Divergence in seasonal hydrology across northern Eurasia: Emerging trends and water cycle linkages

Discharge from large Eurasia rivers increased during the 20th century, yet much remains unknown regarding details of this increasing freshwater flux. Here, for the three largest Eurasian basins (the Ob, Yenisei, and Lena) we examine the nature of annual and seasonal discharge trends by investigating the flow changes along with those for precipitation, snow depth, and snow water equivalent. On the basis of a multiperiod trend analysis and examination of station data, we propose two characteristic regimes to explain the long‐term discharge increase from these large Eurasian rivers. Over the early decades from approximately 1936 to 1965, annual precipitation correlates well with annual discharge, and positive discharge trends are concurrent with summer/fall discharge increases. The latter decades were marked by a divergence between winter/spring flows, which increased, amid summer/fall discharge declines. A comparison of cold season precipitation (CSP) and spring discharge trends across subbasins of the Ob, Yenisei, and Lena shows limited agreement with one precipitation data set but good agreement (R2 \u3e 0.90) when a second is used. While natural variability in the Arctic system tends to mask these emerging trends, spatial and temporal changes can generally be characterized by increased solid precipitation, primarily to the north, along with a drier hydrography during the warm season

Temporal and spatial variations in maximum river discharge from a new Russian data set

Floods cause more damage in Russia than any other natural disaster, and future climate model projections suggest that the frequency and magnitude of extreme hydrological events will increase in Russia with climate change. Here we analyze daily discharge records from a new data set of 139 Russian gauges in the Eurasian Arctic drainage basin with watershed areas from 16.1 to 50,000 km2 for signs of change in maximum river discharge. Several hypotheses about changes in maximum daily discharge and their linking with trends in precipitation over the cold season were tested. For the magnitude of maximum daily discharge we found relatively equal numbers of significant positive and negative trends across the Russian Arctic drainage basin, which draws into question the hypothesis of an increasing risk of extreme floods. We observed a significant shift to earlier spring discharge, which is consistent with documented changes in snowmelt and freeze‐thaw dates. Spatial analysis of changes in maximum discharge and cold season precipitation revealed consistency across most of the domain, the exception being the Lena basin. Trends in maximum discharge of the small‐ to medium‐sized rivers were generally consistent with aggregated signals found for the downstream gauges of the six largest Russian rivers. Although we observe regional changes in maximum discharge across the Russian Arctic drainage basin, no evidence of widespread trends in extreme discharge can be assumed from our analysis

Mapping aerial metal deposition in metropolitan areas from tree bark : a case study in Sheffield, England

We investigated the use of metals accumulated on tree bark for mapping their deposition across metropolitan Sheffield by sampling 642 trees of three common species. Mean concentrations of metals were generally an order of magnitude greater than in samples from a remote uncontaminated site. We found trivially small differences among tree species with respect to metal concentrations on bark, and in subsequent statistical analyses did not discriminate between them. We mapped the concentrations of As, Cd and Ni by lognormal universal kriging using parameters estimated by residual maximum likelihood ({\sc reml}). The concentrations of Ni and Cd were greatest close to a large steel works, their probable source, and declined markedly within 500~metres of it and from there more gradually over several kilometres. Arsenic was much more evenly distributed, probably as a result of locally mined coal burned in domestic fires for many years. Tree bark seems to integrate airborne pollution over time, and our findings show that sampling and analysing it are cost-effective means of mapping and identifying sources

Estimating specific surface area of fine stream bed sediments from geochemistry

Specific surface area (SSA) of headwater stream bed sediments is a fundamental property which determines the nature of sediment surface reactions and influences ecosystem-level, biological processes. Measurements of SSA – commonly undertaken by BET nitrogen adsorption – are relatively costly in terms of instrumentation and operator time. A novel approach is presented for estimating fine (2.5 mg kg−1), four elements were identified as significant predictors of SSA (ordered by decreasing predictive power): V > Ca > Al > Rb. The optimum model from these four elements accounted for 73% of the variation in bed sediment SSA (range 6–46 m2 g−1) with a root mean squared error of prediction – based on leave-one-out cross-validation – of 6.3 m2 g−1. It is believed that V is the most significant predictor because its concentration is strongly correlated both with the quantity of Fe-oxides and clay minerals in the stream bed sediments, which dominate sediment SSA. Sample heterogeneity in SSA – based on triplicate measurements of sub-samples – was a substantial source of variation (standard error = 2.2 m2 g−1) which cannot be accounted for in the regression model. The model was used to estimate bed sediment SSA at the other 1792 sites and at 30 duplicate sites where an extra sediment sample had been collected, 25 m from the original site. By delineating sub-catchments for the headwater sediment sites only those sub-catchments were selected with a dominant (>50% of the sub-catchment area) bedrock formation and land use type; the bedrock and land use classes accounted for 39% and 7% of the variation in bed sediment SSA, respectively. Variation in estimated, fine bed sediment SSA from the paired, duplicate sediment sites was small (2.7 m2 g−1), showing that local variation in SSA at stream sites is modest when compared to that between catchments. How the approach might be applied in other environments and its potential limitations are discussed

Implications of short-range spatial variation of soil bulk density for adequate field-sampling protocols: methodology and results from two contrasting soils

Soil bulk density (BD) is measured during soil monitoring. Because it is spatially variable, an appropriate sampling protocol is required. This paper shows how information on short-range variability can be used to quantify uncertainty of estimates of mean BD and soil organic carbon on a volumetric basis (SOCv) at a sampling site with different sampling intensities. We report results from two contrasting study areas, with mineral soil and with peat. More sites should be investigated to develop robust protocols for national-scale monitoring, but these results illustrate the methodology. A 20 × 20-m2 monitoring site was considered and sampling protocols were evaluated under geostatistical models of our two study areas. At sites with local soil variability comparable to our mineral soil, sampling at 16 points (4 × 4 square grid of interval 5 m) would achieve a root mean square error (RMSE) of the sample mean value of both BD and SOCv of less than 5% of the mean (topsoil and subsoil). Pedotransfer functions (PTFs) gave predictions of mean soil BD at a sample site, comparable to our study area on mineral soil, with similar precision to a single direct measurement of BD. On peat soils comparable to our second study area, the mean BD for the monitoring site at depth 0–50 cm would be estimated with RMSE to be less than 5% of the mean with a sample of 16 cores, but at greater depths this criterion cannot be achieved with 25 cores or fewer

An FGFR1-SPRY2 Signaling Axis Limits Basal Cell Proliferation in the Steady-State Airway Epithelium.

The steady-state airway epithelium has a low rate of stem cell turnover but can nevertheless mount a rapid proliferative response following injury. This suggests a mechanism to restrain proliferation at steady state. One such mechanism has been identified in skeletal muscle in which pro-proliferative FGFR1 signaling is antagonized by SPRY1 to maintain satellite cell quiescence. Surprisingly, we found that deletion of Fgfr1 or Spry2 in basal cells of the adult mouse trachea caused an increase in steady-state proliferation. We show that in airway basal cells, SPRY2 is post-translationally modified in response to FGFR1 signaling. This allows SPRY2 to inhibit intracellular signaling downstream of other receptor tyrosine kinases and restrain basal cell proliferation. An FGFR1-SPRY2 signaling axis has previously been characterized in cell lines in vitro. We now demonstrate an in vivo biological function of this interaction and thus identify an active signaling mechanism that maintains quiescence in the airway epithelium.This study was supported by the Medical Research Council (G0900424 to ER); Wellcome Trust clinical PhD fellowship (JJ). Core grants: Gurdon Institute: Wellcome Trust (092096), Cancer Research UK (C6946/A14492); Stem Cell Initiative: Wellcome Trust/MRC.This is the final version of the article. It first appeared from Elsevier via https://doi.org/10.1016/j.devcel.2016.03.00

Enhancement of antihydrogen formation in antiproton collisions with excited-state positronium

© Published under licence by IOP Publishing Ltd. Antihydrogen formation in positronium scattering on antiprotons is investigated using the two-centre convergent close-coupling method. A several orders of magnitude enhancement in the formation of antihydrogen is found when positronium is in an excited state. The effect is greatest at the lowest energies considered which encompass those achievable in experiment. This suggests a practical approach to creating neutral antimatter for testing its interaction with gravity and for spectroscopic measurements

Scope to predict soil properties at within-field scale from small samples using proximally sensed γ-ray spectrometer and EM induction data

Spatial predictions of soil properties are needed for various purposes. However, the costs associated with soil sampling and laboratory analysis are substantial. One way to improve efficiencies is to combine measurement of soil properties with collection of cheaper-to-measure ancillary data. There are two possible approaches. The first is the formation of classes from ancillary data. A second is the use of a simple predictive linear model of the target soil property on the ancillary variables. Here, results are presented and compared where proximally sensed gamma-ray (γ-ray) spectrometry and electromagnetic induction (EMI) data are used to predict the variation in topsoil properties (e.g. clay content and pH). In the first instance, the proximal data is numerically clustered using a fuzzy k-means (FKM) clustering algorithm, to identify contiguous classes. The resultant digital soil maps (i.e. k = 2–10 classes) are consistent with a soil series map generated using traditional soil profile description, classification and mapping methods at a highly variable site near the township of Shelford, Nottinghamshire UK. In terms of prediction, the calculated expected value of mean squared prediction error (i.e. σ2p,C) indicated that values of k = 7 and 8 were ideal for predicting clay and pH. Secondly, a linear mixed model (LMM) is fitted in which the proximal data are fixed effects but the residuals are treated as a combination of a spatially correlated random effect and an independent and identically distributed error. In terms of prediction, the expected value of the mean squared prediction error from a regression (σ2p,R) suggested that the regression models were able to predict clay content, better than FKM clustering. The reverse was true with respect to pH, however. We conclude that both methods have merit. In the case of the clustering the approach is able to account for soil properties which have non-linearity's with the ancillary data (i.e. pH), whereas the LMM approach is best when there is a strong linear relationship (i.e. clay)

Calculation of antihydrogen formation via antiproton scattering with excited positronium

Detailed presentation of results shown in the ealrier Phys. Rev. Lett. (114, 183201, (2015)).Shows all partial cross sections for antihydrogen formation in collisions of antiprotons with positronium in quantum states n = 1-3