Using Ensemble-based Forecasts as an Irrigation Planning Aid

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Monday, 3 February 2014: 11:00 AM
Room C209 (The Georgia World Congress Center )
Emily Christ, Georgia Institute of Technology, Atlanta, GA; and P. J. Webster and G. Collins

Recent droughts and the continuing water wars between the states of Georgia, Alabama and Florida have made agricultural producers more aware of the importance of managing their irrigation systems more efficiently. Many southeastern states are beginning to consider laws that will require monitoring and regulation of water used for irrigation. In fact, last year, Georgia suspended issuing irrigation permits in some portions of the southwest part of the state to try and limit the amount of water being used in irrigation. However, even in southern Georgia, which receives on average between 23 and 33 inches of rain during the growing season, irrigation can significantly impact crop yields. In fact, studies have shown that when fields do not receive rainfall at the most critical stages in the life of cotton, yield for irrigated fields can be up to twice as much as fields for non-irrigated cotton.

This leads to the motivation for this study, which is to produce a forecast tool that will enable producers to make more efficient irrigation management decisions. First, we will calculate the forecast error associated with ensemble-based forecasts (here the ECMWF model) for a portion of the agricultural region in southern Georgia. We will calculate errors based on observations from the Georgia Automated Environmental Monitoring Network (www.georgiaweather.net). Once the errors have been calculated, we will apply a q-to-q bias correction technique to the data in an effort to improve the precipitation forecasts over the selected region. Once we have applied the bias corrections, then we will use the check-book method of irrigation scheduling to determine the probability of receiving the required amount of rainfall for each week of the growing season. Once established, this tool will allow producers to make more informed decisions concerning irrigation water use. The techniques used here suggest how probabilistic forecasts may be used to optimize agricultural practices in a very general sense.