Assess hydrological responses to a warming climate at the Lysina Critical Zone Observatory in Central Europe

Climate warming is having profound effects on the hydrological cycle by increasing atmospheric demand, changing water availability, and snow seasonality. Europe suffered three distinct heat waves in 2019, and 11 of the 12 hottest years ever recorded took place in the past two decades, which will potentially change seasonal streamflow patterns and long-term trends. Central Europe exhibited six dry years in a row since 2014. This study uses data from a well-documented headwater catchment in Central Europe (Lysina) to explore hydrological responses to a warming climate. We applied a lumped parameter hydrologic model Brook90 and a distributed model Penn State Integrated Hydrologic Model (PIHM) to simulate long-term hydrological change under future climate scenarios. Both models performed well on historic streamflow and in agreement with each other according to the catchment water budget. In addition, PIHM was able to simulate lateral groundwater redistribution within the catchment validated by the groundwater table dynamics. The long-term trends in runoff and low flow were captured by PIHM only. We applied different EURO-CORDEX models with two emission scenarios (Representative Concentration Pathways RCP 4.5, 8.5) and found significant impacts on runoff and evapotranspiration (ET) for the period of 2071–2100. Results from both models suggested reduced runoff and increased ET, while the monthly distribution of runoff was different. We used this catchment study to understand the importance of subsurface processes in projection of hydrologic response to a warming climate.     

Cleantech clusters: Transformational assemblages for a just,green economy or just business as usual?

The rhetorical zeal for green enterprise as a global fix for the tripartite challenges of economic recession, environmental degradation and social inequality is increasingly visible in state and non-state pronouncements around the globe under the banner of ‘The Green Economy’. In particular, many policy-facing statements call for transitions leading to a transformation in development practices. Yet there is little detail either in policy or research regarding the types of transitions needed and how they are to be initiated, nor agreement about what a transformed economy might look like. Despite this, there are emergent activities within the cleantech arena which are being heralded as actually existing examples of green economy activities. One means through which these activities are seeking to exert influence over development trajectories is by clustering both at the subnational and transnational level. While diverse in formation, many of these clusters are hybridised, involving actors from public, private and civil society sectors. Critiquing the efficacy of mainstream industrial cluster theory to analyse hybridised cleantech clustering, this paper presents a unique synthesis of current thought on multiscalar environmental governance and socio-spatial formations to explore the practices and potentialities of these hybridised cleantech clusters. Surveying the landscape of cleantech clustering and meta-clustering, before focusing in depth on one case study, the contribution of clustering to transitioning towards a transformed green economy is considered. Despite strong forces, both within and beyond cleantech clusters, for maintaining neoliberalised approaches to cleantech activity, it is concluded that for as long as cleantech clusters remain open and inclusive of actors proposing alternative pathways they do represent potential, albeit provisional, assemblages for transformation.     

The XMM-Newton Bright Serendipitous Survey: First Extragalactic Results

We present here some initial results from the ongoing XMM-Newton bright serendipitous survey. The survey is aimed at selecting and spectroscopically identifying a large and statistically representative sample of bright (f _x ≳ 7× 10⁻¹⁴ c.g.s) serendipitous X-ray sources in the 0.5–4.5 keV energy band (BSS) and a complementary (smaller) sample in the 4.5–7.5 keV energy band (HBSS). The work is partly based on observations obtained with XMM-Newton, an ESA science mission with instruments and contributors directly founded by ESA member states and the USA(NASA) and on observations collected at TNG. The TNG telescope is operated on the island of La Palma by the Centro Galileo Galilei of the INAF in the Spanish Observatorio del Roque de Los Muchachos of the Instituto de Astrofísica de Canarias. On behalf of the XMM-Newton Survey Science Center.     

Stochastic models in the DORIS position time series: estimates for IDS contribution to ITRF2014

This paper focuses on the investigation of the deterministic and stochastic parts of the Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) weekly time series aligned to the newest release of ITRF2014. A set of 90 stations was divided into three groups depending on when the data were collected at an individual station. To reliably describe the DORIS time series, we employed a mathematical model that included the long-term nonlinear signal, linear trend, seasonal oscillations and a stochastic part, all being estimated with maximum likelihood estimation. We proved that the values of the parameters delivered for DORIS data are strictly correlated with the time span of the observations. The quality of the most recent data has significantly improved. Not only did the seasonal amplitudes decrease over the years, but also, and most importantly, the noise level and its type changed significantly. Among several tested models, the power-law process may be chosen as the preferred one for most of the DORIS data. Moreover, the preferred noise model has changed through the years from an autoregressive process to pure power-law noise with few stations characterised by a positive spectral index. For the latest observations, the medians of the velocity errors were equal to 0.3, 0.3 and 0.4 mm/year, respectively, for the North, East and Up components. In the best cases, a velocity uncertainty of DORIS sites of 0.1 mm/year is achievable when the appropriate coloured noise model is taken into consideration.     

Revised marine reservoir offset (ΔR) values for molluscs and marine mammals from Arctic North America

Appropriate marine–terrestrial reservoir offset (ΔR) values are essential for accurate calibration of marine radiocarbon dates. However, ΔR values are only valid for the specific calibration curve that their calculation is based on. Here, we present revised ΔR values for the Marine20 calibration curve from Arctic North America, based on previously published ¹⁴C dates on pre-bomb live-collected marine molluscs (n = 124) and cetaceans (beluga whales; tooth dentine; n = 12), and bowhead whale–driftwood age comparisons from the same glacio-isostatically uplifted shorelines (n = 18). Molluscan-based ΔR are: Chukchi/Beaufort sea coasts, 265±116 ¹⁴C years; NW Canadian Arctic Archipelago, 188±91 ¹⁴C years; NE Baffin Island, 81±18 ¹⁴C years; SE Baffin Island, 14±58 ¹⁴C years; Hudson Strait, −73±64 ¹⁴C years; Ungava Bay, 0±86 ¹⁴C years; Foxe Basin, 175±89 ¹⁴C years; Hudson Bay, −21±72 ¹⁴C years; James Bay, 209±114 ¹⁴C years; West Greenland, −93±111 ¹⁴C years. Species-specific marine mammal ΔR terms are 107±59 ¹⁴C years for beluga and 24±58 ¹⁴C years for bowheads. Our revised ΔR values are applicable for as long as the same broad oceanographic conditions (circulation, ventilation) have persisted, i.e. through the Holocene. While molluscan values are applicable to other marine carbonate (e.g. foraminifera), cetacean ΔR are valid only for the species they were calculated for and should not be applied to other marine mammals. Importantly, the ΔR terms calculated here are only valid for Marine20 and should not be used with earlier or later calibration curves.     

10Be content in clasts from fallout suevitic breccia in drill cores from the Bosumtwi impact crater,Ghana: Clues to preimpact target distribution

Rocks from drill cores LB‐07A (crater fill) and LB‐08A (central uplift) into the Bosumtwi impact crater, Ghana, were analyzed for the presence of the cosmogenic radionuclide ¹⁰Be. The aim of the study was to determine the extent to which target rocks of various depths were mixed during the formation of the crater‐filling breccia, and also to detect meteoric water infiltration within the impactite layer. ¹⁰Be abundances above background were found in two (out of 24) samples from the LB‐07A core, and in none of five samples from the LB‐08A core. After excluding other possible explanations for an elevated ¹⁰Be signal, we conclude that it is most probably due to a preimpact origin of those clasts from target rocks close to the surface. Our results suggest that in‐crater breccias were well mixed during the impact cratering process. In addition, the lack of a ¹⁰Be signal within the rocks located very close to the lake sediment–impactite boundary suggests that infiltration of meteoric water below the postimpact crater floor was limited. This may suggest that the infiltration of the meteoric water within the crater takes place not through the aerial pore‐space, but rather through a localized system of fractures.     

Magnetic properties of the LL5 ordinary chondrite Chelyabinsk (fall of February 15, 2013)

Here we characterize the magnetic properties of the Chelyabinsk chondrite (LL5, S4, W0) and constrain the composition, concentration, grain size distribution, and mineral fabric of the meteorite's magnetic mineral assemblage. Data were collected from 10 to 1073 K and include measurements of low‐field magnetic susceptibility (χ₀), the anisotropy of χ₀, hysteresis loops, first‐order reversal curves, Mössbauer spectroscopy, and X‐ray microtomography. The REM and REM′ paleointensity protocols suggest that the only magnetizations recorded by the chondrite are components of the Earth's magnetic field acquired during entry into our planet's atmosphere. The Chelyabinsk chondrite consists of light and dark lithologies. Fragments of the light lithology show logχ₀ = 4.57 ± 0.09 (s.d.) (n = 135), while the dark lithology shows 4.65 ± 0.09 (n = 39) (where χ₀ is in 10^?9 m³ kg^?1). Thus, Chelyabinsk is three times more magnetic than the average LL5 fall, but is similar to a subgroup of metal‐rich LL5 chondrites (Paragould, Aldsworth, Bawku, Richmond) and L/LL5 chondrites (Glanerbrug, Knyahinya). The meteorite's room‐temperature magnetization is dominated by multidomain FeNi alloys taenite and kamacite (no tetrataenite is present). However, below approximately 75 K remanence is dominated by chromite. The metal contents of the light and dark lithologies are 3.7 and 4.1 wt%, respectively, and are based on values of saturation magnetization.     

Bayesian and Neural Network Approaches to Estimate Deep Temperature Distribution for Assessing a Supercritical Geothermal System: Evaluation Using a Numerical Model

The temperature distribution at depth is a key variable when assessing the potential of a supercritical geothermal resource as well as a conventional geothermal resource. Data-driven estimation by a machine-learning approach is a promising way to estimate temperature distributions at depth in geothermal fields. In this study, we developed two methodologies—one based on Bayesian estimation and the other on neural networks—to estimate temperature distributions in geothermal fields. These methodologies can be used to supplement existing temperature logs, by estimating temperature distributions in unexplored regions of the subsurface, based on electrical resistivity data, observed geological/mineralogical boundaries, and microseismic observations. We evaluated the accuracy and characteristics of these methodologies using a numerical model of the Kakkonda geothermal field, Japan, where a temperature above 500 °C was observed below a depth of about 3.7 km. When using geological and geophysical knowledge as prior information for the machine learning methods, the results demonstrate that the approaches can provide subsurface temperature estimates that are consistent with the temperature distribution given by the numerical model. Using a numerical model as a benchmark helps to understand the characteristics of the machine learning approaches and may help to identify ways of improving these methods.

     

The sources and budget for dissolved sulfate in a fractured carbonate aquifer,southern Sacramento Mountains,New Mexico,USA

Climate change in the SW USA is likely to involve drier conditions and higher surface temperatures. In order to better understand the evolution of water chemistry and the sources of aqueous SO₄ in these semi-arid settings, chemical and S isotope compositions were determined of springs, groundwater, and bedrock associated with a Permian fractured carbonate aquifer located in the southern Sacramento Mountains, New Mexico, USA. The results suggest that the evolution of water chemistry in the semi-arid carbonate aquifer is mainly controlled by dedolomitization of bedrock, which was magnified by increasing temperature and increasing dissolution of gypsum/anhydrite along the groundwater flow path. The δ³⁴S of dissolved SO₄ in spring and groundwater samples varied from +9.0‰ to +12.8‰, reflecting the mixing of SO₄ from the dissolution of Permian gypsum/anhydrite (+12.3‰ to +13.4‰) and oxidation of sulfide minerals (−24.5‰ to −4.2‰). According to S isotope mass balance constraints, the contribution of sulfide-derived SO₄ was considerable in the High Mountain recharge areas, accounting for up to ∼10% of the total SO₄ load. However, sulfide weathering decreased in importance in the lower reaches of the watershed. A smaller SO₄ input of ∼2–4% was contributed by atmospheric wet deposition. This study implies that the δ³⁴S variation of SO₄ in semi-arid environments can be complex, but that S isotopes can be used to distinguish among the different sources of weathering. Here it was found that H₂SO₄ dissolution due to sulfide oxidation contributes up to 5% of the total carbonate weathering budget, while most of the SO₄ is released from bedrock sources during dedolomitization.