Modeling pollution potential input from the drainage basin into Barra Bonita
     reservoir, São Paulo – Brazil

Prado, RB.; Novo, EMLM.

doi:10.1590/1519-6984.12013

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Articles • Braz. J. Biol. 75 (2) • May 2015 • https://doi.org/10.1590/1519-6984.12013 copy

Modeling pollution potential input from the drainage basin into Barra Bonita reservoir, São Paulo – Brazil

Modelagem do potencial de contribuição da bacia de drenagem para a poluição da qualidade da água do reservatório de Barra Bonita, São Paulo – Brasil

Authorship SCIMAGO INSTITUTIONS RANKINGS

In this study multi-criteria modeling tools are applied to map the spatial distribution of drainage basin potential to pollute Barra Bonita Reservoir, São Paulo State, Brasil. Barra Bonita Reservoir Basin had undergone intense land use/land cover changes in the last decades, including the fast conversion from pasture into sugarcane. In this respect, this study answers to the lack of information about the variables (criteria) which affect the pollution potential of the drainage basin by building a Geographic Information System which provides their spatial distribution at sub-basin level. The GIS was fed by several data (geomorphology, pedology, geology, drainage network and rainfall) provided by public agencies. Landsat satellite images provided land use/land cover map for 2002. Ratings and weights of each criterion defined by specialists supported the modeling process. The results showed a wide variability in the pollution potential of different sub-basins according to the application of different criterion. If only land use is analyzed, for instance, less than 50% of the basin is classified as highly threatening to water quality and include sub basins located near the reservoir, indicating the importance of protection areas at the margins. Despite the subjectivity involved in the weighing processes, the multi-criteria analysis model allowed the simulation of scenarios which support rational land use polices at sub-basin level regarding the protection of water resources.

geotechnology; water quality; nonpoint pollution; multi-criteria analysis; watershed management

Maps and tabular data	Variable	Description
Landsat images /ETM+	Land use / land cover	Land use type and percentage will exert direct influence on soil quality, erosion processes, sediment transport, point and nonpoint pollution sources. This variable offers large impact on the quantity and quality of nutrient input into water resources.
Topographic map (1:250,000)	Sub-basin drainage density	Describes basin potential for transferring watershed pollution output into the fluvial system. The higher the drainage density, the larger is the transference potential.
Topographic map (1:250,000)	Sub-basin distance to reservoir	Describes the potential for dilution or deposition of the input from the watershed. The higher is the distance between the watershed and the reservoir, the shorter is the pollution potential.
Pedology map (1:250,000)	Pedology	Soil characteristics of a watershed will influence infiltration, percolation, absorption and runoff that carry pollutants to water bodies, as well as erosion, among others.
Geology map (1:250,000)	Geology	This variable relates to the composition of rocks. Depending on the type of rocks different elements can reach the water bodies. The geology also controls water infiltration and groundwater contamination.
Geomorphology map (1:250,000)	Geomorphology	This is a factor whose natural features such as terrain slope, slope geometry and dimensions, among others, have great influence on the runoff, and thus the transport of pollutants to the water bodies.
Precipitation data from 75 stations	Precipitation Coefficient of Variation	The intensity of rain will be a determining factor in the processes of runoff and erosion and hence the supply of nutrients and sediments reaching the reservoir.

Precision of the images classification	From 2002 image
Overall performance	90.65%
Confusion average	5.61%
Omission average	3.74%

Land use/land cover classes	Area 2002 (km²)
Urban area	1286.49
Bare soil	1804.96
Pasture	5143.73
Sugar cane	4917.72
Annual crop	878.80
Perennial crop	247.65
Natural vegetation	2665.05
Reforestation	581.50
Macrophytes	19.23
Cloud and shadow	1290.12
Drainage	329.18
Total	19164.43

Scenarios	Simulation
S1	((T1W1)+(T2W3)+(T3W2)+(T4W2)+(T5W2)+(T6W2)+(T7*W3))
S2	((T1W1)+(T2W3)+(T3W2)+(T4W2)+(T5W2)+(T6W2)+(T7*W4))
S3	((T1W1)+(T2W3)+(T3W2)+(T4W2)+(T5W2)+(T6W2)+(T7*W5))
S4	((T1W1)+(T2W3)+(T3W2)+(T4W2)+(T5W2)+(T6W2)+(T7*W7))

Variables	Classes	Weight
Land use / land cover	Urban area	Very High
	Bare soil	Very High
	Pasture	High
	Sugar cane	High
	Annual crop	Medium
	Perennial crop	Low
	Natural vegetation	Very Low
	Reforestation	Low
	Macrophyte	Medium
Sub-basin drainage density (m m^–2)	0.0000000-0.0002056	Very Low
	0.0002056-0.0004042	Low
	0.0004042-0.0006028	Medium
	0.0006028-0.0008014	High
	0.0008014-0.0010000	Very High
Sub-basin distance to reservoir (m)	88800-111000	Very Low
	6600-88800	Low
	44400-66600	Medium
	22200-44400	High
	0000-22200	Very High
Pedology (FAO, 2006Food and Agriculture Organization of the United Nations – FAO, 2006. World reference base for soil resources. Roma: FAO/ISSS/ISRIC. World Soil Resources Reports, no. 103.)	Acrisol	Very High
	Ferralsol	High
	Phaeozem	Low
	Planosol	Medium
	Gleysol	Very Low
	Cambisol	Medium
	Fluvisol and Arenosol	Medium
	Leptosol	Very High
Geology	Alluvion	Very low
	Rio Claro Formation	Very Low
	Itaqueri Formation	Medium
	Marília Formation	Low
	Serra Geral Formation and Basic Intrusive Rocks	High
	Botucatu Formation	Very Low
	Pirambóia Formation	Medium
	Corumbataí Formation	Very High
	Teresina Formation	Very High
	Serra Alta Formation	Very High
	Irati Formation	Very High
	Tatuí Formation	High
	Itararé Formation	Low
	Post tectonic granite suites	Very Low
	São Roque	High
	Amparo Complex	High
	Undifferentiated granite suites	High
	Syntectonic granite suites	High
Geomorphology	Basalt plateau	Very High
	Local canyon watersheds	Very High
	Festooned cliffs	Very High
	Alluvial plain	Very Low
	Plateau	Very Low
	Wide hills	Very Low
	Medium hills	Low
	Small elongated hills and ridges	Medium
	Round hills	Medium
	Elongated and parallel small hills	Medium
	Flat hills	Medium
	Mar de Morros	High
	Parallel hill	High
	Hills with restricted ridges	Very High
	Elongated ridges	Very High

Levels of pollution potential class	Classes area (km²)
	Simulation (weighted sum)
	S1	S2	S3	S4
Very Low	6471.04	5074.22	739.19	5.13
Low	3836.55	2576.38	5677.86	741.92
Medium	4456.41	4546.38	1498.67	4567.38
High	2978.33	4182.81	6435.83	1334.49
Very High	1071.60	2349.94	4462.38	12165.01
Drainage	350.50	350.50	350.50	350.50
Total	19164.43	19164.43	19164.43	19164.43

Instituto Internacional de Ecologia R. Bento Carlos, 750, 13560-660 São Carlos SP - Brasil, Tel. e Fax: (55 16) 3362-5400 - São Carlos - SP - Brazil
E-mail: bjb@bjb.com.br

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