Soil water flux calculation

Figure 14 Comparison of actual volumetric water contents (measured by time domain reflectom-etry) and calculated soil-water flux values (Penman equation) at four soil depths...

Because overland flow depth and infiltration rate are interdependent processes, the transient, spatially variable infiltration rate must also be accounted for. The two-dimensional Richard s equation can be used to calculate the soil water flux at any position x along the slope ... 

PROFILE is a biogeochemical model developed specially to calculate the influence of acid depositions on soil as a part of an ecosystem. The sets of chemical and biogeochemical reactions implemented in this model are (1) soil solution equilibrium, (2) mineral weathering, (3) nitrification and (4) nutrient uptake. Other biogeochemical processes affect soil chemistry via boundary conditions. However, there are many important physical soil processes and site conditions such as convective transport of solutes through the soil profile, the almost total absence of radial water flux (down through the soil profile) in mountain soils, the absence of radial runoff from the profile in soils with permafrost, etc., which are not implemented in the model and have to be taken into account in other ways. 

Soil-related data (HM and BC content in soil parent materials) were included in calculations to account the values of HM weathering. Also we considered the influence of soil types on forest biomass productivity. Runoff data (at scale 0.5 x 0.50 were directly used to get input data on drainage water fluxes, Qie. Forest-type-related data (wood biomass growth and HM content in wood biomass) inserted into our database were subdivided depending on either coniferous, deciduous or mixed forests. 

Even in the simplest cases, comprehensive emission measurements are necessary if a meaningful flux calculation is to be made. This can only be achieved by integrating the measurements over the relevant cycle, i.e. tidal (in the case of H2S from coastal environments), diel (in the case of DMS, CS2 and DMDS from all locations and H2S from non-tidal locations), and seasonal (temperature effects and water coverage effects). In addition, the effect of spatial variability, i.e. the effects of changing vegetation coverage and/or soil inundation need also be considered within some ecosystems. For these reasons, we do not attempt to attempt to average our emissions data, and for the purpose of flux extrapolation, use only the sites that have sufficient emissions data (8,2). 

In Figure 9.21 all of the carbon eventually used in weathering of minerals by CC>2-charged soil water is shown as entering the atmosphere. The difference between the flux of CO2 owing to precipitation of carbonate minerals in the ocean and the total CO2 released from the ocean is that CO2 used to weather silicate minerals on land, and agrees with the calculations of riverine source materials made earlier in this chapter, in which it was shown that 30% of the HCC>3 in river water comes from weathering of silicate minerals. 

This volumetric water flux density directed upward at the soil surface equals (1 x 10-8 m3 m-2 s 1)(l mol/18 x 10-6 m3), or 0.6 x 10-3 mol mT s-1 (= 0.6 mmol m-2 s-1). When discussing water vapor movement in the previous section, we indicated that Jm> emanating from a moist shaded soil is usually 0.2 to 1.0 mmol m-2 s-1, so our calculated flux density is consistent with the range of measured values. The calculation also indicates that a fairly large gradient in hydrostatic pressure can exist near the soil surface. 

Calcium chloride solutions (pH =6.2) labeled with Ca or 36ci were displaced vertically downward through columns of homogeneous, repacked, water-saturated sandy soil by a chemically identical solution labeled with Cl or Ca, respectively. Constant water fluxes, and solution activities of 1 to 15 pCi/dm, were used. Calcium solutions were analyzed by titration with disodium dihydrogen ethylenediamine tetraacetate to a murexide end point (11). The activity of radioactively labeled solutions was obtained by liquid scintillation techniques. Concentrations of adsorbed calcium were calculated from isotope dilution. The concentration of calcium chloride in the influent solution was 0.08 N. Because exchange of calcium for itself was the only chemical process affecting transport, the calcium chloride concentration remained constant at 0.08 N throughout each experiment, both within the column and in the effluent. 

Once values of c (z,t) are estimated, the volatilization flux is calculated from a (diffusion equation that considers the gradient in c and a partially water-filled soil porosity. Such flux calculations are difficult due to the near infinite gradient in gas concentration from soil to atmosphere. Often the volatilization flux is calculated using Equations 10-11 and considering only the very shallowest upper layer of the profile. Volatilization models are not included in most pesticide management models, and in only a few pesticide research models. 

For stationary flow conditions, D and v are independent parameters describing the transport process. In transient conditions, however, the relationship between D and v must be taken into account. Experimental evidences show that for transport in homogeneous saturated porous media, D is a monotoneous function of v. In unsaturated media, this relation becomes extremely complicated since the transport volume 6<,u changes with the water flux. Therefore, the structure of the water fdled pore-space and, hence, the flow field depends on the saturation degree (Flury, M. et al. (1995)) so that the variance of local velocities and the mixing time cannot be simply related to the mean advection velocity. As a consequence, no validated theoretical models exist to calculate the relationship between D and v for unsaturated soils and the dispersivity X cannot be considered to be a material constant, i.e. independent of 0. 

This conclusion is confirmed by the fact that the calculated flux would be 356 ng cm day based on water movement through the soil at 50% RH of 0.274 mL cm day and a Ce of 1300 ng mL in equilibrium with a soil concentration of 10 JLg Reducing the relative humidity to 15% increased the water movement to 0.501 mL cm day and the mass transport of Lindane to 651 ng cm day Note that with dry nitrogen the flux increased to a point, D, and then decreased, which was doubtless due to the soil surface drying out increasing the sorption. Thus evaporation of a compound incorporated in soil will depend first on diffusion through the soil and mass transport to the surface in the soil water when the water is evaporating. Both processes are influenced by properties of the soil and the chemical. 

If either of these last two explanations is the source of the reduced flux from a saturated soil or pure powder compared with saturated water, then the larger lag time calculated for subjects E and F (i.e., 1.56 and 1.04 h, respectively) would represent diffusion through the SC. As a result, flux through the SC should be calculated using these values of rather than 0.58 h estimated for CP absorption from saturated water. When calculated this way, is approximately 7 pg cm" h , which is about 1/lOth of determined from saturated water. This is similar to the in vitro results summarized in Table 11.3 for pure powder and saturated water. These data are also consistent results observed in vitro with hairless mouse skin and for cellulosic membranes (Touraille etal., 1998, 2005) The steady-state fluxes of CP measured from saturated soil and pure powders are approximately an order of magnitude smaller than those from an aqueous saturated solution. 

In the case where the soil is dry, or if no water is flowing in the soil column because of reduced evaporation at the surface, the volatilization is controlled by slow diffusion. Two methods can be used in order to calculate the volatilization in the case of no water flux the relatively simple model of Hartley [32] and the more complex one of Jury et al. [12]. 

MACAL is a model which can be used to assess the critical load of forest soils (de Vries, 1988). Here, critical values for aluminium concentrations and Ca Al ratios are set at 0.2 mmol 1 and 1.0 respectively and the model calculates the yearly averaged calcium and aluminium concentrations in soil compartments of 10 cm up to 80 cm depth. The model predicts the element flux and water flux in given soil compartments at given levels of acidic deposition ... 

Note that the sensitivity of the net flux between the soil and water to the worms activities depends on the relation between the rate R and the solute concentration. For the calculations in Figures 2.13 and 2.14, R varies linearly with concentration as specified in Equation (2.40), and the flux is sensitive to worm activity. But where the rate is independent of concentration, as for NH4+ formation in Equation (2.39), the net flux, which in this case is roughly Ro/a + LRi, is necessarily independent of worm activity, though the distribution of the flux between burrows and the sediment surface and the concentration profile are not. In practice the rate will always depend to some extent on concentration. But the predictions here for the idealized steady state indicate the expected sensitivities. 

Figure 3. Calculated volatilization flux versus time for selected forest pesticides as affected by water evaporation (E) at soil depth L = 10 cm.

SimpleBox is a multimedia mass balance model of the so-called Mackay type. It represents the environment as a series of well-mixed boxes of air, water, sediment, soil, and vegetation (compartments). Calculations start with user-specified emission fluxes into the compartments. Intermedia mass transfer fluxes and degradation fluxes are calculated by the model on the basis of user-specified mass transfer coefficients and degradation rate constants. The model performs a simultaneous mass balance calculation for all the compartments, and produces steady-state concentrations in... 

Our next task is to discuss concentrations and fluxes within a plant community. When we analyze water vapor and CO2 fluxes from the soil up to the top of plants, we are confronted by the great structural diversity among different types of vegetation. Each plant community has its own unique spatial patterns for water vapor and CO2 concentration. The possibility of many layers of leaves and the constantly changing illumination also greatly complicate the analysis. Even approximate descriptions of the gas fluxes within carefully selected plant communities involve complex calculations based on models incorporating numerous simplifying assumptions. We will consider a cornfield as a specific example. 

The flux of trace gases and particles from the atmosphere to the surface is calculated by multiplying concentrations in the lowest model layer by the spatially and temporally varying deposition velocity, which is proportional to the sum of three characteristic resistances (aerodynamic resistance, sublayer resistance, and surface resistance). The surface resistance parametrization developed by Wesely (1989) is used. In this parametrization, the surface resistance is derived from the resistances of the surfaces of the soil and the plants. The properties of the plants are determined using land-use data and the season. The surface resistance also depends on the diffusion coefficient, the reactivity, and water solubility of the reactive trace gas. 

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