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  • Other Sources  (5)
  • ddc:551.49  (3)
  • 551.9  (2)
  • Blackwell Publishing Ltd  (5)
  • American Chemical Society
  • American Chemical Society (ACS)
  • 1
    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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  • 2
    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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  • 3
    Publication Date: 2021-07-05
    Description: Nitrogen (N) fertilization is the major contributor to nitrous oxide (N2O) emissions from agricultural soil, especially in post‐harvest seasons. This study was carried out to investigate whether ryegrass serving as cover crop affects soil N2O emissions and denitrifier community size. A microcosm experiment was conducted with soil planted with perennial ryegrass (Lolium perenne L.) and bare soil, each with four levels of N fertilizer (0, 5, 10 and 20 g N m−2; applied as calcium ammonium nitrate). The closed‐chamber approach was used to measure soil N2O fluxes. Real‐time PCR was used to estimate the biomass of bacteria and fungi and the abundance of genes involved in denitrification in soil. The results showed that the presence of ryegrass decreased the nitrate content in soil. Cumulative N2O emissions of soil with grass were lower than in bare soil at 5 and 10 g N m−2. Fertilization levels did not affect the abundance of soil bacteria and fungi. Soil with grass showed greater abundances of bacteria and fungi, as well as microorganisms carrying narG, napA, nirK, nirS and nosZ clade I genes. It is concluded that ryegrass serving as a cover crop holds the potential to mitigate soil N2O emissions in soils with moderate or high NO3− concentrations. This highlights the importance of cover crops for the reduction of N2O emissions from soil, particularly following N fertilization. Future research should explore the full potential of ryegrass to reduce soil N2O emissions under field conditions as well as in different soils. Highlights This study was to investigate whether ryegrass serving as cover crop affects soil N2O emissions and denitrifier community size; Plant reduced soil N substrates on one side, but their root exudates stimulated denitrification on the other side; N2O emissions were lower in soil with grass than bare soil at medium fertilizer levels, and growing grass stimulated the proliferation of almost all the denitrifying bacteria except nosZ clade II; Ryegrass serving as a cover crop holds the potential to mitigate soil N2O emissions.
    Description: China Scholarship Council http://dx.doi.org/10.13039/501100004543
    Description: The National Science Project for University of Anhui Province
    Keywords: 551.9 ; 631.4 ; denitrification ; perennial ryegrass (Lolium perenne L.) ; soil bacteria ; soil CO2 emissions ; soil N2O emissions
    Type: article
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  • 4
    Publication Date: 2021-07-05
    Description: Sustainable arable cropping relies on repeated liming. Yet, the associated increase in soil pH can reduce the availability of iron (Fe) to plants. We hypothesized that repeated liming, but not pedogenic processes such as lessivage (i.e., translocation of clay particles), alters the Fe cycle in Luvisol soil, thereby affecting Fe isotope composition in soils and crops. Hence, we analysed Fe concentrations and isotope compositions in soil profiles and winter rye from the long‐term agricultural experimental site in Berlin‐Dahlem, Germany, where a controlled liming trial with three field replicates per treatment has been conducted on Albic Luvisols since 1923. Heterogeneity in subsoil was observed at this site for Fe concentration but not for Fe isotope composition. Lessivage had not affected Fe isotope composition in the soil profiles. The results also showed that almost 100 years of liming lowered the concentration of the HCl‐extractable Fe that was potentially available for plant uptake in the surface soil (0–15 cm) from 1.03 (standard error (SE) 0.03) to 0.94 (SE 0.01) g kg−1. This HCl‐extractable Fe pool contained isotopically lighter Fe (δ56Fe = −0.05 to −0.29‰) than the bulk soil (δ56Fe = −0.08 to 0.08‰). However, its Fe isotope composition was not altered by the long‐term lime application. Liming resulted in relatively lower Fe concentrations in the roots of winter rye. In addition, liming led to a heavier Fe isotope composition of the whole plants compared with those grown in the non‐limed plots (δ56FeWholePlant_ + Lime = −0.12‰, SE 0.03 vs. δ56FeWholePlant_‐Lime = −0.21‰, SE 0.01). This suggests that the elevated soil pH (increased by one unit due to liming) promoted the Fe uptake strategy through complexation of Fe(III) from the rhizosphere, which favoured heavier Fe isotopes. Overall, the present study showed that liming and a related increase in pH did not affect the Fe isotope compositions of the soil, but may influence the Fe isotope composition of plants grown in the soil if they alter their Fe uptake strategy upon the change of Fe availability. Highlights Fe concentrations and stocks, but not Fe isotope compositions, were more heterogeneous in subsoil than in topsoil. Translocation of clay minerals did not result in Fe isotope fractionation in the soil profile of a Luvisol. Liming decreased Fe availability in topsoil, but did not affect its δ56Fe values. Uptake of heavier Fe isotopes by graminaceous crops was more pronounced at elevated pH.
    Description: Bundesministerium für Bildung und Forschung http://dx.doi.org/10.13039/501100002347
    Keywords: 551.9 ; liming ; plant‐available Fe pool in soil ; winter rye ; δ56Fe
    Type: article
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  • 5
    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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