Browsing by Author "Cirpka, Olaf A."

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Open Access
Sampling behavioral model parameters for ensemble-based sensitivity analysis using Gaussian process emulation and active subspaces
(2020) Erdal, Daniel; Xiao, Sinan; Nowak, Wolfgang; Cirpka, Olaf A.
Ensemble-based uncertainty quantification and global sensitivity analysis of environmental models requires generating large ensembles of parameter-sets. This can already be difficult when analyzing moderately complex models based on partial differential equations because many parameter combinations cause an implausible model behavior even though the individual parameters are within plausible ranges. In this work, we apply Gaussian Process Emulators (GPE) as surrogate models in a sampling scheme. In an active-training phase of the surrogate model, we target the behavioral boundary of the parameter space before sampling this behavioral part of the parameter space more evenly by passive sampling. Active learning increases the subsequent sampling efficiency, but its additional costs pay off only for a sufficiently large sample size. We exemplify our idea with a catchment-scale subsurface flow model with uncertain material properties, boundary conditions, and geometric descriptors of the geological structure. We then perform a global-sensitivity analysis of the resulting behavioral dataset using the active-subspace method, which requires approximating the local sensitivities of the target quantity with respect to all parameters at all sampled locations in parameter space. The Gaussian Process Emulator implicitly provides an analytical expression for this gradient, thus improving the accuracy of the active-subspace construction. When applying the GPE-based preselection, 70-90% of the samples were confirmed to be behavioral by running the full model, whereas only 0.5% of the samples were behavioral in standard Monte-Carlo sampling without preselection. The GPE method also provided local sensitivities at minimal additional costs.
Open Access
Spectral induced polarization (SIP) of denitrification‐driven microbial activity in column experiments packed with calcareous aquifer sediments
(2023) Strobel, Cora; Abramov, Sergey; Huisman, Johan Alexander; Cirpka, Olaf A.; Mellage, Adrian
Spectral Induced Polarization (SIP) has been suggested as a non-invasive monitoring proxy for microbial processes. Under natural conditions, however, multiple and often coupled polarization processes co-occur, impeding the interpretation of SIP signals. In this study, we analyze the sensitivity of SIP to microbially-driven reactions under quasi-natural conditions. We conducted flow-through experiments in columns equipped with SIP electrodes and filled with natural calcareous, organic-carbon-rich aquifer sediment, in which heterotrophic denitrification was bio-stimulated. Our results show that, even in the presence of parallel polarization processes in a natural sediment under field-relevant geochemical conditions, SIP is sufficiently sensitive to microbially-driven changes in electrical charge storage. Denitrification yielded an increase in imaginary conductivity of up to 3.1 μS cm -1 (+140%) and the formation of a distinct peak between 1 and 10 Hz, that matched the timing of expected microbial activity predicted by a reactive transport model fitted to solute concentrations. A Cole-Cole decomposition allowed separating the polarization contribution of microbial activity from that of cation exchange, thereby helping to locate microbial hotspots without the need for (bio)geochemical data to constrain the Cole-Cole parameters. Our approach opens new avenues for the application of SIP as a rapid method to monitor a system's reactivity in situ. While in preceding studies the SIP signals of microbial activity in natural sediments were influenced by mineral precipitation/dissolution reactions, the imaginary conductivity changes measured in the biostimulation experiments presented here were dominated by changes in the polarization of the bacterial cells rather than a reaction-induced alteration of the abiotic matrix.
Open Access
A stochastic framework to optimize monitoring strategies for delineating groundwater divides
(2020) Allgeier, Jonas; González-Nicolás, Ana; Erdal, Daniel; Nowak, Wolfgang; Cirpka, Olaf A.
Surface-water divides can be delineated by analyzing digital elevation models. They might, however, significantly differ from groundwater divides because the groundwater surface does not necessarily follow the surface topography. Thus, in order to delineate a groundwater divide, hydraulic-head measurements are needed. Because installing piezometers is cost- and labor-intensive, it is vital to optimize their placement. In this work, we introduce an optimal design analysis that can identify the best spatial configuration of piezometers. The method is based on formal minimization of the expected posterior uncertainty in localizing the groundwater divide. It is based on the preposterior data impact assessor, a Bayesian framework that uses a random sample of models (here: steady-state groundwater flow models) in a fully non-linear analysis. For each realization, we compute virtual hydraulic-head measurements at all potential well installation points and delineate the groundwater divide by particle tracking. Then, for each set of virtual measurements and their possible measurement values, we assess the uncertainty of the groundwater-divide location after Bayesian updating, and finally marginalize over all possible measurement values. We test the method mimicking an aquifer in South-West Germany. Previous works in this aquifer indicated a groundwater divide that substantially differs from the surface-water divide. Our analysis shows that the uncertainty in the localization of the groundwater divide can be reduced with each additional monitoring well. In our case study, the optimal configuration of three monitoring points involves the first well being close to the topographic surface water divide, the second one on the hillslope toward the valley, and the third one in between.
Open Access
Strategies for simplifying reactive transport models : a Bayesian model comparison
(2020) Schäfer Rodrigues Silva, Aline; Guthke, Anneli; Höge, Marvin; Cirpka, Olaf A.; Nowak, Wolfgang
For simulating reactive transport on aquifer scale, various modeling approaches have been proposed. They vary considerably in their computational demands and in the amount of data needed for their calibration. Typically, the more complex a model is, the more data are required to sufficiently constrain its parameters. In this study, we assess a set of five models that simulate aerobic respiration and denitrification in a heterogeneous aquifer at quasi steady state. In a probabilistic framework, we test whether simplified approaches can be used as alternatives to the most detailed model. The simplifications are achieved by neglecting processes such as dispersion or biomass dynamics, or by replacing spatial discretization with travel‐time‐based coordinates. We use the model justifiability analysis proposed by Schöniger, Illman, et al. (2015, https://doi.org/10.1016/j.jhydrol.2015.07.047) to determine how similar the simplified models are to the reference model. This analysis rests on the principles of Bayesian model selection and performs a tradeoff between goodness‐of‐fit to reference data and model complexity, which is important for the reliability of predictions. Results show that, in principle, the simplified models are able to reproduce the predictions of the reference model in the considered scenario. Yet, it became evident that it can be challenging to define appropriate ranges for effective parameters of simplified models. This issue can lead to overly wide predictive distributions, which counteract the apparent simplicity of the models. We found that performing the justifiability analysis on the case of model simplification is an objective and comprehensive approach to assess the suitability of candidate models with different levels of detail.