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Enregistrement W4394041534 · doi:10.5281/zenodo.5643392

SWOT River Database (SWORD)

2021· dataset· en· W4394041534 sur OpenAlexaboutno aff
Elizabeth H. Altenau, Tamlin M. Pavelsky, Michael Durand, Xiao Yang, Renato Prata de Moraes Frasson, Liam Bendezu

Notice bibliographique

RevueZenodo (CERN European Organization for Nuclear Research) · 2021
Typedataset
Langueen
DomaineEnvironmental Science
ThématiqueFish Ecology and Management Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSWORDDatabaseSWOT analysisComputer scienceGeographyWorld Wide WebBusiness

Résumé

récupéré en direct d'OpenAlex

If you use the SWORD Database in your work, please cite: Altenau et al., (2021) The Surface Water and Ocean Topography (SWOT) Mission River Database (SWORD): A Global River Network for Satellite Data Products. Water Resources Research. https://doi.org/10.1029/2021WR030054 1. Summary: The upcoming Surface Water and Ocean Topography (SWOT) satellite mission, planned to launch in 2022, will vastly expand observations of river water surface elevation (WSE), width, and slope. In order to facilitate a wide range of new analyses with flexibility, the SWOT mission will provide a range of relevant data products. One product the SWOT mission will provide are river vector products stored in shapefile format for each SWOT overpass (JPL Internal Document, 2020b). The SWOT vector data products will be most broadly useful if they allow multitemporal analysis of river nodes and reaches covering the same river areas. Doing so requires defining SWOT reaches and nodes a priori, so that SWOT data can be assigned to them. The SWOt River Database (SWORD) combines multiple global river- and satellite-related datasets to define the nodes and reaches that will constitute SWOT river vector data products. SWORD provides high-resolution river nodes (200 m) and reaches (~10 km) in shapefile and netCDF formats with attached hydrologic variables (WSE, width, slope, etc.) as well as a consistent topological system for global rivers 30 m wide and greater. 2. Data Formats: The SWORD database is provided in netCDF and shapefile formats. All files start with a two-digit continent identifier (“af” – Africa, “as” – Asia / Siberia, “eu” – Europe / Middle East, “na” – North America, “oc” – Oceania, “sa” – South America). File syntax denotes the regional information for each file and varies slightly between netCDF and shapefile formats. NetCDF files are structured in 3 groups: centerlines, nodes, and reaches. The centerline group contains location information and associated reach and node ids along the original GRWL 30 m centerlines (Allen and Pavelsky, 2018). Node and reach groups contain hydrologic attributes at the ~200 m node and ~10 km reach locations (see description of attributes below). NetCDFs are distributed at continental scales with a filename convention as follows: [continent]_sword_v2.nc (i.e. na_sword_v2.nc). SWORD shapefiles consist of four main files (.dbf, .prj, .shp, .shx). There are separate shapefiles for nodes and reaches, where nodes are represented as ~200 m spaced points and reaches are represented as polylines. All shapefiles are in geographic (latitude/longitude) projection, referenced to datum WGS84. Shapefiles are split into HydroBASINS (Lehner and Grill, 2013) Pfafstetter level 2 basins (hbXX) for each continent with a naming convention as follows: [continent]_sword_[nodes/reaches]_hb[XX]_v2.shp (i.e. na_sword_nodes_hb74_v2.shp; na_sword_reaches_hb74_v2.shp). 3. Attribute Description: This list contains the primary attributes contained in the SWORD netCDFs and shapefiles. x: Longitude of the node or reach ranging from 180°E to 180°W (units: decimal degrees). y: Latitude of the node or reach ranging from 90°S to 90°N (units: decimal degrees). node_id: ID of each node. The format of the id is as follows: CBBBBBRRRRNNNT where C = Continent (the first number of the Pfafstetter basin code), B = Remaining Pfafstetter basin code up to level 6, R = Reach number (assigned sequentially within a level 6 basin starting at the downstream end working upstream), N = Node number (assigned sequentially within a reach starting at the downstream end working upstream), T = Type (1 – river, 3 – lake on river, 4 – dam or waterfall, 5 – unreliable topology, 6 – ghost node). node_length (node files only): Node length measured along the GRWL centerline points (units: meters). reach_id: ID of each reach. The format of the id is as follows: CBBBBBRRRRT where C = Continent (the first number of the Pfafstetter basin code), B = Remaining Pfafstetter basin codes up to level 6, R = Reach number (assigned sequentially within a level 6 basin starting at the downstream end working upstream, T = Type (1 – river, 3 – lake on river, 4 – dam or waterfall, 5 – unreliable topology, 6 – ghost reach). reach_length (reach files only): Reach length measured along the GRWL centerline points (units: meters). wse: Average water surface elevation (WSE) value for a node or reach. WSEs are extracted from the MERIT Hydro dataset (Yamazaki et al., 2019) and referenced to the EGM96 geoid (units: meters). wse_var: WSE variance along the GRWL centerline points used to calculate the average WSE for each node or reach (units: square meters). width: Average width for a node or reach (units: meters). width_var: Width variance along the GRWL centerline points used to calculate the average width for each node or reach (units: square meters). max_width: Maximum width value across the channel for each node or reach that includes island and bar areas (units: meters). facc: Maximum flow accumulation value for a node or reach. Flow accumulation values are extracted from the MERIT Hydro dataset (Yamazaki et al., 2019) (units: square kilometers). n_chan_max: Maximum number of channels for each node or reach. n_chan_mod: Mode of the number of channels for each node or reach. obstr_type: Type of obstruction for each node or reach based on the Globale Obstruction Database (GROD, Whittemore et al., 2020) and HydroFALLS data (http://wp.geog.mcgill.ca/hydrolab/hydrofalls). Obstr_type values: 0 - No Dam, 1 - Dam, 2 - Channel Dam, 3 - Lock, 4 - Low Permeable Dam, 5 - Waterfall. grod_id: The unique GROD ID for each node or reach with obstr_type values 1-4. hfalls_id: The unique HydroFALLS ID for each node or reach with obstr_type value 5. dist_out: Distance from the river outlet for each node or reach (units: meters). type: Type identifier for a node or reach: 1 – river, 2 – lake off river, 3 – lake on river, 4 – dam or waterfall, 5 – unreliable topology, 6 – ghost reach/node. lakeflag: GRWL water body identifier for each reach: 0 – river, 1 – lake/reservoir, 2 – canal, 3 – tidally influenced river. manual_add (node files only): Binary flag indicating whether the node was manually added to the public GRWL centerlines (Allen and Pavelsky, 2018). These nodes were originally given a width = 1, but have since been updated to have the reach width values. meand_len (node files only): Length of the meander that a node belongs to, measured from beginning of the meander to its end in meters. For nodes longer than one meander, the meander length will represent the average length of all meanders belonging to the node (units: meters). sinuosity (node files only): The total reach length the node belongs to divided by the Euclidean distance between the reach end points. slope (reach files only): Reach average slope calculated along the GRWL centerline points. Slopes are calculated using a linear regression (units: meters/kilometer). n_nodes (reach files only): Number of nodes associated with each reach. n_rch_up (reach files only): Number of upstream reaches for each reach. n_rch_down (reach files only): Number of downstream reaches for each reach. rch_id_up (reach files only): Reach IDs of the upstream neighboring reaches. rch_id_dn (reach files only): Reach IDs of the downstream neighboring reaches. swot_obs (reach files only): The maximum number of SWOT passes to intersect each reach during the 21 day orbit cycle. swot_orbits (reach files only): A list of the SWOT orbit tracks that intersect each reach during the 21 day orbit cycle. 4. References: Allen, G. H., & Pavelsky, T. M. (2018). Global extent of rivers and streams. Science, 361(6402), 585-588. Altenau, E. H., Pavelsky, T. M., Durand, M. T., Yang X., Frasson, R. P. d. M., & Bendezu, L. (2021). The Surface Water and Ocean Topography (SWOT) Mission River Database (SWORD): A global river network for satellite data products”. Water Resources Research. Biancamaria, S., Lettenmaier, D. P., & Pavelsky, T. M. (2016). The SWOT mission and its capabilities for land hydrology. In Remote Sensing and Water Resources (pp. 117-147). Springer, Cham. JPL Internal Document (2020b). Surface Water and Ocean Topography Mission Level 2 KaRIn high rate river single pass vector product, JPL D-56413, Rev. A, https://podaac-tools.jpl.nasa.gov/drive/files/misc/web/misc/swot_mission_docs/pdd/D-56413_SWOT_Product_Description_L2_HR_RiverSP_20200825a.pdf Lehner, B., Grill G. (2013): Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrological Processes, 27(15): 2171–2186. Data is available at www.hydrosheds.org. Tessler, Z. D., Vörösmarty, C. J., Grossberg, M., Gladkova, I., Aizenman, H., Syvitski, J. P. M., & Foufoula-Georgiou, E. (2015). Profiling risk and sustainability in coastal deltas of the world. Science, 349(6248), 638-643. Whittemore, A., Ross, M. R., Dolan, W., Langhorst, T., Yang, X., Pawar, S., Jorissen, M., Lawton, E., Januchowski-Hartley, S., & Pavelsky, T. (2020). A Participatory Science Approach to Expanding Instream Infrastructure Inventories. Earth's Future, 8(11), e2020EF001558. Yamazaki, D., Ikeshima, D., Sosa, J., Ba

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,008
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
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 enseignants0,126
Score d'incertitude au seuil0,421

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,008
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0040,006
Études des sciences et des technologies0,0010,000
Communication savante0,0040,005
Science ouverte0,0040,004
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,1260,235

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 Opus0,025
Tête enseignante GPT0,232
Écart entre enseignants0,207 · 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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
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

Citations5
Publié2021
Routes d'admission1
Résumé présentoui

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