riparian vegetation dynamics: insight provided by a process- … · 2013. 10. 2. · riparian area...

ECOHYDROLOGYEcohydrol. 6, 567–585 (2013)Published online 5 December 2012 in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/eco.1348

Riparian vegetation dynamics: insight provided by a process-based model, a statistical model and field data

F. Ye,1 Q. Chen,1,2* K. Blanckaert1,3 and J. Ma11 State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-environmental Sciences (RCEES), Chinese Academy of Sciences

(CAS), Beijing 10085, China2 China Three Gorges University, Yichang 443002, China

3 Department of Limnology of Shallow Lakes and Lowland Rivers, Leibniz-Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany

*CSciCh

Co

ABSTRACT

The dynamics of riparian vegetation in a reach of the Lijiang River, China, are investigated. A new process-based model isdeveloped based on cellular automata, which simulates the key processes in the life cycle of the ten most occurring plant species:germination, normal growth, response to floods and droughts, destruction by high velocities, consumption of resources,colonization and competition. The parameterization of these processes is based on controlled experiments on plants sampled inthe study area. A traditional statistical model is also developed, which relates the vegetation state to four flow-related variables.Both models are assessed based on data from 12 field surveys in the period from 2009 to 2011, during the dry season, the wetseason and at the end of the growing season. Both models predict satisfactorily the spatial distribution of the vegetation cover atthe end of the growing season. Although the statistical model is by definition limited to the steady state conditions at the end ofthe growing season, the process-based model also satisfactorily simulates the temporal dynamics of the vegetation during the dryseason and the wet season. Contrary to the statistical model, the process-based model also satisfactorily simulates the vegetationcover outside the area used for the model parameterization. Thus, process-based models are more robust under flow regimes withspatial heterogeneity and important temporal variations. Field observations and process-based model predictions indicate that theregime of dry season and wet season floods is the main regulator of the vegetation cover in the study area. Copyright © 2012John Wiley & Sons, Ltd.

KEY WORDS riparian vegetation; process-based model; statistical model; field data; flow model

Received 4 February 2012; Revised 28 September 2012; Accepted 24 October 2012

INTRODUCTION

Plant habitats and communities along the river margins andbanks are called riparian vegetation. The merits of riparianvegetation have long been recognized, and efforts havebeen devoted to its study and preservation (Gregory et al.,1991; Naiman and Decamps, 1997; Nilsson and Svedmark,2002). Besides its ecological value in supporting rich plantbiodiversity (Goebel et al., 2003) and maintaining habitatfor fish (Whitledge et al., 2006), birds (Sanders and Edge,1998; Woinarski et al., 2000) and other animals (Giller andTwomey, 1993), riparian vegetation also has stronginteractions with other agents shaping the riparian system,i.e. river flow (Nilsson, 1987; Tabacchi et al., 2000;Nilsson and Svedmark, 2002; Stephan and Gutknecht,2002; Vervuren et al., 2003), sediment and morphology(McBride and Strahan, 1984; Lowrance et al., 1986; Pearceet al., 1998; Abernethy and Rutherfurd, 2000).Traditionally, statistical methods are used to relate the

state of riparian vegetation to hydraulic variables, such asthe time fraction of submergence during growing season,the number of floods, the average submergence depth, the

orrespondence to: Q. Chen, Research Center for Eco-environmentalences (RCEES), Chinese Academy of Sciences (CAS), Beijing 10085,ina. E-mail: [email protected]

pyright © 2012 John Wiley & Sons, Ltd.

average distance to groundwater when not inundated, themaximum durations of submergence and drought, themaximum velocity and flow depth. (Franz and Bazzaz,1977; Auble et al., 1994; Toner and Keddy, 1997; Hillet al., 1998; Shafroth et al., 2002; Henszey et al., 2004). Inthese statistical models, the relations between vegetationand hydraulic variables are solely fitted and do notrepresent physical processes. The main weakness ofstatistical methods mainly lies in their static features: theyare based on the assumption that both the vegetation andthe hydraulic conditions are in an equilibrium steady state.This assumption does not adequately represent the realbehaviour in riparian systems: the recurrence interval ofmajor disturbances is shorter than the life span of the plantin certain cases (Shafroth et al., 2002), and vegetationresponds to extreme events such as floods or droughts(Petts, 1987). Statistical models are inherently unable torepresent the rate of change of riparian vegetationcharacteristics and can, therefore, not be used to predictthe time required for riparian vegetation assemblages toadapt to changing hydraulic conditions. Statistical methodsare also sensitive to over-fitting to sampled data (Hawkins,2004) and require rigorous validation outside the temporaland spatial ranges of the dataset used for fitting the model.Nevertheless, the efficiency and maturity of statisticalmethods make them indispensable when the underlyingprocesses are unclear or hard to parameterize accurately.

568 F. YE et al.

Process-based methods focus on clarifying and model-ling the relevant processes of the investigated system.These methods have been adopted earlier in the study ofterrestrial vegetation dynamics (Simioni et al., 2000;Higgins et al., 2001). Some of the existing analyticalmethods and model configurations can be readily adaptedto the modelling of riparian vegetation, e.g. the logisticfunction describing the plant growth (Ridolfi et al., 2006),the influence of environmental factors on plant growth(Borgogno et al., 2007), the carrying capacity of a givenarea (Fernandez-Illescas and Rodriguez-Iturbe, 2003) andthe competition and colonization behaviours related to thecarrying capacity (Tilman, 1994). Also, in-stream vegeta-tion dynamics have been mechanistically incorporated inthe framework of hydrodynamic and water qualitymodelling for at least two decades (Ambrose et al., 1993;Park et al., 1995). Although they may be less sophisticatedthan their terrestrial counterparts, the treatment of sub-merged plants as an additional term in the flow momentumequation (Park et al., 1995) is very relevant to themodelling of riparian plants, which will be submergedoccasionally during their life cycle. However, riparianvegetation has always been treated as a fixed componentfor the parameterization of bottom roughness or drag force(Stoesser et al., 2003; Straatsma and Baptist, 2008).The application of process-based methods to riparian

vegetation dynamics is still in its early stages: fewapplications have been reported and consistent proceduresfor model development and assessment have not yet beenestablished. The level of sophistication of the modelling ofthe processes and the spatial dimensions of the models varyamong the existing applications: the rules governing theprocesses can be either empirically defined (Stringhamet al., 2001), fuzzy logic based (Glenz et al., 2008) orrealistically describing the physical function (Rood et al.,1999; Dixon and Turner, 2006); whereas the spatialdimension ranges from 0-D analytical formulations(Camporeale and Ridolfi, 2006) to 2-D spatially explicitmodels (Glenz, 2005). As compared with statisticalmodels, process based model should intrinsically be morerobust under different flow regimes and complex flowpatterns, and they should have the potential to providebetter understanding of the underlying processes anddynamics. But the modelling and parameterization ofprocesses require more detailed field data that are scarcelyavailable.Models for riparian vegetation dynamics interact with

models for the flow. The riparian vegetation increases theresistance to flow. This effect is typically accounted for byincreasing the friction coefficient that parameterizes theboundary roughness (Stoesser et al., 2003; Straatsma andBaptist, 2008). The flow model provides all relevanthydraulic variables for the riparian vegetation model, suchas flow depth and flow velocity.The present paper has five major contributions. First, it

develops a new process-based model for the interactions ofriver flow with riparian vegetation. Second, it develops astatistical counterpart of the process-based model. Third, itprovides field data on vegetation dynamics from a 3-year

Copyright © 2012 John Wiley & Sons, Ltd.

observation campaign. Fourth, it makes use of these fielddata to parameterize the processes in the process-basedmodel and to assess the performances of the process-basedand statistical models. Fifth, it enhances insight in thedynamics of the riparian vegetation and the role of flowevents based on the results of the models and the fieldobservations.

METHODS

Study site

The watershed of the Lijiang River, located in SouthwestChina, has a subtropical monsoon climate and strongseasonal fluctuations. The annual average discharge is120m3 s�1 in this area, but the discharge can vary from 12to 12 000m3 s�1. A number of reservoirs operate on theupstream branches of the river, and more reservoirs areplanned. Their purpose is to reduce flow fluctuationsduring flood events and maintain a minimum discharge forcruise ship navigation during the dry season. The alteredflow regime affects the dynamics of the riparian vegetationand other aquatic organisms. Riparian vegetation modelsallow quantifying the effects of the altered flow regime and,hence, assessing the impact of the reservoir operations.

The 2.1 km long studied site is situated in the middlereach of the Lijiang River (25�060N, 110�250E) (Figure 1).The reach starts as a straight compound channel headingsouthwest, and then gradually bends toward the southeast,with multiple bars and shallow areas around the bend. Thetopographical heterogeneity provides potential habitats forriparian plants. During the growing season of most localriparian plants (April to October), the monthly averagedtemperature is above 18 �C, implying that temperature isseldom a limiting factor for active plant growth. Because ofthe onset of the dry season flow recharge by means ofreservoir operation in 1987, the minimum daily averagedflow discharge has been increased, but discharges as low as20m3 s�1 still occur in the dry season (August to Marchnext year). So, drought stresses may have considerableinfluence on some wetland species during their early lifestage and, especially, during their germination period. Inthe wet season (April to July), discharges over 1000m3 s�1

are common during flood events. The entire area, includingthe riparian zones, may be flooded several times when themain flow shifts from the base flow channel in the innerpart of the bend area to the outer part. In some areas, thehigh flow velocities are destructive to most of the riparianspecies. Furthermore, depending on the timings anddurations of the high flow events, inundation stressesmay vary from year to year, affecting both the growingcondition and the survival rate of the riparian plants.

Data collection

Hydrological records, including daily averaged water level,flow discharge and precipitation, have been collected atthree stations since 1976: the upstream Guilin Gaugingstation (25�140N, 110�190E), the downstream YangshuoGauging station (24�460N, 110�300E) and the Chaotian

Ecohydrol. 6, 567–585 (2013)

Figure 1. The study site in themiddle reach of the Lijiang River in SouthwestChina (25�060 N, 110�250 E) and location of sampling transects.

569RIVER RIPARIAN VEGETATION DYNAMICS MODELLING

Gauging station in an upstream tributary (25�110N,110�300E). Cross-sectional velocity patterns were mea-sured in 2010 at base flow conditions and at moderate flowconditions with a SonTek/YSI acoustic Doppler profiler in31 cross-sections with an average stream-wise spacing of100m. Topographic measurements were performed in2009 to delineate both the riparian topography (measuredwith Topcon GPT-1000 total station) and river bedtopography (measured with SonTek/YSI acoustic Dopplerprofiler). Another field survey in 2010 investigated thecharacteristic grain sizes of the bed material both in theriparian area and in the channel, where a Peterson grabberwas used to collect bed material samples below water.Fixed transects (3–10m long in the flow direction and

extending perpendicularly to the flow from the open waterto the mature tree stands) were placed throughout the studysite (Figure 1) to sample the vegetation state. Along eachtransect, the number of plants (for sparse stands) or thepercentage cover (for dense stands) and the average heightwere recorded for each species. Table I summarizes the tenmost occurring species. Plant surveys were conductedthrough a 3-year period during the growing season, withtwo surveys in 2009 (18 April and 12 September), sixsurveys in 2010 (9, 29 March, 12 May, 5 June, 19 July and


20 September) and four surveys in 2011 (19 January, 25March, 31 May and 4 August). Each survey took 2 to5 days. The exact locations of the transects were fixed inthe same year but varied slightly from year to year.

In order to obtain data for the parameterization of theprocess-based model, controlled experiments were con-ducted for investigating the responses of different speciesto stresses. According to plant survey, the simulatedspecies fit into three functional groups: Wetland, Uplandand Facultative/weedy (F/w) (Table I). Typical specieswere selected from each functional group for stressexperiment: Polygonum hydropiper L. for wetland species;Leonurus japonicus Houtt. and Perilla frutescens (L.)Britton for upland species. Because of limited labours, theexperiments for F/w species were not conducted at the localsite. So, another weedy graminoid Cynodon dactylon (L.)Pers., which is similar in growth habit and plant size as thetwo simulated F/w species, was used as a substitute. Theother species in each functional group were parameterizedempirically based on the selected species in experiment,except that Rumex maritimus L. was parameterized basedon existing literatures (Nabben et al., 1999). So theunderlying assumption is that the responses to stresses aresimilar among species in the same group. The mainpurpose of incorporating more than one species in eachgroup is to take into account different plant sizes and otherphysiological attributes (Table I). During the experiments,average sized plant ramets were transplanted into pots andcultivated for 2weeks under controlled soil moisture toscreen out the impaired ones. Then 15 to 20 plants perspecies and per stress regime treatment were selected forthe subsequent experiments. Two major stresses, inunda-tion and drought, were investigated. For the inundationstress of the wetland species, three stress regimes wereimplemented: complete submergence with and withoutlight, and half submergence. The submergence withoutlight is dedicated to the light-limited situations of highstage inundations that are often accompanied by highturbidity. The other two regimes are for less severe stresses.For the inundation stress of upland species, two stressregimes were implemented: complete submergence withand without light. Because upland species lack resistance toeven mild inundation stress, the effect of half submergenceis assumed equivalent to the complete submergence withlight. For the drought stress of wetland and F/w species,four soil moisture levels (19, 15, 11 and 7%) wereimplemented, representing mild to severe stresses. Thedrought stress of upland species was omitted because nosigns of drought-induced impairment were observed at thestudy site during the 3-year survey period. An experimentrepresenting normal growth was performed for eachspecies in order to provide the reference data necessaryfor the interpretation of the experiments with inundationand drought stresses. According to the sensitivity of eachspecies’ response to stresses, observations were madeevery 1–3 days, recording the length of the main stem, thenumber of living leaves and the survival of the subjects inorder to estimate the parameters for impeded growth,biomass loss and survival rate (Table I).

Ecohydrol. 6, 567–585 (2013)

Table

I.Param

etersforthesimulated

species.

Latin

name

Germination

rate

Germination

period

Availableseeds

perm

2Seedweight(g)

Maxim

umbiom

ass(g)

Maxim

umgrow

thrate

Consumption

rate

Lifespan

(days)

Rum

exmaritimus

L.

0.1/0.4a

January–

May

60.0015

2.7

�0.13

0.123

138

Polygonum

hydropiper

L.

0.1/0.4

March–A

pril

500.002

1.65

�0.12

0.017

240

Polygonum

sagitta

tum

L.

0.1/0.4

March–A

pril

470.00363

1.05

�0.12

0.024

240

LeonurusjaponicusHoutt

0.3

March–A

pril

10.00268

3�0

.13

0.333

260

Chrysopogon

aciculatus

(Retz.)Trin.

0.3

February–

April

100

0.000567

0.27

�0.52

0.015

300

Digita

riacilia

ris(Retz.)Koeler

0.3

February–

April

100

0.000567

0.27

�0.52

0.015

300

Euphorbia

pekinensisRupr.

0.3

April–

July

700.002

1�0

.13

0.035

260

Mosla

scabra

(Thunb.)

C.Y.Wuet

H.W.

0.3

April–

July

700.002

1.2

�0.13

0.069

260

Perillafrutescens

(L.)Britto

n0.3

April–

June

150.0012

1.5

�0.13

0.111

260

Urena

lobata

L.

0.3

April–

July

100.002

3�0

.13

0.833

260

Com

petitive

index

Moisturestress

threshold(days)b

(t1/t 2)c

Responseto

mild

moisturestress

b

(klag/R

loss/R

mm)d

Responseto

severe

moisturestress

b

(klag/R

loss/R

mm)d

Responseto

fatal

moisturestress

b

(klag/R

loss/R

mm)d

Fatal

velocity

(ms�

1)

Responseto

fatalvelocity

(Rmv)e

040/40,

5/20

0.0/Nf/N,1.0/N/N

N/0.01/N,0.37/N/N

N/0.01/0.05,N/0.02/0.25

2.5

0.3

115/30,

10/25

0.0/N/N,1.0/N/N

N/0.02/N,0.73/N/N

N/0.02/0.025,

N/0.03/0.03

2.5

0.3

0.8

6/13,20/30

0.0/N/N,1.0/N/N

N/0.03/N,0.83/N/N

N/0.03/0.035,

N/0.01/0.02

2.5

0.3

25/10,1000/1000

N/0.01/N,1.0/N/N

N/0.05/0.1,

1.0/N/N

N/0.05/0.8,

1.0/N/N

20.3

225/25,

1000/1000

N/0.01/N,1.0/N/N

N/0.01/N,1.0/N/N

N/0.01/0.05,1.0/N/N

50.1

220/25,

20/30

N/0.00/N,1.0/N/N

N/0.01/N,0.83/N/N

N/0.01/0.015,

N/0.01/0.015

50.1

24/7,

1000/1000

N/0.01/N,1.0/N/N

N/0.05/0.1,

1.0/N/N

N/0.05/0.8,

1.0/N/N

20.3

24/7,

1000/1000

N/0.01/N,1.0/N/N

N/0.05/0.1,

1.0/N/N

N/0.05/0.8,

1.0/N/N

20.3

24/7,

1000/1000

N/0.01/N,1.0/N/N

N/0.05/0.1,

1.0/N/N

N/0.05/0.8,

1.0/N/N

20.3

24/7,

1000/1000

N/0.01/N,1.0/N/N

N/0.05/0.1,

1.0/N/N

N/0.05/0.8,

1.0/N/N

20.3

aOnlyforwetland

species:germ

inationratesunderdry/wetcondition.b

Value

forinundatio

nstress

anddroughtstress,separatedby

comma.

ct 1,threshold

dividing

mild

andsevere

stresses;t

2,threshold

dividing

severe

andfatal

stresses.d

k lag,lag

coefficientdefining

theretarded

grow

thrate;R

loss,decreasingrateof

plantbiomass;Rmm,deathrateunderm

oisturestresses.e

Rmv:deathrateunderfatalvelocity.fW,w

etland

species,alwaysfoundclosetowater,

rarely

oroccasionally

foundin

uplands.

gU,uplandspecies,alwaysfounduplands,rarely

foundcloseto

water.h

F/w,facultativ

e,weedy

gram

inoidfoundeither

near

water

orin

uplands.

iN,nocorrespondingeffect.

570 F. YE et al.

Copyright © 2012 John Wiley & Sons, Ltd. Ecohydrol. 6, 567–585 (2013)


Process-based riparian vegetation model

The vegetation model consists of a cellular automaton,which discretizes the study area into computationalelements. Plants of the same species within a singlecomputational element are considered homogenous in age,biomass and response to stresses. Hence, care should betaken when defining the appropriate element size. Inpractice, element size is chosen according to the smallestscale of the following measures: (1) the resolution of theavailable topographical data, and (2) the scale ofheterogeneity of the hydraulic parameters that impact onthe plants. To improve its flexibility, the mesh of thecellular automata can be either regular or irregular. All thesimulated species share the same mesh, but the neighbour-hood ranges can be set on a per-species basis, unlike thetraditional cellular automaton. In the present case study, anirregular mesh is implemented to accommodate thedifferent degrees of hydraulic heterogeneity in the studyarea. The smallest element size is about 7m2, which issufficient to resolve the maximum local gradients of water

Figure 2. Simplified flow chart of the integrated model. The flow chart of thcorresponding to the process list numbers (i ~ vii) in the section of ‘Process-ba


levels and flow velocities that primarily determine theobserved plant distribution pattern.

In the plant model, explicit descriptions are made for themost important processes in a plant life cycle and the mostimportant stresses affecting the riparian plants. Figure 2schematizes these processes in a simplified flow chart. Ageneral description of some important processes is given inthe following text.

(i) Germination is a complicated process that is mainlycontrolled by temperature, moisture, oxygen andlight (Raven et al., 2005). The germination processis not treated explicitly in the developed modelbecause the experimental data are lacking. Duringthe simulation, a period (e.g. February to April forP. hydropiper) is assigned to each species withinwhich the plant is allowed to germinate with anempirically defined germination rate. For eachspecies in a computational element, this process isformulated as

e vegetation model is divided by dash lines into several parts and labelledsed riparian vegetation model’. ‘NB’ is the abbreviation of ‘Neighbouring’.

Ecohydrol. 6, 567–585 (2013)

572 F. YE et al.

n ¼ SdaSe�Pg (1)with

Pg ¼sign Rg � Rnd

� �� þ sign Rg � Rnd� �2

(2)

where Sda is the number of available seeds per unitarea (m�2; Table I); Se is the area of the computationalelement (m2). Rg is the germination rate of the species(d�1; Table I); | | indicates an absolute value of a number;Rnd is a random number between 0 and 1; sign() is afunction that extracts the sign of a real number and gives+1 for positive numbers and�1 for negative number; Pgis the indicator of germination or dormancy, which takes1 (germinate if Rnd ≤ Rg) or 0 (dormant if Rnd> Rmm),thus representing the random germination of a plant witha probability equal to the germination rate. The onlyexplicitly modelled process affecting germination is anenhanced germination rate (Table I) after the drawdownof a flood, because the wet substrate exposed after theflood provides favourable conditions for germination,especially for wetland hygrophyte species.

(ii) Normal growth under favourable conditions. Aftergermination, a plant is allowed to grow normally ifthere are no disturbances or stresses. The model uses alogistic curve (Nelder, 1961) to simulate the normalgrowth of each plant species (Figure 3), which isformulated as

Y tð Þ ¼ K1þ ae�bt (3)

where K is the maximum biomass that an individualplant can achieve under favourable conditions (g); a isa non-dimensional intermediate variable related to themaximum biomass K(g) and seed weight, i.e. theinitial weight of a plant Y(0), as a=K/Y(0)� 1; t istime (d); b is the maximum growth rate during a plantlife cycle (d�1). The derivative of Equation (3),

d

dtY tð Þ ¼ bY tð Þ 1� Y tð Þ=K½ � (4)

0 50 100 1500

0.5

1

1.5

2

Bio

mas

s Y

(t)(

g)

Time t(d)

1.65

P. hydropiper

a=824b=0.12(1/d)K=1.65(g)

Figure 3. The logistic curve, according to Equation (3), fitted to thenormal growth, Y(t) of Polygonum hydropiper. The coefficients a, b and K

are defined by Equation (3).


is the time variant growth rate (d�1).In this case study,the values of maximum biomass K and seed weight Y(0) are adopted from the United States Department ofAgriculture plant database (http://plants.usda.gov) andsupplemented by the dry weight measurements of theplant samples collected at the study site, whereas themaximum growth rate is a rough estimation primarilybased on the controlled groups in the experiments.With the values of K and b (Table I), Equation (4) issolved on discrete time steps through

Y t þ Δtð Þ ¼ bY tð Þ 1� Y tð Þ=K½ �Δt þ Y tð Þ (5)with the known initial condition Y(0) (Table I). Thetime step adopted to solve Equation (5) is of the orderof 1 day and determined by the time scale ofperceivable biomass change during the experiments.With this small time step, the error introduced byapplying the Euler scheme of the first order, Equation(5), remains negligible as compared with othersources of uncertainty (see further in the Discussion).

(iii) Moisture stress caused by water level fluctuation. Thisstress includes both the water deficit caused byprolonged low-water level and the anaerobic conditioncaused by inundation. Under the condition of waterdeficits (drought), the decreased cell volume alongwith the increased concentration of cell sap and theformation of abscisic acid lead to a slowing down ofgrowth as well as leaf senescence (Vandersande et al.,2001; Larcher, 2003). Inundation forces a plant toutilize anaerobic respiration, which is less efficientthan aerobic respiration and may result in a significantreduction of the biomass stored in the rhizome and anincrease of ethano, ethylen and abscisic acid in theplant cells, leading to leaf abscission (Vandersman et al.,1993; Larcher, 2003). The light condition duringsubmergence is also important, because high turbidityfurther constrains the photosynthesis (Nabben et al.,1999). Generally, the response of a plant to drought orinundation can be categorized into three forms: retardedgrowth, biomass loss, and death, corresponding to thedegree of stresses, i.e. how long the plant is exposed toadverse conditions. In this study, stress experiments areconducted for the parameterization of the modelledspecies (Table I). For each species, two thresholds ofstress duration (t1, t2) are implemented in the process-based model defining three stress levels (mild: t< t1;severe: t1< t< t2; fatal: t> t2). The particular responseregime under each stress level includes at least one of theaforementioned forms that are expressed by

(a) retarded growth,

Y t þ Δtð Þ ¼ klagbY tð Þ 1� Y tð Þ=K½ �Δt þ Y tð Þ (6)where klag is a non-dimensional coefficient regulatingthe retarded growth rate.

(b) biomass loss,

Y t þ Δtð Þ ¼ 1� Rlossð Þ�Y tð Þ (7)

Ecohydrol. 6, 567–585 (2013)

http://plants.usda.gov


where Rloss is the decreasing rate of plant biomass(d�1).

(c) biomass loss with death,

Y t þ Δtð Þ ¼ 1� Rlossð Þ�Y tð Þ�Pmm (8)

with

Pmm ¼ sign Rnd � Rmmð Þj j þ sign Rnd � Rmmð Þ2(9)

where Rnd is a random number between 0 and 1; Rmmis the death rate under fatal stress (d�1); | | indicates anabsolute value of a number; sign() is a function thatextracts the sign of a real number and gives +1 forpositive numbers and�1 for negative number; Pmm isthe indicator of survival or death under fatal moisturestress, which takes 1 (survival if Rnd>Rmm) or 0(death if Rnd≤Rmm), thus representing the randomdeath of a plant with a probability equal to the deathrate. Table I summarizes the values of the parametersklag (Equation 6), Rloss (Equation 7) and Rmm(Equation 9) for each of the species under each ofthese three stress levels.

(iv) Destruction by high speed flow. This process describesthe physical damage of plant shoots and leaves andsubsequent death of the whole plant caused by the dragforce of the flow. The process is expressed by

Y t þ Δtð Þ ¼ Y tð Þ�Pmv when v≥vmaxð Þ (10)

with

Pmv ¼ sign Rnd � Rmvð Þj j þ sign Rnd � Rmvð Þ2 (11)

where vmax is the threshold for fatal velocity (m s�1);Rmv

is the death rate under fatal velocity (d�1); Pmv is theindicator of survival or death under fatal velocity. Table Ilists the values of vmax and Rmv for each species in thestudy area.

(v) Consumption of available resources by plants. Thecarrying capacity for plant growth of a given spatialextent is limited. In this study, an empirical formu-lation is developed for the modelling of the limitedresources,

R ¼ k�SD2

(12)

whereR (non-dimensional) is the original resources of thegiven spatial extent, not considering the consumption byplants; S is the area of the given spatial extent (m2); D isthe characteristic diameter of the substrate (m), whichaccounts for the effect of soil texture. Generally, finersubstrates support denser vegetation, thanks to their


higher capacity of retaining moisture and nutrients. Thecoefficient k accounts in an approximate way for otherattributes of the soil that may significantly affect habitatqualities, such as shading and external nutrient loads.Despite the occurrences of these effects on other parts ofthe Lijiang River, they are not apparent in the simulatedreach, so the values of k all take 1�0 in the case study. Theresource consumption rate Ci of a species (g

�1), i.e. theresources consumed per unit biomass of plant species i,were empirically defined by field survey through thefollowing procedures: a reference area with a monocul-ture of a stand of species was selected where its biomasswasmost likely to attain a maximum; the area’s resources(R0) for plant growth were estimated according toEquation (12); the plants were harvested and the dryweight Bimax (g) was measured; The resource consump-tion rate was estimated as

Ci ¼ R0=Bimax (13)

Bimax differs from K in Equation (3): Bimax refers to the

maximum biomass that a stand of plants can achieve inmonoculture, whereas K refers to the maximumbiomass that an individual plant can achieve. Becauseof intra-specific competition, Bimax divided by itscorresponding plant number is generally smaller thanK.On the basis of Equations (12) and (13), the availableresources Ra in a computational element can beexpressed as

Ra ¼ Re �Xni¼1

CiBi (14)

where Re is the original resources of a computationalelement calculated by Equation (12); Bi is the totalbiomass of all the plants of species i within thecomputational element (g). In this way, the consumptionof the commonly recognized resources for plant growth,such as open space, nutrients and other soil character-istics (Tilman, 1994; Fernandez-Illescas and Rodriguez-Iturbe, 2003), is related to the plant biomass through thenon-dimensional resources R.

(vi) Colonization and competition. These two processesare closely related to the consumption of availableresources. As Equation (14) indicates, the availableresources of a computational element will decrease asthe simulated plants gain biomass. When the resourcesin a local computational element are depleted, a plantwill either search for neighbouring open space withinits neighbourhood range or compete with coexistingplants. The neighbourhood range is set for each speciesaccording to the attributes showing its spreadingcapability, e.g. the extent of stretched shoots or creepingstems. A competitive index is assigned to each speciesaccording to their morphology and growing habit(Grime, 1973) to identify stronger and weakercompetitors (Table I). The process of competition isformulated as

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574 F. YE et al.

ΔBk ¼Xni¼1

sign ck � cið ÞCiΔBiCk

� �(15)

whereΔB is biomass change (g), which is pre-computedin each time step not considering the effect of resourcelimit; C is the resource consumption rate (g�1); c is thecompetitive index; the subscripts denotes differentspecies. This formulation simply indicates that whencompetition occurs, stronger competitors keep normalgrowth at the cost of weaker competitors. And if thecoexisting species are equally strong or weak, none ofthemwill gain biomass because the term ck� ci equals 0.Although this configuration is somewhat drastic, wewillretain this simple treatment until further experimentaldata are available.

(vii) Life cycle of a plant. The death of an annual species orthe dormant of a perennial species is often regulatedby temperature (e.g. frost). In the study area, the localannual species can have rather short-life spans andmay complete their life cycles well before the coldseason. For this reason, the model uses empirical lifespans for most annual species based on field observa-tions. The life cycle is checked at every time step by the‘Update’ procedure (Figure 2): if a plant’s age reaches itsprescribed life span, it will be treated as dead.

Figure 4. The spatial coupling between hydrodynamic and plant meshes.Solid lines, hydrodynamic elements; dashed lines, polygons formed byconnecting the centres of adjacent hydrodynamic elements; small dots,centres of plant elements; large dots, nodes of hydrodynamic elements.

Hydrodynamic model

The flow simulations were performed with the open sourcemodelling programme SELFE (Gloucester Point, Virginia, U)(Zhang and Baptista, 2008). Some minor modifications wereimplemented to accommodate the integration with thevegetation model. The model is based on the finite elementmethod, which solves the 3D hydrostatic flow equations. Theaverage element size was 96m2 overall, and the mesh wasrefined at the downstream bend area where the topographyand the flow patterns are more complex. Daily meandischarges adjusted from the Guilin gauging station recordswere specified at the upstream boundary; whereas dailymeasurements of water levels at the study site were specifiedas the downstream boundary condition. Flow in rivers isstrongly determined by the boundary roughness. In SELFE,bottom drag is formulated as

CD ¼ 1k0 lndbz0

� ��2(16)

where CD is the non-dimensional drag coefficient; k0 is vonKarman’s constant (0�4); db is the thickness of the bottomcomputational element (m); z0 is the bottom roughness height(m). In this study, the effect of riparian plant is tentativelyincorporated into z0 for each computational element in theplant simulation mesh as

z0 ¼ max zb0;Xni¼1

YiBLiPCið Þ" #

(17)


where zb0 accounts for the grain roughness of the bed materialnot including plant effects; n is the number of species; Yi is thebiomass of a single plant of species i (g);BLi is the biomass tolength ratio of species i, which is obtained by field sampling(Table I); PCi is the percentage cover of species i. Thisformulation indicates the additional roughness heightprovided by a dense cover (100% percentage cover) ofplants equals the average height of the plants, whereas theadditional roughness height of a sparse cover is weightedby the percentage cover. However, this formulation stillneeds more rigorous tests. The hydrodynamic modelprovides all hydraulic parameters that are relevant in thevegetation model.

Coupling of process-based vegetation model andhydrodynamic model

In physical terms, the coupling between the principalvariables of the vegetation and hydrodynamic models isstraightforward. The vegetation model reads the values ofhydraulic parameters (water level and flow velocity) todetermine their effects on plant growth. After updatingthe plants’ state, the plants’ effects on flow feed back tothe hydrodynamic model through a modification of theboundary roughness parameterization (Figure 2). Thenumerical implementation of this coupling is not straight-forward, however, because different spatial and temporaldiscretizations are used in the vegetation and hydrodynam-ic models (Figure 4).

Spatial coupling is needed when different sets of meshesare used for the hydrodynamic and plant models. Inpractice, the element size of the plant model is much finerthan that of the hydrodynamic model. Therefore, bilinearinterpolation is used to pass variables from the hydrodynamicelement to its encircled plant elements. The values areinterpolated to the centres of the plant elements and consideredhomogeneous within a plant element. For example, the valueof plant element ‘1’ (Figure 4) is calculated as

fplant 1ð Þ ¼ fplant xc; ; ycð Þ ¼fhydro ið ÞLi þ fhydro jð ÞLj þ fhydro kð ÞLk (18)

Ecohydrol. 6, 567–585 (2013)


where f is any variable interchanged between the hydro-dynamic and the plant models; i, j, k are the nodes of theencircling hydrodynamic element, and L is the linear shapefunction of the hydrodynamic element

Li x; yð Þ ¼xjyk � xkyj� �þ yj � yk� �xþ xk � xj� �y

2Δijk

Lj x; yð Þ ¼ xkyi � xiykð Þ þ yk � yið Þxþ xi � xkð Þy2ΔijkLk x; yð Þ ¼

xiyj � xjyi� �þ yi � yj� �xþ xj � xi� �y

2Δijk

8>>>>>>><>>>>>>>:

(19)

where Δijk is the area of the hydrodynamic element ijk. In theother direction, when passing values from plant elements tohydrodynamic elements, a set of polygons (dashed lines inFigure 4) are defined by connecting the geometric centres ofthe neighbouring hydrodynamic elements, with each polygonidentified by a node, then each nodal value is determined froma weighted average of all the plant elements encircled by itspolygon. For example, the value of hydrodynamic node i inFigure 4 is calculated as

fhydro ið Þ ¼X8n¼2

fplant nð Þw nð Þ (20)

where w(n) is weight function taken as

w nð Þ ¼ Δn=X8m¼2

Δm (21)

where Δn,Δm are the area of corresponding plant elements.Temporal coupling is needed when different time steps

are used for the two models. In practice, the time step of theplant model is in days or hours (1 day in the present casestudy), which correspond to the time scale of the plantexperiments and the discernible changes of plant biomasswhile the time step of the hydrodynamic model is usuallyin seconds (20 s in the present case study) in order toachieve numerical stability. For the two flow field variablesconcerned, velocity is taken as the maximum value of allthe hydrodynamic time steps between two plant time steps,because the resistance of the plant stem to high speed flowis controlled by a velocity threshold above which the stemis bound to break. On the other hand, surface elevation istaken as the averaged value between two plant time steps,because the effect of submergence or drought is cumula-tive, which is better represented by the average level.

Statistical riparian vegetation model

A statistical model was constructed for the speciesP. hydropiper along the straight compound channelupstream of the bend in the initial part of the investigatedarea (Figure 1). The set-up of the model followed thegeneral idea of the traditional methods (Franz and Bazzaz,1977; Auble et al., 1994; Toner and Keddy, 1997; Hillet al., 1998; Henszey et al., 2004). The dependent variableis the percentage cover at the end of growing season. A totalof 67 quadrats were selected from the sampling transects of


2009 to fit the statistical model. It could be argued that thequadrats in the same transect are not strictly independent.However, the field survey showed that the plant belt on thecompound channel was continuous along the flow directionin 2009, which means that the distribution was not limitedby dispersal. Furthermore, the flow field was quasi uniformalong the straight compound channel, which means that thegradients along the flow direction were much smaller thanthose perpendicular to the flow direction. Therefore, thequadrats in the same transect perpendicular to the flowdirection are considered to have adequate independency forthe set-up of the statistical model. The field data of 2010 and2011 were used for validation. The statistical vegetationmodel was constructed according to the following steps:

1. The explanatory hydraulic variables (predictors) wereselected and calculated. On the basis of the local fieldsurvey and previous studies (Franz and Bazzaz, 1977;Auble et al., 1994; Toner and Keddy, 1997; Hill et al.,1998; Henszey et al., 2004), seven hydraulic variableswere initially selected: the time fraction of submer-gence, the number of floods, the average submergencedepth, the average distance to groundwater when notinundated, the maximum submergence duration, themaximum drought duration and the maximumvelocity. The time span of these variables rangesfrom March (before germination) to August (maturestage) so as to encompass all the environmentalconditions that may affect the plant distribution. Thefirst four variables represent the average disturbance,whereas the last three variables represent the extremeconditions. The level of ground water is assumedequal to the surface level of the nearest open water.These hydraulic variables were calculated from thehydrodynamic model.

2. The functional relations between the dependentvariable and the predicting hydraulic variables weredetermined by means of generalized additive model(GAM; Hastie and Tibshirani, 1990)

G E yð Þ½ � ¼ s0 þXni¼1

si xið Þ (22)

where y is the response variable to be predicted; E(y)is the expected value of y; xi is a set of n predictorvariables; G() is the link function, which relates thelinear predictor on the right-hand side to E(y); si arenon-parametric smooth functions, which means theydo not provide estimated parameters for each predictoras in a regression analysis, thus the form of thefunctions are unspecific. In this study, si was taken asa spline term with 3 degrees of freedom. Because thesmoothing process of GAM is data oriented (Yee andMitchell, 1991) and prone to over-fitting, it is onlyused as a preliminary analysis to identify the likelyfunctional relationships (e.g. linear or quadratic). SASsoftware version 9�2 (SAS Institute Inc, 2008) wasused to perform the computation of GAM analysis andall other statistical analysis.

Ecohydrol. 6, 567–585 (2013)

576 F. YE et al.

3. Once the likely functional relations between theresponse variable and the predictors are determinedby the result of GAM analysis, the non-parametricterms were replaced by parametric functions throughfitting the data into generalized linear models (Nelderand Wedderburn, 1972)

G E yð Þ½ � ¼ p0 þXmj¼1

pjXj (23)

The only difference between Equation (23) andEquation (22) is the non-parametric smoothing,functions si(xi) in GAM are replaced by a set oflinear terms pjXj in generalized linear models, whereXj is any combination of the predictor variables xi. Inthis case study, we took into account linear terms xiand quadratic terms xi

2 while dropping higher-orderterms and cross terms (xixj) to preserve modelinterpretability and avoid data over-fitting. Series ofgeneralized linear models were set up based ondifferent combinations of environmental variablesand the likely functional relationships between eachenvironmental variable and the response variable. Onthe basis of the observations, the model assumes anormal distribution of plant percentage cover alongthe environmental gradients. Because it is reasonableto interpret the effect of the environmental stresses onthe plant biomass as a percentage change of theexisting biomass rather than an absolute value, loglink is used for relating E(y) to the linear predictiveterms on the right-hand side of Equation (23) tostrengthen the linear relationship between them.

4. The fitted model was assessed with five criteria. (1) Isthere any apparent disagreement between modelstructure and ecological processes? (2) Are theestimates of parameters significant as indicated by pvalues. (3) Is there any apparent inadequacy suggestedby the standard residual plot? (4) Is the modeladequately fitted to the sample as indicated, forexample, by the corrected Akaike information criter-ion (AICc)? Akaike information criterion (AIC)measures the relative goodness of fit based on thenumber of parameters and the maximized value of thelikelihood function for the estimated model (Akaike,1974). Its corrected form AICc (Burnham andAnderson, 2004) additionally takes into account theeffect of finite sample sizes and is formulated as

AICc ¼ AICþ 2k k þ 1ð Þn� k � 1 (24)

where k is the number of parameters; n is the samplesize. (5) Is there any inadequacy suggested by modelvalidation? The model candidates were validated withindependent field data that were not used in the modelestablishment. First, the model candidates wereapplied to the flow regime of 2009 in the entirecompound channel to check the performance outsidethe spatial range of the fitted data. Then they wereapplied to the flow regime of 2010 and 2011 to assess


the performance outside the temporal range of thefitted data. When all criteria (1)–(5) are met, themodel with the simplest structure is preferred.

RESULTS

Process-based vegetation model

A 3-year (from 2009�01 to 2011�08) simulation of thedynamics of the flow and the 10 most occurring plantspecies (Table I) of the study area was performed with theprocess-based vegetation model in order to assess themodel’s performances and to gain insight in the vegetationdynamics. The physiological parameters of each modelledspecies are summarized in Table I. Wetland species(hygrophyte) are subject to both inundation and droughtstresses, and their habitats are often located at intermediateelevations where exposure to flow fluctuations during thegrowing season (April to September) is maximum. Thesewetland species are selected as benchmarks for the modelassessment, because the stronger seasonal dynamics andlarger inter-annual variations pose greater challenge to themodel. For the sake of conciseness, this section will focuson the dominant wetland species, P. hydropiper.

The 3 years considered had different hydrologicalregimes that led to considerable differences in the riparianvegetation dynamics. In general, the effects of the flowregime on the wetland species were favourable in 2009,stressful in 2010 and intermediate in 2011. First, thetemporal evolution of the overall vegetation dynamics will beconsidered for these three different hydrological regimes,based on the ‘average cover’, which is defined as the ratio ofthe area occupied by plants to the total area of hygrophytehabitat (area with inundation durations of 10 to 85% duringthe growing season) (Figures 5 and 6). Second, spatialdistributions of the plant coverwill be considered at the end ofthe growing season (Figures 7–10).

1. During the recruitment phase of P. hydropiper(mainly in dry season, before the onset of majorfloods that often occur after April), more seedlingsare found under the favourable flow regime in 2009(17 to 19% observed average cover; Figure 5(a,b)),than under the stressful flow regime in 2010(observed average cover of less than 2%; Figure 5(c)). The simulation results indicate an intermediatesituation in 2011. During the active growing phase(mainly in wet season), observations and simulationsindicate that floods cause an almost 100% loss ofplants in 2010 (Figure 5(c)), whereas simulationsindicate that floods cause losses of 50% in 2009(Figures 5(a,b) and 2011(Figure 5(d)).In general, the agreement of the model predictionswith the field survey data is satisfactory (Figure 5),especially considering the complexity of the mod-elled processes and the inherently high uncertainty inecological models. But the number of surveys ofP. hydropiper (two in 2009, eight in 2010 and four in2011) is insufficient to validate the temporal

Ecohydrol. 6, 567–585 (2013)

Figure 5. Observed and simulated average cover of Polygonum hydropiper and corresponding water level. (a) Along the compound channelupstream of the bend in 2009; (b) in the bar area downstream of the bend in 2009; (c) in the entire study area in 2010; (d) along the compoundchannel upstream of the bend in 2011. The reference (0m) of the relative elevation indicates strong flood, the base flow stage of the dry season is

around �2�6m.

Figure 6. The observed and simulated average cover of Rumexmaritimus in the entire study area in 2010 with corresponding waterlevel fluctuations. The x-axis is truncated at the end of July, which isbeyond the life span of R. maritimus. The reference (0m) of therelative elevation indicates strong flood, the base flow stage of the dry

season is around �2�6m.


evolution of the vegetation cover (Figure 5). Thesurvey data do not capture, for example, the peakvalue and the important variations during the activegrowth phase. Survey data and model predictions forthe species R.Maritimus in 2010 (Figure 6) showsatisfactory agreement and capture the temporalevolution, and lend further credibility to theprocess-based model. Importantly, the process-basedmodel predicts rather accurately the vegetation coverat the end of the growing season.


2. The plant distribution pattern along the upstreamcompound channel shelf at the end of the 2009 growingseason is characterized by a plant belt of P. hydropiper,which is the dominant wetland species. Thewidth of thebelt is 10–30m (perpendicular to the flow direction;solid bars in Figure 7) and an average percentage coverof 15% (Figure 5(a)). The width of this belt at varioussampling locations agrees well with simulation results(Figure 7(a)). In each transect, the percentage covertypically increases gradually outwards from the rivertowards a maximum value at some distance in theriparian area and then gradually decreases towards theouter edge of the riparian belt. This distribution inthe transects is determined by the flood disturbancegradient. Figure 8 compares observations and modelpredictions of the percentage vegetation cover in sometransects that are representative for the model perfor-mances. The locations of these transects are shown inFigure 7. The vegetation cover in the bar areadownstream of the bend is too low to allow for ameaningful comparison. The flood disturbance isrepresented in Figure 8 by the inundation duration,defined as the fraction of time that a zone has beeninundated during the growing season, which servesas a composite variable that accounts for thetopography and the flow regime. The process-basedmodel predictions generally agree satisfactorily withthe field survey data, especially the peak values andthe average values of the percentage cover along thetransect are well predicted. The spatial distribution

Ecohydrol. 6, 567–585 (2013)

Figure 7. Distribution of Polygonum hydropiper along the straight compound channel upstream of the bend at the end of the growing season in 2009.The distributions measured in the transects are indicated by bold solid bars, and the simulated distributions are indicated by the colour code. (a)

Simulations with the process-based model; (b) simulations with the statistical model.

Figure 8. Process-based model prediction (solid line) versus observed (dashed line) percentage cover (PC) of Polygonum hydropiper in somerepresentative transects along the straight compound channel upstream of the bend at the end of the growing season in 2009, and inundation duration (ID)

during the growing season.

578 F. YE et al.

along the transect, and the location of the peakvalues of the percentage coverage, show consider-able discrepancies.

The plant distribution pattern at the end of the 2010growing season reflects extreme conditions. There is noapparent wetland plant belt within the study area andonly scarce individuals of P. hydropiper along theupstream compound channel. The observations and


simulations both suggest that there is no P. hydropipercover exceeding 5% (not shown).

At the end of the 2011 growing season, the P. hydropiperbelt along the upstream compound channel shelf isnarrower than in 2009, with an average cover of 3�8%(Figure 9), whereas the average cover of P. hydropiper inthe downstream bar area remained low throughout thegrowing season. Figures 9 and 10 further compare thesimulated plant distribution patterns and the percentage

Ecohydrol. 6, 567–585 (2013)

Figure 9. Distribution of Polygonum hydropiper along the straight compound channel upstream of the bend at the end of the growing season in 2011.The distributions measured in the transects are indicated by bold solid bars, and the simulated distributions are indicated by the colour code. (a)

Simulations with the process-based model; (b) simulations with the statistical model.

Figure 10. Process-based model prediction (solid line) versus observed (dashed line) percentage cover (PC) of Polygonum hydropiper in somerepresentative transects along the straight compound channel upstream of the bend at the end of the growing season in 2011, and inundation duration (ID)

during the growing season.


cover along transects to observations, leading to similarconclusions with respect to the model performance asdescribed earlier for 2009.

Statistical riparian vegetation model

The statistical model was only constructed for P. hydropiperalong the straight compound channel upstream of the bend.The analysis by means of the GAM procedure of the seven


initially selected variables indicated that the time fraction ofsubmergence during the growing season, average submer-gence depth and maximum submergence duration eachdisplay a single peak quadratic relationship with thedependent variable, whereas the average distance to theground water when not inundated shows a linear relationshipwith the dependent variable. The effect of these fourexplanatory variables has a clear ecological meaning:

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580 F. YE et al.

the adequate growth of wetland species requires anintermediate submergence disturbance through the growingseason (quadratic relationship) and a high moisture demandduring low-flow period (linear relationship). The ecologicalmeaning of the remaining three variables (the number offloods,maximum drought duration andmaximumvelocity) ishard to interpret through the GAManalysis, whichmeans thatcare should be taken when the final fitted model includes anyof these variables.All the combinations of the seven variables were

investigated by a series of generalized linear models.Table II arranges these model candidates by ascendingorder of ΔAICc, which is defined as the difference ofAICc value between each model and the best fit model.The models with ΔAICc values larger than 4 aredismissed. The best fit model (Model 1) performedpoorly during model validation: unrealistically largevalues of percentage cover were predicted outside thesampling area in 2009, and the error in the 2011predictions was also large compared with the observa-tions. The second best fit model (Model 2) displayed noapparent inadequacies when applied to the flow regimes of2009–2011 and was selected as the final model. Model 2relates the percentage cover of P. hydropiper to fourenvironmental variables

log Yð Þ ¼ �49:3294þ 62:4774�i� 43:9644�i2þ74:3�aw� 58:623�aw2þ11:3704�mw� 23:2815�mw2 þ 7:6828�ad

(25)

where Y is percentage cover; i is the time fraction ofsubmergence during the growing season; mw is themaximum submergence duration (d); aw is the averagesubmergence depth (m); ad is the average distance toground water when not inundated (m).The vegetation patterns along the straight compound

channel upstream of the bend at the end of the growingseason predicted by the statistical model are similar tothose predicted by the process-based model, as illustratedin Figures 7 and 9.

Table II. The structures of the statistical model candidatesarranged by ΔAICc.

Model candidates ia nf b mvc mwd md e awf adg AICch

#1 2i 0 j 2 0 0 2 1k 0#2 2 0 0 2 0 2 1 2#3 2 0 2 0 1 2 1 2#4 2 0 2 2 0 2 1 3#5 2 0 2 0 0 2 0 3#6 2 0 0 0 0 2 1 3#7 2 0 2 0 0 0 1 4

a i, the time fraction of submergence during growing season. b nf, numberof floods. c mv, maximum velocity. d mw, maximum submergenceduration. e md, maximum drought duration. f aw, average submergencedepth. g ad, average distance to ground water when not inundated.h ΔAICc, corrected Akaike information criterion.

i 2, quadratic termincluded. j 0, not included. k 1, linear term included.


DISCUSSION

Effects of floods in dry and wet seasons

In the study area, the flow regime during the dry and wetseasons governs the riparian vegetation dynamics anddetermines the distribution of riparian plants at the end ofthe growing season.

The spring floods in dry seasons are shorter in durationand have smaller peak flows compared with the floods inwet seasons. However, spring floods play a critical role inthe recruitment of local riparian species, especially wetlandspecies, by providing moist substrate that enhancesgermination. The timing of the spring floods is alsoimportant. A favourable spring flood should coincide withthe germination period of most local species and allowfor sufficient time for seedling development before the onsetof the wet season. The 2009 spring flood (Figure 5(a,b)),for example, occurred in the germination period ofP. hydropiper, approximately 1month before the onsetof the wet season, leaving sufficient time for P. hydropiperseedlings to develop into juveniles. This explains the highaverage cover attained at the beginning of the 2009 wetseason. The 2010 spring flood occurred too early for mostlocal species (Figure 5(c)). Only the wetland speciesR.maritimus benefited from this flood (Figure 6), but thesubsequent prolonged drought between the spring flood andthe wet season led to a sharp decrease in vegetation cover.The 2011 spring flood, on the contrary, was too late and leftinsufficient time for seedling growth before the wet season(Figure 5(d)).

The floods during the wet season act as a limiting factorof plant growth in the study area. The intensity of wetseason floods usually exceeds the tolerance of riparianspecies and leads to the thinning of the existing vegetationcover. Although wet season floods always exert negativeeffects on riparian upland species, they are indispensiblefor wetland species because frequent flood disturbanceshelp alleviate the competitive pressure from upland species.A favourable regime of wet season floods is characterizedby frequent floods with relatively short durations, lowstages and low peak flows. Among the three samplingyears, the regime of 2009 met these criteria best (Figures 5(a,b)): the base stage remained low during the wet season(April to August) and was close to the base stage during thedry season; floods were frequent (at least six obviousfloods) and occurred at relatively constant time intervals;the duration of each flood was short (mostly less than10 days); the stage fluctuations were mild (mostly within1�5m). All of these characteristics helped exclude uplandcompetitors in the transitional zones and, at the same time,prevented excessive inundation stress for wetland species.The 2010 wet season displayed extreme regimes: the basestage was significantly higher in the wet season than in thedry season; floods had longer durations, with three majorfloods each over 15 days long between April and July; thestage fluctuations were intense, typically more than 2�0m(Figure 5(c)). These characteristics suggest that most of theseedlings that germinated near the base flow of the dryseason were submerged for lengthy periods of time during

Ecohydrol. 6, 567–585 (2013)


the wet season, and that the velocity during flood eventswas more destructive. The 2011 wet season flood regimewas intermediate, with a low stage of wet season base flow,less flood frequency than in 2009 and less flood intensitythan in 2010. It should be noted that flood-induced thinningand regular plant growth occur simultaneously during thewet season. This is evident from the 2011 scenario(Figure 5(d)), when the P. hydropiper cover experienceda 72% increase before and after the wet season (Figure 9).

0

0.2

0.4

0.6

0.8

1

76 77 78 79 81 93 99 04 05 06 07 08 09 10 11

Cor

e ar

ea in

dex

Year

0

5000

10000

15000

20000

25000

76 77 78 79 81 93 99 04 05 06 07 08 09 10 11

Tot

al a

rea

(m2 )

Year

Upland species

Wetland species

Figure 11. Habitat quality of the local upland and wetland speciessimulated by the process-based model. (top) Total area (m2); (bottom) core

area index.

Figure 12. Potential elevation span of local wetland species constrained by thThe reference (0m) of the relative elevation is near the base flow stage of the

too early for the germination period of most local speci


The model validation and assessment (Figures 5–10)indicate that the regime of dry season and wet seasonfloods is the major regulator of the wetland plantdistribution in the study area. Hence, an approximate andpreliminary qualitative assessment of riparian plant habitatcan be obtained by solely examining the flood regimes onhydrographs. The validated process-based model has beenapplied to assess the vegetation for 15 years within the timespan 1976–2011 (Figure 11) using hydrological records asinput data. These 15 years included the years 2009–2011used for the model validation. The results are summarizedin Figure 11 in which ‘total area’ and ‘core area index’ areused to quantify habitat quality of the upstream straightcompound channel. Here, ‘total area’ is defined as the totalarea of all the computational elements with specifiedspecies, and ‘core area index’ is defined as the percentageof ‘total area’ that is comprised of core area. The range ofcore area or edge area is specified by edge depth, in thiscase one computational element. The hydrographs of themost favourable years (1980 and 2009) as well as those ofthe most stressful years (1999 and 2010) are plotted inFigure 12. In each subfigure, a dashed line indicates theupper bound of the potential elevation of wetland habitat,which is constrained by the inundation range of springfloods and the competition from upland species, whereas adotted line indicates the lower bound, which is constrainedby the wet season base flow stage. Generally, the flowregime that produces more and better habitat has a widerspan between the upper and lower bounds (Figure 15left),whereas a narrower or negative (Figure 15 right)

e maximum stage of spring floods and the base stage during the wet season.dry season. In 2010 (bottom right), the first spring flood (January) occurses and is not considered as an adequate spring flood.

Ecohydrol. 6, 567–585 (2013)

582 F. YE et al.

span is more likely to be found in scenarios of less andpoorer habitat. However, this assessment solely based onflow hydrographs should only be used for preliminaryassessment.

Comparison between process-based and statistical models

1. Statistical models are based on the hypothesis ofequilibrium between the explanatory hydraulic vari-ables and the vegetation cover. Therefore, they are bydefinition static and intrinsically unable to account forthe vegetation’s response to hydraulic events, such asfloods or droughts. The results of the process-basedmodels and the field observations (Figures 5 and 6)reveal the important effect of such hydraulic eventson the vegetation dynamics. The application range ofstatistical models is, therefore, mainly limited to theend of the growing season.

2. Even at the end of the growing season, theequilibrium assumption still constrains the applicabilityrange of the statistical model. For the illustrated case ofthe P. hydropiper species, the plants have developedinto a mature stage and are steady at the end of thegrowing season. The explanatory hydraulic variables(Equation 25) represent integral quantities, whichmeans that they encompass the entire growing seasonof the species fromMarch to September. Therefore, theequilibrium assumption is reasonably satisfied for thisspecies. The equilibrium assumption may be inad-equate for species that have longer, perennial lifecycles. For example, the 3-year field survey shows thatthe recruitment of pioneer tree seedlings (mostly

Figure 13. Distribution of Polygonum hydropiper along the bend at the endare indicated by bold solid bars, and the simulated distributions are indicat

simulations with the


Pterocarya stenoptera C. DC. and Triadica sebifera(L. Small) in the study area is primarily controlled bythe flow regime in the weeks after germination. Thispattern agrees with the theory of the recruitment boxmodel (Mahoney and Rood, 1998; Rood et al., 1999;Rood et al., 2003; Dixon and Turner, 2006). Onceestablished, the subsequent survival of these treeseedlings depends mainly on extreme flood events, orspecifically, whether the flow velocity is large enough tobreak the stems.Drawbacks of statisticalmodels for suchperennial species include: (1) the difficulty to determinethe time scale of the relevant hydraulic variables becauseof the age differences among species; (2) the temporalvariation of the seedling’s resistance to flow velocity asopposed to the constant explanatory hydraulic variable.Process-based models are intrinsically more appropriateto simulate the dynamics of such perennial species.

3. Statistical models are sensitive to over-fitting of thesampled data. The parameters and the structure of thestatistical model for P. hydropiper were found to besensitive to the dataset used for model fitting, whichcompromises the reliability of the model’s predictionsoutside the temporal or spatial data ranges used formodel fitting. This is illustrated by the statistical model’sextremely large over-predictions of the cover of P.hydropiper around the bend and in the downstream bararea (Figure 13(b)). The process-based model, onthe contrary, performs satisfactorily in this area(Figures 13(a) and 14), and the accuracy of itspredictions is comparable with that along thecompound channel upstream of the bend (Figures 7–10).

of the growing season in 2011. The distributions measured in the transectsed by the colour code. (a) Simulations with the process-based model; (b)statistical model.

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Figure 14. Process-based model prediction (solid line) versus observed (dashed line) percentage cover (PC) of Polygonum hydropiper in somerepresentative transects along the bend at the end of the growing season in 2011, and inundation duration (ID) during the growing season.


4. In large riparian areas with heterogeneous channelshapes and local flow conditions, statistical models mayrequire a large amount of sampling sites and differentmodel structures corresponding to each characteristicriver section to achieve reasonable results. One singleprocess-basedmodel, on the contrary, may be applicablethroughout the entire heterogeneous area. Process-basedmodels’ main difficulty is the definition of the keyprocesses and the subsequent parameterization of theseprocesses, which require a good understanding of thestudied system and adequately designed experiments.The difficulty of establishing a statistical model is relatedto the spatial and temporal heterogeneities involved,whereas the difficulty of establishing a process-basedmodel is related to the complexity of the processesinvolved.

In summary, the results and discussion indicate thatprocess-based models are more robust under different flowregimes and complex flow patterns than statistical models.Contrary to statistical models, process-based models arenot limited to equilibrium conditions, but can represent thevegetation dynamics, including responses to hydraulicevents such as floods and droughts. Moreover, process-based models have the potential to enhance insight in thevegetation dynamics.

Uncertainties in the process-based model

As mentioned before, the general agreement of the process-based model predictions with the field survey data issatisfactory (Figures 5–10), especially considering thecomplexity of the modelled processes and the inherentlyhigh uncertainty in ecological models. Some further factorsthat contribute to the uncertainty in the model predictionswill now be discussed.

1. The model’s simplifications and assumptions. Thestresses exerted by the river flow are identified as the


major regulators of the vegetation dynamics. Otherfactors, such as temperature and soil fertility aretreated in a rather simplified way. Temperature islikely to affect the timing and rate of germination,whereas soil fertility may have impact on the densityof a patch of plants and the biomass of an individualplant. Another important simplification is that thegroundwater level is taken equal to the water surfacelevel in the river, which may be accurate only in thearea adjacent to the open water. Also, plant rootdynamics are not incorporated in the model, whichaffects the model accuracy under drought conditions.

2. The uncertainty in process parameterizations. Mostof the parameters concerning the stress response areobtained in experiments under controlled conditionsthat isolate the effects of one stress, and which maynot be entirely representative of the more complicatedconditions in the field. During the prolonged droughtin 2010, for example, the movement of insects wassignificantly enhanced and herbivory acted as a sideeffect of drought stress on R.maritimus. Furthermore,several empirically defined parameters such as germin-ation period, fatal velocity and competitive index mayaffect the predictive ability of the vegetation model.Also, in the aspect of model coupling, the currentformulation and parameterization of additional dragforce of plants is far from mature. Further study isneeded on these processes in order to refine theirparameterization.

3. The uncertainty in the plant cover quantification. In theobservational records and the simulation outputs,percentage cover is the main variable describing theplant state. The experimental value of the plant cover isbased on subjective visual estimation, whereas thesimulation value is converted from the biomass and thenumber of the plants. Therefore, the plant coverquantification is inherently approximate.

4. The resolution of the topography and the accuracy of theflow simulation. In the reported study, a spatial scale of a

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584 F. YE et al.

fewmetres is required to adequately resolve the gradientsof the observed plant distribution, which implies that asimilar characteristic spatial scale is required for therelevant hydraulic variables. Therefore, high resolutiontopographic and hydraulic data are required. In thereported study, the topography of the riparian area wasmeasured manually with a total station, whereby thedensity of the sampled elevation points varied based onthe surveyor’s judgement of local elevation gradients.Although the resolution of the measurements is con-sidered sufficient for this study, additional higherresolution data could be obtained, for example, withremote sensing techniques. The riverbed topography ismore difficult to measure with high spatial resolution. Inthis study, the riverbed topographywas interpolated frommeasured transect profiles about 100m apart, whichcorresponds to ameasuring grid that ismuch coarser thanthe resolution of the riparian topography. The accuracyand spatial resolution of the flow simulations, especiallyof the velocity predictions, are affected by the accuracyand spatial resolution of the riverbed topography.

ACKNOWLEDGEMENTS

This research was sponsored by National Basic ResearchProgram 973 (No. 2010CB429004 ), National NatureScience Foundation of China (50920105907, 51279196).The third author was partially funded by the ChineseAcademy of Sciences Visiting Professorship for SeniorInternational Scientists, grant number 2011T2Z24, and bythe Sino-Swiss Science and Technology Cooperation forthe Institutional Partnership, grant number IP13_092911.

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