SIMPLE WATER-BALANCE MODELING APPROACH FOR HIGH PLAINS AQUIFER MANAGEMENT

A water-balance approach based on annual groundwater-level change and water use data can be used as a simple mathematical model to develop management plans for seasonally pumped aquifers that support irrigated agriculture. This method enables the pumping reduction required to obtain stable water levels or a targeted reduction in the water-level decline rate to be readily determined. The approach utilizes the theoretically based linear relationship between annual water-level changes and reported water use (Butler et al., 2016) found for subareas of the High Plains aquifer in Kansas. The approach is analogous to numerical modeling except that the cell sizes are large and the data components few, which allows rapid assessment of an aquifer’s future over the near term (years to a couple decades). In contrast to the multiple inputs needed for the standard numerical model, here only water-level and water-use data are required. Application to the groundwater management districts (GMDs) across the High Plains aquifer (HPA) of Kansas shows the spatial variability of pumping reductions needed to achieve stable water levels within the GMDs. The average reductions for stable water levels for the five GMDs range from 1.5% for GMD2 (the most easterly GMD of the HPA in Kansas) to 34% for GMD3 (southwest Kansas) for 2005-2015 data. Analysis for township subareas of Equus Beds GMD2 gives percent reductions in water use of from near zero to about 20% based on 2005-2014 data. Climate variation scenarios can be incorporated into the simple approach by using correlations between water use and a climatic index for the subareas to estimate the change in average pumping for a particular change in climate. The change in average pumping can then be used in the water balance for the subarea to determine the revised pumping reduction needed for stable water levels. Measures of precipitation such as the standardized precipitation index, PRISM, or radar precipitation work well for these correlations over the HPA of Kansas.