Seasonal connections between meteoric water and streamflow generation along a mountain headwater stream

In snowmelt-driven mountain watersheds, the hydrologic connectivity between meteoric waters and stream flow generation varies strongly with the season, reflecting variable connection to soil and groundwater storage within the watershed. This variable connectivity regulates how streamflow generation mechanisms transform the seasonal and elevational variation in oxygen and hydrogen isotopic composition (δ¹⁸O and δD) of meteoric precipitation. Thus, water isotopes in stream flow can signal immediate connectivity or more prolonged mixing, especially in high-relief mountainous catchments. We characterized δ¹⁸O and δD values in stream water along an elevational gradient in a mountain headwater catchment in southwestern Montana. Stream water isotopic compositions related most strongly to elevation between February and March, exhibiting higher δ¹⁸O and δD values with decreasing elevation. These elevational isotopic lapse rates likely reflect increased connection between stream flow and proximal snow-derived water sources heavily subject to elevational isotopic effects. These patterns disappeared during summer sampling, when consistently lower δ¹⁸O and δD values of stream water reflected contributions from snowmelt or colder rainfall, despite much higher δ¹⁸O and δD values expected in warmer seasonal rainfall. The consistently low isotopic values and absence of a trend with elevation during summer suggest lower connectivity between summer precipitation and stream flow generation as a consequence of drier soils and greater transpiration. As further evidence of intermittent seasonal connectivity between the stream and adjacent groundwaters, we observed a late-winter flush of nitrate into the stream at higher elevations, consistent with increased connection to accumulating mineralized nitrogen in riparian wetlands. This pattern was distinct from mid-summer patterns of nitrate loading at lower elevations that suggested heightened human recreational activity along the stream corridor. These observations provide insights linking stream flow generation and seasonal water storage in high elevation mountainous watersheds. Greater understanding of the connections between surface water, soil water and groundwater in these environments will help predict how the quality and quantity of mountain runoff will respond to changing climate and allow better informed water management decisions.     

Subsurface flow measurements using passive flux meters in variably-saturated cold-regions landscapes

To date, passive flux meters have predominantly been applied in temperate environments for tracking the movement of contaminants in groundwater. This study applies these instruments to reduce uncertainty in (typically instantaneous) flux measurements made in a low-gradient, wetland dominated, discontinuous permafrost environment. This method supports improved estimation of unsaturated and over-winter subsurface flows which are very difficult to quantify using hydraulic gradient-based approaches. Improved subsurface flow estimates can play a key role in understanding the water budget of this landscape.     

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.     

The mass ratio of Charon to Pluto from Hubble Space Telescope astrometry with the fine guidance sensors

The mass ratio of Charon to Pluto is a basic parameter describing the binary system and is necessary for determining the individual masses and densities of these two bodies. Previous measurements of the mass ratio have been made, but the solutions differ significantly (Null et al., 1993; Young et al., 1994; Null and Owen, 1996; Foust et al., 1997; Tholen and Buie, 1997). We present the first observations of Pluto and Charon with a well-calibrated astrometric instrument—the fine guidance sensors on the Hubble Space Telescope. We observed the motion of Pluto and Charon about the system barycenter over 4.4 days (69% of an orbital period) and determined the mass ratio to be 0.122±0.008 which implies a density of 1.8 to 2.1 g cm⁻³ for Pluto and 1.6 to 1.8 g cm⁻³ for Charon. The resulting rock-mass fractions for Pluto and Charon are higher than expected for bodies formed in the outer solar nebula, possibly indicating significant postaccretion loss of volatiles.     

O. Richard Norton,The Cambridge Encyclopedia of Meteorites: Book Review

Earth, Moon, and Planets -     

First evidence of IMF control of Jovian magnetospheric boundary locations: Cassini and Galileo magnetic field measurements compared

The Cassini spacecraft, en route to Saturn, passed close to Jupiter while the Galileo spacecraft was completing its 28th and 29th orbits of Jupiter, thus offering a unique opportunity for direct study of the solar wind-Jovian interaction. Here evidence is given of response of the Jovian magnetopause and bow shock positions to changes of the north-south component of the solar wind magnetic field, a phenomenon long known to occur in equivalent circumstances at Earth. The period analyzed starts with the passage over Cassini of an interplanetary shock far upstream of Jupiter. The shock's arrival at Galileo on the dusk-flank of the magnetosphere caused Galileo to exit into the solar wind. Using inter-spacecraft timing based on the time delay established from the shock arrival at each spacecraft, we point out that Galileo's position with respect to the Jovian bow shock appears to correlate with changes in the disturbed north-south reversing field seen behind the shock. We specifically rule out the alternative of changes in the shape of the bow shock with rotations of the interplanetary magnetic field as the cause.