Effects of the closure of a major sewage outfall on sublittoral, soft sediment benthic communities

The present study examines the benthos within western inner Swansea Bay (Wales, UK), for the period before, during and immediately after the cessation of a major, sewage discharge from Mumbles Head. There have been significant improvements in seawater quality and changes in the species composition of the benthic communities following the cessation of the sewage discharge. There has been an increase in the diversity of deposit feeders, especially the amphipods, and a decrease in the diversity of the filter feeders, especially the polychaetes. Changes are not attributable either to sediment organic matter content or to gross changes in sediment type, but are related to the significant reduction in suspended particulate organic material and sewage contaminants discharged to the Bay. A recovery model has been proposed to describe how soft sediment benthic communities in a shallow, sublittoral, high tidal energy environment respond to the abrupt cessation of a major sewage discharge.     

The effects of spatial autoregressive dependencies on inference in ordinary least squares: a geometric approach

There is a common belief that the presence of residual spatial autocorrelation in ordinary least squares (OLS) regression leads to inflated significance levels in beta coefficients and, in particular, inflated levels relative to the more efficient spatial error model (SEM). However, our simulations show that this is not always the case. Hence, the purpose of this paper is to examine this question from a geometric viewpoint. The key idea is to characterize the OLS test statistic in terms of angle cosines and examine the geometric implications of this characterization. Our first result is to show that if the explanatory variables in the regression exhibit no spatial autocorrelation, then the distribution of test statistics for individual beta coefficients in OLS is independent of any spatial autocorrelation in the error term. Hence, inferences about betas exhibit all the optimality properties of the classic uncorrelated error case. However, a second more important series of results show that if spatial autocorrelation is present in both the dependent and explanatory variables, then the conventional wisdom is correct. In particular, even when an explanatory variable is statistically independent of the dependent variable, such joint spatial dependencies tend to produce “spurious correlation” that results in over-rejection of the null hypothesis. The underlying geometric nature of this problem is clarified by illustrative examples. The paper concludes with a brief discussion of some possible remedies for this problem.     

Partnering between a geography department and a community initiative to provide a wind resource assessment for the Blueskin Bay region,Otago, New Zealand

This paper explores the partnership between a community group and a geography department to assess the wind energy potential of the Blueskin Bay region in southern New Zealand. The partnership has provided opportunities for research and is of strategic importance. Year‐long wind observations and numerical modelling have revealed that the hills surrounding Blueskin Bay show potential for wind energy generation. Despite challenges for both parties, the university–community partnership has allowed a successful research platform to be established.     

The potential transient dynamics of forests in New England under historical and projected future climate change

Projections of vegetation distribution that incorporate the transient responses of vegetation to climate change are likely to be more efficacious than those that assume an equilibrium between climate and vegetation. We examine the non-equilibrium dynamics of a temperate forest region under historic and projected future climate change using the dynamic ecosystem model LPJ-GUESS. We parameterized LPJ-GUESS for the New England region of the United Sates utilizing eight forest cover types that comprise the regionally dominant species. We developed a set of climate data at a monthly-step and a 30-arc second spatial resolution to run the model. These datasets consist of past climate observations for the period 1901?C2006 and three general circulation model projections for the period 2007?C2099. Our baseline (1971?C2000) simulation reproduces the distribution of forest types in our study region as compared to the National Land Cover Data 2001 (Kappa statistic?=?0.54). Under historic and nine future climate change scenarios, maple-beech-basswood, oaks and aspen-birch were modeled to move upslope at an estimated rate of 0.2, 0.3 and 0.5?m?yr^?1 from 1901 to 2006, and continued this trend at an accelerated rate of around 0.5, 0.9 and 1.7?m?yr^?1 from 2007 to 2099. Spruce-fir and white pine-cedar were modeled to contract to mountain ranges and cooler regions of our study region under projected future climate change scenarios. By the end of the 21^st century, 60% of New England is projected to be dominated by oaks relative to 21% at the beginning of the 21^st century, while northern New England is modeled to be dominated by aspen-birch. In mid and central New England, maple-beech-basswood, yellow birch-elm and hickories co-occur and form novel species associations. In addition to warming-induced northward and upslope shifts, climate change causes more complex changes in our simulations, such as reversed conversions between forest types that currently share similar bioclimatic ranges. These results underline the importance of considering community interactions and transient dynamics in modeling studies of climate change impacts on forest ecosystems.     

Validating a rapid-update satellite precipitation analysis across telescoping space and time scales

In order to properly utilize remotely sensed precipitation estimates in hydrometeorological applications, knowledge of the accuracy of the estimates are needed. However, relatively few ground validation networks operate with the necessary spatial density and time-resolution required for validation of high-resolution precipitation products (HRPP) generated at fine space and time scales (e.g., hourly accumulations produced on a 0.25° spatial scale). In this article, we examine over-land validation statistics for an operationally designed, meteorological satellite-based global rainfall analysis that blends intermittent passive microwave-derived rainfall estimates aboard a variety of low Earth-orbiting satellite platforms with sub-hourly time sampling capabilities of visible and infrared imagers aboard operational geostationary platforms. The validation dataset is comprised of raingauge data collected from the dense, nearly homogeneous, 1-min reporting Automated Weather Station (network of the Korean Meteorological Administration during the June to August 2000 summer monsoon season. The space-time RMS error, mean bias, and correlation matrices were computed using various time windows for the gauge averaging, centered about the satellite observation time. For ±10 min time window, a correlation of 0.6 was achieved at 0.1° spatial scale by averaging more than 3 days; coarsening the spatial scale to 1.8° produced the same correlation by averaging over 1 h. Finer than approximately 24-h and 1° time and space scales, respectively, a rapid decay of the error statistics was obtained by trading-off either spatial or time resolution. Beyond a daily time scale, the blended estimates were nearly unbiased and with an RMS error of no worse than 1 mm day^?1.     

The effect of horizontal resolution on simulation of very extreme US precipitation events in a global atmosphere model

We investigate the ability of a global atmospheric general circulation model (AGCM) to reproduce observed 20 year return values of the annual maximum daily precipitation totals over the continental United States as a function of horizontal resolution. We find that at the high resolutions enabled by contemporary supercomputers, the AGCM can produce values of comparable magnitude to high quality observations. However, at the resolutions typical of the coupled general circulation models used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, the precipitation return values are severely underestimated.     

Climate change,weather variability and corn yield at a higher latitude locale: Southwestern Quebec

Climate change has led to increased temperatures, and simulation models suggest that this should affect crop production in important agricultural regions of the world. Nations at higher latitudes, such as Canada, will be most affected. We studied the relationship between climate variability (temperature and precipitation) and corn yield trends over a period of 33 years for the Monteregie region of south-western Quebec using historical yield and climate records and statistical models. Growing season mean temperature has increased in Monterregie, mainly due to increased September temperature. Precipitation did not show any clear trend over the 33 year period. Yield increased about 118 kg ha⁻¹ year⁻¹ from 1973 to 2005 (under normal weather conditions) due mainly to changes in technology (genetics and management). Two climate variables were strongly associated with corn yield variability: July temperature and May precipitation. These two variables explain more than a half of yield variability associated with climate. In conclusion, July temperatures below normal and May precipitation above normal have negative effects on corn yield, and the growing seasons have warmed, largely due to increases in the September temperature.     

Optical and near-infrared colours as a discriminant of the age and metallicity of stellar populations

We present a comprehensive analysis of the ability of current stellar population models to reproduce the optical ( ugriz ) and near-infrared ( JHK ) colours of a small sample of well-studied nearby elliptical and S0 galaxies. We find broad agreement between the ages and metallicities derived using different population models, although different models show different systematic deviations from the measured broad-band fluxes. Although it is possible to constrain simple stellar population models to a well-defined area in age–metallicity space, there is a clear degeneracy between these parameters even with such a full range of precise colours. The precision to which age and metallicity can be determined independently, using only broad-band photometry with realistic errors, is  Δ[Fe/H]≃ 0.18  and  Δlog Age ≃ 0.25  . To constrain the populations and therefore the star formation history further, it will be necessary to combine broad-band optical–IR photometry with either spectral line indices, or else photometry at wavelengths outside this range.