Augmenting Sparse Groundwater Level Data with Earth Observations vis
Machine Learning
- Norm Jones,
- Steven Evans,
- Gustavious Williams,
- Jim Nelson,
- Daniel Ames
Norm Jones
Brigham Young University
Corresponding Author:njones@byu.edu
Author ProfileAbstract
Groundwater development will provide a more stable water source and
enhance food security. Sustainable groundwater development requires
collecting and analyzing data produced at global and national levels and
disseminating that data and knowledge to end users such as States, NGOs,
municipalities, businesses, and agropastoralists in a format that is
useful for planning and decision-making. In developing countries,
analyzing in situ measurements to de can be challenging due to sparsity
of data and lack of tools and expertise. To address these problems we
have developed a web-based geospatial tool that ingests in situ water
level measurements and performs temporal and spatial interpolation to
build interactive animated maps, time series plots, and long-term
aquifer depletion curves. We use machine learning to find correlations
among Earth observation data, such as precipitation or soil moisture,
with water level data and perform more accurate interpolation. This
approach ensures that scarce in situ data are used as effectively and
accurately as possible. This tool helps water managers gain a better
understanding of groundwater resources and determine how aquifers are
responding to groundwater development, droughts, and climate change.