Dataset. Identifying stagnation zones and reverse flow caused by river-aquifer interaction: An approach based on polynomial chaos expansions
This dataset is part of the Supplementary Material of the research: Identifying stagnation zones and reverse flow caused by river-aquifer interaction: An approach based on polynomial chaos expansions. The code infrastructure, the programming scripts, the simulation results, and the dataset files are stored in this online repository. Research Abstract: Fluctuating transient river stages and peak-flow events can significantly influence the interactions between rivers and aquifers and modify the hydraulic gradient, the flux exchange and the subsurface flow paths. As a result, stagnation zones and reverse flow may appear in different parts of an aquifer and at different times. These features of the flow field play a relevant role in the transport, transformation, and residence time of solutes, pollutants, and nutrients in the subsurface. However, their identification using numerical models is complex not only because of highly non-linear dynamics, but also due to significant uncertainties in the model input data which propagate into the quantities of interest. In this work, we use an approach based on polynomial chaos expansions to map the probability of occurrence of stagnation zones and reverse flow during a flood event. We quantify the propagation of uncertainty into the groundwater flow field due to the applied river boundary conditions. Then, we evaluate the responses of the posterior probabilities in an element-wise fashion using a set of flow classification criteria and kernel density estimations. The proposed methodology is flexible because it employs a non-intrusive pseudo-spectral technique and, consequently, it can be applied straightforwardly in pre-existing models. The regions near the confluence of two rivers in the studied area are prone to present transient stagnation and reverse flow.
Steps to reproduce
For reproducing the results of the research, run the files in the following order: 1. sim_alz_pce.py 2. sim_alz_maps.py All the results are automatically stored in 2_Files_gPC_method/res and 2_Files_gPC_method/fig. MODFLOW-2005 must be previously installed in order to run these experiments. We recommend to save the executable file of MODFLOW in the following path: C:\Modflow\MF2005.1_12\bin\mf2005dbl.exe. Otherwise, the MODFLOW batch file MODFLOW_mod.bat must be modify with the proper path where the executable file was located. We recommend to use Spyder as integrated development environment (IDE) to visualize the results.