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Enregistrement W2886151778 · doi:10.1002/hyp.13184

Thermal detection of discrete riparian inflow points (DRIPs) during contrasting hydrological events

2018· article· en· W2886151778 sur OpenAlexaff
Stefan Ploum, Jason A. Leach, Lenka Kuglerová, Hjalmar Laudon

Notice bibliographique

RevueHydrological Processes · 2018
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueHydrology and Sediment Transport Processes
Établissements canadiensSimon Fraser University
Organismes subventionnairesSvenska Forskningsrådet Formas
Mots-clésBiogeochemical cycleRiparian zoneHydrology (agriculture)STREAMSEnvironmental scienceGroundwaterWater tableSnowmeltInflowSurface waterGeologyGeomorphologyEcologyHabitatEnvironmental chemistryOceanographySnowChemistry

Résumé

récupéré en direct d'OpenAlex

Hillslope water is routed through preferential locations in the riparian zone, where its chemical composition can be altered before entering a stream (Ledesma et al., 2018; Lidman, Boily, Laudon, & Köhler, 2017). These locations are often referred to as zero-order streams, preferential inflows, groundwater seepages, or discharge zones (Kuglerová, Ågren, Jansson, & Laudon, 2014; Meinzer, 1923; Tsuboyama, Sidle, Noguchi, Murakami, & Shimizu, 2000). Albeit being frequently used, these terms fail to emphasize the dynamic hydrological and biogeochemical contributions of these locations. Here we group this variety of stream-riparian confluences as discrete riparian inflow points (DRIPs). DRIPs can be associated with groundwater, rain and snowmelt water entering the stream through subsurface flow paths, as well as periods of surface flow when the riparian water table reaches the soil surface. DRIPs can influence stream temperature, nutrient availability, and redox conditions in headwater systems (Crawford et al., 2017; Lowry, Walker, Hunt, & Anderson, 2007) and act as biogeochemical hotspots or control points that have a disproportionally large impact on stream water quality (Bernhardt et al., 2017; McClain et al., 2003). DRIPs are important for shaping stream ecosystems and thus progress has been made on detecting their locations along stream networks using topographic and thermal approaches (Kuglerová et al., 2014; Leach, Lidberg, Kuglerová, Ågren, & Laudon, 2017; Rosenberry, Briggs, Delin, & Hare, 2016). Nevertheless, the seasonal dynamics of DRIPs have not been fully comprehended. Hydrological tracers such as water temperature and chemistry can be used to identify the locations of DRIPs along a stream reach (Abbott et al., 2016; Leach et al., 2017; Matheswaran, Blemmer, Rosbjerg, & Boegh, 2012). The goal of this study was to use animations of detailed stream temperature measurements and descriptive field observations to demonstrate the hydrological influence of DRIPs on a boreal stream during base flow, rain, snowmelt, and rain-on-snow events. We studied a first order stream situated in the Krycklan Catchment Study in northern Sweden (64°14′N, 19°46′E), where the yearly average temperature is 1.8°C, and annual precipitation is 614 mm (Laudon et al., 2013). The stream is bound by two hydrometric stations, referred to as C5 (lake outlet) and C6 (1500 meters downstream of C5), which have catchment areas of 65 and 110 ha, respectively. The differences in discharge between the lake outlet and downstream hydrometric stations represented an estimate of the net gain in hillslope water along the reach (Payn, Gooseff, McGlynn, Bencala, & Wondzell, 2009). A small, shallow lake (4 ha surface area) upstream of the 1500 meter study reach provided a thermal contrast between stream water sourced from the lake and hillslope water sources, due to warming of the lake surface during open water conditions. Previous research detected five major DRIPs along this stream reach which collectively drain about 60% of the catchment surface area of the C5-C6 study reach. We deployed a distributed fibre optic temperature sensing (DTS) system (Selker, van de Giesen, Westhoff, Luxemburg, & Parlange, 2006) along the study reach to thermally detect DRIPs during a summer baseflow period, as well as rain, snowmelt and rain-on-snow events. The DTS system was a Silixa XT-DTS with 25-cm and 6-minute sampling resolution. Full details on the instrument setup and installation can be found in Leach et al. (2017). The video (S1) documents changes in stream thermal profiles during contrasting hydrological events, supported by photographs and videos that were captured along the reach. The discharge records show that in the early stage of the events, stream water was primarily sourced from the hillslopes. After discharge peaked, the stream became dominated by lake water. This shift in water source was also reflected by the stream temperature observations. For example, during the rain event the stream water temperature dropped rapidly at the locations where DRIPs delivered hillslope water to the stream. During the snowmelt event the temperature differences between lake and hillslope water were too small to reliably detect the thermal influence of DRIPs. In contrast, during the rain-on-snow event the DRIPs were again resulting in step-change decreases in the stream water thermal profile. This was the result of the ice-free lake providing warmer water to the stream while the ice- and snow-covered DRIPs delivered colder hillslope water. In this study, we demonstrated a range of field conditions where thermal tracing using DTS provided insights on the delivery of water by DRIPs to a boreal first order stream. Descriptive field observations offered additional information that helped with interpretation of the temperature observations. A combination of thermal tracing with other approaches such as chemical tracing, groundwater monitoring, and hydrometric measurements, could help further characterize the dynamics of DRIPs and their role in stream ecosystems. Video S1 Supporting information Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

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)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,073
Score d'incertitude au seuil0,998

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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 Opus0,011
Tête enseignante GPT0,224
Écart entre enseignants0,213 · 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 tête enseignante, pas un consensus.

Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

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

En bref

Citations44
Publié2018
Routes d'admission1
Résumé présentoui

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