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| Type: | Resource | |
| Storage: | The size of this resource is 67.4 MB | |
| Created: | Aug 25, 2026 at 3:03 p.m. (UTC) | |
| Last updated: | Aug 25, 2026 at 8:40 p.m. (UTC) | |
| Citation: | See how to cite this resource |
| Sharing Status: | Public |
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| Views: | 121 |
| Downloads: | 11 |
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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.
Subject Keywords
Coverage
Spatial
Temporal
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Content
Additional Metadata
| Name | Value |
|---|---|
| Study Area | Logan River watershed, northern Utah, USA |
| Resource Type | Open scientific research dataset and reproducible computational analysis |
| Reproducibility | The accompanying Jupyter notebook documents the computational workflow, analytical methods, transformations, statistical procedures, and visualization steps |
| Research Domain | Hydrology; water resources; environmental monitoring; environmental data science |
| Spatial Setting | Longitudinal river transect representing upstream mountain/headwater, intermediate/downstream, and downstream human-influenced conditions |
| Monitoring Sites | Tony Grove (TG) — LR_TG_BA; Main Street (MS) — LR_MainStreet_BA; Water Lab (WL) — LR_WaterLab_AA |
| Research Purpose | Characterization of spatial and temporal hydrologic variability and evaluation of river-system behavior across the Logan River Observatory transect |
| Spatial Analysis | Cross-site comparison of discharge characteristics along the Logan River transect |
| Temporal Analysis | Time-series visualization, temporal aggregation, smoothing, seasonal comparison, event-scale analysis, and trend assessment |
| Monitoring Network | Logan River Observatory (LRO) |
| Observed Variables | River discharge (streamflow, Q); water temperature (WaterTemp_EXO), where available |
| Data Transformation | Temporal resampling, aggregation, smoothing, and derived hydrologic metrics as documented in the accompanying Jupyter notebook |
| Statistical Methods | Classical and robust statistical methods for characterizing relationships, variability, trends, and uncertainty in hydrologic observations |
| Temporal Resolution | High-frequency observations; source measurements are recorded at 15-minute intervals where applicable |
| Data Quality Control | Assessment and handling of missing, invalid, or inconsistent observations prior to analysis; processing decisions are documented in the accompanying notebook |
| Variable Description | Continuous hydrologic and environmental observations used to characterize spatial and temporal variability along the Logan River Observatory transect |
| Intended Applications | Hydrologic research, water-resource assessment, environmental monitoring, reproducible research, education, and development of subsequent predictive or modeling analyses |
| Data Collection Method | Measurements collected using site-specific environmental monitoring instrumentation (i.e., sensors, etc..) and recorded through automated data-logging systems; instrument and method information should be referenced from the original data files and metadata |
| Data Processing Method | Data quality control, cleaning, temporal alignment, missing-value assessment, aggregation/resampling, statistical summarization, and preparation for hydrologic analysis |
| Hydrologic Analysis Methods | Descriptive statistics, time-series analysis, flow-duration curves, correlation analysis, OLS regression, confidence intervals, Theil–Sen regression, baseflow separation, recession analysis, event/pulse metrics, travel-time analysis, and flow-attenuation analysis |
| Software/Computational Environment | Python and Jupyter Notebook; analysis implemented using open-source scientific Python libraries |
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Related Resources
| The content of this resource is derived from | Logan River Observatory (2026). Logan River Observatory: Logan River near Tony Grove Aquatic Site (LR_TG_BA) Raw Data, HydroShare. |
| The content of this resource is similar to | Scott, M. (2017). Hydrologic Terrain Analysis of Logan Watershed Using Jupyter Notebook TauDEM, HydroShare. This is particularly relevant because it demonstrates an open Jupyter-based hydrologic analysis of the Logan River watershed |
| The content of this resource is similar to | Tarboton, D. (2023). Logan River Flow From Snow Analysis, HydroShare. This resource combines Logan River streamflow, SNOTEL observations, and a Jupyter notebook for regression analysis of snow water equivalent and streamflow |
| The content of this resource is similar to | Development and Implementation of Database and Analyses for High Frequency Data, HydroShare. This resource uses the Logan River watershed as a test case for high-frequency environmental data and includes analyses of flow partitioning, baseflow separation, flow balance, and mass balance. |
| The content of this resource is similar to | Supporting Information: Application of flow and ion data to estimate ungaged inflows and losses in urban and agricultural sub-reaches of the Logan River Observatory, HydroShare. This is relevant to your longitudinal analysis because it uses discharge data from multiple Logan River sites to investigate hydrologic behavior in urban and agricultural reaches. |
| The content of this resource is derived from | Logan River Observatory (2026). Logan River Observatory: Logan River near Main Street Aquatic Site (LR_MainStreet_BA) Raw Data, HydroShare. |
| The content of this resource is derived from | Logan River Observatory (2026). Logan River Observatory: Logan River at the Utah Water Research Laboratory west bridge Aquatic Site (LR_WaterLab_AA) Quality Controlled Data, HydroShare. The LRO collection itself describes these resources as high-frequency observations managed by USU/UWRL. |
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
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| The DAISy (Digital Agro-environment and Intelligent Systems) Lab | None | None |
| Utah Water Research Laboratory | None | None |
| USU Civil & Environmental Engineering Department | None | None |
| USU College of Engineering | None | None |
Contributors
People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.
| Name | Organization | Address | Phone | Author Identifiers |
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| Jeffery S. Horsburgh | Utah State University;Utah Water Research Laboratory | UT, US | +1 (435) 797-2946 | ORCID , ResearchGateID , GoogleScholarID |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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