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Enregistrement W6911120652 · doi:10.5063/aa/duc_merp.26.12

Marsh Ecology Research Program (MERP): Water table levels (1982-1989)

2011· dataset· en· W6911120652 sur OpenAlexaffabout

Notice bibliographique

RevueUC Santa Barbara · 2011
Typedataset
Langueen
Domaine
Thématique
Établissements canadiensDucks Unlimited Canada
Organismes subventionnairesnon disponible
Mots-clésMarshWetlandHydrology (agriculture)Ecosystem ecologyEcosystemWater tableWater yearTable (database)

Résumé

récupéré en direct d'OpenAlex

The Marsh Ecology Research Program (MERP) was a long-term interdisciplinary study on the ecology of prairie wetlands. A scientific team from a variety of disciplines (hydrology, plant ecology, invertebrate ecology, vertebrate ecology, nutrient dynamics, marsh management) was assembled to design and oversee a long-term experiment on the effects of water-level manipulation on northern prairie wetlands. Ten years of fieldwork (1980 -1989), combining a routine long-term monitoring program and a series of short-term studies, generated a wealth of new and diverse information on the ecology and function of prairie wetlands (Murkin, Batt, Caldwell, Kadlec and van der Valk, 2000). This data set includes water table levels, collected as part of the hydrology section of MERP. Studies of marsh ecosystems are frequently hampered by inadequate information about water budgets (Carter et al., 1979). Marshes are often open systems, particularly with respect to movement of water and associated particulate and dissolved materials (Kadlec, 1983). Efforts to evaluate the plant-nutrient relationships in such systems are contingent on knowledge of the hydrology of the system (Kadlec, 1979). Accurate estimates of water budgets are also essential pre-requisites for studies of wetland ecosystem nutrient cycles. Hence, the objective of the measurements described below, together with weather and water level/volume data, was to provide water budgets for nutrient budget calculations for the Marsh Ecology Research Program (MERP; Kadlec, 1989). The approach to hydrology used was the concept of mass balance: Inputs - Outputs = change in volume For this approach, all 3 terms had to be estimated. Change in volume was calculated from daily records of water level and water level - volume tables. Inputs of water to diked cells can be in 3 forms: precipitation, water pumped to maintain design levels, and seepage through the dike or sand ridge forming the north end of the cells. Precipitation inputs were calculated from weather records and pumping was metered, providing direct measurement. Seepage was estimated by difference, with checks based on groundwater topography and hydraulic conductivity, as well as seepage meter spot checks. Outputs of water were evapotranspiration, pumping, and seepage. Seepage out was also derived primarily by difference. Because the hydrologic characteristics of different cells in different years were replicated, estimates of the standard error in the seasonal water budgets were possible and proved to be 10% or less (Kadlec, 1989). In order to refine estimates of the inputs and outputs for the water budget of the cells, additional physical measurements were made. These factors were monitored in order to more accurately define the water storage of the cells, groundwater levels, and losses through seepage. The additional monitoring programs included: supplementary groundwater sampling adjacent to cells for nutrients, and monitoring water storage in the cells (Kadlec, 1989). The groundwater monitoring program was organized on a seasonal basis, due to the more gradual shift in groundwater makeup and supply. Sampling was done monthly for water table level and nutrients in conjunction with the regular groundwater sampling within the cells, and twice during the season for hydraulic conductivity. Note: additional hydrological data sets collected as park of MERP (i.e. water levels, pump meter volumes, precipitation, evaporation) are contained in separate data packages on the KNB. For further information on the Marsh Ecology Research Program (MERP), please visit: http://www.ducks.ca/conserve/research/projects/merp/index.html References: Carter, V., M.S. Bedinger, R.P. Novitski and W.O. Wilen. 1979. Water resources and wetlands. In: Wetland Functions and Values: The State of Our Understanding. (Eds.) P.E. Greeson, J.R. Clark and J.E. Clark, pp. 344-376. American Water Resources Association: Minneapolis, Minnesota. Kadlec, J.A. 1979. Nitrogen and phosphorus dynamics in inland freshwater wetlands. In: Waterfowl and Wetlands: An Integrated Review. (Ed.) T.A. Bookhout, pp. 17-41. North Central Section, The Wildlife Society. Kadlec, J.A. 1983. Water budgets for small diked marshes. Water Resources Bulletin 19: 223-229. Kadlec, J.A. 1989. Hydrology. In: Marsh Ecology Research Program: Long-term Monitoring Procedures Manual. (Eds.) E.J. Murkin and H.R. Murkin, pp. 8-11. Manitoba, Canada: Delta Waterfowl and Wetlands Research Station. Murkin, H.R., B.D.J. Batt, P.J. Caldwell, J.A. Kadlec and A.G. van der Valk. 2000a. Introduction to the Marsh Ecology Research Program. In Prairie Wetland Ecology: The Contribution of the Marsh Ecology Research Program. (Eds) H.R. Murkin, A.G. van der Valk and W.R. Clark. pp. 3-15. Ames: Iowa State University Press. Resulting Publications on MERP Hydrology data: Kadlec, J.A. 1983. Water budgets for small diked marshes. Water Resources Bulletin 19: 223-229. Kadlec, J.A. 1993. Effects of depth of flooding on summer water budgets for small diked marshes. Wetlands 13: 1-9.

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)0,006
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Intégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesMéta-épidémiologie (sens strict), Intégrité de la recherche, Charge 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 enseignants0,263
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0060,001
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0020,000
Bibliométrie0,0020,002
Études des sciences et des technologies0,0010,002
Communication savante0,0010,001
Science ouverte0,0050,003
Intégrité de la recherche0,0030,005
Charge utile insuffisante (le modèle a refusé de juger)0,0510,314

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 Opus0,147
Tête enseignante GPT0,398
Écart entre enseignants0,252 · 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

Citations0
Publié2011
Routes d'admission2
Résumé présentoui

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