IJERD – International Journal of Environmental and Rural Development (2015) 6-1

Research articleerdPeatland Tank Model for Evaluation of Shallow
Groundwater Table Data without Height Reference fromBenchmark
XIN HE*Graduate School of Agriculture, Hokkaido University, Sapporo, Japan
Email: hexin@env.agr.hokudai.ac.jp

TAKASHI INOUE
Research Faculty of Agriculture, Hokkaido University, Sapporo, Japan
Received 14 November 2014

Accepted 1 April 2015(*Corresponding Author)

Abstract Long-term monitoring of shallow groundwater table (GWT) is essential for theevaluation of hydrologic conditions of peatlands, which is meaningful for their conservation
and restoration, especially in certain rural areas. Although there has been research in thisfield, a simple and effective method for the evaluation of GWT fluctuation has not beendeveloped. Peatland Tank Model (PTM) is a one-dimensional water balance model that
represents fluctuations of shallow GWT in peatland. The model contains several parameters,i.e., C (coefficient of GWT increase), Ai (coefficient for the size of plugholes, with i =1, 2,3), and HAi (coefficient for the height of each plughole, i =1, 2, 3). We used PTM with 29
years’ GWT data from Ochiai, Sarobetsu Mire, northern Hokkaido, Japan. These GWT data
do not have a height reference from a benchmark, so that the data have no common
meaning or information in relation to the ground surface or elevation above sea level.
Observation was conducted at three sites; Site D near a drainage ditch along a peat mining
location; Site M at the edge of the peat mining area; and Site U in an unused area far fromthe drainage. A few sets of parameters were obtained on the basis of simulation results.
Smaller simulation errors were attained using the PTM. The 29-year GWT fluctuations atSite D were the greatest and could be characterized by the largest parameter values.
Parameter trends at three sites varied, and were able to reflect the various drainage
conditions. Thus, the PTM is a promising method for the evaluation of long-term variations
and site differences of hydrologic conditions in peatland.
Keywords mire, groundwater table, peatland hydrology, drainage

INTRODUCTION
In recent years, a large number of peatlands have been exploited and converted to agricultural land.
Examples can be found in Canada (Bhatti and Tarnocai, 2009), Sumatra and Kalimantan inIndonesia (Page et al., 2011), and Hokkaido, northern Japan (Umeda, 1980; Fujita et al., 2009). Asa unique terrestrial system, peatland requires proper means of conservation and restoration.
Consequently, balancing agricultural development and peatland conservation has become aresearch subject. The hydrologic environment, especially shallow groundwater table (GWT)
fluctuation, is the most important determinant of the formation and existence of peatland. Longterm monitoring of shallow GWT is essential for the investigation and evaluation of the hydrologicenvironment. Nonetheless, in the past, the majority of monitoring GWT data has been assembled inform of groundwater depth or groundwater level which are not measured with respect to certain
benchmarks (sea level or ground surface). A simple and effective method for evaluating thehydrologic environment using such GWT data has not been developed. In view of this situation,
this paper proposes a simple evaluation method for shallow GWT fluctuation in peatland, based onPeatland Tank Model (PTM). The model provides satisfactory simulation accuracy and flexibility.

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OBJECTIVE
The objective of this study is to show how the PTM detects the spatial variability and temporalchanges of GWT fluctuation patterns even if the GWT data are not measured with respect to certain
benchmarks. The usefulness and effectiveness of the PTM for the shallow peatland GWT arediscussed. The meaning and function of each parameter used in the PTM are also explained.
METHODOLOGY

Fig. 1 Peatland Tank Model
The PTM is a water balance model for simulating the fluctuations of the shallow GWT in peatland(Umeda and Inoue, 1985). In this model, the peatland system is analogous to a water storage tank.
GWT fluctuations can be represented by variations of water levels in the tank. For a bog fed byrainfall, rainwater is the sole inflow and there are almost no other water inputs from thesurrounding environment. Evapotranspiration (ET) and discharge from the bog to surrounding
areas are the main outflows. The PTM (Fig. 1) has three outlet plugholes on the sidewall of the tank.
In theory, the PTM can be characterized by Eqs. 1 and 2.
(1)(2)
Here, GWTn and GWTn−1 represent the GWT at times n and n−1, respectively. C is thecoefficient of GWT increase owing to rain. Rn is the amount of rainfall during time period (n−1) ton. QAin−1 is the amount of groundwater drawdown from the each plughole (i =1, 2, 3) during thesame period. Coefficients Ai (0 ≤ Ai ≤ 1) and HAi are the size and height of each plughole,respectively. The time unit (data sampling interval) of 2 hours was used for estimating GWT by thePTM.
Constant discharge of the peatland and ET were incorporated into water loss from the bottomplughole. Based on such concept, parameter A1 represents the basic discharge (constant dischargeand ET) from the peat layer above height HA1. Similarly, parameters A2 and A3 are introduced torepresent water losses of the layer above heights HA2 and HA3, respectively. These two parameters
are used to characterize more rapid water losses in the shallow surface layer of the peat (Fig. 1).
Parameter C shows the magnitude of GWT increase in response to rainfall in the peatland.
Two simulation methods were implemented (Table 1). Because it is difficult to adjust
simultaneously all seven parameters and this may make the contribution of each parameter
ambiguous, we did not manipulate all seven. Without an explicit principle, determining theparameters is meaningless. Therefore, several rules (mentioned in the footnote of Table 1) wereestablished. Based on these rules, the number of parameters in the PTM was reduced from theoriginal seven to three (Method 1) or two (Method 2).

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Table 1 Simulation methodsMethod12

Manipulated Parameters
C, A1, A2
C, HA1

Fixed or Automatically Determined Parameters
Remarks
A3, HA1, HA2, HA3*1, *2
A1, A2, A3, HA2, HA3*2, *3
In all cases, A3 = 1.
*1: HA1 = HA2−40 cm, HA3 = HA2+10 cm or HA3 = GWTm (if (HA2+10 cm) > GWTm).
*2: HA2 is equal to the value of upper tertile of GWT data calculated via the following steps. In the first step, allobserved GWT data within the calculation period at each site were sorted numerically, in descending order. This set
should include every datum, even if there are overlaps. Then, the value found one third of the way down from themaximum in the organized dataset is defined as the upper tertile.
*3: A1 = 0.0003, A2 = 0.03, HA3 = HA2+10 cm or HA3 = GWTm (if (HA2+10 cm) > GWTm). GWTm: themaximum GWT.

We adopted PTM with 29 years of GWT data recorded (1983–2012, except for 1998) atOchiai, Sarobetsu Mire, northern Hokkaido (approximately 45°8′N, 141°44′E). GWT wasobserved at three sites: Site D near a drainage ditch along the peat mining location; Site M at theedge of the peat mining area; and Site U in an unused area far from the drainage (from 2006onwards, the measurement of Site U could not be continued due to the problems of instrument).
Annual average temperature in this area is ~6.1 °C, varying from −6.5 °C in February to 19.6 °Cin August. Annual average precipitation is 1,073 mm (Japan Meteorological Agency). On
average, the warm period without snow cover lasts from mid-April through late October. All
calculations were performed for July through September. Due to the limitation of equipment andmanpower, the observed GWT data were measured without reference height such as groundsurface or elevation above sea level. Compared with the absolute ground water tables, GWT
without reference height are more difficult to evaluate, because the data have no common
meaning.
In PTM computations, the parameters were adjusted repeatedly until the optimum simulation
result was attained, where the chi-square value of GWT was minimized and the Nash-Sutcliffe
efficiency coefficient and correlation coefficient (r) were maximized. Values of each parameter
were compared by site and examined by one-way analysis of variance (ANOVA). ANOVA
significance levels were set to 0.05 and 0.01. Based on 29 years’ GWT observations at each site,
long-term trends of each parameter were evaluated by the coefficient of determination (R2). All
statistical tests were conducted using SPSS 16.0 statistical software (SPSS Inc., Chicago, IL,USA).
RESULTS AND DISCUSSION
Throughout the results from 1983 to 2012, parameter C was in the order Site D > Site M > Site U,from both methods (Fig. 2). This indicates that the magnitude of GWT increase in response to rain
events was maximum at Site D and minimum at Site U. A larger value of C means less effective
porosity for water storage in peat (Umeda and Inoue, 1985). Therefore, it is concluded that theeffective porosity of peat at the three sites was in the order Site D < Site M < Site U. At the threesites, changes of parameter C over the last 29 years do not show clear trends (P > 0.05) (Fig. 2).
However, there is a strong negative regression relationship between C and total amount of rainfall
from July through September at all sites (Fig. 3). From Method 1, R2 at Site D exceeded 0.80 (P <0.01). This relationship can be explained by the fact that effective porosity increases in peat near
the ground surface. When the amount of rainfall increases, the GWT may spend more time in theupper part of peat layer, where the effective porosity of peat is relatively large and the ratio ofwater table increase (C) becomes small.
In Method 1, GWT fluctuations can be characterized by variations of parameters C, A1, andA2. The difference of A1 between Sites M and U was not obvious (n=21, P > 0.05); however, thisdifference was significant between Site D and the other two sites (n=28 for Site D and M, n=21 forSites D and U, P < 0.01) (Fig. 4). Differences of A2 among the three sites were not obvious (P >0.05). The average A2 at site U (n=21, average = 0.03) is smaller than those at the other two sites(n=28 for Site D, n=29 for Site M, average of both sites = 0.04). Parameters A1 and A2 at all sitesⒸ ISERD18

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over the last 29 years do not show clear trends (P > 0.05). The fact that the value of A1 at Site M
was similar to that at Site U means that characteristics of basic and constant water losses from thepeat layer (mainly from the catotelm) were similar at those two sites. In contrast, a larger A1 at Site
D means that the amount of water loss was greater than at the other two sites. Similarly, A2 at thethree sites was nearly identical, which indicates that water losses from the upper peat layer (part ofthe acrotelm) at the three sites were similar. In conclusion, Site D had greater water loss comparedwith the other two sites, but water loss of the upper peat layer at all three sites were similar.

Fig. 2 Variation of parameter C at three sites from 1983 to 2012

Fig. 3 Relationship between parameter C and total rainfall (July through September)at three sites from 1983 to 2012

Fig. 4 Variation of A1 and A2 in Method 1 at three sites from 1983 to 2012

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Fig. 5 Trends of (HA2–HA1) over 1983–2012 at three sites, from Method 2
In Method 2, GWT fluctuations are represented by parameter C and (HA2–HA1). Figure 5
shows variations of vertical distances between the lower two outlet plugholes (HA2–HA1) at thethree sites. Under the same condition of A1 = 0.0003, A2 = 0.03, and A3 = 1 for the three plugholes,
there were significant differences in (HA2–HA1) among the three sites. Average values of (HA2–HA1) during the last 29 years at sites D, M, and U were 30.7 cm, 22.5 cm, and 17.9 cm,respectively. The greater vertical distance (i.e., depth of bottom plughole) at Site D means that theunit-time GWT drawdown from the bottom plughole QA1 (cf. Eq. 2) at Site D was larger thanthose at sites M or U. Thus, the larger value of (HA2–HA1) indicates more rapid discharge. Overthe same period, (HA2–HA1) at site D increased, with slope 0.29 cm/year (P < 0.01). (HA2–HA1)
at Site M increased by 0.14 cm/year and decreased by –0.22 cm/year at Site U, but the trend was
not significant for either (P > 0.05) (Fig. 5).
The reason for such a change may be inferred as follows. In summer 1983, the drainage ditchnear these three sites was dredged and deepened. In the following few years (1983–1989), leakage
of water by piping from the adjacent mining pool to the ditch was observed. In terms of parametervariation, (HA2–HA1) increased yearly in this period. In the last 29 years, drainage intensity at Site
D clearly increased. However, at sites M and U, variations of drainage intensity were not assignificant. This fact indicates that drainage changed the water loss characteristics of peat and its
influence persisted in the following years.
The three observation sites have different drainage conditions. However, it is difficult todistinguish the difference of GWT fluctuations visually, so these fluctuations appeared similar.
Using the PTM, various characteristics of GWT increase could be represented by parameter C, andGWT decrease could be described by parameters Ai and (HA2–HA1). Large values of parameters
C, Ai and (HA2–HA1) indicate that GWT may response to rain and dry events greatly. Moreover,differences of Ai among observed sites were able to reflect site differences of drainage
characteristics in various peat layers (Method 1). Variations of (HA2–HA1) could depict long-term
trends of drainage characteristics at each site (Method 2). In addition, the parameters could also
represent peat properties. A larger C value indicates less effective porosity and less water storage ofpeat. Larger Ai and (HA2–HA1) values indicate that the GWT decreases rapidly so that the peat
becomes drier during rain-free periods.
There is a strong regression relationship between parameter C and total amount of rainfall.
Therefore, this amount during the simulation period gives hint to the determination of C. For
instance, the value of C in a year with greater rainfall would be smaller. In the case of greatlychanged drainage conditions, simulation by Method 1 was unable to match actual GWTfluctuations, because the distance between HA1 and HA2 was fixed at 40cm. For instance, if adrainage channel was renewed or newly excavated near the observation site, drainage may haveintensified and the drop of GWT from the height of HA2 may have exceeded 40cm. Under suchcircumstances, Method 2 is considered more suitable than Method 1. In practical use of the PTM,
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individual sites. Additionally, the principle of computation, such as simulation rules and methods,
could be adjusted to meet specific peatland conditions.
Average simulation errors of all calculations are shown in Table 2. By applying the rules in
the footnote of Table 1, Method 2 could simplify the computation by decreasing the number ofparameters to be fit, but simulation error might become slightly larger than that of Method 1.
Moreover, through Method 2, site differences were shown more clearly by the obvious differencesof (HA2–HA1) at the three sites, and the trend of long-term drainage effects also became apparent
through yearly decline of the depth of the bottom plughole at Site D.
Table 2 Average errors of all simulations by site for each methodMethod

Site D (n=28)
Site M (n=29)
Site U (n=21)
r
CHI
NSEr
CHI
NSEr
CHI
NSE
1
0.91
9.2
0.84
0.93
5.5
0.86
0.93
5.1
0.86
20.8912.6
0.83
0.92
6.0
0.84
0.93
5.90.85
r: correlation coefficient; CHI: chi-square value; NSE: Nash-Sutcliffe efficiency coefficientn: number of observed years

CONCLUSSIONSThe present study proposed a PTM-based evaluation method for shallow GWT fluctuations ofpeatland, especially bogs. This method can simulate the GWT with accuracy of a few centimeters.
Further, subtle differences in fluctuation patterns can be distinguished by model parameters fordifferent locations or time series.
The observed GWT data used herein were not measured with respect to certain elevationreferences or benchmarks (i.e., sea level or ground surface). In this study, we provide a solution toevaluate the changes in GWT fluctuation patterns or site differences of GWT conditions ofpeatland, when the ground water table data were measured without height reference. Through useof the PTM, such observed GWT data were effectively used for evaluating such changes anddifferences. Especially via Method 2, variations and trends of parameters revealed the site
differences and long-term changes of GWT condition.
The seasonal response of ET and amount of soil moisture in the unsaturated peat surface layer
were not considered in the current version of the model. Hence, improvement could be made in thenear future to achieve more accurate simulation results.
ACKNOWLEDGMENTS
We thank Dr. Tadao Yamamoto and students of the Laboratory of Land Improvement andManagement, Graduate School of Agriculture, Hokkaido University, for their help with long-term
groundwater table measurement at Sarobetsu Mire.
REFERENCES
Bhatti, J. and Tarnocai, C. 2009. Soil carbon sequestration and the greenhouse effect. Soil Science Society ofAmerica, 64.
Fujita, H., Igarashi, Y., Hotes, S., Takada, M., Inoue, T. and Kaneko, M. 2009. An inventory of the mires of
Hokkaido, Japan-their development, classification, decline, and conservation. Plant Ecology, 200, 9-36.
Japan Meteorological Agency. (http://www.jma.go.jp/jma/menu/report.html. Accessed 20 May 2014).
Page, S., Rieley, J. and Banks, C. 2011. Global and regional importance of the tropical peatland carbon pool.
Global Change Biology, 17, 798-818.
Umeda, Y. 1980. Present status and problems for agricultural use of peatland in Hokkaido, Northern Japan.
Proc. 6th Int. Peat Congr. Duluth, Minnesota, USA., 501-505.
Umeda, Y. and Inoue, T. 1985. The influence of evapotranspiration on the groundwater table in peatland.
Journal of the Faculty of Agriculture, Hokkaido University, Japan, 62 (2), 167-181.
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