Results of 2013 Macroalgal Monitoring and Recommendations for Future Monitoring in Great Bay Estuary, New Hampshire

The recently designated nitrogen impairment and reports of elevated macroalgal growth in Great Bay Estuary indicate ecological imbalance. However, reversing the Estuary’s ecological decline will require commitment of considerable resources and is complicated by the variety of sources that deliver nitrogen to the Estuary and the intermittent nature of historic macroalgal monitoring. To advance our understanding of the macroalgal and nitrogen dynamics of the Estuary, data were collected via three approaches: 1) assessing plant cover and biomass along transects; 2) assessing plant cover at randomly selected points; and 3) comparing the nitrogen isotope ratios of macroalgae collected from different habitats. The results offer insight into changes in macroalgal abundance and species composition and the relative importance of various nitrogen sources to macroalgae in Great Bay. Overall, our results corroborate the findings of increasing macroalgal blooms in previous studies and suggests plausible directions for a long-term macroalgal monitoring program

The impact of uncertainty in satellite data on the assessment of flood inundation models

The performance of flood inundation models is often assessed using satellite observed data; however these data have inherent uncertainty. In this study we assess the impact of this uncertainty when calibrating a flood inundation model (LISFLOOD-FP) for a flood event in December 2006 on the River Dee, North Wales, UK. The flood extent is delineated from an ERS-2 SAR image of the event using an active contour model (snake), and water levels at the flood margin calculated through intersection of the shoreline vector with LiDAR topographic data. Gauged water levels are used to create a reference water surface slope for comparison with the satellite-derived water levels. Residuals between the satellite observed data points and those from the reference line are spatially clustered into groups of similar values. We show that model calibration achieved using pattern matching of observed and predicted flood extent is negatively influenced by this spatial dependency in the data. By contrast, model calibration using water elevations produces realistic calibrated optimum friction parameters even when spatial dependency is present. To test the impact of removing spatial dependency a new method of evaluating flood inundation model performance is developed by using multiple random subsamples of the water surface elevation data points. By testing for spatial dependency using Moran’s I, multiple subsamples of water elevations that have no significant spatial dependency are selected. The model is then calibrated against these data and the results averaged. This gives a near identical result to calibration using spatially dependent data, but has the advantage of being a statistically robust assessment of model performance in which we can have more confidence. Moreover, by using the variations found in the subsamples of the observed data it is possible to assess the effects of observational uncertainty on the assessment of flooding risk

An empirical calibration to estimate cool dwarf fundamental parameters from H-band spectra

Interferometric radius measurements provide a direct probe of the fundamental parameters of M dwarfs, but is within reach for only a limited sample of nearby, bright stars. We use interferometrically-measured radii, bolometric luminosities, and effective temperatures to develop new empirical calibrations based on low-resolution, near-infrared spectra. We use H-band Mg and Al features to derive calibrations for effective temperature, radius and log luminosity; the standard deviations in the residuals of our best fits are, respectively, 73K, 0.027Rsun, and 0.049 dex (11% error on luminosity). These relationships are valid for mid K to mid M dwarf stars, roughly corresponding to temperatures between 3100 and 4800K. We apply our calibrations to M dwarfs targeted by the MEarth transiting planet survey and to the cool Kepler Objects of Interest (KOIs). We independently validate our calibrations by demonstrating a clear relationship between our inferred parameters and the absolute K magnitudes of the MEarth stars, and we identify objects with magnitudes too bright for their estimated luminosities as candidate multiple systems. We also use our inferred luminosities to address the applicability of near-infrared metallicity calibrations to mid and late M dwarfs. The temperatures we infer for the KOIs agree remarkably well with those from the literature; however, our stellar radii are systematically larger than those presented in previous works that derive radii from model isochrones. This results in a mean planet radius that is 15% larger than one would infer using the stellar properties from recent catalogs. Our results confirm those of previous in-depth studies of Kepler-42, Kepler-45, and Kepler-186.Comment: Accepted to ApJ. Tables 4 and 5, and machine readable versions of Tables 5 and 7 are available in the ApJ journal articl

Flood-tolerant rice reduces yield variability and raises expected yield, differentially benefitting socially disadvantaged groups.

Approximately 30% of the cultivated rice area in India is prone to crop damage from prolonged flooding. We use a randomized field experiment in 128 villages of Orissa India to show that Swarna-Sub1, a recently released submergence-tolerant rice variety, has significant positive impacts on rice yield when fields are submerged for 7 to 14 days with no yield penalty without flooding. We estimate that Swarna-Sub1 offers an approximate 45% increase in yields over the current popular variety when fields are submerged for 10 days. We show additionally that low-lying areas prone to flooding tend to be more heavily occupied by people belonging to lower caste social groups. Thus, a policy relevant implication of our findings is that flood-tolerant rice can deliver both efficiency gains, through reduced yield variability and higher expected yield, and equity gains in disproportionately benefiting the most marginal group of farmers