ALBERT

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

Description: This collection contains measurements of physical and chemical soil properties on the main experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained in general by bi-annual weeding and mowing. Since 2010, plot size was reduced to 5 x 6 m and plots were weeded three times per year. The following series of datasets are contained in this collection: 1. Physical soil properties - Soil texture: Proportion of sand, silt and clay in the fine soil was measured in April 2002 before plot establishment at 27 locations distributed throughout the experimental site. Undisturbed soil cores were taken to 100 cm depth and separated in depth increments with a resolution of 10 to 20 cm. Grain size fractions according to DIN 19683-2 were then determined by a combined sieve and hydrometer analysis. Values for each plot were interpolated by ordinary kriging. - Bulk density: Bulk density was sampled down to 100 cm depth in 2002 and 30 cm depth in 2004, 2006 and 2008. Several undisturbed soil cores were taken per plot and separated in depth increments before the bulk material was sieved, dried and weighed. - Soil hydraulic properties: Field capacity and permanent wilting point at 10, 20 and 30 cm depth were derived from soil texture data of 2002 and bulk density 2006 by using pedotransfer functions. Applied was equation four and five of Zacharias and Wessolek (2007) to derive parameters of the water retention curve. Water contents at field capacity and permanent wilting point were obtained using the van Genuchte Eq (e.g. eq 1 in Zacharias and Wessolek), and calculating water contents at - 330 cm matric potential (field capacity, 1/3 of atmospheric pressure) and at -15000 cm. -Soil porosity: the fraction of total volume occupied by pores or voids measured at matric potential 0, already published on https://doi.pangaea.de/10.1594/PANGAEA.865254. 2. Chemical soil properties - Lime content: Percentage of CaCO3 in the soil was measured in April 2002 before plot establishment at 27 locations distributed throughout the experimental site. Undisturbed soil cores were taken to 100 cm depth and separated in depth increments with a resolution of 10 to 20 cm. The bulk material was sieved and CaCO3 content of the fine soil was determined as volumetric determination according to DIN 19684-5. - Soil organic matter: Percentage of soil organic matter was measured in April 2002 before plot establishment at 27 locations distributed throughout the experimental site. Undisturbed soil cores were taken to 100 cm depth and separated in depth increments with a resolution of 10 to 20 cm. The bulk material was sieved and organic content of the fine soil was determined using a loss-on-ignition method. - Soil pH value: soil pH value was determined 2002 and 2010 in water and 2002 also in calcium chloride. Five soil samples were taken per plot and bulk material was diluted in water and calcium chloride. PH values were then measured with an electrode.

Description: This data set contains measurements of soil hydraulic properties, i. e. field capacity and permanent wilting point. Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained in general by bi-annual weeding and mowing. Since 2010, plot size was reduced to 5 x 6 m and plots were weeded three times per year. Field capacity and permanent wilting point at 10, 20 and 30 cm depth were derived from soil texture data of 2002 and bulk density 2006 by using pedotransfer functions. Soil texture was determined from undisturbed soil cores at 27 locations distributed throughout the experimental site in spring 2002 before plot establishment. Soil cores were taken to 100 cm depth and separated in depth increments with a resolution of ten cm from ground level to 40 cm depth and 20 cm from 40 cm to 100 cm depth. The bulk material was passed through a sieve with 2 mm mesh size and only fine soil was used for the investigation of soil texture. Grain size fractions according to DIN 19683-2 for every sample were then determined at the laboratory for geoecology of Jena University by a combined sieve and hydrometer analysis. Values for each plot were interpolated by ordinary kriging and the interpolated values were used for the investigation of field capacity and permanent wilting point. Soil bulk density was determined from undisturbed soil samples to a depth of 30 cm. Three soil cores per plot were taken with a split tube sampler with an inner diameter of 4.8 cm and separated in depth increments of five cm. The bulk material was passed through a sieve with 2 mm mesh size, dried to constant weight at 40 °C and subsequently weighed to calculate the density. The determination of field capacity and permanent wilting point was based on pedotransfer functions described in Zacharias and Wessolek (2007). Applied was equation four (where sand content 〈= 66.5%) and five (where sand content 〉 66.5 %) to derive the parameters of the water retention curve. Bulk density for 10 cm was obtained by taking the average of the measured bulk density of 5-10 cm and 10-15 cm, similarly for 20 cm. For 30 cm, bulk density was assumed to be equal to the one measured at 25-30 cm. Water contents at field capacity and permanent wilting point were obtained using the van Genuchte Eq (e.g. eq 1 in Zacharias and Wessolek), and calculating water contents at -330 cm matric potential (field capacity, 1/3 of atmospheric pressure) and at - 15000 cm.

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