We report the discovery of J1953−1019, the first resolved triple white dwarf system. The triplet consists of an inner white dwarf binary and a wider companion. Using Gaia DR2 photometry and astrometry combined with our follow-up spectroscopy, we derive effective temperatures, surface gravities, masses and cooling ages of the three components. All three white dwarfs have pure-hydrogen (DA) atmospheres, masses of 0.60 − 0.63 M ⊙ and cooling ages of 40 − 290 Myr. We adopt eight initial-to-final mass relations to estimate the main sequence progenitor masses (which we find to be similar for the three components, 1.6-2.6 M ⊙ ) and lifetimes. The differences between the derived cooling times and main sequence lifetimes agree for most of the adopted initial-to-final mass relations, hence the three white dwarfs in J1953−1019 are consistent with coeval evolution. Furthermore, we calculate the projected orbital separations of the inner white dwarf binary (303.25±0.01 au) and of the centre of mass of the inner binary and the outer companion (6 398.97 ± 0.09 au). From these values, and taking into account a wide range of possible configurations for the triplet to be currently dynamically stable, we analyse the future evolution of the system. We find that a collision between the two inner white dwarfs due to Lidov-Kozai oscillations is unlikely, though if it occurs it could result in a sub-Chandrasekhar Type Ia supernova explosion.
Surface water availability is a fundamental environmental variable to implement effective climate adaptation and mitigation plans, as expressed by scientific, financial and political stakeholders. Recently published requirements urge the need for homogenised access to long historical records at a global scale, together with the standardised characterisation of the accuracy of observations. While satellite altimeters offer world coverage measurements, existing initiatives and online platforms provide derived water level data. However, these are sparse, particularly in complex topographies. This study introduces a new methodology in two steps (1) teroVIR, a virtual station extractor for a more comprehensive global and automatic monitoring of water bodies, and (2) teroWAT, a multi-mission, interoperable water level processor, for handling all terrain types. L2 and L1 altimetry products are used, with state-of-the-art retracker algorithms in the methodology. The work presents a benchmark between teroVIR and current platforms in West Africa, Kazakhastan and the Arctic: teroVIR shows an unprecedented increase from 55% to 99% in spatial coverage. A large-scale validation of teroWAT results in an average of unbiased root mean square error ubRMSE of 0.638 m on average for 36 locations in West Africa. Traditional metrics (ubRMSE, median, absolute deviation, Pearson coefficient) disclose significantly better values for teroWAT when compared with existing platforms, of the order of 8 cm and 5% improved respectively in error and correlation. teroWAT shows unprecedented excellent results in the Arctic, using an L1 products-based algorithm instead of L2, reducing the error by almost 4 m on average. To further compare teroWAT with existing methods, a new scoring option, teroSCO, is presented, measuring the quality of the validation of time series transversally and objectively across different strategies. Finally, teroVIR and teroWAT are implemented as platform-agnostic modules and used by flood forecasting and river discharge methods as relevant examples. A review of various applications for miscellaneous end-users is given, tackling the educational challenge raised by the community.
<p>Calibration of distributed hydrological models needs to include spatial information of the hydrological processes in order to guarantee a robust spatial representation of the model state variables. Satellite remote sensing monitoring the Earth in a temporal and spatial comprehensive way stands out as a valuable resource of this kind of information. Surface soil moisture (SSM) plays a key role in the description of the hydrological cycle, especially in semi-arid areas. Nevertheless, the coarse resolution of available SSM products has restricted the use of SSM in the calibration of hydrological models to only the temporal approach. The current operational SSM estimates (1km) resulting from new sensor estimates or the application of downscaling methodologies pave the way for this spatial calibration approach. The present study explores the applicability of these spatially enhanced SSM estimates for distributed eco-hydrological modelling in Mediterranean forest basins. On one hand, it contributes to fill the existing research gap on the use of remote sensing SSM spatial patterns within the distributed hydrological modelling framework, in particular in medium/small basins. On the other hand, it serves as an indirect validation method for the spatial performance of satellite SSM products. TETIS eco-hydrological distributed model was implemented in three case studies, named Carraixet (eastern Spain), Hozgarganta (southern Spain), and Ceira (western Portugal), which were strategically selected to perform this research in the Mediterranean Region. The SSM estimates selected for evaluation were: Sentinel-1 SSM provided by the Copernicus Global Land Services (CGLS), SMAP SSM disaggregated using Sentinel-1 provided by the National Aeronautics and Space Administration (NASA), SMOS SSM provided by the Barcelona Expert Center (BEC), and SMOS and SMAP SSM disaggregated using the Dispatch algorithm provided by Lobelia Earth. The methodology employed involved a multi-objective and multi-variable calibration using the considering remote sensing SSM spatial patterns and in-situ streamflow, using the Spatial Efficiency Metric (SPAEF) and the Nash-Sutcliffe efficiency index (NSE) respectively. In spite of the spatial and temporal differences amongst products, the multi-objective calibration approach proposed increased the robustness of the hydrological modelling. Spatial and temporal agreement depends on the selection of the SSM product. The disaggregating methodology determined the spatial agreement to a greater degree than the sensor itself.</p>
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