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Hyrum Tennant

Utah State University

Subject Areas: Hydrology

 Recent Activity

ABSTRACT:

Climate and environmental uncertainties are driving many communities to adopt water conservation strategies related to agricultural water use and irrigation efficiency. While water managers are equipped to understand the technical aspects of improved irrigation efficiencies(e.g. application rate), there are few tools to represent how a water balance responds to changes in management practices. This study seeks to understand the extent to which irrigation excess serves as recharge to streamflow and how this relationship varies with different irrigation practices. We developed a model to predict infiltration and runoff (surrogate for recharge)as a function of applied irrigation,precipitation, evapotranspiration, and soil type. This model was applied to a section of the lower Logan River between Main Street and Mendon Road, which is notably impacted by agricultural operations during the summer growing period. We used this model to evaluate the extent to which irrigation recharge supplements flow at Mendon Road and also evaluated the impact of irrigation timing and irrigation efficiency on recharge. Our model showed that observed flow at Mendon Road had a similar trend and magnitude to modeled recharge during low flow conditions.In addition, we found that irrigation timing had little impact on recharge while reducing the rate of irrigation application by 40% corresponded to a 75% reduction in recharge during summer months. On a broader scale, the results of this case study demonstrate the importance of considering “the paradox of irrigation efficiency” in water resource decision making.

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ABSTRACT:

For environmental data measured by a variety of sensors and compiled from various sources, practitioners need tools that facilitate data access and data analysis. Data are often organized in formats that are incompatible with each other and that prevent full data integration. Furthermore, analyses of these data are hampered by the inadequate mechanisms for storage and organization. Ideally, data should be centrally housed and organized in an intuitive structure with established patterns for analyses. However, in reality, the data are often scattered in multiple files without uniform structure that must be transferred between users and called individually and manually for each analysis. This effort describes a process for compiling environmental data into a single, central database that can be accessed for analyses. We use the Logan River watershed and observed water level, discharge, specific conductance, and temperature as a test case. Of interest is analysis of flow partitioning. We formatted data files and organized them into a hierarchy, and we developed scripts that import the data to a database with structure designed for hydrologic time series data. Scripts access the populated database to determine baseflow separation, flow balance, and mass balance and visualize the results. The analyses were compiled into a package of scripts in Python, which can be modified and run by scientists and researchers to determine gains and losses in reaches of interest. To facilitate reproducibility, the database and associated scripts were shared to HydroShare as Jupyter Notebooks so that any user can access the data and perform the analyses, which facilitates standardization of these operations.

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ABSTRACT:

This resource contains a SQLite database of temperature values recorded at 15-minute intervals between 2014 and 2018 at the iUTAH GAMUT sites in the Logan River Watershed. Additionally contained with in this resource is a Jupyter Notebook that:

1) defines a function for querying the SQLite database and extracting the data for each site
2) gets temperature time series from database using function
3) creates time series for each year of data at each site
4) re samples the complete time series at each site to get the mean daily temperature
5) creates a figure showing (a) a plot of the mean daily temperature at each site between 2014 and 2018 and (b) a plot for each year of data showing a box plot of the temperature data recorded that year for each site.

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Resources
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Geographic Feature 0
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Time Series 0
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Composite Resource Composite Resource
Temperature Visualization for iUTAH GAMUT Sites
Created: Nov. 29, 2018, 11:05 a.m.
Authors: Hyrum Tennant

ABSTRACT:

This resource contains a SQLite database of temperature values recorded at 15-minute intervals between 2014 and 2018 at the iUTAH GAMUT sites in the Logan River Watershed. Additionally contained with in this resource is a Jupyter Notebook that:

1) defines a function for querying the SQLite database and extracting the data for each site
2) gets temperature time series from database using function
3) creates time series for each year of data at each site
4) re samples the complete time series at each site to get the mean daily temperature
5) creates a figure showing (a) a plot of the mean daily temperature at each site between 2014 and 2018 and (b) a plot for each year of data showing a box plot of the temperature data recorded that year for each site.

Show More
Composite Resource Composite Resource

ABSTRACT:

For environmental data measured by a variety of sensors and compiled from various sources, practitioners need tools that facilitate data access and data analysis. Data are often organized in formats that are incompatible with each other and that prevent full data integration. Furthermore, analyses of these data are hampered by the inadequate mechanisms for storage and organization. Ideally, data should be centrally housed and organized in an intuitive structure with established patterns for analyses. However, in reality, the data are often scattered in multiple files without uniform structure that must be transferred between users and called individually and manually for each analysis. This effort describes a process for compiling environmental data into a single, central database that can be accessed for analyses. We use the Logan River watershed and observed water level, discharge, specific conductance, and temperature as a test case. Of interest is analysis of flow partitioning. We formatted data files and organized them into a hierarchy, and we developed scripts that import the data to a database with structure designed for hydrologic time series data. Scripts access the populated database to determine baseflow separation, flow balance, and mass balance and visualize the results. The analyses were compiled into a package of scripts in Python, which can be modified and run by scientists and researchers to determine gains and losses in reaches of interest. To facilitate reproducibility, the database and associated scripts were shared to HydroShare as Jupyter Notebooks so that any user can access the data and perform the analyses, which facilitates standardization of these operations.

Show More
Composite Resource Composite Resource

ABSTRACT:

Climate and environmental uncertainties are driving many communities to adopt water conservation strategies related to agricultural water use and irrigation efficiency. While water managers are equipped to understand the technical aspects of improved irrigation efficiencies(e.g. application rate), there are few tools to represent how a water balance responds to changes in management practices. This study seeks to understand the extent to which irrigation excess serves as recharge to streamflow and how this relationship varies with different irrigation practices. We developed a model to predict infiltration and runoff (surrogate for recharge)as a function of applied irrigation,precipitation, evapotranspiration, and soil type. This model was applied to a section of the lower Logan River between Main Street and Mendon Road, which is notably impacted by agricultural operations during the summer growing period. We used this model to evaluate the extent to which irrigation recharge supplements flow at Mendon Road and also evaluated the impact of irrigation timing and irrigation efficiency on recharge. Our model showed that observed flow at Mendon Road had a similar trend and magnitude to modeled recharge during low flow conditions.In addition, we found that irrigation timing had little impact on recharge while reducing the rate of irrigation application by 40% corresponded to a 75% reduction in recharge during summer months. On a broader scale, the results of this case study demonstrate the importance of considering “the paradox of irrigation efficiency” in water resource decision making.

Show More