Geomorphic classification of rivers: a new methodology applied in an Atlantic Region (Galicia, NW Iberian Peninsula)

Horacio, Jesús; Ojeda, Alfredo Ollero; Pérez‐Alberti, Augusto

doi:10.1007/s12665-017-7072-0

Cited by 10 publications

(4 citation statements)

References 59 publications

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“…En esta línea, la potencia específica y el tamaño medio de sedimento (d 50 ) son dos de las variables geomorfológicas que mejor responden a la evolución de los procesos y formas de un río a escala de cauce/tramo (Schmitt, 2007;CEN, 2002;Knighton, 1998). Horacio et al (2017) Las clasificaciones geomórficas de ríos que más se han utilizado en los últimos años se han basado en el estudio de sus procesos y/o formas (Tadaki et al, 2014), aunque en ocasiones sea complejo definir (o separar) causa y efecto (Kasprak et al, 2016). La forma de un río es el reflejo directo de múltiples procesos que interactúan y, por lo tanto, la forma implica proceso (Kasprak et al, 2016) (i. e., detrás de cada proceso hay una forma asociada).…”

Section: Geodiversidad Fluvial Y Gestión: Una "Necesidad Necesaria"unclassified

Aproximación a la identificación y caracterización de ríos en roca a escala regional mediante variables topo-geomorfológicas (Galicia, Noroeste de la Península Ibérica)

García

Pérez‐Alberti²

2022

CGUV

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Las clasificaciones de ríos son un procedimiento técnico de utilidad para simplificar la realidad y discriminar en tipos con características homogéneas. El ensayo experimental que se presenta, desarrollado sobre la red fluvial de Galicia, tiene el objetivo de combinar Sistemas de Información Geográfica y tres variables geomorfológicas (litología, pendiente y densidad de fracturas) para identificar potenciales tramos de ríos en roca. El método aplicado se estructuró en cuatro etapas secuenciales: (i) inventariado de tramos en roca, (ii) caracterización de los tramos inventariados, (iii) tipificación de la red de drenaje, y (iv) validación del método aplicado. Los resultados obtenidos arrojan una primera cartografía de probabilidad de presencia de ríos en roca para más de 30.000 km de cauces. Un 37% de la red tiene una probabilidad alta de ser río en roca y un 21% una probabilidad baja o nula. El 42% restante se mueve en una probabilidad media. Conocer la distribución espacial de este tipo de ríos debería ser de utilidad para los organismos de cuenca y el establecimiento de protocolos de actuación que consideren, primero, las características de los ríos en roca en la gestión de las masas de agua por su singularidad y repercusión en la dinámica del río, y, segundo, el florecimiento de figuras de protección basadas en la geodiversidad.

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Section: Geodiversidad Fluvial Y Gestión: Una "Necesidad Necesaria"unclassified

Aproximación a la identificación y caracterización de ríos en roca a escala regional mediante variables topo-geomorfológicas (Galicia, Noroeste de la Península Ibérica)

García

Pérez‐Alberti²

2022

CGUV

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“…River channel pattern is a function of a variety of factors including longitudinal gradient, stream power, transport capacity, and bank strength (Buffington & Montgomery, 2022;Church, 2006;Kondolf, Piégay, Schmitt, & Montgomery, 2016). Previous studies on river pattern classification (Demarchi, Bizzi, & Piegay, 2017;Ham & Church, 2012;Horacio, Ollero, & Perez-Alberti, 2017) have been typically approached from a temporally static perspective, focusing on categorising planform at low flow. Whilst this approach is adequate for many meandering rivers in temperate regions, in other climate settings, the aerial proportions of water, exposed sediment and vegetation, which comprise the planform of a river, may substantially vary through a year (Ashworth and Lewin, 2014).…”

Section: Introductionmentioning

confidence: 99%

Seasonal and annual tropical river pattern change detection using machine learning

Li¹,

Williams²,

Hoey³

et al. 2023

Preprint

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Rivers in the tropics are more likely to exhibit seasonal changes in pattern than those in temperate regions because of strongly seasonal rainfall. However, such changes in seasonal tropical river patterns have not been widely investigated. Machine learning methods are used in this study with Sentinel-2 multispectral remote sensing images to classify active channel landforms (water; unvegetated bars; vegetated bars) of the Bislak, Laoag and Abra Rivers, north-west Luzon, the Philippines. River patterns are classified five or six times per year from 2016 to 2020. Spatial and temporal trends were investigated, in the context of the rivers’ active width, valley confinement, tectonic setting and precipitation. Results show a variety of relationships between each landform unit and active width, but a strong correlation was shown between active width and vegetation area in dry and wet seasons. Rivers were divided into sub-reaches based on observed patterns of water frequency and confinement; Ensemble Empirical Mode Decomposition (EEMD) was then used to decompose the landform time series and precipitation record. EEMD indicates that water and vegetated bars commonly show synchronised fluctuations with precipitation, while unvegetated bars have an anti-phase oscillation with precipitation. It also suggests that deviations from periodic consistency in river pattern may reflect the influence of extreme events and/or human disturbance. At the river system scale, faults perpendicular to the channel centreline were associated with an increase in vegetated bar stability. Overall, the interplay of faults, elevation, confinement and tributary locations impact landform stability. This investigation demonstrates that in tropical regions river pattern should be considered as a dynamic entity as characterising pattern from a single time period may misrepresent a river’s character. EEMD is also demonstrated to be an appropriate statistical technique in geomorphology to decompose datasets that are generated from contemporary applications of machine learning to remotely sensed imagery.

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“…Also, the belonging to a given planform category is performed manually based on an expert judgment rather than an automatic procedure. Other attempts (e.g., Bertrand et al [16], Guillon et al [17], and Horacio et al [18]) were rather based on unsupervised ML algorithms, where reaches were described by a set of attributes (quantified by indicators) and then grouped into clusters of similar elements finally assigned-again based on expert judgment-to the different planform typologies. There, attributes are calculated on predefined stretches, although the final reaches may not coincide with them, which may create a discrepancy.…”

Section: Introductionmentioning

confidence: 99%

Automatic River Planform Recognition Tested on Chilean Rivers

Nardini,

Salas,

Carrasco

et al. 2023

Water

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This paper addresses the issue of the automatic identification of river reaches and their planform type given the (observed) set of geomorphic elements and units. It introduces further advances with respect to the original proposal by Nardini and Brierley, and it explores explicitly the ability of the algorithm and associated tools to work properly on significantly different rivers while adopting a given same parametrization. This was indeed an envisaged ability speculated as a challenging conclusion of the previous work. The Duqueco, Laja, and Biobío rivers (Chile) were analyzed for this purpose. The conclusion is definitely positive, which opens future promising application horizons.

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Geomorphic classification of rivers: a new methodology applied in an Atlantic Region (Galicia, NW Iberian Peninsula)

Cited by 10 publications

References 59 publications

Aproximación a la identificación y caracterización de ríos en roca a escala regional mediante variables topo-geomorfológicas (Galicia, Noroeste de la Península Ibérica)

Aproximación a la identificación y caracterización de ríos en roca a escala regional mediante variables topo-geomorfológicas (Galicia, Noroeste de la Península Ibérica)

Seasonal and annual tropical river pattern change detection using machine learning

Automatic River Planform Recognition Tested on Chilean Rivers

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