. Remote Sens Environ 1991; 37: 37-46.
[http://dx.doi.org/10.1016/0034-4257(91)90048-B]
[34]
Foody GM. On the compensation for chance agreement in image classification accuracy assessment. Photogramm Eng Remote Sensing 1992; 58: 1459-60.
[35]
Ma Z, Redmond RL. Tau coefficients for accuracy assessment of classification of remote sensing data. Photogramm Eng Remote Sensing 1995; 61: 435-9.
[36]
Foody GM. Status of land cover classification accuracy assessment. Remote Sens Environ 2002; 80: 185-201.
[http://dx.doi.org/10.1016/S0034-4257(01)00295-4]
[37]
Silva Neto JC. Evaluation of vulnerability to loss of soil in watershed of Salobra river, MS, based on the forms of terrain. Geografia 2013; 22: 05-25.
[38]
Lambin E, Helmut G, Lepers E. Dynamics of Land-use and land-cover change in tropical regions. Annu Rev Environ Resour 2003; 28: 205-41.
[http://dx.doi.org/10.1146/annurev.energy.28.050302.105459]
[39]
Freitas MJCC, Kaetsu PT. A gestão dos recursos hídricos e da estiagem no Oeste Catarinense: contribuição para uma análise sistêmica complexa. Revista Labor & Engenho, Campinas, Brasil 2015; 9(4): 34-50.
[http://dx.doi.org/10.20396/lobore.v9i4.8642497]
[40]
Camargo JA, Alonso A. Ecological and toxicological effects of inorganic nitrogen pollution in aquatic ecosystems: A global assessment. Environ Int 2006; 32(6): 831-49.
[http://dx.doi.org/10.1016/j.envint.2006.05.002] [PMID: 16781774]