Estimating the deep seepage component of the hillslope and catchment water balance within a measurement uncertainty framework

Graham, C. B.; Verseveld, Willem van; Barnard, H. R.; McDonnell, Jeffrey J.

doi:10.1002/hyp.7788

Cited by 76 publications

(78 citation statements)

References 62 publications

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“…The time period used is important if signatures are used for catchment classification: an unusual event such as a large flood may shift the signature values (Casper et al, 2012). Additional uncertainty sources can be important in other catchments, such as catchment boundary uncertainty and flow bypassing the gauge (Graham et al, 2010a).…”

Section: Methods Limitations and Future Developmentsmentioning

confidence: 99%

Uncertainty in hydrological signatures

Westerberg

McMillan

2015

Hydrol. Earth Syst. Sci.

153

141

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Abstract. Information about rainfall-runoff processes is essential for hydrological analyses, modelling and watermanagement applications. A hydrological, or diagnostic, signature quantifies such information from observed data as an index value. Signatures are widely used, e.g. for catchment classification, model calibration and change detection. Uncertainties in the observed data -including measurement inaccuracy and representativeness as well as errors relating to data management -propagate to the signature values and reduce their information content. Subjective choices in the calculation method are a further source of uncertainty.We review the uncertainties relevant to different signatures based on rainfall and flow data. We propose a generally applicable method to calculate these uncertainties based on Monte Carlo sampling and demonstrate it in two catchments for common signatures including rainfall-runoff thresholds, recession analysis and basic descriptive signatures of flow distribution and dynamics. Our intention is to contribute to awareness and knowledge of signature uncertainty, including typical sources, magnitude and methods for its assessment.We found that the uncertainties were often large (i.e. typical intervals of ± 10-40 % relative uncertainty) and highly variable between signatures. There was greater uncertainty in signatures that use high-frequency responses, small data subsets, or subsets prone to measurement errors. There was lower uncertainty in signatures that use spatial or temporal averages. Some signatures were sensitive to particular uncertainty types such as rating-curve form. We found that signatures can be designed to be robust to some uncertainty sources. Signature uncertainties of the magnitudes we found have the potential to change the conclusions of hydrological and ecohydrological analyses, such as cross-catchment comparisons or inferences about dominant processes.

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Section: Methods Limitations and Future Developmentsmentioning

confidence: 99%

Uncertainty in hydrological signatures

Westerberg

McMillan

2015

Hydrol. Earth Syst. Sci.

153

141

View full text Add to dashboard Cite

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“…As a consequence of the lower bypass estimate by Graham et al (2010), they estimated higher deep seepage fractions. However, our simulated bedrock seepage is consistent with the estimated deep seepage at the catchment scale by Graham et al (2010) that was on average approximately 21 % of precipitation at steady state. Our modeling approach of bedrock seepage (with no later exfiltration of bedrock water) is largely in agreement with the study of Gabrielli et al (2012) at the same hillslope.…”

Section: Process Understanding Through the Hillvi Modelmentioning

confidence: 99%

“…Graham et al (2010) showed that deep seepage at the WS10 hillslope scale was 48 ± 35 % of applied water during the same irrigation period (calculated after 10 days of drainage) based on water balance calculations. Our modeling results (Model 2) showed that bedrock seepage accounted for 24 and 26 % based on total applied water and δ 2 H mass of the labeled pulse, respectively.…”

Section: Process Understanding Through the Hillvi Modelmentioning

confidence: 99%

“…Our modeling results (Model 2) showed that bedrock seepage accounted for 24 and 26 % based on total applied water and δ 2 H mass of the labeled pulse, respectively. Graham et al (2010) hypothesized that water that bypassed the trench system was at most 10 % of the measured lateral subsurface flow, while Model 2 simulated that 70 % of lateral subsurface flow was captured by the trench and 30 % bypassed the trench. As a consequence of the lower bypass estimate by Graham et al (2010), they estimated higher deep seepage fractions.…”

Section: Process Understanding Through the Hillvi Modelmentioning

confidence: 99%

“…Graham et al (2010) hypothesized that water that bypassed the trench system was at most 10 % of the measured lateral subsurface flow, while Model 2 simulated that 70 % of lateral subsurface flow was captured by the trench and 30 % bypassed the trench. As a consequence of the lower bypass estimate by Graham et al (2010), they estimated higher deep seepage fractions. However, our simulated bedrock seepage is consistent with the estimated deep seepage at the catchment scale by Graham et al (2010) that was on average approximately 21 % of precipitation at steady state.…”

Section: Process Understanding Through the Hillvi Modelmentioning

confidence: 99%

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A sprinkling experiment to quantify celerity–velocity differences at the hillslope scale

Verseveld

Barnard

Graham³

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Few studies have quantified the differences between celerity and velocity of hillslope water flow and explained the processes that control these differences. Here, we asses these differences by combining a 24-day hillslope sprinkling experiment with a spatially explicit hydrologic model analysis. We focused our work on Watershed 10 at the H. J. Andrews Experimental Forest in western Oregon. Celerities estimated from wetting front arrival times were generally much faster than average vertical velocities of δ 2 H. In the model analysis, this was consistent with an identifiable effective porosity (fraction of total porosity available for mass transfer) parameter, indicating that subsurface mixing was controlled by an immobile soil fraction, resulting in the attenuation of the δ 2 H input signal in lateral subsurface flow. In addition to the immobile soil fraction, exfiltrating deep groundwater that mixed with lateral subsurface flow captured at the experimental hillslope trench caused further reduction in the δ 2 H input signal. Finally, our results suggest that soil depth variability played a significant role in the celerityvelocity responses. Deeper upslope soils damped the δ 2 H input signal, while a shallow soil near the trench controlled the δ 2 H peak in lateral subsurface flow response. Simulated exit time and residence time distributions with our hillslope hydrologic model showed that water captured at the trench did not represent the entire modeled hillslope domain; the exit time distribution for lateral subsurface flow captured at the trench showed more early time weighting.

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Stormflow generation: A meta‐analysis of field evidence from small, forested catchments

Barthold

Woods

2015

Water Resources Research

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Combinations of runoff characteristics are commonly used to represent distinct conceptual models of stormflow generation. In this study, three runoff characteristics: hydrograph response, time source of runoff water, and flow path are used to classify catchments. Published data from the scientific literature are used to provide evidence from small, forested catchments. Each catchment was assigned to one of the eight conceptual models, depending on the combination of quick/slow response, old/new water, and overland/ subsurface flow. A standard procedure was developed to objectively diagnose the predominant conceptual model of stormflow generation for each catchment and assess its temporal and spatial support. The literature survey yielded 42 catchments, of which 30 catchments provide a complete set of qualitative runoff characteristics resulting in one of the eight conceptual models. The majority of these catchments classify as subsurface flow path dominated. No catchments were found for conceptual models representing combinations of quick response-new water-subsurface flow (SSF), slow-new-SSF, slow-old-overland flow (OF) nor new-slow-OF. Of the 30 qualitatively classified catchments, 24 provide a complete set of quantitative measures. In summary, the field support is strong for 19 subsurface-dominated catchments and is weak for 5 surface flow path dominated catchments (six catchments had insufficient quantitative data). Two alternative explanations exist for the imbalance of field support between the two flow path classes: (1) the selection of research catchments in past field studies was mainly to explain quick hydrograph response in subsurface dominated catchments; (2) catchments with prevailing subsurface flow paths are more common in nature. We conclude that the selection of research catchments needs to cover a wider variety of environmental conditions which should lead to a broader, and more widely applicable, spectrum of resulting conceptual models and process mechanisms. This is a prerequisite in studies where catchment organization and similarity approaches are used to develop catchment classification systems in order to regionalize stormflow.

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Estimating the deep seepage component of the hillslope and catchment water balance within a measurement uncertainty framework

Cited by 76 publications

References 62 publications

Uncertainty in hydrological signatures

Uncertainty in hydrological signatures

A sprinkling experiment to quantify celerity–velocity differences at the hillslope scale

Stormflow generation: A meta‐analysis of field evidence from small, forested catchments

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