Remote-sensing data are used extensively to monitor water quality parameters such as clarity, temperature, and chlorophyll-a (chl-a) content. This is generally achieved by collecting in situ data coincident with satellite data collections and then creating empirical water quality models using approaches such as multi-linear regression or step-wise linear regression. These approaches, which require modelers to select model parameters, may not be well suited for optically complex waters, where interference from suspended solids, dissolved organic matter, or other constituents may act as “confusers”. For these waters, it may be useful to include non-standard terms, which might not be considered when using traditional methods. Recent machine-learning work has demonstrated an ability to explore large feature spaces and generate accurate empirical models that do not require parameter selection. However, these methods, because of the large number of included terms involved, result in models that are not explainable and cannot be analyzed. We explore the use of Least Absolute Shrinkage and Select Operator (LASSO), or L1, regularization to fit linear regression models and produce parsimonious models with limited terms to enable interpretation and explainability. We demonstrate this approach with a case study in which chl-a models are developed for Utah Lake, Utah, USA., an optically complex freshwater body, and compare the resulting model terms to model terms from the literature. We discuss trade-offs between interpretability and model performance while using L1 regularization as a tool. The resulting model terms are both similar to and distinct from those in the literature, thereby suggesting that this approach is useful for the development of models for optically complex water bodies where standard model terms may not be optimal. We investigate the effect of non-coincident data, that is, the length of time between satellite image collection and in situ sampling, on model performance. We find that, for Utah Lake (for which there are extensive data available), three days is the limit, but 12 h provides the best trade-off. This value is site-dependent, and researchers should use site-specific numbers. To document and explain our approach, we provide Colab notebooks for compiling near-coincident data pairs of remote-sensing and in situ data using Google Earth Engine (GEE) and a second notebook implementing L1 model creation using scikitlearn. The second notebook includes data-engineering routines with which to generate band ratios, logs, and other combinations. The notebooks can be easily modified to adapt them to other locations, sensors, or parameters.

We analyzed chlorophyll-a (chl-a) concentrations in shallow, turbid Utah Lake using Landsat data from 1984 to 2021. Utah Lake is ~40 km by 21 km, has a surface area of ~390 km², an average depth of ~3 m, and loses ~50% of inflow to evaporation. This limits spatial mixing, allowing us to evaluate impacts on smaller lake regions. We evaluated long-term trends at the pixel level and for areas related to boundary conditions. We created 17 study areas based on differences in shoreline development and nutrient inflows. We expected impacted areas to exhibit increasing chl-a trends, as population growth and development in the Utah Lake watershed have been significant. We used the non-parametric Mann–Kendall test to evaluate trends. The majority of the lake exhibited decreasing trends, with a few pixels in Provo and Goshen Bays exhibiting slight increasing or no trends. We estimated trend magnitudes using Sen’s slope and fitted linear regression models. Trend magnitudes in all pixels (and regions), both decreasing and increasing, were small; with the largest decreasing and increasing trends being about −0.05 and −0.005 µg/L/year, and about 0.1 and 0.005 µg/L/year for the Sen’s slope and linear regression slope, respectively. Over the ~40 year-period, this would result in average decreases of 2 to 0.2 µg/L or increases of 4 and 0.2 µg/L. All the areas exhibited decreasing trends, but the monthly trends in some areas exhibited no trends rather than decreasing trends. Monthly trends for some areas showed some indications that algal blooms are occurring earlier, though evidence is inconclusive. We found essentially no change in algal concentrations in Utah Lake at either the pixel level or for the analysis regions since the 1980′s; despite significant population expansion; increased nutrient inflows; and land-use changes. This result matches prior research and supports the hypothesis that algal growth in Utah Lake is not limited by direct nutrient inflows but limited by other factors.

Utah Lake is unusual due to its large surface area, shallow depth, phosphorus-rich sediments, and well-mixed, unstratified waters. This creates conditions where water column phosphorous concentrations tend toward equilibrium, with lake sediments containing high concentrations of geologic phosphorus. To help understand the potential impact of phosphorous load reductions, we computed a time history of phosphorus mass in the lake using state and federal records of lake volume, dissolved phosphorus concentrations, and outflow. We show that historically, Utah Lake phosphorus concentrations have remained stable over time, in the range of 0.02 to 0.04 mg/L, despite large changes in lake volume and internal phosphorus mass. We performed sorption calculations using data from the literature, demonstrating that it would take unrealistically large load changes to alter water column phosphorus concentrations under sorption processes. The sorption model produces results consistent with historical data that show relatively constant phosphorous concentrations despite large lake volume changes. We show, through several lines of evidence, that water column phosphorus concentrations are insensitive to external loads. Phosphorous load reduction is unlikely to have a significant effect on phosphorus concentrations in Utah Lake and, by extension, in other sorption-dominated shallow lakes with phosphorus-rich sediment.

Atmospheric deposition (AD) is a less understood and quantified source of nutrient loading to waterbodies. AD occurs via settling (large particulates), contact (smaller particulates and gaseous matter), and precipitation (rain, snow) transport pathways. Utah Lake is a shallow eutrophic freshwater lake located in central Utah, USA, with geophysical characteristics that make it particularly susceptible to AD-related nutrient loading. Studies have shown AD to be a significant contributor to the lake’s nutrient budget. This study analyzes nutrient samples from nine locations around the lake and four precipitation gauges over a 6-year study period using three different methods to estimate AD from the precipitation transport pathway. The methods used are simple averaging, Thiessen polygons, and inverse distance weighting, which we use to spatially interpolate point sample data to estimate nutrient lake loads. We hold that the inverse distance weighting method produces the most accurate results. We quantify, present, and compare nutrient loads and nutrient loading rates for total phosphorus (TP), total inorganic nitrogen (TIN), and ortho phosphate (OP) from precipitation events. We compute loading rates for the calendar year (Mg/yr) from each of the three analysis methods along with monthly loading rates where Mg is 10⁶ g. Our estimated annual precipitation AD loads for TP, OP, and TIN are 120.96 Mg/yr (132.97 tons/yr), 60.87 Mg/yr (67.1 tons/yr), and 435 Mg/yr (479.5 tons/yr), respectively. We compare these results with published data on total AD nutrient loads and show that AD from precipitation is a significant nutrient source for Utah Lake, contributing between 25% and 40% of the total AD nutrient load to the lake.

During the 2021 (n = 15) and 2022 (n = 13) summers, we measured the total and dissolved (<0.45 μm) concentration of 25 elements in Utah Lake using Inductively Coupled Plasma–Optical Emission Spectrometry (ICP-OES) with detection limits in the order of a few parts-per-billion (ppb). This resulted in 1400 measurements, which is a unique dataset in terms of sensitivity and temporal resolution. Regulated elements are not commonly measured at the ppb-level; thus, these data provide insight into both the behavior and existence of these elements in an aquatic environment and have implications for both the management and regulation of the lake. Utah regulates twelve of these elements. While ICP-OES has ppb-level sensitivity, it is not the approved regulatory analysis method for these elements. All regulations are for dissolved concentrations, except aluminum (Al) and phosphorus (P), which are for total recovery. We found total Al above the allowable concentration, but dissolved concentrations were well below allowable concentrations. We attribute high total concentrations to suspended clays. This suggests that regulatory methods should be reviewed for lakes with a high suspended-solid content. Dissolved copper (Cu) concentrations were below regulatory levels in 2021, but some samples were above regulatory levels in 2022. This could be related to the use of Cu-based algaecide treatments, or from other sources. Lead (Pb) data were inconclusive; dissolved Pb concentrations were well below the acute (1 h average) limit, but the chronic concentration limit (4 h average) was below the ICP-OES minimal detection limit. Arsenic (As) concentrations exhibited a seasonal trend that we attribute to groundwater inflows—they were below regulatory levels for aquatic environments but around the levels for drinking water. This ppb-level study with high temporal resolution provides insight into regulated elements in Utah Lake previously not available due to the high sensitivity of the method and measurements of both total and dissolved concentrations.

Atmospheric deposition (AD) is a significant source of nutrient loading to waterbodies around the world. However, the sources and loading rates are poorly understood for major waterbodies and even less understood for local waterbodies. Utah Lake is a eutrophic lake located in central Utah, USA, and has high-nutrient levels. Recent research has identified AD as a significant source of nutrient loading to the lake, though contributions from dust particles make up 10% of total AD. To better understand the dust AD sources, we sampled suspected source locations and collected deposition samples around the lake. We analyzed these samples using Inductively Coupled Plasma (ICP) for 25 metals to characterize their elemental fingerprints. We then compared the lake samples to the source samples to determine likely source locations. We computed spectral angle, coefficient of determination, multi-dimensional scaling, and radar plots to characterize the similarity of the samples. We found that samples from local dust sources were more similar to dust in lake AD samples than samples from distant sources. This suggests that the major source of the dust portion of AD onto Utah Lake is the local empty fields south and west of the lake, and not the farther playa and desert sources as previously suggested. Preliminary data suggest that dust AD is associated with dry, windy conditions and is episodic in nature. We show that AD from dust particles is likely a small portion of the overall AD nutrient loading on Utah Lake, with the dry and precipitation sources contributing most of the load. This case identifies AD sources to Utah Lake and provides an example of data and methods that can be used to assess similarity or perform attribution for dust, soil, and other environmental data. While we use ICP metals, any number of features can be used with these methods if normalized.