Aerosol Deposition and Behavior on Leaves in Cool-temperate Deciduous Forests. Part 3: Estimation of Fog Deposition onto Cool-temperate Deciduous Forest by the Inferential Method

Abstract

Fog deposition onto the cool-temperate deciduous forest around Lake Mashu in northern Japan was estimated by the inferential method using the parameterizations of deposition velocity and liquid water content of fog (LWC). Two parameterizations of fog deposition velocity derived from field experiments in Europe and numerical simulations using a detailed multi-layer atmosphere-vegetation-soil model were tested. The empirical function between horizontal visibility (VIS) and LWC was applied to produce hourly LWC as an input data for the inferential method. Weekly mean LWC computed from VIS had a good correlation with LWC sampled by an active string-fog collector. By considering the enhancement of fog deposition due to the edge effect, fog deposition calculated by the inferential method using two parameterizations of deposition velocity agreed with that computed from throughfall data. The results indicated that the inferential method using the current parameterizations of deposition velocity and LWC can provide a rough estimation of water input due to fog deposition onto cool-temperature deciduous forests. Limitations of current parameterizations of deposition velocity related to wind speed, evaporation loss of rain and fog droplets intercepted by tree canopies, and leaf area index were discussed.

Aerosol Deposition and Behavior on Leaves in Cool-temperate Deciduous Forests. Part 2: Characteristics of Fog Water Chemistry and Fog Deposition in Northern Japan

“…The results supported by the fact that the change in the canopy structure caused the variability in the deposition rate (Keim et al, 2005;Nadkarni and Sumera, 2004). The temporal change in the fog water deposition amount is discussed in the companion paper (Katata et al, 2013).…”

“…By combining the fog water sampling and throughfall data, the mean fog water deposition rate (flux) and total material inputs via fog water deposition were estimated. The results were used to evaluate the performance of the current parameterizations of the inferential method (Katata et al, 2013, the companion paper).…”