Open Access

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.

The use of Unmanned Aerial Vehicles (UAVs) and Structure from Motion (SfM) techniques can contribute to increase the accessibility, accuracy, and resolution of Digital Elevation Models (DEMs) used for soil erosion monitoring. This study aimed to evaluate the use of four DEMs obtained over a year to monitor erosion processes in an erosion-degraded area, with occurrence of rill and gully erosions, and its correlation with accumulated rainfall during the studied period. The DEMs of Geomorphic Change Detection (GCD) of horizontal and vertical resolutions of 0.10 and 0.06 m were obtained. It was possible to detect events of erosion and deposition volumes of the order of 2 m3, with a volumetric error of ∼50 %, in rills and gullies in the initial stage denominated R and GS-I, respectively. Events of the order of 100 m3, with a volumetric error around 14 % were found for advanced gullies, a segment denominated GS-II. In the three studied erosion situations, the deposition volume increased with the accumulated rainfall. The segments R and GS-I presented an inverse relationship between erosion volume and accumulated rainfall during the studied period. This behaviour can be explained by the dynamics of the deposition and erosion volumes during the erosion process. In the GS-II segment, erosion and deposition volumes were proportional and a direct relation with the cumulative rainfall over the studied period and a low percentage of volumetric error were found.