scholarly journals Conceptual Models of Flow through a Heterogeneous, Layered Vadose Zone under a Percolation Pond

2004 ◽  
Author(s):  
Kristine Baker ◽  
Larry Hull ◽  
Jesse Bennett ◽  
Shannon Ansley ◽  
Gail Heath
Keyword(s):  
Vadose Zone ◽  
Conceptual Models ◽  
Flow Through

scholarly journals Quantifying structural uncertainty due to discretisation resolution and dimensionality in a hydrodynamic polder model

2009 ◽  
Vol 11 (1) ◽  
pp. 19-30 ◽  
Author(s):  
C. Sehnert ◽  
S. Huang ◽  
K.-E. Lindenschmidt
Keyword(s):  
Conceptual Models ◽  
Flow Characteristics ◽  
Monte Carlo Analysis ◽  
Structural Uncertainty ◽  
Flood Modelling ◽  
River Elbe ◽  
Complex Models ◽  
1D Model ◽  
Flow Through ◽  
Spatial Discretisation

In flood modelling, the structure of conceptual models may have a large influence on the simulation results. Hence, the focus of this paper is on the structural uncertainty in hydrodynamic flood modelling systems. Three different conceptual models with an increasing order of complexity of the spatial discretisation of the flow through a polder system were compared in order to investigate the effect of spatial resolution and dimensionality on flood modelling. The hydrodynamic 1D model DYNHYD was used as a basis for the simulations. The model was extended to incorporate a quasi-2D approach and a Monte Carlo analysis was used to show the effect of structural uncertainty on the resulting flow characteristics of the diverted flood waters. Two flood events of the River Elbe were used to calibrate and test the model. The results of the velocity fields indicate that the simplest 1D model revealed more predictive uncertainty than the other two more complex models. The differences in model structure does not cause large differences in the capping of the peak discharges, but may substantially influence the results of subsequent modelling of sediment and contaminant transport.

scholarly journals Flow dynamics in a vadose shaft – a case study from the Hochschwab karst massif (Northern Calcareous Alps, Austria)

2021 ◽  
Vol 50 (2) ◽  
pp. 157-172
Author(s):  
Eva Kaminsky ◽  
Lukas Plan ◽  
Thomas Wagner ◽  
Barbara Funk ◽  
Pauline Oberender
Keyword(s):  
Water Flow ◽  
Vadose Zone ◽  
Average Delay ◽  
Small Catchment ◽  
Diffuse Infiltration ◽  
Lumped Parameter ◽  
Flow Through ◽  
Rainfall Runoff Model ◽  
Water Transmission

Karst aquifers are highly vulnerable to contamination due to quick water flow through conduits. Their high heterogeneity and the poorly known infiltration effect of the vadose zone make quantification of recharge processes difficult. This study characterizes the water flow and storage in the upper vadose zone with almost four years monitoring of a permanent stream in a vadose shaft (Furtowischacht). Its small catchment of 4,500 m² is located in a former glaciated high Alpine environment (Hochschwab, Austria). High discharge fluctuations between 0.002 and 19 l/s, relatively high hydrograph recession coefficients, and transit velocities between 0.0015 and 2.4 m/s estimated with salt tracer experiments indicate a highly dynamic discharge behavior. A fast point infiltration through open karren and dolines could be observed for rainfall events and indicates a highly karstified network with a rapid water transmission. Snowmelt periods show only a slower flow component and diffuse infiltration. However, condensation within the conduit system is likely superimposed to this signal. A lumped-parameter rainfall-runoff model is used to simulate the discharge with a dual porosity approach. It indicates a low storage volume, which is in accordance with the estimated storage of 22 m³ (or 5 mm), deduced from the recession analyses. In contrary, the physicochemical parameters argue for some storage capability: 1) After an increase of discharge, electric conductivity reacts with an average delay of 50 min; 2) Partly a piston flow can be recognized. These amounts of water may be stored in the partial soil cover alone and therefore the presence of a hydrologically significant epikarst layer is unclear.

Vadose Zone, Part II : Transport

2003 ◽  
Author(s):  
Yoram Rubin
Keyword(s):  
Vadose Zone ◽  
Large Scale ◽  
Field Experiments ◽  
Transport Processes ◽  
Minor Role ◽  
Gravitational Flow ◽  
Flow Through ◽  
Mc Simulation ◽  
A Minor ◽  
Data Requirements

This chapter is an extension of our discussion on transport in chapters 7 to 10. Our goal here is to explore a few aspects of the transport problem which are unique to variably saturated soils. The heterogeneity of soils affects transport of solutes in the vadose zone in different ways. It leads to irregular and hard-to-predict spreading of the solutes. The solutes may be channeled through highly conductive flow channels where diffusion plays only a minor role. This may lead to concentrations which are high and travel times which are fast compared to what one may anticipate by assuming that the medium is homogeneous. Evidence for such behavior was found in field experiments (cf. Wierenga et al., 1991; Ellsworth et al., 1991; Ritsema et al., 1998; Sassner et al., 1994) and in large-scale laboratory experiments (Dagan et al., 1991). Hence, the effects of heterogeneity must be recognized and modeled. The effects of heterogeneity can be modeled by employing the stochastic concepts discussed in earlier chapters. The approach for modeling contaminant transport which is the least restrictive in terms of assumptions introduced is the MC simulation. This approach will be reviewed briefly in section 12.1. Modeling of the mean concentration along our discussion in chapter 8 is computationally less demanding compared to MC simulations, yet is less informative since the concentration in the field can hardly be expected to be equal to its expected value. Applications along that line are limited since deriving the macrodispersion coefficients needed for such an undertaking is difficult. Nonetheless, we shall discussed this approach in section 12.2, for the insight into the transport processes it provides. A few simple models are available for gravitational flow through shallow depths. These methods are of course limited in applications, yet they are less demanding in terms of data requirements and the computational efforts involved. Such methods are the focus of the last section in this chapter. The concept of MC simulation was discussed in earlier chapters.

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