Data‐Driven Estimation of Time‐Variable Catchment Transit Time Distributions

Abstract Transit Time Distributions (TTDs) are widely used to summarize catchment behavior and are useful for understanding solute transport processes. Most methods for estimating TTDs from tracer data involve imposing strong model assumptions, which may affect the results. On the other hand, the development of data‐driven approaches has been severely limited by the small size of tracer data sets. Here, we introduce a novel data‐driven model for estimating TTDs, integrating concepts from traditional TTD modeling, survival analysis, and mixture models. The model leverages both hydrologic and tracer data to infer time‐variable TTDs. We validate the model using virtual benchmark data sets designed to reflect realistic flow and transport dynamics. Results show that the model's tracer data requirements are met by existing long‐term monitoring campaigns or high‐frequency in situ measurements. Our findings highlight the potential of data‐driven methods to enhance TTD estimation and offer new opportunities for advancing catchment‐scale understanding of flow and transport processes.