Do oil and gas platforms off California reduce recruitment of bocaccio (Sebastes paucispinis) to natural habitat? An analysis based on trajectories derived from high-frequency radar

To investigate the possibility that oil and gas platforms may reduce recruitment of rockfishes (Sebastes spp.) to natural habitat, we simulated drift pathways termed “trajectories” in our model) from an existing oil platform to nearshore habitat using current measurements from high-frequency (HF) radars. The trajectories originated at Platform Irene, located west of Point Conception, California, during two recruiting seasons for bocaccio (Sebastes paucispinis): May through August, 1999 and 2002. Given that pelagic juvenile bocaccio dwell near the surface, the trajectories estimate transport to habitat. We assumed that appropriate shallow water juvenile habitat exists inshore of the 50-m isobath. Results from 1999 indicated that 10% of the trajectories represent transport to habitat, whereas 76% represent transport across the offshore boundary. For 2002, 24% represent transport to habitat, and 69% represent transport across the offshore boundary. Remaining trajectories (14% and 7% for 1999 and 2002, respectively) exited the coverage area either northward or southward along isobaths. Deployments of actual drifters (with 1-m drogues) from a previous multiyear study provided measurements originating near Platform Irene from May through August. All but a few of the drifters moved offshore, as was also shown with the HF radar-derived trajectories. These results indicate that most juvenile bocaccio settling on the platform would otherwise have been transported offshore and perished in the absence of a platform. However, these results do not account for the swimming behavior of juvenile bocaccio, about which little is known.

A multi-basin seasonal streamflow model for the Sierra Nevada

Linear regression models are constructed to predict seasonal runoff by fitting streamflow to temperature, precipitation, and snow water content across a range of elevations. The models are quite successful in capturing the differences in discharge between different elevation watersheds and their interannual variations. This exercise thus provides insight into seasonal changes in streamflow at different elevation watersheds that might occur under a changed climate.

Semiempirical down-scaling of GCM output to the local scale for temperature, precipitation, and runoff

EXTRACT (SEE PDF FOR FULL ABSTRACT): An empirically derived multiple linear regression model is used to relate a local-scale dependent variable (either temperature, precipitation, or surface runoff) measured at individual gauging stations to six large-scale independent variables (temperature, precipitation, surface runoff, height to the 500-mbar pressure surface, and the zonal and meridional gradient across this surface). ...The area investigated is the western United States. ... The calibration data set is from 1948 through 1988 and includes data from 268 joint temperature and precipitation stations, 152 streamflow stations (which are converted to runoff data), and 24 gridded 500-mbar pressure height nodes.

Modeling the effect of snow on seasonal runoff within the Truckee River drainage basin

EXTRACT (SEE PDF FOR FULL ABSTRACT): We estimate monthly runoff for a 2-dimensional solution domain containing those areas tributary to Pyramid Lake, Nevada (the Truckee River drainage basin) at a 1-kilometer grid cell spacing. ... To calculate the effect of snow on the hydrologic system, we perform two experiments. In the first we assume that all precipitation falls as rain; in the second we assume that some precipitation falls as snow, thus available water is a combination of rain and snowmelt. We find that considering the effect of snow results in a more accurate representation of mean monthly flow rates, in particular the peak flow during the melt season in the Sierra Nevada. These preliminary results indicate that a relatively simple snow model can improve the representation of Truckee River basin hydrology, significantly reducing errors in modeled seasonal runoff.