Warmer winter under climate change would reshape the winter subsurface drainage pattern in Eastern Canada

Abstract

Subsurface drainage is a key loss pathway for water and nutrients from agricultural land in Eastern Canada. Winter is a dominant period of subsurface drainage and nutrient loss in cold climates. Under climate change, however, future winter drainage patterns may change significantly due to reductions in snow cover and soil freezing. This study evaluated the performance of four machine-learning (ML) models in simulating winter subsurface drainage for five sites in Eastern Canada. The calibrated/trained ML models were then applied to predicted future climate (high emission scenario: RCP8.5) from 1950 to 2100 to comprehend the potential alteration in winter drainage patterns under global warming. Among ML models, the Cubist and SVM-RBF models emerged as the most accurate, offering competing short-term simulation (≤7 years) capabilities compared to the RZ-SHAW model with lower computational demand. However, ML models’ long-term projections under climate change scenarios revealed inconsistencies from insufficient and unbalanced observed winter subsurface drainage data. Simulation by both the RZ-SHAW and ML models predict a significant increase in winter drainage volume by the end of the 21st century (1950–2005 vs. 2070–2100) (RZ-SHAW: 243 mm to 328 mm (+35 %); ML models: 250 mm to 425 mm (+70 %)). RZ-SHAW simulated a shift towards a more evenly spread drainage pattern throughout the winter months from baseline to the end of the century. This shift was driven by the simulated shorter snow coverage periods, advancement of snowmelt timing, and fewer days of freezing soil. Thus, the timing of peak and trough winter drainage is expected to reverse, with February becoming the peak month and April the lowest by the century's end.