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The Arabidopsis tandem-pore K+ (TPK) channels displaying four transmembrane domains and two pore regions share structural homologies with their animal counterparts of the KCNK family. In contrast to the Shaker-like Arabidopsis channels (six transmembrane domains/one pore region), the functional properties and the biological role of plant TPK channels have not been elucidated yet. Here, we show that AtTPK4 (KCO4) localizes to the plasma membrane and is predominantly expressed in pollen. AtTPK4 (KCO4) resembles the electrical properties of a voltage-independent K+ channel after expression in Xenopus oocytes and yeast. Hyperpolarizing as well as depolarizing membrane voltages elicited instantaneous K+ currents, which were blocked by extracellular calcium and cytoplasmic protons. Functional complementation assays using a K+ transport-deficient yeast confirmed the biophysical and pharmacological properties of the AtTPK4 channel. The features of AtTPK4 point toward a role in potassium homeostasis and membrane voltage control of the growing pollen tube. Thus, AtTPK4 represents a member of plant tandem-pore-K+ channels, resembling the characteristics of its animal counterparts as well as plant-specific features with respect to modulation of channel activity by acidosis and calcium
We present cross-hole P- and S-wave seismic experiments that have been performed along a similar to 100 m long transect for the detailed characterization of a contaminated sedimentary site (Bitterfeld research test site, Germany). We invert the corresponding first break arrival times for the P- and S-wave velocity structure and compare two different strategies to interpret these models in terms of pertinent lithological and geotechnical parameter variations. The first (common) approach is based on directly translating the tomographic velocity models into the parameters of interest (e.g., elastic moduli). The second (zonal) approach first reduces the tomographic parameter information to a limited number of characteristic velocity combinations via k-means cluster analysis. Then, for each zone (cluster) further parameters including uncertainties can be estimated. In the presented case study, Our results indicate that the zonal approach provides an effective means for the integrated interpretation of different co-located data.
Large-scale groundwater models are required to estimate groundwater availability and to inform water management strategies on the national scale.
However, parameterization of large-scale groundwater models covering areas of major river basins and more is challenging due to the lack of observational data and the mismatch between the scales of modeling and measurements.
In this work, we propose to bridge the scale gap and derive regional hydraulic parameters by spectral analysis of groundwater level fluctuations.
We hypothesize that specific locations in aquifers can reveal regional parameters of the hydraulic system.
We first generate ensembles of synthetic but realistic aquifers which systematically differ in complexity. Applying Liang and Zhang's (2013), , semi-analytical solution for the spectrum of hydraulic head time series, we identify for each ensemble member and at different locations representative aquifer parameters.
Next, we extend our study to investigate the use of spectral analysis in more complex numerical models and in real settings.
Our analyses indicate that the variance of inferred effective transmissivity and storativity values for stochastic aquifer ensembles is small for observation points which are far away from the Dirichlet boundary.
Moreover, the head time series has to cover a period which is roughly 10 times as long as the characteristic time of the aquifer. In deterministic aquifer models we infer equivalent, regionally valid parameters. A sensitivity analysis further reveals that as long as the aquifer length and the position of the groundwater measurement location is roughly known, the parameters can be robustly estimated.
In many near-surface geophysical studies it is now common practice to collect co-located disparate geophysical data sets to explore subsurface structures. Reconstruction of physical parameter distributions underlying the available geophysical data sets usually requires the use of tomographic reconstruction techniques. To improve the quality of the obtained models, the information content of all data sets should be considered during the model generation process, e.g., by employing joint or cooperative inversion approaches. Here, we extend the zonal cooperative inversion methodology based on fuzzy c-means cluster analysis and conventional single-input data set inversion algorithms for the cooperative inversion of data sets with partially co-located model areas. This is done by considering recent developments in fuzzy c-means cluster analysis. Additionally, we show how supplementary a priori information can be incorporated in an automated fashion into the zonal cooperative inversion approach to further constrain the inversion. The only requirement is that this a priori information can be expressed numerically; e.g., by physical parameters or indicator variables. We demonstrate the applicability of the modified zonal cooperative inversion approach using synthetic and field data examples. In these examples, we cooperatively invert S- and P-wave traveltime data sets with partially co-located model areas using water saturation information expressed by indicator variables as additional a priori information. The approach results in a zoned multi-parameter model, which is consistent with all available information given to the zonal cooperative inversion and outlines the major subsurface units. In our field example, we further compare the obtained zonal model to sparsely available borehole and direct-push logs. This comparison provides further confidence in our zonal cooperative inversion model because the borehole and direct-push logs indicate a similar zonation.