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Agriculture
Soil Science

Modeling Moisture Retention in Peat Soils

Profile image of Jukka AlmJukka Alm

1998, Soil Science Society of America Journal

https://doi.org/10.2136/SSSAJ1998.03615995006200020002X
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Abstract

Soil moisture governs many biogeochemical processes in peatlands. Modeling of those processes relative to climate and anthropogenic influences requires knowledge of the basic hydraulic properties of different peat soils in a function form. Water retention of undisturbed surface peat samples, collected at four depths at each of 38 undrained and drained pine {Pinna sylvestris L.) mire sites, was measured for suction pressures of -0.98, -3.10, -6.19, -9.81, -98.1, and -1554.25 kPa. The obtained data were used to test several well-known water retention models commonly applied to mineral soils. The most suitable model was found to be van Genuchten's model if the residual water content was omitted. Peat characteristics were used to explain the variation in the model's shape parameters. Accounting for the remains of Sphagnum, Carex, Eriophorum, and lignin and the distinction between shallow and deep peat layers considerably improved the moisture retention predictions compared with using bulk density only. The different behavior of the shallow vs. the deep peat layers was mainly attributed to the Sphagnum and lignin residues, but not to the Carex residues. We developed a semiempirical model with only one shape parameter, which was clearly better explained by the peat characteristics than the two shape parameters of the van Genuchten model. We recommend that for statistical investigations or investigations requiring a robust model, the semiempirical model be used. The van Genuchten model is to be preferred in predicting the moisture conditions near saturation.

Figures (9)
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.
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.
*,**, 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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    Mirkka Kotiaho, Kim YrjÃlÃ, Päivi Merilä

    2010

    It is known that Sphagnum associated methanotrophy (SAM) changes in relation to the peatland water table (WT) level. After drought, rising WT is able to reactivate SAM. We aimed to reveal whether this reactivation is due to activation of indigenous methane (CH 4 ) oxidizing bacteria (MOB) already present in the mosses or to MOB present in water. This was tested through two approaches: in a transplantation experiment, Sphagna lacking SAM activity were transplanted into flark water next to Sphagna oxidizing CH 4 . Already after 3 days, most of the transplants showed CH 4 oxidation activity. Microarray showed that the MOB community compositions of the transplants and the original active mosses had become more similar within 28 days thus indicating MOB movement through water between mosses. Methylocystis-related type II MOB dominated the community. In a following experiment, SAM inactive mosses were bathed overnight in non-sterile and sterile-filtered SAM active site flark water. Only mosses bathed with non-sterile flark water became SAM active, which was also shown by the pmoA copy number increase of over 60 times. Thus, it was evident that MOB present in the water can colonize Sphagnum mosses. This colonization could act as a resilience mechanism for peatland CH 4 dynamics by allowing the re-emergence of CH 4 oxidation activity in Sphagnum.

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    Water retention curve and hydraulic conductivity function of highly compressible materials
    Alexandre Cabral

    Canadian Geotechnical Journal, 2007

    A model capable of describing the suction-induced consolidation curve (void ratio function) and water retention curve (WRC) of highly compressible materials (HCM) is developed, validated, and finally applied to describe the WRC of deinking by-products (DBP). DBP are a highly compressible by-product of paper recycling used in geoenvironmental applications. Validation is conducted by modelling the WRC and the void ratio function for a well documented silty sand from Saskatchewan, Canada. The WRC and void ratio function were used to predict its hydraulic conductivity function (k-function). The water content, suction, and volumetric deformation data of DBP are obtained using an experimental technique that allows determination of the WRCs of HCMs that is suitable for prediction of the DBP k-function. The results show that volumetric water contents are underestimated if volume changes are not accounted for, leading to inaccuracies in the WRCs, thus inaccurately predicted k-functions. It is shown that the newly developed model is better suited for HCMs than currently available models, in particular for HCMs that continue to undergo significant volume changes when the applied suction exceeds the air-entry value.

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    Hydrological modeling in swelling/shrinking peat soils
    pietro teatini

    Water Resources Research, 2006

    1] Peatlands respond to natural hydrologic cycles of precipitation and evapotranspiration with reversible deformations due to variations of water content in both the unsaturated and saturated zone. This phenomenon results in short-term vertical displacements of the soil surface that superimpose to the irreversible long-term subsidence naturally occurring in drained cropped peatlands because of bio-oxidation of the organic matter. These processes cause changes in the peat structure, in particular, soil density and void ratio. The consequential changes in the hydrological parameters need to be incorporated in water flow dynamical models. In this paper, we present a new constitutive relationship for the soil shrinkage characteristic (SSC) in peats by describing the variation of porosity with moisture content. This model, based on simple physical considerations, is valid for both anisotropic and isotropic three-dimensional peat deformations. The capability of the proposed SSC to accurately describe the deformation dynamics has been assessed by comparison against a set of laboratory experimental results recently published. The constitutive relationship has been implemented into a Richards' equation-based numerical code and applied for the simulation of the peat soil dynamics as observed in a peatland south of the Venice Lagoon, Italy, in an ad hoc field experiment where the relevant parameters are continuously measured. The modeling results match well a large set of field data encompassing a period of more than 50 days and demonstrate that the proposed approach allows for a reliable reproduction of the soil vertical displacement dynamics as well as the hydrological behavior in terms of, for example, water flow, moisture content, and suction.

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    Stand transpiration and sapflow density in relation to weather, soil moisture and stand characteristics
    Anders Lindroth

    Basic and Applied Ecology, 2002

    Sapflow density was measured in six stands in a boreal forest in central Sweden, to assess its dependence on soil moisture and stand characteristics. The stands were mixed and pure Scots pine and Norway spruce stands, which were between 34 and 105 years old. Sapflow was measured in 12 trees per stand using the Granier method during two contrasting growing seasons; one warm and dry and one wet and cool. The canopy conductance of the stands was estimated by the inverse of the Penman-Monteith equation, using time-lag-adjusted sapflow as input. Maximum canopy conductance varied between 8 mm s-1 and 88 mm s-1 for the stand with the lowest and highest conductance, respectively. Transpiration was higher in the dry, warm season, mean values for the different stands ranging between 1.30 to 4.64 mm day-1 during July to September. The corresponding range in the wet, cool season was 0.95 to 2.65 mm day-1. Besides climatic factors, stand age, stem density and diameter explained most of the variation in sapflow density. By use of multiple regression analysis for 5-day periods it was possible to estimate sapflow density and transpiration for a larger area of the forest. This upscaled areal transpiration was compared with evaporation measured by an eddy-correlation system located centrally in the area. It was shown that areal transpiration constituted 78% of total evaporation in the warm, dry season and 52% in the wet, cool season. It was not possible to establish with confidence a critical limit for soil water at which transpiration began to be reduced, mainly because of wide scatter in the relationship between potential and actual transpiration. Die Saftflussdichte wurde in sechs Beständen des borealen Waldes Zentralschwedens gemessen, um ihre Abhängigkeit von der Bodenfeuchte und den Bestandseigenschaften zu beurteilen. Die Bestände waren reine und gemischte Bestände von Waldkiefer und Fichte, zwischen 34 und 105 Jahre alt. Der Saftfluss wurde mit der Granier-Methode an 12 Bäumen pro Bestand in zwei unterschiedlichen Wachstums-Perioden gemessen: eine warme, trockene und eine nasse, kühle Saison. Die Kronendachleitfähigkeit wurde mit der inversen Penman-Monteith-Gleichung unter Verwendung eines verzögerungskorrigierten Saftflusses als Eingabe abgeschätzt. Die maximale Kronendachleitfähigkeit der Bestände variierte zwischen 8 mm s-1 bei geringster und 88 mm s-1 bei höchster Leitfähigkeit. Die Transpiration in der trockenen, warmen Saison im Zeitraum Juli bis September war mit Mittelwerten zwischen 1.30 und 4.64 mm Tag-1 höher. Die entsprechende Spannweite in der nassen, kalten Saison war 0.95 bis 2.65 mm Tag-1. Neben den klimatischen Faktoren erklärten Bestandsalter, Stammdichte und-durchmesser den größten Teil der Variation in der Saftflussdichte. Unter Verwendung einer multiplen Regressionsanalyse für 5-Tages-Zeiträume war es möglich, Saftflussdichte und Transpiration für ein größeres Areal des Waldes abzuschätzen. Diese hochskalierte Areal-Transpiration wurde mit der Verdunstung verglichen, die mit einem "eddy correlation"-Sys

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