Recently, deep learning (DL) has emerged as a revolutionary tool for transforming industries and scientific disciplines. We argue that DL can offer a complementary avenue toward advancing hydrology. New methods are being developed to interpret the knowledge learned by deep networks. We argue that open competitions, integrating DL and process-based models, more data sharing, data collection from citizen scientists, and improved education will be needed to incubate advances in hydrology.

3D movement of an aquifer system with stress-dependent storage in response to one or more pumping wells is simulated using a new package for MODFLOW, NDIS. Results indicate horizontal movement is consistent with the analytic solution, and vertical movement is smaller and more sensitive to pumping rates and time than that simulated by MODFLOW-SUB. The simulation shows that if the total pumpage is the same, pumping from a single well may induce more land subsidence than pumping from multiple well.