Process diagnostics of snowmelt runoff in global hydrological models: Part II – Are more complex models better?

Abstract. The added value of increased process complexity has long been a central yet unresolved question in hydrological modeling, particularly for snowmelt runoff (SMR), where multiple physical processes interact in complex ways. To address this, we develop a Tree-Based Model Complexity Scoring (TBMCS) method to systematically quantify the complexity of snow-related processes across 13 global hydrological and land surface models. Then by using SMR characteristics, i.e., total runoff (Q_sum), peak discharge (Q_max), and centroid timing (CTQ), as integrated indicators to evaluate these models, we systematically quantify the linkage between model complexity and model performance in 1,513 snow-dominated basins. Results show that (1) models differ substantially in their representation of physical processes, with the largest divergence in melting process treatments, followed by sublimation, interception and rainfall-snowfall partitioning processes. (2) While the model performance for Q_sum >and Q_max shows limited sensitivity to model complexity, CTQ performance exhibits a positive correlation with model complexity (r = 0.56, P < 0.05) particularly in highly complex basins, highlighting the role of process complexity in stern conditions. (3) We also find that the model performance depends more on systematic and balanced representations of key processes than on complexity alone. High-complexity models with well-integrated processes (e.g., DBH) show high robustness, whereas models lacking critical modules exhibit poor accuracy, and even simpler models with well-designed modules (e.g., PCR-GLOBWB) can perform robustly. This study provides a quantitative framework for assessing model complexity and emphasizes that systematic process design is critical for improving SMR simulations in complex environments, offering guidance for future model development.