A particle-tracking scheme for simulating pathlines in coupled surface-subsurface flows

Rooij, Rob de; Graham, Wendy D.; Maxwell, R. M.

doi:10.1016/j.advwatres.2012.07.022

Cited by 30 publications

(24 citation statements)

References 26 publications

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“…Additional information about flow paths combined with the contribution of different ages to streamflow allows direct correlation of, for instance, water chemistry with a specific flow path, which can thus explain variations in water chemistry throughout the year (Benettin et al, 2013;Hrachowitz et al, 2016). The effect of flow paths contributing to streams has already been included in some TTs studies using particle tracking approaches (Basu et al, 2012;de Rooij et al, 2013;Gusyev et al, 2014;Modica et al, 1997;Molénat & Gascuel-Odoux, 2002;Visser et al, 2009); however, these studies focused on stationary or summer/winter TTDs. To date, particle tracking approaches have not been used to characterize dynamic TTDs for real catchments.…”

Section: 1029/2017wr022461mentioning

confidence: 99%

Transient Groundwater Travel Time Distributions and Age‐Ranked Storage‐Discharge Relationships of Three Lowland Catchments

Kaandorp

Louw

Velde

et al. 2018

Water Resources Research

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The contribution of groundwater to streams is controlled by temporally and spatially variable groundwater flow paths with distinctive travel times. The aggregated average travel time distribution (TTD) of all these flow paths functions as a catchment characteristic. Currently, research on TTDs is expanding towards dynamic TTDs and building on this, we present dynamic backward TTDs and residence time distributions using forward particle tracking on a high-resolution spatially distributed groundwater flow model (25*25 m). We show that the dynamic backward TTDs of three Dutch catchments are determined by the interplay between the activation of shallow short flow paths and the intensification of fluxes through all flow paths when groundwater levels rise. In addition, the preference for young water in our lowland catchments appears strongly controlled by drainage density. Variations in catchment mixing with time and between catchments were analyzed using dynamic StorAge Selection (SAS) functions. This showed the effect of differences in geology and topography on the shape of the SAS functions. Additionally, the variability of SAS functions in time was shown to depend on the extent to which new flow paths can be activated. Time-varying SAS functions are required for computation of dynamic TTDs, and this research showed realistic values for the variability in the SAS functions of lowland catchments. The step towards dynamic TTDs is crucial for understanding the temporal and spatial behavior of streams, their chemical composition, and their ecological value.

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Section: 1029/2017wr022461mentioning

confidence: 99%

Transient Groundwater Travel Time Distributions and Age‐Ranked Storage‐Discharge Relationships of Three Lowland Catchments

Kaandorp

Louw

Velde

et al. 2018

Water Resources Research

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“…Physics-based models combined with a particle tracking scheme [e.g., Kollet and Maxwell, 2008;de Rooij et al, 2013], particle tracking approaches, like the Multiple Interacting Pathways (MIPs) model that uses particle tracking for flow and transport [Davies et al, , 2013, and solute transport schemes in rather conceptual models [Sayama and McDonnell, 2009;Hrachowitz et al, 2013] are at hand. Physics-based models require detailed knowledge about hillslope or catchment properties such as hydraulic conductivity of the soil for their use.…”

Section: How This Work Compares To Other Approaches For Time Variant mentioning

confidence: 99%

Temporal dynamics of catchment transit times from stable isotope data

Klaus

Chun

McGuire

et al. 2015

Water Resources Research

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Time variant catchment transit time distributions are fundamental descriptors of catchment function but yet not fully understood, characterized, and modeled. Here we present a new approach for use with standard runoff and tracer data sets that is based on tracking of tracer and age information and time variant catchment mixing. Our new approach is able to deal with nonstationarity of flow paths and catchment mixing, and an irregular shape of the transit time distribution. The approach extracts information on catchment mixing from the stable isotope time series instead of prior assumptions of mixing or the shape of transit time distribution. We first demonstrate proof of concept of the approach with artificial data; the Nash-Sutcliffe efficiencies in tracer and instantaneous transit times were >0.9. The model provides very accurate estimates of time variant transit times when the boundary conditions and fluxes are fully known. We then tested the model with real rainfall-runoff flow and isotope tracer time series from the H.J. Andrews Watershed 10 (WS10) in Oregon. Model efficiencies were 0.37 for the 18 O modeling for a 2 year time series; the efficiencies increased to 0.86 for the second year underlying the need of long time tracer time series with a long overlap of tracer input and output. The approach was able to determine time variant transit time of WS10 with field data and showed how it follows the storage dynamics and related changes in flow paths where wet periods with high flows resulted in clearly shorter transit times compared to dry low flow periods.

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“…The computational efficiency of the PAWS model allows for long-term, large-scale simulations and makes it possible to simulate transport in large watersheds. An operator-splitting strategy (e.g., Phanikumar and McGuire, 2004) combined with a Lagrangian particle transport modeling approach (e.g., de Rooij et al, 2013) with reactions to describe transport in different hydrologic units was applied. Due to the complexity of the processes involved, extensive model testing against analytical solutions for different hydrologic domains is a necessary first step before the performance of the integrated model can be fully evaluated.…”

Section: Introductionmentioning

confidence: 99%

Modeling watershed-scale solute transport using an integrated, process-based hydrologic model with applications to bacterial fate and transport

Niu

Phanikumar

2015

Journal of Hydrology

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A particle-tracking scheme for simulating pathlines in coupled surface-subsurface flows

Cited by 30 publications

References 26 publications

Transient Groundwater Travel Time Distributions and Age‐Ranked Storage‐Discharge Relationships of Three Lowland Catchments

Transient Groundwater Travel Time Distributions and Age‐Ranked Storage‐Discharge Relationships of Three Lowland Catchments

Temporal dynamics of catchment transit times from stable isotope data

Modeling watershed-scale solute transport using an integrated, process-based hydrologic model with applications to bacterial fate and transport

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