Navegando por Assunto "Modeling-prediction"

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Modelagem chuva-vazão-produção de sedimentos via problemas inversos
(Universidade Federal do Pará, 2023-10-05) TORRES FALCÓN, Cindy; ESTUMANO, Diego Cardoso; http://lattes.cnpq.br/5521162828533153; https://orcid.org/0000-0003-4318-4455; BLANCO, Claudio José Cavalcante; http://lattes.cnpq.br/8319326553139808; https://orcid.org/0000-0001-8022-2647
The development of mathematical models and direct methods has made it possible to predict hydrological phenomena such as rainfall-runoff-sediment yield. In order to complement the simulation model, inverse problems can be used to determine the properties of these phenomena and estimate parameters that cannot be measured directly. Therefore, this study was carried out in a small catchment in the Amazon with precipitation data and parameters estimated using the Kineros2 (K2) / direct model (DM). The study proposes solutions to the inverse problem (IP) characterized by the rainfall-runoff-sediment yield phenomenon for events with scarce data, with the aim of estimating the inflow rate, estimating the physical parameters, the runoff depth and the sediment yield of the basin analyzed. The sediment yield data comes from the sediment gauge station in the small catchment. For a more precise and detailed analysis of the model's behavior, combinations of information from observations and the K2 model were also carried out simultaneously with IP. The main scientific contribution is the application of the inverse problem method (Bayesian inference together with a Fourier series) to estimate the parameters of the kinematic wave model and the mass balance, and to estimate the runoff depth and sediment yield for a small watershed in the Amazon. The results showed a good fit between the observed and predicted data via IP, as Nash-Sutcliffe coefficients above 0.70 and RMSE between 0.27 and 1.99 were obtained in the calibration and validation of the rainfall-runoff-sediment yield model. The simulation of the runoff depth and sediment yield showed a 95% degree of reliability, which is consistent with the observed data.