The 2dF Galaxy Redshift Survey: the dependence of galaxy clustering on luminosity and spectral type

We investigate the dependence of galaxy clustering on luminosity and spectral type using the 2dF Galaxy Redshift Survey (2dFGRS). Spectral types are assigned using the principal-component analysis of Madgwick et al. We divide the sample into two broad spectral classes: galaxies with strong emission lines ('late types') and more quiescent galaxies ('early types'). We measure the clustering in real space, free from any distortion of the clustering pattern owing to peculiar velocities, for a series of volume-limited samples. The projected correlation functions of both spectral types are well described by a power law for transverse separations in the range  2<( σ / h ^-1 Mpc)<15  , with a marginally steeper slope for early types than late types. Both early and late types have approximately the same dependence of clustering strength on luminosity, with the clustering amplitude increasing by a factor of ∼2.5 between L * and 4 L *. At all luminosities, however, the correlation function amplitude for the early types is ∼50 per cent higher than that of the late types. These results support the view that luminosity, and not type, is the dominant factor in determining how the clustering strength of the whole galaxy population varies with luminosity.     

Rapid formation of hyperpycnal sediment gravity currents offshore of a semi-arid California river

Observations of sediment dispersal from the Santa Clara River of southern California during two moderately sized river discharge events suggest that river sediment rapidly formed a negatively buoyant (hyperpycnal) bottom plume along the seabed within hours of peak discharge. An array of acoustic and optical sensors were placed at three stations 1 km from the Santa Clara River mouth in 10-m water depth during January–February 2004. These combined observations suggest that fluid mud concentrations of suspended sediment (>10 g/l) and across-shore gravity currents (∼5 cm/s) were observed in the lower 20–40 cm of the water column 4–6 h after discharge events. Gravity currents were wave dominated, rather than auto-suspending, and appeared to consist of silt-to-clay sized sediment from the river. Sediment mass balances suggest that 25–50% of the discharged river sediment was transported by these hyperpycnal currents. Sediment settling purely by flocs (∼1 mm/s) cannot explain the formation of the observed hyperpycnal plumes, therefore we suggest that some enhanced sediment settling from mixing, convective instabilities, or diverging plumes occurred that would explain the formation of the gravity currents. These combined results provide field evidence that high suspended-sediment concentrations from rivers (>1 g/l) may rapidly form hyperpycnal sediment gravity currents immediately offshore of river mouths, and these pathways can explain a significant portion of the river-margin sediment budget. The fate of this sediment will be strongly influenced by bathymetry, whereas the fate of the remaining sediment will be much more influenced by ocean currents.     

Multivariate Correlation in the Framework of Support and Spatial Scales of Variability

This paper extends the concept of dispersion variance to the multivariate case where the change of support affects dispersion covariances and the matrix of correlation between attributes. This leads to a concept of correlation between attributes as a function of sample supports and size of the physical domain. Decomposition of dispersion covariances into the spatial scales of variability provides a tool for computing the contribution to variability from different spatial components. Coregionalized dispersion covariances and elementary dispersion variances are defined for each multivariate spatial scale of variability. This allows the computation of dispersion covariances and correlation between attributes without integrating the cross-variograms. A correlation matrix, for a second-order stationary field with point support and infinite domain, converges toward constant correlation coefficients. The regionalized correlation coefficients for each spatial scale of variability, and the cases where the intrinsic correlation hypothesis holds are found independent of support and size of domain. This approach opens possibilities for multivariate geostatistics with data taken at different support. Two numerical examples from soil textural data demonstrate the change of correlation matrix with the size of the domain. In general, correlation between attributes is extended from the classic Pearson correlation coefficient based on independent samples to a most general approach for dependent samples taken with different support in a limited domain.     

Stormwater plume detection by MODIS imagery in the southern California coastal ocean

Stormwater plumes in the southern California coastal ocean were detected by MODIS-Aqua satellite imagery and compared to ship-based data on surface salinity and fecal indicator bacterial (FIB) counts collected during the Bight'03 Regional Water Quality Program surveys in February–March of 2004 and 2005. MODIS imagery was processed using a combined near-infrared/shortwave-infrared (NIR-SWIR) atmospheric correction method, which substantially improved normalized water-leaving radiation (nLw) optical spectra in coastal waters with high turbidity. Plumes were detected using a minimum-distance supervised classification method based on nLw spectra averaged within the training areas, defined as circular zones of 1.5–5.0-km radii around field stations with a surface salinity of S < 32.0 (“plume”) and S > 33.0 (“ocean”). The plume optical signatures (i.e., the nLw differences between “plume” and “ocean”) were most evident during the first 2 days after the rainstorms. To assess the accuracy of plume detection, stations were classified into “plume” and “ocean” using two criteria: (1) “plume” included the stations with salinity below a certain threshold estimated from the maximum accuracy of plume detection; and (2) FIB counts in “plume” exceeded the California State Water Board standards. The salinity threshold between “plume” and “ocean” was estimated as 32.2. The total accuracy of plume detection in terms of surface salinity was not high (68% on average), seemingly because of imperfect correlation between plume salinity and ocean color. The accuracy of plume detection in terms of FIB exceedances was even lower (64% on average), resulting from low correlation between ocean color and bacterial contamination. Nevertheless, satellite imagery was shown to be a useful tool for the estimation of the extent of potentially polluted plumes, which was hardly achievable by direct sampling methods (in particular, because the grids of ship-based stations covered only small parts of the plumes detected via synoptic MODIS imagery). In most southern California coastal areas, the zones of bacterial contamination were much smaller than the areas of turbid plumes; an exception was the plume of the Tijuana River, where the zone of bacterial contamination was comparable with the zone of plume detected by ocean color.