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Enregistrement W4394353715 · doi:10.6084/m9.figshare.14633022

Global prevalence of non-perennial rivers and streams

2021· dataset· en· W4394353715 sur OpenAlexaboutno aff
Mathis Messager, Bernhard Lehner

Notice bibliographique

RevueFigshare · 2021
Typedataset
Langueen
DomaineEnvironmental Science
ThématiqueWildlife-Road Interactions and Conservation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSTREAMSPerennial streamPerennial plantGeographyEnvironmental scienceEcologyBiologyComputer science

Résumé

récupéré en direct d'OpenAlex

<b>Global prevalence of non-perennial rivers and streams</b>June 2021<b><br></b>prepared by <b>Mathis L. Messager (mathis.messager@mail.mcgill.ca)</b><b>Bernhard Lehner (bernhard.lehner@mcgill.ca)</b><br>1. Overview and background 2. Repository content3. Data format and projection4. License and citations4.1 License agreement4.2 Citations and acknowledgements<br><br><b>1. Overview and background</b>This documentation describes the data produced for the research article: Messager, M. L., Lehner, B., Cockburn, C., Lamouroux, N., Pella, H., Snelder, T., Tockner, K., Trautmann, T., Watt, C. &amp; Datry, T. (2021). Global prevalence of non-perennial rivers and streams. Nature. https://doi.org/10.1038/s41586-021-03565-5<br>In this study, we developed a statistical Random Forest model to produce the first reach-scale estimate of the global distribution of non-perennial rivers and streams. For this purpose, we linked quality-checked observed streamflow data from 5,615 gauging stations (on 4,428 perennial and 1,187 non-perennial reaches) with 113 candidate environmental predictors available globally. Predictors included variables describing climate, physiography, land cover, soil, geology, and groundwater as well as estimates of long-term naturalised (i.e., without anthropogenic water use in the form of abstractions or impoundments) mean monthly and mean annual flow (MAF), derived from a global hydrological model (WaterGAP 2.2; Müller Schmied et al. 2014). Following model training and validation, we predicted the probability of flow intermittence for all river reaches in the RiverATLAS database (Linke et al. 2019), a digital representation of the global river network at high spatial resolution.<br>The data repository includes two datasets resulting from this study:1. a geometric network of the global river system where each river segment is associated with:i. 113 hydro-environmental predictors used in model development and predictions, andii. the probability and class of flow intermittence predicted by the model.2. point locations of the 5,516 gauging stations used in model training/testing, where each station is associated with a line segment representing a reach in the river network, and a set of metadata.<br>These datasets have been generated with source code located at messamat.github.io/globalirmap/.<br>Note that, although several attributes initially included in RiverATLAS version 1.0 have been updated for this study, the dataset provided here is not an established new version of RiverATLAS. <br><br><br><b>2. Repository content</b>The data repository has the following structure (for usage, see section 3. Data Format and Projection; GIRES stands for Global Intermittent Rivers and Ephemeral Streams):<br>— <i><b>GIRES_v10_gdb.zip/ </b>: file geodatabase in ESRI® geodatabase format containing two feature classes (zipped)</i> |——— <b>GIRES_v10_rivers</b> : river network lines |——— <b>GIRES_v10_stations</b> : points with streamflow summary statistics and metadata<br>—<b> </b><i><b>GIRES_v10_shp.zip/ </b>: directory containing ten shapefiles (zipped)</i> Same content as GIRES_v10_gdb.zip for users that cannot read ESRI geodatabases (tiled by region due to size limitations). |——— <b>GIRES_v10_rivers_af.shp</b> : Africa |——— <b>GIRES_v10_rivers_ar.shp</b> : North American Arctic |——— <b>GIRES_v10_rivers_as.shp</b> : Asia |——— <b>GIRES_v10_rivers_au.shp</b> : Australasia|——— <b>GIRES_v10_rivers_eu.shp</b> : Europe|——— <b>GIRES_v10_rivers_gr.shp</b> : Greenland|——— <b>GIRES_v10_rivers_na.shp </b>: North America|——— <b>GIRES_v10_rivers_sa.shp</b> : South America<br>|——— <b>GIRES_v10_rivers_si.shp</b> : Siberia<br>|——— <b>GIRES_v10_stations.shp</b> : points with streamflow summary statistics and metadata<br>— <b><i>Other_technical_documentations.zip/</i></b> :<i> directory containing three documentation files (zipped)</i>|——— <b>HydroATLAS_TechDoc_v10.pdf</b> : documentation for river network framework|——— <b>RiverATLAS_Catalog_v10.pdf</b> : documentation for river network hydro-environmental attributes|——— <b>Readme_GSIM_part1.txt</b> : documentation for gauging stations from the Global Streamflow Indices and Metadata (GSIM) archive<br>—<b> README_Technical_documentation_GIRES_v10.pdf </b>: full documentation for this repository<b><br></b><b><br></b><b>3. Data format and projection</b>The geometric network (lines) and gauging stations (points) datasets are distributed both in ESRI® file geodatabase and shapefile formats. The file geodatabase contains all data and is the prime, recommended format. Shapefiles are provided as a copy for users that cannot read the geodatabase. Each shapefile consists of five main files (.dbf, .sbn, .sbx, .shp, .shx), and projection information is provided in an ASCII text file (.prj). The attribute table can be accessed as a stand-alone file in dBASE format (.dbf) which is included in the Shapefile format. <br>These datasets are available electronically in compressed zip file format. To use the data files, the zip files must first be decompressed.<br>All data layers are provided in geographic (latitude/longitude) projection, referenced to datum WGS84. In ESRI® software this projection is defined by the geographic coordinate system GCS_WGS_1984 and datum D_WGS_1984 (EPSG: 4326).<br><b><br></b><b>4. License and citations</b><i>4.1 License agreement </i>This documentation and datasets are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC-BY-4.0 License). For all regulations regarding license grants, copyright, redistribution restrictions, required attributions, disclaimer of warranty, indemnification, liability, waiver of damages, and a precise definition of licensed materials, please refer to the License Agreement (https://creativecommons.org/licenses/by/4.0/legalcode). For a human-readable summary of the license, please see https://creativecommons.org/licenses/by/4.0/.<br><br><i>4.2 Citations and acknowledgements.</i>Citations and acknowledgements of this dataset should be made as follows:Messager, M. L., Lehner, B., Cockburn, C., Lamouroux, N., Pella, H., Snelder, T., Tockner, K., Trautmann, T., Watt, C. &amp; Datry, T. (2021). Global prevalence of non-perennial rivers and streams. Nature. https://doi.org/10.1038/s41586-021-03565-5 <br>We kindly ask users to cite this study in any published material produced using it. If possible, online links to this repository (https://doi.org/10.6084/m9.figshare.14633022) should also be provided.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Jeu de données · Signal consensuel: Jeu de données
Score de désaccord entre enseignants0,440
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,4410,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,014
Tête enseignante GPT0,250
Écart entre enseignants0,236 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreJeu de données

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2021
Routes d'admission1
Résumé présentoui

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