Monitoring Variations in Lake Water Storage with Satellite Imagery and Citizen Science

Abstract

Despite lakes being a key part of the global water cycle and a crucial water resource, there is limited understanding of whether regional or lake-specific factors control water storage variations in small lakes. Here, we study groups of small, unregulated lakes in North Carolina, Washington, Illinois, and Wisconsin, USA using lake level measurements gathered by citizen scientists and lake surface area measurements from optical satellite imagery. We show the lake level measurements to be highly accurate when compared to automated gauges (mean absolute error = 1.6 cm). We compare variations in lake water storage between pairs of lakes within these four states. On average, water storage variations in lake pairs across all study regions are moderately positively correlated (ρ = 0.49) with substantial spread in the degree of correlation. The distance between lake pairs and the extent to which their changes in volume are correlated show a weak but statistically significant negative relationship. Our results indicate that, on regional scales, distance is not a primary factor governing lake water storage patterns, which suggests that other, perhaps lakes-specific, factors must also play important roles.

Natural and anthropogenic controls on lake water‐level decline and evaporation‐to‐inflow ratio in the conterminous United States

“…Low‐cost in situ hydrologic observatories can collect accurate water balance measurements on small lakes and could be deployed and monitored by citizen scientists (Watras et al 2019, 2021). Remote sensing technology with improved sensors and methods to quantify lake surface area dynamics (Eilander et al 2014; Pekel et al 2016; Pickens et al 2020), in combination with citizen science lake‐level observations can potentially expand the temporal and spatial coverage of lake water storage observations (Little et al 2021).…”

“…The NLA design is better suited to capturing spatial trends rather than temporal trends in lake‐level variation, and regionally specific climate oscillations were not included in our model. While these temporal oscillations and their drivers are important to understand hydroclimatic effects on lakes, we expect that in the NLA dataset, the spatial variation across CONUS may exceed temporal signals that are likely conditioned to geographic region and local scale factors (McGregor 2017; Little et al 2021). Other climate metrics besides the PHDI may capture climatic variation that drives lake water‐level declines.…”

Semantic Conceptual Framework for Environmental Monitoring and Surveillance—A Case Study on Forest Fire Video Monitoring and Surveillance

“…This information can be used in fire prevention [40], the assessment of fire effects [41] and water quality monitoring [42]. Citizen science data are typically combined with other, more reliable sources of data (e.g., lake level data from measurements collected through citizen science with satellite data [43]), or crowdsourced data for event detection in urban environments with fixed sensors, as discussed in [44]. This kind of data is unstructured and requires manual or automatic processing, which means it cannot be used in its raw format.…”

Applications of GIS and remote sensing in public participation and stakeholder engagement for watershed management

“…For example, Secchi-depth measurements obtained through networks of citizen scientists have been applied to validate satellite products of water quality (Deutsch et al, 2021;George et al, 2021;Menon et al, 2021). In addition, water level measurements from citizen scientists have been integrated with lake surface area measurements from remote sensing to develop water storage estimates (Little et al, 2021) and remote sensing has been used to validate observed flood heights from citizen scientists (Graham & Butts, 2005). There are also ways for citizen scientists to derive their own water quality estimates without the need for special equipment through smart phone apps that can estimate water quality based upon analysis of pictures from smartphones (Malthus et al, 2020).…”