THE WATERS OF THE AGUAPEÍ AND PEIXE RIVERS AND THEIR DRAINAGE AREAS, SÃO PAULO, BRAZIL

• Grant number: 23/16718-0
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: February 01, 2024
• End date: August 11, 2024
• Field of knowledge: Engineering - Sanitary Engineering - Water Resources
• Principal Investigator: Renata Ribeiro de Araújo
• Grantee: Lisiane Garofallo de Lima
• Host Institution: Faculdade de Ciências e Tecnologia (FCT). Universidade Estadual Paulista (UNESP). Campus de Presidente Prudente. Presidente Prudente , SP, Brazil

Abstract

Lotus ecosystems are systems of a dynamic and hierarchical nature, they develop in responseto natural and anthropic phenomena and processes, where internal and external exchanges ofmatter and energy occur over space and time, promoting connectivity between aquatic andterrestrial. Correlations between use classes and land cover, plant health and limnologicalvariables are expected as a result of this connection. Therefore, the present research projectaims to analyze possible similarities between land use and cover classes, NormalizedDifference Vegetation Index (NDVI) values and limnological variables in the Aguapeí andPeixe Rivers, located in the Water Management Unit. Water Resources 20 and 21, in the stateof São Paulo. The limnological variables will correspond to surface water from two samplingpoints on the Peixe River and two on the Aguapeí River, in two different hydrological periodsof the year (high and low water seasons). To this end, in situ measurements will be carried outusing portable digital devices of the variables Water Temperature, Electrical Conductivity,Turbidity, pH and Dissolved Oxygen, and also the Transparency of the water column usingthe Secchi disk. Through laboratory protocols, values for total phosphorus, dissolvedphosphorus, chlorophyll a, soluble reactive phosphorus, ammonia-N and total suspendedmaterial will be determined. Finally, the research results will be tabulated and analyzed usingdescriptive statistics using Microsoft Excel and Statistica for Windows, as well as multipleregression analyzes through the application of the hierarchical classification technique ofCluster Analysis, identifying similarities with the variables.