Fishing in the Amazonian Forest: A Gendered Social Network Puzzle

We employ social network analysis (SNA) to describe the structure of subsistence fishing social networks and to explore the relation between fishers’ emic perceptions of fishing expertise and their position in networks. Participant observation and quantitative methods were employed among the Tsimane’ Amerindians of the Bolivian Amazon. A multiple-regression quadratic assignment procedure was used to explore the extent to which gender, kinship, and age homophilies influence the formation of fishing networks. Logistic regressions were performed to determine the association between fishers’ expertise, their sociodemographic identities, and network centrality. We found that fishing networks are gendered and that there is a positive association between fishers’ expertise and centrality in networks, an association that is more striking for women than for men. We propose that a social network perspective broadens understanding of the relations that shape the intracultural distribution of fishing expertise, as well as natural resource access and use.     

Measuring Community and University Impacts of Critical Civic Geography: Insights from Chicago

Geographers have increasingly adopted community-based learning and research into their teaching and scholarly activities since Bunge and Harvey called for an applied public geography that is both useful and challenges societal inequalities. With few exceptions, however, there has been little discussion of methods for measuring this work. Many published assessments focus on the impacts of projects on students but overlook the impacts on community partners. Impacts on faculty and the larger university community are also often ignored. This article discusses literature on the evaluation of community–university research and service learning from a critical perspective. A discussion of service learning and community-based research (CBR) projects at two Chicago universities, DePaul and Chicago State, is presented. In both cases challenges were encountered to achieve full evaluation of projects, yet both included an evaluation of university and community partners that allowed for assessment of the projects’ value to all partners.     

Developing a model of climate change behavior among adolescents

Research on adolescent climate change perceptions has uncovered key insights about how knowledge, concern, and hope might relate to behavior and the potential for educational interventions to influence these factors. However, few of these studies have employed treatment/control designs that might address causality and none have addressed how these factors might interact to influence behavior. We developed a model of behavior change where a climate education treatment impacted knowledge, knowledge impacted hope and concern, and hope and concern together impacted behavior. We empirically tested the utility of this model and the causal relationships within it using a pre/post, treatment/control evaluation of climate education among adolescents in North Carolina, USA (n?=?1041). We found support for a causal relationship between the treatment and gains in knowledge, but not between treatment and behavior. However, we did find support for a path model in which climate change knowledge positively relates to increased climate change concern and hope, and increases in concern and hope predict changes in pro-environmental behavior. Low SES was related to smaller gains in knowledge, concern, and behavior. Our results contribute to a theoretical understanding of climate change behaviors among adolescents and suggest that climate education aiming to change behavior should focus on building hope and concern.     

Adaptation and poverty reduction in Mozambique: an opportunity for developing countries to lead§

Climate change disproportionately impacts the world’s poorest countries. A recent World Bank report highlighted that over 100 million people are at risk of falling into extreme poverty as a result of climate change. There is currently a lack of information about how to simultaneously address climate change and poverty. Climate change challenges provide an opportunity for those impacted most to come up with new and innovative technologies and solutions. This article uses an example from Mozambique where local and international partners are working side-by-side, to show how developing countries can simultaneously address climate change and poverty reduction using an ecosystem-based adaptation approach. Using ecosystem-based adaptation, a technique that uses the natural environment to help societies adapt to climate change, developing countries can lead the way to improve climate adaptation globally. This paradigm shift would help developing countries become leaders in ecosystem-based adaptation and green infrastructure techniques and has implications for climate policy worldwide.

POLICY RELEVANCE

The Paris Agreement resulting from the United Nations Framework Convention on Climate Change (UNFCCC) 21st Conference of Parties (COP 21) in December 2015 was rightly lauded for its global commitment to cut greenhouse gas emissions. However, COP 21 was also historic because of its call for non-party stakeholders to address climate change, inclusion of a global goal of ‘enhancing adaptive capacity, strengthening resilience and reducing vulnerability’, and the United States’ commitment of $800 million to adaptation funding. The combination of recognizing the need for new stakeholders to commit to climate change adaptation, the large impact climate change will have on the developing world, and providing access to funds for climate change adaptation creates a unique opportunity for developing countries to pave the way in adaptation policies in practices. Currently, developing countries are creating National Adaptation Plans (NAPs) for the UNFCCC. Through including a strong component of ecosystem-based adaptation in NAPs, developing countries can shape their countries’ policies, improve local institutions and governments, and facilitate a new generation of innovative leaders. Lessons learned in places like Mozambique can help lead the way in other regions facing similar climatic risks.     

Macroseepage of methane and light alkanes at the La Brea tar pits in Los Angeles

Geologic seepage of methane and light (C₂-C₅) alkanes was measured at the La Brea Tar Pits in Los Angeles. Samples were collected using flux chambers with stainless steel canisters and analyzed using gas chromatography. Average seepage rates from individual seepage sites were 970 ± 330 mg/h of methane, 14.0 ± 5.5 mg/h of ethane, 9.1 ± 3.7 mg/h of propane, 3.7 ± 1.6 mg/h of i-butane, 0.33 ± 0.16 mg/h of n-butane, 260 ± 120 μg/h of i-pentane, and 5.3 ± 1.9 μg/h of n-pentane, while maximum seepage rates exceeded 17 g/h of methane, 270 mg/h of ethane, 190 mg/h of propane, 95 mg/h of i-butane, 10 mg/h of n-butane, 7 mg/h of i-pentane, and 0.1 mg/h of n-pentane. These absolute fluxes have an additional unknown amount of error associated with them due to sampling methodology, and should be taken as the lower limit of emissions. Samples collected revealed generally dry gas, with high methane emissions relative to the light alkanes. Overall emissions from the tar pits were found to come not only from the active geologic seepage, but also from the outgassing of the standing asphalt at the site. Using the gas ratios, which are negligibly affected by errors introduced by sampling methodology, observed in this study, daily emissions of C₂ – C₅ alkanes from the La Brea area were estimated to be 4.7 ± 1.6 Mg, which represents 2–3 % of total emissions in the entire Los Angeles region.     

Measurement of Kodaikanal White-Light Images – II. Rotation Comparison and Merging with Mount Wilson Data

Sunspot umbral positions and areas were measured for 82 years (1906–1987) of daily, full-disk photoheliogram observations at the Kodaikanal station of the Indian Institute of Astrophysics. The measurement technique and reduction procedures used were nearly identical to those used earlier for the reduction of Mount Wilson daily full-disk photoheliograms, covering an overlapping interval of 69 years. In this paper we compare the differential rotation of the Sun from the analysis of the Kodaikanal data with the Mount Wilson results. In addition, we analyze the data set formed by combining the data from the two sites for differential rotation. While doing this, it has become apparent to us that small, subtle optical effects at both sites produce systematic errors that have an influence on rotation (and other) results from these data. These optical effects are analyzed here, and corrections are made to the positional data of the sunspots from both sites. A data set containing the combined positional data of sunspots from both sites, corrected for these optical aberrations, has been constructed. Results for both sunspot groups and individual sunspots are presented. It is pointed out that optical aberrations similar to those found in the Kodaikanal data may also exist in the Greenwich photoheliograph data, because these two sets of solar images were made with similar telescopes.     

Measurement of Kodaikanal white-light images – IV. Axial Tilt Angles of Sunspot Groups

The Kodaikanal sunspot data set, covering the interval 1906–1987, is used in conjunction with the similar Mount Wilson sunspot data set, covering the interval 1917–1985, to examine characteristics of sunspot group axial tilt angles. Good agreement is demonstrated between various results derived from the two independent data sets. In particular, the tendency for sunspot groups near the average tilt angle to be larger than those far from the average tilt angle is confirmed. Similarly the faster residual rotation rate for groups near the average tilt angle is also confirmed. Other confirmations are made for the relationships between latitude drift of sunspot groups and tilt angle, polarity separations, and axial expansion. Evidence is presented that tilt angles averaged over these long time intervals differ between the north and south hemispheres by about 1.4 deg. It is suggested that residual tilt angles show a slight systematic variation with phase in the activity cycle.     

Trend analysis of nutrient loadings in a large prairie catchment

Nutrient loadings in many river catchments continue to increase due to rapid expansion of agriculture, urban and industrial development, and population growth. Nutrient enrichment of water bodies has intensified eutrophication which degrades water quality and ecosystem health. In this study, we carried out a trend analysis of total phosphorus and total nitrogen loads in the South Saskatchewan River (SSR) catchment using a novel approach to analyse nutrient time series. Seasonal analysis of trends at each of the water quality stations was performed to determine the relationships between annual flow regimes and nutrient loads in the catchment, in particular, the influence of the high spring runoff on nutrient export. Decadal analysis was also performed to determine the long-term relationships of nutrients with anthropogenic changes in the catchment. Although it was found that seasonal and historical variability of nutrient load trends is mainly determined by streamflow regime changes, there is evidence that increases in nitrogen concentration can also be attributed to anthropogenic changes.     

Enhanced identification of a hydrologic model using streamflow and satellite water storage data: A multicriteria sensitivity analysis and optimization approach

Hydrologic model development and calibration have continued in most cases to focus only on accurately reproducing streamflows. However, complex models, for example, the so‐called physically based models, possess large degrees of freedom that, if not constrained properly, may lead to poor model performance when used for prediction. We argue that constraining a model to represent streamflow, which is an integrated resultant of many factors across the watershed, is necessary but by no means sufficient to develop a high‐fidelity model. To address this problem, we develop a framework to utilize the Gravity Recovery and Climate Experiment's (GRACE) total water storage anomaly data as a supplement to streamflows for model calibration, in a multiobjective setting. The VARS method (Variogram Analysis of Response Surfaces) for global sensitivity analysis is used to understand the model behaviour with respect to streamflow and GRACE data, and the BORG multiobjective optimization method is applied for model calibration. Two subbasins of the Saskatchewan River Basin in Western Canada are used as a case study. Results show that the developed framework is superior to the conventional approach of calibration only to streamflows, even when multiple streamflow‐based error functions are simultaneously minimized. It is shown that a range of (possibly false) system trajectories in state variable space can lead to similar (acceptable) model responses. This observation has significant implications for land‐surface and hydrologic model development and, if not addressed properly, may undermine the credibility of the model in prediction. The framework effectively constrains the model behaviour (by constraining posterior parameter space) and results in more credible representation of hydrology across the watershed.     

Multicriteria sensitivity analysis as a diagnostic tool for understanding model behaviour and characterizing model uncertainty

Complex hydrological models are being increasingly used nowadays for many purposes such as studying the impact of climate and land‐use change on water resources. However, building a high‐fidelity model, particularly at large scales, remains a challenging task, due to complexities in model functioning and behaviour and uncertainties in model structure, parameterization, and data. Global sensitivity analysis (GSA), which characterizes how the variation in the model response is attributed to variations in its input factors (e.g., parameters and forcing data), provides an opportunity to enhance the development and application of these complex models. In this paper, we advocate using GSA as an integral part of the modelling process by discussing its capabilities as a tool for diagnosing model structure and detecting potential defects, identifying influential factors, characterizing uncertainty, and selecting calibration parameters. Accordingly, we conduct a comprehensive GSA of a complex land surface–hydrology model, Modélisation Environmentale–Surface et Hydrologie (MESH), which combines the Canadian land surface scheme with a hydrological routing component, WATROUTE. Various GSA experiments are carried out using a new technique, called Variogram Analysis of Response Surfaces, for alternative hydroclimatic conditions in Canada using multiple criteria, various model configurations, and a full set of model parameters. Results from this study reveal that, in addition to different hydroclimatic conditions and SA criteria, model configurations can also have a major impact on the assessment of sensitivity. GSA can identify aspects of the model internal functioning that are counter‐intuitive and thus help the modeller to diagnose possible model deficiencies and make recommendations for improving development and application of the model. As a specific outcome of this work, a list of the most influential parameters for the MESH model is developed. This list, along with some specific recommendations, is expected to assist the wide community of MESH and Canadian land surface scheme users, to enhance their modelling applications.