© IWA Publishing 2011 Water Science & Technology | 64.1 | 201114

Numerical modelling: a tool for better constructedwetland design?
Guenter Langergraber

ABSTRACT
There is a need for a simpliﬁed computer-based design tool for subsurface ﬂow constructedwetlands (CWs) which is based on process-based numerical models. Parameters of existing design
guidelines and rules have been derived from experiments under speciﬁc conditions. Therefore
designing CWs using these parameters is limited to these conditions (i.e. temperature, wastewatercomposition, ﬁlter material, etc.). Process-based numerical models describe the main processes in

Guenter LangergraberInstitute for Sanitary Engineering and Water
Pollution Control,
University of Natural Resources and Life Sciences,
Vienna (BOKU),
Muthgasse 18, A-1190 Vienna,
AustriaE-mail: guenter.langergraber@boku.ac.at

CWs in detail. If the design of CWs is based on these models it will be possible to design CWs for avariety of different boundary conditions and therefore the main limitation of existing design
guidelines and rules could be overcome. The use of process-based models is currently limited mainly
due to their complexity in structure and use. To make numerical modelling a useful and applicable
tool for design, a simpliﬁed computer-based design tool that does not require special knowledge ofnumerical modelling is needed. Additionally, simple models for pre- and post-treatments are also
required. Besides allowing designs for various boundary conditions, design tools based on processbased models can also predict the dynamic behaviour of the designed system thus showing e.g., thehigher robustness of CWs against ﬂuctuating inﬂows and peak loads compared to other treatment
solutions. Such a tool could increase the quality of CW design and the acceptance and use of CW
simulation in practice.
Key words | constructed wetland, design, process-based numerical model, simulation tool, subsur-

face ﬂow

INTRODUCTION
Constructed wetlands (CWs) are engineered water treatment
systems that optimize the treatment processes found in natural environments. CWs are popular systems which efﬁciently
treat different kinds of polluted water and are therefore sustainable environmentally friendly solutions. A large numberof physical, chemical and biological processes are active inparallel and mutually inﬂuence each other (e.g. Kadlec &Wallace ). Therefore CWs are complex systems and fora long time have been often considered as ‘black boxes’.
Little effort has been made to understand the main processes
leading to contaminant removal. Recently, some efforts havebeen made to understand the processes in CWs in more detail,and modern tools from environmental microbiology, plantbiology, ecology and molecular biology have been used forthis purpose (e.g. Faulwetter et al. ).
Because of the complexity of the processes in CWs, all theavailable design guidelines and rules are based on empirical
doi: 10.2166/wst.2011.520

rules of thumb, such as those using speciﬁc surface area requirements (e.g. Brix & Johansen ; DWA-A 262 ; ÖNORM
B 2505 ) or simple ﬁrst-order decay models (e.g. Rousseau
et al. ; Kadlec & Wallace ). Rousseau et al. ()compared different design models for horizontal ﬂow (HF)
CWs whereby ﬁrst-order degradation models have beenshown to be the most suitable for design purposes. However,
the authors claimed that accurate results can be obtained
only when the parameters have been gained from systems operating under similar conditions (climatic conditions, wastewatercomposition, porous ﬁlter material, plant species, etc.).
During the last years an increasing interest in numericalmodelling of subsurface ﬂow (SSF) CWs can be observed.
The main objective of numerical modelling of CWs is toobtain a better understanding of the processes governing
the biological and chemical transformation and degradation
processes, thus providing insights into the ‘black box’, and15

G. Langergraber | Numerical modelling: a better tool for CW design?

last but not least to evaluate and improve existing design criteria (Langergraber ). By using dynamic process-based
numerical models, i.e., models that do not consider CWs
as a ‘black box’ but describe main processes in detail, as
the basis for design tools, it can be expected that thedesign tools will be more reliable and could be applied toa wide range of different boundary conditions.
In this paper the currently available process-based numerical models for subsurface ﬂow CWs are presented brieﬂy. The
limitations of these models to be applicable as design tools are
discussed and research questions are presented that need to beaddressed on the way towards making process-based numerical models applicable for design purposes.

Water Science & Technology | 64.1 | 2011

• Forquet et al. (): diphasic numerical model (based onﬁnite-elements), description of the parallel movement of
air and water in a VF ﬁlter.

2. Reactive transport models for saturated ﬂow
conditions• Reactive transport models applicable only for constant
ﬂow rates:

○

○

○

PROCESS-BASED NUMERICAL MODELS FORCONSTRUCTED WETLANDS
During the last few years models with different complexities
have been developed for describing processes in subsurface
ﬂow CWs. In subsurface ﬂow CWs no free water level is visible, and water ﬂows either horizontally or verticallythrough the porous ﬁlter media. Only a few models havebeen published that are able to model biochemical transformation and degradation processes.
HF systems can be simulated when only water ﬂow saturated conditions are considered. A series or network ofcontinuously-stirred tank reactors (CSTRs) is most frequently
used to describe the hydraulics, and reactions are modelledwith various complexities. For modelling vertical ﬂow (VF)CWs with intermittent loading, transient variably-saturated
ﬂow models are required. Due to the intermittent loading,these systems are highly dynamic, adding to the complexity
needed to model the overall system. Models applicable to
VF CWs use either the Richards equation or simpliﬁed
approaches to describe variably-saturated ﬂow.
The following list summarizes available process-based
numerical models for subsurface ﬂow CWs in which only
models with minimum complexity in describing water ﬂow
and/or biochemical processes are listed. More informationon the models can be found in recently published review
papers (Langergraber , ; and Langergraber et al.
a) and in the original references, respectively.

• Reactive transport models with a tanks-in-series
approach for water ﬂow:

○
○

○

○

simulation of tracer experiments and oxygen transport
in intermittent sand ﬁlters.

Chen et al. (): only carbon transformation processes.
Wynn & Liehr (): carbon and nitrogen transformation processes.
Marsili-Libelli & Checchi (): carbon and nitrogentransformation processes.
Rousseau (): carbon and nitrogen transformation
processes, reaction model in matrix notation basedon the mathematical formulation of the ActivatedSludge Models (ASMs; Henze et al. ).

• Reactive transport models coupled to a complex groundwater ﬂow model:○

PHWAT (Brovelli et al. a, b): carbon and nitrogentransformation processes, reaction model in matrix
notation based on ASMs, coupled with the groundwater ﬂow model MODFLOW, an extension of
MODFLOW for unsaturated zones is on the way tobeing implemented.

3. Reactive transport models for variably-saturated ﬂow• Reactive transport models with simpliﬁed approached for
simulating the variably-saturated water ﬂow:○

1. Complex ﬂow models but single-solute transport only

• Schwager & Boller (): ﬁnite-element ﬂow model,

Mashauri & Kayombo (): only carbon transformation processes.
Mayo & Bigambo (): only nitrogen transformations processes.
Wang et al. (): only nitrogen transformations
processes.

○

McGechan et al. (): different horizontal layers to
describe variably-saturated water ﬂow, considers poolsof organic matter, ammonium, nitrate and oxygen;microbiologically controlled transformations between
these pools.
FITOVERT (Giraldi et al. ): different horizontal
layers to describe variably-saturated water ﬂow,

16○

G. Langergraber | Numerical modelling: a better tool for CW design?

reaction model in matrix notation based on ASMs fordescription of carbon and nitrogen transformation
processes, implemented in Matlab®.
Freire et al. (): combination of CSTRs and deadzones to describe variably-saturated ﬂow, descriptionof the removal processes for the dye AO7 only.

• Reactive transport models coupled with ﬂow models that
use the Richards equation to describe variably-saturated
water ﬂow:

○

○

○

○

CW2D (Langergraber ; Langergraber & Šimůnek): implemented in the HYDRUS software, reaction model in matrix notation based on ASMs fordescription of carbon, nitrogen and phosphoroustransformation processes, has the most publishedapplications.
Ojeda et al. (): implemented in the RetrasoCodeBright (RCB) ﬂow model, simpliﬁed description oforganic matter, nitrogen and sulphur transformation
processes.
Wanko et al. (): considers organic matter removaland oxygen transport in VF ﬁlters.
Maier et al. (): implemented in the MIN3P ﬂow
and transport code, describes processes in CWs forthe remediation of contaminated groundwater.
W

The Constructed Wetland Model N 1 (CWM1), a generalmodel to describe biochemical transformation and degradation processes for organic matter, nitrogen and sulphurin subsurface ﬂow CWs, has been published with the main
aim to provide a widely accepted model formulation for biochemical transformation and degradation processes inconstructed wetlands that can then be implemented in various simulation tools (Langergraber et al. b). CWM1
describes all relevant aerobic, anoxic and anaerobic biokinetic processes occurring in HF and VF CWs requiring
predicting efﬂuent concentrations of organic matter,
nitrogen and sulphur. 17 processes and 16 components(8 solute and 8 particulate components) are considered.

FROM MODELLING TOWARDS DESIGN
Limitations of existing design guidelines and rules
Most of the existing design guidelines for CWs are based on
empirical rules of thumb and/or simple ﬁrst-order decay
models. All these design models use parameters that havebeen derived from experiments with pilot CWs. Therefore

Water Science & Technology | 64.1 | 2011

the parameters are only valid for the speciﬁc boundary conditions for which they have been obtained i.e., under which
the experiments have been carried out. These boundary
conditions include: climatic conditions, wastewater composition, porous ﬁlter material, plant species, etc. For thedesign of a CW, this means that the parameters of these simpliﬁed design models can only be used for designing systems
operating under similar conditions, such as climatic conditions, wastewater composition, porous ﬁlter material,
plant species, etc. (Rousseau et al. ).

Experiences from applying existing simulation tools
A review of existing simulation tools for CWs showed thatmeasured data from CWs can be matched (Langergraber
et al. a). Based on the experiences from the practical application of the HYDRUS/CW2D simulation tool
Langergraber () concluded that simulation results
match the measured data when the hydraulic behaviour ofthe system is well described. The inﬂuence of the parametersof the hydraulic properties of the ﬁlter material is muchhigher compared to the inﬂuence of the parameters of
the biokinetic model. Therefore once the ﬂow model iscalibrated well, a good match of experimental data to reactive transport simulations can be obtained using default
parameter for the biokinetic model (such as given byLangergraber & Šimůnek ).
To obtain reasonable simulation results for water ﬂow in
VF beds, Langergraber () advises to measure at least theporosity and saturated hydraulic conductivity of the ﬁlter
material. For good ﬁts of the ﬂow model the measurementof the volumetric efﬂuent ﬂow rate between loadings isrequired. Additionally, tracer experiments should be carriedout to get a better idea of the hydraulics and internal mixing.
Usually, the data obtained from CWs are based on grab
samples that are collected from the inﬂuent and efﬂuent ona daily, weekly or even only monthly basis. When using
dynamic process-based models more frequent data areneeded for calibration of the model to be able to utilize its
full capacity, i.e., also to model and predict the changes inconcentration over time. The number of additional samples
needed for simulating CW depends on the objectives of themodelling study. Objectives of and the resulting efforts formodelling studies have been discussed in guidelines on theuse of activated sludge models (e.g. Gillot et al. ). In
the case that the variation of the inﬂuent concentrations isvery low reactive tracers can be used to generate concentration changes. Toscano et al. () showed the use of

17

G. Langergraber | Numerical modelling: a better tool for CW design?

an ammonium peak as reactive tracer for a modelling studyof two-stage experimental CWs.
Modern biotechnological tools can help to gain new
insights into the functioning of CWs. However, dataobtained from these experiments are usually not in a form
and/or have the appropriate units to be used directly formodelling purposes. As an example, Langergraber et al.
() showed the conversion of measured data on the quantity of microbial biomass for modelling purposes. The
measurements of Tietz et al. () have been used as a starting point. They used several methods to measure microbial
biomass including conversion from microscopic direct
counts into biomass, fumigation-extraction for biomass-C,
ATP measurements for biomass-C, and substrate induced
respiration (SIR) for biomass-C (Figure 1, left). Langergraber
et al. () converted these data into microbial biomass
COD which is the unit used in the simulation tools andshowed that the simulation results matched the measured
data well (Figure 1, right).
Another example is that the information gained fromexperiments and/or measurements can be of too muchdetail compared to the needs of the simulation tools andtheir underlying numerical methods. Figure 2 (left) showsa simpliﬁed picture of the reality in the subsurface:• Between the sand grains there are pores ﬁlled with water,••roots and (in the case of unsaturated conditions) air.
The microbial bioﬁlm is growing on the sand grains.
Concentration gradients can be measured in the bioﬁlm
as well as in the pore water.

A number of simulation tools use the ﬁnite element
method to numerically solve the governing partial differential equations for water ﬂow and contaminant transport.
Such a ﬁnite element is depicted in Figure 2 (right). A triangle is shown because most numerical methods use

Water Science & Technology | 64.1 | 2011

Figure 2 | Schematic sketch of the subsurface: the simpliﬁed ‘reality’ (left) and the ﬁniteelement approach (right).

triangular shaped ﬁnite elements. In such a ﬁnite element
it is assumed that there is a homogeneous distribution ofsand grains, water and air, roots, biomass and contaminants.
There is no distinction between the different phases in theﬁnite element thus in every point in the ﬁnite element thesame properties can be found, e.g., 55% sand, 30% water,
8% air, 4% roots and 3% microbial biomass. Information
on bioﬁlm thickness, concentration gradients, etc. is notavailable in a ﬁnite element. Data on e.g., bioﬁlm thickness
therefore can not be directly used for modelling but have to
be converted into average biomass concentrations in theﬁnite element. Gradients (of water content and/or biomass
and contaminant concentrations) can only be foundbetween different ﬁnite elements.
Modellers have to communicate to specialists fromother ﬁelds (e.g. microbiologists, plant physiologists, hydrologists, CW designers, etc.) which data are needed formodelling. However, these specialists also have to get abasic understanding of the input data requirements of themodels. Only if a common language and understanding isfound and joint efforts are made it is possible to design
experiments and measurement campaigns in a way that
useful data for modelling are produced.

Figure 1 | Measured microbial biomass quantity (adapted from Tietz et al. 2007, left) and simulation results (adapted from Langergraber et al. 2007, right).

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G. Langergraber | Numerical modelling: a better tool for CW design?

Limitations of existing simulation tools
Most of the papers on CW modelling published focus on thedescription of the biochemical transformation and degradation processes. However, a number of other processes
have to be considered for the formulation of a full model
for CWs (Langergraber et al. b):• The ﬂow model (describing water ﬂow in the porous
media is of utmost importance).

• The inﬂuence of plants (growth, decay, decomposition,•••

nutrient uptake, root oxygen release, etc.)
The transport of particles/suspended matter and thedescription of clogging processes
Adsorption and desorption processes
Physical re-aeration

Even if simulation tools fulﬁl most of these requirements,
there are two important aspects required for designing a
CW, that none of the existing simulation tools has included
yet. Two simple models for pre-and post treatment units
would be needed:
1. a simple model for prediction of TSS and COD removal
based on the design of the mechanical pre-treatment
unit, and2. a model for pre- and/or post-treatment of phosphorus
with pre-precipitation in the mechanical pre-treatment
unit and/or adsorption ﬁlters after the ﬁlter beds,
respectively.
One of the main obstacles for the simulation tools available is that they are rather complicated and difﬁcult to run.
Although most of the tools have a graphical user-interface, ittakes quite some time and experience to be able to produce
realistic simulation results. Therefore only a few people in
the organisations in which the tools have been developed
are able to run them. Meyer et al. () showed how,
based on experiences from simulations with a complex
simulation tool, a simpliﬁed but robust and reliable modelfor design purposes for CWs treating combined sewer overﬂow can be developed. This simple model has only a few
parameters and is therefore simple to use.

The key factors inﬂuencing the treatment performanceof CWs and their link to modelling
Microbial community
It is generally agreed that the microbial community plays
a major role in the transformation processes andWater Science & Technology | 64.1 | 2011degradation processes in CWs. The resulting process conditions and the usually low ﬂow rates in CWs lead to ahigh microbial diversity. Although there are a number ofmolecular-biological methods available to investigate
these questions, knowledge of microbiological dynamics
and the correlation of biological and non-biologicalprocesses in CWs is still insufﬁcient (e.g. Faulwetter
et al. ).
For modelling purposes the biomass characterizationin subsurface ﬂow CWs in terms of quantity and speciation is of utmost importance. In VF beds mainly aerobic
processes occur and therefore mainly aerobic microorganisms are present. Additionally, the number ofheterotrophic micro-organisms is known to be muchlarger compared to the number of autotrophic microorganisms (Langergraber et al. ). The situation ismuch more complicated in HF CWs where mainly anaerobic processes occur thus also requiring the considerationof sulphur transformation and degradation processes. At
the moment there have been no investigations of the distribution of the microbial biomass and the actual
concentrations of the speciﬁc bacteria groups that havebeen deﬁned in biokinetic models, e.g., in CWM1 besides
heterotrophic and autotrophic nitrifying bacteria, 4 otherbacteria groups are also deﬁned: fermenting, acetotrophic
methanogenic, acetotrophic sulphate reducing and sulphide oxidising bacteria (Langergraber et al. b).
Thus information on the quantity and the distribution for
all bacteria groups in the ﬁlter is required to be able tomodel realistic biomass distributions in the VF and HF
beds.
To describe the kinetic rates of the biokinetic processes a
determination of bacterial activities and rate constants
is required. Respirometric tests are a promising tool, such as
the advanced CW respirometry technology prototyped by(Andreottola et al. (), Figure 3) and further developedby Ortigara et al. () and Morvannou et al. ().

Vegetation
Several major functions have been attributed to plants in
SSF CWs, including physical ﬁltering by root-mats,provision of surface area for attached microbes growth,
insulation by aerial tissue and litter during winter,
uptake and storage of nutrients and other elements,
release of oxygen and other substances by the roots, andthe contribution of organic carbon to the substrate (Brix).

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G. Langergraber | Numerical modelling: a better tool for CW design?

Water Science & Technology | 64.1 | 2011

Figure 3 | Respirometer for monitoring oxygen consumption (left) and examples for a respirogramm (right) (Andreottola et al. 2007).

Plants are therefore an indispensable part of subsurface
ﬂow CWs. Therefore especially the following processes
associated with plants are important:• the water balance and the inﬂuence of plants on the••hydraulics (e.g. evapotranspiration),the oxygen release and organic carbon release throughthe plant roots which inﬂuence the microbial processes
in the subsurface, as well as
the nutrient cycling (uptake and release of nitrogen andphosphorus) which inﬂuences the overall treatment
performance.

Physical and other processes
The physical and physicochemical characteristics of theﬁlter material used in a CW can greatly inﬂuence the overall
performance of the system. Physical properties (such as porosity, hydraulic conductivity and grain size distribution)
inﬂuence hydraulic efﬁciency, which is a critical componentof good design. Poor hydraulic efﬁciency leads to shortcircuiting and reduced treatment performance (Kadlec &Wallace ). The importance of the description of waterﬂow for modelling deﬁnes the need to know the physicalproperties of the ﬁlter material (Langergraber ).
Additionally, the following physical and other processes
are important:• the physicochemical properties of the ﬁlter material are•••

important to model e.g., adsorption,
the hydraulic retention time in CWs that is an importantfactor for their treatment efﬁciency,
the oxygen transfer, as oxygen is one of the most importantenvironmental conditions for biokinetic processes, andthe processes inﬂuencing clogging of the ﬁlter media andtherefore changing the hydraulics and further inﬂuencing
the performance of the CW.

SUMMARY AND CONCLUSIONS
Process-based numerical models for CWs have gained
increasing attention in the last couple of years. As well as
aiming to increase understanding of the system, models
aim to improve existing design criteria for CWs. All the existing design guidelines and rules are based on empirical rulesof thumb and/or simple ﬁrst-order decay models and use
parameters that have been derived from experiments. Therefore if the parameters derived from these experiments areused for design they are only valid for the speciﬁc experimental boundary conditions, e.g., climatic conditions,wastewater composition, porous ﬁlter material, plant
species. It can be expected that when dynamic processbased numerical models are used as the basis for design
tools, that the design tools can be applied to a wide rangeof boundary conditions and the limitations of existing
design guidelines and rules can be overcome.
Can existing simulation tools for CWs now be used forthe design of CWs? Existing simulation tools are able todescribe the processes in SSF CWs in detail. However,development is still needed in terms of a better characterisation of the microbial biomass to be able to model realistic
biomass distributions in HF and VF beds. Additionally,
there are two main limitations of existing simulation tools
being applicable as a design tool:
1. Although the simulation tools have graphical userinterfaces, they are complicated to use and it takes timeand experience to be able to produce realistic simulation
results.
2. Models for pre- and post-treatments are not available in
the existing simulation tools but are required to carryout a design of a CW.
Therefore, to make numerical simulation a useful andapplicable tool for CW design it can be concluded that a

20

G. Langergraber | Numerical modelling: a better tool for CW design?

further development of the existing models is needed. A simpliﬁed computer-based CW design tool based on processbased numerical models should be developed that:• can be used with knowledge of CW design but does not
require special knowledge of numerical modelling,• allows designing CWs for different boundary conditions•(such as climatic conditions, wastewater characterization, ﬁlter material, climates, etc.), andmakes the description of the dynamic behaviour of thedesigned CW possible thus allowing the demonstrationof the greater robustness of CW treatment systems e.g.,against ﬂuctuating inﬂows and peak loads.

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First received 29 October 2010; accepted in revised form 10 January 2011