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avai lab le at www.sc iencedi rec t .com

journa l homepage: www.e lsev ier .com/ locate /eco lmodel

actors controlling vegetation succession in Kushiro Mire

adanobu Nakayama ∗

sian Environment Research Group, National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba,baraki 305-8506, Japan

r t i c l e i n f o

rticle history:

ublished on line 7 April 2008

eywords:

rying phenomenon

lder invasion

ire shrinking

wo-species competition

ICE-VEG

a b s t r a c t

The NIES integrated catchment-based eco-hydrology (NICE) model was expanded to include

the vegetation succession processes in the Kucyoro River catchment, Japan (NICE-VEG). The

NICE-VEG simulated the water/heat budget and dynamic vegetation processes iteratively.

The simulation results indicated that the spatial occupation rate of alder invasion is posi-

tively correlated with hydrogeological changes. Some discrepancies are attributable to local

heterogeneity of changes in groundwater level, porosity, deposited sediments, and other

limiting factors (nutrients, soil moisture, light, temperature). Furthermore, simulation of

channelized rivers showed that the recharge rate of Kushiro Mire decreases greatly. This

indicates that channelization will also cause an increase of sedimentation/nutrient load

imiting factor and flooding in the downstream area around the mire. The NICE-VEG reproduced excel-

lently the invasion of alder in the mire over the last 30 years, which represents a dramatic

advance in our understanding of the drying phenomenon associated with alder invasion.

The reproducibility of these simulation results suggests that the NICE-VEG includes some

of the important factors affecting vegetation succession in Kushiro Mire.

succession (Chapin et al., 1994); (ii) experimental determina-

. Introduction

ushiro Mire (the largest mire in Japan) and the Kushiro Riveratchment (area: 2204.7 km2 located in northern Japan) (Fig. 1)Digital National Land Information GIS Data of Japan, 1976,997) have undergone changes as a result of conversion torban or agricultural use since 1884. Because of the channel-

zation of meandering rivers in the northern part of Kushiroire in the 1970–1980s in order to smoothly drain runoff and

rotect farmlands from flooding, runoff containing nutrientsrom farmland and sediments from short-cut channels haveowed directly into the mire and deposited flood-borne sedi-ent. The dominant species in this mire include alder (Alnus

aponica), reed (Phragmites australis), moss (Polytrichum spp.,

phagnum spp.), sedge (Eriophorum vaginatum), willow (Salixpp.), Japanese ash (Fraxinus mandshurica var. japonica), andeadow sweet (Spiraea salicifolia). Alder has propagated widely

∗ Tel.: +81 29 850 2564; fax: +81 29 850 2584.E-mail address: nakat@nies.go.jp.

304-3800/$ – see front matter © 2008 Elsevier B.V. All rights reserved.oi:10.1016/j.ecolmodel.2008.02.017

© 2008 Elsevier B.V. All rights reserved.

around Kushiro Mire since channelization, due mainly to low-ering of the groundwater level and increased nutrient input,resulting in gradual shrinkage of the mire, as shown in Table 1(Ministry of Environment, 2004). This drying phenomenonshows that human activities have changed the water cycle,and thus vegetation succession, in the mire (Nakayama andWatanabe, 2004, 2006).

Some previous studies have investigated the environmen-tal factors and primary succession of alder, mainly throughfield observations, field experiments, sampling and analysis.These have included: (i) the effect of environmental factors(soil texture, soil depth, litterfall, accumulation rate, root den-sity, organic content, moisture, and nutrients) on primary

tion of relative growth rates under different relative additionrates and nitrogen uptake/fixation in seedlings (Burgess andPeterson, 1986); (iii) biomass increase about 1.2/1.7 times in

226 e c o l o g i c a l m o d e l l i n g 2 1 5 ( 2 0 0 8 ) 225–236

Table 1 – Vegetation change in the Kushiro Mire (Ministry of Environment, 2004)

1947 (ha) 1977 (ha) 1996 (ha) 1947–1977 (ha/year) 1977–1996 (ha/year)

Mire (A) 22,476 19,586 12,303 −96.3 −383.3Alder (B) 2,097 2,941 7,127 28.1 220.3Willow 712 873 976 5.4 5.4Forest 11,218 8,976 10,479 −74.7 79.1Meadow 1,452 2,263 552 27.0 −90.1Farm 2,165 3,789 6,024 54.1 117.6Residential 33 780 1,655 24.9 46.1Road 498 822 1,061 10.8 12.6Bare 0 676 432 22.5 −12.8River&Lake 1,742 1,684 1,782 −1.9 5.2

19,43

Mire + Alder (C) 24,573 22,527A/C 0.91 0.87B/C 0.09 0.13

the presence of increased phosphorus/alkalinity (Ministry ofEnvironment, 2004); (iv) growth conditions in submerged con-ditions, water level variations, pH, EC (electrical conductivity),and DO (dissolved oxygen) determined using Principal Com-ponent Analysis (PCA) (Yabe and Onimaru, 1997; Hotes et al.,2001); (v) oxygen uptake and nitrogen fixation based on cham-ber measurements and determinations conducted in the field(Hendrickson et al., 1990; Grosse et al., 1993); (vi) environ-mental factors related to reed and mire vegetation (Glaseret al., 1990; Wassen et al., 1990, 1995; Yabe and Onimaru,1997). Because these previous studies allowed effective clas-sification of the characteristics of alder and mire vegetation,it is powerful to combine these results with numerical sim-

ulation in order to reproduce the drying phenomenon inthe mire, to evaluate the relationships among water, heat,nutrient, sediment and vegetation, and to simulate or fore-cast the influence of river channelization/meandering and

Fig. 1 – Land cover and observation points of the study area (Kuccatchment) in (a) 1976, and (b) 1997 (Digital National Land Informis 22 km wide by 46 km long, covering the whole Kucyoro River clight-blue lines represent the Kushiro main river and other tribuborder of the study area for vegetation succession simulation inthe river has been channelized in the downstream area of the Ku

0 −68.2 −163.00.630.37

land-cover change on vegetation change downstream in themire.

The objective of the present study was to estimate theinfluence of river channelization and land-cover changes onvegetation change downstream in Kushiro Mire by addingthe interrelationships among water, heat, nutrients, sedi-ment, and vegetation. The author expanded the NICE (NIESIntegrated Catchment-based Eco-hydrology) model series(Nakayama and Watanabe, 2004, 2006, 2007; Nakayama et al.,2006, 2007) to include the vegetation succession processesincluding competition between two species in the KucyoroRiver catchment, Japan (NICE-VEG). The NICE-VEG simulatedthe water/heat budget and vegetation succession processes

iteratively by adding the environmental limiting factor ofcontrolling vegetation succession in relation to submergeddepth, which is the newly developed part of this study. Theauthor conducted a simulation to reproduce the alder inva-

yoro River catchment, a tributary of the Kushiro Riveration GIS Data of Japan). The simulation area in this figureatchment. The dark-blue line is the Kucyoro River, and thetaries of the Kushiro River catchment. The black line is thethe downstream area of the Kucyoro River catchment. In (b),cyoro River.

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e c o l o g i c a l m o d e l l i n

ion that has occurred up to now, and evaluated the influencef river channelization and land-cover change on the vegeta-ion changes that have occurred downstream in Kushiro Mire.he results showed that the new nature restoration project

aunched in this area would be very effective for recovery ofhe mire vegetation, which is now very seriously affected byhe drying phenomenon associated with vegetation changeaused by the invasion of alder.

. Study area

he Kucyoro River catchment (area: 123.0 km2), a tributary ofhe Kushiro River catchment, is located in Hokkaido, north-rn Japan (Fig. 1; Digital National Land Information GIS Dataf Japan, 1976, 1997). The annual mean temperature is about–6 ◦C, making it one of the coldest regions in Japan. Meannnual precipitation is about 1100 mm. In summer, the meanemperature is 17–19 ◦C, and fog is common. Kushiro Mire,ocated in the downstream region of the Kucyoro River, haseen protected under the Ramsar Convention since 1980 andas declared a national park in 1987. Nevertheless, the water

ycle has recently changed and a drying phenomenon hasccurred in the mire (Fig. 1), closely associated with vege-ation change caused by inflow of increased sediment loadrom the surrounding areas due to river channelization, con-ersion of the surrounding areas to urban or agricultural use,nd subsequent invasion of alder into the mire (Nakayamand Watanabe, 2004). These phenomena are closely related toiver flooding, not only in the typhoon season but also duringpring snowmelt (Nakayama and Watanabe, 2006).

Some previous studies have investigated the responses ofgravel bed river to meander straightening and rectification

Brookes, 1985; Schilling and Wolter, 2000; Talbot and Lapointe,002). When reaches are shortened and slopes are steepenedt meander cut-offs, increased sedimentation and floodingccur downstream (Talbot and Lapointe, 2002), causing dry-

ng of Kushiro Mire (Nakamura et al., 1997; Nakayama andatanabe, 2004). In order to arrest sediment-load influx and

llow recovery of the mire, the Japanese government startednew project in 2002, the Kushiro Mire Conservation Plan, to

e-meander the channelized rivers in the catchment (Ministryf Environment, 2002), which included river restoration andssessment of ecological integrity (Jungwirth et al., 2002).herefore, it is very important to assess the effect of riverhannelization on Kushiro Mire, in order to clarify the factorsontrolling species competition between alder, reeds, and wil-ows, and to reproduce the alder invasion in the mire so far, inrder to prevent sediment loading from riparian forests ando recover the mire in the future.

. Model description of NICE-VEG

.1. NICE model series

p to now, the author has developed the NICE (NIES Integratedatchment-based Eco-hydrology) model series (Nakayamand Watanabe, 2004, 2006, 2007; Nakayama et al., 2006,007), which includes surface-unsaturated–saturated water

5 ( 2 0 0 8 ) 225–236 227

processes and assimilates land-surface processes describingvariations of LAI (leaf area index) and FPAR (fraction of pho-tosynthetically active radiation) derived from MODIS satellitedata (Fig. 2). LAI and FPAR are important parameters for eval-uating vegetation growth (Justice et al., 1998). The NICE modelconnects several sub-models from the ground to the surface byconsidering water/heat fluxes, for example, (i) the gradient ofhydraulic potential between the deepest layer of unsaturatedflow and the groundwater level, (ii) effective precipitation cal-culated from the precipitation rate, infiltration of precipitationinto the upper soil moisture store, and evapotranspirationrates, and (iii) seepage between river and groundwater. Detailsare have been described in Nakayama and Watanabe (2004).

3.2. Vegetation succession model

The forest model used in this study is ZELIG (Urban et al.,1991, 1993; Cumming and Burton, 1994), a recent reformationof the basic JABOWA (Bonan, 1989) and FORET (Shugart andWest, 1977) models. Although gap models have been appliedto a variety of forests and differ in some details, they allshare the same basic structure and logic (Urban and Shugart,1992), simulating the establishment, annual diameter growth,and mortality of each tree. Studies comparing the simulationresults of some models have shown that ZELIG is more accu-rate and suitable, despite having some limitations (Busing andSolomon, 2004).

ZELIG simulates the establishment, annual diametergrowth, and mortality of each tree on an array of modelplots (Fig. 2). The primary zone of influence of a singlecanopy-dominant tree defines plot size. The plot is consideredhomogeneous horizontally, but vertical heterogeneity (canopyheight and height to base of crown) is simulated in some detail.Adjacent cells interact through light interception at low sunangles. Seedling establishment, mortality, and regenerationare solved stochastically by using Monte-Carlo simulations,while the growth stage is largely deterministic. The compet-itive environment of the plot is defined by the height, leafarea, and woody biomass of each individual tree determinedby allometric relationships with diameter as follows (Urbanand Shugart, 1992)

H = 137 + b2D − b3D2, where b2 = 2Hmax − 137

Dmaxand b3

= Hmax − 137

D2max

(1)

where D (cm) is diameter at breast height (DBH) and H (cm)is height. The crown diameter CD (m) is derived in Eq. (2)(Garman, 2003).

CD = exp[ln(D)b0 + b1]. (2)

The species coefficients were set as b0 = 0.5212634 andb1 = 0.6300169 for reed alder (Garman, 2003). CD was used tocalculate the simulated predominant species at each mesh

because vegetation cover of GIS data (Fig. 1), which wascompared with the simulated value in the results of thisstudy, was basically categorized from aerial photographs (Dig-ital National Land Information GIS Data of Japan). The tree

228 e c o l o g i c a l m o d e l l i n g 2 1 5 ( 2 0 0 8 ) 225–236

Fig. 2 – Flow diagram of the NICE-VEG model in the expansion of the NICE model series (Nakayama and Watanabe, 2004,2006; Nakayama et al., 2006). Components inside the double frame show environmental limiting factors for vegetationgrowth in Eqs. (4)–(10).

g 2 1

dcL(

wabtcl(Tisttsgaars1

G

r

r

r

wrttidaTdlaTsc

ib

e c o l o g i c a l m o d e l l i n

iameter growth is given in Eq. (3), assuming a logisticurve (JABOWA-derived gap model) and L = cD2 (Prentice andeemans, 1990) where L is leaf area and c is a parameter=0.160694):

dD

dt= GD(1 − DH/DmaxHmax)

(274 + 3b2D − 4b3D2)(3)

here G is a growth rate scalar. The growth rate is calculateds a function of several growth reduction factors as describedelow. Several studies have pointed out that the multiplica-ive approach in ZELIG in the same way as that in JABOWAauses rapid convergence to zero and results in unrealisticallyow growth rates when many growth factors are consideredBugmann, 1996, 2001; Bugmann et al., 1996; Yaussy, 2000).hough some studies have applied “Liebig’s Law of the Min-

mum” (Kienast, 1987; Bugmann, 1996, 2001) to use only themallest of all the growth factors in view of these limita-ions, this approach is based on the unrealistic assumptionhat only the smallest factor limits tree growth and that oneingle environmental factor explains all the variability of treerowth during any given year. In this study, the author appliedstepwise procedure by Bugmann (1996) in order to satisfy thebove two requirement for the environmental limiting factorsfor light Qh, nutrients F, soil moisture M, temperature T, andubmerged depth SD are given as follows (Urban and Shugart,992)

= Gmax[r(Qh)r(F)r(M)r(T)r(SD)]1/3 (4)

r(Qh) = c1{1 − exp[−c2(Qh − c3)]},where Qh = Q0 exp[−kL(h′)] (5)

(F) = c4 + c5F − c6F2 (6)

(M) =[

M ∗ −M

M∗]1/2

(7)

(T) = 4(T − Tmin)(Tmax − T)

(Tmax − Tmin)2(8)

here Gmax is the maximum growth rate, Qh is the incidentadiation at height h (1 = full sun), h′ is the height greaterhan h, k is a light extinction coefficient (=0.4), F is rela-ive soil fertility (dimensionless on 0–1), M is soil moisturendex (on 0–1), M* is maximum tolerable for a species, T isegree-day index (5.56 ◦C base), Tmim and Tmax are minimumnd maximum degree-day limits for species (Tmim = 1420,

max = 3084) used in this study, and SD (m) is submergedepth of vegetation from the ground surface. The available

ight Qh falls off negative-exponentially within the canopyccording to the Beer–Lambert law (Urban and Shugart, 1992).he parameters ci (i = 1–6) are fitted constants dependent onhade-stress and nutrient-stress tolerance (c = 1.11, c = 2.52,

1 2

3 = 0.07, c4 = 0.2133, c5 = 1.789, c6 = −1.014).The author added the effect of submerged depth as a limit-

ng factor r(SD) into Eq. (4) because this effect is very differentetween growths of reed and alder (Hotes et al., 2001; Ohmi

5 ( 2 0 0 8 ) 225–236 229

Environment Preservation Foundation, 2001). In this study theauthor added the limiting factor by using previous data forreed (Ohmi Environment Preservation Foundation, 2001) andalder (Hotes et al., 2001):

rr(SD) = 0.01 for SD < 0.0 or SD ≥ 0.8= 0.01 + 4.95SD for 0.0 ≤ SD < 0.2= 1.0 for 0.2 ≤ SD < 0.7= 7.93 − 9.9SD for 0.7 ≤ SD < 0.8

(9)

ra(SD) = 1.0 for SD < 0.06= 2.98 − 33.0SD for 0.06 ≤ SD < 0.09(= 0.06 + 2�)= 0.01 for SD ≥ 0.09

. (10)

The subscripts r and a in the above Eqs. (9) and (10) showreed and alder, respectively. Although the above Eq. (4) lacks amechanistic basis, this approach yields intuitively reasonableresults, probably superior to both the multiplicative approachand Liebig’s Law (Bugmann, 1996).

3.3. Integration of models

Because the NICE-VEG simulates water/heat budget, masstransport (Itakura, 1984; Shimizu and Arai, 1988; KushiroBranch Office, 2002; Toda et al., 2002), and vegetation succes-sion processes iteratively (Fig. 2), it is possible to estimate theinfluence of river channelization/meandering and land-coverchange on vegetation change downstream in Kushiro Mireby adding the relationships between water, heat, nutrients,sediment, and vegetation. It is very powerful to simulate thespatial and temporal variations of environmental factors con-trolling vegetation change, such as soil moisture, submergeddepth, nutrient loading, and sediment accumulation, and toinput the dynamic changes of these variables into the vege-tation succession model as the boundary of model expansion(Fig. 2).

In the first step, the model simulates water/heat valuessuch as evapotranspiration, river flow discharge and depth,soil moisture, soil temperature, and groundwater level in eachgrid at each time step by inputting the meteorological forcingdata. In the second step, the model simulates the vegetationsuccession processes by inputting the simulated results ofthe first step and the meteorological data. These simulationsdo not include feedback from vegetation change to climaticchange because this study area is not so large, and thereforethis process can be considered negligible.

4. Input data and boundary conditions forsimulation

4.1. Input data

The hourly observation data for downward short- andlong-wave radiation, precipitation, atmospheric pressure, airtemperature, air humidity, and wind speed at a reference

level were calculated from the AMeDAS (Automated Meteo-rological Data Acquisition System) meteorological data (JapanMeteorological Agency, 1970–2003) obtained at Tsurui observa-tion station in the catchment (Fig. 1; 43◦13′48′′N, 144◦19′30′′E,

i n g

230 e c o l o g i c a l m o d e l l

mean elevation 42 m) collected by the Japan MeteorologicalBusiness Support Center for forcing simulation data, and thesevalues were input at each grid because the study area is notlarge.

Mean elevation data (Geographical Survey Institute ofJapan, 1999), soil texture data (Hokkaido National AgriculturalExperiment Station, 1985), vegetation class data (EnvironmentAgency of Japan, 1993) and land cover data (Digital NationalLand Information GIS Data of Japan, 1976, 1997) were con-verted and input to the NICE-VEG with a resolution of 100 m.The simulation area in the vertical direction was divided into20 layers with a weighting factor of 1.1 (finer at the upperlayers). The upper layer was set at 2 m depth, and the 20thlayer was defined as an elevation of −200 m from sea level.Geological structure was divided into four types on the basisof hydraulic conductivity (Kh and Kv), the specific storage ofporous material (Ss), and specific yield (Sy) by using soil sam-ples taken at two depths (0.1 and 1.0 m) and previous data from150 sample data points in the Kushiro River catchment (Oharaet al., 1975). Kushiro Mire consists largely of soils finer thansilt, mainly peat. Details have been presented in Nakayamaand Watanabe (2004).

For the upstream boundaries, reflecting conditions on thehydraulic head were used assuming that there is no inflowfrom the mountains in the opposite direction. The hydraulichead values parallel to the ground level were input as the ini-tial conditions for the groundwater flow model. For the modelsimulating hillslope hydrology, the flow depth and the dis-charge at the uppermost ridges of the mountains were set aszero throughout the simulation. In river cells, outflows fromriverbeds of −1 m mean elevation from the ground surfacewere considered in the same way as in the study by Nakayamaand Watanabe (2006). Important data for alder and reed in thisstudy input to the NICE-VEG were summarized from previousstudies (Burgess and Peterson, 1986; Chapin et al., 1994; Hoteset al., 2001; Ohmi Environment Preservation Foundation, 2001;Ministry of Environment, 2004) (Table 2). Because previousresearch has shown that the nutrient load to the mire hasincreased about 20% during the past 30 years (Ministry ofEnvironment, 2004), the author increased the environmentallimiting factor for soil fertility in Eq. (6) from 0.5 to 1.0 linearlyby considering the relative growth rates (Burgess and Peterson,1986).

4.2. Observed data

The author set groundwater level meters at 10 points, flowdepth meters at 5 points, and a water sampler/turbidity sen-sor at 1 point around the catchment for model validation.Measurements were made over a 3-year period in 2000–2002.The water level (KADEC, MIZU-II) was automatically recordedto data-loggers at hourly intervals. During winter, when thewater level meters were not set up, river discharge data (sup-plied by Hokkaido Regional Development Bureau) at one pointwere used as the observed data. Details have been describedby Nakayama and Watanabe (2004, 2006).

The water turbidity concentration and water temperature(CTI, C105) were automatically recorded to data-loggers at10-min intervals from July 2002 to June 2003. Furthermore,samples of the turbid water below the water surface were

2 1 5 ( 2 0 0 8 ) 225–236

automatically taken using sample bottles (ISCO, Model-3700)at hourly intervals six times during periods of high precipi-tation (July, August, October 2002, and May, June, July 2003)in order to validate the water turbidity concentration andobserve the various nutrient concentrations (total phosphorusand total nitrogen).

4.3. Running the simulation

The simulation area for water/heat budget and mass transportwas 22 km wide by 46 km long, covering the whole KucyoroRiver catchment (Fig. 1). This area is discretized into a gridof 226 × 460 blocks with a grid spacing of 100 m, which issufficiently fine to describe the shapes of meandered chan-nels. The time-step in the water/heat budget simulation was�t = 30 min in order to facilitate numerical stability. The sim-ulation was conducted on an NEC SX-6 supercomputer forthe period 1970 (meandering channel, Fig. 1a) to 2003 (presentchannelized river, Fig. 1b) by using the same data for soil tex-ture, vegetation class, geological structure, and the differentshapes of the river channels. The first 6 months were used asa warm-up period until equilibrium conditions were reached,and parameters were estimated by comparison of simulatedresults using the observed values published in the literature.After simulation results for water/heat budget such as riverdischarge, soil moisture, groundwater level, and soil temper-ature, had been calibrated and validated by the observed datafor 2001–2002 (Nakayama and Watanabe, 2004, 2006), the sim-ulation was able to back-cast the water/heat budget during1970–2000. Then, the mass transport was simulated at thehillslope and the river (Itakura, 1984; Shimizu and Arai, 1988;Kushiro Branch Office, 2002; Toda et al., 2002). The time-stepsused in the hillslope and stream network models were �t = 50and 10 s in order to facilitate numerical stability. The simu-lated results of nutrient and sediment loads were validated bythe observed data for 2002–2003.

The simulation area for vegetation succession was 5 kmwide by 13 km long in the downstream section of the KucyoroRiver in the mire (Fig. 1). Mesh size was 10 m × 10 m, which wassufficient to represent a vegetation colony of plot size (Shinshoet al., 1988; Shinsho and Tsujii, 1996). Simulated results forsoil moisture, groundwater level, submerged depth, nutrientloading, and sediment accumulation averaged at every monthwere input to the vegetation succession model in order tosimulate vegetation growth for every year after a warm-upsimulation of 300 years (Fig. 2). The simulated tree height wascalibrated using values obtained in previous studies. Finally,the simulation of vegetation succession was conducted forthe period 1970–2003, and the results were compared with thevegetation distribution of GIS data (Fig. 1), as described in thefollowing section.

5. Results

5.1. Validation of ecohydrological characteristics in the

mire

The simulated groundwater level was compared and vali-dated by the observed value combining the NIES observation

e c o l o g i c a l m o d e l l i n g 2 1 5 ( 2 0 0 8 ) 225–236 231

Table 2 – Summarized inputted data to the NICE-VEG about alder and reed from the previous researches (Burgess andPeterson, 1986; Chapin et al., 1994; Hotes et al., 2001; Ohmi Environment Preservation Foundation, 2001; Ministry ofEnvironment, 2004)

Alder Reed References

Soil texture (% of total)Sand 64.8 ± 1.9 74.6 ± 4.3 Chapin et al. (1994)Silt 24.7 ± 1.5 15.8 ± 2.8 Chapin et al. (1994)Clay 10.5 ± 0.7 9.6 ± 1.8 Chapin et al. (1994)

Soil depth (cm/horizon)Litter 1.7 ± 0.2 0 Chapin et al. (1994)Organic horizon 2.8 ± 0.8 0 Chapin et al. (1994)A + B horizons 8.8 ± 0.2 5.2 ± 0.5 Chapin et al. (1994)

Litterfall (g/(m2 year))Fine litter 203–260 1.3–1.8 Chapin et al. (1994)Wood 45–47 0 Chapin et al. (1994)Total litter 248–307 1.3–1.8 Chapin et al. (1994)

Biomass for limiting factor of seedlings growth (mg/plant)Control 298 Chapin et al. (1994)N added 823 Chapin et al. (1994)P added 1110 Chapin et al. (1994)

Survivorship of seedlings (% of total) 81 Chapin et al. (1994)

Seedling growth rateHeight growth (cm) 3.28–5.45 Chapin et al. (1994)Total biomass (g/plant) 3.2–3.8 Chapin et al. (1994)Aboveground production (g/(plant year)) 0.3–0.8 Chapin et al. (1994)Relative growth rate (g/(g year)) 0.3–0.8 Chapin et al. (1994)Height increment after 3-year (cm) 2.1–9.2 Chapin et al. (1994)

(Noninoculated) relative growth rates5% N added 3.8–4.9 Burgess and Peterson (1986)10% N added 9.2–10.1 Burgess and Peterson (1986)15% N added 15.1–17 Burgess and Peterson (1986)

(Inoculated) relative growth rates0% N added 5.2–8.0 Burgess and Peterson (1986)5% N added 6.2–7.6 Burgess and Peterson (1986)10% N added 10.3–13.2 Burgess and Peterson (1986)15% N added 14.3–16.8 Burgess and Peterson (1986)

Submerged depth for living (cm) Eq. (10) Eq. (9) Hotes et al. (2001), Ohmi Environment Preservation Foundation (2001)Diameter at breast height (m) 2–16 (Peak:4–6) Ministry of Environment (2004)

MM

daEttrhNeoavuRsvic

Effect of P on livingMonitoring site (total drying weight; g) 0.05P added (total drying weight; g) 0.067

ata (Nakayama and Watanabe, 2004, 2006) and the scannednd digitized data from the previous research (Ministry ofnvironment, 2004) in the mire at the downstream area ofhe Kucyoro River in 2001–2002 (Fig. 3). The relative groundwa-er level takes a minus value downstream of the channelizediver because sediment accumulation by coarser materialsas increased the surface elevation (Nakamura et al., 1997;akayama and Watanabe, 2004). The groundwater level recov-rs further downstream of the channelized river because mostf the sediment is deposited in the upper area. The annual-veraged simulation result reproduces these characteristicsery well in the Kucyoro River catchment, although the sim-lated value overestimates the observed data in the Kottaroiver catchment (in the northeastern area in this figure). This

imulated distribution is very similar to that observed in a pre-ious study by Nakayama and Watanabe (2004), which directlynput changes in meteorological forcing data and vegetationlasses (Fig. 1) between 1977 and 2001.

inistry of Environment (2004)inistry of Environment (2004)

The simulated tree height H (cm) was validated by valuesobtained in previous studies (Shinsho et al., 1988; Shinshoand Tsujii, 1996) (Fig. 4). The observed data are more scat-tered because alder thickets show different types of zonationand vegetation growth in natural river areas (with germi-nated alder thickets showing similar height and DBH) andimproved river areas (with alder thickets raised from seedshowing different height and DBH) (Shinsho and Tsujii, 1996).The simulated value depends greatly on the maximum treeheight Hmax (m), and the following simulation uses Hmax = 9(m) on the assumption that there are no differences in thetypes of zonation and vegetation growth of alder thickets inthe mire.

5.2. Evaluation of hydrogeological changes in the mire

Hydrogeological changes from the 1970 to 2000s were evalu-ated by long-term simulation (Fig. 5). The changes in elevation

232 e c o l o g i c a l m o d e l l i n g 2 1 5 ( 2 0 0 8 ) 225–236

Fig. 3 – Averaged groundwater level relative to ground surface in the mire around the downstream area of the Kucyoro Riverin 2001–2002: (a) observed value combining the NIES observation data (Nakayama and Watanabe, 2004, 2006) and thescanned and digitized data from the previous research (Ministry of Environment, 2004); and (b) the value simulated byNICE-VEG. The red colored region shows the higher submerged achannelization in 1976, and the black cell the straightened river

Fig. 4 – Comparison between simulated tree height H (cm)and observed value versus diameter at breast height (DBH).Lines are results of NICE-VEG simulation, and dots are thepreviously observed values (Shinsho et al., 1988; Shinshoand Tsujii, 1996). The Onnenai and Chiruwatsunai Riversare outside the Kucyoro River catchment, and their data arealso plotted as a reference to show the spatialheterogeneity of alder growth.

rea. The white cell indicates the meandering river beforeafter channelization in 1997, as shown in Fig. 1.

from during this period were evaluated using the GIS-database(Hokkaido Regional Development Bureau, 1975; GeographicalSurvey Institute of Japan, 1999) (Fig. 5a). During this 30-yearperiod, the elevation increases predominantly in the down-stream areas of rivers flowing into the mire (Kushiro mainriver and tributaries; Kucyoro River, Chiruwatsunai River, Set-suri River, Ashibetsu River, Hororo River, Onnenai River, etc.)particularly around the downstream area of the Kucyoro River,which indicates that the increase of sediment delivery hascaused morphological changes in the mire (Nakamura et al.,1997; Nakayama and Watanabe, 2004).

The NICE-VEG simulation shows that the groundwater levelhas decreased predominantly around the channelized riversand the downstream area in the mire (Fig. 5b). The simula-tion result shows that the maximum value of groundwaterdegradation is more than 1 m. The increase of river dischargecaused by channelization results in a decrease of seepage infil-tration from the river to the aquifer, which accounts for thedecrease in the groundwater level downstream of the chan-nelized rivers. The change in groundwater level relative to theground surface (Fig. 5c) was calculated by summation of boththe elevation change (Fig. 5a) and the absolute groundwaterlevel change (Fig. 5b). It was assumed that the alder invasion(Fig. 1) and the decrease in groundwater level relative to theground surface (Fig. 5c) have a close correlation.

5.3. Reproduction of the drying phenomenon and alderinvasion in the mire

The long-term NICE-VEG simulation for the period 1970–2002reproduces qualitatively and quantitatively the invasion of

alder in the mire (Fig. 6a). The GIS data show that reed waspredominant in 1976 (proportion of reed 85.3%; that of alder14.7%), and that alder spread in 1997 (proportion of reed 52.9%;that of alder 47.1%) (Fig. 1), and this was reproduced well by the

e c o l o g i c a l m o d e l l i n g 2 1 5 ( 2 0 0 8 ) 225–236 233

Fig. 5 – Hydrogeological changes from 1970 to 2000s: (a) elevation change evaluated by using the GIS-database (HokkaidoRegional Development Bureau, 1975; Geographical Survey Institute of Japan, 1999); (b) simulated result for groundwaterl (c) eg er a

Nt(i1t1

asi

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evel change (above sea level) after river channelization; andround surface). The white cell indicates the meandering riv

ICE-VEG simulation for both 1976 (proportion of reed 84.0%;hat of alder 16.0%) and 1997 (67.0 and 33.0%, respectively)Fig. 6a). The model forecasts that reed would be predom-nant and that alder would not have invaded the mire in997 if the river had not been channelized in the 1970s (dot-ed line in the figure; proportion of reed 85.0%; that of alder5.0%).

The NICE-VEG also simulated the spatial distribution oflder invasion up to the present time (Fig. 6b and c). Inva-ion of alder is very sensitive to submerged depth, nutrientnput, and other factors. The simulation result indicated that

ig. 6 – Results of simulation of alder invasion in the mire: (a) propatial distribution of alder and reed in 1976 and 1997, respectivhe rivers were channelized in the 1970–1980s (solid line: alder, bepresents the simulated results that would have been expectedotted-line: alder, bold dotted-line: reed). Circles and triangles rehe GIS data (Fig. 1). In (b) and (c), the dark-blue cell represents thownstream area. The simulated results in (b) and (c) reproduce

stimated value of groundwater level change (relative tond the black cell the channelized river.

the degradation of groundwater level relative to ground sur-face and submerged depth are the most predominant factorsassociated with mire shrinkage, which is greatly affected byriver channelization (Talbot and Lapointe, 2002) (Fig. 5). Theincrease in elevation agrees quantitatively with a previous insitu study using Cs-137 fallout concentration analysis, whichindicated that the aggradation was about 2 m at the down-

stream end of the channelized reach of the Kucyoro River(Nakamura et al., 1997; Mizugaki et al., 2006). Although thesimulation result for alder invasion in 1997 underestimatesthe GIS data (Fig. 1b) at the center of the mire, the simulation

portion of alder and reed during 1970–2000, (b) and (c)ely. In (a), the lines are actual simulated results for whenold line: reed), and the area represented by dotted linesif the rivers had not been channelized in the past (solidpresent the proportions of alder and reed calculated frome Kucyoro River. In (c), the river has been channelized in itswell the GIS data shown in Fig. 1(a) and (b), respectively.

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values reproduce qualitatively the spatial distribution of alderinvasion, similar to the GIS data (Fig. 1).

6. Discussion

The drying phenomenon in Kushiro Mire (Fig. 1) is closelyrelated to the increased influx of sediments from the sur-rounding area, where agricultural development, reclamation,and channelization of the river occurred (Nakayama andWatanabe, 2004). The spatial occupation rate of alder invasionis positively correlated with hydrogeological changes (Fig. 5).Some discrepancies in Fig. 6 are attributable to local changesin groundwater level (Nakayama and Watanabe, 2004), het-erogeneity of porosity due to the particle size distributionof deposited sediments (Nakamura et al., 1997), and otherlimiting factors (nutrients, soil moisture, light, temperature)(Urban and Shugart, 1992). The higher nutrient concentrationin the groundwater has also affected the vegetation changein the mire (Ministry of Environment, 2004). Furthermore, itwas noteworthy that the simulation of river channelizationshowed that the recharge rate in the mire decreased greatly(Fig. 5). This indicates that channelization will also cause anincrease of sedimentation/nutrient loading and flooding in thedownstream area around the mire (Talbot and Lapointe, 2002).

The NICE-VEG reproduced very accurately the invasion ofalder in the mire over the last 30 years (Fig. 6) in compari-son with the vegetation cover derived from GIS data (Fig. 1).This result represents a dramatic advance in our understand-ing of the drying phenomenon associated with alder invasion.The reproducibility of the simulation result implies that theNICE-VEG includes some of the important factors associatedwith vegetation succession in the mire. In particular, themodel clarified that the change in submerged depth (Yabeand Onimaru, 1997; Hotes et al., 2001) is one of the crucialfactors effecting alder invasion, accompanied by aggrada-tion of sediment flowing in from the surrounding catchmentsdue to urban or agricultural land use (Nakamura et al., 1997;Nakayama and Watanabe, 2004). Collecting data for variousconditions affecting mire species such as alder, reed, moss,sedge, willow, Japanese ash, and meadow sweet, are importantfor reproducing more correctly the actual vegetation change.These would include hydrology (water level fluctuations;Hotes et al., 2001), water quality (nitrogen, phosphate, potas-sium, pH, electrical conductivity, dissolved oxygen; Burgessand Peterson, 1986; Chapin et al., 1994; Yabe and Onimaru,1997) (Table 2), radiation and temperature, in order to simu-late/forecast the competition among mixtures of plant speciesand to preserve biodiversity (UNEP, 2002) in the mire with highaccuracy (Fig. 1). It is further necessary to clarify interactionsamong water, heat, sediment, nutrients and vegetation usingstatistical approaches such as canonical correspondence anal-ysis and cluster analysis in order to evaluate more favorableenvironmental conditions for the mire and to reproduce theinvasion of alder.

Alder is distributed as thickets in the mire, which show dif-

ferences in zonation and vegetation growth (tree height, crowndiameter, and DBH) along the river, in the mire, and betweenthe natural river and the improved river (Shinsho and Tsujii,1996; Ministry of Environment, 2004). Germinated alder thick-

2 1 5 ( 2 0 0 8 ) 225–236

ets that consist of trees of similar height and DBH, togetherwith bushes of meadow sweet on the forest floor, are foundin natural rivers because of limited sediment supply. An alderthicket raised from seed, which consists of trees of differentheights and DBH, together with reed and sedge on the forestfloor, is found along improved rivers together with small-scalegerminated alder thickets, because the sediment supply is fre-quent and the soil layer resulting from sediment accumulationis thick (Nakamura et al., 1997; Mizugaki et al., 2006). It is nec-essary to include these types of heterogeneity in Eqs. (1)–(10)of NICE-VEG as a function of hydrology, geology, and nutri-ent conditions in order to reproduce more accurately the alderinvasion at the center of the mire (Fig. 6). The characteristicsof alder thickets are closely associated with soil conditions(sand and silt, gravel, organic content, peat soil, etc.). Analysisof isotopes such as Cs-137, Ru-103, Ba-140, Be-7, and Th-232is also an attractive approach for estimating the thickness ofsediment accumulation (Mizugaki et al., 2006).

It is further necessary to clarify the relationship betweenseedling establishment/mortality/regeneration and theexpansion of alder distribution by both airborne and water-borne alder seeds. This is closely related to not only the abovezonation heterogeneity but also the spatial localization ofalder growth. Alder usually grows above the local moundof the mire in the shallow groundwater (Fig. 3). In someparts, this phenomenon is included as a limiting factor forsubmerged depth in Eqs. (9) and (10) (Yabe and Onimaru,1997; Hotes et al., 2001; Ohmi Environment PreservationFoundation, 2001). Because the spatial scale of this moundis often smaller than the grid spacing of the NICE-VEG sim-ulation (100 m), it will be necessary to simulate it by using afiner grid in the future. The NICE-VEG can also include theeffect of airborne seedling distribution by using the forcingmeteorological data for wind speed or coupling with theatmospheric model. This is also related to the differences inevapotranspiration between alder and other mire vegetationspecies such as reed. Investigations of the genetic diversityand population structure of alder (Huh, 1999) are very pow-erful for specifying the source and process of alder growthin combination with ground truth data in addition to plantmaps, aerial photographs, and satellite data by using GPS(Global Positioning System) and GIS.

In order to stop sediment loading and nutrient influx,and to assist the recovery of Kushiro Mire, several ministriesin Japan initiated a new project in 2002 to re-meander thechannelized rivers, to establish a riparian buffer, and to cre-ate a sediment retention pond (Ministry of Environment,2002). This Japanese project will be very informative for clar-ifying certain aspects of the controversy associated withriver restoration in the United States (Palmer and Bernhardt,2006), because US practitioners rarely consider vegetationin their restoration efforts. For effective policy-making, it isnecessary to forecast the effects of re-meandering of chan-nelized rivers and to assess whether reed will recover ifthe current channelized rivers are re-meandered and sedi-ment/nutrient loads are reduced by using the NICE-VEG, as

currently there is a very serious drying phenomenon asso-ciated with the vegetation change caused by alder invasion(Ministry of Environment, 2002). Possible mitigation measuresto preserve the mire would include the establishment of ripar-

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cknowledgements

ome of the simulations in this study were run on an NEC SX-6upercomputer at the Center for Global Environment ResearchCGER), NIES. Dr. M. Watanabe, Keio University, Japan, waselpful in many discussions about the model constructionnd the drying phenomenon in Kushiro Mire. Furthermore,he author would like to thank the Kushiro Development andonstruction Department, Hokkaido Regional Developmentureau, for providing the discharge and agricultural data forhe Kushiro River catchment.

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