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Multiorder hydrologic Position for Europe — a Set of Features for Machine Learning and Analysis in Hydrology

The presented dataset EU-MOHP v013.1.1 provides multiscale information on the hydrologic position (MOHP) of a geographic point within its respective river network and catchment as gridded maps. More precisely, it comprises the three measures “divide to stream distance” (DSD) as sum of the distances...

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Autores principales: Nölscher, Maximilian, Mutz, Michael, Broda, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9617849/
https://www.ncbi.nlm.nih.gov/pubmed/36309509
http://dx.doi.org/10.1038/s41597-022-01787-4
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author Nölscher, Maximilian
Mutz, Michael
Broda, Stefan
author_facet Nölscher, Maximilian
Mutz, Michael
Broda, Stefan
author_sort Nölscher, Maximilian
collection PubMed
description The presented dataset EU-MOHP v013.1.1 provides multiscale information on the hydrologic position (MOHP) of a geographic point within its respective river network and catchment as gridded maps. More precisely, it comprises the three measures “divide to stream distance” (DSD) as sum of the distances to the nearest stream and catchment divide, “lateral position” (LP) as a relative measure of the position between the nearest stream and divide and “stream distance” (SD) as the distance to the nearest stream. These three measures are calculated for nine hydrologic orders to reflect different spatial scales from local to continental. Its spatial extent covers major parts of the European Economic Area (EEA39) which also largely coincides with physiographical Europe. Although there are multiple potential use cases, this dataset serves predominantly as valuable static environmental descriptor or predictor variable for hydrogeological and hydrological modelling such as mapping or forecasting tasks using machine learning. The generation of this dataset uses free open source software only and therefore can be transferred to other regions or input datasets.
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spelling pubmed-96178492022-10-31 Multiorder hydrologic Position for Europe — a Set of Features for Machine Learning and Analysis in Hydrology Nölscher, Maximilian Mutz, Michael Broda, Stefan Sci Data Data Descriptor The presented dataset EU-MOHP v013.1.1 provides multiscale information on the hydrologic position (MOHP) of a geographic point within its respective river network and catchment as gridded maps. More precisely, it comprises the three measures “divide to stream distance” (DSD) as sum of the distances to the nearest stream and catchment divide, “lateral position” (LP) as a relative measure of the position between the nearest stream and divide and “stream distance” (SD) as the distance to the nearest stream. These three measures are calculated for nine hydrologic orders to reflect different spatial scales from local to continental. Its spatial extent covers major parts of the European Economic Area (EEA39) which also largely coincides with physiographical Europe. Although there are multiple potential use cases, this dataset serves predominantly as valuable static environmental descriptor or predictor variable for hydrogeological and hydrological modelling such as mapping or forecasting tasks using machine learning. The generation of this dataset uses free open source software only and therefore can be transferred to other regions or input datasets. Nature Publishing Group UK 2022-10-29 /pmc/articles/PMC9617849/ /pubmed/36309509 http://dx.doi.org/10.1038/s41597-022-01787-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
Nölscher, Maximilian
Mutz, Michael
Broda, Stefan
Multiorder hydrologic Position for Europe — a Set of Features for Machine Learning and Analysis in Hydrology
title Multiorder hydrologic Position for Europe — a Set of Features for Machine Learning and Analysis in Hydrology
title_full Multiorder hydrologic Position for Europe — a Set of Features for Machine Learning and Analysis in Hydrology
title_fullStr Multiorder hydrologic Position for Europe — a Set of Features for Machine Learning and Analysis in Hydrology
title_full_unstemmed Multiorder hydrologic Position for Europe — a Set of Features for Machine Learning and Analysis in Hydrology
title_short Multiorder hydrologic Position for Europe — a Set of Features for Machine Learning and Analysis in Hydrology
title_sort multiorder hydrologic position for europe — a set of features for machine learning and analysis in hydrology
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9617849/
https://www.ncbi.nlm.nih.gov/pubmed/36309509
http://dx.doi.org/10.1038/s41597-022-01787-4
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