A decision scheme to indicate dominant hydrological flow processes on temperate grassland

Scherrer, Simon C.; Naef, Félix

doi:10.1002/hyp.1131

Cited by 130 publications

(142 citation statements)

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“…A 28 precipitation collector (Shimada et al, 1992, Yamanaka et al, 2004) that can prevent the evaporation of 29 stored precipitation was used for collecting monthly precipitation, and the mixed value representing average 30 of precipitation isotope composition for the relative month (Ma and Yamanaka, 2013). Hydrogen and 31 oxygen stable isotope ratios ( 2 H/ 1 H and 18 O/ 16 O) of the collected water samples were measured using a 32 tunable diode laser isotope analyzer (L11020-I, Picarro, CA, USA). The measurement errors for this 33 analyzer were 0.1‰ for δ 18 O and 1‰ for δD (Yamanaka and Onda, 2011).…”

Section: / 42mentioning

confidence: 99%

“…The measurement errors for this 33 analyzer were 0.1‰ for δ 18 O and 1‰ for δD (Yamanaka and Onda, 2011). For each SC, the mean values of 34 δ 18 O and δD of precipitation were estimated considering regional altitudinal effects (1.6‰/100 m for δ 18 O 35 and 6.4‰/100 m for δD), which were determined from the data set of Makino (2013). 36 37…”

Section: / 42mentioning

confidence: 99%

“…The possible range was set to be 44 ±5% around the newest optimal value for each parameter in the iteration calculations. In the procedure of 45 calibration for isotope balance, the combined-RMSE (≡{RMSE δD /8 + RMSE δ18O }/2) was used for selecting 46 the best parameter set for both δ 18 O and δD, because a set of parameters providing the best result for δ 18 O is 47 not always the best for δD, and vice versa. The contribution of δD was divided by 8, according to theory ofRMSE rather than NSE as a measure of model performance, because variation range of isotopic data is 50 relatively small and thus NSE was too sensitive.…”

Section: Calibration and Validation 32mentioning

confidence: 99%

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Factors controlling inter-catchment variation of mean transit time with consideration of temporal variability

Yamanaka

2016

Journal of Hydrology

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17The catchment transit time, a lumped descriptor reflecting both time scale and spatial structure of 18 catchment hydrology can provide useful insights into chemical/nuclear pollution risks within a catchment. 19Despite its importance, factors controlling spatial variation of mean transit time (MTT) are not yet well 20 understood. In this study, we estimated time-variant MTTs for about ten years (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) in five 212 / 42 mesoscale sub-catchments of the Fuji River catchment, central Japan, to establish the factors controlling 22 their inter-catchment variation with consideration of temporal variability. For this purpose, we employed a 23 lumped hydrological model that was calibrated and validated by hydrometric and isotopic tracer 24 observations. Temporal variation patterns of estimated MTT were similar in all sub-catchments, but with 25 differing amplitudes. Inter-catchment variation of MTT was greater in dry periods than wet periods, 26 suggesting spatial variation of MTT is controlled by water 'stock' rather than by 'flow'. Although the 27 long-term average MTT (LAMTT) in each catchment was correlated with mean slope, coverage of forest (or 28 conversely, other land use types), coverage of sand-shale conglomerate, and groundwater storage, the 29 multiple linear regression revealed that inter-catchment variation of LAMTT is principally controlled by the 30 amount of groundwater storage. This is smaller in mountainous areas covered mostly by forests and greater 31 in plain areas with less forest coverage and smaller slope. This study highlights the topographic control of 32 MTT via groundwater storage, which might be a more important factor in mesoscale catchments, including 33 both mountains and plains, rather than in smaller catchments dominated by mountainous topography. 34 35

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Section: / 42mentioning

confidence: 99%

Section: / 42mentioning

confidence: 99%

Section: Calibration and Validation 32mentioning

confidence: 99%

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Factors controlling inter-catchment variation of mean transit time with consideration of temporal variability

Yamanaka

2016

Journal of Hydrology

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“…SSF generally results if an impermeable horizon or impermeable bedrock occurs on a steep slope with very shallow or less shallow soil and an effective system of lateral flow paths. DP is however expected on permeable and thick soils or on permeable shallow soils with very permeable subsoil and bedrock (Scherrer and Naef, 2003).…”

Section: Introductionmentioning

confidence: 98%

“…Theprocess which dominates depends on the site characteristics and the rainfall event. The DRP on a site is the process that contributes most to runoff for a given rainfall event (Naef et al, 2002) DRP can be classified as Hortonian Overland Flow (HOF), Saturated Overland Flow (SOF), Subsurface Flow (SSF) and Deep Percolation (DP) (Scherrer and Naef, 2003). Overland flow is the movement of water over the land, downslope toward a surface water body.…”

Section: Introductionmentioning

confidence: 99%

Identifying Dominant Runoff Processes at a Regional Scale – A GIS - Based Approach

Joseph¹,

Franklin²,

Olufemi³

2017

Present Environment and Sustainable Development

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Abstract:Identifying landscapes with similar hydrological characteristics is useful for the determination of dominant runoff process (DRP) and flood prediction. Several approaches used for DRP-mapping differ in respect to time and data requirement. Manual approaches based on field investigation and expert knowledge are time consuming and difficult to implement at regional scale. Automatic GIS-based approach on the other hand require simplification of data but are easier to implement and it is applicable on regional scale.

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Subsurface Stormflow

Weiler

McDonnell

Meerveld

et al. 2005

Encyclopedia of Hydrological Sciences

130

113

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Subsurface stormflow is a runoff producing mechanism operating in most upland terrains. In a humid environment and steep terrain with conductive soils, subsurface stormflow may be the main mechanism of storm runoff generation. In drier climates and in lowlands with gentler topography, subsurface stormflow may occur only under certain extreme conditions (high antecedent soil moisture), when transient water tables form and induce lateral flow to the channel. While an important contributor to the volume of flow in the stream, subsurface stormflow is also responsible for the transport of labile nutrients into surface water bodies. Since the flow path of water in the subsurface often determines the chemistry of waters discharging into the stream and hence the water quality, characterizing this subsurface flow path and the water's age and origin is important. Subsurface stormflow may also enhance positive pore pressure development in steep terrain and may be responsible for landslide initiation. Thus, subsurface stormflow is of great interest and importance beyond the conventional hydrological literature. This article examines the history of the study of subsurface stormflow processes, reviews theories on the generation of subsurface stormflow, and gives a detailed overview of current research in subsurface flow processes and the implication for future research.

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A decision scheme to indicate dominant hydrological flow processes on temperate grassland

Cited by 130 publications

References 8 publications

Factors controlling inter-catchment variation of mean transit time with consideration of temporal variability

Factors controlling inter-catchment variation of mean transit time with consideration of temporal variability

Identifying Dominant Runoff Processes at a Regional Scale – A GIS - Based Approach

Subsurface Stormflow

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