Productivity,detritus flux,and nutrient cycling in a Carex lyngbyei tidal marsh

Net annual primary production of a sedge Carex lyngbyei dominated tidal marsh in the Fraser River estuary, British Columbia, Canada was 634 g ash-free dry weight (AFDW) per m² per yr (687 g dry weight per m² per yr). Mean maximum shoot elongation during the short (May to August) growing season was 1.88 cm per day from overwintering shoots. The maximum aboveground standing crop of 690 g AFDW per m² represented only 25% of the total below-ground biomass, which appears to be controlling most of the critical life history processes of the sedge marsh. An estimate of 14 percent of the aboveground standing crop was lost through leaching of dissolved organic carbon from the growting plant. Aboveground tissue losses, which were negligible during the growing season, occurred primarily via translocation in autumn and tidal export during the winter. In situ measurements showed that of the original maximum standing crop, approximately 38%, 37%, and 25% were lost by downward translocation, tidal export, and sediment burial, respectively. Based on changes in above and belowground nutrient pools, rapid spring (May to late June) uptake rates of 109 mg N per m² per day and 23.0 mg P per m² per day by shoots were followed by downward translocation rates of 44.8 mg N per m² per day and 12.2 mg P per m² per day during late June to the end of August. Aboveground leaching rates were estimated as 23.9 mg N per m² per day and 7.8 mg P m² per day and belowground uptake rates as 100 mg N per m² per day and 26 mg P per m² per day; root uptake occurred primarily after late June. Nutrient levels in decomposing litter more than doubled over the winter period showing a pattern of nutrient enrichment characteristic of marsh ecosystems. *** DIRECT SUPPORT *** A01BY023 00004     

Chironomids as indicators of climate change: a 100‐lake training set from a subarctic region of northern Sweden (Lapland)

Multivariate numerical analyses (DCA, CCA) were used to study the distribution of chironomids from surface sediments of 100 lakes spanning broad ecoclimatic conditions in northern Swedish Lapland. The study sites range from boreal forest to alpine tundra and are located in a region of relatively low human impact. Of the 19 environmental variables measured, ordination by CCA identified mean July air temperature as one of the most significant variables explaining the distribution and the abundance of chironomids. Lossonignition (LOI), maximum lake depth and mean January air temperature also accounted for significant variation in chironomid assemblages. A quantitative transfer function was created to estimate mean July air temperature from sedimentary chironomid assemblages using weightedaveraging partial least squares regression (WAPLS). The coefficient of determination was relatively high (r² = 0.65) with root mean squared error of prediction (RMSEP, based on jack-knifing) of 1.13 °C and maximum bias of 2.1 °C, indicating that chironomids can provide useful quantitative estimates of past changes in mean July air temperature. The paper focuses mainly on the relationship between chironomid composition and July air temperature, but the relationship to LOI and depth are also discussed.     

Regional Energy Demand Responses To Climate Change: Methodology And Application To The Commonwealth Of Massachusetts

Climate is a major determinant of energy demand. Changes in climate may alter energy demand as well as energy demand patterns. This study investigates the implications of climate change for energy demand under the hypothesis that impacts are scale dependent due to region-specific climatic variables, infrastructure, socioeconomic, and energy use profiles. In this analysis we explore regional energy demand responses to climate change by assessing temperature-sensitive energy demand in the Commonwealth of Massachusetts. The study employs a two-step estimation and modeling procedure. The first step evaluates the historic temperature sensitivity of residential and commercial demand for electricity and heating fuels, using a degree-day methodology. We find that when controlling for socioeconomic factors, degree-day variables have significant explanatory power in describing historic changes in residential and commercial energy demands. In the second step, we assess potential future energy demand responses to scenarios of climate change. Model results are based on alternative climate scenarios that were specifically derived for the region on the basis of local climatological data, coupled with regional information from available global climate models. We find notable changes with respect to overall energy consumption by, and energy mix of the residential and commercial sectors in the region. On the basis of our findings, we identify several methodological issues relevant to the development of climate change impact assessments of energy demand.     

Towards evaluating cloud response to climate change using clustering technique identification of cloud regimes

Most of the discrepancies in the climate sensitivity of general circulation models (GCMs) are believed to be due to differences in cloud radiative feedback. Analysis of cloud response to climate change in different ‘regimes’ may offer a more detailed understanding of how the cloud response differs between GCMs. In which case, evaluation of simulated cloud regimes against observations in terms of both their cloud properties and frequency of occurrence will assist in assessing confidence in the cloud response to climate change in a particular GCM. In this study, we use a clustering technique on International Satellite Cloud Climatology Project (ISCCP) data and on ISCCP-like diagnostics from two versions of the Hadley Centre GCM to identify cloud regimes over four different geographical regions. The two versions of the model are evaluated against observational data and their cloud response to climate change compared within the cloud regime framework. It is found that cloud clusters produced by the more recent GCM, HadSM4, compare more favourably with observations than HadSM3. In response to climate change, although the net cloud response over particular regions is often different in the two models, in several instances the same basic processes may be seen to be operating. Overall, both changes in the frequency of occurrence of cloud regimes and changes in the properties (optical depth and cloud top height) of the cloud regimes contribute to the cloud response to climate change.     

Integration of Remote Sensing and GIS to Detect Pockets of Urban Poverty: The Case of Rosario, Argentina

The advent of high spatial resolution, multispectral satellite imagery has allowed analysis of remotely sensed images of urban land cover to become more useful to urban planning and decision making than in the past. The addition of radar imagery at relatively high spatial resolution (6 metres at best), with the advantages that it is not affected by cloud and diurnal light conditions and that it is sensitive to the target's geometric shape, surface roughness and moisture content offers additional capability in this regard. This paper incorporates analysis of Canadian RADARSAT-1 and American Landsat TM satellite imagery and ground-based GIS data to identify known pockets of urban poverty. Poverty is defined, based on a limited number of census variables related to dwelling construction materials and per household overcrowding. The objective is to provide a proof of concept that remote sensing data, especially from synthetic aperture radar, and ground-based GIS data can be successfully integrated for urban planning purposes. The results suggest that the approach used is reasonable and that, with future refinement, it offers planners and decision makers a timely and cost effective means to locate and monitor poverty pockets in urban areas. This is especially important in large, rapidly urbanising areas in the developing world.     

Self-organised clustering for road extraction in classified imagery

The extraction of road networks from digital imagery is a fundamental image analysis operation. Common problems encountered in automated road extraction include high sensitivity to typical scene clutter in high-resolution imagery, and inefficiency to meaningfully exploit multispectral imagery (MSI). With a ground sample distance (GSD) of less than 2 m per pixel, roads can be broadly described as elongated regions. We propose an approach of elongated region-based analysis for 2D road extraction from high-resolution imagery, which is suitable for MSI, and is insensitive to conventional edge definition. A self-organising road map (SORM) algorithm is presented, inspired from a specialised variation of Kohonen's self-organising map (SOM) neural network algorithm. A spectrally classified high-resolution image is assumed to be the input for our analysis. Our approach proceeds by performing spatial cluster analysis as a mid-level processing technique. This allows us to improve tolerance to road clutter in high-resolution images, and to minimise the effect on road extraction of common classification errors. This approach is designed in consideration of the emerging trend towards high-resolution multispectral sensors. Preliminary results demonstrate robust road extraction ability due to the non-local approach, when presented with noisy input.