ABSTRACT:

Lakes, an important components of terrestrial ecosystems, vary widely in size, depth, and ecological characteristics. Small lakes, which are more common than larger ones, play vital roles in local ecosystems. however, these water bodies, particularly those that are terminated, exhibit high sensitivity to climate and environmental changes risking lives of hundred species. In such lakes, any alteration in water inflow significantly impacts the lake’s level and size, thereby affecting its physical parameters like temperature, pH, and turbidity. To address these impacts, remote sensing methods can be used to find the changes in any lake water level and water quality. This project aims to study the aspects using Utah Lake as a case study, utilizing remote sensing imagery to understand and illustrate the dynamics of lake size and water quality. LANDSAT images are extracted from Google Earth Engine and analyzed to positively define the proportional correlation between the lake level and the turbidity. Wet and dry years are selected for this analysis based on literature. The outcomes of this analysis will aid in identifying critical changes and inform decision-makers on protecting different species and habitants and planning and managing the lake's water resources to ensure both societal and ecological benefits.

ABSTRACT:

Understanding the subsurface water depth fluctuations is highly required for any further groundwater studies. In this project, the historical water level of the groundwater was studied for Cache Valley, Utah. Data of wells were called from USGS using Python, through the web service ‘NWIS’ function. Only the six wells that have enough observations were selected and then located spatially on a map. The data were plotted and statistically analyzed using different tools. Also, the data of some wells showed an abrupt change in the threshold conditions of the time series after 1987. The water table depth correlation of every two wells was visually related to the distance between the wells as they fitted the exponential correlation curve more than the linear one. Quantitatively, the Pearson test proved that the correlation between wells is correlated to the distance between them.

ABSTRACT:

Karst aquifers are important water resources all over the globe. Due to the fractured and dissolved geology in karst aquifers, the exchange between stream and groundwater is difficult to quantify. One method for estimating stream and groundwater exchange is solving a mass balance at the reach scale (Neilson et al. 2018). Primarily, a mass balance requires discharge and concentration data for a conservative constituent such as sodium or chloride. Secondarily, a mass balance requires several assumptions to limit the unknowns. Variations in which concentration data and assumptions are used in the mass balance produce numerous estimations of exchange rates between stream and groundwater for many scenarios. The purpose of this resource was to effectively store exchange rate estimates for various scenarios and provide an accessible interface to analyze the scenario estimates for 26 reaches in Logan Canyon. This resource includes an SQLite database that stores the estimate and scenario data. Then, the database was populated by linking it to the mass balance model. Additionally, GIS was used to obtain geologic layer data, a portion of this data was also added to the database. The Jupyter Notebook included in this resource analyzes the model output and examines the sensitivity of the model to parameters such as assumptions and data input.