RÉSUMÉ
The biospheric water and carbon cycles are intimately coupled, so simulating carbon fluxes by vegetation also requires modelling of the water fluxes, with each component influencing the other. Observations of river streamflow integrate information at the catchment scale and are widely available over a long period; they therefore provide an important source of information for validating or calibrating vegetation models. In this paper, we analyse the performance of the Sheffield dynamic global vegetation model (SDGVM) for predicting river streamflow and quantifying how this information helps to constrain carbon flux predictions. The SDGVM is run for 29 large catchments in the United Kingdom. Annual streamflow estimates are compared with long time-series observations. In 23 out of the 29 catchments, the bias between model and observations is less than 50 mm, equivalent to less than 10% of precipitation. In the remaining catchments, larger errors are because of combinations of unpredictable causes, in particular various human activities and measurement issues and, in two cases, unidentified causes. In one of the catchments, we assess to what extent a knowledge of annual streamflow can constrain model parameters and in turn constrain estimates of gross primary production (GPP). For this purpose, we assume the model parameters are uncertain and constrain them by the streamflow observations using the generalized likelihood uncertainty estimation method. Comparing the probability density function of GPP with and without constraint shows that streamflow effectively constrains GPP, mainly by setting a low probability to GPP values below about 1100 g C-1 m2 yr-1 . In other words, streamflow observations allow the rejection of low values of GPP, so that the potential range of possible GPP values is almost halved.
RÉSUMÉ
Methods to evaluate the performance of segmentation algorithms for synthetic aperture radar (SAR) images are developed, based on known properties of coherent speckle and a scene model in which areas of constant backscatter coefficient are separated by abrupt edges. Local and global measures of segmentation homogeneity are derived and applied to the outputs of two segmentation algorithms developed for SAR data, one based on iterative edge detection and segment growing, the other based on global maximum a posteriori (MAP) estimation using simulated annealing. The quantitative statistically based measures appear consistent with visual impressions of the relative quality of the segmentations produced by the two algorithms. On simulated data meeting algorithm assumptions, both algorithms performed well but MAP methods appeared visually and measurably better. On real data, MAP estimation was markedly the better method and retained performance comparable to that on simulated data, while the performance of the other algorithm deteriorated sharply. Improvements in the performance measures will require a more realistic scene model and techniques to recognize oversegmentation.
RÉSUMÉ
The development of a linear filter to optimise the signal-to-noise ratio of the auditory cortical evoked potential is described. The filter characteristics were derived from the frequency spectra of cortical potentials taken from 40 normal and 20 hearing impaired adult ears, at two test frequencies (120 tests). The performance of the filter was compared with a typical filter used in clinical practice (1.5 Hz to 15 Hz second-order Butterworth filter). Results showed that the filter produced an average increase in the signal-to-noise ratio of approximately 38%. Further comparisons were made using 14 different Butterworth filters (all second order) and the best of these, the 5 Hz to 9 Hz filter, produced a 28% improvement in the signal-to-noise ratio. The signal-to-noise ratio was calculated by comparing the absolute integral area of the average post-stimulus data to that of the pre-stimulus data. This improvement in the signal-to-noise ratio enhances signals for objective machine scoring analysis or alternatively, allows for a reduction in the number of sweeps (and hence time) required to record the evoked potential.
