T71 Addressing Complex Problems in Hydrogeology with Big Data and Machine Learning

Monday, 11 October 2021
8:00 AM - 12:00 PM
D135

MAPPING GROUNDWATER USE WITH SATELLITE SENSOR FUSION AND MACHINE LEARNING

Paper number: T71-12
Program: Technical Sessions
Time: 10:45 AM – 11:05 AM

Authors

Abstract

Changes in groundwater storage due to excessive withdrawals threatens food, water and energy security. Globally, groundwater withdrawals are one of the primary drivers creating changes in storage. However, very few regions of the world monitor groundwater withdrawals at the local scale necessary to implement sustainable management solutions. Multiple satellite sensors are sensitive to different elements of groundwater flux, yet integrating these sensors to predict withdrawals is a challenge due to a lack of water use data, and complexities in the theory relating sensor measurement to withdrawals. Machine learning methods have been shown to be effective in relating earth science observations to various processes and are a promising technique for improving our ability to model and monitor groundwater systems. Here, we present a machine learning framework for integrating multiple satellite sensors and derivative data products related to water use. Using primarily data from Landsat, Sentinel-1, GRACE, GRACE-FO, and MODIS, as well as products derived from sensors on these satellites, we are able to estimate groundwater withdrawals at the local (1-5 km resolution) scale. The satellite products measure different components of the water balance at resolutions ranging from 10 m to 100s of km, and are integrated using data driven algorithms including random forests and neural networks. We are currently implementing these models in portions of three regional aquifer systems: the Mississippi Alluvial Aquifer in Mississippi, Arkansas, Missouri, and Louisiana; the High Plains Aquifer, in Kansas; and the Basin and Range Aquifer System, in Arizona. Complexity in irrigation practices and the aquifer itself results in varying model quality with r2 ranging from 0.5 to 0.9 across these study areas. Ultimately, we plan to extend these withdrawal estimates to the conterminous United States, producing high resolution, annual estimates of groundwater use.

Geological Society of America Abstracts with Programs. Vol. 53, No. 6

doi: 10.1130/abs/2021AM-369568

© Copyright 2021 The Geological Society of America (GSA), all rights reserved. Permission is hereby granted to the author(s) of this abstract to reproduce and distribute it freely, for noncommercial purposes. Permission is hereby granted to any individual scientist to download a single copy of this electronic file and reproduce up to 20 paper copies for noncommercial purposes advancing science and education, including classroom use, providing all reproductions include the complete content shown here, including the author information. All other forms of reproduction and/or transmittal are prohibited without written permission from GSA Copyright Permissions.