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We analyzed the possibility of introducing a single stochastic scaling parameter a to describe the spatial variability of soil hydraulic properties, using the soil hydraulic properties of the Hamra field (Russo and Bresler 1981) and the Panache field (Nielsen, Biggar, and Erh 1973). In the traditional approach (Peck, Luxmoore, and Stolzy 1977; Russo and Bresler 1980; Warrick, Mullen, and Nielsen 1977), sets of scaling factors are estimated from the h(s) and K(s) functions. For "perfectly similar media," the two sets of a should be identical. Even though the sets of a in these studies were found to be correlated (table 2), they possessed different statistical properties, and were not identical. Results of structural analyses of the sets of a from the two fields suggested that the spatial structures of the two a-sets are quite distinct, reflecting the different spadal behavior of the h(θ) and the K(θ) functions. Moreover, there was poor correlation between the uncorrelated residuals of the a-sets, indicating that part of the high correlation between the a-sets found in earlier work must stem from the presence of an undetected drift and from correlation between nearby measurements. Under field conditions, the saturated hydraulic conductivity is controlled by the flow of water through large structural voids (macropores), which drain at very small negative values of water pressure. Because of this, we tried eliminating Ks by using relative hydraulic properties instead of the hydraulic properties themselves to estimate the scaling factor sets. For the Hamra field, for which we assumed that the hydraulic properties could be described by the model of Brooks and Corey (1964), we found the resultant sets of scaling factors to be highly correlated (R2 = 0.996) with the same spatial structure, but with slightly different variance. By examining the relationships between the two a-sets implied by the Brooks and Corey (1964) model we saw that (1) in general, both sets will be functions of the range of water saturation values used to estimate them, (2) the correlation between the two sets can be improved for media with broad pore-size distributions, and (3) the two sets will be identical if and only if the relative hydraulic conductivity function K,.(hr) is described by the deterministic function Kr(hr) = hy -2 ("strictly similar media"). This analysis suggests that, for media that are not well described by Kr = hr -2, a scaling factor would be required in addition to a in order to achieve agreement between scaled values of hr(θ) and Kr(θ) at all points. A general model Kr = hr -η was proposed, with η as a second stochastic scaling factor for media that do not obey the restrictive assumptions of macroscopic Miller similitude. In the Hamra field, this modified scaling procedure produced perfect agreement between the scaling hydraulic properties. In the Panache field, with values of η determined from linear regression analysis of the logarithmic transformations of Kr and h,., agreement was improved considerably between the scaled hydraulic properties as compared to the more restrictive scaling procedure. In contrast to the Hamra field, however, there remained some significant differences between the scaled properties. These differences may have been artifacts of the different methods used to estimate the hIs) and the K(s) functions for the Panache field. The results of our analysis suggest that in any transient transport problem involving both K(s) and h(s), the description of their spatial variability requires the use of at least three stochastic variates-Ks , α, and η-not a alone.
Summary:
1) Three types of forest, evergreen seasonal forest, heath forest and Melaleuca swamp forest, were distinguished and studied in the vicinity of Cheko in southwestern Cambodia, where moist tropical climate with a pronounced dry season in three winter months prevails.
2) These three forest types respectively occupied deep latosol derived from sandstone, very sandy soil around the swamp forest, and deep deposit of silica sand with underground hardpan in shallow valleys.
3) Total plant biomass was estimated by the allometric method based on some 140 sample trees (DBH24.5 cm) which were felled in four sample plots (two 50 mX50 m plots in the evergreen seasonal forest, and each one 20 m x 50 m plot in the other two types). Biomass of ground vegetation was estimated separately by similar technique and clipping.
4) The biomass of evergreen seasonal forest was estimated as follows. Stem 215 ton/ha, branch 99 ton/ha, root 61 ton/ha, leaf 7.3 ton/ha, leaf area index 7.4 ha/ha, density of trees over 4.5 cm DBH 1,280/ha, relative basal area of Whole stand 3.19 o/oo.
5) The biomass of heath forest was as follows. Stem 111 ton/ha, branch 35 ton/ha·, root 19 ton/ha, leaf 7.7 ton/ha, leaf area index 7.1 ha/ha, tree density 2,570/ha, relative basal area 2.3 o/oo.
6) The biomass of M elaleuca swamp forest was as follows. Stem 7.4 ton/ha, branch 3.9 ton/ha, root 2.6 ton/ha, leaf 0.79 ton/ha, leaf area index 0.37, undergrowth of sedge 2.57 ton/ha, tree density 200/ha, relative basal area of trees 0.35 o/oo.
7) It was found that the biomass of small trees (4.5 cm>DBH>1 cm) and ground vegetation (4.5 cm <= DBH) was so unevenly distributed over the forest floor that a few hundred square meters of sample area would be needed for estimating them at a moderate level of statistical reliability.
8) The estimated biomass of the evergreen seasonal forest was compared with the data hitherto obtained in moist tropical forests of Cote d'Ivoire and Thailand. The forest of Cheko was found to have the biomass equivalent to other rain forests, but to be characterized by a specific DBH-tree height curve, a rather small leaf area index and a high value of leaf area/leaf weight ratio.