Aquaculture and Irrigation Water Use Model (AIWUM) 2.0 input and output datasets

Datasets are inputs and outputs of Aquaculture and Irrigation Water Use Model (AIWUM) 2.0.  AIWUM 2.0 employs remote sensing data sets and machine learning utilizing Distributed Random Forests, an ensemble machine learning algorithm to estimate annual and monthly groundwater use for irrigation and aquaculture (2014–20) throughout this region at 1 km resolution, using annual pumping data from flowmeters in Mississippi and real-time flowmeters in Arkansas, Louisiana, Mississippi, Missouri, and Tennessee. Aquaculture and irrigation estimates contained in this data release are representative of groundwater withdrawal for six different categories: aquaculture, cotton, corn, rice, soybeans, and other crops. 

Model results are intended to be used primarily as input to a groundwater model of the Mississippi River Valley alluvial aquifer. The groundwater model has been developed for the Mississippi Alluvial Plain (MAP) Regional Water Availability Study. Model results are also useful for general education, and to inform research, discussion, and decision making for the scientific and management communities