Abstract. Climate change and land use are rapidly changing the amount and temporal distribution of recharge in northern aquifers. This paper presents a novel method for distributing Monte Carlo simulations of 1-D soil profile spatially to estimate transient recharge in an unconfined esker aquifer. The modeling approach uses data-based estimates for the most important parameters controlling the total amount (canopy cover) and timing (depth of the unsaturated zone) of groundwater recharge. Scots pine canopy was parameterized to leaf area index (LAI) using forestry inventory data. Uncertainty in the parameters controlling soil hydraulic properties and evapotranspiration was carried over from the Monte Carlo runs to the final recharge estimates. Different mechanisms for lake, soil, and snow evaporation and transpiration were used in the model set-up. Finally, the model output was validated with independent recharge estimates using the water table fluctuation method and baseflow estimation. The results indicated that LAI is important in controlling total recharge amount, and the modeling approach successfully reduced model uncertainty by allocating the LAI parameter spatially in the model. Soil evaporation compensated for transpiration for areas with low LAI values, which may be significant in optimal management of forestry and recharge. Different forest management scenarios tested with the model showed differences in annual recharge of up to 100 mm. The uncertainty in recharge estimates arising from the simulation parameters was lower than the interannual variation caused by climate conditions. It proved important to take unsaturated depth and vegetation cover into account when estimating spatially and temporally distributed recharge in sandy unconfined aquifers.
Monte Carlo-Based Random Modeling Approach for the Description and Electromagnetic Analysis of Multilayer Structures
