ABSTRACT:

This study evaluates the feasibility of developing a new reservoir on Temple Fork of the Logan River using Geographic Information System based hydrologic analysis and computational tools, terrain, and land-cover analysis. A 10 m Digital Elevation Model (DEM) was used to delineate the watershed and simulate reservoir inundation under multiple pool elevation scenarios, including 1865 m, 1885 m, 1920 m, and 1980 m. The analysis involved extracting terrain below selected water surface elevations, converting inundation extents into polygons, and quantifying affected areas within the watershed. Among the tested scenarios, a pool elevation of 1920 m was identified as optimal, producing a continuous and hydraulically connected reservoir while limiting the inundated area to approximately 1.05 km², or about 4.5% of the total watershed. To assess environmental impacts, the selected reservoir extent was overlaid with National Land Cover Database (NLCD) data. Results indicate that the inundation area is dominated by shrub/scrub land cover, accounting for approximately 0.79 km² (~76%), followed by forested areas totaling approximately 0.22 km² (~21%), while developed land comprises only about 0.03 km² (~3%). These findings suggest that the proposed reservoir would primarily impact natural landscapes with minimal direct effects on human infrastructure. Overall, this study demonstrates the effectiveness of GIS-based methods for integrating terrain analysis, hydrologic assessment, and land-cover evaluation to support preliminary reservoir planning and decision-making.

ABSTRACT:

The Normalized Difference Vegetation Index (NDVI) quantifies plant greenness and serves as an indicator of vegetation biomass and density. In arid regions such as Utah, where soil properties vary spatially within agricultural fields, NDVI-based spatial analysis provides a comprehensive assessment of crop health. In this study, NDVI data was collected from an alfalfa field near Pelican Lake in Randlett, Utah, and integrated with meteorological data from the Utah Climate Center’s Pelican Lake weather station. An Observations Data Model (ODM) implemented within an SQLite database was used to manage and stream the data into a web application live monitoring dashboard. The system visualizes NDVI time series alongside environmental variables including temperature, precipitation, and humidity to support interpretation of alfalfa vegetation dynamics. The developed web interface will enable users (i.e., alfalfa farmers) to track temporal patterns in crop development, assess vegetation condition, and identify optimal harvest timing based on both NDVI thresholds and meteorological weather indicators. Results demonstrate that integrating NDVI with real-time environmental data improves the interpretability of crop conditions and supports more informed agricultural decision-making and planning. Our approach has the potential to enhance yield and harvesting optimization while reducing alfalfa losses in water-limited environments like Northern Utah.

ABSTRACT:

This study presents a hydrologic analysis (i.e., through Environmental and Hydrologic Data Analysis and Experimentation) of daily streamflow discharge (Q) at three sites along the Logan River located in Logan Northern Utah, spanning mountain headwater to an urban reach (2014 - 2025). Time series analysis reveals strong seasonal snowmelt peaks, with greater relative variability upstream. A log transformation and KDE show that upstream flows are more dispersed and positively skewed, while downstream distributions are more compressed. A 20-day rolling means smoothing daily variability and highlights recurring seasonal and storm-driven discharge pulses. Flow-duration curves show a downstream increase in high-flow exceedance, suggesting cumulative upstream inputs. OLS regression residuals show systematic departures from randomness, with nonlinear and heteroscedasticity behavior, indicating poorer model fit at low flows. Spearman correlation (ρ≈0.57-0.90) indicates strong spatial connectivity. Kruskal–Wallis and Dunn’s tests confirm significant differences between sites. Results support the role of headwaters as a snowmelt-driven “water tower,” with downstream flow variability influenced by human activities such as dams, diversions, or land-use change.

ABSTRACT:

This resource presents a reproducible hydrologic and environmental data-analysis workflow developed by Abdulganiyu Jimoh using observations from the Logan River Observatory (LRO) in northern Utah. The analysis examines spatial and temporal variability in river discharge and water temperature across a longitudinal transect spanning contrasting hydrologic settings, from upstream mountain conditions to downstream and urban-influenced reaches.

The workflow applies exploratory data analysis, time-series analysis, statistical modeling, flow-duration analysis, smoothing and resampling, robust trend estimation, flashiness metrics, baseflow separation, recession analysis, and event-based hydrologic characterization. These methods are used to quantify flow variability, examine discharge–temperature relationships, characterize hydrologic extremes, and evaluate how hydrologic behavior changes along the river corridor.

Results demonstrate strong seasonal structure, an inverse relationship between discharge and water temperature, and differences in flow variability among monitoring locations. The analysis also examines downstream attenuation of hydrologic variability and watershed response characteristics. Together, these analyses provide a reproducible framework for examining spatially and temporally varying hydrologic conditions using continuous environmental observations.

The resource is designed to support transparent and reproducible hydrologic analysis and to provide a reusable computational framework for researchers, students, water-resource scientists, and other users working with environmental monitoring data. The underlying observations are associated with the Logan River Observatory; this resource documents the analytical workflow, processing, visualization, and interpretation applied to those observations.

This public resource contributes an openly accessible computational example of how continuous hydrologic observations can be transformed into interpretable information about watershed behavior, variability, and environmental conditions.