Search

This study introduces a new automated system that blends multi-satellite information and citizen science data for reliable and timely observations of lake and river ice in under-observed northern regions. The system leverages the Google Earth Engine resources to facilitate the analysis and visualization of ice conditions. The adopted approach utilizes a combination of moderate and high-resolution optical data, along with radar observations. The results demonstrate the system's capability to accurately detect and monitor river ice, particularly during key periods, such as the freeze-up and the breakup. The integration citizen science data showed added values in the validation of remote sensing products, as well as filling gaps whenever satellite observations cannot be collected due to cloud obstruction. Moreover, it was shown that citizen science data can be converted to valuable quantitative information, such as the case of ice thickness, which is very useful when combined with ice extent derived from remote sensing. In this study, citizen science data were employed for the quantitative assessment of the remote sensing product. Obtained results showed a good agreement between the product and observed river status, with a Critical Success Index of 0.82. Notably, the system has shown effectiveness in capturing the spatial and temporal evolution of snow and ice conditions, as evidenced by its application in analyzing specific ice jam events in 2023. The study concludes that the developed system marks a significant advancement in river ice monitoring, combining technological innovation with community engagement. [ABSTRACT FROM AUTHOR]

The formation, growth, and decay of freshwater ice on lakes and rivers are fundamental processes of northern regions with wide-ranging implications for socio-ecological systems. Ice thickness at the end of winter is perhaps the best integration of cold-season weather and climate, while the duration of thick and growing ice cover is a useful indicator for the winter travel and recreation season. Both maximum ice thickness (MIT) and ice travel duration (ITD) can be estimated from temperature-driven ice growth curves fit to ice thickness observations. We simulated and analyzed ice growth curves based on ice thickness data collected from a range of observation programs throughout Alaska spanning the past 20–60 years to understand patterns and trends in lake and river ice. Results suggest reductions in MIT (thinning) in several northern, interior, and coastal regions of Alaska and overall greater interannual variability in rivers compared to lakes. Interior regions generally showed less variability in MIT and even slightly increasing trends in at least one river site. Average ITD ranged from 214 d in the northernmost lakes to 114 d across southernmost lakes, with significant decreases in duration for half of sites. River ITD showed low regional variability but high interannual variability, underscoring the challenges with predicting seasonally consistent river travel. Standardization and analysis of these ice observation data provide a comprehensive summary for understanding changes in winter climate and its impact on freshwater ice services. [ABSTRACT FROM AUTHOR]