<p> Across the planet, climate change is altering the way human societies interact with the environment. Amplified climate change at high latitudes is significantly altering the structure and function of ecosystems, creating challenges and necessitating adaptation by societies in the region that depend on local ecosystem services for their livelihoods. Rural communities in Interior Alaska rely on plants and animals for food, clothing, fuel and shelter. Previous research suggests that climate-induced changes in environmental conditions are challenging the abilities of rural residents to travel across the land and access local resources, but detailed information on the nature and effect of specific conditions is lacking. My objectives were to identify climate-related environmental conditions affecting subsistence access, and then estimate travel and access vulnerability to those environmental conditions. I collaborated with nine Interior Alaskan communities within the Yukon River basin and provided local residents with camera-equipped GPS units to document environmental conditions directly affecting access for 12 consecutive months. I also conducted comprehensive interviews with research participants to incorporate the effects of environmental conditions not documented with GPS units. Among the nine communities collaborating on this research, 18 harvesters documented 479 individual observations of environmental conditions affecting their travel with GPS units. Environmental conditions were categorized into seven condition types. I then ranked categories of conditions using a vulnerability index that incorporated both likelihood (number of times a condition was documented) and sensitivity (magnitude of the effect from the condition) information derived from observations and interviews. Changes in ice conditions, erosion, vegetative community composition and water levels had the greatest overall effect on travel and access to subsistence resources. Environmental conditions that impeded travel corridors, including waterways and areas with easily traversable vegetation (such as grass/sedge meadows and alpine tundra), more strongly influenced communities off the road network than those connected by roads. Combining local ecological knowledge and scientific analysis presents a broad understanding of the effects of climate change on access to subsistence resources, and provides information that collaborating communities can use to optimize adaptation and self-reliance. </p><p>

<p> The geographic variability and uneven distribution of climate-related impacts in urban environments pose serious challenges to achieving social-ecological resilience and environmental justice. There are no generalizable solutions for the anticipated climate challenges facing urban environments, which vary from increased frequency and intensity of extreme weather events to flooding, heat waves, droughts, and worsening air quality. Densely populated coastal urban areas, like Long Beach, California, are further exposed to sea level rise, coastal erosion, and saltwater intrusion. In response, ecosystem-based adaptation plans have gained traction in the scientific literature and policy circles as viable, multi-beneficial strategies to build urban resilience to withstand anticipated climate threats. Green infrastructure (GI) offers flexible, place-based solutions and as such, has surged in popularity as an urban planning strategy, reflecting the focus of planners and policy-makers to design and implement location-specific interventions. Utilizing a mixed-methods approach, this empirical case study analyzes the spatial distribution and projected intensity of climate-related impacts in Long Beach, California. Integrating geospatial data, surveys, and key informant interviews, this study explores citizen perception of climate risk and desirability of GI solutions to increase adaptive capacity across two high risk communities with unevenly distributed biophysical and social vulnerabilities.</p><p>

A 147cm sediment core from Lake DV09, northern Devon Island, Nunavut, Canada (75&deg; 34'34"N, 89&deg; 18'55"W) contains annually-laminated (varved) sediments, providing a 1600-year record of climate variability. A minerogenic lamina deposited during the annual thaw period and a thin deposit of organic matter deposited during the summer and through the winter, together form a clastic-organic couplet each year. The thinnest varves occur from AD800-1050, and the thickest from AD1100-1300, during the Medieval Warm Period. The relative sediment density is also highest during this period suggesting increased sediment transport energy. The coldest period of the Little Ice Age appears to be during the AD 1600s. Varve widths over the past century indicate climate warming in the region.

<p> The assumption of hydrologic stationarity has formed the basis of coastal design to date. At the beginning of the 21^st century, the impact of climate variability and future climate change on coastal water levels has become apparent through long term tide gauge records, and anecdotal evidence of increased nuisance tidal flooding in coastal areas. Recorded impacts of global sea rise on coastal water levels have been documented over the past 100 to 150 years, and future water levels will continue to change at increasing, unknown rates, resulting in the need to consider the impacts of these changes on past coastal design assumptions. New coastal infrastructure plans, and designs should recognize the paradigm shift in assumptions from hydrologic stationarity to non-stationarity in coastal water levels. As we transition into the new paradigm, there is a significant knowledge gap which must address built coastal infrastructure vulnerability based on the realization that the underlying design assumptions may be invalid. </p><p> A framework for the evaluation of existing coastal infrastructure is proposed to effectively assess vulnerability. The framework, called the Climate Preparedness and Resilience Register (CPRR) provides the technical basis for assessing existing and future performance. The CPRR framework consists of four major elements: (1) datum adjustment, (2) coastal water levels, (3) scenario projections and (4) performance thresholds. The CPRR framework defines methodologies which: (1) adjust for non-stationarity in coastal water levels and correctly make projections under multiple scenarios; (2) account for past and future tidal to geodetic datum adjustments; and (3) evaluate past and future design performance by applying performance models to determine the performance thresholds. The framework results are reproducible and applicable to a wide range of coastal infrastructure types in diverse geographic areas. </p><p> The framework was applied in two case studies of coastal infrastructure on the east and west coasts of the United States. The east coast case study on the Stamford Hurricane Barrier (SHB) at Stamford CT, investigated the navigation gate closures of the SHB project. The framework was successfully applied using two performance models based on function and reliability to determine the future time frame at which relative sea level rise (RSLR) would cause Navigation Gate closures to occur once per week on average or 52 per year. The closure time analysis also showed the impact of closing the gate earlier to manage internal drainage to the Harbor area behind the Stamford Hurricane Barrier. These analyses were made for three future sea level change (SLC) scenarios. </p><p> The west coast case study evaluated four infrastructure elements at the San Francisco Waterfront, one building and three transportation elements. The CPRR framework applied two performance models based on elevation and reliability to assess the vulnerability to flooding under four SLC scenarios. An elevation-based performance model determined a time horizon for flood impacts for king tides, 10 and 100-year annual exceedance events. The reliability-based performance model provided a refinement of results obtained in the elevation-based model due to the addition of uncertainty to the four infrastructure elements. </p><p> The CPRR framework and associated methodologies were successfully applied to assess the vulnerability of two coastal infrastructure types and functions in geographically diverse areas on the east and west coasts of the United States.</p><p>

This study has made use of an experimental method to investigate if and how exposure to an integrated 'socioeconomic- and climate justice' framing, and a 'catastrophe' framing, alter Swedish post highschool students attitudes towards climate change mitigation efforts, both on a personal- and societal level. The experiment was conducted through 287 surveys where equal shares of the respondents were given different stimuli through a short text at the beginning of the surveys. Results indicate that contrary to the hypotheses, exposure to the 'catastrophe' frame had a larger and more positive effect on especially attitudes towards personal climate change mitigation efforts, but also to some extent on societal efforts, than did exposure to a 'justice' framing. This is possibly due to the salience of catastrophe framings in the Swedish public discourse, and /or the integrated approach the educational system has taken on the subject of sustainable development.

<p> Animal movement influences ecological and biogeographical dynamics, and studying it reveals helpful insights at a time when anthropogenic activities have accelerated rates of climatic and land cover change. This dissertation addresses three fundamental questions in ecology and biogeography linked to the movement and distribution of animals. First, how do animal movements affect their environments? Second, how do the effects of land use change interact with atmospheric climate change to alter species distributions? Third, how do organisms track their climatic niches through time and space? Each question is addressed with a separate study, each generating methods and results with implications for future academic work, management, and conservation. </p><p> In the first study, I tracked the daily movements of the common hippopotamus, <i> Hippopotamus amphibius</i>, a megaherbivore that transports nutrient-rich biomass between terrestrial and aquatic ecosystems. I developed a spatially explicit biomass transfer model that relates rates of ingestion and egestion to movement behavior states derived from the movement data. The biomass transfer model revealed the process by which <i>H. amphibius</i> generates patterned landscapes of nutrient removal and deposition hotspots. In addition, the model generated maps of these nutrient transfer landscapes, making it possible to explore the spatial dynamics of nutrient transfers, and showing that the amount of biomass transferred reaches levels equivalent to rates of aboveground net primary productivity. In addition to revealing the influences of <i>H. amphibius</i> on ecosystem ecology, this study also provided metrics of home range size, habitat use, and movement behavior useful for conservation planning. </p><p> The first study provides a method for nutrient transfer mapping which could be applied to many other species, and leverages increasing quantities of high-resolution movement tracking data to map transfers of nutrients across landscapes. This can help predict the landscape-scale ecological changes resulting from the loss of animal movements that provide nutrient transfers. The approach can also be used to map other material transport dynamics, such as animal-transported seed dispersal or the movement of persistent organic pollutants. </p><p> In the second study, I used species distribution modeling to identify the interacting effects of climate and land use change on the distribution of <i>H. amphibius</i>. Hydrologic change is likely to result from ongoing shifts from rain-fed to irrigated agriculture across much of sub-Saharan Africa, where <i>H. amphibius</i> occurs. A lack of spatial data on hydrology, especially data temporally consistent with atmospheric climate datasets, has made it difficult to build species distribution models for semiaquatic species, such as <i>H. amphibius</i>, which are physiologically dependent on surface water. I overcame this challenge by coupling a simple hydrologic model to scenarios of land use and climate change, identifying potential effects on <i>H. amphibius</i> distributions. I found that increased levels of streamflow abstraction from irrigation will lead to much greater declines in <i>H. amphibius</i> habitat suitability than arise from scenarios of climate change alone. I also contrasted predictions of <i>H. amphibius</i> distributions that incorporated only atmospheric climate variables to predictions that also incorporated hydrologic variables, and found significant improvements in model performance when hydrology was incorporated. </p><p> The second study provides support for using predictive variables with strong mechanistic links to the physiology or ecology of the focal species when building species distribution models. The study also outlines a way to generate surfaces of key hydrologic variables from the climate surfaces commonly used for species distribution modeling. These surfaces have the potential to greatly improve forecasts generated by other semiaquatic species distribution models. From a conservation perspective, the second study highlights the potential for substantial losses of <i>H. amphibius</i> habitat across Africa as a result of increases in irrigation development. Other semiaquatic species in the region, as well as those dependent on the keystone ecological role of <i>H. amphibius</i> and its nutrient-transporting movements, may be similarly affected. </p><p> In the third study, I explored the role of movement in shaping species distributions in variable climates. Climatic variability at multiple time scales causes suitable climatic conditions to shift across geographic space. Recent scholarship has proposed that two species traits, the ability to colonize suitable locations, referred to as dispersal, and the ability to continue to occupy an area with unsuitable conditions, referred to as persistence, facilitate niche tracking, the process by which species follow suitable conditions moving through geographic space. By developing a model that simulates niche tracking through historically observed patterns of temporal and spatial variability, I quantified how different dispersal and persistence abilities affect niche tracking potential. I found that both dispersal and persistence facilitate niche tracking, and that small increases in persistence ability result in surprisingly large increases in niche tracking potential. </p><p> The third study makes two main contributions to ecological niche theory and distribution modeling. (Abstract shortened by ProQuest.) </p><p>

<p> The Interior Alaska boreal forest ecosystem is one of the largest ecosystems on earth and lies between the warmer southerly temperate and colder Arctic regions. The ecosystem is underlain by discontinuous permafrost. The presence or absence of permafrost primarily controls water pathways and ecosystem composition. As a result, the region hosts two distinct ecotypes that transition over a very short spatial scale&mdash;often on the order of meters. Accurate mesoscale hydrological modeling of the region is critical as the region is experiencing unprecedented ecological and hydrological changes that have regional and global implications. However, accurate representation of the landscape heterogeneity and mesoscale hydrological processes has remained a big challenge. This study addressed this challenge by developing a simple landscape model from the hill-slope studies and in situ measurements over the past several decades. The new approach improves the mesoscale prediction of several hydrological processes including streamflow and evapotranspiration (ET). </p><p> The impact of climate induced landscape change under a changing climate is also investigated. In the projected climate scenario, Interior Alaska is projected to undergo a major landscape shift including transitioning from a coniferous-dominated to deciduous-dominated ecosystem and from discontinuous permafrost to either a sporadic or isolated permafrost region. This major landscape shift is predicted to have a larger and complex impact in the predicted runoff, evapotranspiration, and moisture deficit (precipitation minus evapotranspiration). Overall, a large increase in runoff, evapotranspiration, and moisture deficit is predicted under future climate. Most hydrological climate change impact studies do not usually include the projected change in landscape into the model. In this study, we found that ignoring the projected ecosystem change could lead to an inaccurate conclusion. Hence climate induced vegetation and permafrost changes must be considered in order to fully account the changes in hydrology.</p><p>

Magister Artium (Development Studies) - MA(DVS) / Agriculture in Ghana is dominated by smallholder farmers who are faced with unpredictable rainfall and extreme weather events. Climate modelling forecasts that the rate at which precipitation will decrease in the country is far more than the rate at which it will increase during the wet season. It is predicted that rain-fed maize output will decrease below 25 percent in all the ten regions of the country by 2020 if nothing is done. To mitigate the effect of climate change and safeguard food security, the country must undertake measures to adapt to the changing climate. The process of adaptation, therefore, involves the interdependence of agents through their relation with each other. This includes the institution in which the agents reside and the resource based on which they depend. The resource embedded in such relationship has been termed social capital. Empirical studies on social capital and climate change adaptation is lacking, especially in Ghana. Based on this, the study assesses the influence of social capital on climate change adaptation strategies among smallholder farmers in the Central region of Ghana. Both primary and secondary data were used for the study. Primary data was collected using household questionnaires, focus group discussions, and key informant interviews. K-means cluster analysis was used to identify weak and strong ties and four individual social capital variables. Twenty-year maize and rainfall data were analysed using trend analysis. The influence of individual social capital and other controlled variables were analysed using Multinomial logit model. Using 225 sampled households the results of the study showed that all the four identified individual social capital variables differ by sex. The perceptions of climate change among smallholder farmers also differ significantly by location. The four individual social capital variables as well as other controlled variables influence at least one indigenous adaptation strategy and one introduced adaptation strategy. The study recommends, among others, that transfer of climate change adaptation techniques or technology to smallholder farmers should not be solely accomplished through the usual technology transfer network of agricultural researchers and extension agents. Rather, it will be imperative to increased contact with a wide variety of local actors who provide information and resources for agricultural production.

No abstract available. / Dissertation (LLM)--University of Pretoria, 2013. / Centre for Human Rights / unrestricted

<p> Community solar is a renewable energy practice that&rsquo;s been adopted by multiple U.S. states and is being considered by many more, including the state of Oregon. A recent senate bill in Oregon, called the &ldquo;Clean Electricity and Coal Transition Plan&rdquo;, includes a provision that directs the Oregon Public Utility Commission to establish a community solar program for investor-owned utilities by late 2017. Thus, energy consumers in Portland will be offered participation in community solar projects in the near future. Community solar is a mechanism that allows ratepayers to experience both the costs and benefits of solar energy while also helping to offset the proportion of fossil-fuel generated electricity in utility grids, thus aiding climate change mitigation. </p><p> For community solar to achieve market success in the residential sector of Portland, ratepayers of investor-owned utilities must socially accept this energy practice. The aim of this study was to forecast the potential social acceptance of community solar among Portland residents by measuring willingness to participate in these projects. Additionally, consumer characteristics, attitudes, awareness, and knowledge were captured to assess the influence of these factors on intent to enroll in community solar. The theory of planned behavior, as well as the social acceptance, diffusion of innovation, and dual-interest theories were frameworks used to inform the analysis of community solar adoption. These research objectives were addressed through a mixed-mode survey of Portland residents, using a stratified random sample of Portland neighborhoods to acquire a gradient of demographics. 330 questionnaires were completed, yielding a 34.2% response rate. </p><p> Descriptive statistics, binomial logistic regression models, and mean willingness to pay were the analyses conducted to measure the influence of project factors and demographic characteristics on likelihood of community solar participation. Roughly 60% of respondents exhibited interest in community solar enrollment. The logistic regression model revealed the percent change in utility bill (essentially the rate of return on the community solar investment) as a dramatically influential variable predicting willingness to participate. Community solar project scenarios also had a strong influence on willingness to participate: larger, cheaper, and distant projects were preferred over small and expensive local projects. Results indicate that community solar project features that accentuate affordability are most important to energy consumers. Additionally, demographic characteristics that were strongly correlated with willingness to enroll were politically liberal ideologies, higher incomes, current enrollment in green utility programs, and membership in an environmental organization. Thus, the market acceptance of community solar in Portland will potentially be broadened by emphasizing affordability over other features, such as community and locality. </p><p> Additionally, I explored attitudinal influences on interest in community solar by conducting exploratory factor analysis on attitudes towards energy, climate change, and solar barriers and subsequently conducting binomial logistic regression models. Results found that perceiving renewable energy as environmentally beneficial was positively correlated with intent to enroll in community solar, which supported the notion that environmental attitudes will lead to environmental behaviors. The logistic regression model also revealed a negative correlation between community solar interest and negative attitudes towards renewable energy. Perceptions of solar barriers were mild, indicating that lack of an enabling mechanism may be the reason solar continues to be underutilized in this region.</p><p>