Data/hora: 02/06/2024 02:37:47

Provedor de dados: | Ciência e Agrotecnologia
País: | Brazil
Título: | Hybrid kriging methods for interpolating sparse river bathymetry point data
Autores: | Batista,Pedro Velloso Gomes; Silva,Marx Leandro Naves; Avalos,Fabio Arnaldo Pomar; Oliveira,Marcelo Silva de; Menezes,Michele Duarte de; Curi,Nilton.
Data: | 2017-07-01
Ano: | 2017
Palavras-chave: | Geostatistics; Spatial prediction; Regression kriging; Riverbed morphology.
Resumo: | ABSTRACT Terrain models that represent riverbed topography are used for analyzing geomorphologic changes, calculating water storage capacity, and making hydrologic simulations. These models are generated by interpolating bathymetry points. River bathymetry is usually surveyed through cross-sections, which may lead to a sparse sampling pattern. Hybrid kriging methods, such as regression kriging (RK) and co-kriging (CK) employ the correlation with auxiliary predictors, as well as inter-variable correlation, to improve the predictions of the target variable. In this study, we use the orthogonal distance of a (x, y) point to the river centerline as a covariate for RK and CK. Given that riverbed elevation variability is abrupt transversely to the flow direction, it is expected that the greater the Euclidean distance of a point to the thalweg, the greater the bed elevation will be. The aim of this study was to evaluate if the use of the proposed covariate improves the spatial prediction of riverbed topography. In order to asses such premise, we perform an external validation. Transversal cross-sections are used to make the spatial predictions, and the point data surveyed between sections are used for testing. We compare the results from CK and RK to the ones obtained from ordinary kriging (OK). The validation indicates that RK yields the lowest RMSE among the interpolators. RK predictions represent the thalweg between cross-sections, whereas the other methods under-predict the river thalweg depth. Therefore, we conclude that RK provides a simple approach for enhancing the quality of the spatial prediction from sparse bathymetry data.
Tipo: | Info:eu-repo/semantics/article
Idioma: | Inglês
Identificador: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1413-70542017000400402
Editor: | Editora da Universidade Federal de Lavras
Relação: | 10.1590/1413-70542017414008617
Formato: | text/html
Fonte: | Ciência e Agrotecnologia  v.41 n.4 2017
Fonte: | info:eu-repo/semantics/openAccess