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  • ddc:551.49  (3)
  • advective flux  (1)
  • Blackwell Publishing Ltd  (4)
  • American Chemical Society
  • English  (4)
  • 1
    Publication Date: 2022-04-01
    Description: We present a workflow to estimate geostatistical aquifer parameters from pumping test data using the Python package welltestpy. The procedure of pumping test analysis is exemplified for two data sets from the Horkheimer Insel site and from the Lauswiesen site, Germany. The analysis is based on a semi‐analytical drawdown solution from the upscaling approach Radial Coarse Graining, which enables to infer log‐transmissivity variance and horizontal correlation length, beside mean transmissivity, and storativity, from pumping test data. We estimate these parameters of aquifer heterogeneity from type‐curve analysis and determine their sensitivity. This procedure, implemented in welltestpy, is a template for analyzing any pumping test. It goes beyond the possibilities of standard methods, for example, based on Theis' equation, which are limited to mean transmissivity and storativity. A sensitivity study showed the impact of observation well positions on the parameter estimation quality. The insights of this study help to optimize future test setups for geostatistical aquifer analysis and provides guidance for investigating pumping tests with regard to aquifer statistics using the open‐source software package welltestpy.
    Description: Article impact statement: We present a workflow to infer parameters of subsurface heterogeneity from pumping test data exemplified at two sites using welltestpy.
    Description: German Federal Environmental Foundation (DBU) http://dx.doi.org/10.13039/100007636
    Keywords: ddc:551.49
    Language: English
    Type: doc-type:article
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  • 2
    Publication Date: 2022-09-27
    Description: Little research attention has been given to validating clusters obtained from the groundwater geochemistry of the waterworks' capture zone with a prevailing lake‐groundwater exchange. To address this knowledge gap, we proposed a new scheme whereby Gaussian finite mixture modeling (GFMM) and Spike‐and‐Slab Bayesian (SSB) algorithms were utilized to cluster the groundwater geochemistry while quantifying the probability of the resulting cluster membership against each other. We applied GFMM and SSB to 13 geochemical parameters collected during different sampling periods at 13 observation points across the Barnim Highlands plateau located in the northeast of Berlin, Germany; this included 10 observation wells, two lakes, and a gallery of drinking production wells. The cluster analysis of GFMM yielded nine clusters, either with a probability ≥0.8, while the SSB produced three hierarchical clusters with a probability of cluster membership varying from 〈0.2 to 〉0.8. The findings demonstrated that the clustering results of GFMM were in good agreement with the classification as per the principal component analysis and Piper diagram. By superimposing the parameter clustering onto the observation clustering, we could identify discrepancies that exist among the parameters of a certain cluster. This enables the identification of different factors that may control the geochemistry of a certain cluster, although parameters of that cluster share a strong similarity. The GFMM results have shown that from 2002, there has been active groundwater inflow from the lakes towards the capture zone. This means that it is necessary to adopt appropriate measures to reverse the inflow towards the lakes.
    Description: Article impact statement: The probability of cluster membership quantified using an algorithm should be validated against another probabilistic‐based classifier.
    Description: Federal Ministry of Education and Research http://dx.doi.org/10.13039/501100002347
    Keywords: ddc:551.9 ; ddc:551.49
    Language: English
    Type: doc-type:article
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  • 3
    Publication Date: 2024-02-09
    Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉Gas transport in soils is usually assumed to be purely diffusive, although several studies have shown that non‐diffusive processes can significantly enhance soil gas transport. These processes include barometric air pressure changes, wind‐induced pressure pumping and static air pressure fields generated by wind interacting with obstacles. The associated pressure gradients in the soil can cause advective gas fluxes that are much larger than diffusive fluxes. However, the contributions of the respective transport processes are difficult to separate. We developed a large chamber system to simulate pressure fields and investigate their influence on soil gas transport. The chamber consists of four subspaces in which pressure is regulated by fans that blow air in or out of the chamber. With this setup, we conducted experiments with oscillating and static pressure fields. CO〈sub〉2〈/sub〉 concentrations were measured along two soil profiles beneath the chamber. We found a significant relationship between static lateral pressure gradients and the change in the CO〈sub〉2〈/sub〉 profiles (R〈sup〉2〈/sup〉 = 0.53; 〈italic toggle="no"〉p〈/italic〉‐value 〈2e‐16). Even small pressure gradients between −1 and 1 Pa relative to ambient pressure resulted in an increase or decrease in CO〈sub〉2〈/sub〉 concentrations of 8% on average in the upper soil, indicating advective flow of air in the pore space. Positive pressure gradients resulted in decreasing, negative pressure gradients in increasing CO〈sub〉2〈/sub〉 concentrations. The concentration changes were probably caused by an advective flow field in the soil beneath the chamber generated by the pressure gradients. No effect of oscillating pressure fields was observed in this study. The results indicate that static lateral pressure gradients have a substantial impact on soil gas transport and therefore are an important driver of gas exchange between soil and atmosphere. Lateral pressure gradients in a comparable range can be induced under windy conditions when wind interacts with terrain features. They can also be caused by chambers used for flux measurements at high wind speed or by fans used for head‐space mixing within the chambers, which yields biased flux estimates.〈/p〉
    Description: Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659
    Keywords: ddc:631.4 ; advective flux ; chamber flux measurements ; static air pressure fields ; wind‐induced pressure pumping
    Language: English
    Type: doc-type:article
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  • 4
    Publication Date: 2024-02-28
    Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉Hydrogeological information about an aquifer is difficult and costly to obtain, yet essential for the efficient management of groundwater resources. Transferring information from sampled sites to a specific site of interest can provide information when site‐specific data is lacking. Central to this approach is the notion of site similarity, which is necessary for determining relevant sites to include in the data transfer process. In this paper, we present a data‐driven method for defining site similarity. We apply this method to selecting groups of similar sites from which to derive prior distributions for the Bayesian estimation of hydraulic conductivity measurements at sites of interest. We conclude that there is now a unique opportunity to combine hydrogeological expertise with data‐driven methods to improve the predictive ability of stochastic hydrogeological models.〈/p〉
    Description: 〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉〈italic〉Article impact statement〈/italic〉: This article introduces hierarchical clustering as a method for defining a notion of site similarity; the aim of this method is to improve the derivation of prior distributions in Bayesian methods in hydrogeology.〈/p〉
    Description: Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659
    Description: https://github.com/GeoStat-Bayesian/geostatDB
    Description: https://github.com/GeoStat-Bayesian/exPrior
    Description: https://github.com/GeoStat-Bayesian/siteSimilarity
    Keywords: ddc:551.49 ; hydrogeological sites ; hydrogeological modeling
    Language: English
    Type: doc-type:article
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