Avaliação do modelo J2000/JAMS para modelagem hidrológica em bacias hidrográficas brasileiras

Machado, Aline Ribeiro; Mello, Arisvaldo Vieira; Wendland, Edson

doi:10.1590/s1413-41522016145177

Cited by 7 publications

(8 citation statements)

References 25 publications

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“…Overall, the calculated RMSE index was 0.07 m 3 s −1 , which is indication of an acceptable "downscaling." Similar values of RMSE in hydrological models that presented good performance can be consulted in the works of Monte et al [79] and Machado et al [80].…”

Section: Resultssupporting

confidence: 54%

Conjunctive Water Resources Management in Densely Urbanized Karst Areas: A Study in the Sete Lagoas Region, State of Minas Gerais, Brazil

et al. 2019

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Headwater catchments store valuable resources of quality water, but their hydraulic response is difficult to assess (model) because they are usually deprived of monitoring stations, namely hydrometric stations. This issue becomes even more pertinent because headwater catchments are ideal for the practice of conjunctive water resources management involving the supply of towns with groundwater and surface water, a solution that can be used to mitigate overexploitation of groundwater resources in densely urbanized and populated areas. In this study, a stepwise approach is presented whereby, in a first stage, a gauged basin was modeled for stream flow using the JAMS J2000 framework, with the purpose to obtain calibrated hydraulic parameters and ecological simulated stream flow records. Having validated the model through a comparison of simulated and measured flows, the simulated record was adjusted to the scale of an ungauged sub-basin, based on a new run of JAMS J2000 using the same hydraulic parameters. At this stage, a second validation of modeled data was accomplished through comparison of the downscaled flow rates with discharge rates assessed by field measurements of flow velocity and water column height. The modeled basin was a portion of Jequitiba River basin, while the enclosed sub-basin was the Marinheiro catchment (state of Minas Gerais, Brazil). The latter is a peri-urban watershed located in the vicinity of Sete Lagoas town, a densely urbanized and populated area. This town uses 15.5 hm3 year−1 of karst groundwater for public water supply, but the renewable resources were estimated to be 6.3 hm3 year−1. The impairment between abstraction and renewable resources lasts for decades, and for that reason the town experiences systemic water table declines and sinkhole development. The present study claims that the storage of quality water in the Marinheiro catchment, in a dam reservoir, would help alleviate the depletion of groundwater resources in the karst aquifer because this catchment could deliver 4.73 hm3 year−1 of quality surface water to the municipality without endangering ecologic flows. The construction of a small dam at the outlet of Marinheiro catchment could also improve aquifer recharge. Presently, the annual recharge in this catchment approaches 1.47 hm3 but could be much larger if the small dam was installed in the water course and the captured stream water managed properly.

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Section: Resultssupporting

confidence: 54%

Conjunctive Water Resources Management in Densely Urbanized Karst Areas: A Study in the Sete Lagoas Region, State of Minas Gerais, Brazil

et al. 2019

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“…Esas diferencias pueden ser oriundas de la acentuada fluctuación de condiciones climáticas en la región, bajo la influencia de las variables humedad relativa, temperatura y velocidad del aire utilizadas en este trabajo. Por otro lado, los valores del coeficiente de eficiencia de Nash Sutcliffe (CNS) fueron superiores a los estimados por Sousa et al (2015); Brighenti et al (2016); Chaves et al (2016); Lopes et al (2016); Machado et al (2017). Esas discrepancias pueden estar asociadas al elevado número de observaciones utilizadas en la presente investigación, lo que resultó en una mayor eficiencia de las redes desarrolladas.…”

Section: Discussionunclassified

Pronóstico Anual De La Carga Térmica Radiante Aplicándose Inteligencia Artificial

Borges

Mendoza

Morais

2021

NAT

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En este estudio se desenvolvieron redes neuronales artificiales para predecir el conforto térmico animal, en función de la temperatura ambiente y la velocidad del aire para cada día del año en el calendario juliano. Los datos fueron obtenidos en el sitio del Instituto Nacional de Meteorología para una serie histórica de 30 años, coleccionada en la Estación Convencional Padre Ricardo Remetter, municipio de Santo Antonio de Leverger-MT. Para la elaboración de las redes se adoptó como variable de entrada el día del año y como variable de salida la carga térmica de radiación. El número de neuronas varió entre 2 y 15, utilizándose una y dos camadas ocultas. El ajuste de las redes se verificó por el coeficiente de determinación, error absoluto medio, porcentaje medio del error absoluto, la normalidad de los residuos y la prueba de t-Student. No hubo discrepancias entre los valores estimados por las redes y los obtenidos de la serie histórica. Finalmente se seleccionaron diez arquitecturas con adecuados índices de desempeño y las cuatro mejores se sometieron al análisis de residuos. Se concluyó que las redes neuronales del tipo perceptron con dos camadas ocultas fueron apropiadas para pronosticar la carga térmica radiante, conforme el día Juliano. Palabras-clave: conforto térmico; red neuronal artificial; series temporales. Annual prognostic of the radiant thermal using artificial intelligence ABSTRACT: In this research, artificial neural networks were developed to predict the animal thermal comfort based on the room temperature and air velocity for the year day in the Julian calendar. The data were obtained from the website of the National Institute of Meteorology for a 30-year historical series, collected at the Padre Ricardo Remetter Meteorological Station, municipality of Santo Antônio de Leverger-MT. To elaborate the networks, the day of the year was adopted as the input variable and the radiation thermal load as the output variable. The number of neurons ranged varied from 2 to 15, being used one and two hidden layers. The adjustment of the networks was verified by the determination coefficient, mean absolute error, mean percentage of the absolute error, the normality of residues and the t-Student test. The values estimated by the networks and those obtained from the historical series did not differ. Finally, ten architectures with adequate performance and efficiency indexes were selected and among them the four best were submitted to the residue analysis. It was concluded that the artificial perceptron neural networks formed by two-layer hidden were suitable for the prognosis of the radiant thermal load, as a function of Julian day. Keywords: thermal comfort; artificial network; time series.

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“…In Brazil, water resource management is hampered by the lack of hydrological information. Although this gap can be supplied by hydrological models that simulate the behavior of river watersheds (Machado et al 2017), the challenge is to determine rainfall-runoff models that have a good representation of the parameters involved. Estimate runoff in urban watersheds, especially in areas that suffer from possible irregular occupations, is a complex and dynamic process, especially in the context of its spatial variability.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis of rainfall–runoff parameters models to estimate flows

et al. 2021

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Determinate the runoff of a watershed is a challenge due to the complexity of representing all “inlets” and “outlets” involved in a rainfall–runoff model. Therefore, methodologies applied for this purpose should have a good representation of the variables that most influence in this process. One of the models used to calculate the design flow is the (USDA in Urban Hydrology for Small. Technical release, no 55 (TR-55). Soil Conservation Service. Washigton, DC, http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Urban+Hydrology+for+Small+watersheds#1, 1986), which considers the analysis changes in soil coverage, time of concentration (tc), and recurrence period (T). In this way, this study sought to evaluate the hydrological behavior of a watershed with an increase in soil waterproofing. These modifications were correlated with the variation of runoff coefficients (CN), modifications of the periods of recurrence indicated by the literature, and different equations of the time of concentration. Its application was carried out in the Ribeirão do Suru watershed, Santana de Parnaíba, SP, Brazil. The CN {75; 80; 85; 90} increased 3.14, 5.61, 10.90 and 15.85%, respectively. In the most critical situation, runoff was 15.85% higher in estimated CN. The variation of precipitation as a function of T (2, 5, 10, 25, 50, 100 and 500) and application of 11 time of concentration methods designed 132 hydrographs and flow values that were statistically treated in T of Student and in the Analysis of Variance (ANOVA). Except for Bransby Willians associated Cinematic Method, Dooge with Johnstone and CTH with Tsuchyia, the pairs showed degrees of correlation below 59%. The greatest correlation was observed in Jonhstone with Dooge (90%), followed by the Kinematic Method with the Soil Conservation Service Method (83%) and with Dodge (74%). As a result, it was possible to demonstrate the behavior of the SCS parameters to minimize subjectivities and revealing how each parameter impacts the flow of the watershed. Finally, the sensitivity attributed to T was the highest among the three analyzed.

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Avaliação do modelo J2000/JAMS para modelagem hidrológica em bacias hidrográficas brasileiras

Cited by 7 publications

References 25 publications

Conjunctive Water Resources Management in Densely Urbanized Karst Areas: A Study in the Sete Lagoas Region, State of Minas Gerais, Brazil

Conjunctive Water Resources Management in Densely Urbanized Karst Areas: A Study in the Sete Lagoas Region, State of Minas Gerais, Brazil

Pronóstico Anual De La Carga Térmica Radiante Aplicándose Inteligencia Artificial

Sensitivity analysis of rainfall–runoff parameters models to estimate flows

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