Partially penetrating lake-aquifer interaction in a laboratory-scale tidal setting

Hydrodynamic relationships between gravel pit lakes and aquifers: brief review and insights from numerical investigations

“…Their simulated amplitude H and phase φ values are summarised in Table 6. Relative to the nolake case, the results show that the presence of the gravel pit lake dampens the groundwater wave propagation, as the water table exhibits smaller amplitude and increased phase lag (see also Figure 7d), underlining the dominant role of the storage effect of the lake, as pointed out by Jazayeri et al [2021]. Regarding the gravel pit lake's level itself, slower readjustments also occur in response to the sinusoidal variation in groundwater recharge, as compared to the aquifer, to be related to the response time τ c of the gravel pit lake.…”

“…Such numerical studies of lake-groundwater interactions are mainly conducted on natural lakes, from the early work of Winter [1976] who examined the general principles of these interactions to the more recent investigations of Jazayeri et al [2021] who modelled the effects of lakes on groundwater wave propagation. They are also instructive with respect to artificial lakes when they aim to identify the main factors controlling lake-groundwater systems by varying lake and aquifer characteristics [e.g., Genereux and Bandopadhyay, 2001].…”

Riparian Freshwater Lens Response to Flooding

“…The tank has internal dimensions of 1.200 m long, 0.330 m high, and 0.030 m wide. The tank was packed with coarse sand (with d 10 = 1.22 ± 0.05 mm; where d 10 is the sand particle diameter at which 10% of a sample's mass consists of smaller particles) to a depth of 0.310 ± 0.001 m using a wet‐packing method (e.g., Jazayeri, Werner, & Cartwright, 2021; Jazayeri, Werner, Wu, et al., 2021). Where a low‐lying floodplain was considered, its depth was 0.300 ± 0.001 m above the tank's base.…”

“…Regularization was applied within the calibration methodology to constrain the deviation of calibrated parameters from the measured parameters (i.e., L , H nr , H s , ρ f , ρ s , B r , Κ , κ , α L ). Consequently, the calibration objective function was defined as the weighted sum of “prediction error” (i.e., the squared deviation between analytical predictions and laboratory measurements) and “regularization mismatch” (i.e., the squared difference between the calibrated and measured parameter values; e.g., Jazayeri, Werner, & Cartwright, 2021; Jazayeri, Werner, Wu, et al., 2021; Werner et al., 2016). The Evolutionary Solving Method (ESM) in Microsoft Excel ® was used to search for the representative parameter set (i.e., L , H nr , H s , ρ f , ρ s , B r , Κ , κ , α L ) that minimized the objective function.…”

“…As an alternative, laboratory experiments can assist in evaluating complex subsurface processes under simplified, controlled conditions that allow for rigorous observations and analysis of lens transience, which is challenging to characterize within field settings. Laboratory-scale physical models are commonly applied to various forms of lenses, including riparian lenses (e.g., Jazayeri, Werner, & Cartwright, 2021;Jazayeri, Werner, Wu, et al, 2021;Wu et al, 2020), as well as the lenses within coastal zones and oceanic islands (e.g., Lu et al, 2019;Stoeckl & Houben, 2012;Tang et al, 2021;Yan et al, 2021). More recently, Jazayeri et al (2022) conducted laboratory experiments to study the transient response of lenses (adjacent to fully penetrating, gaining rivers) to instantaneous boundary head changes due to variations in the river water level or saltwater head at the inland boundary.…”