A land surface model including cloud (fog) water deposition on vegetation was developed to better predict the heat and water exchanges between the biosphere and atmosphere. A new scheme to calculate cloud water deposition on vegetation was implemented in this model. High performance of the model was confirmed by comparison of calculated heat and cloud water flux over a forest with measurements. The new model provided a better prediction of measured turbulent and gravitational fluxes of cloud water over the canopy than the commonly used cloud water deposition model. In addition, simple linear relationships between wind speed over the canopy ( | U | ) and deposition velocity of cloud water (V dep ) were found both in measurements and in the calculations. Numerical experiments using the model were performed to study the influences of two types of leaves (needle and broad leaves) and canopy structure parameters (total leaf area index and canopy height) on V dep . When the size of broad leaves is small, they can capture larger amounts of cloud water than needle leaves with the same canopy structure. The relationship between aerodynamic and canopy conductances for cloud water at a given total leaf area density (LAD) strongly influenced V dep . From this, it was found that trees whose LAD Ϸ 0.1 m 2 m Ϫ3 are the most efficient structures for cloud water deposition. A simple expression for the slope of V dep plotted against LAD obtained from the experiments can be useful for predicting total cloud water deposition to forests on large spatial scales.

Vertical turbulent fluxes of water vapour, carbon dioxide, and sensible heat were measured from 16 August to the 28 September 2006 near the city centre of Münster in northwest Germany. In comparison to results of measurements above homogeneous ecosystem sites, the CO 2 fluxes above the urban investigation area showed more peaks and higher variances during the course of a day, probably caused by traffic and other varying, anthropogenic sources. The main goal of this study is the introduction and establishment of a new gap filling procedure using radial basis function (RBF) neural networks, which is also applicable under complex environmental conditions. We applied adapted RBF neural networks within a combined modular expert system of neural networks as an innovative approach to fill data gaps in micrometeorological flux time series. We found that RBF networks are superior to multilayer perceptron (MLP) neural networks in the reproduction of the highly variable turbulent fluxes. In addition, we enhanced the methodology in the field of quality assessment for eddy covariance data. An RBF neural network mapping system was used to identify conditions of a turbulence regime that allows reliable quantification of turbulent fluxes through finding an acceptable minimum of the friction velocity. For the data analysed in this study, the minimum acceptable friction velocity was found to be 0.15 m s −1 . The obtained CO 2 fluxes, measured on a tower at 65 m a.g.l., reached average values of 12 µmol m −2 s −1 and fell to nighttime minimum values of 3 µmol m −2 s −1 . Mean daily CO 2 emissions of 21 g CO 2 m −2 d −1 were obtained during our 6-week experiment. Hence, the city centre of Münster appeared to be a significant source of CO 2 . The half-hourly average values of water vapour fluxes ranged between 0.062 and 0.989 mmol m −2 s −1 and showed lower variances than the simultaneously measured fluxes of CO 2 .

No abstract

No abstract

No abstract