ABSTRACT:

The Upper Basin of the Colorado River, under current agreements, must prioritize releases between 7.48 and 9.0 million-acre feet of water releases to the Lower Basin each year. These releases control Lower Basin deliveries which, until recently, were at least 8.23 million-acre feet per year. This delivery represents downstream allocations for Mexico, Native American tribes, and Lower Basin states. This report presents a management alternative that allows for proportional releases from Lake Powell based on inflows. Our results in RiverWare (CRSS) modeling show that Lake Powell is kept above power pool elevation longer than without the rule in place.

ABSTRACT:

The Upper Basin of the Colorado River, under current agreements, must prioritize releases between 7.48 and 9.0 million-acre feet of water releases to the Lower Basin each year. These releases control Lower Basin deliveries which, until recently, were at least 8.23 million-acre feet per year. This delivery represents downstream allocations for Mexico, Native American tribes, and Lower Basin states. This report presents a management alternative that allows for proportional releases from Lake Powell based on inflows. Our results in RiverWare (CRSS) modeling show that Lake Powell is kept above power pool elevation longer than without the rule in place.

ABSTRACT:

This Resource serves to explain and contain the methodology, R codes, and results of the PRISM freshwater supply key indicator analysis for my thesis. For more information, see my thesis at the USU Digital Commons.

Freshwater availability in the state can be summarized using streamflow, reservoir level, precipitation, and temperature data. Climate data for this study have a period of record greater than 30 years, preferably extending beyond 1950, and are representative of natural conditions at the county-level.

Oregon State University, Northwest Alliance for Computational Science and Engineering PRISM precipitation and temperature gridded data are representative of statewide, to county-level, from 1895-2015. These data are available online from the PRISM Climate Group. Using the R ‘prism’ package, monthly PRISM 4km raster grids were downloaded. Boundary shapefiles of Utah state, and each county, were obtained online from the Utah Geospatial Resource Center webpage. Using the R ‘rgdal’ and ‘sp’ packages, these shapefiles were transformed from their native World Geodetic System 1984 coordinate system to match the PRISM BIL raster’s native North American Datum 1983 coordinate system. Using the R ‘raster’ package, medians of PRISM precipitation grids at each spatial area of interest were calculated and summed for water years and seasons. Medians were also calculated for PRISM temperature grids and averaged over water years and seasons. For analysis of single months, the median results were used for all PRISM indicators. Seasons were analyzed for the calendar year which they are in, Winter being the first season of each year. Freshwater availability key indicators were non-parametrically separated per temporal/spatial delineation into quintiles representing Very Wet/Very High/Hot (top 20% of values), Wet/High/Hot (60-80%), Moderate/Mid-level (40-60%), Dry/Low/Cool (20-40%), to Very Dry/Very Low/Cool (bottom 20%). Each quintile bin was assigned a rank value 1-5, with ‘5’ being the value of the top quintile, in preparation for the Kendall Tau-b correlation analysis. These results, along with USGS irrigation withdrawal and acreage data, were loaded into R. State-level quintile results were matched according to USGS report year. County quintile results were matched with corresponding USGS irrigation withdrawal and acreage county-level data per report year for all other areas of interest. Using the base R function cor(), with the “kendall” method selected (which is, by default, the Kendall Tau-b calculation), relationship correlation matrices were produced for all areas of interest. The USGS irrigation withdrawal and acreage data correlation analysis matrices were created using the R ‘corrplot’ package for all areas of interest.

See Word file for an Example PRISM Analysis, made by Alan Butler at the United States Bureau of Reclamation, which was used as a guide for this analysis.

ABSTRACT:

This Resource serves to explain and contain the methodology and results of the Bureau of Reclamation (BOR) reservoir levels freshwater supply key indicator analysis for my thesis. For more information, see my thesis at the USU Digital Commons.

Freshwater availability in the state can be summarized using streamflow, reservoir level, precipitation, and temperature data. Climate data for this study have a period of record greater than 30 years, preferably extending beyond 1950, and are representative of natural conditions at the county-level.

BOR reservoir level data are representative of major statewide surface water supplies. These data are available online and were downloaded as comma-separated files from the BOR website. For BOR reservoir levels, 1 June was selected to represent each water year. This date is towards the beginning of the summer irrigation season and typically after spring snow melt. When 1 June data were missing, the closest available adjacent date in June or May was chosen.

Freshwater availability key indicators were non-parametrically separated per temporal/spatial delineation into quintiles representing Very Wet/Very High/Hot (top 20% of values), Wet/High/Hot (60-80%), Moderate/Mid-level (40-60%), Dry/Low/Cool (20-40%), to Very Dry/Very Low/Cool (bottom 20%).
Each quintile bin was assigned a rank value 1-5, with ‘5’ being the value of the top quintile, in preparation for the Kendall Tau-b correlation analysis. These results, along with USGS irrigation withdrawal and acreage data, were loaded into R. State-level quintile results were matched according to USGS report year. County quintile results were matched with corresponding USGS irrigation withdrawal and acreage county-level data per report year for all other areas of interest. Using the base R function cor(), with the “kendall” method selected (which is, by default, the Kendall Tau-b calculation), relationship correlation matrices were produced for all areas of interest. The USGS irrigation withdrawal and acreage data correlation analysis matrices were created using the R ‘corrplot’ package, for all areas of interest.

ABSTRACT:

This Resource serves to explain and contain the methodology and results of the United States Geological Survey (USGS) Hydro-Climatic Data Network 2009 (HCDN-2009) freshwater supply key indicator analysis for my thesis. For more information, see my thesis at the USU Digital Commons.

Freshwater availability in the state can be summarized using streamflow, reservoir level, precipitation, and temperature data. Climate data for this study have a period of record greater than 30 years, preferably extending beyond 1950, and are representative of natural conditions at the county-level.

A limited number of USGS HCDN-2009 streamflow gages were found that can represent statewide conditions, are unimpaired (which produces natural hydrographs), and have measurements that extend as far back as water year 1915, making them suitable for this study. USGS HCDN-2009 streamgage data for Utah are available and downloaded as tab-separated files from the USGS National Water Information System: Web Interface. To relate HCDN-2009 streamflow measurements as water supply indicators to every five-year annual USGS irrigation withdrawal and acreage data, they were summarized at water year timesteps. Annual summations of each daily average cubic feet per second measurement over each water year (1 OCT – 30 SEP) were calculated. This gives an annual volume of streamflow at each gage for each available water year. For the known periods of time when there were measurement malfunctions, USGS models and approves data to fill these gaps.

Freshwater availability key indicators were non-parametrically separated per temporal/spatial delineation into quintiles representing Very Wet/Very High/Hot (top 20% of values), Wet/High/Hot (60-80%), Moderate/Mid-level (40-60%), Dry/Low/Cool (20-40%), to Very Dry/Very Low/Cool (bottom 20%). Each quintile bin was assigned a rank value 1-5, with ‘5’ being the value of the top quintile, in preparation for the Kendall Tau-b correlation analysis. These results, along with USGS irrigation withdrawal and acreage data, were loaded into R. State-level quintile results were matched according to USGS report year. County quintile results were matched with corresponding USGS irrigation withdrawal and acreage county-level data per report year for all other areas of interest.

See Word file for an Example PRISM Analysis, made by Alan Butler at the United States Bureau of Reclamation, which was used as a guide for this analysis.

ABSTRACT:

This Resource serves to explain and contain the methodology, R codes, and results of the United States Geological Survey (USGS) irrigation freshwater withdrawals and irrigated acreages analysis for my thesis. For more information, see my thesis at the USU Digital Commons.

Statewide USGS estimates of total, ground, and surface withdrawal estimates are available one year in every five years for the period between 1950 and 2015. Statewide USGS estimates of total irrigated acres are available one year in every five years for the period between 1960 and 2015. USGS county-level irrigation withdrawal and acreage estimates, including sprinkler and flood irrigated acres, are available for the period between 1985 and 2015. Spatial delineations in Utah for this analysis include statewide, northern, and southern divisions (roughly splitting the state into two halves), the Great and Colorado River basins, and all 29 counties. Sub-state correlation analyses were only possible using USGS county-level data for the period between 1985 and 2015.
USGS irrigation withdrawal data were compiled for statewide and county-levels for all years available in Utah. Statewide irrigation estimates data were made available in the USGS national water use reports. County-level irrigation data for Utah were available from the USGS National Water Information System: Web Interface.

USGS irrigation data in Utah are limited. At most, 14 points of data can be analyzed. Using a density function, these data do not satisfy normal distribution assumptions. These data also have tied values (i.e., subsequent estimates in the analysis that are identical). Kendall’s Tau-b statistic handles situations such as these robustly. Kendall’s Tau-b is a non-parametric statistic that measures the correlation between ranked pairs, via their number of concordant and discordant pairs. Being a non-parametric test, values can be continuous, such as irrigation withdrawal estimates, or ordinal, such as the water-year quintile rank of freshwater availability key indicators discussed in the other resources connected with this resource.

If X and Y represent two datasets of interest, for instance total irrigation withdrawals and sprinkler irrigated acres, concordance and discordance are defined as follows:

Given the pairs (X_0,Y_0) and (X_1,Y_1 ):
Concordant pair= (Y_1-Y_0)/(X_1-X_0 )>0
Discordant pair= (Y_1-Y_0)/(X_1-X_0 )<0
Tied pair= (Y_1-Y_0)/(X_1-X_0 )=0

These equations reveal how well the pair of data follow each other, indicating a whether a significant relationship exists. The result of Kendall’s Tau-b test is a number between -1 and 1. A value of 1 means there is perfect positive association, or agreement i.e., when the pair are sorted in descending order according to one parameter, their ranks, or order, are mirrored. Conversely, a value of -1 shows perfect negative correlation, or inversion, i.e., as one increases or decreases, the other does the opposite.
The formula in R software for Kendall’s Tau-b (Yao 2021) is:
τ_b= (n_c-n_d)/(√(N_1 ) × √(N_2 ))
where
n_c=number of concordant pairs
n_c=number of discordant pairs
N_1=number of data pairs not tied in a target feature
N_2=number of data pairs not tied in the other target feature
To test the significance of Kendall Tau-b results, where ‘n’ is the number of observations, the following formula can be used to obtain a Z-score that can be referred to the normal distribution:

Z= (3τ_b*√(n(n-1)))/√(2(2n+5))

Once a Z-score is obtained, it can then be mapped to a p-value from the normal distribution to check for statistical significance. A Z-score of ≥1.96 denotes that with 95% confidence a statistically significant relationship exists. Using a Z-score of 1.96, along with 14, 12 and 7 as the numbers of observations, τ_b is equal to 0.39, 0.43 and 0.62 respectively. These thresholds were chosen as indicating that, with 95% confidence, a significant relationship exists between the pair of parameters.

For sub-state analyses’ spatial delineations, Konieczki and Heilman (2004), Ramsey et al. (2009), and Wallace et al. (2012) were used as references to break up the Great Basin and Colorado River basins according to counties in Utah (see thesis for more information). Northern and southern area counties were split according to visual inspection, each representing roughly half of the state (see thesis for maps of these areas).

The R programming software (R version 4.1.0 (2021-05-18) -- "Camp Pontanezen") was used to analyze statewide and county-level USGS irrigation data for Utah, utilizing Kendall’s Tau-b test to discover significance of relationships between parameters. R software produces matrices of Kendall Tau-b results. Using the R ‘corrplot’ package, the lower half of results matrices were plotted.

ABSTRACT:

This collection comprises the data, codes, analyses and results for my thesis. See the thesis in USU Digital Commons for more information.

PUBLIC ABSTRACT

According to the United States Geological Survey, irrigation represents about 80% of the freshwater withdrawn in Utah. On average, Utah is the second driest state in the U.S., with drier conditions projected throughout the century. Periods fluctuating between drought and flood are typical in the region. Understanding what factors affect Utah’s largest water user can inform sustainable irrigation practices, which could lead to conservation of Utah’s vital surface and groundwater. National, regional, and state analyses have identified total irrigated acreages, more efficient irrigation technologies, and freshwater availability as significant factors of irrigation withdrawals. This study sought to bring these findings up to date in Utah, and validate them at state, sub-state, and county levels. Utilizing Kendall’s Tau-b correlation test, factor relationships were assessed between the survey’s irrigation withdrawal and acreage data, as well as water year, season, and monthly freshwater availability key indicators including natural stream flows, reservoir levels, precipitation, and ambient temperatures. At the state level, no significant correlations were found between total irrigation withdrawals and total, or sprinkler, irrigated acres. This could indicate that practices such as fallowing fields, or converting to sprinkler systems, may not significantly reduce irrigation withdrawals. Relatively few significant results were found between irrigation withdrawals and water year key indicators, suggesting that historical infrastructure and practices have been adequate at overcoming annual freshwater availability fluctuations. Total and surface irrigation withdrawals were significantly negatively correlated with ground withdrawals, suggesting that conjunctive management principles have played a key role mitigating annual freshwater availability fluctuations. Groundwater stores have been overdrawn in many areas of the state, and this practice may not be sustainable under projected drier conditions. With further analyses, some significant correlations between irrigation withdrawals, and early months and seasons’ precipitation and ambient temperatures may be utilized in irrigation demand projections. Though sub-state results were similar with statewide results, county results varied, demonstrating the importance of finer scales of analyses and localized decision making. It also highlights areas that may be more affected by freshwater availability, as well as the many unique irrigation withdrawal reduction opportunities that exist within areas of Utah.