Treatment wetland placement and preliminary design by geospatial analysis: A case study of the Tarawera Watershed

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Treatment wetland placement and preliminary

design by geosp
atial analysis: A case study of the
Tarawera Watershed

May 25
, 2010


Brian C. Peacock

University of Auckland School of Engineering, Auckland, New Zealand

Lafayette College, Easton, PA, Department of Civil and Environmental Engineering


Contents:

1.

Abstract

2.

Introduction

3.

Background


Tarawera Watershed


Constructed Wetlands


Geographic Information Systems (GIS)

4.

Methods


Financial Cost
Estimates



Pollutant Availability & Removal Rates



Mauri Model Assessment


Land
Score
Computation

5.

Results
& Discussion

Error

Financial
Cost Estimates



Pollutant
Availability & Removal Rates

Mauri Model Assessment

The Effect of

γ

General Applicability

6.

Conclusion

7.

References


Data Sources

8.

Figures

9.

Tables

Brian
C.
Peacock



2



1.

Abstract

Constructed wetlands (CW) are an excellent low cost solution water quality issues and runoff
management.
Placement of CWs within a watershed must be optimized to minimize cost, social
impacts and cultural impacts whilst maximizing
pollutant removal and environmental
benefits
.
A

land score system for siting CWs
was developed
that
combines
conventional
cost benefit analysis
with a
cultural assessment based on Maori values.
The result is a
simple
wetland siting tool
that
utilizes readily available data and can easily be implemented by
land planners
in a variety of

watersheds.
This model was
applied to the Tarawera Watershed in t
he Bay of Plenty, New Zealand

to ascertain the areas most suitable for wetland development and to predict the cost of
implementation
.
Although the
model was successful in siting wetlands within the watershed,
further
study is needed to reduce error in the final result.

2.

Introduction

Constructed wetlands (CW) are a common Best Management Practice (BMP) for
contaminated
runoff treatment and management. The benefits of CWs include: high biological productivity that
qu
ickly transforms pollutants, integration of pollutants into natural cycles, additional habitat for
wildlife and flood buffering (Kadlec and Wallace, 2009).

CWs are capable of treating

wastewaters
containing a diverse range of pollutants such as nutrients, heavy metals,
organic waste
, organic
compounds and suspended solids
.
However, in order to be effective, wetlands must be tailored for
the removal of certain
pollutant groups
.

Hydrolo
gy, topography and climate further constrain
treatment wetland
viability within a
spatia
l context.

Optimal design of networks of CWs on the watershed scale
is a topic of needed research (
Mitsch
and Day, 2006
; Crumpton, 2001).

Balancing the factors
that
af
fect treatment effectiveness
against
economics and cultural requirements is a complex task involving
extensive calculation. Geographic
information systems (GIS) provide a
flexible
framework for this type of spatial analysis
. The
Tarawera Watershed is hig
hly impacted by
non
-
point source nutrient loading, point source
pollution and wetland draining.
Also, denizens of the region have
both economic
and cultural
interests
. These factors make the watershed
an optimal case study that will
yield results general

enough for application in other watersheds around the world. The findings of this study can also be
used by policy makers in devising remediation strategies for the watershed.

Brian
C.
Peacock



3



3.

Background

Tarawera Watershed

The Tarawera W
atershed is a catchment of 980
km
2
locate
d in the Bay of Plenty region of
New
Zealand
(Raucher and Hoefner, 2009).
Figure 1 shows an overview of this watershed.
65

km from
the sea, t
he headwaters of the Tarawera
river
are
relatively pristine
(see Fig. 2
; Raucher and
Hoefner, 2009
).

A
s the Tarawera River reaches its
flood plains
, anthropoge
nic influences become
more preva
lent. First, the Tarawera R
iver has been redirected many times. The lo
w lying land that
the r
iver

now flows through was
historically
a wetland environment that has
been drained. Becaus
e
of natural and artificial leve
es, in many places the Ta
rawera R
iver surface is actually higher than the
sur
r
ounding land. This coupled with wetland destruction
has increased
the severity of floods in the
area. The
imple
me
ntation of
CWs in the flood plain
would help to restore the natural hydrology
and decrease damage due to flooding

(Kadlec and Wallace, 2009)
.

The major
natural
water chemistry influences to the Tarawera Watershed
are
geogenic
and
geothermal.
G
eothermal activity with
in the watershed contributes arsenic, boron, antimony,
thallium, sulfu
r and mercury (Mroczek, 2005;
McKenzie et al., 2001).

By definition, these m
etals
are present as ‘baseline
’ constituents. However, because of their high toxicity removal is often
desira
ble.


The land

cover within the Tarawera Watershed is 21.51% ‘high producing exotic grassland’ that is
used for dairy production (Hoefner, 2009). Runoff from this land use generally contains high
concentrations of nitrogen, phosphorus and fe
cal coliform
bacteria.

The
Tasman pulp and paper mill
is located on the banks of the Tarawera River
in Kawerau, New
Zealand
. This mill uses
260,000 m
3
of water from the Tarawera river every day (
Bruere
, 2003
).
The
waste
water
generated (approximately equal in volume)
is treated before discharge by
clarification with a floculant (magnafloc155 and aluminum sulphate) and secondary biochemical
oxygen demand (BOD)
removal through ae
r
ation (See
Fig. 3 and Fig. 4
;
Norske Skog).
De
spite
treatment, waters of higher turbidit
y
and likely other contaminants
are present downstream of this
discharge point.

According to Wilcock and Davies
-
Colley (1986), effluent volume from the plant
would have to be reduced by 95% in order for odor and co
lor to no longer be noticable by

comparison
to upstream water. The brown color of the Tasman wastewater is likely due to
high
Brian
C.
Peacock



4



concentrations of
water s
oluble poly
-
phenolic compounds, commonly known as tannins (Saez,
1999)
.
These naturally occuring compounds hin
der macronutrient cycling within eco
systems by
disrupting nitrogen fixation and mineralization

(Kraus et al., 2003)
.

Interestingly, polyphenols are
also capable of forming complexes with heavy metals, most commonly iron and
aluminum
(Kraus et
al., 2003)
.
The incorpo
ration of new data into
the GIS map will better quantify the effects of the
plant on water quality.

Adjacent
to the plant is a disposal site where mill waste
was once
dumped. This site contains a
significant
amount o
f toxic material that is
lea
ching
into groundwater (
Tull, 200
8;
Wolfe, 2009).
Also, the direction of groundwater transport within the waste site is from
the
west to
the east
where
the Tarawera River is located, suggesting the possablility of contaminant transfer from the site into
the river (Sinclair Knight Mertz Lt
d., 2007).
The concentrations of many ions within this waste
site, including zinc and lead, are
in excess of
the
Australian and New Zealand Guidelines for Fresh
and Marine Water Quality (ANZECC) guidelines by several orders of magnitude
(Tull, 2008;
Wolfe, 2009).


According to Tull
(2008)
, pollutants from
the waste site
include: lead, cyanide, zinc,
chromium, ammonia, polychlorinated biphenyls (PCBs), dioxins, furans, phenols, chlorine, oil and

oxygen demanding waste sludge
(Carbonaceous Bi
ochemica
l O
xygen Demand or CBOD)
.

There are a number of different
stakeholders
within the Tarawera watershed. Farms, logging
operations and the pulp and paper mill are the most significant capitalistic interests that could be
affected by CW implementation. The indi
genous Maori people own a large portion of the lands
within the watershed. However, their culture dictates treatment of land not as a capital commodity
but as a sacred resource to be conserved. The principal of
kaitiakita
nga
,

best translated as

guardians
hip
, strongly
influences the land use decisions of the Maori people
(Marsden, 1992)
.
Land also has

m
auri

or ‘life
-
force’ that must be maintained and improved for future generations.
Because of this difference in viewpoint between capitalist society and indigenous people,
a decision
support tool based on improvement of mauri
, called the Mauri Model
(Morgan, 2006)
will be
incorporated into this study.


Constructed Wetlands

Of the pollut
ants present in the watershed, CWs are capable of
removing

nitrogen, phosphorous
,
ammonia, lead, cyanide, zinc, chromium,
mercury, sulfur,
arsenic,
boron,

phenols, chlorine, oil,
Brian
C.
Peacock



5



PCBs,
CBOD
and fecal coliform b
acteria
(Kadlec
and Wallace, 2009
; Imfeld
et al., 2009
).

Unfortunately,
current CW technology is not capable of removing color (tannins) derived from pulp
and paper mill waste (Knight and Hilleke, 1994). Still, t
h
e
capacity of CWs to remove
a wide
variety of
pollutants
is very attractive in a h
ighly impacted watershed
like Tarawera
.
However,
different CW designs
tailor
to removal of
specific group
s
of pollutants.

This study will focus on
surface flow wetlands because of their low maintenance requirements and simil
arity to natural
wetlands. Th
e anaerobic conditions present in these wetlands are ideal for removal of heavy metals
and nutrients (Kadlec and Wallace, 2009).

The
primary

function
s
of
macrophytes in CWs

are
nutrient storage, organic sediment production,
microbial habitat and carbon
fixation (Kadlec and Wallece, 2009).
Contaminants are directly
removed through wetland vegetation only if plants are harvested (Peacock and Kney, 2010).

Therefore, the selection
criteria for treatment wetland macrophytes are
typically limited to
polluta
nt
resilience and indigeneity.
Plants native to New Zealand that have been proven effective in CWs
include
Typha orientalis
(cattail),
Scripus spp.
(Bulrush) and
Phormium spp.
(Flax);

(Chague
-
Goff et
al., 1999
; Johnson and Brooke, 1989;
Gebremariam
and
B
eutel
, 2008).

Optimization of a network of CWs is a topic of current interest for application
in
many
watersheds
.
For example, wetland restoration in the Mississippi
-
Ohio
-
Missouri (MOB) River Basin has been
proposed to
ameliorate
water quality in the Gulf
of Mexico. However, before this project begins,
“…there is a need for formal and rigorous large
-
scale research…” (Mitsch and Day, 2006).
The
current practice is to site CWs individually rather than engineering a network of CWs. According
to Crumpton,
it is necessa
ry to approach CW placement on the
watershed scale to maximize water
quality benefits (2001).
The GIS techniques developed in this project will be directly applicable to
the MOB project and many other watersheds around the world.



G
eographic
Information Systems (GIS)

Wetland placement is affected by many parameters, most of which are spatial.

For processing this
type of data, analysis utilizing GIS
can be a powerful tool.
Moreover, GIS provides extensive tools
for
rapidly
making multivariate
,
areal calculations, which are essential when considering the
watershed scale.

Brian
C.
Peacock



6



Palmeri and Trepel
developed a land score system

for siting and sizing
CWs

within a GIS interface
(2002).
Their raster
-
based model returns a score for each cell by computing
a weighted average of
six criteria: climatic, hydrological, geological, environmental and social and economic.
The values
for each of these criteria were derived from elevation, land use, soil type, river network
, population

and historical wetland locality data layers.
Palmeri and Trepel
also
mention contaminant
concentration dependence
as a n
eeded improvement to their system.

4.

Methods

The independent
spatial
variables
that
will influence
the algorithm developed in this
project
include:

c
hemical
concentrations, to
pography, soil type
,
river/lake
network
,

land use
,
water
temperature
,
historical vegetation, historical floods, geothermal sites and
cultural
sites
.

The
three
primary factors
that influence the viability of a
site for wetland construction are
:

financial

cost
s
,
pollutant
removal
capacity
and
cultural significance
.

The metrics for measuring these

parameters and the method
s
used to calculate
the final land score
are presented below
.

Financial
Cost Estimates

The costs associated with
wetland construction include land cost, demolition & clearing,
grading,
soil conditioning and planting. In
addition,
culverts and flow regulation devices are necessary
,
but
lack
predictable areal
costs
. Therefore, these costs ar
e included in the planting costs.
The cost
determination structure
and
predicted costs for the Tarawera Watershed are

shown
in Table 1.
M
odification costs were
sourced from
Rawlinson
s
New Zealand Construction Handbook (2009)
.
L
and
costs
and
planting
costs were not readily available and are based on the author’s best estimate.

The land classification system is based on the
New Zealand Land Cover Database (LCDB)
to
U
nited States National Land Cover
Database
(NLCD)
conversion presented by Hoefner (2009)
.

The raw data was provided by
Environment Bay of Plenty, 2010
.
All costs were calculated in New
Zealand Dollars.

The slope of a CW area must be less then .05%
grade to prevent excessive flow
(United States
Environmental Protection Agency, 1993)
.

The
cost given in Table 1 for grading is volumetric
($/m
3
) and must be
converted into an areal figure
using
the following
approximation based o
n the
geometry shown in Figure 5
:

Brian
C.
Peacock



7




€
C
A

SGC
V
8









Equation 1


Where C
A
is areal cost,
C
V
is volumetric cut to fill cost
, G is the grade as a fraction and S is
the
minimum step width.

The step width used in the Tarawera Watershed assessment is 25m.

Pollutant Availability & Removal Rate

The rate of most
wetland
treatment processes depends
on
t
he
concentration
of the pollutant
. Often
a first
-
order removal model is used to approximate this relati
onship (Kadlec and Wallace, 2009
):

€
dC
dt


kC








Equation 2

Where
C
is
areal
pollutant concentration
(
g m
-
2
),
k
is the first
-
order rate constant (day
-
1
) and
t
is
time (days). From this relationship it is clear that pollutants present in higher concentrations are
removed at a higher rate. Thus, wetlands should be placed where
available pollutant concentration
is a
s high as
possible.

Because of lack of data and lack of time
, sophisticated pollutant transport modeling was not possible
for the Tarawera Watershed.
As these constraints are commonly experienced by land planners, the
application of a simple approximation

for pollutant availability
was deemed appropriate.
Three
chemicals
were considered: nitrate, phosphate and
sulf
ate. Su
l
f
ate was considered, not as a
pollutant but as a me
chanism for heavy metal removal. Sulfate is first reduced to sulfide
via sulfate
reducing bacteria
(Faulwet
t
er et al., 2009)
.

The anaerobic environment provided by free surface
constructed wetlands encourages this process
(Kadlec and Wallace, 2009)
.
Sulfide ions bond readily
with heavy metals to form
less toxic
metal
sulfide compound
s
(
Kadlec and Wallace, 2009).

€
M
2
+
+
S
2
-

MS







Equation 3

The availability of these compounds to constructed wetlands was determined by time averaged,
exponentially distance
weighted the
issen polygon
iso
-
concentrations.
The available concentration
was

found
by the following formula:



€
C
A

C
0

.98
D
200













Equation 4

Brian
C.
Peacock



8



Where C
A
is
the available concentration, C
0
is the
time averaged
concentration throughout the
polygon and D is distance
in meters from the main cour
se of the Tarawera River or Lake Tarawera.

The sample points and theissen polygons
are shown
overlay
ing
the
distance
from the river
in Figure
6
.
The water quality data was sou
rced from Frontiers Abroad
and is shown in Figure 7
-

11
(Unpublished)
.


The
pollutant removal rate
was determined per square meter by
multiplying the available
concentration (C
A
) by
the reaction rate constant (K)

by the average water
depth, which for the
Tarawera W
atershed was assumed to be .3m. The reaction rate constants
used i
n this study are
provided in T
able 2.
One
will note that dissolved oxygen and tempera
ture are needed to compute
the K
value for nitrogen and ph
osphorous. Because of the nature of these water quality parameters,
the
ir
availability was taken to be uniform
throughout
each of the polygons.

A final zero order reaction was assumed to take place to compensate for
additional
pollutants that
have not been quantified. Also, the a
ncillary
benefits provided by wetlands such as wildlife habitat
are pr
esent
regardless of location
. The value of this compensation
factor
in the Tarawera study is
taken to be

25
.

Mauri Model Assessment

The
Mauri Model
, developed by Morgan provides an ideal framework for the assessment of cultural
values (2006). This model is base
d on four primary well beings: economic, environmental,
social
and cultural.
Typically, a set of metrics is devised to assess each of these well beings based on the
nature of the project.
Then, an integer value from
-
2 to +2 is assigned for each metric a
ccording to
th
e rating system shown in Table 3
.
T
o adopt this model to GIS
, data layers
were used as metrics
and the effect on mauri is based on th
e value of the data layer. Mauri M
odel assess
ment criteria for
the Tarawera W
atershed is shown in Table 4
.
The data
incorporated
into the
Tarawera Mauri
Model evaluation
included
land cover
, historical wetland cover, historical flooding and
geothermal
features.

To calculate the score
for each of the well beings, a
weighted average was applied to the layers
i
nvolved. The method used to determine
this weighting was a compar
ative matrix as shown in
Table 5
.

The final score is a weighted average of the four well beings. The weighting for this
depends heavily on one’s point of view. A good compromise between we
stern view points and
Brian
C.
Peacock



9



indigenous viewpoints suggested by Morgan is 30% economic, 30% environmental, 20% social and
20% cultural
(2009)
.

This weighting will be used for analysis of the Tarawera Watershed.

Land Score Computation

Suitability of sites for CWs
will be based on a combination of traditional cost
-
benefit analysis and
Mauri Model analysis. The final
land
score (S)
was
based on the following formula:

€
S

0
M

0
k
i
P
i
i

1
n

C
M
γ
M

0
















Equation 5

Where P is pollutant removal
in g/day
, C is the cost, k is a
constant with dimensions cost
time
over
mass, M is the Mauri Model assessment output (normalized to
-
1 ≤ M ≤ 1),
γ
is a weighting
constant (0 ≤
γ
< ∞) and n is the number of pollutants treated. This equation accounts for benefits
of multiple pollutants an
d factors that are difficult to assign a monetary value.
The k values

for
the
Tarawera Watershed were
estimated to be: .
11
for
nitrate, .39
for
phosphate, .28 for sulfate and .22
for ancillary benefits.

Finally
, the score is normalized with respect to th
e highest value such
all
values lie on a
scale from 0 to 10.

5.

Results
& Discussion

It was hypothesized that the North East corner of the watershed would be the best area for
placement of CWs
because of the flat grade and high pollutant availability
downstream of Kawarau
and the mill
. The final score
presented
in Figure 12
strongly

confirms this
conjecture
, showing a
concentrated region of
high wetland suitability
.
This is the result of a low cost per square meter
(Figure 13) and relatively high Ma
uri Model scores (Figure 14).
Figure 15 shows the break down
of the Mauri Model assessment into economic, environmental, social and cultural parameters. It is
interesting to note the
d
ichotomy between the
economic and environmental factors
within the
wat
ershed
.

Also, cultural values tend to align with environmental values while social values
correlate
with economic values.


Error

Because of the
extent of parameters involved in this treatment wetland placement model,
it is
expected that
error is not negligible
. Also many of the
values
included in the model have
Brian
C.
Peacock



10



considerable uncertainty
accompanying
them
. To further compound error within the model, there
is imprecision in
the
geographic locations of up to ±10m.
Finally
, layers such as
soil
type
define
hard boundaries
between values
when in fact there is a
continuous
gradient between
these

values
.
A
detailed
quantitative
assessment of error within this model
is beyond the scope of this project
,
but
would be worthwhile for future study.

Financial
Cost Estimates

The error
propagated
within the c
ost estimation
section of the model
is at best ±$100.62
and at
worst ±$317
.41
. The majority of this error comes from the imprecision of land cost estimates that
are based solely upon
land use. This error is estimated to be
from ±$100 up to ±$300.
Because
wetland projects would likely involve eminent domain for land acquisition, us
e of
an appraised value

layer

as produced for assessing landowner rates

would
significantly reduce
error
.

In addition, this
error would only be on the positive side because landowners will certainly n
ot stand for being
compensated
less than their tax
-
determining appraisal.

This would result in an approximate error
between
[
+$11.18,
-
$0] and [+$104.18,
-
$103
.70]
.

The
second
largest source of error is in the land
modification costs
(demolition and clearing)
, which range from ±
$5 to ±$100.05
.
These errors are
difficult to eliminate without a detailed cost estimate for each particular site
which
would be time
consuming, expensive and impractical
.
Rather, once the resulting CW sites identified from this
model, a more detailed cost an
alysis should be undertaken.
The precision of
the
grading costs

could
be improved significantly by taking into acc
ount not only the slope of the cell being calculated, but
of those surrounding it. With this method, the assumed surface would no longer be a strait line
at the
gradient but one that follows topography. This would add significant complexity to
the
model but
would result in far more precise cost analysis.

Pollutant Availability
&
Removal Rates

Of three factors that are taken into account to form the final score, the p
ollutant availability and
removal capacity is by far the leas
t
precise. The
first cause fo
r this imprecision is lack of
extensive

water quality data. Only 11 points have been sampled within the watershed over the course of three
years with one to two samples per year.
In addition, not all the parameters were assessed each year.

For instance, nitrate and phosphate concentrations are based on a single sampling.

The pollutant
removal assessment for this watershed was only based on three pollutants because of lack of data
availability for a wide range of pollutants. To
compensa
te for the other
pollutants removed, a zero
Brian
C.
Peacock



11



order rate constant was introduced but with little certainty as to its value.
Also, the use of sulfate
concentration to estimate metal removal should be verified empirically.

The second reason for a lack of prec
ision is inadequate understanding of hydrology and pollutant
tra
nsport within the watershed.
Hydrologic modeling of the watershed has been performed by
Hoefner (2009) using the
KINEROS2 model. This model is
designed
to assess
the effects of single
large r
ainfall events. Because of the large size of the watershed and continuous time scale desired for
wetland placement, the Soil and Water Assessment Tool (SWAT) would likely be better suited to
this application (
Hoefner, 2009;
Arnold et al., 1994).
Also, SWAT
provides within it a means for
estimating sediment, nitrate and phosphate flux throughout the watershed.
Hydrologic information
could also be used to
accurately
pr
edict
water availability,
hydraulic loading and pollutant loading of
CWs. However, it is
d
ifficult to take into account the
effects of potential wetlands upstream on
those downstre
am without iterative calculation that is not possible within the simple raster
calculator functions of GIS application. In addition, t
his would result in a model tha
t requires the
input
of
total cost, total wetland area or total
pollutant removal in order to yield a set of
potential
wetlands.

It would be interesting to compare a more complex analysis to this simple analysis to
establish weather the added complexity i
s worth the benefits of
precision
.

Finally, the weighting factors applied to the
pollutant removal rates were assigned rough estimates.
With further study these factors could be assigned values that are very precise and reflect
very
accurately the
comparative benefits of pollutant removal per gram.

Mauri Model
Assessment

The Mauri M
odel assessment was rather precise compared to the other
two parameters
,
with a
propagated
error of
±
.125.
The capacity for improvement of this
error
is limited
by the c
oarseness
of the Mauri Model scale (Table 3).
Because the scale rates based on integers, the error for each
measured value is ±.5. To decrease this
error, one would have to assign decimal values.
By nature,
most of the metrics
used to assess the Mauri M
odel become
rather subjective when assessed on a
scale finer than that of Table 3. Therefore, there would be very little
to gain by increasing the
precision of the Mauri Model
scale
.
One feasible way to decrease the error of Mauri Model
assessment is to increase the number of
independent
metrics used to determine the final score. For
Brian
C.
Peacock



12



instance, if a cultural sites layer
were to be
included in the
Tarawera Watershed
assessment the
propa
gated
error would decrease to ±.112.

The Effect of
γ

As shown in
Equation 5, the Mau
ri model result is raised to a power,
γ
, in order to weight it a
gainst
the other two factors.
Figure 16 shows the effect of
γ
on the final output of the model.
Notice th
at
because
the high mauri model scores generally align with the low cost scores, the mauri model has
the effec
t of clarifying the results and increasing the contrast between the high scores and the low
scores.
In the final result
γ

was taken to be 1.5 bec
ause
of the relevance of the Mauri Model to local
values
yet a higher value
seemed to overshadow the effects of cost on placement.
In watersheds
where the Mauri model assessment contrasts more with the pollutant removal rate and the cost
this
constant may be more difficult to ascertain because it is dependent on the values of the inhabitants
of the watershed
.
The decision then lies
not in engineering
, but in the political arena.

General Applicability

This
model makes use of readily avail
able data in a simple format that can be understood and
applied by those with a non
-
technical background.
However, as noted above, the complexity of the
pollutant removal rate expression may need to be increased to reduce error within the model. The
resu
lts
of the application of the model to the Tarawera Watershed,
show very clearly
and accurately
a
region of h
igh CW
suitability
. In F
igure
12 one will note that within this region
, to the south,
there is a large area of even higher scores marked by an arr
ow. This entire area is affected
significantly by
a single sampling point that
had a relatively high dissolved oxygen and
a
high
phosphate level at that one moment
.
Clearly, one cannot be certain that this are
a is better suited for
CWs
than the north base
d on this data. Therefore, a more in depth
pollutant availability and
removal
model
, based on more extensive data
is necessary to make
enable
decision
-
making
based on
small increments
of the wetland suitability scale
.


In general the methods devised here are easily transferable to other watersheds.
Although
the
Mauri M
odel was developed with Maori culture in mind, many other indigenous cultures share
similar views (Morgan, 2006).
Even beyond indigenous culture, populat
ions of all nations can
appreciate and the
four well beings of the Mauri M
odel, but the weighting between them may be
different.
Still, t
he Mauri Model has the distinct advantage of being able to evaluate parameters
Brian
C.
Peacock



13



possessing intrinsic value rather than
monetary value making it an essential part of any watershed
assessment.

6
.

Conclusion

This project
has developed a viable

means
for siting CWs within a large
-
scale watershed.
The
output of the model
is a set
of potential wetland sites that are desirable b
ased on efficiency,
economics and culture.

The method is simple enough to be applied by land

planners with limited
technical background
and makes use of readily available data
.
However,
m
ore work is needed to
assess
error associated with the model. One area of particular po
tential for error reduction is in

pollutant availability
and removal rate.

Overall, the model was successful at identifying favorable wetland sites in the Tarawera Watershed
and determining the
associated costs.

The most promising sites for CW development are situated in
the North East corner on the flat Rangitaiki Plains. Here, costs are low, pollutants are high and
Mauri improvements are significant.
However,
the current model is too coarse t
o identify sites
within this broad region. The
se
results must be refined
by
implementing
a more complex model.


Although, not yet a honed tool, this CW placement model represents a flexible framework to
be
augmented
with future study. The result will be
a
versatile
paradigm and powerful tool
applicable
in watersheds throughout the world.


Acknowledgements
:

Frontiers Abroad, Dan Hikuroa, Dietrich Hoefner
,
Kepa Morgan,
Angela Slade
and Environment
Bay of Plenty

Brian
C.
Peacock



14



7
.

References

1.

Arnold, J. G., J. R. Williams, R. Srinivasan, K. W. King, R. H. Griggs.
(1994)
SW
AT
-

soil water assessment tool.

Technical Report, United States Department of Agriculture;
Agricultu
ral Research Service; Grassland
, Soil and Water Research Laboratory.

2.

Brue
re, A. (2003) Pulp and Paper Mills in the Bay of Plenty.

Environment Bay of Plenty
Environmental Publication.

3.

Crumpton, W. G. (2001) Using wetlands for water quality improvement; the importance of
a watershed scale approach.
Water Science

and Technology,
44
(11
-
12): 559
-
564.

4.

Faulwet
ter, J. L., V. Gagnon, C. Sundb
erg, F. Chazarenc, M. Burr, J. Brisson, A. Camper,
O. Stein. (2009) Microbial processes influencing
performance
of treatment wetlands: A
review.
Ecological Engineering, 35
:
987
-
1004.

5.

Gebremariam, S.
Y., Beutel, M. W. (2008) Nitrate removal and DO levels in batch wetland
mesocosms: Cattail (Typha spp.) versus bulrush (Scirpus spp.).
Ecological Engineering
, 55:
1
-
6.

6.

Hoefner, D. (2009) Geospatial analysis of the Tarawera River catchment.
Unpublished
r
esearch project,
University of Auckland, Geography 333.

7.

Imfeld, G., M. Braeckevelt, P. Kuschk, H. H. Richnow. (2009
) Monitoring
and assessing
processes of organic chemicals removal in constructed wetlands.
Chemosphere, 74
(3):
349
-
362.

8.

Johnson, P., P. Broo
ke. (1989) Wetland plants in New Zealand.
Wellington: DSIR
Publishing.

9.

Ka
dlec, R. H., S. D. Wallace. (2009
). Treatment wetlands, second edition. Boca Raton:
CRC Press.

10.

Knight, R., J. Hilleke. (1994)
Design and performance of the Champion
pilot
-
constructed
wetland treatment system.

Tappi Journal, 77
(5): 240
-
245.

11.

Marsden, Maori. (1992) Kaitiakitanga: A definitive introduction to the holistic world view
of the Maori.
Retrieved 28 March 2010
, from
www.marinenz.org.nz/documents/Marsden_1992_Ka
itiakitanga.pdf
.

12.

McKenzie, E. J., K. L. Brown, S. L. Cady, K. A. Campbell. (2001) Trace metal chemis
try
and silicificatio
n of microorganisms in geothermal sinter, Taupo Volcanic Zone, New
Zealand.
Geothermics, 30
:
483
-
502.

Brian
C.
Peacock



15



13.

Mitsch, W. J., J. W. Day
Jr
.
(2006
) Restoration of wetlands in the Mississippi
-
Ohio
-
Missouri (MOM) River Basin: Experience and needed research.

Ecological Engineering,
26
(1):
55
-
69.

14.

Morgan, T. K. K. B. (2006) Decision
-
support tools and the indigenous paradigm.
Engineering Sustainabi
lity, 159
(ES4):
169
-
177.

15.

Mroczek, E. K. (2005) Contributions of
arsenic and chloride from the K
awerau geothermal
field to the Tarawera River, New Zealand.
Geothermics, 34
:
223
-
238.

16.

Norske Skog. Tasman Mill induction video.

17.

Palmeri, L., M. Trepel. (2002)
A GIS
-
based score system for siting and sizing of created or
restored wetlands: two case studies.
Water Resources Management, 16
:
307
-
328.

18.

Peacock, B.C
., A. D. Kney. (2010)
Development
of a Constructed Wetland System
Integrating Agricultural Production with Runoff Treatment.


ASCE World Environmental
& Water Resources Congress, Providence, RI
.

19.

Raucher, L., D. Hoefner. (2009) Tarawera River water quality project. Unpublished
research pro
ject, University of Auckland, Geography 333.


20.

Rawlinsons
. (2009) Rawlinsons New Zealand Construction Handbook, 24
th
edition.

Rawlinsons Media Limited: Auckland.

21.

Saez, G. V. (1999) Bibliographic review about the organic compounds produced in the
wood pulp
and paper industry: Incidence in toxicity and anaerobial biodegradation of their
effluents.

22.

Sinclair Knight Mertz Inc. (2007) Tasman primary solids waste landfill: hydrogeological
investigation. Rep. Vol. 1.

Auckland: Sinclair Knight Mertz Inc.

23.

Tull
, K. (2008) “Mini
-
superfund” site in Kawerau, New Zealand: A closer look at water
quality.
(Masters Thesis, Duke University)

24.

United States Environmental Protection Agency. (1993) Constructed and natural wetlands
for controlling nonpoint source pollution.
Boca Raton: CRC press.

25.

Wilcock, R. J., R. J. Davies
-
Colley. (1986) Panel tests for evaluating the appearance and
odour of th
e
lower Tarawera River.
New Zealand Journal of Marine and Freshwater Research,
20
(4): 699
-
708.

26.

Wolfe, L. (2009) Concentrations of
selected ions in groundwater at Tasman Pulp and Paper
waste site. Unpublished research project, University of Auckland, Geography 333.

Brian
C.
Peacock



16




Data Sources

27.

Environment Bay of Plenty (2010) GIS Data Layers Including: Land Use, Soil Type,
2004
Flooding, Geotherm
al
Extents and Pre
-
human Vegetation.

28.

Frontiers Abroad (unpublished) Tarawera River Water Quality Data from 2008 to 2010.

29.

Hoefner, D. (2009)
Digital Elevation Model of the Tarawera Watershed.
Unpublished
research project, University of Auckland, Geography 33
3.

Brian
C.
Peacock



17



8
.

Figures

Figure 1


An overview of the Tarawera Watershed showing the Tarawera River, Lake Tarawera,
hill
-
shade topography and the water quality sampling points.
(Compiled
from data produced by
Environment Bay of Plenty, 2010
and Hoefner, 2009)
Brian
C.
Peacock



18












Figure

2

-

The Tarawera
river breifly
becomes subterrane
an before flowing
out the side of a cliff. Here one can
see the clear
clean
waters present
throughout the top part of the river.


Figure

3

-

The outflow from the
Tasman pulp and pap
er mill.
Note
high turbidity of
these waters.


Figure 4
-

A secondary BOD removal
pond at the Tasman pulp and paper
mill. The jet of water in the center of
the pond is for aeration.


Brian
C.
Peacock



19



Figure 5


The assumption used to quantify the cut to fill
grading
cost
. The cost is averaged over
the length S resulting in Eq. 1.





Brian
C.
Peacock



20



Figure 6



The distance of points throughout the watershed from the main course of the Tarawera
River and Lake Tarawera
overlaid
by the th
iesse
n polygon regions that define areas of eq
ual
concentration.
Also shown are the water sampling points throughout the watershed.
All of this
data was combined to
determine the pollutant availability within the watershed.

Brian
C.
Peacock



21



Fig
ure

7



Mean d
issolved oxygen concentrations within the Tarawera River.



Figure
8


Mean temperature the Tarawera River.


0

1

2

3

4

5

6

7

8

9

0

10

20

30

40

50

60

70

Concentration
(mg/L)


Distance
from
Headwaters
(km)


Mean
Dissolved
Oxygen
Concentration

in
the
Tarawera
River

Kawerau
Pulp

and
Paper
Mill

0

5

10

15

20

25

30

35

40

45

0

10

20

30

40

50

60

70

Concentration
(mg/L)


Distance
from
Headwaters
(km)


Mean
Temperature
of
the
Tarawera

River

Kawerau
Pulp

and
Paper
Mill

Brian
C.
Peacock



22



Figure 9


Mean
nitrate
concentrations within the Tarawera River.


Figure
10


Mean phosphate concentrations within the Tarawera River.


0

0.5

1

1.5

2

2.5

3

3.5

0

10

20

30

40

50

60

70

Concentration
(mg/L)


Distance
from
Headwaters
(km)


Nitrate
Concentration
in
the


Tarawera
River

Kawerau
Pulp

and
Paper
Mill

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0

10

20

30

40

50

60

70

Concentration
(mg/L)


Distance
from
Headwaters
(km)


Phosphate
Concentration
in
the

Tarawera
River

Kawerau
Pulp

and
Paper
Mill

Brian
C.
Peacock



23



Figure 11


Mean sulfate concentrations within the Tarawera River.


0

20

40

60

80

100

120

140

0

10

20

30

40

50

60

70

Concentration
(mg/L)

Distance
from
Headwaters
(km)

Mean
Suplfate
Concentration


in
the
Tarawera
River

Kawerau
Pulp

and
Paper
Mill

Brian
C.
Peacock



24



Figure 12


The final wetland suitability landscore for the the Tarawera Watershed.


Wetland Suitability Land Score

High
Score

Caused
by
High

Pollutant

Concentrations

Brian
C.
Peacock



25



Figure 13


Cost
(NZ$)
per square meter of constructed wetland.


Brian
C.
Peacock



26



Figure 14

The overall Mauri Model assessment for the Tarawera Watershed.



Brian
C.
Peacock



27



Figure 15

The scores for the four well beings of the Mauri Model.

Brian
C.
Peacock



28



Figure 16


C
omparison of the final wet
land placement land score when
γ
is varied.


Brian
C.
Peacock



29



9
.

Tables

Table
1



The cost determination structure
for the wetland placement model
. Costs
(NZ$)
are
broken down by the influencing layer then the classification within that layer
and given in New
Zealand dollars
.


Classification
NLCD
#
Modification
Land
Cost
Quarries/Strip Mines/Gravel Pits
32
None
$250.00
$0.00
/m
2
Bare Rock/Sand/Clay
31
None
$250.00
$0.00
/m
2
Transitional
33
None
$250.00
$0.00
/m
2
Emergent Herbaceous Wetlands- Exotic
92
None
$250.00
$0.00
/m
2
Shrubland- Exotic
51
None
$250.00
$0.00
/m
2
Shrubland- Native
51
None
$250.00
$0.00
/m
2
Grassland/Herbaceous- Exotic
71
None
$250.00
$0.00
/m
2
Grassland/Herbaceous- Native
71
None
$250.00
$0.00
/m
2
Pasture/Hay
81
None
$250.00
$0.00
/m
2
Row Crops
82
None
$250.00
$0.00
/m
2
Orchards/Vineyards/Other
61
Clearing
$250.00
$60.00
/m
2
Deciduous Forest- Exotic
41
Clearing
$250.00
$100.00
/m
2
Deciduous Forest- Native
41
Clearing
$250.00
$100.00
/m
2
Evergreen Forest- Exotic
42
Clearing
$250.00
$100.00
/m
2
Evergreen Forest- Native
42
Clearing
$250.00
$100.00
/m
2
Mixed Forest- Exotic
43
Clearing
$250.00
$100.00
/m
2
Mixed Forest- Native
43
Clearing
$250.00
$100.00
/m
2
Low Intensity Residential
21
Demolition
$480.00
$110.00
/m
2
Commercial/Industrial/Transportation
23
Demolition
$800.00
$300.00
/m
2
Emergent Herbaceous Wetlands- Native
92
Open Water
11
Sand/Light Soil
-
Leveling
-
$5.00
/m
3
Well/Moderately Well Drained
-
Clay Liner
-
$6.44
/m
2
Imperfectly/Poorly Drained
-
none
-
$0.00
/m
2
Clay
-
Leveling
-
$6.10
/m
3
Well/Moderately Well Drained
-
Topsoil
-
$5.60
/m
2
Imperfectly/Poorly Drained
-
Topsoil
-
$5.60
/m
2
Light Rock
-
Leveling
-
$16.75
/m
3
Well/Moderately Well Drained
-
Liner & Topsoil
-
$10.38
/m
2
Imperfectly/Poorly Drained
-
Topsoil
-
$8.30
/m
2
Rock
-
Leveling
-
$65.00
/m
3
Well/Moderately Well Drained
-
Liner & Topsoil
-
$38.84
/m
2
Imperfectly/Poorly Drained
-
Topsoil
-
$33.60
/m
2
Planting Cost
-
-
-
$30.00
/m
2
Modification
Cost
Soil Type
Landuse/Exotic
Final Land Score = 0
Final Land Score = 0
Brian
C.
Peacock



30



Table 2



The rate
constants (K) used to assess pollutant removal rate in the Tarawera Watershed
case study.


Table 3



The scale for Mauri M
odel assessment.


Pollutant
Source
Nitrate
(.176T - 1.9)
d
-1
Peacock and Kney, 2010
Phosphate
(.0445DO - .19)
d
-1
Peacock and Kney, 2010
Sulphate
0.62
d
-1
Based on complete reduction and reaction with a 60AMU, +2
charged metal and a hydraulic retention time of 1 day/m
2
K
!
Mauri
Score
Fully Restored Mauri
+2
Improved Mauri
+1
Neutral Mauri
0
Degraded Mauri
-1
Destroyed Mauri
-2
Brian
C.
Peacock



31



Table 4


The spatial mauri model assessment criteria.

The scoring structure
broken down by the
influencing layer
;
then the
score for each possible value
within that layer
is given
.


*The Cultural Site layer was not available at the time of computation and is therefore not included in the
Tarawera Watershed
assessment.


Table 5


The
comparative matrix used to weight the input layers for the Mauri Model
assessment
.


NLCD
#
Economic
Environment
Social
Cultural
32
-2
2
-1
0
31
0
1
0
0
33
0
1
1
0
92
0
1
0
1
51
0
2
0
1
51
0
-1
0
0
71
0
2
0
1
71
0
0
0
0
81
-2
2
-1
0
82
-2
2
-1
0
61
-2
2
-1
0
41
0
2
1
1
41
0
0
0
0
42
-1
2
0
1
42
0
0
0
0
43
-1
2
0
1
43
0
0
0
0
21
-1
2
-2
0
23
-2
2
-1
0
92
11
Historic Flood Area
-
2
1
1
1
Not Historic Flood Area
-
1
0
0
0
Historic Wetland
-
0
2
0
2
Not Historic Wetland
-
0
0
0
-1
Geothermal Site
-
-1
-1
-1
-2
No Geothermal Site
-
0
0
0
0
Cultural Site
-
-1
0
-1
-2
Not Cultural Site
-
0
0
0
0
-
30%
30%
20%
20%
Open Water
Weight
Classification
Pasture/Hay
Row Crops
Orchards/Vineyards/Other
Deciduous Forest- Exotic
Shrubland- Exotic
Shrubland- Native
Grassland/Herbaceous- Exotic
Evergreen Forest- Exotic
Evergreen Forest- Native
Deciduous Forest- Native
Mixed Forest- Native
Mixed Forest- Exotic
Grassland/Herbaceous- Native
Historic Flood
Historic Wetland
Geothermal
Final Land Score = 0
Final Land Score = 0
Cultural Sites*
Landuse/Exotic
Quarries/Strip Mines/Gravel Pits
Bare Rock/Sand/Clay
Transitional
Emergent Herbaceous Wetlands- Exotic
Low Intensity Residential
Commercial/Industrial/Transportation
Emergent Herbaceous Wetlands- Native
Landuse
Historical
Wetland
Geothermal
Flooding
Total
Weight
Landuse
0
-1
-1
-1
-3
19.00%
Historical
Wetland
1
0
1
0
2
29.00%
Geothermal
Site
1
-1
0
0
0
25.00%
Flooding
1
0
0
0
1
27.00%
Weight = .25 + .02 * Total
POSITIVE
NEGATIVE