ABSTRACT:

Characteristics of spatial and temporal variability in surface water quality can have important implications for remote sensing model development and applications. Temporal variability is important for defining appropriate use of near-coincident matches between satellite overpasses and field samples, identifying critical periods for monitoring, and understanding algae bloom dynamics. Spatial variability is important for selecting satellite instruments with appropriate spatial resolutions.

This resource contains various water quality parameters for the Great Salt Lake. Chlorophyll-a data were collected by students at the University of Utah (U of Utah) using a Hydrolab DS5 (OTT Hydromet) multi-parameter sonde equipped with a submersible fluorescence Chlorophyll-a sensor (range of 0.03–500 ug/L). The Gilbert Bay sites (prefixed with GB) were located approximately 1000 m apart, which is the same scale as the coarsest MODIS spatial resolution. At each of these sites, data were also collected at offsets to the site center to represent sub-Landsat and sub-SENTINEL-2 resolution. These offset samples were spaced at approximately 7.5 m increments (i.e., 7.5, 15, 22.5 and 30 m) from the original sites GB2, GB3 and GB4. The offsets were identified with suffixes a, b, c and d, so that the first offset (7.5 m) from GB2 was identified as GB2a, the second offset (15 m) from GB2 was GB2b, etc.) Data collection at the GSL1 site also included sampling at offsets at the same increments (7.5, 15,22.5 and 30 m) east of the original site.

The GB sites also include measurements over various depths in order to evaluate relationships between surface (0-1m) water quality and water quality throughout the water column.

ABSTRACT:

My name is Carly Hansen, and I was born and raised in Wisconsin, and now live in Salt Lake City, UT. I am currently a PhD student at the University of Utah in the Civil and Environmental Engineering program.

My research interests include remote sensing, urban water systems, and lake health.

Email: carly.hansen@utah.edu

Outside of academics, I really enjoy cooking/trying new foods, running, and traveling.

ABSTRACT:

A flood inundation analysis for the Onion Creek Watershed

ABSTRACT:

The .Rdata datasets were created to be used in the Snowbedo R Package, available on GitHub at : <a href="https://github.com/cahhansen/Snowbedo" rel="nofollow">https://github.com/cahhansen/Snowbedo</a>. The Snowbedo package and models were developed in part with support of the iUTAH project, under the Research Focus Area 3: Understanding the ties between human and environmental water systems.

The datasets contain a collection of time series of streamflow, meteorological, and land surface/atmospheric data which can be used to calibrate streamflow models. Each dataset corresponds to a different watershed/stream in the Wasatch Mountains.

Variables and their definitions are as follows:
Date - date, Year-Month-Day
Streamflow - streamflow rate in cms (from Salt Lake City Department of Public Utilities)
Tmax_C - maximum temperature, degrees Celsius from sub-daily measurements (SNOTEL)
Tmin_C - minimum temperature, degrees Celsius from sub-daily measurements (SNOTEL)
SWE_cm - snow water equivalent, centimeters (SNOTEL)
Albedo - average watershed albedo (derived from MOD01A1 product)
SolarRad_Whm2d - shortwave downwelling radiation, W-h/m2/day (from CERES SYN1deg product)
SnowCover - percentage of watershed covered by snow (derived from MOD01A1 product)
SnowDepth_cm - depth of snow, centimeters (SNOTEL)
Precip_cm - precipitation, centimeters/day (SNOTEL)

Additional parameters are also included for exploring the effects of the lagged parameters (lagged by one day) on streamflow.

Streams and the locations of the SLCDPU Gage Location and their corresponding SNOTEL sites are as follows:
City Creek - 40.7841, -111.883; SNOTEL Site: Louis Meadow (972)
Little Cottonwood - 40.579, -111.798; SNOTEL Site: Snowbird (766)
Lambs Creek -40.7548, -111.709; SNOTEL Site: Parley's Summit (684)
Dell Creek - 40.7809, -111.681; SNOTEL Site: Lookout Peak (596)
Big Cottonwood - 40.618, -111.780; SNOTEL Site: Mill-D (628)

The scripts are intended to be used with the Snowbedo R Package (github.com/cahhansen/Snowbedo. The scripts may be run using R Statistical Software and the dependent external packages (listed in the scripts). The Snowbedo package was developed in order to model streamflow as a result of changing snowpack dynamics (particularly albedo). The purpose of the NeuralNetwork script is to train and build a neural network model of streamflow based on climate and watershed characteristics.Results of the model are a daily time-series of streamflow covering the same time period as the input datasets. Different scenarios (with adjusted albedo) can be created with the ModelDifferencesInAlbedo.R script. The readme.txt file explains how the package can be used.

ABSTRACT:

Surface reflectance measured by satellite remote sensing can be used to evaluate optical water quality in lakes and reservoirs.

This resource contains surface reflectance data from the Landsat 5 and Landsat 7 sensors at locations throughout the Great Salt Lake, Utah Lake, and Farmington Bay in Utah, USA. Calibration data contain near-coincident samples of surface chlorophyll (from the Utah Division of Water Quality, USGS, and Jordan River-Farmington Bay Water Quality Council), while historical reflectance data contain only surface reflectance data over the historic record of 1984-2016. Reflectance data are provided for each of the visible, near infrared, and shortwave infrared bands (scaled from 0-10000). These data were downloaded via Google Earth Engine, which hosts the surface reflectance products that are produced by the USGS. Cloud Mask bands indicate data with potential cloud/haze/atmospheric interferences (any value >1).

ABSTRACT:

Forecasting conditions that are indicative of algal blooms can help provide an early warning for monitoring and water management agencies. This script creates a seasonal (monthly) forecasting model which uses hydrologic and climate data from earlier in the season to predict chlorophyll concentrations throughout the late summer months. The accompanying data includes time series of monthly average extreme chlorophyll values, average streamflows, snow water equivalent, temperatures, and precipitation totals in or near Utah Lake.