Water Resources Research Act Program

Details for Project ID 2015OK320B

Threats to the Lugert-Altus Irrigation District: Untangling the Effects of Drought, Land Use Change, and Groundwater Development

Institute: Oklahoma
Year Established: 2015 Start Date: 2015-03-01 End Date: 2016-02-29
Total Federal Funds: $25,000 Total Non-Federal Funds: $50,000

Principal Investigators: Tyson Ochsner, Erik Kruger, Yohannes Yimam

Project Summary: As of October 1, 2014, Lake Altus-Lugert, the primary water supply for the Lugert-Altus Irrigation District (LAID) in southwest Oklahoma, was only 10% full, was recovering from a golden algae bloom which killed all fish in the lake, and has not contained enough water to produce an irrigated cotton crop since 2010. Severe drought in 2011 and 2012 played a major role in the demise of the lake, but local residents suspect upstream land use change and groundwater development may have contributed. Furthermore, according to the Southern Climatic Impact Planning Program (SCIPP), the climate of the region is already changing in both precipitation and evapotranspiration, and the region may face increased frequency and severity of drought. The relative importance of these various contributing factors is unknown, and the future of the lake, the irrigation district, and the Altus community which depends on both is highly uncertain. There is a pressing need for research to better understand the drivers of change in this regionally-significant watershed. Objectives: The long term goal of this research group is to identify strategies by which the community of Altus can successfully adapt to changing water availability. The objective of this proposal is to evaluate the effects of drought, land use change, and groundwater development on streamflow into Lake Altus-Lugert. The team is well qualified to lead this project as evidenced by strong backgrounds and achievement records in hydrology and soil science related research. In addition, we have already begun preliminary work for the project by compiling and analyzing hydro-meteorological data for the region, characterizing land use change within the watershed, and initiating a working relationship with stakeholders. The specific aims for this project are to: Specific Aim #1. Quantify changes in weather, land use, number of groundwater wells, and streamflow in the Lake Altus-Lugert watershed from 1970-2014. A combination of parametric and non-parametric procedures will be used to identify and quantify statistically significant trends in precipitation, temperature, land use (e.g. planted acres), number of wells, and streamflow for the 45-yr period from 1970-2014 and also three sub-periods. Specific Aim #2. Develop a statistical model describing the impacts of weather, land use, and number of groundwater wells on streamflow in the Lake Altus-Lugert watershed. Proven multiple regression approaches for modeling annual streamflow based on precipitation, temperature, and number of groundwater wells will be adapted to include the possible effect of land use changes, e.g. significant decreases in planted acres within the watershed. Methods: In Specific Aim #1, we will use the non-parametric Kendall’s Tau statistic to identify trends in environmental variables and Kendall’s slope to quantify changes (Smith and Wahl, 2003). Trends in anthropogenic factors will be assessed using linear regression, with significant changes quantified by slopes of regressions. These analyses will build the foundation for statistical models to assess the relative importance of natural and anthropogenic impacts on streamflow in the watershed. In Specific Aim #2, we will develop a multiple regression model for log-transformed annual streamflow with candidate input variables of average watershed precipitation and temperature, number of groundwater wells, and planted acres. The 95% confidence intervals on the coefficients for each variable will be examined to identify any coefficient that is not statistically significant. Non-significant variables will be removed from the model, and the regression will be repeated to arrive at a final model which retains only statistically significant terms.