Morsa case study status
report (Deliverable D29) Norwegian AQUAMONEY case study on valuation of environmental
and resource costs of water services
Author
Date
David N. Barton
10.05.07
Contact information AquaMoney Partners
Colophone
This report is part of the EU funded project AquaMoney, Development and Testing of Practical Guidelines for the
Assessment of Environmental and Resource Costs and Benefits in the WFD, Contract no SSPI-022723.
General
Deliverable
D29. Case study report
Deadline
17th April
Complete reference
Barton (2007). Morsa case study status report. Norwegian AQUAMONEY case study on
valuation of environmental and resource costs of water services.
Status
Author(s)
Date
Approved / Released
D.N. Barton
17.04.07
Comments
Date
Reviewed by
Pending for Review
Second draft
First draft for Comments
Under Preparation
Confidentiality
Public
Restricted to other programme participants (including the Commission Service)
Restricted to a group specified by the consortium (including the Advisory Board)
Confidential, only for members of the consortium
Accessibility
Workspace
Internet
Paper
Copyright © 2006
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any
means, electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the copyright holder.
Content
Summary
II
1. INTRODUCTION - GENERAL CASE STUDY CHARACTERISTICS
1.1 Location of the case study area
1.2 Geographical characteristics
1.3. Water system characteristics
1.3 Characterisation of water use
1.4 Cost recovery of water and sewage fees in Morsa
2. PRESSURE, IMPACT, AND RISK ANALYSIS WITH REGARDS TO THE WFD ENVIRONMENTAL
OBJECTIVES
2.1 Significant pressures impacting on water status
2.2. Impacts on surface and groundwater bodies
2.3. Water bodies at risk of not achieving a good status
2.4. Diagnosis of water quality and ecological issues (aquatic and related terrestrial ecosystems)
2.5. General trends and future pressures
3. POLICY ISSUES
3.1 Water management framework and major issues
3.2 Water policy and research relevance in the basin
3.3 Information sources and stakeholder involvement
4. ERC ANALYSIS AND METHODOLOGICAL ISSUES
4.1 List of main water-related goods and services provided in the basin
4.2 Objectives: type of ERC analysis to performe
4.3 Proposed methods and tools for the valuation of ERC
4.3.1 Choice experiment
4.3.2 Benefit transfers
4.3.3 Mitigation cost
4.4 Methodological issues
4.4.1 Methodological steps preparing CE studies
4.4.2 Target group(s)
4.4.3 Sample size requirements
4.4.4 Valuation scenario(s) design in relation to ERCB & WFD
4.4.5 Spatial implementation scale
4.4.6 Temporal issues
4.4.7 Methodological tests (e.g. sensitivity to scope, distance-decay etc)
4.4.8 Value transfer tests
4.4.9 Aggregation and use of GIS (feasibility of a GIS based value map)
4.4.10
Planning of activities and their timing
4.4.11
Other issues
Appendix 1
Appendix 2
4
4
4
4
6
8
8
8
8
8
15
15
15
15
15
16
16
16
17
17
17
18
18
18
18
18
19
20
22
22
22
22
23
23
23
26
28
I
Summary
The following bullets correspond to the categories in the AQUAMONEY case study characterisation matrix.
They summarise the key characteristics of the Morsa case study for purposes of case study comparability.
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
II
Objective:
Problem:
Good/bad valued:
Valuation method:
Survey method:
Survey timing:
Part of CVD:
Scale:
Use-nonuse / users-nonusers:
Standard (effluent) values:
Sensitivity to scope testing:
Substitution effects:
Aggregation:
Benefit transfer:
Use of GIS:
disproportionate cost analysis
eutrophication and toxic algal blooms
valuation of different attribute levels of lake visitation (goods and bads)
choice experiment
in-person survey of users; web-based survey of users and non-users
September-December 2007
will participate in a common water quality design for choice experiments
regional scale ; target water body and substitute lakes
Yes, both
No, but values for selected water quality attributes (sight depth)
Yes, implicit in choice experiment of multi-level water quality attribute
Yes, planned multi-site choice sets and actual site selection
Yes, distance decay evaluated using a regionally representative hh sample
Not within Norway. Temporal stability tested against 1995 CVM study
Yes, but subject to CVD guidance on common explanatory variables
1.
INTRODUCTION - GENERAL CASE STUDY CHARACTERISTICS
1.1
area
Location of the case study
The Morsa catchment is an approximately
700 km2 area in south-eastern Norway,
including outskirts of Oslo in the north, a
number of smaller lakes draining to the
Hobøl river which runs into Storefjorden and
Vannemfjorden in the south, which the drains
to the Oslofjord through the city of Moss
(¡Error! No se encuentra el origen de la
referencia.) .
Figure 1. The Morsa catchment draining to the Vansjø
Lakes and the Oslofjord
Source: NVE Atlas
1.2
Geographical characteristics
Except for land use the geographical
characteristics1 of the Morsa catchment have
not been included in this case study report as they can be found in the river basin characterisation report
(Lyche_Solheim, Borgvang et al. 2003). They are not deemed directly relevant for valuation study design, but
can be added later if this is shown to be required data for e.g. the value mapping.
Figure 2. Land uses and nutrient loading pressures.
Source: NIVA and (Lyche_Solheim, Vagstad et al. 2001)
Note land use: Mainly forest (green areas) and agriculture
(light brown areas) with the main urban area being Moss city
(dark brown areas) in the southern end of the catchment.
Vannemfjorden is a highly eutrophic lake
with frequent cyanobacteria blooms in MayJune. Total P loading in 2000 approximately
17 000 kg TotP. Main sources of nurient
loading are agriculture(57%), septic tanks
from individual households (11%), municipal
wastewater (6%) and natural background
run-off (26%) . Nitrogen loading relevant to
marine areas in Oslofjord in the context of
complying with the North Sea Treaty. Little
industry of significance. Other water quality
issues are marginal compared to nutrient
loading issue.
Relative P-loading
contribution by sector by sub-catchment
(¡Error! No se encuentra el origen de la
referencia.).
Source apportionment has
identified near-shore irrigated vegetable
agriculture as probable main contributor to
surplus P.
1.3. Water system characteristics
Most of watershed currently at risk of not
meeting ”good status” under WFD (Figure 3, Table 1, Table 2). Storefjorden provides drinking water supply for
60 000 people through the MOVAR water supply facility. Due to algal blooms and heavy turbidity in spring
floods MOVAR recently invested approximately 10 million Euro in upgraded water treatment (flushing, ozone
1
- Climate (include rainfall, T, ET, and runoff average values, and spatial and temporal patterns)
- Lithology
- River length, basin area, altitude, etc.
- Biotic framework (describe main ecosystems, including components & functions)
4
treatment).Storefjorden and Vannemfjorden serve as the aquatic recreational area for at least 33 000 people
living locally. North Sea Treaty obligations in Oslofjord.
Table 1. Temporary estimation of ecological quality ratio (EQR) in waterbodies of the Morsa catchment (see
Annex E i Lyche-Solheim et al. 2003 b).
Lake
no.
NVE
5572
294
293
292
295
2
291
291
291
Water bodies - lakes
EQR
SFT class
>0,6?
0,7
0,6
0,3
0,1
<0,3?
0,25
0,15
0,1
I-II?
II
II
IV
IV-V
V?
IV
IV-V
IV-V
Water bodies – water courses
EQR
SFT class
Rivers upstream of Langen
Tangenelva (from Våg til Mjær)
Hobølelva from Mjær til Tomter
Hobølelva from Tomter til Kråkstadelva
Kråkstadelva
Hobølelva from Kråkstadelva to Høyfoss (Kure)
Hobølelva from Høyfoss (Kure) to Vansjø
Mørkelva
Veidalselva
Svinna upstream of Sæbyvannet
Svinna downstream of Sæbyvannet
Mosseelva
Source: (Lyche_Solheim, Vagstad et al. 2001)
>0,6?
0,4
0,25
0,3
0,2
0,2
0,2
0,2
0,2
0,25
0,3
0,3
I-II?
IV
IV
IV
V
V
V
IV-V
V
IV-V
IV-V
IV
Bindingsvann
Langen
Våg
Mjær
Sæbyvannet
Bjørnerødvann
Vansjø, Storefjorden
Vansjø, Vanemfjorden
Vansjø, Grepperødfjorden
Subcatchm
ent no.
(REGINE)
003.E
003.D
003.D
003.CZ
003.C0
003.C0
003.B3Z
003.B5A
003.B1C
003.B1A
003.A3
The EQR is based on the “one-out all out” principle where the quality element with the lowest EQR value
determines the EQR of the water body. Values <0,6 indicate moderate or poor sattus. SFT-class refers to the
Norwegian Pollution Control Authority’s waterbody status classification. “?” indicates determination of status
based on somewhat lacking data.
2
No number in NVEs Lake database
5
Table 2.
Integrated evaluation criteria for
evaluating risk of not achieving ”good ecological
status” (good ecological potential for HMWB).
Source: (Lyche_Solheim, Vagstad et al. 2001)
Risk of not
attaining
Biological environome
ntal
status
objective
criteria
Pressure
criteria
Pysical
chemical
status
criteria
Lakes
Bindingsvann
Langen
Våg
Mjær
Sæbyvannet
Bjørnerødvann
Vansjø-Storefjorden
Vansjø-Vanemfjorden
Vansjø-Grepperødfjorden
?
+
+
+
+
+
+
+
+
?
+
+
?
+
+
+
?
?
?
?
+
?
+
+
+
uncertain
low
low
high
very high
uncertain
very high
very high
very high
Rivers
Tangenelva
Hobølelva ovenfor Tomter
Hobølelva ovenfor Kråkstadelva
Hobølelva ovenfor Vansjø
Høbølelva ovenfor Høyfoss
Kråkstadelva*
Svinna oppstrøms Sæbyvannet
Svinna nedstrøms Sæbyvannet
Mørkelva
Veidalselva
Mosseelva
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
?
+
?
+
+
+
+
?
?
+
?
high
very high
high
very high
very high
very high
very high
high
high
very high
high
Water body
Figure 3. Morsa catchment waterbodies Risk of not
attaining good ecological status (GES)
Note: Based on the WFD principle of "one-out - allout" the table shows a “+” where at least one of the
pressure criteria has been evaluated as somewhat
significant or significant, and where the EQR < 0,6
(see above). Question marks indicate lacking data.
* heavily modified water body (HMWB).
Note: “lav risiko”=low risk. “høy risiko”=high risk of
not attaining GES in 2015
1.3
Characterisation of water use
The Morsa catchment includes 9 different
municipalities in two counties (Østfold and
Akershus). The municipalities constitute the socalled “Morsa Project” which coordinates financing
at a catchment level of nutrient mitigation measures
(http://www.morsa.org/hvaermors.html).
¡Error!
No se encuentra el origen de la referencia.
illustrates the county boundaries around the Morsa
catchment. They include large urban areas which
are highly unlikely to use the Vansjø Lakes because
of distance and available substitutes. In conducting
an internet-based survey using a panel at county
level this becomes a relevant methodological issue
which is discussed further below.
Figure 4. Morsa catchment municipalities covers 9
different municipalities in two counties (Østfold
and Akershus)
Source: NIVA
From a recreational water use perspective what is immediately evident is
the large number of lakes in close vicinity to Vansjø, as well as the
Oslofjord. There are in other words a large number of substitute sites for
water recreation although Vansjø is by far the largest.
Population density of Akershus and Østfold Counties (Source: SSB)
6
Figure 6.
also shows that population density is high near Vansjø (Moss town), as well as in Oslo to the north and
Fredrikstad to the south. Populations in these areas use Vansjø to a very limited degree based on anecdotal
evidence, suggesting high distance decay of any WTP for nutrient abatament measures and better water quality.
See appendix 2 for a list of the municipalities in and around the catchment.
Figure 5. Administrative boundaries of Akershus and Østfold Counties (Source: SSB)
Figure 6. Population density of Akershus and Østfold Counties (Source: SSB)
7
A mitigating factor for distance
decay (at least in Østfold county) is
that a large number of the water
bodies in the county are heavily
eutrophied according to Norwegian
State Pollution Control Authority
criteria (¡Error! No se encuentra el
origen de la referencia. ). No work
has been done on recreational water
use patterns in the region and there
is very little basis for establishing a
“policy relevant” population over
which to aggregate recreation
benefits for a particular water body
such as Vansjø.
1.4
Cost recovery of water
and sewage fees in Morsa
Figure 7. Water quality status in substitute lakes
Source: Miljøstatus Norge
Note: water quality legend (“meget god”=”very good”; “meget
dårlig”= “very poor”).
Given that water and sewage fees
are a possible payment vehicle the
water and sewage utility cost
recovery analysis is perhaps the
most relevant part of the Economic
Analysis of Water Users under the
WFD (Annex III).
(Lyche_Solheim, Vagstad et al. 2001) (in Norwegian) has a detailed analysis of water and sewage rates and
transfers between municipalities in the catchment. The data in this report can be used to calculate the annual cost
recovery rate for water and sewage fees in the area, which in turn can be used as the status quo level of the
payment attribute in the choice experiment.
2. PRESSURE, IMPACT, AND RISK ANALYSIS WITH
REGARDS TO THE WFD ENVIRONMENTAL OBJECTIVES
2.1
Significant pressures impacting on water status
Figure 8 shows a water user conflict matrix based on the pressure-impact approach for the Morsa catchment
(Lyche_Solheim, Borgvang et al. 2003). The matrix covers both pollution, water abstraction and morphological
pressures, but it is immediately evident that the principle conflict relate to nutrient pressures as stated previously.
2.2. Impacts on surface and groundwater bodies
As stated previously, due to algal blooms and heavy turbidity in spring floods the water utility MOVAR recently
invested approximately 10 million Euro in upgraded water treatment (flushing, ozone treatment). Storefjorden
and Vannemfjorden serve as the aquatic recreational area for at least 33 000 people living locally. Impacts in
terms of numbers of water users affected by municipality has not been evaluated and will be a contribution of the
stated choice survey.
8
2.3. Water bodies at risk of not achieving a good status
See Figure 3.
9
Pressure (cause) Households:
Drinking
water
Impact (effect)
Households:
Drinking water
Households:
Sewage emissions
Agriculture:
irrigation
Agriculture: Runoff
Agriculture:
Morphological
changes
Bathing
Households:
Sewage
emissions
÷
÷/0
Agriculture:
irrigation
Agriculture:
Run-off
Agriculture:
Morphological
changes
Bathing Fishing Hydropower Industry:
Water
supply
Industry:
emissions
Recreation:
scenic
÷/0
÷
÷/0
÷/0
÷/0
÷/0
0
÷
0
÷/0
0
0
0
0
÷/0
0
0
0
0
0
0
÷/0
÷/0
0
0
0
÷/0
÷/0
÷
0
÷/0
÷/0
0
÷/0
÷/0
÷/0
0
÷/0
0
÷/0
÷/0
÷/0
÷/0
0
0
0
÷/0
0
÷
÷/0
÷/0
0
÷
0
0
0
÷
0
÷
0
0
0
÷/0
0
0
0
÷
÷/0
0
0
0
0
0
÷
÷/0
÷/0
÷/0
÷
÷/0
÷/0
÷/0
÷/0
÷/0
0
÷/0
÷/0
÷
0
÷
÷/0
Fishing
÷/0
÷
÷/0
÷
÷
0
Hydropower
÷/0
÷/0
÷/0
÷/0
0
0
0
0
÷
÷/0
÷/0
0
0
0
0
÷/0
÷/0
÷/0
÷/0
0
÷/0
÷/0
0
÷/0
0
÷
÷/0
÷
÷
0
0
÷/0
0
÷/0
0
÷/0
÷/0
÷/0
÷/0
0
0
0
0
0
0
0
÷
÷/0
÷
÷
÷/0
÷/0
÷
÷/0
÷
÷/0
Industry: Water
supply
Industry:
emissions
Recreation:
scenic
Boating
Nature reserves
Boating Nature
reserves
÷/0
÷/0
Figure 8. Matrix of water use conflicts Morsa catchment
Source: (Hovik, Selvik et al. 2003). Water resource use conflicts in Morsa catchment: ÷ = conflict, 0 = little or no impact. Red: Main use conflicts, Yellow:
Secondary use conflicts
10
2.4. Diagnosis of water quality and ecological issues (aquatic and related terrestrial ecosystems)
See Table 1 and Table 2.
2.5. General trends and future pressures
No separate trends analysis has been performed for the Morsa catchment apart from the evaluation of risk of not
attaining GES in 2015.
3.
POLICY ISSUES
3.1
Water management framework and major issues
The principle focus of the case study is water quality issues, specifically related to excessive nutrient loading of the
Vansjø Lakes.
3.2
Water policy and research relevance in the basin
Nutrient abatement as a priority is illustrated in the pressure-impact matrix (
). (Barton, Saloranta et al. 2007) used a Bayesian belief network to show how uncertain nutrient mitigation costs could
be compared to uncertain benefits of water quality improvements. The study was based on a more than 10 year old
contingent valuation study (Magnussen, Bergland et al. 1995) from the Vansjø Lakes. Whereas the CV study looked at
willingness to pay for increasing water quality throughout the lakes to suitable levels for drinking water and bathing,
they were not able to show any scope effects of different levels of improvement. The WTP estimates are therefore
much coarser than the predictions of suitability on several different water quality parameters derived from the Bayesian
belief network. Furthermore, the study did not evaluate health risks due to toxic algal blooms, which have been a
recurring problem since 2000. Also, disproportionate cost analysis ideally requires that benefits can be calculated for
several different levels of ambition in the programme of measures. Valuing alternative levels of water quality is easier
in choice experiments (CE) than contingent valution, given the same budget for sampling, as more information on
preferences is obtained per respondent.
The following focuses specifically on the policy relevance of the planned choice experiment,
Reearch relevance of the choice experiments
NIVA has carried out water quality monitoring of algal toxins since 2004 (particularly in Østfold and Akershus
Counties). In 2006 a total of 37 lakes were analysed; in 25 of these we found the algal toxin microcystin - the situation
was worse than previously assumed. Because of this several lakes had bathing advisories and water treatment plants
increased their preparedness. The Norwegian Health Institute has been contacted frequently regarding health effects of
contact with blue green algae.
NIVA has taken the initiative for a national monitoring programme and system for risk evaluation of algal toxins in
Norway. Risk analysis has generally been the purvue of experts, but there is a growing international awareness that
health advisories to the public should be based on better information of how water users experience water quality and
health risk subjectively (WHO 2001). Health advisories (bathing, dietary) play a large role in determining the use
value of water resources and as such the evaluation of whether costs of measures are disproportionate to the benefits of
avoiding advisories.
An understanding of subjective water use suitability criteria (Smith, Cragg et al. 1991) will also make it easier for local
environmental authorities to give more relevant risk information and portray monitoring data in a more meaningful way
to the public. There is also a need to understand how health risk and suitability is percieved across different water user
groups. Survey-based methods – particularly choice experiments – can be used to evaluate these preferences across
relevant populations around the water resource in question (Bateman, Carson et al. 2002).
15
AquaMoney
The newest choice experiment methods such as latent choice models are capable of studying user preferences for
different water quality attributes for different user segments of the population (classes such as bathers, boaters, property
owners, hikers) (Morey and Breffle 2006). Choice experiments also have the capability to look at more flexible forms
of the utility function - the mixed logit approach allows for different distributional forms of choice attributes, including
positive and negative values for different segments of the population (Train 2003).
As far as we know there are no previous choice experiments of water quality in Norway, and only a few internationally
(Hanley, Adamowicz et al. 2002; Hensher, Shore et al. 2004; Adamowicz, Dupont et al. 2005). As far as we know
there have been no choice experiments on health risks related to algal toxins. In that sense, the AQUAMONEY project
also fills an important research gap with its case studies.
3.3
Information sources and stakeholder involvement
Principle secondary information sources for the river basin are as follows:
• River basin characterisation: (Lyche_Solheim, Borgvang et al. 2003)
• Cost-effectiveness analysis of programme of measures (pre-WFD): (Lyche_Solheim, Vagstad et al. 2001)
• Prior non-market valuation studies of Lake Vansjø (CVM): (Magnussen, Bergland et al. 1995)
• Uncertainty analysis of programme of measures (Bayesian belief networks): (Barton, Saloranta et al. 2005;
Barton, Saloranta et al. 2007)
The following stakeholders were interviewed for the AQUAMONEY decision-makers survey:
• Norwegian Pollution Control Authority (SFT)
• Norwegian Directorate for Water Resources and Energy (NVE)
• The Morsa Project
4.
ERC ANALYSIS AND METHODOLOGICAL ISSUES
4.1
List of main water-related goods and services provided in the basin
The following list of water –related goods and services is ranked in rough order of importance from the water use
conflicts matrix (
). The conflict matrix evaluation was based on expert judgment of the severity and number of users affected. The
indirect use values of sustainable land use in the catchment are derived from the main pressure-impact linkages leading
to conflict between water users.
Direct Use values of the
Vansjø Lakes (Morsa
catchment)
Bathing / recreation
Fishing / recreation
Drinking water
Irrigation
water
(vegetables)
USE VALUES
Indirect Use values
relating to land use
management in the
catchment and effect on
water quality in Vansjø
Lakes
Nutrient
retention,
including
sewage
treatment
Flood control
Option and
Option value
Quasi-
Unknown given that there
are many substitute water
bodies for practicing
bathing, fishing and also
for
drinking
water
transfer.
NON-USE VALUES
Existence value
Unkown, but no wetland
nature reserves.
No
charismatic or publicly
known aquatic red-list
species
Benefits and costs from water services are self-explanatory. Benefits accrue to direct uses of the Vansjø Lakes.
Drinking, irrigation water supply and sewage treatment have direct costs and are in principle “cost recovered” (by
regulation). Nutrient retention is the single most important indirect use value related to land use management, involving
costs of ploughing, fertilisation reduction and buffer zone measures.
16
4.2
Objectives: type of ERC analysis to performe
Keywords for the ERC analysis are environmental costs and benefit-cost analysis (disproportionate cost analysis).
I. The primary objectives of the Norwegian valuation study in AQUAMONEY:
1.
2.
3.
to evaluate the marginal willingness to pay for different aspects of water quality for recreation in the Vestre
Vansjø Lake and Storefjorden Lake (Eastern Vansjø). (and substitute lakes in Østfold and Akershus Counties,
South Eastern Norway).
to conduct benefit transfer (BT) tests across different water user populations using the Vansjø Lakes.
o BT type 1(Figure 9): to evaluate benefit transfer across different user populations using Vestre Vansjø
Lake
o BT type 4: to evaluate distance decay of response rates and willingness to pay within the counties
where the water bodies are located. Evaluate “relevant population” for disproportionate cost analysis.
to evaluate the temporal “stability” and convergent validity of valuation estimates comparing contingent
valuation for water quality (Magnussen, Bergland et al. 1995) with valuation based on choice experiment
results
II. Secondary objectives (analysis based on data from previous studies, applied in the AQUAMONEY context):
1.
linking marginal attribute values of specific water quality indicators to cost-effectiveness estimates for those
indicators modelled using Bayesian networks (Barton, Saloranta et al. 2006)
a. visibility depth
b. probability of bathing advisories
2. conduct disproportionate cost evaluation combining uncertainty in non-market valuation
estimates (transferred and primary) with uncertainty in costs of measures.
3. evaluate costs of attaining good ecological status using cost-based valuation only. Evaluate
transferability issues using Bayesian networks.
4.3
Proposed methods and tools for the valuation of ERC
4.3.1
Choice experiment
Re Objectives: I.1
A choice experiment is planned in two phases:
1.
Pilot study on identifying significant water user suitability attributes by water user type
This study will be conducted during the spring and early summer 2007 using a web-based survey of a small panel
drawn from stakeholder organisations representing water users around Lake Vansjø (environmental NGO, boat owners
association, land owners association). The survey will look at how different water users trade-off water quality and user
attributes. A share-ware internett-software will be tested and the panel will be constructed by NIVA (with the help of
an M.Sc. student from UMB).
2.
Main survey of marginal willingness to pay for water user suitability attributes
This survey is also internett-based, and will use only the significant water quality and user attributes uncovered in the
pilot study. The sample will be much larger and cover two whole counties within which the Lake Vansjø waterbody
17
AquaMoney
and catchment is located. The internet survey tool and panel for these two counties will be sub-contracted from a
professional survey company, Norstat3.
4.3.2
Benefit transfers
Re Objectives: I.2-3
Figure 9 Benefit transfer tests
Morrison and Bergland (2006) review different approaches to
benefit transfer testing of choice experiment models (Figure 9).
The benefit transfer tests conducted with the data sets in the
Morsa case study will be Type 1. Type 4 test will be possible
by comparing choices of populations living nearshore to the
county/region at large. Between European case study sites
Type 2 tests may be conducted (provided similar choice
experiment survey instruments are employed).
4.3.3
Mitigation cost
Re Objectives II.1-3
From the Morsa catchment costs of nutrient mitigation
measures in agriculture and sewage treatment have been
Source: Morrison and Bergland (2006)
quantified (Barton, Saloranta et al. 2006). This has been
evaluated against the effects of these measures in Bayesian belief networks which account for uncertainty of cost and
effect estimates. The Bayesian networks can be used “in reverse” (inductively) to estimate implementation costs of
given changes in lake water quality. This constitutes an alternative and conservative approach to valuing attainment of
“good ecological status” (assuming that WTP is unknown, but higher than total mitigation costs).
4.4
Methodological issues
4.4.1
Methodological steps preparing CE studies
The following methodological steps are common to the preparation of choice experiments(Kanninen 2006). Some of
these steps are discussed under the common AQUAMONEY headings for methodological issues.
1.
2.
3.
4.
5.
6.
Identify attributes, levels;
Number of alternatives in choice set
Treatment of attributes(continuous, categorical); number of parameters and degrees of freedom, required
number of choice sets
Maximum number of choice occasions/questions feasible; decision on whether to use blocks
Determine minimum sample size
Factorial design
a. OMED (1993, Sloane, 2004, or Warren Kuhfeld’s online catalog:
http://support.sas.com/techsup/technote/ts723_Designs.txt)
7.
8.
9.
4.4.2
b. Computer generated (software choice?)
Decide whether to include interactive terms
Evaluate dominated alternatives
Evaluate possibility to adjust attributes levels during field work in order to balance probabilities
Target group(s)
The choice experiment will be conducted on a panel of respondents from Østfold and Akershus Counties Figure 5.
Problem 1: For the general population in these counties we have no way of selecting water recreational users only.
The sample is likely to include a large number of non-users.
3
http://www.norstat.no/en/content.asp?uid=201
18
Solution 1: option values will be an important element of the survey. Questions identifying current and intended use
patterns will be included in the survey. This will be important in order to estimate a latent class model (heterogenous
preferences across user groups).
Problem 2: Nor is it possible to select specific users of Lake Vansjø, as there are many substitute marine and freshwater
recreation sites in the two counties. Distance decay for Lake Vansjø is expected to be high if available subsitute lake
recreation options are pointed out to the respondents.
Solution 2: Questions will have to be introduced to separate distance decay effects from substitute effects on marginal
utility of water quality attributes in Vansjø Lakes.
Problem 3: representative sampling within the two counties is possible with the Norstat panel, but not so for a selection
of municipalities within the counties (e.g. within the catchment or around the lakes). The sample will probably be
adequate for evaluating distance decay and substitution effects with other water recreation sites, but will have relatively
few users of the Vansjø lakes themselves with a specific knowledge of local water quality required to evaluate user
thresholds in that particular lake.
Solution 3: water quality descriptions must be more generic (not specific for eutrophication conditions in Vansjø).
Users of other lakes in the counties should be asked to evaluate thresholds in the lakes where they live. Some map user
interface is necessary in the survey so that respondents can select the lake they are most familiar with when answering
choice questions.
4.4.3
Sample size requirements
Question 1: What is the minimum sample size of pilot survey in order to identify significant water quality attributes?
Questions 2: What is the minimum sample size of the main survey in order to identify significant explanatory
background variables of choice?
In question 1 we are concerned with estimating the marginal value of attribute levels. In this case it may be an OK
approximation to use the rule of thumb proposed by Orme (Kanninen 2006; Orme 2006), who suggests the following
formula:
Equation 1
N= 500 (NLEV)/ (NALT x NREP)
NLEV= largest number of levels in any atribute, including interactions
NALT= number of alternatives per choice set
NREP=number of choice questions per respondent
The final sample size estimation will be based on the number of attributes and choice occasions which is selected.
In question 2 we are concerned with hypothesis testing of explanatory variables. Personal communication with an
econometrician4 suggested that at least double sample size of that estimated using Equation 1 would be necessary to
test for a few very significant relationships (e.g. with socio-economic background variables). We currently have no
priors on choice proportions. The pilot choice experiment will be used to narrow down the number of choice attributes
so that observation of any signficant relationships with background variables becomes easier.
Orme (2006) states that for investigative work and establishing hypotheses about a market samples sizes as low as 3060 respondents may be sufficient5. For robust quantitative work without strata Orme suggests a samples size of 300
(presumably dependent on use of Equation 1).
Further rules of thumb when budgetary restrictions take precendece over thoeretical sample size considerations
(Hensher, Rose et al. 2005). With no priors they suggest a “quota strategy” aimed at obtaining a minimum of 50
responses for a given choice (they further assume that this is based on 16 choice sets, completely generic / non-labeled
alternatives, and no context or covariate effects to be modelled).
4
5
O.Bergland, UMB
It is not clear whether this assumes the adaptive conjoint analysis of Sawtooth software for which Orme is a proponent.
19
AquaMoney
Morey and Breffle (2006) provide an example of sample size which produced significant results in a latent class study
appllication fo choice experiments. The authors had a sample of 640 valid responses from which the could identify 4
significantly distinct latent classes.
Sample stratification
Norstat recommends conducting an additional mail based survey for respondents over 60 as they are not well
represented in the panel. This will also be sub-contracted to Norstat. In addition it will be desirable to tratify the
sample by municipality or postcode if possible in order to have enough respondents at different distances from the
Vansjø Lakes. TO BE CONFIRMED WITH NORSTAT.
Further practical advice is that individual strata should contain at least 200 respondents (Orme 2006)
4.4.4
Valuation scenario(s) design in relation to ERCB & WFD
Section 4 made a case for the use of choice experiments in the context of benefit-cost analysis of disproportionte costs
under implementation of the WFD. Using a choice experiment also makes it possible to evaluate “good ecological
status” as a policy priority, versus “good water use suitability”, which has to date been the focus of Norwegian water
management. Furthermore, we want to test whether respondents consider suitability in the same way the WFD defines
“ecological status” with its “all out one out” rule. Perhaps recreational water users are willing to trade some quality
attributes off against others. Water quality attributes can be grouped in some broad classes:
• “esthetic attributes” (continuous)
• “health risk attributes”(continuous)
• “advisory attributes” (binary or categorical)
The choice experiment will test :
H1) which types of attributes are significant for choosing whether to use the water water body for recreation? Do
significant attributes vary with the type of recreational activity?
H2) whether water users (bathers, boaters, shoreline users) show any suitability thresholds for these attrbutes
H3) if so whether these thresholds correspond to limit values for “good ecological status”.
These “hypotheses” are discussed below.
H1)
(Selected) Water quality
attributes are significant in site
choice
An initial list of attributes that are
candidates for evaluation in focus
groups and pilot tests is given in Table
4.
This list of attributes was narrowed
down further for the first focus group
(Figure 10).
Proposed attributes to be discussed in
focus groups for importance for their
recreational
experience
before
selection. Once selected we will do
further work on getting appropriate photos. Ideally we will have a set of attributes that are consistent from a water
quality perspective, i.e. that represent the same concentrations of nutrients, algal biomass. This may be difficult in
practice, but at NIVA we will try to find photo illustrations to that effect.
Figure 10 Attributes and levels to be evaluated in focus groups
20
Table 3. Proposal for recreational attribute descriptions to be tested in choice experiments
Factor
Proposed attributes*
Visibility depth swimming
Relationship
WTP=f(attribute)
Hypothesis
negative
Suggested levels
Visibility depth viewing
negative
Shoreline esthetics - foam
Water colour
uncertain
negative
Algal /macrophyte growth on
shoreline surfaces
Bathing advisory
negative
negative
Binary 1=yes 0=no
Bathing advisory risk levels
Risk of accute symptoms from
algal
cyanotoxins
(gastroenteric, allergenic, head
aches, fever)
Travel time to substitute
bathing sites of at least similar
water quality
Increase in water and sewage
utility fee
negative
negative
Risk levels
negative
minutes
negative
kroner/year
See appendix 1 examples
0.2 m (foam on surface)
0.5 m
1m
2m
4m
See appendix 2 for examples
0.2 m (foam on surface)
0.5 m
1m
2m
4m
See appendix for example
1=Milky green (bloom)
0=Other [Brown (natural humus, windy)
Grey-blue transparent (clear water)}
See examples of levels from Magnussen,
Navrud et al. 1995
Visualisation of probability based on
percentage of population predisposed to
allergenic reactions
H2) water users (bathers, boaters, shoreline users) show suitability thresholds for selected water quality
attributes
The Norwegian Pollution Control Authority (SFT) water suitability levels for bathing are given in Table 4.
Table 4. SFT water suitability levels for bathing
Bathing and recreation
Suitability classes
Effects of:
Parametre
Very suitable Suitable Less suitable Not suitable
Nutrients
TotP (ug P/l)
<7
7-11
11-20
>20
ChlA (ug/l)
<2
2-4
4-8
>8
Secchi depth(m)
>4
2-4
1-2
<1
Organic material Colour number
<25
>25
Source: translated from SFT (1997). Note the colour number refers to the intensity of brown colour
We are especially interested in evaluating whether there is any threshold for recreational bathing at the limit values for
“suitable”bathing water given by the SFT (11 ug Chla /l, 2m sightdepth).
Our hypothesis is that after several years of water quality problems, users have become more used to poor conditions,
but continue to use the lake. The local population m ay have adapted to algal blooms and the fact that the lake might be
humous, and so have a greater tollerance for poor sight depth and opaque water colour. We want to test whether
respondents have a higher tollerance for poor water quality than the recognized suitability limit values. This would
imply that benefits of improvement are lower than in the previous CV survey conducted in 1995 (Magnussen, Bergland
et al. 1995).
21
AquaMoney
H3) Suitability thresholds correspond to limit values for “good ecological status”
The classification (lake type) for Vestre Vansjø is still uncertain. However classified, good ecological status would
comply with bathing recreational suitability criteria established by the SFT.
Table 5. Suitability versus ecological status
Limit value “Not Good ecological status
suitable”
(Chla A biomass)
Economic use:
(Chla A biomass)
(draft recommendations of Intercalibration
(SFT, 1995)
projects, pers. com. Anne Lyche Solheim,
NIVA)
Raw drinking
water
Irrigation water
8 ug/l
Recreational fishing
20 ug/l
Bathing water
8 ug/l
20 ug/l
5 ug/l
(large lime poor nonhumous lakes)
10.5 ug/l
(large lime rich
humous lakes)
If classified as “lime poor” good ecological status would require algal biomass concentrations that are half of what is
regulated as “suitable” for bathing. From an economist’s point of view good status would require a programme of
measures that was excessively costly, assuming bathing water quality is the primary economic use affected by excessive
nutrient levels (if characterised as humus rich “suitability and ecological status” thresholds are compatible). If the
choice experiment uncovers less sensitivity to water quality, this conclusion would be even more true.
The
implications would be that “good ecological status” limit values are strict in an economic sense and biased towards
accepting a derrogation from “good ecological status” on dispropotionate cost grounds.
4.4.5
Spatial implementation scale
The in-person interviews of lake users will be focused on the main water body in the Morsa catchment, Lake Vansjø
(Vestre Vansjø and Storefjorden). The web-based survey will cover the counties of Østfold and Akershus and is
“regional”; wider than the river basin.
4.4.6
Temporal issues
The initialpayment attribute proposed was an annual water and sewage fee. Historically, there have been a number of
sewage fee price increases to pay for measures in the catchment. Initial focus groups have shown that a county-wide
tax shows less protest reactions.
4.4.7
Methodological tests (e.g. sensitivity to scope, distance-decay etc)
The choice experiment is planned at county level (Østfold and Akershus) which exceeds the limits of the Lake Vansjø
catchment. While this is necessary to test distance decay, the county level panel is not very efficient as it will probably
include a large proportion of water users not using the Vansjø Lakes themselves.
Sensitivity to scope will be tested implictely by using more than 2 attributes levels for several of the water quality
attributes in the choice experiment.
4.4.8
Value transfer tests6
Several different tests of benefit transfers are discussed as alternative approaches to evaluating the reliability of BT
between the case study sites :
1. convergent validity of implicit prices and WTP (unit value transfer)
2. convergent validity of individuals choices conditional on socio-economic and attitude parameters
3. similarity of value functions (similarity of coefficients)
6
The following section was prepared in connection with the EU project Thresholds (unpublished memo).
22
4.
5.
convergent validity of rankings and resulting welfare loss function
damage function transfer (conditional on pollution loading)
Approach 1 and 3 area “traditional” tests conducted on transfer of CV results. Approach 2 and 4 are specific to choice
experiments, although both these approaches have elements of benefit function transfer (using site characteristics with
transferred model coefficients) also seen in the CV literature. Approach 5 is (as far as we know) a new approach in the
BT literature7. We therefore propose running test 1, 3 and 5 (if detailed water quality data is available at other
comparable sites to Lake Vansjø).
Introducing some notation for the following discussion; subscript (p) will refer to the policy site to which estimates are
transferred. Subscript (s) will refer to the study site from which estimates are transferred/ where the primary valuation
study was carried out.
There are a number of possible benefit transfer tests that could be conducted in comparing this study to others in
AQUAMONEY. These are discussed further in appendix.
4.4.9
Aggregation and use of GIS (feasibility of a GIS based value map)
¡Error! No se encuentra el origen de la referencia. shows the water quality of major water bodies in the whole of the
Østfold county. If respondents within the county were asked to indicate which lakes they visit, how often and to what
extent water quality attributes affected their choices between lakes that were otherwise similar, it may be feasible to
identify boundaries for relevant user populations around the different water bodies in the county. This may be the basis
for a county-wise value map. If this county-wise approach is taken the sample size will not be large enough to estimate
marginal utilities for individual water bodies. Values applied will be averages across the county.
4.4.10
Planning of activities and their timing
Survey implementation is planned for August and December 2007.
4.4.11
Other issues
Information-optimal experimental design?
• SPSS
• JUMP
• GAUSS (Kanninen et al. 2006)…
• catalogue based orthogonal main effects designs (OMED) ?
Web-based/computer-based surveys with illustration capabilities?
• Questback
• SPSS Dimensions(?)
• Sawtooth(?)
Estimation software (incl. mixed logit, latent dependent variable)
• NLOGIT/LIMDEP(reliable?)
• STATA(too limited?)
• GAUSS(programming required)
• BIOGEME (freeware)
7
Currently being evaluated in the EU project Thresholds. This requires detailed water quality data and damage functions from the case study sites.
23
AquaMoney
References
Adamowicz, V., D. Dupont, et al. (2005). "The Value of good quality drinking water to Canadians and the role of risk
perceptions: a preliminary analysis." Journal of Toxicology and Environmental Health Part A Volume 67,
(Number 20-22 / October–November 2004): 1825 - 1844.
Barton, D. N., T. Saloranta, et al. (2005). "Using Bayesian network models to incorporate uncertainty in the economic
analysis of pollution abatement measures under the water framework directive." Water Science and
Techology: Water Supply 5(6): 95–104.
Barton, D. N., T. Saloranta, et al. (2006). "Pros and cons of using Bayesian networks to evaluate nutrient abatement
decisions under uncertainty in a Norwegian river basin." Ecological Economics (manuscript submitted
November 2006).
Barton, D. N., T. Saloranta, et al. (2007). "Pros and cons of using Bayesian networks to evaluate nutrient abatement
decisions under uncertainty in a Norwegian river basin." Ecological Economics (forthcoming).
Barton, D. N., T. Saloranta, et al. (2006). Using belief networks in pollution abatement planning. Example from Morsa
catchment, South Eastern Norway. NIVA Report SNo.5213-2006, Norwegian Institute for Water Research
(NIVA).
Bateman, I., R. T. Carson, et al. (2002). Economic Valuation with Stated Preferences Techniques. A Manual, Edward
Elgar.
Hanley, N., W. Adamowicz, et al. (2002). Price vector effects in choice experiments: an empirical test. World Congress
for Environmental and Resource Economists, Monterrey, California, June 2002.
Hanley, N., R. E. Wright, et al. (2006). "Estimating the economic value of improvement in river ecology using choice
experiments: an application to the water framework directive." Journal of Environmental Management(78):
183-193.
Hensher, D., N. Shore, et al. (2004). "Households' willingness to pay for water service attributes." Environmental and
Resource Economics Volume 32 (Number 4/ December, 2005): 509-531.
Hensher, D. A., J. M. Rose, et al. (2005). Applied Choice Analysis. A Primer. Cambridge, New York, Cambridge
University Press.
Hovik, Selvik, et al. (2003). Demonstrasjonsprosjekt for Implementering av EUs Vanndirektiv i Vansjø-Hobølvassdraget: Fase I, NIVA.
Jiang, Y., S. K. Swallow, et al. (2006). "Context-Sensitive Benefit Transfer Using Stated Choice Models: Specification
and Convergent Validity for Policy Analysis." Environmental and Resource Economics(31): 477-499.
Kanninen, B., Ed. (2006). Valuing environmental amenities using stated choice studies. A Common sense approach to
theory and practice. The Economics of non-market goods and resources. Dordrecht, Springer.
Kristofersson, D. and S. Navrud (2005). "Validity Tests of Benefit Transfer - Are We Performing the Wrong Tests?"
Environmental and Resource Economics(30): 279-286.
Lyche_Solheim, A., S. A. Borgvang, et al. (2003). Demonstrasjonsprosjekt for implementering av EUs Vanndirektiv i
Vannsjø-Hobøl. Fase 2: Skisse til veildere for karakteriseringsoppgavene i 2004, samt forslag til
overvåkningsprogram, NIVA.
Lyche_Solheim, A., N. Vagstad, et al. (2001). Tiltaksanalyse for Morsa. Vansjø-Hobøl vassdraget. Sluttrapport, NIVA.
Magnussen, K., O. Bergland, et al. (1995). Overføring av nytte-estmater: status for Norge og utprøving knyttet til
vannkvalitet. Del II. Utprøving knyttet til vannkvalitet. NIVA Report 1995-3258.
Morey, E. and J. T. W. Breffle (2006). "Using angler characteristics and attitudinal data to identify environmental
preference classes: a latent-class model." Evironmental & Resource Economics(34): 91-115.
24
Orme, B. (2006). Sample Size Issues for Conjoint Analysis (Chapter 7). Getting Started with Conjoint Analysis:
Strategies for Product Design and Pricing Research. Reprinted from Orme, B. (2006). Madison, Wis.,
Research Publishers LLC.
Poe, G. L., K. L. Giraud, et al. (2005). "Computational methods for measuring the difference of empirical distributions."
American Journal of Agricultural Economics 82(2): 353-365.
Smith, D. G., A. M. Cragg, et al. (1991). "Water clarity criteria for bathing waters based on user perception." Journal of
Environmental Management(33): 285-299.
Train, K. E. (2003). Discrete choice methods with simulation. New York, Cambridge University Press.
WHO (2001). World Health Organization (WHO). Water Quality: Guidelines, Standards and Health., Published by
IWA Publishing, London, UK.
25
AquaMoney
Appendix 1
Further details on possible benefit transfer approaches
1. Convergent validity of implicit prices and WTP (unit value transfers)
1.1 Convergent validity of implicit price of water quality attributes
Implicit price: IP =
MU v β v
=
(8)
MU c α c
v= choice attribute
c= cost attribute
Mixed logit gives standard errors for parameter coefficients through simulation. However, the parameters are not
independent(?). In order to simulate confidence interval for IP correctly you need to simultaneously estimate the
parameters and calculate IP through bootstrapping the model (Krinsky and Robb (1986)). If simulated IP for the study
site and policy site are approximately normal the two-onesided t-test (TOST) (Kristofersson and Navrud 2005) can be
used to evaluate the hypothesis that the implicit prices are equivalent.
An alternative approach that allows for other
distributional assumptions (??) is to calculate p-values for differences between bootstrapped non-normal distributions
(Poe, Giraud et al. 2005). Poe et al. point out that inspection of overlapping confidence intervals tend to accept too
many transfers when confidence intervals are wide (due to small sample size, poor survey design and implementation
etc.).
1.2 Convergent validity of WTP for policies (bundles of attributes)
Willingness to pay can also be calculated as :
WTPq = −
1
αc
(Vi − Vn ) (9)
where
WTPq= willingness to pay for programme choice with change in attribute level q
αc= parameter on cost attribute
Vi-Vn=difference in indirect utility of choice set i versus the no programme choice8
Following (Jiang, Swallow et al. 2006) WTP can then be estimated across all attribute bundles (choice permutations) at
each site and then the resulting distributions compared across sites. The authors check whether the WTP distribution is
normal for all combinations of programmes using the Kolmogorov-Smirnov (KS) test (Lapin 1993) of normality.
Upon confirming normality they then use a “Paired sample p-test” for the differences between distributions which
sounds much the same as the TOST proposed by (Kristofersson and Navrud 2005).
2.
convergent validity of individuals choices conditional on socio-economic and attitude parameters
This approach compares the % of correct predictions of choices using the study site (transferred) model coefficients on
policy site respondent characteristics, with the actual choices made at the policy site(Jiang, Swallow et al. 2006).
3. convergence of utility functions across sites (equality of parameter estimates)
A straight-forward approach is to conduct a likelihood ratio test of equivalence of coefficient parameters and scale
parameters between site specific and pooled models (Hanley, Wright et al. 2006; Jiang, Swallow et al. 2006). Jiang et
al.(2006) use two tests to avoid confounding equality of coefficient and scale parameters (that needed to be e
programmed in Gauss).
Test 1:
8
Φ site = Φ pooled , λ site = λ pooled
Does the apporach used in Jiang et al. require that the “no programme” choice be defined explicitely in terms of attribute levels?
26
H1:
Φ site ≠ Φ pooled , λ site ≠ λ pooled
λ site ≠ λ pooled
≠ Φ pooled , λ site ≠ λ pooled
Test 2: Φ site = Φ pooled ,
H1: Φ site
4. convergent validity of rankings and resulting welfare loss function
Jiang et al. (2006) use Spearman’s and Kendal’s correlation coefficients to compare rankings of attribute bundles
between study and policy sites.
They also derive a loss function based on the difference between the cumulative WTP for the highest ranked choices T,
out of a total N choices, chosen at the study site, with the model coefficients and respondent characteristics of the policy
site. These are compared to the cumulative WTP for the highest ranked choices at the policy site, with the policy site
model coefficients and policy site respondent characteristics. The difference between WTP estimates is due to the
different source of ranking results (study site versus policy site). WTP is calculated as shown in equation (9) above.
The Loss function summarises the transfer error in terms of reduction in WTP relative to the optimal ranking at the
policy site:
Loss(N)=100[WP|P(N)-WP|S(N)]/WP|P(N)
(10)
where
WP|P(N) is the sum of non-negative WTP estimates using only policy site rankings, coefficients and respondent
characteristics
WP|S(N) is the sum of non-negative WTP estimates using study site rankings, policy site coefficients and respondent
characteristics
5. damage function transfer (conditional on pollution loading)
Several different tests of transfer reliability (convergent validity) can be carried out on the damage function given
availability of data at the policy site. Each additional function or linkage transferred introduces a potential error.
Table 6 Damage function transfer possibilities (conditional on site characteristics)
Policy
site
data
availability
Nutrient concentration
(N) only
or
Algal biomass (B) and
duration(D)
and
Congestion (Z)
Policy site transferred WTP given study site estimates (p|s),
given difference (δ)in site attributes (p-s)
Study
WTP
site
WTPp|s,1= ws(gs(fs(δN p-s)+ef)+eg) +ew
WTPs|s
WTPp|s,2= ws(gs(δΒ p-s, δD p-s) +eg) +ew
WTPp|s,3= ws(δ Z p-s , δΒ p-s, δD p-s) +ew
Task 2.5 calls for evaluating whether GIS can be used to improve benefit transfers based on state variables. This will be
relevant if the study site teams obtain GIS maps showing water quality monitoring data at their study site and perhaps
neighbouring beaches, or other beaches in the same country. We could then use the GIS maps to generate WTP at each
site with water quality data using the function in Table 6.
27
AquaMoney
Appendix 2
Municipalities in and around the Morsa Catchment.
Østfold County
0101 Halden
0104 Moss
0105 Sarpsborg
0106 Fredrikstad
0111 Hvaler
0118 Aremark
0119 Marker
0121 Rømskog
0122 Trøgstad
0123 Spydeberg
0124 Askim
0125 Eidsberg
0127 Skiptvedt
0128 Rakkestad
0135 Råde
0136 Rygge
0137 Våler
0138 Hobøl
28
Akershus County
0211 Vestby
0213 Ski
0214 Ås
0215 Frogn
0216 Nesodden
0217 Oppegård
0219 Bærum
0220 Asker
0221 Aurskog-Høland
0226 Sørum
0227 Fet
0228 Rælingen
0229 Enebakk
0233 Nittedal
0235 Ullensaker
0238 Nannestad
0239 Hurdal
0230 Lørenskog
0231 Skedsmo
0234 Gjerdrum
0236 Nes
0237 Eidsvoll
Coding:
Adjacent to target lake
In lake catchment
Other municipalities in county