Method for Estimating Sediment Mass Movement from Delta Recutting: A Case Study Using Single Beam Sonar in Deer Creek Reservoir

The recutting of delta sediments typically occurs during reservoir drawdown in the summer months. It can affect various reservoir processes and can impact water quality because of resuspending nutrients during warm periods supports phytoplankton growth. Quantifying this sediment movement is a key element for evaluating the life and quality of a reservoir. This study targets reservoirs in the intermountain region of the U.S. These reservoirs are filled in the spring, then drawdown through the summer to provide irrigation water. Incoming sediment loads are generally restricted to spring high flows, with little new sediment entering the reservoirs during the remainder of the year. As the reservoirs undergo drawdown, the sediment deposited in the delta region during spring flows is re-cut from the exposed delta and moved into submerged delta region. The majority of flow and sediment movement both above and below the water surface occurs in channels cut into the sediments during spring deposition. During recutting, channels in the exposed sediments often move, but the submerged channels are more stationary. Traditional single-beam sonar surveys are performed on a grid and changes are used to quantify sediment movements. This approach is not applicable to delta recutting as the grid resolution is not sufficient to resolve the relevant changes that occur in the narrow excised flow channels. This study explores the ability to quantify and monitor sediment mass movement in Deer Creek Reservoir (DCR) using a single beam sonar. Our method uses surveyed cross-sections across the flow channels. It is difficult to position boat passes exactly on previous survey lines, and small location differences in an up-stream or down-stream location can be significant because of the slope of the channel. To address this, we surveyed each line in two directions, then interpolated both the position and elevation data. We performed periodic surveys over a two-month period. We were able to document and quantify both sediment deposition and erosion areas. As expected, sediment movement was from the inlet areas toward the reservoir. The data showed both deposition and erosion depending on the distance from the reservoir head, which changed over the survey period. This method can be used to quantify sediment recutting and resuspension that can affect nutrient loads during critical warm, low-reservoir conditions, but is difficult to implement accurately

Evaluating Remote Sensing Model Specification Methods for Estimating Water Quality in Optically Diverse Lakes throughout the Growing Season

Spectral images from remote sensing platforms are extensively used to estimate chlorophyll-a (chl-a) concentrations for water quality studies. Empirical models used for estimation are often based on physical principles related to light absorption and emission properties of chl-a and generally relying on spectral bands in the green, blue, and near-infrared bands. Because the physical characteristics, constituents, and algae populations vary widely from lake to lake, it can be difficult to estimate coefficients for these models. Many studies select a model form that is a function of these bands, determine model coefficients by correlating remotely-measured surface reflectance data and coincidentally measured in-situ chl-a concentrations, and then apply the model to estimate chl-a concentrations for the entire water body. Recent work has demonstrated an alternative approach using simple statistical learning methods (Multiple Linear Stepwise Regression (MLSR)) which uses historical, non-coincident field data to develop sub-seasonal remote sensing chl-a models. We extend this previous work by comparing this method against models from literature, and explore model performance for a region of lakes in Central Utah with varying optical complexity, including two relatively clear intermountain reservoirs (Deer Creek and Jordanelle) and a highly turbid, shallow lake (Utah Lake). This study evaluates the suitability of these different methods for model parameterization for this area and whether a sub-seasonal approach improves performance of standard model forms from literature. We found that while some of the common spectral bands used in literature are selected by the data-driven MLSR method for the lakes in the study region, there are also other spectral bands and band interactions that are often more significant for these lakes. Comparison of model fit shows an improvement in model fit using the data-driven parameterization method over the more traditional physics-based modeling approaches from literature. This suggests that the sub-seasonal approach and exploitation of information contained in other bands helps account for lake-specific optical characteristics, such as suspended solids and other constituents contributing to water color, as well as unique (and season-specific) algae populations, which contribute to the spectral signature of the lake surface, rather than only relying on a generalized optical signature of chl-a. Consideration of these other bands is important for development of models for long-term and entire growing season applications in optically diverse water bodies

Groundwater Level Data Imputation Using Machine Learning and Remote Earth Observations Using Inductive Bias

Sustainable groundwater management requires an accurate characterization of aquifer-storage change over time. This process begins with an analysis of historical water levels at observation wells. However, water-level records can be sparse, particularly in developing areas. To address this problem, we developed an imputation method to approximate missing monthly averaged groundwater-level observations at individual wells since 1948. To impute missing groundwater levels at individual wells, we used two global data sources: Palmer Drought Severity Index (PDSI), and the Global Land Data Assimilation System (GLDAS) for regression. In addition to the meteorological datasets, we engineered four additional features and encoded the temporal data as 13 parameters that represent the month and year of an observation. This extends previous similar work by using inductive bias to inform our models on groundwater trends and structure from existing groundwater observations, using prior estimates of groundwater behavior. We formed an initial prior by estimating the long-term ground trends and developed four additional priors by using smoothing. These prior features represent the expected behavior over the long term of the missing data and allow the regression approach to perform well, even over large gaps of up to 50 years. We demonstrated our method on the Beryl-Enterprise aquifer in Utah and found the imputed results follow trends in the observed data and hydrogeological principles, even over long periods with no observed data

Extending Multi-Beam Sonar with Structure from Motion Data of Shorelines for Complete Pool Bathymetry of Reservoirs

Bathymetric mapping is an important tool for reservoir management, typically completed before reservoir construction. Historically, bathymetric maps were produced by interpolating between points measured at a relatively large spacing throughout a reservoir, typically on the order of a few, up to 10, meters or more depending on the size of the reservoir. These measurements were made using traditional survey methods before the reservoir was filled, or using sonar surveys after filling. Post-construction issues such as sedimentation and erosion can change a reservoir, but generating updated bathymetric maps is difficult as the areas of interest are typically in the sediment deltas and other difficult-to-access areas that are often above water or exposed for part of the year. We present a method to create complete reservoir bathymetric maps, including areas above the water line, using small unmanned aerial vehicle (sUAV) photogrammetry combined with multi-beam sonar data—both established methods for producing topographic models. This is a unique problem because the shoreline topographic models generated by the photogrammetry are long and thin, not an optimal geometry for model creation, and most images contain water, which causes issues with image-matching algorithms. This paper presents methods to create accurate above-water shoreline models using images from sUAVs, processed using a commercial software package and a method to accurately knit sonar and Structure from Motion (SfM) data sets by matching slopes. The models generated by both approaches are point clouds, which consist of points representing the ground surface in three-dimensional space. Generating models from sUAV-captured images requires ground control points (GCPs), i.e., points with a known location, to anchor model creation. For this study, we explored issues with ground control spacing, masking water regions (or omitting water regions) in the images, using no GCPs, and incorrectly tagging a GCP. To quantify the effect these issues had on model accuracy, we computed the difference between generated clouds and a reference point cloud to determine the point cloud error. We found that the time required to place GCPs was significantly more than the time required to capture images, so optimizing GCP density is important. To generate long, thin shoreline models, we found that GCPs with a ~1.5-km (~1-mile) spacing along a shoreline are sufficient to generate useful data. This spacing resulted in an average error of 5.5 cm compared to a reference cloud that was generated using ~0.5-km (~1/4-mile) GCP spacing. We found that we needed to mask water and areas related to distant regions and sky in images used for model creation. This is because water, objects in the far oblique distance, and sky confuse the algorithms that match points among images. If we did not mask the images, the resulting models had errors of more than 20 m. Our sonar point clouds, while self-consistent, were not accurately georeferenced, which is typical for most reservoir surveys. We demonstrate a method using cross-sections of the transition between the above-water clouds and sonar clouds to geo-locate the sonar data and accurately knit the two data sets. Shore line topography models (long and thin) and integration of sonar and drone data is a niche area that leverages current advances in data collection and processing. Our work will help researchers and practitioners use these advances to generate accurate post-construction reservoir bathometry maps to assist with reservoir management

A Spatial Long-Term Trend Analysis of Estimated Chlorophyll-a Concentrations in Utah Lake Using Earth Observation Data

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

A Spatial Long-Term Trend Analysis of Estimated Chlorophyll-a Concentrations in Utah Lake Using Earth Observation Data

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

Groundwater Level Data Imputation Using Machine Learning and Remote Earth Observations Using Inductive Bias

Sustainable groundwater management requires an accurate characterization of aquifer-storage change over time. This process begins with an analysis of historical water levels at observation wells. However, water-level records can be sparse, particularly in developing areas. To address this problem, we developed an imputation method to approximate missing monthly averaged groundwater-level observations at individual wells since 1948. To impute missing groundwater levels at individual wells, we used two global data sources: Palmer Drought Severity Index (PDSI), and the Global Land Data Assimilation System (GLDAS) for regression. In addition to the meteorological datasets, we engineered four additional features and encoded the temporal data as 13 parameters that represent the month and year of an observation. This extends previous similar work by using inductive bias to inform our models on groundwater trends and structure from existing groundwater observations, using prior estimates of groundwater behavior. We formed an initial prior by estimating the long-term ground trends and developed four additional priors by using smoothing. These prior features represent the expected behavior over the long term of the missing data and allow the regression approach to perform well, even over large gaps of up to 50 years. We demonstrated our method on the Beryl-Enterprise aquifer in Utah and found the imputed results follow trends in the observed data and hydrogeological principles, even over long periods with no observed data

Nutrient Atmospheric Deposition on Utah Lake: A Comparison of Sampling and Analytical Methods

We describe modified sampling and analysis methods to quantify nutrient atmospheric deposition (AD) and estimate Utah Lake nutrient loading. We address criticisms of previous published collection methods, specifically collection table height, screened buckets, and assumptions of AD spatial patterns. We generally follow National Atmospheric Deposition Program (NADP) recommendations but deviate to measure lake AD, which includes deposition from both local and long-range sources. The NADP guidelines are designed to eliminate local contributions to the extent possible, while lake AD loads should include local contributions. We collected side-by-side data with tables at 1 m (previous results) and 2 m (NADP guidelines) above the ground at two separate locations. We found no statistically significant difference between data collected at the different heights. Previous published work assumed AD rates would decrease rapidly from the shore. We collected data from the lake interior and show that AD rates do not significantly decline away from the shore. This demonstrates that AD loads should be estimated by using the available data and geostatistical methods even if all data are from shoreline stations. We evaluated screening collection buckets. Standard unscreened AD samples had up to 3-fold higher nutrient concentrations than screened AD collections. It is not clear which samples best represent lake AD rates, but we recommend the use of screens and placed screens on all sample buckets for the majority of the 2020 data to exclude insects and other larger objects such as leaves. We updated AD load estimates for Utah Lake. Previous published estimates computed total AD loads of 350 and 153 tons of total phosphorous (TP) and 460 and 505 tons of dissolve inorganic nitrogen (DIN) for 2017 and 2018, respectively. Using updated collection methods, we estimated 262 and 133 tons of TP and 1052 and 482 tons of DIN for 2019 and 2020, respectively. The 2020 results used screened samplers with lower AD rates, which resulted in significantly lower totals than 2019. We present these modified methods and use data and analysis to support the updated methods and assumptions to help guide other studies of nutrient AD on lakes and reservoirs. We show that AD nutrient loads can be a significant amount of the total load and should be included in load studies