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Rainfall-runoff modeling for flood forecasting: application of global methodologies to a medium-size basin in Brazil

Congress: 2008
Author(s): Bruno Rabelo Versiani (1), Marcus Felipe Matos Cruz (2) , Alan Fábio de Bastos (2)
(1) Corresponding author: Associated Professor - Universidade Federal de Minas Gerais - Departamento de Engenharia Hidráulica e Recursos Hídricos - Av. do Contorno, 842 - 30110-060 – Belo Horizonte – Minas Gerais – Brazil – Phone: ++ 55 31 34994821 – Fax: ++ 55 31 34994823 – Email: (2) Universidade Federal de Minas Gerais - Departamento de Engenharia Hidráulica e Recursos Hídricos - Av. do Contorno, 842 - 30110-060 – Belo Horizonte – Minas Gerais – Brazil.

Keyword(s): rainfall -runoff modeling, DPFT methodology, GR3 model, Artificial Neural Network model
Article: Poster:
AbstractThis paper deals with the hydrologic modeling of rainfall-runoff relationship, employing a global approach, applied to a medium-size basin in Brazil. In general the Brazilian hydrologic gauging stations network display only data for precipitation and runoff and, in the majority of cases, only daily data is available. Due to the difficulty in the calibration of the large number of parameters that conceptual models generally require, global empirical methods are currently used in studies and technological applications. It was used the DPFT ( Différences Premières de la Fonction de Transfert - First Differences of the Transfer Function) methodology to identify the Unit Hydrograph and effective precipitation. Otherwise, a non-linear Artificial Neural Network ( ANN ) model approach was used to compare with the DPFT methodology. Basically, the Unit Hydrograph method proposes that, for a given basin, runoff is the result of a loss function and of a linear transfer function. The loss function is strongly non-linear, and transforms the total measured precipitation, either weighted or arithmetic mean, into effective precipitation, which produces the surface runoff. The linear transfer function increases over time the effective rainfall, so as to obtain the surface runoff. Classically, a loss function model (assumed to be the most appropriate for the basin) is required, a priori, to obtain the effective rainfall for each event. As opposed to this classic approach, the DPFT methodology, proceeding iteratively from an array of events of total rainfall – runoff, establishes the Transfer Function, and the effective rainfall for each event. This distinctiveness permits a comparison and choice, a posteriori, of the best loss function for a given hydrographic basin. Two simple models of loss function were studied and calibrated: a Reservoir model (with three parameters) and the GR3 model (with one parameter) An Artificial Neural Network (ANN) is a flexible mathematical structure which is capable of identifying complex non-linear relationships between input and output data sets, as rainfall-runoff processes. The employed ANN model is based on the class of higher order feedforward neural networks, the Non-linear Sigmoidal Regression Blocks Networks (NSRBN), which deploy a constructive incremental learning algorithm that is responsible for the network´s definition. The DPFT methodology was applied to the Rio das Velhas basin, at Honório Bicalho cross section ( 1655 km2 ), located in the State of Minas Gerais – Brazil, using 15 significant events of daily rainfall and runoff data. The results were analyzed and compared with regards to their efficiency. When analyzing the performance of the Reservoir and GR3 models in the calibration phase, it was established that the loss function models produced similar results However, in the validation stage, a superior performance of the GR3 model was observed. With respect to the ANN approach, the results suggest that it may provide a superior alternative to the other used models for developing input-output simulation and forecasting, in situations that do not require modeling of the internal structure of the basin. Nevertheless, the ANN approach presented does not provide models that have physically realistic components and parameters.
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