A dry lunar mantle reservoir for young mare basalts of Chang’E-5

Abstract The distribution of water in the Moon’s interior carries key implications for the origin of the Moon1, the crystallisation of the lunar magma ocean2, and the duration of lunar volcanism2. The Chang’E-5 (CE5) mission returned the youngest mare basalt samples, dated at ca. 2.0 billion years ago3, from the northwestern Procellarum KREEP Terrane (PKT), providing a probe into the spatio-temporal evolution of lunar water. Here we report the water abundance and hydrogen isotope composition of apatite and ilmenite-hosted melt inclusions from CE5 basalts, from which we derived a maximum water abundance of 370 ± 30 μg.g-1 and a δD value (-330 ± 160‰) for their parent magma. During eruption, hydrogen degassing led to an increase in the D/H ratio of the residual melts up to δD values of 300-900‰. Accounting for low degrees of mantle partial melting followed by extensive magma fractional crystallisation4, we estimate a maximum mantle water abundance of 2-6 μg.g-1, which are too low for water contents alone to account for generating the Moon’s youngest basalts. Such modest water abundances for the lunar mantle are at the lower end of those estimated from mare basalts that erupted from ca. 4.0-2.8 Ga5, 6, suggesting the mantle source of CE5 basalts dried up by ca. 2.0 Ga through previous melt extraction from the PKT mantle during prolonged volcanic activity.

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Mud volcano gas hydrates in the Caspian Sea

Bulletin of the Geological Society of Denmark ◽

10.37570/bgsd-1995-41-10 ◽

1994 ◽

Vol 41 ◽

pp. 95-100

Author(s):

G. D. Ginsburg ◽

V. A. x Soloviev

Keyword(s):

Isotope Composition ◽

Mud Volcano ◽

Light Hydrocarbons ◽

The Caspian Sea ◽

Gravity Corer ◽

Water Composition ◽

Isotopic Studies ◽

Hydrogen Isotope Composition ◽

Deep Source ◽

Water Contents

Two gas hydrate accumulations associated with mud volcano craters were investigated by means of sparker survey, bottom water and sediment sampling using gravity corer and subsequent chemical and isotopic studies of gas, bottom and porewater and carbonate inclusions. Hydrate contents in sediments were up to 35% per volume. Sometimes hydrates were encountered immediately on the seabed. Correlations have been established between hydrate contents, water contents (after dissociation of hydrates), chlorinity and the oxygen and hydrogen isotope composition of the pore water. The hydrate water is enriched with respect to deuterium. Liquid water in sediments contains higher 180 as result of isotopic exchange with carbonates. Hydrates are thought to have formed from the mud volcano brines (from their water and from dissolved light hydrocarbons which are thermogenic in origin). Each accumulation has its own deep source. The developed approach presents a thorough study of the hydrate and water contents in sediments along with the water composition.

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Differences in morpho-physiological leaf traits reflect the response of growth to drought in a seeder but not in a resprouter Mediterranean species

Functional Plant Biology ◽

10.1071/fp11232 ◽

2012 ◽

Vol 39 (4) ◽

pp. 332 ◽

Cited By ~ 19

Author(s):

David A. Ramírez ◽

Antonio Parra ◽

Víctor Resco de Dios ◽

José M. Moreno

Keyword(s):

Water Availability ◽

Isotope Composition ◽

Carbon Assimilation ◽

Late Summer ◽

Leaf Traits ◽

Severe Drought ◽

Photosynthetic Gas Exchange ◽

Maximum Water ◽

Monthly Changes ◽

Projected Changes

Understanding the mechanisms underlying the response of different plant functional types to current and projected changes in rainfall is particularly important in drought-prone areas like the Mediterranean. Here, we report the responses of two species with contrasting leaf characteristics and post-fire regeneration strategies (Cistus ladanifer L., malacophyllous, seeder; Erica arborea L., sclerophyllous, resprouter) to a manipulative field experiment that simulated a severe drought (45% reduction of historical average rainfall). We measured monthly changes in relative growth rate (RGR), specific leaf area (SLA), bulk leaf carbon isotope composition (δ13C), predawn water potential (Ψpd), photosynthetic gas exchange, bulk modulus of elasticity and osmotic potential at maximum turgor (π). Temporal (monthly) changes in RGR of C. ladanifer were correlated with all measured leaf traits (except π) and followed Ψpd variation. However, the temporal pattern of RGR in E. arborea was largely unrelated to water availability. SLA monthly variation reflected RGR variation reasonably well in C. ladanifer, but not in E. arborea, in which shoot growth and δ13C increased at the time of maximum water stress in late summer. The relationship between water availability, and RGR and carbon assimilation in C. ladanifer, and the lack of any relationship in E. arborea suggest that the former has an enhanced capacity to harness unpredictable rainfall pulses compared with the latter. These contrasting responses to water availability indicate that the projected changes in rainfall with global warming could alter the competitive ability of these two species, and contribute to changes in plant dominance in Mediterranean shrublands.

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Melt inclusions from Permian-Triassic komatiites, northern Vietnam, reveal evidence for a deep hydrous mantle reservoir

10.7185/gold2021.6304 ◽

2021 ◽

Author(s):

Charbel Kazzy ◽

Alexander Sobolev ◽

Andrey Gurenko ◽

Evgeny Asafov ◽

Eero Hanski ◽

...

Keyword(s):

Melt Inclusions ◽

Northern Vietnam ◽

Mantle Reservoir

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Spatio-temporal stable isotope variation of a benthic primary consumer in a simple food web in a strongly acidic lake

PeerJ ◽

10.7717/peerj.7890 ◽

2019 ◽

Vol 7 ◽

pp. e7890 ◽

Cited By ~ 1

Author(s):

Hideyuki Doi ◽

Eisuke Kikuchi ◽

Shigeto Takagi ◽

Shuichi Shikano

Keyword(s):

Stable Isotope ◽

Temporal Variation ◽

Food Web ◽

Isotope Composition ◽

Isotope Ratios ◽

Stable Isotope Composition ◽

Nitrogen Stable Isotope ◽

Chironomid Larvae ◽

Carbon And Nitrogen ◽

Spatio Temporal

Analysis of aquatic food webs is typically undertaken using carbon and nitrogen stable isotope composition of consumer and producer species. However, the trophic consequences of spatio-temporal variation in the isotope composition of consumers have not been well evaluated. Lake Katanuma, Japan, is highly acidic and has only one dominant species of benthic alga and one planktonic microalga, making it a prime system for studying trophic relationships between primary consumers and producers. In this simple lake food web, we conducted a field survey to evaluate spatial and temporal variation in the carbon and nitrogen stable isotope composition of a chironomid larvae in association with a single benthic and planktonic alga. We found a significant correlation between carbon stable isotope ratios of the chironomid larvae and the benthic diatom species in the lake. Thus, chironomid larvae may represent a reliable isotopic baseline for estimating isotope values in benthic diatoms. However, although the correlation held in shallow water, at four m depths, there was no significant relationship between the isotope ratios of chironomids and benthic diatoms, probably because deep-water larvae spend part of their life cycle migrating from the lake shore to deeper water. The differing isotope ratios of deeper chironomid tissues likely reflect the feeding history of individuals during this migration.

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Measurements of leaf sucrose to explain variability in hydrogen isotope composition of leaf cellulose

10.5194/egusphere-egu21-8918 ◽

2021 ◽

Author(s):

Meisha Holloway-Philips ◽

Jochem Baan ◽

Daniel Nelson ◽

Guillaume Tcherkez ◽

Ansgar Kahmen

Keyword(s):

Hydrogen Isotope ◽

Metabolic Flux ◽

Species Differences ◽

Dark Respiration ◽

Isotope Composition ◽

Turnover Time ◽

Water Soluble ◽

Soluble Carbohydrates ◽

Model Parameters ◽

Hydrogen Isotope Composition

The hydrogen isotope composition (&#948;2H) of cellulose has been used to assess ecohydrological processes and carries metabolic information, adding new understanding to how plants respond to environmental change. However, experimental approaches to isolate drivers of &#948;2H variation is limited to the Yakir & DeNiro model (1990), which is difficult to implement and largely unvalidated. Notably, the two biosynthetic fractionation factors in the model, associated with photosynthetic (&#949;A) and post-photosynthetic (&#949;H) processes are currently accepted as constants, and the third parameter &#8211; the extent to which organic molecules exchange hydrogen (fH) with local water &#8211; is usually tuned in order to resolve the difference between modelled and observed cellulose &#948;2H values. Thus, by virtue, the metabolically interpretable parameter is only fH, whilst from theory, metabolic flux rates will also impact on the apparent fractionations. To overcome part of this limitation, we measured the &#948;2H of extracted leaf sucrose from fully-expanded leaves of seven species and a phosphoglucomutase &#8216;starchless&#8217; mutant of tobacco to estimate the isotopic offset between sucrose and leaf water (&#949;sucrose). Sucrose &#948;2H explained ~60% of the &#948;2H variation observed in cellulose. In general, &#949;sucrose&#160;was higher (range: -203&#8240; to -114&#8240;; mean: -151 &#177; 21&#8240;) than the currently accepted value of -171&#8240; (&#949;A) reflecting 2H-enrichment downstream of triose-phosphate export from the chloroplast, with statistical differences in &#949;sucrose&#160;observed between species estimates. The remaining &#948;2H variation in cellulose was explained by species differences in fH&#160;(estimated by assuming &#949;H = +158&#8240;). We also tested possible links between model parameters and plant metabolism. &#949;sucrose&#160;was positively related to dark respiration (R2=0.27) suggesting an important branch point influencing sugar &#948;2H. In addition, fH was positively related to the turnover time (&#964;) of water-soluble carbohydrates (R2=0.38), but only when estimated using fixed &#949;A = -171&#8240;. To decipher and isolate the &#8220;metabolic&#8221; information contained within &#948;2H values of cellulose it will be important to assess &#948;2H values of non-structural carbohydrates so that hydrogen isotope fractionation during sugar metabolism can be better understood. This study provides the first attempt at such measurements showing species differences in both source and sink processes are important in understanding &#948;2H variation of cellulose.

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Efficient simulation of flood events using machine learning

10.5194/egusphere-egu2020-6254 ◽

2020 ◽

Author(s):

Jihane Elyahyioui ◽

Valentijn Pauwels ◽

Edoardo Daly ◽

Francois Petitjean ◽

Mahesh Prakash

Keyword(s):

Machine Learning ◽

Water Depth ◽

Input Data ◽

Learning Model ◽

Water Levels ◽

Training Dataset ◽

Time Step ◽

Machine Learning Model ◽

Maximum Water ◽

Spatio Temporal

Flooding is one of the most common and costly natural hazards at global scale. Flood models are important in supporting flood management. This is a computationally expensive process, due to the high nonlinearity of the equations involved and the complexity of the surface topography. New modelling approaches based on deep learning algorithms have recently emerged for multiple applications.This study aims to investigate the capacity of machine learning to achieve spatio-temporal flood modelling. The combination of spatial and temporal input data to obtain dynamic results of water levels and flows from a machine learning model on multiple domains for applications in flood risk assessments has not been achieved yet. Here, we develop increasingly complex architectures aimed at interpreting the raw input data of precipitation and terrain to generate essential spatio-temporal variables (water level and velocity fields) and derived products (flood maps) by training these based on hydrodynamic simulations.An extensive training dataset is generated by solving the 2D shallow water equations on simplified topographies using Lisflood-FP.As a first task, the machine learning model is trained to reproduce the maximum water depth, using as inputs the precipitation time series and the topographic grid. The models combine the spatial and temporal information through a combination of 1D and 2D convolutional layers, pooling, merging and upscaling. Multiple variations of this generic architecture are trained to determine the best one(s). Overall, the trained models return good results regarding performance indices (mean squared error, mean absolute error and classification accuracy) but fail at predicting the maximum water depths with sufficient precision for practical applications.A major limitation of this approach is the availability of training examples. As a second task, models will be trained to bring the state of the system (spatially distributed water depth and velocity) from one time step to the next, based on the same inputs as previously, generating the full solution equivalent to that of a hydrodynamic solver. The training database becomes much larger as each pair of consecutive time steps constitutes one training example.Assuming that a reliable model can be built and trained, such methodology could be applied to build models that are faster and less computationally demanding than hydrodynamic models. Indeed, in with the synthetic cases shown here, the simulation times of the machine learning models (< seconds) are far shorter than those of the hydrodynamic model (a few minutes at least). These data-driven models could be used for interpolation and forecasting. The potential for extrapolation beyond the range of training datasets will also be investigated (different topography and high intensity precipitation events).&#160;

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