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Katata, Genki; Kajino, Mizuo*; Hiraki, Takatoshi*; Aikawa, Masahide*; Kobayashi, Tomiki*; Nagai, Haruyasu
no journal, ,
Water and matter input via fog deposition onto a mountainous forest (Mt. Rokko, Japan) was investigated using detailed land surface model that includes fog deposition onto vegetation (SOLVEG). Simulations using SOLVEG were carried out under meteorological and chemical fields produced by off-line coupled meso-scale meteorological/aerosol chemical transport model (WRF/EMTACS). The SOLVEG clearly underestimated the cumulative fog deposition calculated from throughfall data. This suggests that an enhancement of fog deposition by "edge effect" which is the phenomenon that fog droplets carried by horizontal advection are captured by leaves under canopy clustering and inhomogeneity. The deposition of atmospheric pollutants onto the forest floor due to fog deposition was estimated from the fog deposition by SOLVEG and chemical concentrations in fog water predicted by WRF/EMTACS.
Kajino, Mizuo*; Katata, Genki; Hiraki, Takatoshi*; Aikawa, Masahide*; Kobayashi, Tomiki*
no journal, ,
In order to predict water and matter deposition to forests, accurate estimation of chemical and physical properties of fog and aerosols are indispensable. We have developed a new aerosol chemical transport model (EMTACS) coupled with a meteorological model (WRF) and applied it to investigate uplift fog events occurred over a mountainous forest (Mt. Rokko, Japan). The EMTACS model is unique to dynamically solve temporal evolutions of mixing states of fog and aerosols, in addition to their chemical compositions and size distributions, and thus aerosol-fog interaction processes are considered in one coherent framework. The model performance was evaluated using meteorological and chemical observation data. Formation, evolution and acidification processes of fog and aerosols over the forest region were discussed.
Ritter, A.*; Katata, Genki; Regalado, C.*; Nagai, Haruyasu
no journal, ,
SOLVEG is a one-dimensional multilayer atmosphere-soil-vegetation model including fog deposition onto vegetation. However the complexity of the processes described by SOLVEG involves the use of many parameters not always readily available. We describe how an inverse parameter estimation algorithm, based on the Global Multilevel Coordinate Search (GMCS), coupled to SOLVEG can be used to determine model parameters. Focusing on the soil hydraulic parameters, which typically suffer from large uncertainty, we applied this inverse GMCS procedure to optimize such parameters from top soil water content time series measured in a laurel cloud forest of the Garajonay National Park (Canary Islands, Spain).