Generality of fractal 1/f scaling in catchment tracer time series, and its implications for catchment travel time distributions

Godsey, Sarah E.; Aas, Wenche; Clair, Thomas A.; Wit, Heleen de; Fernandez, Ivan J.; Kahl, Jeffrey S.; Malcolm, I. A.; Neal, Colin; Neal, Margaret; Nelson, Sarah J.; Norton, Stephen A.; Palucis, M. C.; Skjelkvåle, B.L.; Soulsby, Chris; Tetzlaff, Doerthe; Kirchner, James W.

doi:10.1002/hyp.7677

Cited by 148 publications

(207 citation statements)

References 32 publications

(25 reference statements)

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“…As stated by Soulsby et al (2010) gamma distributions with α < 1 are most suitable to represent non-linear processes. Similarly several other studies found α values significantly smaller than 1 (McGuire et al, 2005;Hrachowitz et al, 2009Hrachowitz et al, , 2010Godsey et al, 2010;Kirchner et al, 2010;Speed et al, 2010;Birkel et al, 2012;Heidbüchel et al, 2012;Muñoz-Villers and McDonnell, 2012). On the other hand, our results reported that when coarse temporal resolutions were used (monthly or bi-monthly), the value of α approached 1, which could lead to erroneous deductions.…”

Section: Comparison Of Distribution Functionssupporting

confidence: 65%

“…Previous research accounted the groundwater contribution in 85 % of the total runoff, characterized by MTTs on the order of 2 to 4 years (Timbe et al, 2014;Crespo et al, 2012). A detailed description of the physical, hydro- logical and land cover characteristics of the catchment and main tributaries are given in Timbe et al (2014), whereas additional information on the climate and ecosystem gradients of the research area can be found in Bendix et al (2008), Fiedler and Beck (2008) and Wilcke et al (2008).…”

Section: Study Areamentioning

confidence: 99%

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Sampling frequency trade-offs in the assessment of mean transit times of tropical montane catchment waters under semi-steady-state conditions

Timbe

Windhorst²,

Célleri

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. Precipitation event samples and weekly based water samples from streams and soils were collected in a tropical montane cloud forest catchment for 2 years and analyzed for stable water isotopes in order to understand the effect of sampling frequency in the performance of three lumped-parameter distribution functions (exponential-piston flow, linear-piston flow and gamma) which were used to estimate mean transit times of waters. Precipitation data, used as input function for the models, were aggregated to daily, weekly, bi-weekly, monthly and bi-monthly sampling resolutions, while analyzed frequencies for outflows went from weekly to bi-monthly. By using different scenarios involving diverse sampling frequencies, this study reveals that the effect of lowering the sampling frequency depends on the water type. For soil waters, with transit times on the order of few weeks, there was a clear trend of over predictions. In contrast, the trend for stream waters, which have a more damped isotopic signal and mean transit times on the order of 2 to 4 years, was less clear and showed a dependence on the type of model used. The trade-off to coarse data resolutions could potentially lead to misleading conclusions on how water actually moves through the catchment, notwithstanding that these predictions could reach better fitting efficiencies, fewer uncertainties, errors and biases. For both water types an optimal sampling frequency seems to be 1 or at most 2 weeks. The results of our analyses provide information for the planning of future fieldwork in similar Andean or other catchments.

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Section: Comparison Of Distribution Functionssupporting

confidence: 65%

Section: Study Areamentioning

confidence: 99%

Sampling frequency trade-offs in the assessment of mean transit times of tropical montane catchment waters under semi-steady-state conditions

Timbe

Windhorst²,

Célleri

et al. 2015

Hydrol. Earth Syst. Sci.

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“…The issue has long been disregarded by hydrologists dealing with the quantification of input/output water fluxes, and has now rightfully become a cornerstone of hydrochemical studies. Typical applications include both atmospheric compounds entering the catchment through precipitation [Shaw et al, 2008;Godsey et al, 2010] or anthropogenic compounds injected onto the catchment, for example, due to farming activities [e.g., Basu et al, 2010;Rouxel et al, 2011;Kennedy et al, 2012]. Chlorides are an example of solutes which can be considered either geogenic or anthropogenic, because the mass input to the terrestrial part of the hydrologic cycle, whose proper quantification is sometimes problematic [Neal et al, 1988;Tetzlaff et al, 2007;Dunn and Bacon, 2008], originates either from atmospheric deposition and/or from the application of inorganic fertilizers.…”

Section: Introductionmentioning

confidence: 99%

Chloride circulation in a lowland catchment and the formulation of transport by travel time distributions

Benettin

Velde

Zee

et al. 2013

Water Resources Research

118

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[1] Travel times are fundamental catchment descriptors that blend key information about storage, geochemistry, flow pathways and sources of water into a coherent mathematical framework. Here we analyze travel time distributions (TTDs) (and related attributes) estimated on the basis of the extensive hydrochemical information available for the Hupsel Brook lowland catchment in the Netherlands. The relevance of the work is perceived to lie in the general importance of characterizing nonstationary TTDs to capture catchment transport properties, here chloride flux concentrations at the basin outlet. The relative roles of evapotranspiration, water storage dynamics, hydrologic pathways and mass sources/sinks are discussed. Different hydrochemical models are tested and ranked, providing compelling examples of the improved process understanding achieved through coupled calibration of flow and transport processes. The ability of the model to reproduce measured flux concentrations is shown to lie mostly in the description of nonstationarities of TTDs at multiple time scales, including short-term fluctuations induced by soil moisture dynamics in the root zone and long-term seasonal dynamics. Our results prove reliable and suggest, for instance, that drastically reducing fertilization loads for one or more years would not result in significant permanent decreases in average solute concentrations in the Hupsel runoff because of the long memory shown by the system. Through comparison of field and theoretical evidence, our results highlight, unambiguously, the basic transport mechanisms operating in the catchment at hand, with a view to general applications.

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“…[18] The gamma distribution [Kirchner et al, 2000;Godsey et al, 2010] with shape parameter (dimensionless) and scale parameter (¼ mTT/) [T]:…”

mentioning

confidence: 99%

The master transit time distribution of variable flow systems

Heidbüchel

Troch

Lyon

et al. 2012

Water Resources Research

160

241

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[1] The transit time of water is an important indicator of catchment functioning and affects many biological and geochemical processes. Water entering a catchment at one point in time is composed of water molecules that will spend different amounts of time in the catchment before exiting. The next water input pulse can exhibit a totally different distribution of transit times. The distribution of water transit times is thus best characterized by a time-variable probability density function. It is often assumed, however, that the variability of the transit time distribution is negligible and that catchments can be characterized with a unique transit time distribution. In many cases this assumption is not valid because of variations in precipitation, evapotranspiration, and catchment water storage and associated (de)activation of dominant flow paths. This paper presents a general method to estimate the time-variable transit time distribution of catchment waters. Application of the method using several years of rainfall-runoff and stable water isotope data yields an ensemble of transit time distributions with different moments. The combined probability density function represents the master transit time distribution and characterizes the intraannual and interannual variability of catchment storage and flow paths. Comparing the derived master transit time distributions of two research catchments (one humid and one semiarid) reveals differences in dominant hydrologic processes and dynamic water storage behavior, with the semiarid catchment generally reacting slower to precipitation events and containing a lower fraction of preevent water in the immediate hydrologic response.

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Generality of fractal 1/f scaling in catchment tracer time series, and its implications for catchment travel time distributions

Cited by 148 publications

References 32 publications

Sampling frequency trade-offs in the assessment of mean transit times of tropical montane catchment waters under semi-steady-state conditions

Sampling frequency trade-offs in the assessment of mean transit times of tropical montane catchment waters under semi-steady-state conditions

Chloride circulation in a lowland catchment and the formulation of transport by travel time distributions

The master transit time distribution of variable flow systems

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