Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/11024
Título: Modelagem da erosão hídrica e métodos de interpolação de batimetria fluvial na bacia do Alto Rio Grande (MG)
Título(s) alternativo(s): Water erosion modeling and river bathymetry interpolation methods in the Upper Grande River Basin (MG)
Autores: Silva, Marx Leandro Naves
Curi, Nilton
Oliveira, Marcelo Silva de
Spalevic, Velibor
Quinton, John
Palavras-chave: Erosão
RUSLE
SEDD
Krigagem por regressão
Batimetria
Erosion
Regression kriging
Bathymetry
Data do documento: 13-Abr-2016
Editor: Universidade Federal de Lavras
Citação: BATISTA, P. V. G. Modelagem da erosão hídrica e métodos de interpolação de batimetria fluvial na bacia do Alto Rio Grande (MG). 2016. 214 p. Dissertação (Mestrado em Ciência do Solo)-Universidade Federal de Lavras, Lavras, 2016.
Resumo: Water erosion negatively affects soil fertility, soil structure, and water availability to plants. Moreover, the off-site erosion impacts contribute to the sedimentation and eutrophication of water courses. At watershed scale, erosion models are used to evaluate such impacts in a distributed manner. Bathymetric surveys from rivers and reservoirs can supply important information regarding off-site erosion effects, since geomorphologic changes due to sedimentation can be assessed from bathymetry-derived terrain models. However, since extensive bathymetric surveys may prove to be costly and time consuming, water depth measurements are usually made through cross-sectional surveys, which may lead to a sparse sampling pattern. The aim of this study was to estimate the soil losses and sediment yield in the Upper Grande River Basin, using the Revised Universal Soil Loss Equation (RUSLE) and the Sediment Delivery Distributed model (SEDD); and also, to quantify the sediment delivery to the main hydroelectric power plant reservoirs in the basin. Moreover, it aimed to evaluate hybrid kriging methods for interpolating bathymetry point data from a flooded segment of the Grande River. The orthogonal distance to river centerline was used as an auxiliary variable for regression kriging (RK) and co-kriging (CK). The results from the hybrid kriging methods were compared to the ones from ordinary kriging, inverse distance weighting and topogrid, through an external validation. RUSLE predictions estimated that the average soil losses in the Upper Grande River Basin were of 22.35 t ha -1 yr -1 , and that bare soils, eucalypt and agriculture suffered the highest erosion rates among the identified land use classes. The average specific sediment yield (SSY) in the basin was of 1.93 t ha -1 yr -1 . According to the SEDD model calibration, the SSY predictions showed an error of 0.02 t ha -1 yr -1 , or 1.25%. The model predictions estimated that 1.45 million t yr -1 of sediments are delivered to the Camargos/Itutinga power plant reservoir, whereas the Funil power plant reservoir receives a sediment input of 1.59 million t yr -1 . Although model calibration yielded small errors in relation to the observed data, the lack of field measurements has impaired a more thorough validation of the employed models. Nevertheless, the results indicate that the RUSLE/SEDD approach may be useful for analyzing sediment transport in Brazilian watersheds, where limited input data is available. The external validation of the hybrid kriging methods indicated that the employed RK approach yielded the lowest RMSE among the analyzed interpolators. Moreover, the RK predictions were able to represent the river thalweg between the widely spaced cross-sections, whereas the other methods under-predicted the thalweg in such gaps. Therefore, we concluded that the employed RK method provided a simple approach for enhancing the quality of the spatial prediction from sparse bathymetry data.
URI: http://repositorio.ufla.br/jspui/handle/1/11024
Aparece nas coleções:Ciência do Solo - Mestrado (Dissertações)



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