Subsurface and Surface Water Flow Interactions

Hantush, Mohammad M.; Kalin, Latif; Govindaraju, Rao S.

doi:10.1061/9780784411766.ch09

Cited by 4 publications

(3 citation statements)

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“…For instance, groundwater-flow patterns and their interactions with surface water at the watershed scale are influenced by topography, geology, and the climate of the region. Furthermore, physically based watershed models vary in complexity in handling the surface-water-groundwater interactions (Hantush et al 2011). Such interactions in wetlands are complex and an understanding of basic principles and physical laws governing exchange between groundwater and surface water is needed for modeling the interactions at multiple scales (Hantush et al 2011).…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, physically based watershed models vary in complexity in handling the surface-water-groundwater interactions (Hantush et al 2011). Such interactions in wetlands are complex and an understanding of basic principles and physical laws governing exchange between groundwater and surface water is needed for modeling the interactions at multiple scales (Hantush et al 2011). In this study, accurate prediction was more important than understanding the physical processes.…”

Section: Discussionmentioning

confidence: 99%

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Wetland Water-Level Prediction Using ANN in Conjunction with Base-Flow Recession Analysis

2017

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This study introduces two artificial neural network (ANN)-based methodologies to predict hourly water levels (WLs) in wetlands characterized by water tables at or near the surface that respond rapidly to precipitation. The first method makes use of hourly precipitation data and WL data from nearby sites. The second method is a combination of ANN, recursive digital filter, and recession curve method and does not require any nearby site. The proposed methods were tested at two headwater wetlands in coastal Alabama. Site 17 had two nearby sites whose WLs were highly correlated with Site 17's. The root-mean-square error and Nash-Sutcliffe efficiencies were 2.9 cm and 0.98, respectively, when the first method was applied to Site 17. The second method was tested at Site 32. For this, the WL time series was separated into quick-and slow-response components. A combination of ANN and base-flow separation methods proved to be very efficient for WL prediction at this site, especially when the duration of quick-response components of individual events was less than 6 h. The proposed methodologies, therefore, proved useful in predicting WLs in wetlands dominated by both surface water and groundwater. individual papers. This paper is part of the Journal of Hydrologic Engineering, © ASCE, ISSN 1084-0699/D4015003 (11)/$25.00. © ASCE D4015003-1 J. Hydrol. Eng. J. Hydrol. Eng. Downloaded from ascelibrary.org by Auburn University on 09/24/15. Copyright ASCE. For personal use only; all rights reserved. © ASCE D4015003-5 J. Hydrol. Eng. J. Hydrol. Eng. Downloaded from ascelibrary.org by Auburn University on 09/24/15. Copyright ASCE. For personal use only; all rights reserved. © ASCE D4015003-8 J. Hydrol. Eng. J. Hydrol. Eng. Downloaded from ascelibrary.org by Auburn University on 09/24/15. Copyright ASCE. For personal use only; all rights reserved. © ASCE D4015003-9 J. Hydrol. Eng. J. Hydrol. Eng. Downloaded from ascelibrary.org by Auburn University on 09/24/15. Copyright ASCE. For personal use only; all rights reserved. © ASCE D4015003-11 J. Hydrol. Eng. J. Hydrol. Eng. Downloaded from ascelibrary.org by Auburn University on 09/24/15.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Wetland Water-Level Prediction Using ANN in Conjunction with Base-Flow Recession Analysis

2017

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“…Understanding infiltration behaviour in non‐uniform soils has ramifications for surface water hydrology and streamflow generation. Traditional overland flow generation mechanisms primarily rely on the Hortonian model where the rainfall intensity exceeds infiltration capacity at the soil surface, or from saturation excess that builds up from below because of an impeding layer or a groundwater table (Hantush et al , ). However, the particular class of non‐uniformity investigated here gives rise to overland flow generation mechanisms that do not fall into either of these established categories.…”

Section: Discussionmentioning

confidence: 99%

Local‐ and field‐scale infiltration into vertically non‐uniform soils with spatially‐variable surface hydraulic conductivities

Govindaraju

Corradini

Morbidelli

2012

Hydrological Processes

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Abstract:We studied the problem of local-and field-scale infiltration over a particular class of heterogeneous soils. At the local scale, the soils are described as being vertically non-uniform, with the saturated hydraulic conductivity continuously decreasing with depth according to a power law function. Analogous to the Green-Ampt model, analytical expressions are first developed for local-scale infiltration using a sharp front approximation, and model results are compared with numerical solutions of the Richards equation. These results show that saturation does not occur from below in soils with such vertical non-uniformity, thereby allowing for the use of a sharp front approximation. Because of vertical non-uniformity, ponding conditions are achieved locally even for rainfall rates less than the surface saturated hydraulic conductivity. Furthermore, infiltration rates asymptotically approach zero at long times. To determine field-scale infiltration properties, the spatial variability in the surface saturated hydraulic conductivity is represented by a log-normal random field. Using cumulative infiltration as the independent variable, expressions are developed for the ensemble mean of field-scale infiltration and the expected time for a given depth of water to infiltrate over the field. Surface horizontal heterogeneity is found to control fieldscale infiltration at small times, whereas local vertical non-uniformity exerts a strong control at long times.

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Current and Future Challenges in Groundwater. I: Modeling and Management of Resources

2015

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Subsurface and Surface Water Flow Interactions

Cited by 4 publications

References 193 publications

Wetland Water-Level Prediction Using ANN in Conjunction with Base-Flow Recession Analysis

Wetland Water-Level Prediction Using ANN in Conjunction with Base-Flow Recession Analysis

Local‐ and field‐scale infiltration into vertically non‐uniform soils with spatially‐variable surface hydraulic conductivities

Current and Future Challenges in Groundwater. I: Modeling and Management of Resources

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