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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.

Inland surface waters in tropical environments play a major role in the water and carbon cycle. Remote sensing techniques based on passive, active microwave or optical wavelengths are commonly used to provide quantitative estimates of surface water extent from regional to global scales. However, some of these estimates are unable to detect water under dense vegetation and/or in the presence of cloud coverage. To overcome these limitations, the brightness temperature data at L-band frequency from the Soil Moisture and Ocean Salinity (SMOS) mission are used here to estimate flood extent in a contextual radiative transfer model over the Amazon Basin. At this frequency, the signal is highly sensitive to the standing water above the ground, and the signal provides information from deeper vegetation density than higher-frequencies. Three-day and (25 km × 25 km) resolution maps of water fraction extent are produced from 2010 to 2015. The dynamic water surface extent estimates are compared to altimeter data (Jason-2), land cover classification maps (IGBP, GlobeCover and ESA CCI) and the dynamic water surface product (GIEMS). The relationships between the water surfaces, precipitation and in situ discharge data are examined. The results show a high correlation between water fraction estimated by SMOS and water levels from Jason-2 (R > 0.98). Good spatial agreements for the land cover classifications and the water cycle are obtained.