Mapping anthropogenic water cycle impacts in a future climate: A global digital twin for scenario-driven exploration
Description
This project develops an emergent constraint emulator for future changes in water storage estimates based on the available historical record of GRACE and GRACE-FO measurements.
Detailed example
Predictions of liquid water equivalent thickness in centimeters.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
An emulator to predict groundwater will reduce computation time relative to larger physical models. Additionally, predicting future changes in water storage estimates will better inform water managers for upcoming periods of water surplus or scarcity. Current results suggest we can predict 2-3 months into the future, and future work intends to identify the maximum number of months we can predict.
Controls / human review
ATO: Not reported; PIA: Not published