07-13, 11:25–11:55 (America/Chicago), Grand Salon C
Recharging ground aquifers is an urgent task for improving groundwater sustainability in California. Geophysical data can provide a capability to image the subsurface where the major data gap lies. However, neither data nor analytic tools required to derive subsurface information is readily accessible. We present an interactive web application that utilizes a public database, GIS capabilities and directly integrates Jupyter Notebooks and Python packages from researchers to guide recharge site location. Our demonstration showcases how this technology can contribute to improving groundwater recharge in California and how integrating the research knowledge directly into a web application can increase the impact.
California's Central Valley is one of the world's most productive farmland, but the region faces a serious threat to groundwater sustainability due to population growth and climate change. Recharging ground aquifers is essential to address this challenge, however a major data gap exists in the subsurface. Geophysical data can provide crucial information about the subsurface, but neither the data nor the analytic tools required to derive subsurface information is readily accessible to those working on the recharge problem.
In this talk, we will present our development of a web-application and companion public database for accelerating groundwater recharge in California, which is a part of the Sustainability Accelerator Project funded by Stanford Doerr School of Sustainability. Our application uses electrical resistivity data obtained from electromagnetic geophysical surveys, as well as ancillary data from driller's logs (containing information about sediment/rock) and water level/quality measurements, to create 2D maps of recharge metrics. These maps guide the location of recharge sites, and the public resistivity and ancillary data are compiled into an online database using Redivis and displayed in a custom web-application. The application provides project partners the ability to utilize research codes without requiring knowledge of Python, and is flexible to allow updates by researchers to support rapid changes and feedback from partners to meet their specific needs for a recharge site location.
The development of the web-application was a collaborative effort between academic researchers and software engineers at Curvenote. The application enables direct use of research code by front-facing practitioners tackling the recharge problem in California. We utilized open source Python packages, to create Jupyter Notebooks that can execute each stage of the workflow.
Dr. Kang completed his PhD in Geophysics at University of British Columbia, Canada, in 2018. His thesis work focused on electromagnetic imaging and its application to mining problems. Currently, he is a Postdoctoral Researcher in the Geophysics Department at Stanford. His research focus is on maximizing the value of sensor data for advancing groundwater science and management. He is a co-creator of an open-source geophysical software, SimPEG.