Agricultural irrigation strongly affects groundwater pollution in cultivated areas. Groundwater vulnerability was evaluated using the DRASTIC method by considering agricultural activities that affect water infiltration and pollutant transport to the groundwater. Three scenarios, using different water recharge sources and calculation methods, were considered for the Tongliao area of northern China. For Scenario 1, only precipitation contributed to the net recharge estimation and ~33.77% of the area was under high pollution risk. For Scenarios 2 and 3, both precipitation and irrigation return water were considered for net recharge estimations. The fractional areas of high pollution risk regions were 40.60% and 19.22% for Scenarios 2 and 3, respectively. The modified infiltration coefficients for this study area were used in Scenario 3, and the fractional area of the high-risk region was 21.38% lower than for Scenario 2. The use of empirical infiltration coefficients in Scenario 2 overestimated the water infiltration ability in the cultivated areas, which also overestimated the fractional area of high-risk regions in this study. Accurate assessment of the impact of agricultural activities on the groundwater pollution risk is essential for cultivated areas. Emphasis should be placed on the calculation method of proper parameters for DRASTIC model construction.

Climate warming is altering seasonal snow regimes and vegetation phenology in cold regions, but the influence of snowmelt may extend well beyond spring thaw. We examined how snow-related associations with net primary productivity (NPP) changed across the growing season in the Naoli River Basin during 2014–2024. Monthly NPP was reconstructed using the Carnegie–Ames–Stanford Approach and evaluated against MODIS NPP. The growing season was divided into early (EGS), peak (PGS), and late (LGS) stages based on vegetation phenology. Generalized additive models identified contemporaneous, lagged, and cumulative associations of air temperature, precipitation, solar radiation, and snow water equivalent (SWE), while random forest and partial dependence analyses were used to compare driver importance and SWE–NPP responses. SWE showed antecedent associations, with AIC-selected April-referenced windows A0L4 (EGS; R² = 0.23) and A0L3 (LGS; R² = 0.24). Solar radiation remained the most important predictor, but its relative importance declined from 53.77% in the EGS to 42.56% in the LGS. SWE was more important than precipitation in the EGS (18.96% vs. 12.38%) and PGS (17.31% vs. 13.86%), whereas precipitation became more important in the LGS (19.75% vs. 15.13%). The SWE–NPP relationship also shifted from an overall declining response in the EGS to a nonlinear response in the PGS and a predominantly positive response in the LGS. These results highlight the value of considering antecedent snowpack conditions alongside growing-season climate in vegetation productivity monitoring, with implications for sustainable watershed management under climate change.