A bridge over troubled water: A contextual analysis of social vulnerability to climate change in a riverine landscape in south-east Norway

Local communities in the Gudbrandsdalen region in Norway are increasingly exposed to climate-induced hazards such as floods and landslides. A core question is how community members respond to climate change and what factors contribute to more resilient communities. The authors used a contextual approach to analyze data from semi-structured interviews along five dimensions. In Gaustad Muncipality they found that individuals’ motivation to adapt to climate change depended largely on subjective values such as identity, place attachment, cultural values, and social networks among individuals, which means it is crucial that strategic plans for adaptation to climate change at different policy levels are experienced as relevant by community members. While the studied community has experienced heavy floods in river systems and streams, little evidence of adaptation was observed. Instead, they appeared to adopt coping strategies. Landowners may have limited incentives to adapt to climate change due to contraproductive policy measures such as economic compensation for direct losses without requiring improved practices. Effective adaptation to climate change on the local level is likely to require making compensation mechanisms contingent upon landowners showing willingness to change from coping to adaptive practices, as well as a contextualized approach integrating local and scientific forms of knowledge.     

China’s national carbon emissions trading scheme: lessons from the pilot emission trading schemes,academic literature,and known policy details

Upon completion, China’s national emissions trading scheme (C-ETS) will be the largest carbon market in the world. Recent research has evaluated China’s seven pilot ETSs launched from 2013 on, and academic literature on design aspects of the C-ETS abounds. Yet little is known about the specific details of the upcoming C-ETS. This article combines currently understood details of China’s national carbon market with lessons learned in the pilot schemes as well as from the academic literature. Our review follows the taxonomy of Emissions Trading in Practice: A Handbook on Design and Implementation (Partnership for Market Readiness & International Carbon Action Partnership. (2016). Retrieved from www.worldbank.org): The 10 categories are: scope, cap, distribution of allowances, use of offsets, temporal flexibility, price predictability, compliance and oversight, stakeholder engagement and capacity building, linking, implementation and improvements.

Key policy insights

• Accurate emissions data is paramount for both design and implementation, and its availability dictates the scope of the C-ETS.

• The stakeholder consultative process is critical for effective design, and China is able to build on its extensive experience through the pilot ETSs.

• Current policies and positions on intensity targets and Clean Development Mechanism (CDM) credits constrain the market design of the C-ETS.

• Most critical is the nature of the cap. The currently discussed rate-based cap with ex post adjustment is risky. Instead, an absolute, mass-based emissions cap coupled with the conditional use of permits would allow China to maintain flexibility in the carbon market while ensuring a limit on CO₂ emissions.

     

High-frequency climate oscillations drive ice-off variability for Northern Hemisphere lakes and rivers

Climatic Change - This study examines the climatic drivers of ice-off dates for lakes and rivers across the Northern Hemisphere. Most lakes and rivers have trended toward earlier ice-off dates over...     

Water Cooperation Priorities in the Lancang-Mekong River Basin Based on Cooperative Events Since the Mekong River Commission Establishment

The Lancang-Mekong River has attracted much attention from researchers, but the cooperation on water issues in this river basin has been limited, even after the establishment of the Mekong River Commission(MRC). Cooperation on water resources has been determined as one of the key priority areas in the Lancang-Mekong Cooperation Mechanism, but there are no details of targets. In order to establish the priorities of water cooperation under the mechanism, we adopted nine categories to classify the objectives of 87 water cooperation events based on the ‘Lancang-Mekong Water Cooperative Events Database' from 1995 to 2015. Based on the occurrence of cooperative events, cooperative objectives, cooperative scales, and approaches to cooperation, we conducted statistical, correlation, and text analyses. Our analyses indicated the following results: under the impact of economic conditions inside and outside the river basin, full cooperation appeared more difficult than bilateral and multilateral cooperation. Each of the partners adopted different preferences for cooperation targets. Cooperation with more definite objectives was easier to establish than cooperation with broader and more complex objectives. The potential objectives for water cooperation were navigation, hydropower, joint management, data sharing, flood control and water use. Because hydropower development is controversial, and because water cooperation is avoided by most existing regional cooperation mechanisms due to its complexity, we suggest the following priority areas for water cooperation in the Lancang-Mekong River Basin. 1) Navigation and flood control/drought relief are attractive objectives for all the riparian countries across the whole watershed. 2) Data sharing should be a priority for cooperation in the watershed due to its laying the foundation for the equitable and reasonable utilization of transboundary waters. 3) Hydropower is an objective best implemented mainly through bilateral cooperation, and on tributaries.     

Network analysis to inform invasive species spread among lakes

Often facilitated by human-mediated pathways,aquatic invasive species are a threat to the health and biodiversity of global ecosystems.We present a novel approach incorporating survey data of watercraft movement in a social network analysis to reconstruct potential pathways of aquatic invasive species spread between lakes.As an example,we use the green alga Nitellopsis obtusa,also known as starry stonewort,an aquatic invasive species affecting the Great Lakes region in the United States and Canada.The movement of algal fragments via human-mediated pathways(i.e.,watercraft)has been hypothesized as the primary driver of starry stonewort invasion.We used survey data collected at boat ramps during the 2013 and 2014 openwater seasons to describe the flow of watercraft from Lake Koronis,where N.obtusa was first detected in Minnesota,to other lakes in the state.Our results suggest that the risk of N.obtusa expansion is not highly constrained by geographic proximity and management efforts should consider highly connected lakes.Estimating human movement via network analysis may help to explain past and future routes of aquatic invasive species infestation between lakes and can improve evidence-based prevention and control efforts.     

Network analysis to inform invasive species spread among lakes

Often facilitated by human-mediated pathways,aquatic invasive species are a threat to the health and biodiversity of global ecosystems.We present a novel approach incorporating survey data of watercraft movement in a social network analysis to reconstruct potential pathways of aquatic invasive species spread between lakes.As an example,we use the green alga Nitellopsis obtusa,also known as starry stonewort,an aquatic invasive species affecting the Great Lakes region in the United States and Canada.The movement of algal fragments via human-mediated pathways(i.e.,watercraft)has been hypothesized as the primary driver of starry stonewort invasion.We used survey data collected at boat ramps during the 2013 and 2014 openwater seasons to describe the flow of watercraft from Lake Koronis,where N.obtusa was first detected in Minnesota,to other lakes in the state.Our results suggest that the risk of N.obtusa expansion is not highly constrained by geographic proximity and management efforts should consider highly connected lakes.Estimating human movement via network analysis may help to explain past and future routes of aquatic invasive species infestation between lakes and can improve evidence-based prevention and control efforts.     

Spatially explicit models for exploring COVID‐19 lockdown strategies

This article describes two spatially explicit models created to allow experimentation with different societal responses to the COVID‐19 pandemic. We outline the work to date on modeling spatially explicit infective diseases and show that there are gaps that remain important to fill. We demonstrate how geographical regions, rather than a single, national approach, are likely to lead to better outcomes for the population. We provide a full account of how our models function, and how they can be used to explore many different aspects of contagion, including: experimenting with different lockdown measures, with connectivity between places, with the tracing of disease clusters, and the use of improved contact tracing and isolation. We provide comprehensive results showing the use of these models in given scenarios, and conclude that explicitly regionalized models for mitigation provide significant advantages over a “one‐size‐fits‐all” approach. We have made our models, and their data, publicly available for others to use in their own locales, with the hope of providing the tools needed for geographers to have a voice during this difficult time.     

A probabilistic framework for improving reverse geocoding output

Reverse geocoding, which transforms machine‐readable GPS coordinates into human‐readable location information, is widely used in a variety of location‐based services and analysis. The output quality of reverse geocoding is critical because it can greatly impact these services provided to end‐users. We argue that the output of reverse geocoding should be spatially close to and topologically correct with respect to the input coordinates, contain multiple suggestions ranked by a uniform standard, and incorporate GPS uncertainties. However, existing reverse geocoding systems often fail to fulfill these aims. To further improve the reverse geocoding process, we propose a probabilistic framework that includes: (1) a new workflow that can adapt all existing address models and unitizes distance and topology relations among retrieved reference data for candidate selections; (2) an advanced scoring mechanism that quantifies characteristics of the entire workflow and orders candidates according to their likelihood of being the best candidate; and (3) a novel algorithm that derives statistical surfaces for input GPS uncertainties and propagates such uncertainties into final output lists. The efficiency of the proposed approaches is demonstrated through comparisons to the four commercial reverse geocoding systems and through human judgments. We envision that more advanced reverse geocoding output ranking algorithms specific to different application scenarios can be built upon this work.     

Multibeam sonar backscatter data processing

Multibeam sonar systems now routinely record seafloor backscatter data, which are processed into backscatter mosaics and angular responses, both of which can assist in identifying seafloor types and morphology. Those data products are obtained from the multibeam sonar raw data files through a sequence of data processing stages that follows a basic plan, but the implementation of which varies greatly between sonar systems and software. In this article, we provide a comprehensive review of this backscatter data processing chain, with a focus on the variability in the possible implementation of each processing stage. Our objective for undertaking this task is twofold: (1) to provide an overview of backscatter data processing for the consideration of the general user and (2) to provide suggestions to multibeam sonar manufacturers, software providers and the operators of these systems and software for eventually reducing the lack of control, uncertainty and variability associated with current data processing implementations and the resulting backscatter data products. One such suggestion is the adoption of a nomenclature for increasingly refined levels of processing, akin to the nomenclature adopted for satellite remote-sensing data deliverables.