资源环境科技发展态势分析平台

One of the primary means of addressing the depletion of aquifers supporting irrigated agriculture is to reduce pumping. In this work, we address the key question of how much data is needed to reliably predict the impact of pumping reductions. Previous studies have demonstrated the effectiveness of a simple water balance‐based approach for predicting the near‐term impact of changes in total pumping on changes in average water levels for areas ranging in size from 256 to 21,600 km² in the High Plains aquifer (HPA) in the state of Kansas in the central U.S. This method shows considerable promise as a management tool in the Kansas HPA and potentially other highly stressed aquifers. However, one characteristic that sets the Kansas HPA apart from many other aquifers is an abundance of data on both water levels, from a decades‐old annual water level measurement program, and annual pumping volumes, due to reporting requirements and regulatory oversight. Most regions will have considerably less information. This study investigates the impact of more limited data availability through random subsampling of the water level and water use data sets from Groundwater Management District 4 (GMD4) in northwest Kansas and GMD5 in south‐central Kansas. The results indicate that accurate estimates of net inflow and specific yield, the key parameters needed to predict near‐term aquifer responses to proposed pumping reductions, can be obtained with much sparser water level and water use data than are available in the Kansas HPA, as long as the data are not systematically biased.