Proceedings of the 2019 Mississippi Water Resources Conference

Year: 2019 Authors: Wilson J., Painter J., Torak L., Kress W.

Water use is a critical and often uncertain component of quantifying the water cycle and securing reliable and sustainable water supplies. Recent water-level declines in the Mississippi Alluvial Plain, especially in the Mississippi Delta, pose a threat to water sustainability. Currently, the U.S. Geological Survey (USGS) Water Availability and Use Program Mississippi Alluvial Plain Water Availability Study is developing a hydrologic decision-support system to help manage water resources in this area, one of the most productive agricultural regions in the Nation.

To improve water-use estimates needed as input to the hydrologic decision-support system, an aquaculture and irrigation water-use model, Aquaculture and Irrigation Water-Use Model (AIWUM) 1.0, was developed and compared to other reported and estimated irrigation water-use values within the study area for 1999-2017. AIWUM 1.0 is primarily driven by annual flowmeter data provided by the Mississippi Department of Environmental Quality's Delta Voluntary Metering Program as well as historical flowmeter data from the Yazoo Mississippi Delta Joint Water Management District. The model quality incorporates remote sensing and flowmeter data and outputs monthly estimates at a fine spatial (100 meters) and temporal (monthly) resolution used directly in the Mississippi Embayment Regional Aquifer Study (MERAS) groundwater model 2.1.

Results indicate annual total irrigation water-use estimates ranged from about 3 to 9 billion gallons per day and a majority of the irrigation water use was applied to soybeans (52%), followed by aquaculture and rice (26%), other crops (10%), corn (8%), and cotton (4%). Comparisons indicate that annual total irrigation water-use estimates from AIWUM 1.0 generally were smaller than all other sources of water-use data, but within the Mississippi Delta the total annual water use is approximately equal between AIWUM 1.0 and the MERAS groundwater model 2.1.

This and other models included in the decision-support system are developed in Python and interconnected, resulting in a dynamic, instead of the traditional static, model. This approach allows models to quickly evolve as better data are available (e.g., additional flowmeter data, improved remote sensing data), providing the current best estimates of water resources to cooperators and the public. Future planned work includes (a) determination of irrigation rates based on machine learning and geostatistical methods using daily precipitation and temperature data and regional irrigation water-use from flowmeter data, (b) improved classification of crop type and irrigated versus unirrigated lands, (c) back- and forecasting estimates from 1890-2100, and (d) establishment of a public-facing, real-time irrigation water-use model.