Investigating a high resolution, stream chloride time series from the Biscuit Brook catchment, Catskills, NY

Shaw, Stephen B.; Harpold, Adrian A.; Taylor, Jason M.; Walter, M. Todd

doi:10.1016/j.jhydrol.2007.10.009

Cited by 39 publications

(44 citation statements)

References 32 publications

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“…Weiler and McDonnell, 2004) to infer first order controls on TTDs under state-steady assumptions. Similarly, Shaw et al (2008) elegantly linked steady-state TTDs to model structure. However, the full potential of such integrated conceptual models to serve as diagnostic tools (cf.…”

Section: Hrachowitz Et Al: What Can Flux Tracking Teach Us About mentioning

confidence: 99%

“…Here these combined imbalances in catchment Cl − budgets were accounted for by rescaling the input concentrations using lumped adjustment factors as successfully applied in earlier studies (e.g. Tetzlaff et al, 2007;Dunn and Bacon, 2008;Shaw et al, 2008;McMillan et al, 2012). Although, this strategy ignores potential temporal variability in unobserved Cl − cycling processes, the effects of these processes are limited in the wet and cool Scottish climate and in the absence of more detailed information considered negligible.…”

Section: Hydrological and Geochemical Datamentioning

confidence: 99%

“…3; e.g. Shaw et al, 2008;Fenicia et al, 2010). Note, that in the absence of detailed knowledge of the actual mixing processes, the term "mixing" here refers to the combined processes of potential dispersive and diffusive mixing in the soil itself as well as in-stream mixing of water particles entering the catchment at different times and locations and being routed to the stream through different flow routes.…”

Section: Mixing Modelsmentioning

confidence: 99%

“…Lovett et al, 2005), it was in the cool Scottish climate, also in the absence of suitable data warranting the inclusion of this process, considered negligible as individual process in the models (cf. Kirchner et al, 2000;Page et al, 2007;Shaw et al, 2008) due to low transpiration rates (and thus limited sap flow) and limited organic matter turnover in the Heather moorland vegetation and the coniferous forests of the study catchments (cf. Liu et al, 2004).…”

Section: Dynamic Partial Mixingmentioning

confidence: 99%

“…The question arising is thus what is the origin of these longer-than-exponential or possibly long tails in the sense of power law tails. While some argued (Kirchner et al, 2000(Kirchner et al, , 2001Godsey et al, 2010) that the tails of TTDs are manifestations of fractal 1/f scaling which can be explained by catchment-scale advection and dispersion processes, others pointed out the possibility that the shapes of TTDs in general and their tails in particular can be a reflection of the process heterogeneity and flow path connectivity and thus casting some doubt on the generality of strict 1/f scaling (Shaw et al, 2008;Hrachowitz et al, 2010b). Similarly, Birkel et al (2012b) reasoned that the behavior of tails, in the tracer response is an expression of the process complexity of a given catchment.…”

Section: Long Tails Of P F and P Tmentioning

confidence: 99%

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What can flux tracking teach us about water age distribution patterns and their temporal dynamics?

Hrachowitz

Savenije

Bogaard

et al. 2013

Hydrol. Earth Syst. Sci.

269

443

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Abstract. The complex interactions of runoff generation processes underlying the hydrological response of streams remain not entirely understood at the catchment scale. Extensive research has demonstrated the utility of tracers for both inferring flow path distributions and constraining model parameterizations. While useful, the common use of linearity assumptions, i.e. time invariance and complete mixing, in these studies provides only partial understanding of actual process dynamics. Here we use long-term (< 20 yr) precipitation, flow and tracer (chloride) data of three contrasting upland catchments in the Scottish Highlands to inform integrated conceptual models investigating different mixing assumptions. Using the models as diagnostic tools in a functional comparison, water and tracer fluxes were then tracked with the objective of exploring the differences between different water age distributions, such as flux and resident water age distributions, and characterizing the contrasting water age pattern of the dominant hydrological processes in the three study catchments to establish an improved understanding of the wetness-dependent temporal dynamics of these distributions.The results highlight the potential importance of partial mixing processes which can be dependent on the hydrological functioning of a catchment. Further, tracking tracer fluxes showed that the various components of a model can be characterized by fundamentally different water age distributions which may be highly sensitive to catchment wetness history, available storage, mixing mechanisms, flow path connectivity and the relative importance of the different hydrological processes involved. Flux tracking also revealed that, although negligible for simulating the runoff response, the omission of processes such as interception evaporation can result in considerably biased water age distributions. Finally, the modeling indicated that water age distributions in the three study catchments do have long, power-law tails, which are generated by the interplay of flow path connectivity, the relative importance of different flow paths as well as by the mixing mechanisms involved. In general this study highlights the potential of customized integrated conceptual models, based on multiple mixing assumptions, to infer system internal transport dynamics and their sensitivity to catchment wetness states.

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Section: Hrachowitz Et Al: What Can Flux Tracking Teach Us About mentioning

confidence: 99%

Section: Hydrological and Geochemical Datamentioning

confidence: 99%

Section: Mixing Modelsmentioning

confidence: 99%

Section: Dynamic Partial Mixingmentioning

confidence: 99%

Section: Long Tails Of P F and P Tmentioning

confidence: 99%

See 3 more Smart Citations

What can flux tracking teach us about water age distribution patterns and their temporal dynamics?

Hrachowitz

Savenije

Bogaard

et al. 2013

Hydrol. Earth Syst. Sci.

269

443

View full text Add to dashboard Cite

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Assessing urbanization impacts on catchment transit times

Soulsby

Birkel

Tetzlaff

2014

Geophysical Research Letters

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Stable isotopes have potential for assessing the hydrologic impacts of urbanization, although it is unclear whether established methods of isotope modeling translate to such disturbed environments. We tested two transit time modeling approaches (using a gamma distribution and a two‐parallel linear reservoir (TPLR) model) in a rapidly urbanizing catchment. Isotopic variability in precipitation was damped in streams with attenuation inversely correlated with urban cover. The models captured this reasonably well, although the TPLR better represented the integrated dual response of urban and nonurban areas with reduced uncertainty. Percent urban cover influenced the shape of the catchment transit time distribution. Total urban cover reduced the mean transit time to <10 days compared with ~1 year and ~2–3 years with 63% and 13% urbanization, respectively, while it was at >4 years for nonurban sites.

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Time-variable transit time distributions and transport: Theory and application to storage-dependent transport of chloride in a watershed

2015

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Transport processes and pathways through many hydrodynamic systems vary over time, often driven by variations in total water storage. This paper develops a very general approach to modeling unsteady transport through an arbitrary control volume (such as a watershed) that accounts for temporal variability in the underlying transport dynamics. Controls on the selection of discharge from stored water are encapsulated in probability distributions X Q ðS T ; tÞ of age-ranked storage S T (the volume of water in storage ranked from youngest to oldest). This framework is applied to a long-term record of rainfall and streamflow chloride in a small, humid watershed at Plynlimon, UK. While a time-invariant gamma distribution for X Q produced a good fit to data, the fit was significantly improved when the distribution was allowed to vary with catchment storage. However, the variation was inverse to that of a ''well-mixed'' system where storage has a pure dilution effect. Discharge at high storage was predicted to contain a larger fraction of recent event water than at low storage. The effective volume of storage involved in transport was 3411 mm at mean catchment wetness, but declined by 71 mm per 1 mm of additional catchment storage, while the fraction of event water in discharge increased by 1.4%. This ''inverse storage effect'' is sufficient to reproduce the observed long-memory 1=f fractal spectral structure of stream chloride. Metrics quantifying the strength and direction of storage effects are proposed as useful signatures, and point toward a unified framework for observing and modeling coupled watershed flow and transport.

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Investigating a high resolution, stream chloride time series from the Biscuit Brook catchment, Catskills, NY

Cited by 39 publications

References 32 publications

What can flux tracking teach us about water age distribution patterns and their temporal dynamics?

What can flux tracking teach us about water age distribution patterns and their temporal dynamics?

Assessing urbanization impacts on catchment transit times

Time-variable transit time distributions and transport: Theory and application to storage-dependent transport of chloride in a watershed

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