Enhanced runoff simulation with improved evapotranspiration accounting for vegetation response to climate variability

• Penman-Monteith PET shows bias in runoff simulation. • PET equation neglects the vegetation response to key environmental drivers. • Mixed generalised additive model simulates nonlinear stomatal conductance. • Integrating nonlinear stomatal conductance into PET improves runoff simulation. Rainfall-runoff simulation plays a crucial role in predicting high runoff events. Hydrological models that use potential evapotranspiration (PET) equations are biased in runoff simulation because they neglect the role of vegetation responses to environmental variables such as CO 2 concentration, air temperature (T a ), net radiation (R n ), and vapor pressure deficit (VPD). The modification of Penman-Monteith PET (PET PM ) by incorporating vegetation response to environmental variables through stomatal conductance (g s ) leads to complexity and uncertainty. This study used a mixed generalised additive model (MGAM) to simulate g s as a nonlinear function of environmental variables. By integrating MGAM derived g s into PET PM, the modified model, PET MGAM was developed. Using data from three eddy covariance flux tower sites with different vegetation types, PET MGAM outperformed PET PM , showing higher Nash-Sutcliffe Efficiency (NSE) and Kling–Gupta efficiency (KGE) values for runoff simulations of the catchments associated with the flux towers. The results showed that PET MGAM moderated the runoff underestimation simulated by PET PM , under both wet and dry conditions. shapley additive explanations (SHAP) analysis highlighted the contribution of key environmental variables to PET estimation under wet and dry climates. PET MGAM accounts for the interactive effects of CO 2 , T a , and VPD in a modified PET equation, leading to more accurate estimates of water balance components under wet and dry climate conditions.