Basin-wide hydromorphological analysis of ephemeral streamsusing machine learning algorithms

Sustainable river management now encompasses a much wider concept that includes hydromorphological and fluvial habitat studies. In ephemeral streams, the geomorphological characterization of channels is complex due to episodic flows and riparian vegetation dynamics. Stream channel survey and classif...

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Detalles Bibliográficos
Autores: Rabanaque, María Pilar, Martínez-Fernández, Vanesa, Calle, Mikel, Benito, Gerardo
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/257795
Acceso en línea:http://hdl.handle.net/10261/257795
Access Level:acceso abierto
Palabra clave:Ephemeral rivers
Geomorphological assessment
Machine learning
Network segmentation
Remote sensing
River management
Stream classification
Tribute
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dc.title.none.fl_str_mv Basin-wide hydromorphological analysis of ephemeral streamsusing machine learning algorithms
title Basin-wide hydromorphological analysis of ephemeral streamsusing machine learning algorithms
spellingShingle Basin-wide hydromorphological analysis of ephemeral streamsusing machine learning algorithms
Rabanaque, María Pilar
Ephemeral rivers
Geomorphological assessment
Machine learning
Network segmentation
Remote sensing
River management
Stream classification
Tribute
title_short Basin-wide hydromorphological analysis of ephemeral streamsusing machine learning algorithms
title_full Basin-wide hydromorphological analysis of ephemeral streamsusing machine learning algorithms
title_fullStr Basin-wide hydromorphological analysis of ephemeral streamsusing machine learning algorithms
title_full_unstemmed Basin-wide hydromorphological analysis of ephemeral streamsusing machine learning algorithms
title_sort Basin-wide hydromorphological analysis of ephemeral streamsusing machine learning algorithms
dc.creator.none.fl_str_mv Rabanaque, María Pilar
Martínez-Fernández, Vanesa
Calle, Mikel
Benito, Gerardo
author Rabanaque, María Pilar
author_facet Rabanaque, María Pilar
Martínez-Fernández, Vanesa
Calle, Mikel
Benito, Gerardo
author_role author
author2 Martínez-Fernández, Vanesa
Calle, Mikel
Benito, Gerardo
author2_role author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia e Innovación (España)
University of Turku
Academy of Finland
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Ephemeral rivers
Geomorphological assessment
Machine learning
Network segmentation
Remote sensing
River management
Stream classification
Tribute
topic Ephemeral rivers
Geomorphological assessment
Machine learning
Network segmentation
Remote sensing
River management
Stream classification
Tribute
description Sustainable river management now encompasses a much wider concept that includes hydromorphological and fluvial habitat studies. In ephemeral streams, the geomorphological characterization of channels is complex due to episodic flows and riparian vegetation dynamics. Stream channel survey and classification at the watershed scale provide the basis for geomorphological conservation, process interpretation, assessing sensitivity to disturbance, and identifying reaches that supply and store sediment. Here, we present a stream classification based on a two-step approach: (1) automatic river segmentation based on spatial variability in channel/valley morphology from topographic measurements (LiDAR, light, detection and ranging), and (2) fluvial landform and vegetation density mapping derived from multispectral opensource satellite images (Sentinel-2) using support vector machine (SVM) and Random Forest (RF) algorithms. These analyses provide continuous, quantitative spatial values of geometric (channel/valley width, slope gradient, and route distance), landform (active channel and gravel bars with five densities of vegetation cover), and hydraulic (specific stream power) variables. Four stream types were identified in the Rambla de la Viuda catchment (1500 km2), an ephemeral gravel-bed river in eastern Spain. The spatial distribution of channel types is explained by differences in geometry (active channel width, valley width, and slope gradient) and a hydraulic parameter (specific stream power). The landforms/vegetation patterns provided insight on causal relationships between erosion and deposition processes during high flow periods and the time since the most recent large disruptive flood event. Channel type distribution provided first-order predictions about the location of reaches that supply and store sediment and thus information on sediment continuity along the river. Dam effects on downstream reaches resulted in geomorphological disequilibrium, producing narrowing of the active channel, slope reduction, and a decrease of gravel bar areal extension. The proposed catchment scale analysis provides a comprehensive and replicable methodology for environmental planning in Mediterranean ephemeral streams to guide further hydromorphological surveys at the reach scale.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022
2022
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dc.publisher.none.fl_str_mv John Wiley & Sons
publisher.none.fl_str_mv John Wiley & Sons
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spelling Basin-wide hydromorphological analysis of ephemeral streamsusing machine learning algorithmsRabanaque, María PilarMartínez-Fernández, VanesaCalle, MikelBenito, GerardoEphemeral riversGeomorphological assessmentMachine learningNetwork segmentationRemote sensingRiver managementStream classificationTributeSustainable river management now encompasses a much wider concept that includes hydromorphological and fluvial habitat studies. In ephemeral streams, the geomorphological characterization of channels is complex due to episodic flows and riparian vegetation dynamics. Stream channel survey and classification at the watershed scale provide the basis for geomorphological conservation, process interpretation, assessing sensitivity to disturbance, and identifying reaches that supply and store sediment. Here, we present a stream classification based on a two-step approach: (1) automatic river segmentation based on spatial variability in channel/valley morphology from topographic measurements (LiDAR, light, detection and ranging), and (2) fluvial landform and vegetation density mapping derived from multispectral opensource satellite images (Sentinel-2) using support vector machine (SVM) and Random Forest (RF) algorithms. These analyses provide continuous, quantitative spatial values of geometric (channel/valley width, slope gradient, and route distance), landform (active channel and gravel bars with five densities of vegetation cover), and hydraulic (specific stream power) variables. Four stream types were identified in the Rambla de la Viuda catchment (1500 km2), an ephemeral gravel-bed river in eastern Spain. The spatial distribution of channel types is explained by differences in geometry (active channel width, valley width, and slope gradient) and a hydraulic parameter (specific stream power). The landforms/vegetation patterns provided insight on causal relationships between erosion and deposition processes during high flow periods and the time since the most recent large disruptive flood event. Channel type distribution provided first-order predictions about the location of reaches that supply and store sediment and thus information on sediment continuity along the river. Dam effects on downstream reaches resulted in geomorphological disequilibrium, producing narrowing of the active channel, slope reduction, and a decrease of gravel bar areal extension. The proposed catchment scale analysis provides a comprehensive and replicable methodology for environmental planning in Mediterranean ephemeral streams to guide further hydromorphological surveys at the reach scale.The research conducted in this study was funded by the Ministry of Science and Innovation through the projects EPHIMED (CGL2017-86839-C3-1-R) and EPHIDREAMS (PID2020-116537RBI00), co-financed with FEDER funds. M.P. Rabanaque and V. Martínez-Fernández were funded by Spanish Ministry of Science and Innovation contracts, namely from the PhD FPI programme (PRE2018-086771) and Post-doc Juan de la Cierva programme (FJC2018-035451-I) respectively. M.C. was partly financed by the EPHIMED project, Turku Collegium of Science, Medicine and Technology (TCSMT) and Hydro-RDI-Network, Academy of Finland funding ID: 337279.Peer reviewedJohn Wiley & SonsMinisterio de Ciencia e Innovación (España)University of TurkuAcademy of FinlandConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202220222022info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/257795reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/MICINN/Plan Estatal de Investigación Científica y Técnica y de InnovaciónCGL2017-86839-C3-1-R/info:eu-repo/grantAgreement/MICINN/Plan Estatal de Investigación Científica y Técnica y de InnovaciónPID2020-116537RBI00/info:eu-repo/grantAgreement/MICINN/Plan Estatal de Investigación Científica y Técnica y de InnovaciónPRE2018-086771/info:eu-repo/grantAgreement/MICINN/Plan Estatal de Investigación Científica y Técnica y de InnovaciónFJC2018-035451-I/https://onlinelibrary.wiley.com/doi/10.1002/esp.5250?af=RSíinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2577952026-05-22T06:33:51Z
score 15.812429

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