This study demonstrates a number of methods for using field sampling and observed lake characteristics and patterns to improve techniques for development of algae remote sensing models and applications. As satellite and airborne sensors improve and their data are more readily available, applications of models to estimate water quality via remote sensing are becoming more practical for local water quality monitoring, particularly of surface algal conditions. Despite the increasing number of applications, there are significant concerns associated with remote sensing model development and application, several of which are addressed in this study. These concerns include: (1) selecting sensors which are suitable for the spatial and temporal variability in the water body; (2) determining appropriate uses of near-coincident data in empirical model calibration; and (3) recognizing potential limitations of remote sensing measurements which are biased toward surface and near-surface conditions. We address these issues in three lakes in the Great Salt Lake surface water system (namely the Great Salt Lake, Farmington Bay, and Utah Lake) through sampling at scales that are representative of commonly used sensors, repeated sampling, and sampling at both near-surface depths and throughout the water column. The variability across distances representative of the spatial resolutions of Landsat, SENTINEL-2 and MODIS sensors suggests that these sensors are appropriate for this lake system. We also use observed temporal variability in the system to evaluate sensors. These relationships proved to be complex, and observed temporal variability indicates the revisit time of Landsat may be problematic for detecting short events in some lakes, while it may be sufficient for other areas of the system with lower short-term variability. Temporal variability patterns in these lakes are also used to assess near-coincident data in empirical model development. Finally, relationships between the surface and water column conditions illustrate potential issues with near-surface remote sensing, particularly when there are events that cause mixing in the water column.

Reconstructed historical streamflow time series can supplement limited streamflow gauge observations. However, there are common challenges of typical modeling approaches: process‐based hydrologic models can be data/computation‐intensive, and statistics‐based models can be region/stream‐specific. Here we present a nationally scalable modeling framework integrating the simulated runoff from the Variable Infiltration Capacity (VIC) model with the Routing Application for Parallel computatIon of Discharge (RAPID) routing model leveraging high‐performance computing. We demonstrate an efficient method of assimilating streamflow at US Geological Survey (USGS) streamflow monitoring sites using a simple hierarchical approach in the VIC‐RAPID framework. The result is a reconstructed 36‐year (1980–2015) daily and monthly streamflow dataset (Dayflow) at ∼2.7 million NHDPlusV2 stream reaches in the conterminous US (CONUS). We perform a comprehensive evaluation at 7,526 USGS sites and characterize their error statistics. The results demonstrate that 49% of the USGS sites demonstrate Kling–Gupta Efficiency (KGE) > 0.5 and 58% of the sites show percentage bias within ±20% for the daily naturalized streamflow. Streamflow data assimilation across CONUS shows an overall improvement over naturalized streamflow, notably in the western semiarid‐to‐arid regions. Comparison to other national and global streamflow reanalysis datasets such as the National Water Model and Global Reach‐scale A priori Discharge Estimates for SWOT demonstrates improved KGE, reduced bias, and directions for Dayflow improvements. Investigations of error statistics with key hydrologic, hydroclimatic, and geomorphologic basin characteristics reveal region‐specific patterns which may help improve future framework applications. Overall, Dayflow may enable a better understanding of hydrologic conditions in a changing environment, especially in locations currently not represented by streamflow monitoring networks.

Hansen CH, Burian SJ, Dennison PE, Williams GP. 2019. Evaluating historical trends and influences of meteoorological and seasonal climate conditions on lake chlorophyll a using remote sensing. Lake Reserv Manage. XX:XX–XX. Evaluations of long-term water quality trends and patterns in lakes and reservoirs are often inhibited by irregular historical records. This study uses historical Landsat satellite imagery to construct a more complete historical record of algal biomass (measured via chlorophyll a [Chl-a]) and presents a framework for developing seasonal algal estimation models using open source tools for processing and model development. This approach is both physically based (using observed patterns of variability and algal succession in the lake) and data driven (relying on statistical methods for model development). We use a generalized linear regression modeling technique to develop lake-specific, seasonal models for each lake in the multilake Great Salt Lake system in Utah. The 32-yr constructed history of estimated Chl-a enables analysis of long-term trends within the lake system as well as evaluations of local climate influences on Chl-a concentrations. The estimated historical record exhibits a shift in seasonality (i.e., maximum Chl-a occurs earlier in the growing season), as well as increasing trends of extreme Chl-a concentrations. We also evaluated relationships between meteorological conditions and Chl-a using the enhanced historical record and found localized sensitivity to short-term weather events such as high wind, high temperatures, or precipitation events. Seasonal climate conditions including high winter precipitation, summer temperatures, and early spring snow water equivalent are consistent with higher Chl-a extremes in the historical record. Improved understanding of the trends and climate influences provides useful context and guidance for future monitoring efforts and management strategies.