Applied hydrology and sedimentology for disturbed areas

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Prediction of rainfall runoff‐induced sediment load from bare land surfaces by generalized regression neural network and empirical model

Based on three rainfall run-off-induced sediment transport data for bare surface experimental plots, the generalized regression neural network (GRNN) and empirical models were developed to predict sediment load. Rainfall intensity, slope, rainfall duration, soil particle median diameter, clay content of the soil, rill density and soil particle mass density constituted the input variables of the models while sediment load was the target output. the GRNN model was trained and tested. the GRNN model was found successful in predicting sediment load. Sensitivity analysis by the GRNN model revealed that slope and rainfall duration were the most sensitive parameters. in addition to the GRNN model, two empirical models were proposed: (1) in the first empirical model, all the input variables were related to the sediment load, and (2) in the second empirical model, only rainfall intensity, slope and rainfall duration were related to the sediment load. the empirical models were calibrated and validated. At the calibration stage, the coefficients and the exponents of the empirical models were obtained using the genetic algorithm optimization method. the validated empirical models were also applied to two more experimental data sets: (1) one data set was from a field experiment, and (2) one set was from a laboratory experiment. the results indicated the success of the empirical models in predicting sediment load from bare land surfaces

Modelagem da retenção de herbicidas em zonas ripárias Modeling of herbicide retention in riparian zones

O presente trabalho teve como objetivo investigar a retenção de atrazina e picloram, transportados via escoamento superficial, em zona ripária. Para isto, simulou-se um escoamento superficial, contendo uma mistura de caulinita, atrazina e picloram, dentro de zonas ripárias estabelecidas ao longo de plantações de pinheiros do Nordeste do Estado da Geórgia, EUA. Cinco parcelas foram instaladas dentro de zonas ripárias, apresentando declividades diferentes (2, 5, 10, 15 e 20%). Os efeitos da umidade do solo e da presença do horizonte O na retenção dos dois herbicidas foram avaliados. Um modelo exponencial, comumente utilizado na estimativa de redução da DBO e de nutrientes em tratamento por escoamento superficial, foi empregado na estimativa de redução de herbicidas e caulinita em zonas ripárias. O modelo possibilitou estimar com razoável precisão, a remoção de caulinita e atrazina da mistura em escoamento ao longo de zonas ripárias de 10 m de comprimento. Em geral, a declividade foi o parâmetro que apresentou melhor correlação com a retenção dos contaminantes presentes na mistura em escoamento na zona ripária. O horizonte O, mais espesso nas maiores declividades, favoreceu tanto a sedimentação da caulinita como a adsorção da atrazina.<br>This work aimed to investigate the retention of atrazine and picloram, carried by surface flow, in riparian zones. The surface flow, containing a mixture of both herbicides and kaolin, was then simulated within riparian zones established in pine plantations in north-eastern Georgia, USA. Five plots were established within riparian zones, each with a different slope (2, 5, 10, 15 and 20%). The influence of the initial moisture and of the O horizon condition in herbicide retention was analyzed. An exponential model, commonly used for the estimate of biochemical demand of oxygen (BOD) and nutrients attenuation in overland flow treatment, was used to estimate the attenuation of kaolin and herbicides in riparian zones. The model allowed an accurate estimate of the attenuation of kaolin and atrazine from the surface runoff mixture along 10 m of riparian zone. Generally, slope showed the best correlation with retention of the pollutants presented in the runoff mixture within the riparian zone. The O horizon present in the steeper slopes improved the sedimentation of kaolin and the atrazine adsorption