ABSTRACT:

This resource contains data inputs and a Jupyter Notebook that is used to introduce Hydrologic Analysis using Terrain Analysis Using Digital Elevation Models (TauDEM) and Python. TauDEM is a free and open-source set of Digital Elevation Model (DEM) tools developed at Utah State University for the extraction and analysis of hydrologic information from topography. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

In this activity, the student learns how to (1) derive hydrologically useful information from Digital Elevation Models (DEMs); (2) describe the sequence of steps involved in mapping stream networks, catchments, and watersheds; and (3) compute an approximate water balance for a watershed-based on publicly available data.

Please note that this exercise is designed for the Logan River watershed, which drains to USGS streamflow gauge 10109000 located just east of Logan, Utah. However, this Jupyter Notebook and the analysis can readily be applied to other locations of interest. If running the terrain analysis for other study sites, you need to prepare a DEM TIF file, an outlet shapefile for the area of interest, and the average annual streamflow and precipitation data.
- There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects).
- If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS.
- You also need to obtain average annual streamflow and precipitation data for the watershed of interest to assess the annual water balance and calculate the runoff ratio in this exercise. In the U.S., the streamflow data can be obtained from the USGS NWIS website (https://waterdata.usgs.gov/nwis) and the precipitation from PRISM (https://prism.oregonstate.edu/normals/). Note that using other datasets may require preprocessing steps to make data ready to use for this exercise.

ABSTRACT:

This resource contains data inputs and an iPython Jupyter Notebook used to simulate semi-distributed variable source area runoff generation in a tributary to the Logan River. This resource is part of the HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about.

In this activity, the student learns how to (1) calculate the topographic wetness index using digital elevation models (DEMs) following up on a previous module on DEMs and GIS in Hydrology; (2) apply TOPMODEL concepts and equations to estimate soil moisture deficit and runoff generation across a watershed given necessary watershed and storm characteristics; and (3) critically assess concepts and assumptions to determine if and why TOPMODEL is an appropriate tool given information about a specific watershed.

Please note that this exercise sets up the data needed to estimate runoff in the Spawn Creek watershed using TOPMODEL. Spawn Creek is a tributary of the Logan River, Utah. This exercise uses some of the same data as the Logan River Exercise in Digital Elevation Models and GIS in Hydrology at https://www.hydroshare.org/resource/9c4a6e2090924d97955a197fea67fd72/. If running the TOPMODEL for other study sites, you need to prepare a DEM TIF file and an outlet shapefile for the area of interest.
- There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects).
- If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS.

ABSTRACT:

Hydrologic Data Analysis, and Conservation Laws

ABSTRACT:

This resource contains a Jupyter Notebook that is used to introduce hydrologic data analysis and conservation laws. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

In this activity, the student learns how to (1) calculate the residence time of water in land and rivers for the global hydrologic cycle; (2) quantify the relative and absolute uncertainties in components of the water balance; (3) navigate public websites and databases, extract key watershed attributes, and perform basic hydrologic data analysis for a watershed of interest; (4) assess, compare, and interpret hydrologic trends in the context of a specific watershed.

Please note that in problems 3-8, the user is asked to use an R package (i.e., dataRetrieval) and select a U.S. Geological Survey (USGS) streamflow gage to retrieve streamflow data and then apply the hydrological data analysis to the watershed of interest. We acknowledge that the material relies on USGS data that are only available within the U.S. If running for other watersheds of interest outside the U.S. or wishing to work with other datasets, the user must take some further steps and develop codes to prepare the streamflow dataset. Once a streamflow time series dataset is obtained for an international catchment of interest, the user would need to read that file into the workspace before working through subsequent analyses.

ABSTRACT:

Hydrologic Data Analysis, and Conservation Laws

ABSTRACT:

A river classification was developed for major regions of the State of California USA using a field surveying protocol that can now be used to collect data at additional sites. The field protocols provide a framework for systematically collecting a variety of physical geomorphic data using uniform sampling, including channel slope, cross-sectional morphology, sediment composition, and longitudinal depth and width variability. The standard sampling layout consists of a stream length 15 times the active channel width (measured along the thalweg) divided into 10 equidistant transects that are arranged perpendicular to the stream channel. Details of the river classification of the Sacramento Basin region are available in Byrne et al (2020).

This resource contains the field surveying protocols and field data collection template.

ABSTRACT:

A river classification was developed for major regions of the State of California USA using a field surveying protocol that can now be used to collect data at additional sites. The field protocols provide a framework for systematically collecting a variety of physical geomorphic data using uniform sampling, including channel slope, cross-sectional morphology, sediment composition, and longitudinal depth and width variability. The standard sampling layout consists of a stream length 15 times the active channel width (measured along the thalweg) divided into 10 equidistant transects that are arranged perpendicular to the stream channel. Details of the river classification of the Sacramento Basin region are available in Byrne et al (2020).

This resource contains the field surveying protocols and field data collection template.

ABSTRACT:

In the western US, major landscape modifications for flood conveyance and conversion of floodplains to crops have reduced the natural pathways of recharge and groundwater discharge. Combined with direct flow diversions for irrigation, these modifications result in depleted streamflows during the critical summer low flow period. Depleted streams are much more susceptible to extreme spatial and temporal temperature variability, which is inextricably linked to aquatic habitat suitability. However, in depleted streams, even small amounts of colder water (e.g., cool lateral inflows) can moderate temperatures and provide critical thermal refugia. While irrigation diversions reduce the amount of water instream, seepage from nearby irrigated areas and canal networks can enhance baseflows and moderate stream temperatures downstream of diversions. Some rivers now depend on these human-mediated return flows to maintain suitable flow and temperature conditions for river ecosystems over the dry season, making them sensitive to changes in land and water management. To improve our understanding of the role of irrigation diversions and shallow return flows on stream temperature patterns, we collected flow and temperature measurements along a diversion-depleted reach of the Blacksmith Fork River in northern Utah over three summers. We determined the significance of site-specific properties (shading, weather), channel morphology, and lateral inflows on spatial and temporal stream temperature patterns. We found that lateral inflows, most likely sourced from unlined canals, were a critical component for maintaining suitable river temperatures. This study informs local and regional water management efforts during low flow periods and highlights potential unintended consequences of irrigation efficiency projects that reduce seepage.

ABSTRACT:

This note is created to help calculate the infiltration rate of soil using the Green-Ampt infiltration model.

ABSTRACT:

This resource contains raw data collected for the project "Increasing the Water Conservation Impact of Utah State University’s (USU) Extension Water Check Program with 5 Second Metering" (https://uwrl.usu.edu/water-check-study). The data is for ~ 78 households in Logan and Hyde Park, Utah collected in Summer and Fall 2022. 5-second water use data was collected over the entire period using a Flume Smart Home Water Monitoring Device. After ~ two weeks, a USU Extension Water Check was conducted during a site visit. There are 6 data sets in this resource. Data are anonymized and can be linked -- joined -- by the SiteID field.

1_Database_CSVFiles/
1) FlumePropertyData.csv => Metadata for the households collected by Flume when a device is installed and the Flume phone App was installed.
2) Sites.csv => Metadata for the households including city, state, and zipcode.
3) WaterCheckData.csv => Parcel, landscape, and irrigation system data collected as part of the USU Extension Water Check during a 1-hour visit to the household. Data also include Water Check recommendations to reduce irrigation water use.
4) RawWaterUseData/SITE_XXX.csv => Raw 5-second water use data collected by Flume Smart Home Water Monitoring Devices (http:/FlumeWater.com). One file for each household/SiteID. XXX is the SiteID.
5) daily_WeatherData_GVFarm.csv => Weather data from the nearest station - Greenville Farm, Cache Valley, Utah.
6) TrainingData.csv => Irrigation events identified by duration (minutes), volume_gal (gallons), average_fr_GPM (gallons per minute), label (type of event). These data are used to train a model that uses the raw 5-second data to classify irrigation events.
The code to classify the raw 5-second water use data is in a separate code repository - https://github.com/cjbas22/HelpUSUExtensionP.

2_AdditionalData => Folder with duplicate copies of the weather station and training data.

3_Database => Empty folder. Code in the repository https://github.com/cjbas22/HelpUSUExtensionP reads the raw csv files and creates a database with tables for each data file.