In Situ Isotopic Analysis of Uraninite Microstructures from the Oklo-Okélobondo Natural Fission Reactors, Gabon

ABSTRACTUranium deposits can provide important information on the long-term performance of radioactive waste forms because uraninite (UO2+X) is similar to the UO2 in spent nuclear fuel. The Oklo-Okélobondo U-deposits, Gabon, serve as natural laboratory where the long-term (hundreds to billions of years) migration of uranium and other radionuclides can be studied over large spatial scales (nm to km). The natural fission reactors associated with the Oklo- Okélobondo U-deposits occur over a range of depths (100 to 400 m) and provide a unique opportunity to study the behavior of uraninite in near surface oxidizing environments versus more reducing conditions at depth. Previously, it has been difficult to constrain the timing of interaction between U-rich minerals and post-depositional fluids. These problems are magnified because uraninite is susceptible to alteration, it continuously self-anneals radiation damage, and because these processes are manifested at the nm to μm scale. Uranium, lead and oxygen isotopes can be used to study fluid-uraninite interaction, provided that the analyses are obtained on the micro-scale. Secondary ionization mass spectrometry (SIMS) permits in situ measurement of isotopic ratios with a spatial resolution on the scale of a few μm. Preliminary U-Pb results show that uraninite from all reactor zones are highly discordant with ages aaproaching the timing of fission chain reactions (1945±50 Ma) and resetting events at 1180±47 Ma and 898±46 Ma. Oxygen isotopic analyses show that uraninite from reactors that occur in near surface environments (δ18O= −14.4‰ to −8.5‰) have reacted more extensively with groundwater of meteoric origin relative to reactors located at greater depths (μ18O= −10.2‰ to −7.3‰). This study emphasizes the importance of using in situ high spatial resolution analysis techniques for natural analogue studies.

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Global downscaling of remotely sensed soil moisture using neural networks

Hydrology and Earth System Sciences ◽

10.5194/hess-22-5341-2018 ◽

2018 ◽

Vol 22 (10) ◽

pp. 5341-5356 ◽

Cited By ~ 10

Author(s):

Seyed Hamed Alemohammad ◽

Jana Kolassa ◽

Catherine Prigent ◽

Filipe Aires ◽

Pierre Gentine

Keyword(s):

Soil Moisture ◽

Spatial Resolution ◽

Land Surface ◽

Vegetation Index ◽

Spatial Scales ◽

Ancillary Data ◽

Repeat Time ◽

Spatiotemporal Scales ◽

Better Than

Abstract. Characterizing soil moisture at spatiotemporal scales relevant to land surface processes (i.e., of the order of 1 km) is necessary in order to quantify its role in regional feedbacks between the land surface and the atmospheric boundary layer. Moreover, several applications such as agricultural management can benefit from soil moisture information at fine spatial scales. Soil moisture estimates from current satellite missions have a reasonably good temporal revisit over the globe (2–3-day repeat time); however, their finest spatial resolution is 9 km. NASA's Soil Moisture Active Passive (SMAP) satellite has estimated soil moisture at two different spatial scales of 36 and 9 km since April 2015. In this study, we develop a neural-network-based downscaling algorithm using SMAP observations and disaggregate soil moisture to 2.25 km spatial resolution. Our approach uses the mean monthly Normalized Differenced Vegetation Index (NDVI) as ancillary data to quantify the subpixel heterogeneity of soil moisture. Evaluation of the downscaled soil moisture estimates against in situ observations shows that their accuracy is better than or equal to the SMAP 9 km soil moisture estimates.

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A Review of In Situ Isotopic Studies of the Oklo and Bangombé Natural Fission Reactors Using Microbeam Analytical Techniques

Minerals ◽

10.3390/min10121060 ◽

2020 ◽

Vol 10 (12) ◽

pp. 1060

Author(s):

Hiroshi Hidaka

Keyword(s):

Nuclear Reactions ◽

Fission Products ◽

Analytical Techniques ◽

Laser Probe ◽

The Past ◽

Isotopic Analyses ◽

Isotopic Studies ◽

Reactor Materials ◽

Fission Reactors

Isotopic analyses of elements in the natural reactor materials have often been performed to understand the distribution behaviors of the fission products and to evaluate the function of nuclear reactions since the first discovery of a natural reactor in 1972. Several types of unique microminerals, including significant amounts of fission products, have been found in and around the Oklo and the Bangombé natural reactors. In the past two decades, microbeam techniques using ion and laser probe facilities have been effectively applied for the in situ isotopic analyses of individual microminerals to investigate the migration behaviors of fissiogenic radioisotopes produced in the reactors. This paper presents a review of interpretations of the isotopic results of microminerals found in and around the natural reactors.

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Exploring the use of machine-learning techniques to integrate ground- and remote sensing-based observations for efficient near-surface soil moisture mapping

10.5194/egusphere-egu2020-9092 ◽

2020 ◽

Author(s):

Sarah Schönbrodt-Stitt ◽

Paolo Nasta ◽

Nima Ahmadian ◽

Markus Kurtenbach ◽

Christopher Conrad ◽

...

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Spatial Resolution ◽

Surface Soil ◽

Bare Soil ◽

Small Scale ◽

Surface Soil Moisture ◽

Near Surface ◽

Campania Region

Mapping near-surface soil moisture (&#952;) is of tremendous relevance for a broad range of environment-related disciplines and meteorological, ecological, hydrological and agricultural applications. Globally available products offer the opportunity to address &#952; in large-scale modelling with coarse spatial resolution such as at the landscape level. However, &#952; estimation at higher spatial resolution is of vital importance for many small-scale applications. Therefore, we focus our study on a small-scale catchment (MFC2) belonging to the &#8220;Alento&#8221; hydrological observatory, located in southern Italy (Campania Region). The goal of this study is to develop new machine-learning approaches to estimate high grid-resolution (about 17 m cell size) &#952; maps from mainly backscatter measurements retrieved from C-band Synthetic Aperture Radar (SAR) based on Sentinel-1 (S1) images and from gridded terrain attributes. Thus, a workflow comprising a total of 48 SAR-based &#952; patterns estimated for 24 satellite overpass dates (revisit time of 6 days) each with ascendant and descendent orbits will be presented. To enable for the mapping, SAR-based &#952; data was calibrated with in-situ measurements carried out with a portable device during eight measurement campaigns at time of satellite overpasses (four overpass days in total with each ascendant and descendent satellite overpasses per day in November 2018). After the calibration procedure, data validation was executed from November 10, 2018 till March 28, 2019 by using two stationary sensors monitoring &#952; at high-temporal (1-min recording time). The specific sensor locations reflected two contrasting field conditions, one bare soil plot (frequently kept clear, without disturbance of vegetation cover) and one non-bare soil plot (real-world condition). Point-scale ground observations of &#952; were compared to pixel-scale (17 m &#215; 17 m), SAR-based &#952; estimated for those pixels corresponding to the specific positions of the stationary sensors. Mapping performance was estimated through the root mean squared error (RMSE). For a short-term time series of &#952; (Nov 2018) integrating 136 in situ, sensor-based &#952; (&#952;insitu) and 74 gravimetric-based &#952; (&#952;gravimetric) measurements during a total of eight S1 overpasses, mapping performance already proved to be satisfactory with RMSE=0.039 m&#179;m-&#179; and R&#178;=0.92, respectively with RMSE=0.041 m&#179;m-&#179; and R&#178;=0.91. First results further reveal that estimated satellite-based &#952; patterns respond to the evolution of rainfall. With our workflow developed and results, we intend to contribute to improved environmental risk assessment by assimilating the results into hydrological models (e.g., HydroGeoSphere), and to support future studies on combined ground-based and SAR-based &#952; retrieval for forested land (future missions operating at larger wavelengths e.g. NISARL-band, Biomass P-band sensors).

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An experimental 2DVAR retrieval using AMSR2

10.5194/amt-2019-178 ◽

2019 ◽

Author(s):

David Ian Duncan ◽

Patrick Eriksson ◽

Simon Pfreundschuh

Keyword(s):

Wind Speed ◽

Spatial Resolution ◽

Spatial Scales ◽

Surface Wind ◽

Optimal Estimation ◽

Surface Wind Speed ◽

Model Errors ◽

Near Surface ◽

Noise Characteristics ◽

Microwave Imager

Abstract. A two-dimensional variational retrieval (2DVAR) is presented for a passive microwave imager. The overlapping antenna patterns of all frequencies from the Advanced Microwave Scanning Radiometer-2 (AMSR2) are explicitly simulated to attempt retrieval of near surface wind speed and surface skin temperature at finer spatial scales than individual antenna beams. This is achieved, with the effective spatial resolution of retrieved parameters shown by analysis of 2DVAR averaging kernels. Sea surface temperature retrievals achieve about 30 km resolution, with wind speed retrievals at about 10 km resolution. It is argued that multi-dimensional optimal estimation permits greater use of total information content from microwave sensors than other methods, with no compromises on target resolution needed; instead, various targets are retrieved at the highest possible spatial resolution, driven by the channels' sensitivities. All AMSR2 channels can be simulated within near their published noise characteristics for observed clear-sky scenes, though calibration and emissivity model errors are key challenges. This experimental retrieval shows the feasibility of 2DVAR for cloud-free retrievals, and opens the possibility of standalone 3DVAR retrievals of water vapour and hydrometeor fields from microwave imagers in the future. The results have implications for future satellite missions and sensor design, as spatial oversampling can somewhat mitigate the need for larger antennas in the push for higher spatial resolution.

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Assessing the efficiency of a “buried wall” barrier in the establishment of near-surface long-term storage and disposal facilities for RW

Radioactive waste ◽

10.25283/2587-9707-2021-1-96-105 ◽

2021 ◽

Vol 14 (1) ◽

pp. 96-105

Author(s):

V. V. Suskin ◽

◽

I. V. Kapyrin ◽

F. V. Grigorev ◽

◽

...

Keyword(s):

Near Surface ◽

Transfer Processes ◽

Long Term Storage ◽

Mass Transfer Processes ◽

High Degree ◽

The Impact ◽

Term Storage

The article evaluates the impact of a “buried wall” barrier on the long-term safety during the long-term storage1 or in-situ disposal of nuclear legacy facilities, in particular, industrial reservoirs, as well as during the development of near-surface disposal facilities for radioactive waste (RWDF). For assessment purposes, filtration and mass transfer processes have been numerically modelled in the GeRa code based on a case study of a reference near-surface facility. The study explores in which way the available covering screen affects the dynamics of contaminant spread. It evaluates the sensitivity of the results to the dispersion parameter commonly characterized by a high degree of uncertainty.

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Tropical Refuges with Exceptionally High Phylogenetic Diversity Reveal Contrasting Phylogenetic Structures

International Journal of Biodiversity ◽

10.1155/2015/758019 ◽

2015 ◽

Vol 2015 ◽

pp. 1-17 ◽

Cited By ~ 7

Author(s):

Lara Pugliesi ◽

Alessandro Rapini

Keyword(s):

Phylogenetic Diversity ◽

Spatial Scales ◽

Northeastern Brazil ◽

Ex Situ ◽

Habitat Filtering ◽

Phylogenetic Structure ◽

Chapada Diamantina ◽

Limited Dispersal

Loss of phylogenetic diversity (PD) has gained increasing attention in conservation biology. However, PD is not equally distributed in a phylogeny and can be better assessed when species relatedness (phylogenetic structure: PS) is also considered. Here, we investigate PD and PS in two refuges of biodiversity in northeastern Brazil: the Bahia Costal Forest (BCF) in the Atlantic Forest domain and Chapada Diamantina (CD) in the Caatinga domain. We used geographic data of 205 species at two spatial scales and a chronogram of Apocynaceae based on matK sequences to estimate PD and PS. Our results show an exceptionally high PD in both refuges, overdispersed in BCF and clustered in CD, although this difference is less evident or absent for recent relationships, especially at a smaller spatial scale. Overall, PS suggests long-term competitive exclusion under climatic stability, currently balanced by habitat filtering, in BCF, and biome conservatism and limited dispersal leading to in situ diversification and high density of microendemics in CD. The phylogenetically clustered flora in CD, also threatened by climate changes, are naturally more vulnerable than BCF. Therefore, while in situ conservation may ensure protection of biodiversity in BCF, emergency ex situ conservation is strongly recommended in CD.

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Retrieving and Validating Leaf and Canopy Chlorophyll Content at Moderate Resolution: A Multiscale Analysis with the Sentinel-3 OLCI Sensor

Remote Sensing ◽

10.3390/rs13081419 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1419

Author(s):

Charlotte De Grave ◽

Luca Pipia ◽

Bastian Siegmann ◽

Pablo Morcillo-Pallarés ◽

Juan Pablo Rivera-Caicedo ◽

...

Keyword(s):

Chlorophyll Content ◽

Spatial Resolution ◽

Spatial Scales ◽

Fluorescence Signal ◽

Scaling Analysis ◽

Terrestrial Vegetation ◽

Area Index ◽

In Situ Data ◽

Linear Behavior

ESA’s Eighth Earth Explorer mission “FLuorescence EXplorer” (FLEX) will be dedicated to the global monitoring of the chlorophyll fluorescence emitted by vegetation. In order to properly interpret the measured fluorescence signal, essential vegetation variables need to be retrieved concomitantly. FLEX will fly in tandem formation with Sentinel-3 (S3), which conveys the Ocean and Land Color Instrument (OLCI) that is designed to characterize the atmosphere and the terrestrial vegetation at a spatial resolution of 300 m. In support of FLEX’s preparatory activities, this paper presents a first validation exercise of OLCI vegetation products against in situ data coming from the 2018 FLEXSense campaign. During this campaign, leaf chlorophyll content (LCC) and leaf area index (LAI) measurements were collected over croplands, while HyPlant DUAL images of the area were acquired at a 3 m spatial resolution. A multiscale validation strategy was pursued. First, estimates of these two variables, together with the combined canopy chlorophyll content (CCC = LCC × LAI), were obtained at the HyPlant spatial resolution and were compared against the in situ measurements. Second, the fine-scale retrieval maps from HyPlant were coarsened to the S3 spatial scale as a reference to assess the quality of the OLCI vegetation products. As an intermediary step, vegetation products extracted from Sentinel-2 data were used to compare retrievals at the in-between spatial resolution of 20 m. For all spatial scales, CCC delivered the most accurate estimates with the smallest prediction error obtained at the 300 m resolution (R2 of 0.74 and RMSE = 26.8 μg cm−2). Results of a scaling analysis suggest that CCC performs well at the different tested spatial resolutions since it presents a linear behavior across scales. LCC, on the other hand, was poorly retrieved at the 300 m scale, showing overestimated values over heterogeneous pixels. The introduction of a new LCC model integrating mixed reflectance spectra in its training enabled to improve by 16% the retrieval accuracy for this variable (RMSE = 10 μg cm−2 for the new model versus RMSE = 11.9 μg cm−2 for the former model).

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Seasonal Spatial and Temporal Distribution of Chlorophyll-a Concentration over Kuwait and the Arabian Gulf using Satellite and In-Situ Data

10.20944/preprints202107.0232.v1 ◽

2021 ◽

Author(s):

Jasem A. Albanai

Keyword(s):

Spatial Resolution ◽

Arabian Gulf ◽

Temporal Distribution ◽

Spatial And Temporal Distribution ◽

Important Indicator ◽

Near Surface ◽

In Situ Data ◽

Level 3 ◽

Level 2

The concentration of chlorophyll-a (chlor-a) is an important indicator of marine water quality, as it is considered an indicator of the phytoplankton density in a specific area. Remote sensing techniques have been developed to measure the near-surface concentration of chlor-a in water across the correlation between spectral bands and in situ data. This algorithm applies to sensors of varying spatial, temporal and spectral resolutions. However, in this study, chlor-a level 2 and 3 products of SNPP – VIIRS spectrometer (Equation OC3) of NASA OceanColor suite was relied upon to study the spatial and temporal distribution of chlor-a concentration in the Arabian Gulf (also known as the Persian Gulf) and the State of Kuwait’s water (located to the north-eastern part of the Arabian Gulf) from 2012 to 2019. Ground truthing points (n = 192) matched to the level 2 products have been used to build an empirical model and cross-validate it. The correlation was positive where was 0.79 and the validation RMSE was = ± 0.64 mg/m-3. The derived algorithm was then applied to chlor-a level 3 seasonal products. Additionally, the chlor-a concentration values of Kuwaiti waters have been enhanced using the IDW algorithm to increase the spatial resolution, as it is considered as a small area compared to the spatial resolution of level 3 chlor-a products. The model derived from IDW was tested using the Mann Whitney test (Sig = 0.948 p > 0.01). However, the result showed that the chlor-a concentration is higher in Kuwait Bay compared to Kuwaiti water, and it is higher in Kuwaiti water compared to the Arabian Gulf. The coasts have higher concentrations too, when compared to the open water. Generally, the chlor-a increases in winter and makes a semi-regular cycle during the years of study; this cycle is more regular in the Gulf’s waters than in Kuwait’s.

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Global Downscaling of Remotely-Sensed Soil Moisture using Neural Networks

10.5194/hess-2017-680 ◽

2018 ◽

Cited By ~ 3

Author(s):

Seyed Hamed Alemohammad ◽

Jana Kolassa ◽

Catherine Prigent ◽

Filipe Aires ◽

Pierre Gentine

Keyword(s):

Neural Networks ◽

Soil Moisture ◽

Spatial Resolution ◽

Land Surface ◽

Vegetation Index ◽

Spatial Scales ◽

Ancillary Data ◽

Repeat Time ◽

Spatio Temporal

Abstract. Characterizing soil moisture at spatio-temporal scales relevant to land surface processes (i.e. of the order of a kilometer) is necessary in order to quantify its role in regional feedbacks between land surface and the atmospheric boundary layer. Moreover, several applications such as agricultural management can benefit from soil moisture information at fine spatial scales. Soil moisture estimates from current satellite missions have a reasonably good temporal revisit over the globe (2–3 days repeat time); however, their finest spatial resolution is 9 km. NASA's Soil Moisture Active Passive (SMAP) satellite estimates soil moisture at two different spatial scales of 36 km and 9 km since April 2015. In this study, we develop a neural networks-based downscaling algorithm using SMAP observations and disaggregate soil moisture to 2.25 km spatial resolution. Our approach uses mean monthly Normalized Differenced Vegetation Index (NDVI) as an ancillary data to quantify sub-pixel heterogeneity of soil moisture. Evaluation of the downscaled soil moisture estimates against in situ observations shows that their accuracy is better than or equal to the SMAP 9 km soil moisture estimates.

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How runoff components affect the export of DOC and nitrate: a long-term and high-frequency analysis

10.5194/hess-2017-416 ◽

2017 ◽

Cited By ~ 2

Author(s):

Michael P. Schwab ◽

Julian Klaus ◽

Laurent Pfister ◽

Markus Weiler

Keyword(s):

High Frequency ◽

Initial Conditions ◽

Shallow Groundwater ◽

High Temporal Resolution ◽

Near Surface ◽

Nitrate Concentrations ◽

Nitrate Export ◽

Dry Conditions

Abstract. We monitored dissolved organic carbon (DOC) and nitrate concentrations and fluxes in situ with a UV-Vis spectrometer for two years at a high temporal resolution of 15 minutes in the forested Weierbach headwater catchment. The catchment exhibits a characteristic double peak runoff response to incident rainfall during periods with wet initial conditions. When initial conditions are dry, only the first discharge peak occurs. During our observations, both DOC and nitrate concentrations increased during the first discharge peak, while only nitrate concentrations were elevated during the second discharge peak. Relying on additional biweekly end-member data of precipitation, throughfall, soil water and groundwater, we linked the first peak to near surface flowpaths and the second peak to shallow groundwater reactions and subsurface flowpaths. The mass export of DOC and nitrate is largely controlled by the discharge yield. Nevertheless, this relationship is altered by changing flowpaths during different wetness conditions in the catchment. Due to the absence of second discharge peaks during dry conditions, the DOC export is more relevant and the nitrate export is less relevant during dry catchment states. The study highlights the benefits of in-situ, long-term, and high-frequency monitoring for comparing DOC and nitrate export with runoff components that are changing rapidly during events as well as gradually between seasons.

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