Evaluation of analytical and numerical approaches for the estimation of groundwater travel time distribution

Basu, N. B.; Jindal, Priyanka; Schilling, Keith E.; Wolter, Calvin F.; Takle, Eugene S.

doi:10.1016/j.jhydrol.2012.08.052

Cited by 57 publications

(42 citation statements)

References 38 publications

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“…The TTD, f ( τ ), was estimated using the geographic information system approach that was proposed by Schilling and Wolter () and Basu et al () and that has been shown to adequately capture groundwater travel times in Iowa landscapes. In this methodology, the travel time τ corresponding to each point in the landscape is described by

τ = \frac{length}{average linear velocity} = \frac{L}{((), K * i) / n}

where L is the flow path length from each cell in the landscape to the nearest stream (m), K is the hydraulic conductivity (m/s), i is the hydraulic gradient, and n is the aquifer porosity.…”

Section: Methodsmentioning

confidence: 99%

“…In this methodology, the travel time τ corresponding to each point in the landscape is described by

τ = \frac{length}{average linear velocity} = \frac{L}{((), K * i) / n}

where L is the flow path length from each cell in the landscape to the nearest stream (m), K is the hydraulic conductivity (m/s), i is the hydraulic gradient, and n is the aquifer porosity. The primary assumption of this approach is that the water table follows the topography (Basu et al, ; Schilling & Wolter, ), and thus the hydraulic gradient can be approximated as the ratio of the elevation difference (as estimated from the digital elevation map) and the flow path distance L between the cell of interest and the nearest outlet.…”

Section: Methodsmentioning

confidence: 99%

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A Race Against Time: Modeling Time Lags in Watershed Response

Ilampooranan

Meter

Basu

2019

Water Resources Research

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Land use change and agricultural intensification have increased food production but at the cost of polluting surface and groundwater. Best management practices implemented to improve water quality have met with limited success. Such lack of success is increasingly attributed to legacy nutrient stores in the subsurface that may act as sources after reduction of external inputs. However, current water‐quality models lack a framework to capture these legacy effects. Here we have modified the SWAT (Soil Water Assessment Tool) model to capture the effects of nitrogen (N) legacies on water quality under multiple land‐management scenarios. Our new SWAT‐LAG model includes (1) a modified carbon‐nitrogen cycling module to capture the dynamics of soil N accumulation, and (2) a groundwater travel time distribution module to capture a range of subsurface travel times. Using a 502‐km2 Iowa watershed as a case study, we found that between 1950 and 2016, 25% of the total watershed N surplus (N Deposition + Fertilizer + Manure + N Fixation − Crop N uptake) had accumulated within the root zone, 14% had accumulated in groundwater, while 27% was lost as riverine output, and 34% was denitrified. In future scenarios, a 100% reduction in fertilizer application led to a 79% reduction in stream N load, but the SWAT‐LAG results suggest that it would take 84 years to achieve this reduction, in contrast to the 2 years predicted in the original SWAT model. The framework proposed here constitutes a first step toward modifying a widely used modeling approach to assess the effects of legacy N on the time required to achieve water‐quality goals.

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τ = \frac{length}{average linear velocity} = \frac{L}{((), K * i) / n}

where L is the flow path length from each cell in the landscape to the nearest stream (m), K is the hydraulic conductivity (m/s), i is the hydraulic gradient, and n is the aquifer porosity.…”

Section: Methodsmentioning

confidence: 99%

“…In this methodology, the travel time τ corresponding to each point in the landscape is described by

τ = \frac{length}{average linear velocity} = \frac{L}{((), K * i) / n}

Section: Methodsmentioning

confidence: 99%

A Race Against Time: Modeling Time Lags in Watershed Response

Ilampooranan

Meter

Basu

2019

Water Resources Research

Self Cite

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“…A disconnect seems to exist between catchment and groundwater TT communities. Whereas catchment studies using lumped models and stable isotopes often focus on short TTs of days and months (e.g., Birkel et al, 2012;Dunn et al, 2010;Peralta-Tapia et al, 2016) the TTs studied by the groundwater community using groundwater models and tracers such as dissolved gases and radioactive isotopes are generally in the order of years and decades (e.g., Basu et al, 2012;Eberts et al, 2012;Gilmore et al, 2016;Solder et al, 2016;Stewart & Morgenstern, 2016;Visser et al, 2009;Visser et al, 2013). The coupling between groundwater TTDs and stream discharge is especially used in the assessment and prediction of stream discharge from (nitrate) polluted aquifers (e.g., Böhlke & Denver, 1995;Duffy & Lee, 1992;Zhang et al, 2013).…”

Section: Introductionmentioning

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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“…The groundwater travel time distribution in the Bear Creek watershed was evaluated using a GIS platform (ESRI, 2010) to estimate the average linear velocity in 3‐m cells (Schilling and Wolter, 2007; Basu et al, 2012). The model estimates the travel time ( t ) corresponding to each grid cell:

t = \frac{L}{υ} = \frac{n L}{K i}

where v (L t −1 ) is the average linear velocity, K (L t −1 ) is the hydraulic conductivity, and i is the hydraulic gradient, L is the flow path length, and n is porosity.…”

Section: Methodsmentioning

confidence: 99%

“…The close agreement among the modeling approaches was noteworthy given the differences in their levels of sophistication (the GIS model had intermediate complexity). The advantage of the GIS model was its ability to represent spatial distributions of watershed‐scale travel times with relative simplicity compared with MODFLOW simulation (Basu et al, 2012).…”

Section: Methodsmentioning

confidence: 99%

Tile Drainage Density Reduces Groundwater Travel Times and Compromises Riparian Buffer Effectiveness

et al. 2015

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Strategies to reduce nitrate-nitrogen (nitrate) pollution delivered to streams often seek to increase groundwater residence time to achieve measureable results, yet the effects of tile drainage on residence time have not been well documented. In this study, we used a geographic information system groundwater travel time model to quantify the effects of artificial subsurface drainage on groundwater travel times in the 7443-ha Bear Creek watershed in north-central Iowa. Our objectives were to evaluate how mean groundwater travel times changed with increasing drainage intensity and to assess how tile drainage density reduces groundwater contributions to riparian buffers. Results indicate that mean groundwater travel times are reduced with increasing degrees of tile drainage. Mean groundwater travel times decreased from 5.6 to 1.1 yr, with drainage densities ranging from 0.005 m (7.6 mi) to 0.04 m (62 mi), respectively. Model simulations indicate that mean travel times with tile drainage are more than 150 times faster than those that existed before settlement. With intensive drainage, less than 2% of the groundwater in the basin appears to flow through a perennial stream buffer, thereby reducing the effectiveness of this practice to reduce stream nitrate loads. Hence, strategies, such as reconnecting tile drainage to buffers, are promising because they increase groundwater residence times in tile-drained watersheds.

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Evaluation of analytical and numerical approaches for the estimation of groundwater travel time distribution

Cited by 57 publications

References 38 publications

A Race Against Time: Modeling Time Lags in Watershed Response

A Race Against Time: Modeling Time Lags in Watershed Response

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

Tile Drainage Density Reduces Groundwater Travel Times and Compromises Riparian Buffer Effectiveness

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