Identifying Stagnation Zones and Reverse Flow Caused by River-Aquifer Interaction: An Approach Based on Polynomial Chaos Expansions

Fluctuating stream stages and peak-flow events can significantly influence the interactions between streams 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 nonlinear 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 nonintrusive pseudo-spectral technique and, consequently, it can be applied straightforwardly in preexisting models. The regions near the confluence of two streams in the studied area are prone to present transient stagnation and reverse flow.publishedVersio

Dataset for the research "Propagation of hydropeaking waves in heterogeneous aquifers: effects on flow topology and uncertainty quantification"

This repository contains the dataset used in the research "Propagation of hydropeaking waves in heterogeneous aquifers: effects on flow topology and uncertainty quantification," written by P. Merchán-Rivera, M. Basilio Hazas, G. Marcolini, and G. Chiogna.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Dataset and algorithms for the Bayesian framework to assess and create risk maps of groundwater flooding

This repository is part of the research "A Bayesian Framework to Assess and Create Maps of Groundwater Flooding", written by Pablo Merchán-Rivera, Alexandra Geist, Markus Disse, Jingshui Huang, and Gabriele Chiogna. The algorithms and scripts apply the framework to create maps of groundwater flooding susceptibility in a numerical model that simulates the groundwater flood event. The algorithm includes the application of the elementary effects method, a Markov Chain Monte Carlo inference using the DREAM algorithm, and the exploration of the predictive posterior distributions of the groundwater heads. The simulation performed in the research can be run using this repository to reproduce the research results.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

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

Dataset for the research "Propagation of hydropeaking waves in heterogeneous aquifers: effects on flow topology and uncertainty quantification"

This repository contains the dataset used in the research "Propagation of hydropeaking waves in heterogeneous aquifers: effects on flow topology and uncertainty quantification," written by P. Merchán-Rivera, M. Basilio Hazas, G. Marcolini, and G. Chiogna.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Dataset and algorithms for the probabilistic assessment of groundwater flooding occurrence

This repository is part of the research "A Bayesian Framework to Assess and Create Maps of Groundwater Flooding", written by Pablo Merchán-Rivera, Alexandra Geist, Markus Disse, Jingshui Huang, and Gabriele Chiogna. The algorithms and scripts apply the framework to create maps of groundwater flooding susceptibility in a numerical model that simulates the groundwater flood event. The algorithm includes the application of the elementary effects method, a Markov Chain Monte Carlo inference using the DREAM algorithm, and the exploration of the predictive posterior distributions of the groundwater heads. The simulation performed in the research can be run using this repository to reproduce the research results.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Identifying Stagnation Zones and Reverse Flow Caused by River-Aquifer Interaction: An Approach Based on Polynomial Chaos Expansions

Fluctuating stream stages and peak-flow events can significantly influence the interactions between streams 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 nonlinear 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 nonintrusive pseudo-spectral technique and, consequently, it can be applied straightforwardly in preexisting models. The regions near the confluence of two streams in the studied area are prone to present transient stagnation and reverse flow