Machine Learning Integration Strategies for Process-based Ecohydrological Modeling: Addressing Epistemic Uncertainties of Water Mixing Dynamics in Tree Water

Compared to process-based models (PBMs), higher prediction accuracy of machine learning models (MLMs) has been repeatedly reported in ecohydrological research. This might indicate the higher efficiency of data-driven MLMs for extracting and generalising information from the data, especially when traditional PBMs are often challenged by epistemic uncertainties in process representation. To preserve ‘modelling as a learning tool’, integrating MLMs into PBMs is a promising avenue to leverage MLMs for data assimilation, and PBMs for holistic explainability of processes across the Critical Zone (i.e., the thin crust of the Earth including vegetation).
One example of an ecohydrological process with high epistemic uncertainties is the mixing mechanism of root uptake water from soils by trees. Due to limited process understanding together with high uncertainties of isotope measurements in trees, usually mixing dynamics in tree water storage in ecohydrological models show poor representation.
Here, we use data from a comprehensive monitoring campaign which has been conducted during the growing season of 2020 at a plot site with two willow trees and grass in southeastern Berlin, Germany, including daily or sub-daily in-situ measurements of hydrological characteristics and stable water isotopes in precipitation, soils, vegetation, and neighboring open water bodies. Using the data, a baseline ecohydrological PBM (EcoHydroPlot) was used to simulate water flow and isotope dynamics across the Critical Zone. In addition, MLMs with different strategies for integration were applied: Firstly, as an additional module to the PBM, a post-hoc result-analyzing MLM was trained with the error of the PBM. Secondly, a hybrid model was built that replaces equations for mixing mechanism of root-uptake water in PBM with a data-driven ML algorithm. An eXplainable AI (XAI) tool was applied to help understand uncertainties in the PBM and process representation in MLM.
By comparing these approaches using different criteria of prediction accuracy and interpretability, we identified an optimal strategy for leveraging MLM capabilities within PBM frameworks in addressing the process of tree water mixing with high epistemic uncertainties, potentially extending the concept of ‘modeling as a learning tool’ to MLM-integrated PBMs.