Elucidation of the relationship between geographic and time sources of stream water using a tracer approach in a headwater catchment

Katsuyama, Masanori; Kabeya, Naoki; Ohte, Nobuhito

doi:10.1029/2008wr007458

Cited by 39 publications

(38 citation statements)

References 60 publications

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“…While transport process studies provided crucial insights in small-scale dynamics, black-box model inter-comparison studies have shed light on the physical controls of the long-term average TTDs on the catchment scale (e.g. Laudon et al, 2007;Broxton et al, 2009;Hrachowitz et al, 2009a;Tetzlaff et al, 2009a;Katsuyama et al, 2009Katsuyama et al, , 2010Lyon et al, 2010a;Speed et al, 2010;Ali et al, 2012;Asano and Uchida, 2012;Capell et al, 2012a;McGrane et al, 2012).…”

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

confidence: 99%

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%

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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“…We estimated η values between 2.15 and 3.23, the largest values we found in related studies that used the same distribution function. Reported values are normally lower than 2 (e.g., Hrachowitz et al, 2009;Katsuyama et al, 2009;McGuire and McDonnell, 2006;Viville et al, 2006;Kabeya et al, 2006), indicating that a large portion of old water is released first to the river as depicted by the isotopic composition of the stream. On the contrary, when analyzing the behavior of water flow as derived from GM, the tracer signal's peak at the outflow occurs instantaneously, meaning that a considerable portion of the event rainfall water rapidly contributes to discharge, as for instance via lateral flow from near-surface deposits.…”

Section: Comparison Of Distribution Functionsmentioning

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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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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“…These observations strongly suggest changes in dominant flow paths as a consequence of land use change, but no scientific evidence is available to confirm this. In this study, we combined MTT analysis and EMMA (Crespo et al, 2012;Katsuyama et al, 2009) to assess the effect of land use on spatial and temporal dynamics of water sources and flow paths in catchments with contrasting land use (i.e. natural forest, smallholder agriculture and commercial tea and tree plantations) in the Mau Forest Complex.…”

Section: Stable Water Isotopes ( 2 Hmentioning

confidence: 99%

“…Barthold et al, 2011;Chaves et al, 2008;Crespo et al, 2012;Katsuyama et al, 2009). Access to groundwater in the study area is complicated by the absence of wells or boreholes in NF and TTP, and the existing wells in SHA are often not properly sealed, which means that groundwater can mix with water from shallower soil layers and precipitation, obscuring the groundwater signal.…”

Section: Dominant Water Sources 20mentioning

confidence: 99%

Land use alters dominant water sources and flow paths in tropical montane catchments in East Africa

Jacobs¹,

Timbe²,

Weeser³

et al. 2018

Preprint

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Abstract. Conversion of natural forest to other land uses could lead to significant changes in catchment hydrology, but the nature of these changes has been insufficiently investigated in tropical montane catchments, especially in Africa. To address 20 this knowledge gap, we identified stream water sources and flow paths in three tropical montane sub-catchments (27-36 km²) with different land use (natural forest, smallholder agriculture and commercial tea plantations) within a 1 021 km² catchment in the Mau Forest Complex, Kenya. Weekly samples were collected from stream water, precipitation and soil water for 75 weeks and analysed for stable water isotopes (δ 2 H and δ 18 O) for mean transit time estimation, whereas trace element samples from stream water and potential end members were collected over a period of 55 weeks for end member mixing analysis. 25Stream water mean transit time was similar (~4 years) in the three sub-catchments, and ranged from 3.2-3.3 weeks in forest soils and 4.5-7.9 weeks in pasture soils at 15 cm depth to 10.4-10.8 weeks in pasture soils at 50 cm depth. The contribution of springs and wetlands to stream discharge increased from 18, 1 and 48 % during low flow to 22, 51 and 65 % during high flow in the natural forest, smallholder agriculture and tea plantation sub-catchments, respectively. The dominant stream water source in the tea plantation sub-catchment was spring water (56 %), while precipitation was dominant in the smallholder 30 agriculture (59 %) and natural forest (45 %) sub-catchments. These results confirm that catchment hydrology is strongly influenced by land use, which could have serious consequences for water-related ecosystem services, such as provision of clean water.Hydrol. Earth Syst. Sci. Discuss., https://doi

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Elucidation of the relationship between geographic and time sources of stream water using a tracer approach in a headwater catchment

Cited by 39 publications

References 60 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?

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

Land use alters dominant water sources and flow paths in tropical montane catchments in East Africa

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