Despite several high-profile data breaches and business models that commercialize user data, participation in social media networks continues to require users to trust corporations to safeguard the...

Citizen science initiatives span a wide range of topics, designs, and research needs. Despite this heterogeneity, there are several common barriers to the uptake and sustainability of citizen science projects and the information they generate. One key barrier often cited in the citizen science literature is data quality. Open-source tools for the analysis, visualization, and reporting of citizen science data hold promise for addressing the challenge of data quality, while providing other benefits such as technical capacity-building, increased user engagement, and reinforcing data sovereignty. We developed an operational citizen science tool called the Community Water Data Analysis Tool (CWDAT)—a R/Shiny-based web application designed for community-based water quality monitoring. Surveys and facilitated user-engagement were conducted among stakeholders during the development of CWDAT. Targeted recruitment was used to gather feedback on the initial CWDAT prototype’s interface, features, and potential to support capacity building in the context of community-based water quality monitoring. Fourteen of thirty-two invited individuals (response rate 44%) contributed feedback via a survey or through facilitated interaction with CWDAT, with eight individuals interacting directly with CWDAT. Overall, CWDAT was received favourably. Participants requested updates and modifications such as water quality thresholds and indices that reflected well-known barriers to citizen science initiatives related to data quality assurance and the generation of actionable information. Our findings support calls to engage end-users directly in citizen science tool design and highlight how design can contribute to users’ understanding of data quality. Enhanced citizen participation in water resource stewardship facilitated by tools such as CWDAT may provide greater community engagement and acceptance of water resource management and policy-making.

Abstract. Despite the high historical losses attributed to flood events, Canadian flood mitigation efforts have been hindered by a dearth of current, accessible flood extent/risk models and maps. Such resources often entail large datasets and high computational requirements. This study presents a novel, computationally efficient flood inundation modeling framework (“InundatEd”) using the height above nearest drainage (HAND)-based solution for Manning's equation, implemented in a big-data discrete global grid system (DGGS)-based architecture with a web-GIS (Geographic Information Systems) platform. Specifically, this study aimed to develop, present, and validate InundatEd through binary classification comparisons to recently observed flood events. The framework is divided into multiple swappable modules including GIS pre-processing; regional regression; inundation models; and web-GIS visualization. Extent testing and processing speed results indicate the value of a DGGS-based architecture alongside a simple conceptual inundation model and a dynamic user interface.

Abstract. Despite the high historical losses attributed to flood events, Canadian flood mitigation efforts have been hindered by a dearth of current, accessible flood extent/risk models and maps. Such resources often entail large datasets and high computational requirements. This study presents a novel, computationally efficient flood inundation modeling framework (InundatEd) using the height above the nearest drainage-based solution for Manning's equation, implemented in a big-data discrete global grid systems-based architecture with a web-GIS platform. Specifically, this study aimed to develop, present, and validate InundatEd through binary classification comparisons to known flood extents. The framework is divided into multiple swappable modules including GIS pre-processing; regional regression; inundation model; and web-GIS visualization. Extent testing and processing speed results indicate the value of a DGGS-based architecture alongside a simple conceptual inundation model and a dynamic user interface.

Cowichan Lake lamprey ( Entosphenus macrostomus) is a threatened species resident to Mesachie Lake, Cowichan Lake, and adjoining Bear Lake and their major tributaries in British Columbia. Decreases in trapping success have created concerns that the population is declining. Some potential threats include water use, climate change, and management actions. Owing to the absence of long-term data on population trends, little information is available to estimate habitat quality and factors that influence it. We sought to fill this gap by examining associations between habitat area and variables representing suspected key drivers of habitat availability. Critical habitat areas were imaged using an unmanned aerial vehicle over a period of three years at three sites at Cowichan Lake and a subsequent habitat area was classified. Meteorological and anthropogenic controls on habitat area were investigated through automatic relevance detection regression models. The major driver of habitat area during the critical spawning period was water level during the storage season, which also depends on the meteorological variables and anthropogenic control. It is recommended that regulation of the weir should aim to ensure that the water level remains above the 1 m mark, which roughly equates to the 67% coverage of water on the habitat area used for spawning.

The continuous growth of available geospatial data requires new methods for its integration, analysis, and visualization to be explored and implemented in software available to the geospatial community. Discrete Global Grid Systems (DGGS) are an emerging method for spatial data handling in the digital earth framework. DGGS are hierarchical data structures for discretizing the Earth’s surface that have seen considerable theoretical development over the last two decades. In this paper, four software implementations are reviewed, dggridR, H3, OpenEAGGR, and S2, to explore their potential applications in data modelling and GIS, as well as their performance. These software implementations were also evaluated against the recently published Open Geospatial Consortium (OGC) abstract specification. The results indicate great potential and versatility for utilizing such systems in geospatial analysis, if basic methods for converting and handling spatial features are further developed. The performance of these systems is shown to be highly scalable and operational with datasets of various sizes. Yet, it is demonstrated that the current software implementations generally fall short of fulfilling all of the OGC requirements or it was not possible to confirm their compliance. The assessment here identified that further enhancements, endorsement of OGC criteria, and their explicit acknowledgment within official documentation remain key research needs for the evaluated software packages. Further work developing operational DGGS that solve real world problems may promote greater community adoption and integration of DGGS data structures into commonly used geospatial platforms.

This study conducted linear and change-point analyses of historical trends since 1942 in the length and number of days suitable for skating on backyard rinks in the “Original Six” National Hockey League cities of Boston, Chicago, Detroit, Montreal, New York, and Toronto. Analysis is based on the relationship between ambient air temperatures and the probability of skating, using thresholds identified through the RinkWatch citizen science project. In all cities, coefficient estimates suggest the number of high-probability skating days per winter is declining, with easternmost cities displaying notable declines and growing inter-annual variability in skating days in recent decades. Linear analysis shows a statistically significant decline in Toronto, with a step-change emerging in 1980, after which there is on average one-third fewer skating days compared with preceding decades. The outdoor skating season trends towards later start dates in Boston, Montreal, New York, and Toronto. Future monitoring of outdoor rinks provides an opportunity for engaging the public in identification of winter warming trends that might otherwise be imperceptible, and for raising awareness of the impacts of climate change.

Abstract Discrete global grid systems (DGGS) have been proposed as a data model for a digital earth framework. We introduce a new data model and analytics system called IDEAS – integrated discrete environmental analysis system to create an operational DGGS-based GIS which is suitable for large scale environmental modelling and analysis. Our analysis demonstrates that DGGS-based GIS is feasible within a relational database environment incorporating common data analytics tools. Common GIS operations implemented in our DGGS data model outperformed the same operations computed using traditional geospatial data types. A case study into wildfire modelling demonstrates the capability for data integration and supporting big data geospatial analytics. These results indicate that DGGS data models have significant capability to solve some of the key outstanding problems related to geospatial data analytics, providing a common representation upon which fast and scalable algorithms can be built.

Despite numerous studies in statistical downscaling methodologies, there remains a lack of methods that can downscale from precipitation modeled in global climate models to regional level high resolution gridded precipitation. This paper reports a novel downscaling method using a Generative Adversarial Network (GAN), CliGAN, which can downscale large-scale annual maximum precipitation given by simulation of multiple atmosphere-ocean global climate models (AOGCM) from Coupled Model Inter-comparison Project 6 (CMIP6) to regional-level gridded annual maximum precipitation data. This framework utilizes a convolution encoder-dense decoder network to create a generative network and a similar network to create a critic network. The model is trained using an adversarial training approach. The critic uses the Wasserstein distance loss function and the generator is trained using a combination of adversarial loss Wasserstein distance, structural loss with the multi-scale structural similarity index (MSSIM), and content loss with the Nash-Sutcliff Model Efficiency (NS). The MSSIM index allowed us to gain insight into the model’s regional characteristics and shows that relying exclusively on point-based error functions, widely used in statistical downscaling, may not be enough to reliably simulate regional precipitation characteristics. Further use of structural loss functions within CNN-based downscaling methods may lead to higher quality downscaled climate model products.

Detection of changes in spatial processes has long been of interest to quantitative geographers seeking to test models, validate theories, and anticipate change. Given the current “data-rich” environment of today, it may be time to reconsider the methodological approaches used for quantifying change in spatial processes. New tools emerging from computer vision research may hold particular potential to make significant advances in quantifying changes in spatial processes. In this article, two comparative indices from computer vision, the structural similarity (SSIM) index, and the complex wavelet structural similarity (CWSSIM) index were examined for their utility in the comparison of real and simulated spatial data sets. Gaussian Markov random fields were simulated and compared with both metrics. A case study into comparison of snow water equivalent spatial patterns over northern Canada was used to explore the properties of these indices on real-world data. CWSSIM was found to be less sensitive than SSIM to changing window dimension. The CWSSIM appears to have significant potential in characterizing change and/or similarity; distinguishing between map pairs that possess subtle structural differences. Further research is required to explore the utility of these approaches for empirical comparison cases of different forms of landscape change and in comparison to human judgments of spatial pattern differences.