Bohmian trajectories and Klein's paradox

We compute the Bohmian trajectories of the incoming scattering plane waves for Klein's potential step in explicit form. For finite norm incoming scattering solutions we derive their asymptotic space-time localization and we compute some Bohmian trajectories numerically. The paradox, which appears in the traditional treatments of the problem based on the outgoing scattering asymptotics, is absent.Comment: 14 pages, 3 figures; minor format change

Integrated management of a Swiss cropland is not sufficient to preserve its soil carbon pool in the long term

Croplands are involved in the exchange of carbon dioxide (CO2) between the atmosphere and the biosphere. Furthermore, soil carbon (C) stocks play an important role in soil fertility. It is thus of great interest to know whether intensively managed croplands act as a net source or sink of atmospheric CO2 and if soil C stocks are preserved over long timescales. The FluxNet site CH-Oe2 in Oensingen, Switzerland, has been operational since the end of 2003. This cropland is managed under the Swiss framework of the Proof of Ecological Performance (PEP, a variant of integrated management) with a crop rotation centred on winter wheat, which also includes winter barley, winter rapeseed, peas, potato and intermediate cover crops. In addition to eddy covariance measurements, meteorological and soil measurements were available along with information on C imports and exports from organic fertilisation, sowing and harvesting. This study investigates cropland C budgets over 13 years and assesses whether the PEP regulations resulted in a balanced C budget. The strongest CO2 uptake was observed during cereal seasons. C export through harvest, however, offset the strong uptake of the cereal crops. The largest net CO2 emissions to the atmosphere were observed during pea and cover crop seasons. Net biome production, representing the overall C budget (assuming carbon leaching to groundwater to be negligible), typically ranged between close to C neutral to C losses of up to 407&thinsp;g&thinsp;C&thinsp;m−2 per season, with peas being the largest source. Overall, the field lost 1674&thinsp;g&thinsp;C&thinsp;m−2 over 13 years (129&thinsp;g&thinsp;C&thinsp;m−2&thinsp;yr−1), which was confirmed by soil C stock measurements at the beginning and the end of the study period. Although managing the field under the regulations of PEP did not result in an overall C sink, model simulations showed that the use of cover crops reduced the C losses compared to leaving the field bare. The use of solid manure improved the C budget by importing substantial amounts of C into the soil, while liquid manure had only a small effect. We thus conclude that additional efforts are needed to bring Swiss management practices closer to the goal of preserving soil C in the long term.</p

Low-flow analysis of the rivers in the Ljubljanica watershed

Methanol is the second most abundant volatile organic compound in the troposphere and plays a significant role in atmospheric chemistry. While there is consensus about the dominant role of living plants as the major source and the reaction with OH as the major sink of methanol, global methanol budgets diverge considerably in terms of source/sink estimates reflecting uncertainties in the approaches used to model, and the empirical data used to separately constrain these terms. Here we compiled micrometeorological methanol flux data from eight different study sites and reviewed the corresponding literature in order to provide a first cross-site synthesis of the terrestrial ecosystem-scale methanol exchange and present an independent data-driven view of the land–atmosphere methanol exchange. Our study shows that the controls of plant growth on the production, and thus the methanol emission magnitude, and stomatal conductance on the hourly methanol emission variability, established at the leaf level, hold across sites at the ecosystem-level. Unequivocal evidence for bi-directional methanol exchange at the ecosystem scale is presented. Deposition, which at some sites even exceeds methanol emissions, represents an emerging feature of ecosystem-scale measurements and is likely related to environmental factors favouring the formation of surface wetness. Methanol may adsorb to or dissolve in this surface water and eventually be chemically or biologically removed from it. Management activities in agriculture and forestry are shown to increase local methanol emission by orders of magnitude; they are however neglected at present in global budgets. While contemporary net land methanol budgets are overall consistent with the grand mean of the micrometeorological methanol flux measurements, we caution that the present approach of simulating methanol emission and deposition separately is prone to opposing systematic errors and does not allow taking full advantage of the rich information content of micrometeorological flux measurements

Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)

Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

The following authors were omitted from the original version of this Data Descriptor: Markus Reichstein and Nicolas Vuichard. Both contributed to the code development and N. Vuichard contributed to the processing of the ERA-Interim data downscaling. Furthermore, the contribution of the co-author Frank Tiedemann was re-evaluated relative to the colleague Corinna Rebmann, both working at the same sites, and based on this re-evaluation a substitution in the co-author list is implemented (with Rebmann replacing Tiedemann). Finally, two affiliations were listed incorrectly and are corrected here (entries 190 and 193). The author list and affiliations have been amended to address these omissions in both the HTML and PDF versions