Lake trophic state is a key ecosystem property that integrates a lake’s physical, chemical, and biological processes. Despite the importance of trophic state as a gauge of lake water quality, standardized and machine-readable observations are uncommon. Remote sensing presents an opportunity to detect and analyze lake trophic state with reproducible, robust methods across time and space. We used Landsat surface reflectance data to create the first compendium of annual lake trophic state for 55,662 lakes of at least 10 ha in size throughout the contiguous United States from 1984 through 2020. The dataset was constructed with FAIR data principles (Findable, Accessible, Interoperable, and Reproducible) in mind, where data are publicly available, relational keys from parent datasets are retained, and all data wrangling and modeling routines are scripted for future reuse. Together, this resource offers critical data to address basic and applied research questions about lake water quality at a suite of spatial and temporal scales.
Multiple instruments and methods exist for collecting discrete streamflow measurements in small streams with low flows, defined here as less than 5.7 m3/s (200 ft3/s). Included in the available methods are low‐cost approaches that are infrequently used, in part, because their uncertainty is not well known. In this work, we evaluated the accuracy and suitability of three low‐cost velocity measurement methods (surface float [SF], velocity head rod [VR], and rising body [RB]) and three conventional current meters (acoustic Doppler velocimeter, and mechanical Price type AA and Price Pygmy meters) relative to discharge calculated from stable artificial hydraulic controls. A total of 231 measurements were made by 20 individuals during 88 site visits to 24 sites in eight states. Accuracies were assessed for all methods and precision was evaluated for the low‐cost methods. The median percent error was below 5% for conventional methods, and below 20% for the low‐cost methods. The SF was the most accurate (median absolute percent error 14%) and precise (mean percent precision of 11%) low‐cost method. The RB and VR, respectively, had 15% and 20% median absolute percent error and 29% and 12% mean percent precision. Results suggest that low‐cost methods, when used appropriately, can be used to estimate discharge data under low flow conditions when measurements with conventional methods are not feasible and the associated accuracies meet end‐user measurement objectives.
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