Modelagem hidrológica sob escassez de dados na Bacia do Alto Mundaú, Nordeste do Brasil

Abstract

A B S T R A C TStudies focused on the understanding of hydrological processes are of paramount importance to assist in the management and decision-making related to water resources in watersheds. Hydrological models are considered powerful tools in understanding these processes. However, such models require a range of data that is not easily accessible. This study aimed to apply and test the SWAT model, following a recommended sequence of procedures in a sub-basin of the Mundaú River, in the Northeast of Brazil. Input data were used as a land use map, a digital elevation map and a soil type map, as well as a period data of 2000 to 2016, in daily time step. The accuracy of the model based on the Nash-Sutcliffe coefficient (CNS), by percentage of trend (PBIAS) and by the coefficient of determination (R²). The values of CNS, PBIAS and R² were 0.53; -7.1 and 0.56, respectively, for the calibration period. It was possible to perform the hydrological modeling with the SWAT model in the Mundaú river sub-basin, obtaining the satisfactory performance, according to the CNS and very good considering the PBIAS in the calibration. With the calibrated model, future studies can be carried out with the SWAT to validate it and predict possible impacts of climate change and land use in the studied basin. Studies such as this have shown that, increasingly, experimental data are essential for adequate representation and understanding of processes through hydrological modeling.

Projections of climate change in streamflow and affluent natural energy in the Brazilian hydroelectric sector of CORDEX models

“…NASH is an objective function that measures the efficiency of adjustment between an observed model and observed data, being one of the most adopted/used criteria in Hydrology (Andrade et al 2017). According to the formulation presented by Nash & Sutcliffe (1970) and considering N as the number of samples, o i as the data observed, m i as the modeled data, and i o as the average of the observed data, NASH is calculated through Equation 6: Silva et al…”

“…The reference values for NASH, suggested by Andrade et al (2017), are classified as: good (between 0.4 and 0.6), very good (0.6 and 0.8) and excellent (above 0.8). Being its maximum value corresponding to 1, which indicates/means a perfect adjustment between the modeled and the observed data.…”