Multifactorial Principal‐Monotonicity Inference for Macro‐Scale Distributed Hydrologic Modeling

Macro-scale distributed hydrologic modeling advanced hydro-system understandings and scientized relevant human activities. However, it faces challenges of hydrometeorological heterogeneities, parametric interactions, data uncertainty, computational expensiveness, and other complexities, especially over cold regions with intense climatic changes. As an effort to address them, a multifactorial principal-monotonicity inference (MFPMI) method is developed through integrating, extending, or improving climate classification, hydrologic modeling, and sensitivity analysis. MFPMI is applied to an undammed macro-scale high-latitude cold-region watershed, Athabasca River Basin (ARB) in Canada. MFPMI mitigates the underestimation of climatic impacts on streamflows in process-based models, hydrologic classification, large-scale hydroclimatic data deconstruction, parametric-interaction neglection, and climatic homogenization; its superiority is particularly evident for highly heterogeneous climates. Dominant climatic impacts on ARB streamflows of various regimes increase from tributaries to the mainstem and decrease from up- through down- to mid-stream catchments, possibly due to the offset effects of non-climatic factors (e.g., vegetation and soil). The impacts also decline with streamflow magnitudes, and vary with seasons, spatial scales and lead months rather than temporal resolutions. Streamflow magnitudes, catchments and metrics differ compositions of the climate conditions explaining cross-scale uppermost discharge variations. In spite of this, streamflow increases with temperature and precipitation, and headwater climates play part of dominant roles in forcing discharges throughout ARB. Accuracy metrics differentiate parameters and accordingly structures of MFPMI models, and hydroclimatic data uncertainty is high for high flows, fine temporal scales or low climatic impacts, increasing uncertainty in hydrologic simulations and climatic-impact estimates. This study helps advance modeling and understandings of macro-scale cold-region hydrology.