Incorporating domain knowledge in estimating subsurface permeability using machine learning methods
Abstract
Subsurface permeability is a key parameter in watershed models that controls the contribution from the subsurface flow to stream flows, yet it is difficult and expensive to measure directly at the spatial extent and resolution required by fully distributed watershed models. The wide availability of stream surface flow data and remote-sensing data products, compared to groundwater monitoring data, provides new data sources for inverse modeling to infer the soil and geologic properties using integrated surface and subsurface hydrologic models. We have successfully applied deep neural networks (DNNs) to develop inverse mapping that captures complex, highly nonlinear relationships between model parameters and observed system responses across a number of watersheds within the United States. Given the increasing computational cost of fully distributed, mechanistic watershed models, we found that domain knowledge gained from multi-step sensitivity analyses can effectively reduce the dimensionality and hence size of ensemble forward simulation required for training accurate DNNs. Instead of mapping all observations to all model parameters to be estimated, we strategized the estimation by systematically understanding the spatial and temporal information content in multiple types of data for each parameter. Using multi-year streamflow observations at the watershed outlets, we found that including a dry-year streamflow response is more important than the wetter years for estimating subsurface properties. The evapotranspiration (ET) data products from remote sensing may not add additional information to watershed parameter estimation when streamflow observations are available. However, they could still be valuable for ungaged watersheds. Our studies highlight the importance of developing and incorporating domain knowledge when applying machine learning methods to assist watershed modeling, shedding new light on broader applications of machine learning methods in various Earth science domains.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFM.H33B..03C