Exploration of DHSVM modeling of the Rio Jucu
To complement the integrated DHSVM work on the Rios Jucu and Santa Maria de Vitoria, and to learn the technology, Mr. Kenny Oliveira conducted his Master's degree research at UFES Jeronimo Monteiro, on a detailed application of DHSVM to the Jucu basin, including data and setup trade-offs.
OLIVEIRA, Kenny Delmonte. Hydrologic modeling of Jucú River basin using the physically distributed DHSVM model. 2014. Dissertation (Master’s degree in Forest Science) – Federal University of Espírito Santo, Jerônimo Monteiro, ES. Adviser: Prof. Dr. Sidney Sára Zanetti. Co-adviser: Prof. Dr. Roberto Avelino Cecílio. Co-adviser: PhD. Jeffrey Edward Richey.
The aim of this study was to enhance the knowledge of the complex interactions of soil-vegetation-atmosphere system interface, in the basin of river Jucu, using a dynamic, physical and DHSVM distributed model, which explicitly represents the integrated processes of hydrological phenomena in main purpose of obtaining sufficiently representative simulations of local standards for future applications in management, sanitation and reforestation of the basin and surrounding. The first step consisted in the careful twork obtaining the input data of the model, both through literature review and by the use geotechnology. The second step was to hydrologic modeling. The model was calibrated and validated to simulate the flow of Finance Jucuruaba, the main train station Jucu River basin, following the same procedure for the validation of the flow further upstream of this station. This analysis yielded simulations that met a high prediction accuracy with moderate expenditure of time on demand technical efforts for calibration. The DHSVM performed well, showing its ability to be used in areas different from those for which it was developed and tested. The functional representation of land, with hydrological significance was demonstrated in the application of the methodology for obtaining a matrix layer with properties of interest in the direct use of the model, due to the strong association of the variables with spatial properties of soil are necessary to modeling.