Location: Forage and Livestock Production ResearchTitle: Improving regional groundwater flow models for the Texas portion of the Ogallala Aquifer Author
|Singaraju, Sreeram - Texas Tech University|
|Uddamari, Venki - Texas Tech University|
|Bailey, Ryan - Colorado State University|
Submitted to: Miscellaneous Publishing Information Bulletin
Publication Type: Abstract Only
Publication Acceptance Date: 7/17/2017
Publication Date: 6/13/2017
Citation: Singaraju, S., Uddamari, V., Gowda, P.H., Bailey, R. 2017. Improving regional groundwater flow models for the Texas portion of the Ogallala Aquifer[abstract]. 2017 UCOWR/NIWR Conference, June 13-15, 2018, Fort Collins, Colorado. Session 9, p. 32.
Interpretive Summary: Abstract Only.
Technical Abstract: The Ogallala aquifer is the largest aquifer underlying parts of eight states (Texas, New Mexico, Oklahoma, Colorado, Kansas, Nebraska, South Dakota and Wyoming) in the United States. While about 20% of the aquifer lies in Texas, the state uses over 5 Million acre-feet of water per year to be one of the major producers of winter wheat, sorghum, corn, and cotton in the nation. Several regional groundwater flow models have been developed to simulate the flow of water in the Texas portion of the Ogallala aquifer. However, most of the existing models were developed using a spatial resolution of 1 mile X 1 mile which is not suitable to study intra-section interferences between wells which is becoming a serious issue as declining water levels in the aquifer is causing farmers to use multiple wells to meet their groundwater needs. Periodic updating of groundwater flow models also allows for inclusion of new data and better model parameterizations of ET, recharge rates and other hydrological processes of significance. The primary goal of this study is to refine existing regional groundwater flow models using a spatial resolution of 1 km X 1 km. The refined model will provide us with an opportunity to incorporate new information such as data from new wells and other physical properties of the aquifer and incorporate better descriptions of hydrologic processes. To this end, the models will then be coupled with Decision Support System for Agrotechnology Transfer (DSSAT) and Soil and Water Assessment Tool (SWAT) to better characterize crop water requirements (which are supplied through groundwater pumping) and spatially variable recharge rates. The refined models will not only allow us to capture the interactions between closely spaced wells but is also likely to provide a better picture of water availability across the aquifer in Texas and as such foster scientifically-credible groundwater resources management in this groundwater dependent region.