A Computer Aided Approach for River Styles—Inspired Characterization of Large Basins: A Structured Procedure and Support Tools

Abstract

This paper presents a systematic procedure for developing a characterization and classification of river reaches inspired by the River Styles Framework, through which insight can be gained about the understanding of river behavior. Our procedure takes advantage of several computer based “tools”, i.e., algorithms implemented in software packages of various types, from “simple” Excel sheets to sophisticated algorithms in Python language, in general all supported by Geographic Information Systems (GIS). The main potentially useful, existing tools for this specific aim are discussed here, revealing their strengths and weaknesses. New, complementary or alternative tools that have been developed in the project feeding this paper are presented, which can contribute to the scientific community and stakeholders of the topic. The main result of our research is a structured and practical guide (a ToolBox Manual) that can support practitioners and researchers wishing to characterize and classify large rivers, based on the River Styles Framework. The main contribution is that this set of ideas, solutions, and tools, makes this type of exercise significantly more transparent and at the same time much less subjective. Moreover, the procedure is applicable to large systems and does not require more information than that generally available also in developing or emerging countries.

A Systematic, Automated Approach for River Segmentation Tested on the Magdalena River (Colombia) and the Baker River (Chile)

“…Last on the right: reaches obtained. It can be noticed that, after applying the categorical reductionist-holistic algorithm (Nardini et al [25]), a short reach of current segmentation (exactly on the elbow) has been incorporated within the T20 reach because it is shorter than the significant length (5 km).…”

“…However, the output of Hubert's test (stretches of different colors in Figure 3) can be questionable. For instance, the stretch marked by the two red segments in Figure 3 would certainly be classified by expert judgment as narrower with respect to the one above it (notice that there is no criterion linked to the length as Hubert test determined both short and long segments) (source: Nardini et al [25]). there is no criterion linked to the length as Hubert test determined both short and long segments) (source: Nardini et al [25]).…”

“…For instance, the stretch marked by the two red segments in Figure 3 would certainly be classified by expert judgment as narrower with respect to the one above it (notice that there is no criterion linked to the length as Hubert test determined both short and long segments) (source: Nardini et al [25]). there is no criterion linked to the length as Hubert test determined both short and long segments) (source: Nardini et al [25]). Martinez-Fernandez et al [26] applied similar tests by comparing three different techniques: the Pettitt test, the Mann-Kendall test and the non-parametric Multi Response Permutation Procedure (MRPP) test by using three different river attributes (VB width, active channel width, slope).…”

“…This is clearly an abstraction (e.g., bed material can vary virtually continuously longitudinally-and laterally/vertically), and all the attributes require some kind of spatial averaging, for instance smoothing out where too frequent variations occur along the river. Still, we believe that this procedure is reasonably practical for segmentation and that it can be supported by suitable reductionist-holistic algorithms (e.g., the Hubert test, as proposed by the FCT, for quantitative, continuous attributes, like VB width; or the Categorical Holistic algorithm proposed in Nardini et al [25,34]).…”

“…intersection of the segmentations of each "core attribute" thus obtaining potential reaches; • refinement of the output: too short reaches (with respect to the L S ) are eliminated by incorporating them within the closest, major segment, either manually or through a computerized, possibly automated algorithm like the one presented in Nardini et al [25].…”