Algal blooms are pervasive in many freshwater environments and can pose risks to the health and safety of humans and other organisms. However, monitoring and tracking of potentially harmful blooms often relies on in-person observations by the public. Remote sensing has proven useful in augmenting in situ observations of algal concentration, but many hurdles hinder efficient application by end users. First, numerous approaches to estimate aquatic chlorophyll-a are available and can produce inconsistent results. Second, lack of quantitative in situ observations limits opportunities to train models for specific waterbodies, such that models developed for other systems must be used instead. We (1) implement univariate and multivariate logistic regression models to estimate the probability that aquatic chlorophyll-a concentrations exceed an accepted threshold beyond which harmful effects become likely and (2) evaluate the use of visually classified bloom/no-bloom satellite imagery to augment in situ training data. Using a binary classification of aquatic chlorophyll-a exceeding 10 μg∕L, we found that (1) logistic regression models were ∼80% accurate, (2) univariate models trained with visually classified data produce nearly the same accuracy (79%) as models trained with in situ observations (80%), and (3) augmenting in situ chlorophyll-a observations with visual classifications outperformed (82% accuracy) models trained on in situ observations alone (80% accuracy). These results provide a framework for evaluating multiple spectral indices in retrieving algal bloom presence or absence and illustrate that training data derived directly from satellite imagery can be useful in augmenting in situ observations.

Understanding streamflow in montane watersheds on regional scales is often incomplete due to a lack of data for small-order streams that link precipitation and snowmelt processes to main stem discharge. This data deficiency is attributed to the prohibitive cost of conventional streamflow measurement methods and the remote location of many small streams. Expedient and low-cost streamflow measurement methods used by resource professionals or citizen scientists can provide scientifically useful solutions to this data deficiency. To this end, four current velocity measurement methods were evaluated in a laboratory flume: the surface float, rising body, velocity head rod, and rising air bubble methods. The methods were tested under a range of stream velocities, cross-sectional depths, and streambed substrates. The resulting measurements provide estimates of precision and bias of each method, as well as method-specific insight and calibration formulas. The mean values of the coefficient of variation, a measure of precision, were 10% for the surface float, 10% for the velocity head rod, 14% for the rising body, and 9% for the air bubble method. The values of scaled mean error, a measure of bias, were -8% for the surface float, -4% for the velocity head rod, -1% for the rising body, and 4% for the air bubble. The velocity head rod and surface float methods were the easiest methods to use, providing greater precision at large (> = 0.6 m/s) and small (<0.6 m/s) velocities, respectively. However, the reliance on a velocity ratio for each of these methods can generate inaccuracy in their results. The rising body method is more challenging to execute and of lower precision than the former two methods but provides low bias measurements. The rising air bubble method has a complex instrument assembly that is considered impractical for potential field user groups.

While many studies on tribal water resources of individual tribal lands in the United States (US) have been conducted, the importance of tribal water resources at a national scale has largely gone unrecognized because their combined totals have not been quantified. Thus, we sought to provide a numerical estimate of major water budget components on tribal lands within the conterminous US and on USGS hydrologic unit codes (HUC2) regions. Using existing national-scale data and models, we estimated mean annual precipitation, evapotranspiration, excess precipitation, streamflow, and water use for the period 1971–2000. Tribal lands represent about 3.4 percent of the total land area of the conterminous US and on average account for 1.9 percent of precipitation, 2.4 percent of actual evapotranspiration, 0.95 percent of excess precipitation, 1.6 percent of water use, and 0.43 percent of streamflow origination. Additionally, approximately 9.5 and 11.3 percent of US streamflow flows through or adjacent as boundaries to tribal lands, respectively. Streamflow through or adjacent to tribal lands accounts for 42 and 48 percent of streamflow in the Missouri region, respectively; and for 86 and 88 percent in the Lower Colorado region, respectively. On average, 5,600 million cubic meters of streamflow per year was produced on tribal lands in the Pacific Northwest region, nearly five times greater than tribal lands in any other region. Tribal lands in the Great Lakes, Missouri, Arkansas-White-Red, and California regions all produced between 1,000 and 1,400 million cubic meters per year.

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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