MétaCan
Menu
Back to cohort
Record W2886151778 · doi:10.1002/hyp.13184

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

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

Bibliographic record

VenueHydrological Processes · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsSimon Fraser University
FundersSvenska Forskningsrådet Formas
KeywordsBiogeochemical cycleRiparian zoneHydrology (agriculture)STREAMSEnvironmental scienceGroundwaterWater tableSnowmeltInflowSurface waterGeologyGeomorphologyEcologyHabitatEnvironmental chemistryOceanographySnowChemistry

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.224
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations44
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueHydrological ProcessesSame topicHydrology and Sediment Transport ProcessesFrench-language works237,207