Superhumps in the helium dwarf nova KL Draconis

We report on the discovery of a 25.5-min superhump period for the suspected helium dwarf nova system KL Draconis in a high state. The presence of superhumps combined with the previously observed helium spectrum and large-amplitude photometric variations confirm that KL Dra is an AM CVn system similar to CR Bootis, V803 Cen and CP Eridani. We also find a low-state photometric period at 25.0 min that we suggest may be the orbital period. With this assumption, we estimate   q =0.075  ,   M ₁=0.76 M_⊙  and   M ₂=0.057 M_⊙  .     

SPIRE point source photometry: within the Herschel interactive processing environment (HIPE)

The different algorithms appropriate for point source photometry on data from the SPIRE instrument on-board the Herschel Space Observatory, within the Herschel Interactive Processing Environment (HIPE) are compared. Point source photometry of a large ensemble of standard calibration stars and dark sky observations is carried out using the 4 major methods within HIPE: SUSSEXtractor, DAOphot, the SPIRE Timeline Fitter and simple Aperture Photometry. Colour corrections and effective beam areas as a function of the assumed source spectral index are also included to produce a large number of photometric measurements per individual target, in each of the 3 SPIRE bands (250, 350, 500μm), to examine both the accuracy and repeatability of each of the 4 algorithms. It is concluded that for flux densities down to the level of 30mJy that the SPIRE Timeline Fitter is the method of choice. However, at least in the 250 and 350μm bands, all 4 methods provide photometric repeatability better than a few percent down to at approximately 100mJy. The DAOphot method appears in many cases to have a systematic offset of ～8 % in all SPIRE bands which may be indicative of a sub-optimal aperture correction. In general, aperture photometry is the least reliable method, i.e. largest scatter between observations, especially in the longest wavelength band. At the faintest fluxes, <30mJy, SUSSEXtractor or DAOphot provide a better alternative to the Timeline Fitter.     

Integrated Storage and Querying of Spatially Varying Data Quality Information in a Relational Spatial Database

This article investigates a new, integrated technique for storing and retrieving spatially varying data quality information in a relational spatial database. Rather than storing global data quality statements, the system enables data quality information to be referenced to a spatial framework, individual spatial objects, or even parts of spatial objects. The integrated model, called as RDBMS for Spatial Variation in Quality (RSVQ), allows flexible storage of spatially varying data quality information, and seamless querying irrespective of the underlying storage model. RSVQ is founded on a formal model of relational databases, defining a new derived, polymorphic query operator      to join quality data with spatial data. The      operator is implemented in an extension to SQL as a new WITHQUALITY keyword. A performance evaluation of RSVQ was conducted, using an Oracle Spatial database and a case study of cadastral data for parts of Victoria, Australia. The results of this evaluation demonstrated that the system is practical and efficient for a wide range of queries, as well as indicating the performance trade-offs associated with the different data quality storage models. Using the integrated RSVQ approach provides the potential for a single, consistent, database engine for a wide range of existing and proposed spatial data quality management systems.     

Upscaling site‐scale ecohydraulic models to inform salmonid population‐level life cycle modeling and restoration actions – Lessons from the Columbia River Basin

With high‐resolution topography and imagery in fluvial environments, the potential to quantify physical fish habitat at the reach scale has never been better. Increased availability of hydraulic, temperature and food availability data and models have given rise to a host of species and life stage specific ecohydraulic fish habitat models ranging from simple, empirical habitat suitability curve driven models, to fuzzy inference systems to fully mechanistic bioenergetic models. However, few examples exist where such information has been upscaled appropriately to evaluate entire fish populations. We present a framework for applying such ecohydraulic models from over 905 sites in 12 sub‐watersheds of the Columbia River Basin (USA), to assess status and trends in anadromous salmon populations. We automated the simulation of computational engines to drive the hydraulics, and subsequent ecohydraulic models using cloud computing for over 2075 visits from 2011 to 2015 at 905 sites. We also characterize each site's geomorphic reach type, habitat condition, geomorphic unit assemblage, primary production potential and thermal regime. We then independently produce drainage network‐scale models to estimate these same parameters from coarser, remotely sensed data available across entire populations within the Columbia River Basin. These variables give us a basis for imputation of reach‐scale capacity estimates across drainage networks. Combining capacity estimates with survival estimates from mark–recapture monitoring allows a more robust quantification of capacity for freshwater life stages (i.e. adult spawning, juvenile rearing) of the anadromous life cycle. We use these data to drive life cycle models of populations, which not only include the freshwater life stages but also the marine and migration life stages through the hydropower system. More fundamentally, we can begin to look at more realistic, spatially explicit, tributary habitat restoration scenarios to examine whether the enormous financial investment on such restoration actions can help recover these populations or prevent their extinction. Copyright © 2017 John Wiley & Sons, Ltd.