ALBERT

Description: Many aquifers that are highly contaminated by arsenic in South and Southeast Asia are in the floodplains of large river networks. Under natural conditions, these aquifers would discharge into nearby rivers; however large-scale groundwater pumping has reversed the flow in some areas so that rivers now recharge aquifers. At a field site near Hanoi Vietnam, we find river water recharging the aquifer becomes high in arsenic, reaching concentrations above 1000 μg/L, within the upper meter of recently (〈 ∼10 yrs ) deposited riverbed sediments as it is drawn into a heavily pumped aquifer along the Red River. Groundwater arsenic concentrations in aquifers adjacent to the river are largely controlled by river geomorphology. High (〉 50 μg/L) aqueous arsenic concentrations are found in aquifer regions adjacent to zones where the river has recently deposited sediment and low arsenic concentrations are found in aquifer regions adjacent to erosional zones. High arsenic concentrations are even found adjacent to a depositional river reach in a Pleistocene aquifer, a type of aquifer sediment which generally hosts low arsenic water. Using geochemical and isotopic data we estimate the in-situ rate of arsenic release from riverbed sediments to be up to 1000 times the rates calculated on inland aquifer sediments in Vietnam. Geochemical data for riverbed porewater conditions indicate that the reduction of reactive, poorly crystalline iron oxides controls arsenic release. We suggest that aquifers in these regions may be susceptible to further arsenic contamination where riverine recharge drawn into aquifers by extensive groundwater pumping flows through recently deposited river sediments before entering the aquifer. This article is protected by copyright. All rights reserved.

Description: Abstract Meltwater from glaciers is not only a stable source of water but also affects downstream streamflow dynamics. One of these dynamics is the interannual variability of streamflow. Glaciers can moderate streamflow variability, because the runoff in the glacierised part, driven by temperature, correlates negatively with the runoff in the non‐glacierised part of a catchment, driven by precipitation, thereby counterbalancing each other. This is also called the glacier compensation effect (GCE) and the effect is assumed to depend on relative glacier cover. Previous studies found a convex relationship between streamflow variability and glacier cover of different glacierised catchments, with lowest streamflow variability at a certain optimum glacier cover. In this study we aim to revisit these previously found curves to find out if a universal relationship between interannual streamflow variability and glacier cover exists, which could potentially be used in a space‐for‐time substitution analysis. Moreover, we test the hypothesis that the dominant climate drivers (here precipitation and temperature) switch around the suggested optimum of the curve. First a set of virtual nested catchments, with the same absolute glacier area but varying non‐glacierised area, were modelled to isolate the effect of glacier cover on streamflow variability. The modelled relationship was then compared to a multi‐catchment dataset of gauged glacierised catchments in the European Alps. In a third step, changes of the GCE curve over time were analysed. Model results showed a convex relationship and the optimum in the simulated curve aligned with a switch in the dominant climate driver. However, the multi‐catchment data and the time change analyses did not suggest the existence of a universal convex relationship. Overall, we conclude that GCE is complex due to entangled controls and changes over time in glacierised catchments. Therefore, care should be taken to use a GCE curve for estimating and/or predicting interannual streamflow variability in glacierised catchments.

Description: ABSTRACT This response expands on the analysis performed in ‘Candidate Distributions for Climatological Drought Indices (SPI and SPEI)’ by explaining several topics in greater detail and by testing the conclusions of our original article against the claims made in the comment by Drs Vicente-Serrano and Begueria. Tests using the same 11 climate time series confirm the original findings from Stagge et al. (2015) that the Generalized Extreme Value (GEV) distribution produces consistently better fits. Claims that the GEV distribution exaggerates extreme SPEI values were found to be false by comparing Log-Logistic and GEV-generated SPEI values directly to the baseline normal distribution, rather than to one another. Once compared with the theoretical normal distribution, the GEV distribution was shown to better model the extreme tails, while the Log-Logistic distribution consistently underestimated these values. Analysis of the tails was shown to introduce significant uncertainty due to extrapolation regardless of the distribution. We thus strongly disagree with claims made in the comment by Vicente-Serrano and Begueria that their results clearly recommend the Log-Logistic distribution. Instead, we prove that differences tend to be small, but consistently support the use of the GEV distribution for SPEI analysis across multiple data sources and goodness of fit metrics.

Description: We analyse a high-resolution spectropolarimetric data set collected for the He-weak B3p IV star HR 2949. The Zeeman effect is visible in the circularly polarized component of numerous spectral lines. The longitudinal magnetic field varies between approximately –650 and +150 G. The polar strength of the surface magnetic dipole is calculated to be 2.4 $^{+0.3}_{-0.2}$  kG. The star has strong overabundances of Fe-peak elements, along with extremely strong overabundances of rare-earth elements; however, He, Al, and S are underabundant. This implies that HR 2949 is a chemically peculiar star. Variability is seen in all photospheric lines, likely due to abundance patches as seen in many Ap/Bp stars. Longitudinal magnetic field variations measured from different spectral lines yield different results, likely a consequence of uneven sampling of the photospheric magnetic field by the abundance patches. Analysis of photometric and spectroscopic data for both HR 2949 and its companion star, HR 2948, suggests a revision of HR 2949's fundamental parameters: in particular, it is somewhat larger, hotter, and more luminous than previously believed. There is no evidence of optical or ultraviolet emission originating in HR 2949's magnetosphere, despite its moderately strong magnetic field and relatively rapid rotation; however, when calculated using theoretical and empirical boundaries on the initial rotational velocity, the spin-down age is compatible with the stellar age. With the extensive phase coverage presented here, HR 2949 will make an excellent subject for Zeeman Doppler imaging.

Description: The Standardized Precipitation Index (SPI), a well-reviewed meteorological drought index recommended by the World Meteorological Organization (WMO), and its more recent climatic water balance variant, the Standardized Precipitation-Evapotranspiration Index (SPEI), both rely on selection of a univariate probability distribution to normalize the index, allowing for comparisons across climates. Choice of an improper probability distribution may impart bias to the index values, exaggerating or minimizing drought severity. This study compares a suite of candidate probability distributions for use in SPI and SPEI normalization using the 0.5° × 0.5° gridded Watch Forcing Dataset (WFD) at the continental scale, focusing on Europe. Several modifications to the SPI and SPEI methodology are proposed, as well as an updated procedure for evaluating SPI/SPEI goodness of fit based on the Shapiro–Wilk test. Candidate distributions for SPI organize into two groups based on their ability to model short-term accumulation (1–2 months) or long-term accumulation (〉3 months). The two-parameter gamma distribution is recommended for general use when calculating SPI across all accumulation periods and regions within Europe, in agreement with previous studies. The generalized extreme value distribution is recommended when computing the SPEI, in disagreement with previous recommendations.

Description: Abstract Time series of groundwater head measurements serve as a primary source of information on groundwater systems. In different groundwater systems, and across several scales, we observe a multitude of patterns in groundwater time series, resulting from complex hydrogeological setups. Unlike in surface hydrology, there is no generalized classification to categorize and quantify the dynamics in groundwater time series. This leads to a lack of tools that could help us disentangle the information contained in groundwater time series in a systematic way. To approach such a classification, we present a principle for organization to qualitatively describe and quantify groundwater dynamics in a non‐redundant and data efficient way. We devise a descriptive typology of groundwater dynamics and assign quantitative measures, mathematically expressing these dynamics. Based on an extensive data set of daily groundwater hydrographs from Central Europe, we analyze the relationship between indices and typology based on Principal Component Analysis (PCA). The PCA is also used to investigate and discuss redundancy, i.e. indices expressing similar information content of hydrographs. Further, we investigate the indices’ sensitivity to measurement interval and length of the overall observed period. A case study demonstrates the potential of the typology and index approach to link groundwater dynamics to the underlying hydrogeological process controls. The tools provided for characterization and quantification of groundwater dynamics should improve future efforts of groundwater classification and prediction in ungauged aquifers and other applications.

Description: Abstract Widespread contamination of groundwater with geogenic arsenic is attributed to microbial dissolution of arsenic‐bearing iron (oxyhydr)oxides minerals coupled to the oxidation of organic carbon. The recharge sources to an aquifer can influence groundwater arsenic concentrations by transport of dissolved arsenic or reactive constituents that affect arsenic mobilization. To understand how different recharge sources affect arsenic contamination – in particular through their influence on organic carbon and sulfate cycling – we delineated and quantified recharge sources in the arsenic affected region around Hanoi, Vietnam. We constrained potential end‐member compositions and employed a novel end‐member mixing model using an ensemble approach to apportion recharge sources. Groundwater arsenic and dissolved organic carbon concentrations are controlled by the dominant source of recharge. High arsenic concentrations are prevalent regardless of high dissolved organic carbon or ammonium levels, indicative of organic matter composition, where the dominant recharge source is riverine. In contrast, high dissolved organic carbon and significant organic matter decomposition are required to generate elevated groundwater arsenic where recharge is largely non‐riverine. These findings suggest that in areas of riverine recharge, arsenic may be efficiently mobilized from reactive surficial environments and carried from river‐aquifer interfaces into groundwater. In groundwaters derived from non‐riverine recharge areas, significantly more organic carbon mineralization is required to obtain equivalent levels of arsenic mobilization within inland sediments. This method can be broadly applied to examine the connection between hydrology, geochemistry and groundwater quality.

Description: The compound Bi(TeSi t Bu 2 Ph) 3 ( 1 ) was obtained from the reaction of BiCl 3 with t Bu 2 PhSiTeSiMe 3 in diethyl ether. The single crystal structure analysis revealed a trigonal pyramidal structure of the BiTe 3 core and weak Bi···C (arene) contacts. Compound 1 was characterized by multinuclear NMR spectroscopy, IR spectroscopy and elemental analysis. Thermogravimetric experiments show that compound 1 decomposes with formation of Bi 2 Te 3 or a mixture of elemental bismuth and Bi 4 Te 3 , depending on the pressure conditions.