T17 Remote Sensing Applications in Hydrology

Sunday, 22 September 2019
8:00 AM - 12:00 PM
Room 104AB, West Building

ESTIMATING GROUNDWATER EXTRACTION WITH INTEGRATED SATELLITE DATASETS AND MACHINE LEARNING

Paper number: T17-5
Program: Technical Sessions
Time: 9:05 AM – 9:20 AM

Authors

Abstract

Groundwater is a key component of the water cycle, and monitoring groundwater withdrawals is a crucial step in water resources management and mitigating storage loss. However, very few regions of the U.S. and world monitor their groundwater withdrawals at the local scale necessary to implement sustainable management solutions. In this study, we use data from various satellite sensors that measure different components of the water balance at resolutions ranging from 10 m to 100s of km. Since the measurements these satellites collect are related in different ways to the water balance, combining them can result in a powerful monitoring tool. However, due to their varying resolutions and the complexity of combining them in a water balance, approaches to use them to estimate groundwater withdrawals have been limited. Here we overcome these limitations using a hybrid water balance/machine learning approach. Using this approach, we estimate groundwater withdrawals at ~1 km resolution. Our study area is the High Plains Aquifer in Kansas, where withdrawals have depleted the aquifer substantially.

We use a combination of evapotranspiration estimates from MODIS, precipitation estimates from TRMM, GPM and PRISM, and crop type and land use estimates from NASS as proxies for water demand, as well as surface water extent maps with weighted spatial filters to estimate quantity of and proximity to surface water. When combined in a machine learning framework, these variables can predict groundwater withdrawals at the local scale. We use the random forests approach to assess the impact of each variable on the outcome and determine the mechanisms driving groundwater demand, and find that land use as well as proxies for surface water availability are strong predictors of groundwater demand. This method can be used to estimate groundwater withdrawals in regions with limited data, or to fill in temporal or spatial gaps in areas with moderate data. Implementing this method to estimate groundwater could enable water managers to proactively implement sustainable solutions.

Geological Society of America Abstracts with Programs. Vol. 51, No. 5

doi: 10.1130/abs/2019AM-340862

© Copyright 2019 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.