A Participatory Science Approach to Expanding Instream Infrastructure Inventories. Issue 11 (11th November 2020)

• Record Type: Journal Article
• Authors: Whittemore, Aaron; Ross, Matthew R. V.; Dolan, Wayana; Langhorst, Theodore; Yang, Xiao; Pawar, Sayali; Jorissen, Michiel; Lawton, Eric; Januchowski‐Hartley, Stephanie; Pavelsky, Tamlin
• Is Part Of: Earth's future. Volume 8:Issue 11(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-11-11
• Subjects: global -- river obstructions -- ecohydrology -- dam -- machine learning -- participatory science; Environmental sciences -- Periodicals; Environmental sciences; Periodicals; 550
• DOI: 10.1029/2020EF001558
• Languages: English
• ISSNs: 2328-4277

Abstract

Abstract: Over the past decade, remote sensing data have improved in resolution and become more widely available, bringing new opportunities for its use in environmental science and conservation. One potential application is to identify and map instream infrastructure across the world, with important implications for fisheries, hydrology, flooding, and more. To date, databases of instream infrastructure focus on larger dams with reservoirs that are comparatively easy to detect with remotely sensed imagery. Despite their impact on freshwater ecosystems, smaller infrastructure is often overlooked. To overcome these challenges, we require more systematic approaches, such as the Global River Obstruction Database (GROD) presented here, to map instream infrastructure. We present a participatory approach to identify, map, and validate infrastructure and provide an initial data set for the contiguous United States ( n = 4, 197). We highlight the value of participatory methods that include the public and suggest ways they could be fused with machine learning for future applications. Plain Language Summary: In recent years, imagery retrieved from Earth‐observing satellites has improved in quality and become more widely available. We can use the improved satellite imagery to observe the Earth's surface in entirely new ways. One potential application is to identify and map dams and other river obstructions that represent disturbances to the Earth's freshwater systems. Keeping record of river obstructions is valuable for many purposes from fisheries management to understanding flood dynamics. Currently, global dam databases focus on larger structures, while smaller dams and infrastructure are often overlooked. Small river obstructions greatly outnumber their larger counterparts and collectively have a significant impact on freshwater ecosystems. To further knowledge on the location and type of global river obstructions, we created the Global River Obstruction Database (GROD). GROD uses a uniform approach that maps river obstructions across the globe regardless of size or reservoir presence. We present and validate our citizen science‐based approach to map obstructions and provide an initial data set of 4, 197 obstructions for the contiguous United States. We also highlight the value of incorporating members of the public in data collection and suggest ways citizen science‐based methods could be fused with more technical applications like machine learning for future projects. Key Points: We identify and map instream infrastructure in the contiguous United States as part of the Global River Obstructions Database We validate our approach against highly accurate regional data sets and find that we correctly identify a large fraction of infrastructure We discuss how our participatory approach can be used with machine learning to further the mapping of global instream infrastructure