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Table 5. Shape parameter regression results. + p = bulk density, C = Carex, Er = Eriophorum, L = lignin, S = Sphagnum, Amor = amorphous mass, Layer 1 = 0-10 cm below peat surface, Laye p 2 = 10-20 cm, Layer 3 = 25-35 cm, Layer 4 = 50-60 cm.

Table 5 Shape parameter regression results. + p = bulk density, C = Carex, Er = Eriophorum, L = lignin, S = Sphagnum, Amor = amorphous mass, Layer 1 = 0-10 cm below peat surface, Laye p 2 = 10-20 cm, Layer 3 = 25-35 cm, Layer 4 = 50-60 cm.

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error criterion (Séderstr6m and Stoica, 1989), and the criterion of minimum description length (Rissanen, 1983), see Table 2. According to these criteria, the model reduced from Eq. [1]:
error criterion (Séderstr6m and Stoica, 1989), and the criterion of minimum description length (Rissanen, 1983), see Table 2. According to these criteria, the model reduced from Eq. [1]:
Table 1, Value range and sample distributions of the 152 peat cores analyzed.
Table 1, Value range and sample distributions of the 152 peat cores analyzed.
*,**, and *** Significant at the 0.05, 0.01, and 0.001 probability levels, respectively. | p = bulk density, C = Carex, Er = Eriophorum, L = lignin, § = Sphagnum, Amor = amorphous mass, Layer 1 = 0-10 cm below peat surface, Layer 2 = 10-20 cm, Layer 3 = 25-35 cm, Layer 4 = 50-60 cm. Table 4. Correlation coefficients of the peat characteristics} considered.
*,**, and *** Significant at the 0.05, 0.01, and 0.001 probability levels, respectively. | p = bulk density, C = Carex, Er = Eriophorum, L = lignin, § = Sphagnum, Amor = amorphous mass, Layer 1 = 0-10 cm below peat surface, Layer 2 = 10-20 cm, Layer 3 = 25-35 cm, Layer 4 = 50-60 cm. Table 4. Correlation coefficients of the peat characteristics} considered.
Table 7. Parameter functions of the optimal van Genuchten model. + p = bulk density, C = Carex, Layer 1 = 0-10 cm below the peat surface, S = Sphagnum. + R’ is for the complete model (must not be compared to R? in Table 5). § The right-hand column (R? valid) displays the results of the cross-validation.
Table 7. Parameter functions of the optimal van Genuchten model. + p = bulk density, C = Carex, Layer 1 = 0-10 cm below the peat surface, S = Sphagnum. + R’ is for the complete model (must not be compared to R? in Table 5). § The right-hand column (R? valid) displays the results of the cross-validation.
t p = bulk density, C = Carex, Er = Eriophorum, 1 = lignin, S = Sphagnum, Amor = amorphous mass, Layer 1 = 0-10 cm below peat surface, Layer 2 = 10-20 cm, Layer 3 = 25-35 cm, Layer 4 = 50-60 cm, Slay 1 = S x Layer 1, Llay 1 = L x Layer 1. + R’ is for the complete model (must not be compared with R? in Table 5). Set 0 = parameters 6,, 8,9, and k estimated directly from water retention data, not explained by peat characteristics. § The right-hand column displays the results (R? valid) of the cross-validation. Table 6. Parameter functions of the semiempirical model (Eq. [3]).
t p = bulk density, C = Carex, Er = Eriophorum, 1 = lignin, S = Sphagnum, Amor = amorphous mass, Layer 1 = 0-10 cm below peat surface, Layer 2 = 10-20 cm, Layer 3 = 25-35 cm, Layer 4 = 50-60 cm, Slay 1 = S x Layer 1, Llay 1 = L x Layer 1. + R’ is for the complete model (must not be compared with R? in Table 5). Set 0 = parameters 6,, 8,9, and k estimated directly from water retention data, not explained by peat characteristics. § The right-hand column displays the results (R? valid) of the cross-validation. Table 6. Parameter functions of the semiempirical model (Eq. [3]).
Fig. 2. Model performance indicated by coefficient of determination (R’) at the local soil water suction levels where the measurements were made. Local point regressions with the shape parameters explained by peat Character Sets 1 and 4 (see Table 3), semiempiri- cal model (Eq. [3]) with Set 4, and van Genuchten model (Eq. [7]) with Set 4 are compared with their maximum fit limit and theoretical maximum limit values.
Fig. 2. Model performance indicated by coefficient of determination (R’) at the local soil water suction levels where the measurements were made. Local point regressions with the shape parameters explained by peat Character Sets 1 and 4 (see Table 3), semiempiri- cal model (Eq. [3]) with Set 4, and van Genuchten model (Eq. [7]) with Set 4 are compared with their maximum fit limit and theoretical maximum limit values.
Fig. 3. Examples of peat water content as predicted by model Eq. [7] (solid line) and 3b (dotted line, see Table 1) with the shape parameters explained by peat Character Set 4 (see Table 3) and Model [3] with peat Character Set 5 (dashed line). Measured water content (dots) and measured sample characteristics (sample origins in peat depth profile = Layer, bulk density = p, botanical components S, C, L and Er) are given for each sample. Approximate (6%) error bars due to potential measurement errors in water content are also indicated. Samples are named by the dominant botanical peat component (Sphagnum, Carex, or lignin) followed by the visual field characterization of the peat (combination of symbols S, C, L, and Er). Nomenclature of the botanical components as in Table 1. The models developed and tested in this research show that continuous water retention models apply well A distinction between the top sample layer (0-10 cm from the lower level of the green moss layer) and the deeper sample layers (10-60 cm from the reference level) also proved to be a clearly statistically significant variable. The inclusion of this variable considerably in- creased the determination degree of peat water reten- tion, but could not be further explained by investigating the shape parameters n and « of the reduced van Gen- uchten model (Eq. [7]). Fortunately, the distribution of the single shape parameter k in the semiempirical model
Fig. 3. Examples of peat water content as predicted by model Eq. [7] (solid line) and 3b (dotted line, see Table 1) with the shape parameters explained by peat Character Set 4 (see Table 3) and Model [3] with peat Character Set 5 (dashed line). Measured water content (dots) and measured sample characteristics (sample origins in peat depth profile = Layer, bulk density = p, botanical components S, C, L and Er) are given for each sample. Approximate (6%) error bars due to potential measurement errors in water content are also indicated. Samples are named by the dominant botanical peat component (Sphagnum, Carex, or lignin) followed by the visual field characterization of the peat (combination of symbols S, C, L, and Er). Nomenclature of the botanical components as in Table 1. The models developed and tested in this research show that continuous water retention models apply well A distinction between the top sample layer (0-10 cm from the lower level of the green moss layer) and the deeper sample layers (10-60 cm from the reference level) also proved to be a clearly statistically significant variable. The inclusion of this variable considerably in- creased the determination degree of peat water reten- tion, but could not be further explained by investigating the shape parameters n and « of the reduced van Gen- uchten model (Eq. [7]). Fortunately, the distribution of the single shape parameter k in the semiempirical model
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Applied MathematicsSoil ScienceForestryORGANIC SOILSoil retention waterPeat BogPeat Soil
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