2015
DOI: 10.1002/hyp.10515
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A preliminary assessment of water partitioning and ecohydrological coupling in northern headwaters using stable isotopes and conceptual runoff models

Abstract: We combined a conceptual rainfall‐runoff model and input–output relationships of stable isotopes to understand ecohydrological influences on hydrological partitioning in snow‐influenced northern catchments. Six sites in Sweden (Krycklan), Canada (Wolf Creek; Baker Creek; Dorset), Scotland (Girnock) and the USA (Dry Creek) span moisture and energy gradients found at high latitudes. A meta‐analysis was carried out using the Hydrologiska Byråns Vattenbalansavdelning (HBV) model to estimate the main storage change… Show more

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Cited by 67 publications
(55 citation statements)
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“…The uncertainty of the snowmelt end‐member was highest in 2011, potentially due to a long‐duration snowmelt season compounding the inherent spatio‐temporal variability of infiltrating snowmelt. For example, snowmelt at the beginning of the season may be more enriched than later in the snowmelt season, due to evaporation of light water isotopes in the snow accumulation period and a longer transit time (Tetzlaff et al, ). Notwithstanding, Euclidean distances and tracer reconstructions in the context of our uncertainty and young water fraction analyses suggest that the tracer‐based results were not obviously subject to deleterious effects from hydrodynamic mixing and/or molecular diffusion at this spatial scale (e.g., Jones, Sudicky, Brookfield, & Park, ; Park, Sudicky, Brookfield, & Jones, ).…”
Section: Discussionmentioning
confidence: 99%
“…The uncertainty of the snowmelt end‐member was highest in 2011, potentially due to a long‐duration snowmelt season compounding the inherent spatio‐temporal variability of infiltrating snowmelt. For example, snowmelt at the beginning of the season may be more enriched than later in the snowmelt season, due to evaporation of light water isotopes in the snow accumulation period and a longer transit time (Tetzlaff et al, ). Notwithstanding, Euclidean distances and tracer reconstructions in the context of our uncertainty and young water fraction analyses suggest that the tracer‐based results were not obviously subject to deleterious effects from hydrodynamic mixing and/or molecular diffusion at this spatial scale (e.g., Jones, Sudicky, Brookfield, & Park, ; Park, Sudicky, Brookfield, & Jones, ).…”
Section: Discussionmentioning
confidence: 99%
“…Average annual precipitation is 1020 mm yr -1 at Dorset and 622 mm yr -1 at Krycklan. A detailed comparison of the hydro-meteorological conditions at the three catchments was presented by 15 Tetzlaff et al (2015). Soils of the four sites were characterized as freely draining podzols of generally coarse texture ranging between loamy or silty sands to sand with an overlying organic layer of about 10-20 cm thickness.…”
Section: Study Sitesmentioning
confidence: 99%
“…With the model presented here, we improve the conceptualisation of these internal catchment processes by using a more spatially distributed approach. We have also changed the interception of precipitation in the model compared with previous model work to more accurately characterise the influence of vegetation on water partitioning (Tetzlaff et al, ). At the same time, the aim was to keep the model relatively simple in order to derive a generic tool that can be applied across northern sites (Tetzlaff et al, ).…”
Section: Introductionmentioning
confidence: 99%
“…We have also changed the interception of precipitation in the model compared with previous model work to more accurately characterise the influence of vegetation on water partitioning (Tetzlaff et al, ). At the same time, the aim was to keep the model relatively simple in order to derive a generic tool that can be applied across northern sites (Tetzlaff et al, ). The main research aims of this study are therefore to: (1) develop a spatially explicit representation of water and tracer fluxes at the catchment scale using a conceptual spatially distributed, tracer‐aided runoff model, (2) understand how landscape structure affects the routing and mixing of water, (3) characterise how these interactions affect the dynamics of water ages in different components of the system and how this affects the non‐stationarity in stream water ages.…”
Section: Introductionmentioning
confidence: 99%