Spatial interpolation of snow water equivalency using surface observations and remotely sensed images of snow‐covered area

As demand for water continues to escalate in the western Unites States, so does the need for accurate monitoring of the snowpack in mountainous areas. In this study, we describe a simple methodology for generating gridded‐estimates of snow water equivalency (SWE) using both surface observations of SWE and remotely sensed estimates of snow‐covered area (SCA). Multiple regression was used to quantify the relationship between physiographic variables (elevation, slope, aspect, clear‐sky solar radiation, etc.) and SWE as measured at a number of sites in a mountainous basin in south‐central Idaho (Big Wood River Basin). The elevation of the snowline, obtained from the SCA estimates, was used to constrain the predicted SWE values. The results from the analysis are encouraging and compare well to those found in previous studies, which often utilized more sophisticated spatial interpolation techniques. Cross‐validation results indicate that the spatial interpolation method produces accurate SWE estimates [mean R² = 0·82, mean mean absolute error (MAE) = 4·34 cm, mean root mean squared error (RMSE) = 5·29 cm]. The basin examined in this study is typical of many mid‐elevation mountainous basins throughout the western United States, in terms of the distribution of topographic variables, as well as the number and characteristics of sites at which the necessary ground data are available. Thus, there is high potential for this methodology to be successfully applied to other mountainous basins. Copyright © 2010 John Wiley & Sons, Ltd.     

Controls on surface water chemistry in two lake‐watersheds in the Adirondack region of New York: differences in nitrogen solute sources and sinks

The southwestern Adirondack region of New York receives among the highest rates of atmospheric nitrogen (N) deposition in the USA. Atmospheric N deposition to sensitive ecosystems, like the Adirondacks, may increase the acidification of soils through losses of exchangeable nutrient cations, and the acidification of surface waters associated with enhanced mobility of nitrate (NO₃^?). However, watershed attributes, including surficial terrestrial characteristics, in‐lake processing, and geological settings, have been found to complicate the relationships between atmospheric N deposition and N drainage losses. We studied two lake‐watersheds in the southwestern Adirondacks, Grass Pond and Constable Pond, which are located in close proximity (～26 km) and receive similarly high N deposition, but have contrasting watershed attributes (e.g. wetland area, geological settings). Since the difference in the influence of N deposition was minimal, we were able to examine both within‐ and between‐watershed influences of land cover, the contribution of glacial till groundwater inputs, and in‐lake processes on surface water chemistry with particular emphasis on N solutes and dissolved organic carbon (DOC). Monthly samples at seven inlets and one outlet of each lake were collected from May to October in 1999 and 2000. The concentrations of NO₃^? were high at the Grass Pond inlets, especially at two inlets, and NO₃^? was the major N solute at the Grass Pond inlets. The concentrations of likely weathering products (i.e. dissolved Si, Ca²⁺, Mg²⁺, Na⁺) as well as acid neutralizing capacity and pH values, were also particularly high at those two Grass Pond inlets, suggesting a large contribution of groundwater inputs. Dissolved organic N (DON) was the major N solute at the Constable Pond inlets. The higher concentrations of DON and DOC at the Constable Pond inlets were attributed to a large wetland area in the watershed. The DOC/DON ratios were also higher at the Constable Pond inlets, possibly due to a larger proportion of coniferous forest area. Although DON and DOC were strongly related, the stronger relationship of the proportion of wetland area with DOC suggests that additional factors regulate DON. The aggregated representation of watershed physical features (i.e. elevation, watershed area, mean topographic index, hypsometric‐analysis index) was not clearly related to the lake N and DOC chemistry. Despite distinctive differences in inlet N chemistry, NO₃^? and DON concentrations at the outlets of the two lakes were similar. The lower DOC/DON ratios at the lake outlets and at the inlets having upstream ponds suggest the importance of N processing and organic N sources within the lakes. Although an inverse relationship between NO₃^? and DOC/DON has been suggested to be indicative of a N deposition gradient, the existence of this relationship for sites that receive similar atmospheric N deposition suggest that the relationship between NO₃^? and the DOC/DON ratio is derived from environmental and physical factors. Our results suggest that, despite similar wet N deposition at the two watershed sites, N solutes entering lakes were strongly affected by hydrology associated with groundwater contribution and the presence of wetlands, whereas N solutes leaving lakes were strongly influenced by in‐lake processing. Copyright © 2006 John Wiley & Sons, Ltd.     

Beiträge zur Gliederung des Grundgebirges von Bornholm

Ohne Zusammenfassung     

Feminism and Social Theory in Geography: An Introduction*

This essay introduces a collection of articles based on papers developed for a Fall 2004 speaker series at the University of Minnesota. The articles address the continued relevance of feminist geography and the unique contributions of feminist perspectives in various areas of geographic research. They also point out directions for needed future research. This introduction briefly reviews the successes of and remaining challenges to feminist geography, including material inequities yet unresolved in two other (nonresearch) “places” of academic life: teaching and the workplace. We discuss the ongoing underrepresentation of women and people of color on our faculties and in the front of classrooms.     

Evolving Turbulence Realizations of Atmospheric Flow

A significant difference exists between estimates of contaminant atmospheric transport and dispersion calculated by an ensemble-averaged model and the turbulent details of any particular atmospheric transport and dispersion realization. In some cases, however, it is important to be able to make inferences of these realizations using ensemble-averaged models. It is possible to make such inferences if there are sensors in the field to report contaminant concentration observations. Any information determined about the atmospheric transport and dispersion realization can then be assimilated into a forecast model. This approach can enhance the accuracy of the atmospheric transport and dispersion forecast of a particular event. This work adopts that approach and reports on a genetic algorithm used to optimize the variational problem. Given contaminant sensor measurements and a transport and dispersion model, one can back-calculate unknown source and meteorological parameters. In this case, we demonstrate the dynamic recovery of unknown meteorological variables, including the transport variables that comprise the “outer variability” (wind speed and wind direction) and the dispersion variables that comprise the “inner variability” (contaminant spread). The optimization problem is set up in an Eulerian grid space, where the comparison of the concentration field variable between the predictions and the observations forms the cost function. The transport and dispersion parameters, which are determined from the optimization, are in Lagrangian space. This calculation is applied to continuous and instantaneous releases in a horizontally homogeneous wind field such as that observed during traditional transport and dispersion field experiments. The method proves to be successful at recovering the unknown transport and dispersion parameters for a numerical experiment.     

Confronting the challenge of integrated assessment of climate adaptation: a conceptual framework

Key limitations of integrated assessment models (IAMs) are their highly stylized and aggregated representation of climate damages and associated economic responses, as well as the omission of specific investments related to climate change adaptation. This paper proposes a framework for modeling climate impacts and adaptation that clarifies the relevant research issues and provides a template for making improvements. We identify five desirable characteristics of an ideal integrated assessment modeling platform, which we elaborate into a conceptual model that distinguishes three different classes of adaptation-related activities. Based on these elements we specify an impacts- and adaptation-centric IAM, whose optimality conditions are used to highlight the types of functional relationships necessary for realistic representations of adaptation-related decisions, the specific mechanisms by which these responses can be incorporated into IAMs, and the ways in which the inclusion of adaptation is likely to affect the simulations’ results.     

Climate change and mental health: an exploratory case study from Rigolet, Nunatsiavut, Canada

As the impacts from anthropogenic climate change are increasing globally, people are experiencing dramatic shifts in weather, temperature, wildlife and vegetation patterns, and water and food quality and availability. These changes impact human health and well-being, and resultantly, climate change has been identified as the biggest global health threat of the 21st Century. Recently, research is beginning to indicate that changes in climate, and the subsequent disruption to the social, economic, and environmental determinants of health, may cause increased incidences and prevalence of mental health issues, emotional responses, and large-scale sociopsychological changes. Through a multi-year, community-led, exploratory case study conducted in Rigolet, Nunatsiavut, Labrador, Canada, this research qualitatively explores the impacts of climate change on mental health and well-being in an Inuit context. Drawing from 67 in-depth interviews conducted between January 2010 and October 2010 with community members and local and regional health professionals, participants reported that changes in weather, snow and ice stability and extent, and wildlife and vegetation patterns attributed to climate change were negatively impacting mental health and well-being due to disruptions in land-based activities and a loss of place-based solace and cultural identity. Participants reported that changes in climate and environment increased family stress, enhanced the possibility of increased drug and alcohol usage, amplified previous traumas and mental health stressors, and were implicated in increased potential for suicide ideation. While a preliminary case study, these exploratory findings indicate that climate change is becoming an additional mental health stressor for resource-dependent communities and provide a baseline for further research.