Chris Soulsby

Learning from model failure: insights from an AI/SHAP-based temporal error decomposition of tracer-aided ecohydrological modelling

Abstract. Process-based models (PBM) have served as testing grounds for hypotheses, being falsified and refined through model evaluation based on temporally complex PBM errors. Yet, conventional evaluation practices (e.g., performance metrics, visual inspection across time series...