Understanding Parameter Uncertainty in Community Land Model Version 5 with Machine Learning for Enhanced Hydrological Predictions

Presented by Kachinga Silwimba, Computing PhD student, Data Science emphasis

Abstract: The terrestrial water cycle plays a pivotal role in Earth's climate system, with soil moisture acting as a critical variable influencing land-atmosphere interactions, ecosystem dynamics, and hydrological processes. However, accurate representation of soil moisture in Land Surface Models (LSMs), such as the Community Land Model Version 5 (CLM5), remains a challenge due to uncertainties in soil hydraulic parameterization. These uncertainties propagate errors in hydrological variables, including total water storage (TWS), evapotranspiration, and leaf area index (LAI), limiting predictive capabilities, particularly in ungauged basins. This dissertation systematically evaluates and mitigates parameterization uncertainties in CLM5 by employing empirical orthogonal function (EOF) analysis, self-organizing maps (SOM), and evidential deep neural networks (EDNNs) to enhance hydrological simulations. First, EOF analysis is used to assess the influence of soil hydraulic parameters on soil moisture variability across the Contiguous United States (CONUS), identifying dominant spatial and temporal patterns while distinguishing parameter-driven effects from climatic influences. Next, an integrated EOF-SOM approach clusters soil moisture patterns based on nonlinear relationships, refining parameter calibration strategies for diverse climatic and geographic regions. To address uncertainty quantification, EDNNs are applied to model LAI, capturing both epistemic and aleatoric uncertainties in CLM5 simulations, thereby improving the robustness of vegetation and water cycle predictions. Finally, the EDNN framework is extended to predict hydrological variables in ungauged basins, leveraging perturbed parameter ensembles to enhance TWS forecasts in data-scarce regions. Collectively, these methodological advancements contribute to improving soil moisture representation in LSMs, providing a scalable framework for hydrological predictions, climate adaptation strategies, and water resource management. By integrating statistical techniques and machine learning, this research enhances the accuracy of CLM5 simulations, bridging gaps between model uncertainty and real-world hydrological processes.

Committee: Dr. Alejandro Flores (Advisor), Dr. Jodi Mead, Dr. Linnia Hawkins, Dr. David Gagne