scholarly journals MODELAGEM CHUVA-VAZÃO EM UMA BACIA TROPICAL UTILIZANDO O MODELO IPH II / RAINFALL-RUNOFF MODELING IN A TROPICAL BASIN USING THE IPH II MODEL

Geo UERJ ◽  
2018 ◽  
pp. e30557
Author(s):  
Eduardo Morgan Uliana ◽  
Donizete Dos Reis Pereira ◽  
Demetrius David da Silva ◽  
Frederico Terra de Almeida ◽  
Adilson Pacheco de Souza

O objetivo do presente trabalho foi avaliar a aplicabilidade do modelo hidrológico IPH II para a estimativa de vazões diárias na bacia hidrográfica do rio Pomba assim como verificar a sua acurácia na simulação de eventos extremos, de forma a obter informações para o planejamento e gestão dos recursos hídricos, além da previsão e mitigação de eventos de cheia no local. A sub-bacia selecionada para o estudo teve como seção de controle a estação fluviométrica Guarani, a qual drena uma área de 1.650 km2, localizada no estado de Minas Gerais. Os dados de precipitação e evapotranspiração de referência, requeridos como dados de entrada no modelo IPH II, foram obtidos pelos métodos de Thiessen e Hargreaves-Samani, respectivamente. A calibração do modelo foi realizada de forma automática utilizando o algoritmo Shuffled Complex Evolution (SCE-UA), que possibilitou a estimativa dos parâmetros do modelo de forma rápida e eficiente. Os resultados obtidos com a utilização do modelo IPH II mostraram que as estimativas das vazões diárias foram adequadas e boas, com base no coeficiente de Nash-Sutcliffe, incluindo as máximas e mínimas diárias anuais e, também, as vazões mínimas de referência para fins de outorga, o que permite concluir que o modelo tem potencial para ser utilizado na gestão de recursos hídricos, na previsão de vazões de cheias e na mitigação de seus efeitos, assim como para análise de consistência e preenchimento de falhas nos dados de vazões.

RBRH ◽  
2019 ◽  
Vol 24 ◽  
Author(s):  
Eduardo Morgan Uliana ◽  
Frederico Terra de Almeida ◽  
Adilson Pacheco de Souza ◽  
Ibraim Fantin da Cruz ◽  
Luana Lisboa ◽  
...  

ABSTRACT Parameterization and performance analysis of a hydrological model allow its consolidation, so that water-resource management strategies could be evaluated and extreme events forecast. In this context, this study aimed to evaluate the performance of the Sacramento Soil Moisture Accounting (SAC-SMA) and IPH II models for runoff estimation in the Teles Pires River basin, which is located in the Amazon region, State of Mato Grosso, Brazil. Both models were automatically calibrated using Shuffled Complex Evolution algorithm (SCE-UA) and validated for five runoff monitoring units. Our results showed that both are suitable for daily runoff modeling in the Teles Pires River basin with higher performance in larger drainage area basins. We can also infer that the simple use of complex rainfall-runoff models might not provide improved estimates. Although the SAC-SMA is the most complex and detailed model for hydrological processes, it has not outperformed IPH II in any of the monitoring units in the Teles Pires River.


10.29007/66vq ◽  
2018 ◽  
Author(s):  
Mehdi Sheikh Goodarzi ◽  
Bahman Jabbarian Amiri ◽  
Shabnam Navardi

Regarding to importance of modeling calibration, this study will be focused on probabilistic role of different strategies in calibration and verification steps. Tank lumped conceptual model was selected as a hydrological platform to investigate the effects of each optimization strategy on model performance.However, much considerable efforts are required to calibrate a large number of parameters in conceptual models to obtain better results. With development of artificial intelligence, three probabilistic Global Search Algorithms (GSAs) including Shuffled Complex Evolution (SCE), Genetic Algorithm (GA) and Rosenbrock Multi-Start Search (RBN) and also three Objective Functions (OFs) consisted of Nash-Sutcliffe (NSE), Root Mean Square Error (RMSE) and mean absolute error (MAE) were employed for model calibration (comparing the performance of different GSAs versus OFs). The best set of parameters, which is derived from the calibration step, will be used as prediction coefficients for the model verification stage. Performance evaluation of the simulation results was undertaken using Coefficient of Correlation (r) and Descriptive Statistics.Results indicated that all of optimization strategies have a relative ability to retrieve optimal values of eighteen parameters of the Tank model. However, the best GSAs for daily runoff simulation are SCE (0.871) and GA (0.864), respectively, for calibration and verification phases. In case of the OFs result, NSE (0.763) and RMSE (0.834) are more performant for calibration and verification of the model. Finally, the best strategy was selected by combining the results of GSAs and OFs models. Finally, SCE*MAE (0.906) and GA*RMSE (0.868) were selected as a top series.


Hydrology ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 57
Author(s):  
Konstantinos Vantas ◽  
Epaminondas Sidiropoulos

The identification and recognition of temporal rainfall patterns is important and useful not only for climatological studies, but mainly for supporting rainfall–runoff modeling and water resources management. Clustering techniques applied to rainfall data provide meaningful ways for producing concise and inclusive pattern classifications. In this paper, a timeseries of rainfall data coming from the Greek National Bank of Hydrological and Meteorological Information are delineated to independent rainstorms and subjected to cluster analysis, in order to identify and extract representative patterns. The computational process is a custom-developed, domain-specific algorithm that produces temporal rainfall patterns using common characteristics from the data via fuzzy clustering in which (a) every storm may belong to more than one cluster, allowing for some equivocation in the data, (b) the number of the clusters is not assumed known a priori but is determined solely from the data and, finally, (c) intra-storm and seasonal temporal distribution patterns are produced. Traditional classification methods include prior empirical knowledge, while the proposed method is fully unsupervised, not presupposing any external elements and giving results superior to the former.


2001 ◽  
Author(s):  
Fred L. Ogden ◽  
Ehab A. Meselhe ◽  
Justin Niedzialek ◽  
Ben Smith

2021 ◽  
pp. 127043
Author(s):  
Kang Xie ◽  
Pan Liu ◽  
Jianyun Zhang ◽  
Dongyang Han ◽  
Guoqing Wang ◽  
...  

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Chao Zhang ◽  
Ru-bin Wang ◽  
Qing-xiang Meng

Parameter optimization for the conceptual rainfall-runoff (CRR) model has always been the difficult problem in hydrology since watershed hydrological model is high-dimensional and nonlinear with multimodal and nonconvex response surface and its parameters are obviously related and complementary. In the research presented here, the shuffled complex evolution (SCE-UA) global optimization method was used to calibrate the Xinanjiang (XAJ) model. We defined the ideal data and applied the method to observed data. Our results show that, in the case of ideal data, the data length did not affect the parameter optimization for the hydrological model. If the objective function was selected appropriately, the proposed method found the true parameter values. In the case of observed data, we applied the technique to different lengths of data (1, 2, and 3 years) and compared the results with ideal data. We found that errors in the data and model structure lead to significant uncertainties in the parameter optimization.


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