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Water for Wajir

Decision modeling for the Habaswein-Wajir Water Supply Project

in Northern Kenya

Eike Luedeling, Jan De Leeuw

World Agroforestry Centre, Nairobi, Kenya

August 2014

1

Water for Wajir – decision modeling for the Habaswein-Wajir

Water Supply Project in Northern Kenya

Contents Background ................................................................................................................................................... 3

Process .......................................................................................................................................................... 4

Modeling approach ....................................................................................................................................... 4

Major model elements ................................................................................................................................... 5

Stakeholders .............................................................................................................................................. 5

Costs .......................................................................................................................................................... 5

Benefits ..................................................................................................................................................... 6

Risks .......................................................................................................................................................... 7

Model implementation .............................................................................................................................. 7

Estimate elicitation and consolidation ...................................................................................................... 8

Post processing.......................................................................................................................................... 9

Consideration of risks ............................................................................................................................... 9

Results ......................................................................................................................................................... 10

Residents of Wajir ................................................................................................................................... 10

Residents of Habaswein .......................................................................................................................... 13

Pipeline communities .............................................................................................................................. 16

Upstream areas ........................................................................................................................................ 17

Downstream areas ................................................................................................................................... 18

Donors ..................................................................................................................................................... 19

Water company ....................................................................................................................................... 20

Overall project benefits ........................................................................................................................... 23

Discussion ................................................................................................................................................... 27

Recommendations ....................................................................................................................................... 27

References ................................................................................................................................................... 28

Annex 1: Model details ............................................................................................................................... 30

Wajir, Habaswein and pipeline communities...................................................................................... 30

Upstream and downstream areas ......................................................................................................... 30

Water company ................................................................................................................................... 30

Donor .................................................................................................................................................. 30

2

Overall net present benefits from the project ...................................................................................... 31

Risks .................................................................................................................................................... 31

Sampling ............................................................................................................................................. 31

Varying values .................................................................................................................................... 31

Discounting ......................................................................................................................................... 32

Annex 2: Results from the Monte Carlo analysis for pipeline communities, upstream and downstream

areas ............................................................................................................................................................ 33

Annex 3: Estimates ..................................................................................................................................... 39

All-risks scenario .................................................................................................................................... 39

Cooperation scenario .............................................................................................................................. 48

3

Water for Wajir – decision modeling for the Habaswein-Wajir

Water Supply Project in Northern Kenya

Eike Luedeling, Jan De Leeuw

World Agroforestry Centre, Nairobi, Kenya

Background The city of Wajir in Northern Kenya, the capital of the county of the same name, has experienced rapid

population growth in recent decades. The city has so far never had a reliable supply of clean drinking

water or a sanitation system. To improve the situation, plans are currently considered to construct a water

pipeline from Habaswein, another locale in Wajir County that is about 110 km away (Figure 1).

Figure 1. Map of the study region showing the approximate course of the proposed pipeline.

In Habaswein, the Merti aquifer would be tapped and water abstracted and piped to Wajir. Even though

the Kenyan government and the Dutch donor ORIO stand ready to fund this undertaking, the project is

currently stalled for a number of reasons, including the delayed completion of various feasibility and

4

impact studies and because of resistance in Habaswein. Local residents fear that the pipeline would

undermine their own water supply and many therefore oppose the project. It is also not clear, which of the

various stakeholders would benefit or suffer from the intervention, and whether it is prudent for the donor

to invest in this pipeline.

With funding from the UK National Environment Research Council, the World Agroforestry Centre,

together with the Centre for Training and Integrated Research in ASAL Development (CETRAD, Kenya),

University College London (United Kingdom) and Acacia Water (The Netherlands), conducted this study

to make a business case for the pipeline project that considers all relevant costs, benefits and risks.

Process The project kicked off with a 1-day stakeholder workshop (November 4, 2013) that gathered around 30

people with an interest in the pipeline project. During this meeting, a smaller group of 8 experts was

formed that spent the next two days (November 5 and 6) delineating all important aspects of the business

case. This was followed by coding of the model by a decision analyst. Following a few rounds of

feedback that helped improve the model, estimates of the probability distributions of all uncertain

variables in the model were elicited from the experts. Expert estimates were consolidated into group

estimates. After some more refinement of the model, it was run as a Monte Carlo simulation (many model

runs with varying input values). Results were compiled and summarized in a first version of this report,

which was shared with the modeling team. After a further consultative workshop (May 26, 2014), during

which more feedback was elicited and following inclusion of results from detailed hydrological modeling,

final model runs were executed and this report updated to its current version.

Modeling approach In order to build a comprehensive business case model of the proposed project, all major costs and

benefits, as well as all risks to the project were first compiled and relationships between them identified.

In this step, the modeling team aimed at comprehensiveness – identifying all relevant variables – rather

than at a high level of mechanistic detail. Based on the relationships between variables, equations were

defined to translate the various variables into the net present benefits of the project. These are the sum of

all benefits minus the sum of all costs, while considering the various risks that are involved. Such

calculations were done separately for all major stakeholders. In order to adjust for the time preference of

the stakeholders – i.e. the fact that they value benefits less highly if they accrue far in the future – all

results were discounted using standard economic procedures.

For running the model, estimates of all uncertain variables were required. Such estimates were elicited

from the members of the modeling team, who were asked to provide confidence intervals or probability

distributions that represented their uncertainty about the variables. For instance, an estimator might state

that the risk of political interference is between 10 and 50%, with a 90% chance that the real value is in

this interval. Since most people are not initially very good at making such estimates, all team members

were subjected to calibration training. During this training they were introduced to a number of

techniques to help them accurately estimate their uncertainty. Once estimates from all team members had

been collected for all uncertain variables, the analyst consolidated these into probability distributions that

represented the team’s level of uncertainty.

5

Based on the resulting distributions, Monte Carlo simulations of the net benefits of the project were run.

In such simulations, outcomes are calculated with the model many times – in this case 10,000 times –

with slightly varying values for all input variables. The values are determined by randomly drawing

samples from the defined distributions. Each run of the model provides one estimate of net present

benefits, and the totality of all model runs generates a probability distribution of these outcomes that

illustrates the probabilities, with which the project results in net gains and losses. The distribution also

indicates the likely magnitude of these gains and losses.

While the outcome distribution already provides a useful result, it can be analyzed further to identify

high-value variables in the calculation. These are the key uncertainties that determine the outcome. It is

these variables that could be measured to reduce the uncertainty of the decision maker about likely

decision outcomes.

Where the above analysis already provides a clear idea of which decision alternative is preferable, a

recommendation can immediately be made. Where both gains and losses are possible, measurement of

high-value variables is often justified. Once such measurements have been taken, the simulation

procedure can be repeated, possibly followed by more measurements. It is likely that after a few such

rounds, a clearly preferable decision option emerges.

Major model elements

Stakeholders

Separate analyses were conducted for the following stakeholder groups to see who stands to benefit or

lose from the proposed project:

Residents of Wajir

Residents of Habaswein

Communities along the pipeline

Upstream communities

Downstream communities

Water company (charged with operating the system)

Donor consortium

Costs and benefits for all these stakeholders were considered in different model modules. These were

similar in structure for the residents of the two communities and along the pipeline, for whom the same

types of costs and benefits may arise. Upstream and downstream effects were modeled at a much

aggregated level only, with the option to add more detail if results indicated that this was warranted. For

the water company, particular attention was paid to profitability of the company as a business.

Costs

Costs considered in the model were mostly the costs of operating the system, as well as fees for water

purchases by residents. Negative environmental impacts were considered for the upstream and

downstream communities only. All cost items and the stakeholders to which they applied are listed in

Table 1.

6

Table 1. Cost categories considered in the model and stakeholders to which they were applied (X means that the

respective cost was considered for a stakeholder).

Cost category

Res

iden

ts o

f

Wa

jir

Res

iden

ts o

f

Ha

ba

swei

n

Res

iden

ts o

f

pip

elin

e

com

mu

nit

ies

Up

stre

am

are

as

Do

wn

stre

am

are

as

Wa

ter

com

pa

ny

Do

no

r

con

sort

ium

Initial investment - - - - - X X

Running costs and metering - - - - - X -

Salaries - - - - - X -

Repairs - - - - - X -

Aquifer monitoring - - - - - X -

Infrastructure maintenance - - - - - X -

Pipeline security - - - - - X -

Payments for ecosystem services X - - - - X -

Negative environmental impacts - - - X X - -

Expenses for water purchases X X X - - - -

Benefits

The water company stands to benefit mainly through sale of water to its customers in Wajir, Habaswein

and along the pipeline. Upstream and especially downstream communities may benefit from payments for

ecosystem services, but are otherwise unlikely to experience positive impacts from the intervention. The

residents of Wajir, Habaswein and along the pipeline are expected to receive a wide range of benefits,

mostly related to new economic opportunities and improved public health (Table 2).

Table 2. Benefit categories considered in the model and stakeholders to which they were applied (X means that the

respective benefit was considered for a stakeholder).

Cost category

Res

iden

ts o

f

Wa

jir

Res

iden

ts o

f

Ha

ba

swei

n

Res

iden

ts o

f

pip

elin

e

com

mu

nit

ies

Up

stre

am

are

as

Do

wn

stre

am

are

as

Wa

ter

com

pa

ny

Do

no

r

con

sort

ium

Reduced infant mortality X X X - - - -

Reduced costs for disease

treatment X X X - - - -

Higher productivity X X X - - - -

Job creation X X X - - - -

Higher investments X X X - - - -

Reduced brain drain X X X - - - -

Revenue from more demand for

local products and services X X X - - - -

Higher tax revenue X X X - - - -

Reduced reliance on shallow

wells X X X - - - -

Sanitation benefits X X X - - - -

Water during the dry season X X X - - - -

General livelihood improvement X X X - - - -

Revenue from water sales - - - - - X -

Revenue from PES - X - X X - -

7

Risks

A number of risk factors threaten the feasibility of the project or have potential to reduce its operational

efficiency. Residents and opinion leaders have concerns about the hydrological sustainability of the

project, i.e. they are worried that it may undermine the reliability of their own water supply, which has

historically been very stable. This perception has led to substantial risk of political interference and

unwillingness to cooperate in project development, constituting another risk factor. Some risks to the

project threaten to completely prevent the intervention from happening. Other risks may make the project

fail at some point in the future (e.g. salinity build-up). Other risk factors, particularly regional conflict or

poor maintenance, may lead to temporary failure of the system. The last risk category contains factors that

merely reduce the efficiency of the pipeline, either permanently or temporarily. Specifically, the

following risk factors were considered:

Factors that lead to immediate project cancellation:

Negative feasibility report

Water yield too low

Inadequate benefit sharing

Political interference

Factors that lead to cancellation later:

Wells run dry

Increasing water salinity

Oil development (raising the risks of wells running dry or turning saline)

Dam development (raising the risks of wells running dry or turning saline)

Factors that cause temporary failure of project benefits

Maintenance problems

Water price is too high

Regional conflict

Factors that reduce project performance from the beginning:

Poor project design

Factors that temporarily reduce project performance:

Poor maintenance and operation

Illegal abstractions

Lack of cooperation

Model implementation

Mathematically, all risks were organized into two factors. The first factor described whether any benefits

accrued in a given year. In the case of immediate project cancellation, this factor was set to zero for all

8

years; if cancellation occurred later, zeroes were inserted for all years that followed. For temporary

failure, only the respective year received a zero weight. All non-zero years received a factor value of 1.

The second factor was used to scale the benefits. This factor comprised all the performance-reducing

risks, which were considered by reducing the amount of benefits by a certain percentage. These

percentages were multiplied for all risks, producing an overall benefit-scaling factor with the following

form:

∏ , in which nRisks is the number of individual risks and

is the benefit reduction (ranging between 0 and 1) expected due to risk i.

The analysis was run over a 30-year time horizon. All benefits were multiplied by both risk factors for all

years and costs subtracted to produce annual project budgets. These were produced for each stakeholder

group separately. Stakeholder time preference was then considered by discounting future profits by a

user-specified (and uncertain) discount rate. The resulting annual values were then added to produce an

overall Net Present Value for the intervention. Finally, results for all stakeholders were summed and

overall net present benefits for the project calculated, in addition to the individual stakeholder results.

Details of the model are provided in an annex to this report.

Estimate elicitation and consolidation

All members of the modeling team were ‘calibrated’. This process involved instruction in techniques to

improve their capacity to estimate the state of their own uncertainty. Training participants also took a

series of estimation tests to gauge their initial skill level and track progress throughout the training.

Calibration training is a standard procedure developed by Hubbard Decision Research (Hubbard, 2014),

which has been shown to strongly increase people’s capacity to provide accurate estimates.

All calibrated estimators were asked to fill estimation spreadsheets, which contained all uncertain

variables in the model. For each variable, they provided the upper and lower bounds of their own 90%

confidence interval. They were also encouraged to specify the distribution type for each variable, though

most variables were ultimately assumed to be normally distributed.

All estimates were then processed into diagrams that illustrated the different ranges provided by the

estimators. Based on these diagrams, as well as some intuitive weighting, when certain estimators were

deemed more credible informants than others (e.g. European team members were likely not well informed

about local issues of Wajir County), consolidated team estimates were then prepared by the analyst. These

were used to run the Monte Carlo simulation. For selected variables, some co-variation was assumed in

sampling variables for the simulation. The water prices and the numbers of water users at Wajir,

Habaswein and the communities along the pipeline were set to be negatively correlated. This was entered

into the model by drawing random samples in such a way that the resulting distributions correlated with a

coefficient of determination (r) of approximately -0.7. In the ‘all risks’ scenario, the Payment for

Ecosystem Services rate charged to all water sales was assumed to be negatively correlated (r = -0.7) with

the risks of inadequate benefit sharing and political interference. These two risk factors were assumed to

be proportional (r = 1).

9

Post processing

The Monte Carlo simulation for the pipeline project, which included 10,000 model runs, produced 10,000

projected results. These 10,000 results were displayed in single histograms to provide a visual impression

of likely distribution of project outcomes. In addition, all outcomes for all stakeholders were statistically

related to all uncertain input variables using Partial Least Squares (PLS) regression (Wold, 1995; Wold et

al., 2001; Luedeling and Gassner, 2012). This procedure calculates the so-called ‘Variable Importance in

the Projection’ statistic for each variable. This measure is an expression of the extent to which variation in

the independent variables explains variation in the outcome. Plotting this metric for all variables allows

identification of the key uncertainties in the calculation whose reduction through measurement would

increase the certainty with which outcomes can be projected. In such plots, bars are shown in red, when

low values for the respective variable are associated with high values for project outcomes, and in green

when there was a positive relationship between the two.

Consideration of risks

There are two ways to look at the present study, which reflect on how risks must be considered. One

interpretation of this model is that it is a forecast of what is likely to happen. In that case, all risks must be

considered in the model, including the chance that the project is not implemented. To fill the stakeholder

desire for an evaluation of the technical feasibility and environmental and financial sustainability of the

pipeline, however, it makes more sense to exclude certain risk factors, such as the chance of political

interference, from the simulation runs. This is useful, because the study itself may sway opinions, and

various political tools may also be employed to improve stakeholder perceptions of the project (e.g.

awareness building or payments for ecosystem services). The model then fulfills the desire by decision-

makers to obtain as objective an overview as possible of the general desirability of the pipeline project, so

that political positions on the issue can be developed. Because of these two interpretations of the

modeling procedure, all simulations were therefore run with and without politics-related risk factors.

10

Results Separate results were produced for each stakeholder group: the residents of Wajir, Habaswein and the

communities along the pipeline, areas upstream and downstream, the donor consortium and the water

company. In general, when considering the entire spectrum of risks that were identified, the project

currently seems unlikely to succeed. This is not so much because the intervention is infeasible or would

not benefit the stakeholders, but rather because key stakeholders are opposed to the project idea and are

likely to interfere during the politicized decision making process or the implementation of the project.

This high risk is clearly visible in results for all stakeholders, many of whom are most likely to see neither

net benefits nor net costs from the intervention, when all risks are considered. When excluding all risks

related to contrasting and conflicting stakeholder perceptions, the outlook for the project becomes more

favorable, though substantial risks of losses remain for all stakeholders.

Residents of Wajir

When considering all risks of the project, residents of Wajir are likely to see neither net benefits not net

costs (41.7% of all simulation runs; Table 3). Thirty-four percent of runs resulted in a loss and 24.3% in a

net benefit from the project. Plausible losses (10% quantile) were up to 262.0 million USD, while

plausible benefits (90% quantile) ranged up to 112.4 million USD). The full distribution is shown in the

inset of Figure 2. The most influential variables responsible for the wide range of projected outcomes

were related to the cost of water, the number of water users and the valuation and extent of reductions in

infant mortality and the number of disease treatments needed for water-borne diseases (Figure 2). Poor

project design and trends in water revenue were also identified as important factors.

Table 3. Characteristics of the net present benefit distribution for the residents of Wajir, for the two risk consideration

scenarios.

All risks considered

Net present benefit quantiles (million USD)

0% 10% 25% 50% 75% 90% 100%

-1550.4 -262.0 -68.9 0.0 0.0 112.4 714.3

Chance (%) of

Mean net present benefit (million USD) loss zero Benefits

-40.9 34.0 41.7 24.3

Assuming no political obstacles


0% 10% 25% 50% 75% 90% 100%

-1315.5 -348.2 -176.6 -15.9 67.4 170.7 744.6

Chance (%) of

Mean net present benefit (million USD) loss zero Benefits

-62.8 53.6 5.9 40.5

11

Figure 2. Important factors that limit the precision of outcome projections for the residents of Wajir, for the scenario in

which all risks are considered, according to the Variable-Importance-in-the-projection statistic of Partial Least Squares

regression. Inset: Net present benefit (in USD) distribution for the residents of Wajir, considering all relevant risks.

Assuming that political obstacles can be cleared, the chance of zero net benefits was reduced to 5.9%

(Table 3). Net losses to the residents of Wajir remained more likely (53.6% chance) than net benefits

(40.5%). Plausible losses ranged up to 348.2 million USD, while plausible benefits up to a value of 170.7

million USD appeared possible. The full distribution is shown in the inset of Figure 3. Important

uncertainties worth reducing were related to water purchases and prices and the magnitude and valuation

of improvements in public health and infant mortality (Figure 3). Trends in water prices and the quality of

project design were also worth closer scrutiny.

12

Figure 3. Important factors that limit the precision of outcome projections for the residents of Wajir, assuming that all

political obstacles have been cleared, according to the Variable-Importance-in-the-projection statistic of Partial Least

Squares regression. Inset: Net present benefit (in USD) distribution for the residents of Wajir, assuming that all political

obstacles have been cleared.

13

Residents of Habaswein

Also for the residents of Habaswein, the high chance of project failure made it likely that no net benefits

or costs would arise (41.7%; Table 4). Among the remaining model runs, the chance of net benefits

(51.3%) outweighed the chance of net costs (7.0%). The range of plausible outcomes (10-90% quantiles)

was 0 to 71.7 million USD. The full distribution for this scenario is shown in the inset of Figure 4.

Influential variables in the model were the number and valuation of additionally surviving infants, the

amount of water purchased by residents from the water company and the number and valuation of disease

treatments that will no longer be necessary (Figure 4). As was the case for Wajir, the risk of political

interference, inadequate benefit sharing and the chance of poor project design were also major sources of

uncertainty. Productivity gains at Habaswein and the rate of Payments for Environmental Services, which

are partly assumed to benefit Habaswein, also had important impacts on projected outcomes. Finally, the

number of water users was also identified as important.

Table 4. Characteristics of the net present benefit distribution for the residents of Habaswein, for the two risk

consideration scenarios.



0% 10% 25% 50% 75% 90% 100%

-116.5 0.0 0.0 2.6 37.7 71.7 323.7

Chance (%) of

Mean net present benefit (million USD) loss zero benefits

22.4 7.0 41.7 51.3



0% 10% 25% 50% 75% 90% 100%

-102.1 -1.5 8.6 30.5 60.0 95.0 276.1

Chance (%) of


38.7 11.0 5.9 83.0

14

Figure 4. Important factors that limit the precision of outcome projections for the residents of Habaswein, for the

scenario in which all risks are considered, according to the Variable-Importance-in-the-projection statistic of Partial

Least Squares regression. Inset: Net present benefit (in USD) distribution for the residents of Habaswein, considering all

relevant risks.

The assumption that all political hurdles can be overcome made prospects for Habaswein appear more

positive (Table 4). The chance of net gains for residents of this town increased to 83.0% in this scenario,

as opposed to an 11.0% chance of net losses and a 5.9% chance of no net costs or gains due to project

failure. For this scenario, plausible outcomes ranged between -1.5 and 95.0 million USD. The full

outcome distribution is shown in the inset of Figure 5.

Again, the key variables that determined the wide distribution of outcomes were the number and valuation

of surviving infants (Figure 5). The number and price of disease treatments was also important. Important

variables were also related to water purchases, with the number of water buyers, the water price and the

quantity of water purchased by each buyer emerging as important determinants of project outcomes. Poor

project design was an important risk factor. The time preference of Habaswein’s residents was a further

source of uncertainty. This is indicated by the residents’ discount rate, which is a standard economic

parameter used to adjust for the fact that most people prefer immediate benefits to benefits arising at a

later date. All future benefits and costs are then progressively ‘discounted’ by this rate for each future

year. Adding up all discounted costs and benefits of the project produces the Net Present Value (NPV),

which is used to judge decision outcomes for all stakeholders.

15

Figure 5. Net present benefit (in USD) distribution for the residents of Habaswein, assuming that all political obstacles

have been cleared.

16

Pipeline communities

For communities along the pipeline, chances of project failure were the same as for Wajir and Habaswein.

For these settlements, the chance of benefits outweighed the chance of losses for both scenarios (Table 5).

Overall the chance of benefiting from the project was 43.0% for the all-risks scenario and 70.3% for the

no-obstacles scenario. Distributions of likely outcomes for the pipeline communities had the same shape

as for Habaswein because of similar cost and benefit structures. Also for pipeline communities, number

and valuation of surviving infants and disease treatments, questions about the basic economics of water

sales (number of customers, price) and the quality of project design were of importance. Inadequate

benefit sharing and political interference emerged as important uncertain risk factors. Figures for the

pipeline communities are provided in the annex.

Table 5. Characteristics of the net present benefit distribution for the residents of communities along the pipeline, for the

two risk consideration scenarios.



0% 10% 25% 50% 75% 90% 100%

-88.9 -6.1 0.0 0.0 14.8 35.6 174.3

Chance (%) of


8.4

15.3 41.7 43.0



0% 10% 25% 50% 75% 90% 100%

-119.9 -13.8 0.0 10.7 28.1 48.6 222.3

Chance (%) of


14.6

23.8 5.9 70.3

17

Upstream areas

Upstream areas were not expected to be much affected by the proposed intervention, but they were

included in the model because some impacts might be possible. When the risk of project failure due to

political factors was included, losses (49.7%) were about equally likely as gains (48.4%; Table 6). When

no political obstacles remained, the chance of benefits (78.9%) clearly outweighed the chance of losses

(20.7%). Important variables were mostly related to Payments for Ecosystem Services (PES), which were

assumed to be financed through a levy on water sales. It therefore depended on the share of total collected

PES going to upstream areas and the rate of the PES levy. Since most water revenue and therefore PES

payments would be collected in Wajir, water purchases and water price in Wajir were also important.

Important uncertain risk factors were the chance of political interference, inadequate benefit sharing and

poor project design. Habaswein’s PES share also emerged as an uncertainty, because a higher proportion

of PES revenue allocated to Habaswein would reduce the payments to upstream areas. Finally, the

environmental impact of the intervention on upstream areas was one of the key uncertainties. Figures for

upstream areas are shown in the annex.

Table 6. Characteristics of the net present benefit distribution for upstream areas, for the two risk consideration

scenarios.



0% 10% 25% 50% 75% 90% 100%

-0.5 -0.2 -0.1 0.0 0.3 0.7 4.2

Chance (%) of


0.1 49.7 1.9 48.4



0% 10% 25% 50% 75% 90% 100%

-1.0 -0.1 0.0 0.2 0.5 0.8 4.6

Chance (%) of


0.3 20.7 0.4 78.9

18

Downstream areas

For downstream areas, there was only a very small chance (2.9%) of net benefits from the project, which

rose to 4.5% when political risks were excluded (Table 7). Potential benefits were almost entirely

negative, plausibly ranging from 41.5-41.6 to 4.8-4.9 million USD. It should be noted, however, that

these calculations were only based on one variable, which was estimated by the members of the modeling

team, as well as the discount rate applied by downstream users. Payments for Ecosystem Services are not

currently considered for downstream areas. Figures for downstream areas are provided in the annex.

Table 7. Characteristics of the net present benefit distribution for downstream areas, for the two risk consideration

scenarios.



0% 10% 25% 50% 75% 90% 100%

-80.5 -41.5 -32.4 -22.7 -13.5 -4.9 0.4

Chance (%) of


-23.4 95.0 2.1 2.9



0% 10% 25% 50% 75% 90% 100%

-89.0 -41.6 -32.4 -22.7 -13.3 -4.8 0.6

Chance (%) of


-23.3 95.0 0.5 4.5

19

Donors

No concrete benefits were assumed to arise for the donor, so the only factor considered for this

stakeholder was the initial investment. Of course, the extent to which this expenditure helps the donor

consortium, composed of the government of Kenya and the Dutch investor ORIO, achieve its objectives

could also be factored into this calculation. However, with the main focus of the modeling on evaluation

of the overall feasibility of the investment, considerations about the donor were restricted to financial

flows only. Given this approach, it is not surprising that there is 100% chance of losses for the donor in

both scenarios, with the extent of these losses ranging plausibly between 67 and 49 million USD (Table

8).

Table 8. Characteristics of the net present benefit distribution for the donor consortium, for the two risk consideration

scenarios.



0% 10% 25% 50% 75% 90% 100%

-86.2 -66.6 -61.1 -55.1 -48.9 -43.4 -23.3

Chance (%) of


-55.0 100.0 - -



0% 10% 25% 50% 75% 90% 100%

-91.7 -66.7 -61.3 -55.1 -48.8 -43.4 -22.2

Chance (%) of


-55.0 100 - -

20

Water company

Profitability for the water company of operating the pipeline may be the most important determinant of

project feasibility. Risks to this are quite high in the all-risks scenario, which indicated a 51.4% chance of

losses (Table 9). Due to the model structure, some losses were incurred even when the project failed.

When political risks were excluded, the chance of failure dropped to 21.0%, as opposed to a chance of

79.0% that the company would generate a profit. Plausible profits and losses were substantial in both

scenarios. In the all-risks scenario, net returns ranged from -109.3 to 447.7 million USD (10% to 90%

quantile), while the outlook in the no-obstacles scenario was brighter at -62.3 to 564.7 million USD. The

full distribution of projected outcomes for the all-risks scenario is shown in the inset of Figure 6 and for

the no-obstacles scenario in the inset of Figure 7.

Table 9. Characteristics of the net present benefit distribution for the water company, for the two risk consideration

scenarios.



0% 10% 25% 50% 75% 90% 100%

-755.7 -109.3 -88.3 -15.0 227.7 447.7 2000.9

Chance (%) of


95.6 51.4 - 48.6



0% 10% 25% 50% 75% 90% 100%

-866.0 -62.3 26.3 182.1 370.0 564.7 2165.9

Chance (%) of


227.5 21.0 - 79.0

For the water company, key uncertainties were related mainly to their main market for water in Wajir,

most notably the amount of water likely to be sold to each customer, the water price and the trend in

water revenue (Figure 6). In the all-risks scenario, the risk of political interference, inadequate benefit

sharing and poor project design were major risk factors. The rate of Payments for Ecosystem Services

was also an important factor. Interestingly a higher PES rate, which at the surface might look like a loss to

the water company, was related to high profits, because these payments were assumed to ease political

resistance to the project and foster collaboration. Further variables worth measuring were the salaries paid

to water company employees, running costs of the company and the base risks of drying wells and

increasing salinity. In the no-obstacles scenario, the key uncertainties were similar to the all-risks

scenario, except for the political risks, which were excluded (Figure 7).

21

Figure 6. Important factors that limit the precision of outcome projections for the water company, for the scenario in


regression. Inset: Net present benefit (in USD) distribution for the water company, considering all relevant risks.

22

Figure 7. Important factors that limit the precision of outcome projections for the water company, assuming that all


Squares regression. Inset: Net present benefit (in USD) distribution for the water company, assuming that all political


23

Overall project benefits

Adding up all net present benefits for all stakeholders provided an illustration of the overall desirability,

expressed by a high Net Present Value of the project, of constructing the pipeline. It should be noted here

that the investment is considered to aim at maximizing overall benefits for all stakeholders rather than

those of the investor only. In fact, according to the analyst’s understanding of the planned investment, the

investor is certain to make a loss, which, however, may be appropriate in a government measure or

benevolent development project.

When considering all risks, the project was more likely to result in a net loss (55.9% chance) than a net

benefit (44.1%; Table 10). The 10% quantile of the distribution, which can be considered the poorest

plausible outcome, was equivalent to a loss of 192.3 million USD. The highest plausible result, however,

the 90% quantile, was a net benefit of 302.2 million USD. The full distribution is shown in the inset of

Figure 8, which clearly shows the large spike in the distribution that is related to the high chance of

project failure. In most such cases, some costs are incurred, shifting the distribution towards negative

numbers.

As for several of the stakeholders, the value of a surviving infant was the most influential uncertainty in

the model (Figure 8). This was followed by the extent to which poor project design reduced system

performance and the number of additional infants that survive in Wajir. The value of a disease treatment

was also identified as influential. Among the top 8 variables were 3 that were related to political will: the

risk of political interference, inadequate benefit sharing and the Payment for Ecosystem Services rate

(which was correlated to the political risk factors). This finding underscores the necessity to build

consensus on the project among all stakeholders and ensure that they all perceive the intervention as

beneficial.

Table 10. Characteristics of the net present benefit distribution for the project, for the two risk consideration scenarios.



0% 10% 25% 50% 75% 90% 100%

-403.6 -192.3 -166.3 -52.8 142.1 302.2 1180.2

Chance (%) of


7.2 55.9 - 44.1



0% 10% 25% 50% 75% 90% 100%

-533.3 -115.9 -5.3 121.0 259.6 404.8 1275.6

Chance (%) of


139.9 25.9 - 74.1

24

Further important variables were the discount rates of the water company and the residents of Wajir, the

downstream environmental impacts, the number of disease treatments that will no longer be necessary in

Wajir and the water company’s salaries and running costs. The size of the initial investment by the donors

also emerged as an important variable, along with losses due to illegal abstractions or poor maintenance.

Finally, reduction in infant mortality in Habaswein was also important.

Figure 8. Important factors that limit the precision of outcome projections for the overall project, for the scenario in


regression. Inset: Net present benefit (in USD) distribution for the overall project, considering all relevant risks.

Prospects for the project were much better under the assumption that political obstacles can be removed.

The chance of net benefits from the intervention improved to 74.1%, as opposed to a chance of loss of

25.9%. The range of plausible outcomes was -115.9 to 404.8 million USD, indicating that substantial

gains are possible, but there is also the chance of high losses. The full distribution is shown in the inset of

Figure 9. With the political risks removed, the value of a surviving infant remained the key uncertainty,

followed by the impacts of poor project design and the number of additional surviving infants in Wajir

(Figure 9). The value of disease treatment followed, along with the discount rate applied by residents of

Wajir. Next in importance came the number of saved disease treatments in Wajir, the number of

25

additional surviving infants in Habaswein, the losses from illegal abstractions and poor maintenance and

the salaries paid by the water company.

Figure 9. Important factors that limit the precision of outcome projections for the overall project, assuming that all


Squares regression. Inset: Net present benefit (in USD) distribution for the overall project, assuming that all political


Visualization of projected outcome distributions for all stakeholder groups indicated that none of the two

scenarios provided unambiguous certainty about either positive or negative outcomes of the project. The

only exception was the financial prospect of the donor (Figure 10). There was thus substantial uncertainty

about the desirability of the Habaswein-Wajir Water Supply project for all stakeholders.

26

Figure 10. Overview of projected outcome distributions for all stakeholders for both risk consideration scenarios.

27

Discussion Given the current state of uncertainty, and the current political climate, the proposed project is very risky,

with plausible outcomes varying widely for all stakeholders (Figure 10). Considering estimates about the

risk of political interference and related variables, the chance of project failure is very high, possibly too

high for most investors. At the moment, political resistance comes mainly from the direction of

Habaswein, whose inhabitants are concerned about the sustainability of their water resources. This

resistance stands in contrast to the relatively small hydrological risks identified by the hydrological

studies that accompanied this assessment. In fact, when weighing all evidence and considering the

possibility that the town of Habaswein could be compensated through a PES scheme, the likelihood that

the pipeline project would generate net benefits was actually higher for Habaswein than for Wajir, where

water prices are expected to be quite a bit higher. So it seems possible that political resistance can be

overcome, which may be an essential step that must be undertaken, before the project can move forward.

In designing the details of this project, attention must be paid to fair sharing of benefits. The perception

that water will be taken away from Habaswein without fair compensation has already interfered with the

planning process. The importance of variables related to this issue was evident in many of the stakeholder

models. It will also be important to conduct environmental impact assessments on areas upstream and

downstream from Habaswein. These impacts emerged as key uncertainties for some stakeholders.

The uncertainty that will be hardest to address might be the valuation of a surviving infant. While this is

obviously a delicate and ethically charged judgment, it is critical for evaluating whether this project is

worth pursuing from the perspective of society as a whole. However, social benefits that are difficult to

quantify in an uncontroversial manner are not important for the cost-benefit considerations of the water

company. Our analysis revealed that even without political risk this project remains risky from the

perspective of the water company. This is worrying, because profitability of the water company is a major

factor determining the sustainability of the benefits of the project. The risk of bankruptcy of the water

company undermines this sustainability. The risk of negative returns of the water company is the result of

high uncertainties in a number of variables: the volume of water purchased and the price paid for the

water are the most important ones. Adequate pre-project market surveys on the demand and willingness to

pay for water could reduce these uncertainties and produce gains in certainty about project outcomes for

the water company. We propose undertaking additional research on these two and a number of other

variables, which have the largest influence on the current uncertainty around the financial outcomes for

the water company.

Recommendations The analysis clearly showed that this project involves substantial risks, which at present make it difficult

to decide whether this project will generate net benefits or losses. Targeted measurements, along with

modifications to the project design, could add clarity.

In terms of measurements, attention should be paid to infant mortality and water-borne diseases. This

analysis had no concrete data on these issues, except some official statistics on current infant mortality. In

particular, estimates about the reduction in diseases and the number of additional surviving infants were

highly uncertain. Precision could be added by investigating the infection pathways for water-borne

diseases and the causal factors for infant mortality in the particular context of Wajir County. This should

28

help with estimates of how the pipeline project is likely to affect both issues in the future. An evaluation

of the treatment costs for water-borne diseases should also be conducted. This should be easily achievable

by interviewing local health professionals. The greatest benefit-related uncertainty, which affected

outcomes for several stakeholders, was the value that a human life has to whoever makes the decision

about proceeding with this project. The current 90% confidence interval in the model is 200 to 100,000

USD. While it may be impossible to find a precise uncontroversial number for this variable, it may

nevertheless be possible to reduce this range.

We recommend one measurement that did not emerge as a key uncertainty but would likely contribute to

reducing political resistance to the project. The hydrological modeling showed only a small chance of

wells running dry but a greater chance that salinity might intrude into the aquifer from below the

freshwater layer. A relatively inexpensive test borehole into the deeper layers of the aquifer would help

clarify this issue and possibly contribute to overcoming political resistance.

Regarding project design, two issues emerged as important. Poor project design was identified as one of

the major risks to project success. The importance of doing adequate planning at the beginning of the

implementation phase cannot be overemphasized. Furthermore, activities to build consensus around the

intervention and ensure that all stakeholders approve of the intervention is critical. Payments for

Environmental Services were included in the model, but other benefit-sharing mechanisms, as well as

awareness-raising measures, should also be explored. While not considered as an option in the present

model, financial compensation of downstream areas for potentially negative environmental impacts could

also be considered. At present, downstream water users are the only clear losers of the intervention. Since

they would not strictly provide an environmental service to the project, they are not currently included in

the PES scheme, but project planners may consider adding damage compensation measures to the project

design.

Acknowledgment We acknowledge funding for this work by the UK National Environment Research Council and support

from the Cooperative Research Program on Water Land and Ecosystems (WLE) of the CGIAR.

Furthermore we are grateful to all members of the modeling team and to all participants of our workshops

for invaluable insights into the pipeline project.

References Hubbard, D.W., 2014. How to Measure Anything - Finding the Value of Intangibles in Business. Wiley.

Luedeling, E. and Gassner, A., 2012. Partial Least Squares regression for analyzing walnut phenology in

California. Agricultural and Forest Meteorology, 158: 43-52.

Wold, S., 1995. PLS for multivariate linear modeling. In: H. van der Waterbeemd (Editor), Chemometric

methods in molecular design: methods and principles in medicinal chemistry. Verlag-Chemie,

Weinheim, Germany, pp. 195-218.

Wold, S., Sjostrom, M. and Eriksson, L., 2001. PLS-regression: a basic tool of chemometrics. Chemom.

Intell. Lab. Syst., 58(2): 109-130.

30

Annex 1: Model details

Wajir, Habaswein and pipeline communities

Model modules for these three communities were identical. Benefits at all sites were derived by adding up

the following individual benefits: Reduced infant mortality, reduced costs for disease treatment, higher

productivity, job creation, higher investments, reduced brain drain, revenue from more demand for local

products and services, higher tax revenue, reduced reliance on shallow wells, sanitation benefits, water

during the dry season, general livelihood improvement (also listed earlier in the report). Payments for

Ecosystem Services (PES), i.e. availing water to Wajir, were considered for Habaswein. PES were

calculated as described below in the water company section. The option of PES payments for the other

communities was also built into the model, even though such payments are unlikely. Where model users

do not expect such payments to happen, the respective uncertain variable can be set to zero.

On the cost side, only the costs for purchasing water were considered. These were derived by multiplying

the number of water users, the amount of water purchased per person and the future water price. Since

people of these locations already purchase some water, often in bottles or jerry cans, the current water

price and consumption rate was also estimated, multiplied by the number of water users and subtracted

from the water costs. All costs and benefits were discounted to account for people’s time preference (see

below).

Upstream and downstream areas

The possibility was considered that downstream and upstream areas might experience negative impacts

from the project. This was not so much based on hydrological considerations, but entered the model as a

stand-alone estimate of the costs of damage that could possibly be caused. Revenues for both stakeholder

groups arose exclusively from PES. These payments were introduced, because the project might benefit

from certain management measures to conserve water in the upstream areas and extraction of water at

Habaswein could possibly affect development prospects downstream. PES payments are meant to

compensate for these costs or foregone profits.

Water company

For the water company, benefits arose exclusively through the sale of water. This resulted from simple

summation of the water costs paid by the three client stakeholders (Wajir, Habaswein and the

communities along the pipeline). Out of these revenues, a certain percentage was extracted to be used for

PES. These funds were distributed among all other stakeholders according to a weighting system

specified by the analyst (as uncertain variables).

On the cost side, a number of cost items were considered: Initial investment costs, running costs (incl.

metering), salaries, repairs, costs of aquifer monitoring, infrastructure maintenance, pipeline security,

costs incurred through waste of water and costs for wildlife conservation.

Donor

On the side of the donor, assumed to be a consortium of the Government of Kenya and the Dutch

company ORIO, only the initial investment costs were factored into the calculations.

31

Overall net present benefits from the project

The overall costs and benefits were derived by adding up all stakeholder benefits and subtracting costs to

all stakeholders. Risks were considered as described in the risk section below and all values were

discounted as outlined in the discounting section (also below).

Risks

Risks were considered in two different ways: through a project success factor and through a benefit

scaling factor.

Project success factor

The project success factor could only assume two values for a given year: zero and one. Its effect was that

it could set the benefits arising from the project to zero for any year, if the project suffered from

permanent or temporary failure.

Different risks had different effects on this factor. Risks such as a negative feasibility report, inadequate

water yield or political interference set the project success factor to zero for all years, meaning that no

benefits arise at all. Other risks, such as salinity increases or drying wells led the project success factor to

be set to zero for all years, starting in the year that the problem first arose. These two risks were expressed

by a base risk, to which a risk enhancement component was added. This component depended on the

development of oil or dam projects in the region, which was specified by a probability estimate. Finally,

temporary failure, which may be caused by regional conflict or lack of cooperation, led to the project

success factor to be set to zero for individual years only.

Benefit scaling factor

The benefit scaling factor was used to reduce the amount of benefits generated by the project, either

permanently or temporarily. It took the shape of a factor, ranging between 0 and 1, with which all benefits

were multiplied. Poor project design led to a permanent reduction in revenues, implemented by an

adjustment of the benefit scaling factor by a fixed amount in all years. Other factors were variable,

including poor maintenance or illegal abstractions, which were applied to different extents in each year.

Sampling

All uncertain variables were sampled from user-specified distributions. In most cases, these were normal

distributions characterized by 90% confidence intervals. A range of other distribution types could also be

specified but were rarely chosen by the estimators.

For some variables, co-variation was introduced. This was motivated by the realization that, e.g., the price

of something and the demand for it are normally not independent but follow some sort of negative

correlation. Consequently, the option to correlate variables was built into the algorithms, with users

having to specify the variables that were to co-vary, the confidence interval for each variable and the

desired coefficient of correlation (R2). This function currently only works for normally distributed

variables.

Varying values

For most variables, it is unreasonable to expect exactly the same value for each year of the simulation. A

value varying function was therefore introduced, which required as inputs the mean expected value for a

given variable and an estimate of its coefficient of variation. From these numbers, a time series that

32

included a measure of random variation was generated. This function also included the option to

introduce absolute or relative trends in the data, around which annual values varied. This function was

applied to the majority of variables in the model.

Discounting

It is common practice in economic analyses to discount costs and benefits that arise in the future by a

certain percentage for each year. This is motivated by the realization that most people and most

businesses prefer immediate returns over those that arise in the future. Discounting was applied to all

costs and benefits for all stakeholders, according to discount rates specified (as uncertain distributions) by

the estimators.

33

Annex 2: Results from the Monte Carlo analysis for pipeline communities,

upstream and downstream areas

Figure 11. Important factors that limit the precision of outcome projections for the residents of the communities along the

pipeline, for the scenario in which all risks are considered, according to the Variable-Importance-in-the-projection

statistic of Partial Least Squares regression. Inset: Net present benefit (in USD) distribution for the residents of the

communities along the pipeline, considering all relevant risks.

34

Figure 12. Important factors that limit the precision of outcome projections for upstream areas, assuming that all political

obstacles have been cleared, according to the Variable-Importance-in-the-projection statistic of Partial Least Squares

regression. Inset: Net present benefit (in USD) distribution for the residents of the communities along the pipeline,

assuming that all political obstacles have been cleared.

35

Figure 13. Important factors that limit the precision of outcome projections for upstream areas, for the scenario in which

all risks are considered, according to the Variable-Importance-in-the-projection statistic of Partial Least Squares

regression. Inset: Net present benefit (in USD) distribution for upstream areas, considering all relevant risks.

36

Figure 14. Important factors that limit the precision of outcome projections for upstream areas, assuming that all political

obstacles have been cleared, according to the Variable-Importance-in-the-projection statistic of Partial Least Squares

regression. Inset: Net present benefit (in USD) distribution for upstream areas, assuming that all political obstacles have

been cleared.

37

Figure 15. Important factors that limit the precision of outcome projections for downstream areas, for the scenario in


regression. Inset: Net present benefit (in USD) distribution for downstream areas, considering all relevant risks.

38

Figure 16. Important factors that limit the precision of outcome projections for downstream areas, assuming that all


Squares regression. Inset: Net present benefit (in USD) distribution for downstream areas, assuming that all political


39

Annex 3: Estimates

All-risks scenario

Variable Description

Lower

bound

Upper

bound

Distrib-

ution* general variables

n_years number of years to run simulation 30 30 constant

cost_CV coefficients for introducing annual variation 10 15 pos_normal

benefit_CV coefficients for introducing annual variation 10 20 pos_normal

general_CV coefficients for introducing annual variation 10 15 pos_normal

general benefit valuation

value_of_surviving_infant How much should a avoided infant death be valued at? 200 100,000 pos_normal

value_of_disease_treatment How much does a treatment for water-borned disease cost? 3 300 pos_normal

future_water_price_m3_Habaswein

Price of a m3 of water in the project in Habaswein with the

project 1 5 correlated

future_water_price_m3_Wajir

Price of a m3 of water in the project in Wajir with the


future_water_price_m3_pipeline_com

munities

Price of a m3 of water in the project in along the pipeline

with the project 1 10 correlated

present_water_price_m3_Habaswein

Price of a m3 of water in the project in Habaswein at

present 5 20 normal

present_water_price_m3_Wajir Price of a m3 of water in the project in Wajir at present 5 20 normal

present_water_price_m3_pipeline_com

munities

Price of a m3 of water in the project in along the pipeline at

present 5 20 normal

Wajir_water_users Number of water users in Wajir 80,000 300,000 correlated

Habaswein_water_users Number of water users in Habaswein 20,000 100,000 correlated

Pipeline_communities_water_users

Number of water users in the communities along the

pipeline 1,500 50,000 correlated

Wajir_present_water_purchase_per_pe

rson_day_l Water purchase per person per day in Wajir at present 10 20 pos_normal

40


Lower

bound

Upper

bound

Distrib-

ution*

Habaswein_present_water_purchase_p

er_person_day_l Water purchase per person per day in Habaswein at present 10 20 pos_normal

Pipeline_communities_present_water_

purchase_per_person_day_l

Water purchase per person per day in communities along

the pipeline at present 10 20 pos_normal

Wajir_future_water_purchase_per_per

son_day_l

Water purchase per person per day in Wajir with the

project 20 80 pos_normal

Habaswein_future_water_purchase_pe

r_person_day_l

Water purchase per person per day in Habaswein with the


Pipeline_communities_future_water_p

urchase_per_person_day_l


the pipeline with the project 20 80 pos_normal

water_revenue_trend Water cost change trend (integrates price and #users; in %) 2 5 pos_normal

water_tax_rate Tax rate on water sales 0.01 0.08 pos_normal

GOK_ORIO_initial_investment Initial investment 40 million 70 million normal

Payment for Ecosystem Services

PES_rate

Share of water company revenue used for Payments for

Ecosystem services 0.01 0.02 correlated

PES_share_Wajir Share for Wajir 0 0 pos_normal

PES_share_Habaswein Share for Habaswein 0.5 0.8 pos_normal

PES_share_pipeline_communities Share for pipeline communities 0 0.1 pos_normal

PES_share_downstream Share for upstream 0 0 pos_normal

PES_share_upstream Share for downstream 0 0.3 pos_normal

WAJIR

Costs for Wajir

Wajir_initial_investment Initial investment 0 0 constant

Wajir_running_costs_metering Running costs /metering 0 0 constant

Wajir_salaries Salaries 0 0 constant

Wajir_repairs Repairs 0 0 constant

Wajir_aquifer_monitoring Costs for aquifer monitoring 0 0 constant

Wajir_infrastructure_maintenance Infrastructure maintenance 0 0 constant

41


Lower

bound

Upper

bound

Distrib-

ution* Wajir_pipeline_security Pipeline security 0 0 constant

Benefits for Wajir

Wajir_additional_surviving_infants Number of additional surviving infants 200 1,200 pos_normal

Wajir_disease_treatments_saved Number of disease treatments that are no longer necessary 20,000 200,000 pos_normal

Wajir_higher_productivity_benefits Benefits through higher productivity 200,000 12.5 mill. pos_normal

Wajir_revenue_from_more_demand_f

or_local_products_services

Revenue from more demand for local services and

products 1 400,000 pos_normal

Wajir_reduced_brain_drain_benefits Benefits of reduced brain drain, fewer people leaving town 1 2,000,000 pos_normal

Wajir_higher_investment_benefits Benefits through higher investment 100,000 2,500,000 pos_normal

Wajir_job_creation_benefits Benefits through job creation 100,000 2,000,000 pos_normal

Wajir_reduced_water_treatment_costs Reduced water treatment costs 1 100,000 pos_normal

Wajir_sanitation_benefits Benefits from better sanitation (not captured above) 1 1,000,000 pos_normal

Wajir_reduced_reliance_on_shallow_

wells_benefits Benefits from reduced reliance on shallow wells 200,000 2,000,000 pos_normal

Wajir_water_during_dry_season_drou

ght_benefits

Benefits from increased water availability during the dry

season 200,000 2,000,000 pos_normal

Wajir_livelihood_improvement Other livelihood benefits 1 1,000,000 pos_normal

Wajir_revenue_from_water_sale Revenue from water sales 0 0 constant

Wajir_discount_rate Discount rate applied to estimates for Wajir 8 13 pos_normal

HABASWEIN

Costs for Habaswein

Habaswein_initial_investment Initial investment 0 0 constant

Habaswein_running_costs_metering Running costs /metering 0 0 constant

Habaswein_salaries Salaries 0 0 constant

Habaswein_repairs Repairs 0 0 constant

Habaswein_aquifer_monitoring Costs for aquifer monitoring 0 0 constant

Habaswein_infrastructure_maintenanc

e Infrastructure maintenance 0 0 constant

Habaswein_pipeline_security Pipeline security 0 0 constant

42


Lower

bound

Upper

bound

Distrib-

ution* Benefits for Habaswein

Habaswein_additional_surviving_infan

ts Number of additional surviving infants 20 480 pos_normal

Habaswein_disease_treatments_saved Number of disease treatments that are no longer necessary 2,000 40,000 pos_normal

Habaswein_higher_productivity_benef

its Benefits through higher productivity 100,000 4,000,000 pos_normal

Habaswein_revenue_from_more_dema

nd_for_local_products_services



Habaswein_reduced_brain_drain_bene

fits Benefits of reduced brain drain, fewer people leaving town 1 600,000 pos_normal

Habaswein_higher_investment_benefit

s Benefits through higher investment 10,000 600,000 pos_normal

Habaswein_job_creation_benefits Benefits through job creation 50,000 500,000 pos_normal

Habaswein_reduced_water_treatment_

costs Reduced water treatment costs 1 50,000 pos_normal

Habaswein_sanitation_benefits Benefits from better sanitation (not captured above) 1 500,000 pos_normal

Habaswein_reduced_reliance_on_shall

ow_wells_benefits Benefits from reduced reliance on shallow wells 1 10,000 pos_normal

Habaswein_water_during_dry_season_

drought_benefits


season 1 10,000 pos_normal

Habaswein_livelihood_improvement Other livelihood benefits 1 400,000 pos_normal

Habaswein_revenue_from_water_sale Revenue from water sales 0 0 constant

Habaswein_discount_rate Discount rate applied to estimates for Habaswein 8 13 pos_normal

PIPELINE COMMUNITIES

Costs for pipeline communities

Pipeline_communities_initial_investm

ent Initial investment 0 0 constant

Pipeline_communities_running_costs_

metering Running costs /metering 0 0 constant

Pipeline_communities_salaries Salaries 0 0 constant

Pipeline_communities_repairs Repairs 0 0 constant

43


Lower

bound

Upper

bound

Distrib-

ution* Pipeline_communities_aquifer_monito

ring Costs for aquifer monitoring 0 0 constant

Pipeline_communities_infrastructure_

maintenance Infrastructure maintenance 0 0 constant

Pipeline_communities_pipeline_securit

y Pipeline security 0 0 constant

Benefits for pipeline communities

Pipeline_communities_additional_surv

iving_infants Number of additional surviving infants 3 288 pos_normal

Pipeline_communities_disease_treatme

nts_saved Number of disease treatments that are no longer necessary 500 25,000 pos_normal

Pipeline_communities_higher_producti

vity_benefits Benefits through higher productivity 10,000 500,000 pos_normal

Pipeline_communities_revenue_from_

more_demand_for_local_products_ser

vices



Pipeline_communities_reduced_brain_

drain_benefits Benefits of reduced brain drain, fewer people leaving town 1 200,000 pos_normal

Pipeline_communities_higher_investm

ent_benefits Benefits through higher investment 1 200,000 pos_normal

Pipeline_communities_job_creation_b

enefits Benefits through job creation 1 200,000 pos_normal

Pipeline_communities_reduced_water_

treatment_costs Reduced water treatment costs 1,000 30,000 pos_normal

Pipeline_communities_sanitation_bene

fits Benefits from better sanitation (not captured above) 1 50,000 pos_normal

Pipeline_communities_reduced_relianc

e_on_shallow_wells_benefits Benefits from reduced reliance on shallow wells 1 20,000 pos_normal

Pipeline_communities_water_during_d

ry_season_drought_benefits


season 10,000 500,000 pos_normal

Pipeline_communities_livelihood_imp

rovement Other livelihood benefits 10,000 50,000 pos_normal

44


Lower

bound

Upper

bound

Distrib-

ution* Pipeline_communities_revenue_from_

water_sale Revenue from water sales 0 0 constant

Pipeline_communities_discount_rate

Discount rate applied to estimates for the pipeline

communities 8 13 pos_normal

WATER COMPANY

Costs for water company

Water_company_initial_investment Initial investment 1 5,000,000 pos_normal

Water_company_running_costs_meteri

ng Running costs /metering 100,000 2,000,000 pos_normal

Water_company_salaries Salaries 1,000,000 4,000,000 pos_normal

Water_company_repairs Repairs 100,000 1,000,000 pos_normal

Water_company_aquifer_monitoring Costs for aquifer monitoring 200,000 1,000,000 pos_normal

Water_company_infrastructure_mainte

nance Infrastructure maintenance 1,000,000 3,500,000 pos_normal

Water_company_pipeline_security Pipeline security 70,000 175,000 pos_normal

waste_of_water_costs Costs incurred through waste of water 1 300,000 pos_normal

wildlife_protection_and_conservation_

costs Costs for wildlife conservation 1 500,000 pos_normal

Benefits for water company

Water_company_additional_surviving

_infants Number of additional surviving infants 0 0 constant

Water_company_disease_treatments_s

aved Number of disease treatments that are no longer necessary 0 0 constant

Water_company_higher_productivity_

benefits Benefits through higher productivity 0 0 constant

Water_company_revenue_from_more_

demand_for_local_products_services


products 0 0 constant

Water_company_reduced_brain_drain

_benefits Benefits of reduced brain drain, fewer people leaving town 0 0 constant

Water_company_higher_investment_b

enefits Benefits through higher investment 0 0 constant

Water_company_job_creation_benefits Benefits through job creation 0 0 constant

45


Lower

bound

Upper

bound

Distrib-

ution* Water_company_reduced_water_treat

ment_costs Reduced water treatment costs 0 0 constant

Water_company_sanitation_benefits Benefits from better sanitation (not captured above) 0 0 constant

Water_company_reduced_reliance_on

_shallow_wells_benefits Benefits from reduced reliance on shallow wells 0 0 constant

Water_company_water_during_dry_se

ason_drought_benefits


season 0 0 constant

Water_company_livelihood_improvem

ent Other livelihood benefits 0 0 constant

Water_company_discount_rate Discount rate applied to estimates for the water company 5 10 pos_normal

DOWNSTREAM USERS AND ECOSYSTEMS

Costs for downstream

downstream_environmental_impact Total valuation of environmental impacts per year 1 5,000,000 pos_normal

Benefits for downstream

downstream_other_benefits Other downstream benefits 1 50,000 pos_normal

downstream_discount_rate

Discount rate applied to estimates for downstream users

and ecosystems 8 13 normal

UPSTREAM USERS AND ECOSYSTEMS

Costs for upstream

upstream_environmental_impact Total valuation of environmental impacts per year 1 30,000 pos_normal

Benefits for upstream

upstream_other_benefits Other upstream benefits 1 30,000 pos_normal

upstream_discount_rate

Discount rate applied to estimates for upstream users and

ecosystems 8 13 normal

RISK FACTORS

Other developments

chance_dam_development

Chance of big dam projects that affect water supply to

Habaswein (chance 0..1) 0.1 0.5 normal_0_1

chance_oil_development

Chance of oil development that affects water supply to


46


Lower

bound

Upper

bound

Distrib-

ution* Factors that cause the project to fail instantly

negative_feasibility_report Risk of negative feasibility report (chance 0..1) 0 0 constant

low_water_yield Risk that water yield is too low (chance 0..1) 0.01 0.05 normal_0_1

inadequate_benefit_sharing Risk of inadequate benefit sharing (chance 0..1) 0.15 0.2 correlated

political_interference Risk of political interference (chance 0..1) 0.3 0.5 correlated

Factors that cause the project to fail later

drying_wells_base

Risk of drying wells under current conditions, no dams and

no oil (chance per year 0..1) 0.005 0.01 normal_0_1

drying_wells_dams

Added risk of drying wells by dam developments (increase

in chance per year 0..1) 0.001 0.005 normal_0_1

drying_wells_oil

Added risk of drying wells by oil developments (increase


increased_salinity_base Risk of excessive salinity (chance per year 0..1) 0.01 0.02 normal_0_1

increased_salinity_dams

Added risk of excessive salinity by dam developments

(increase in chance per year 0..1) 0 0.001 normal_0_1

increased_salinity_oil

Added risk of excessive salinity by oil developments


Factors that cause project to fail in some years

maintenance_problems

Maintenance problems cause benefits to fail in some years

(chance per year 0..1) 0.01 0.05 normal_0_1

pipeline_failure

Pipeline failure causes benefits to fail in some years


water_price_too_high

High water price causes benefits to fail in some years


regional_conflict

Regional conflict causes benefits to fail in some years


Factors that reduce performance in all years

poor_project_design

Poor project design reduces system performance (reduction

in benefits 0..1) 0.02 0.25 normal_0_1

Factors that reduce performance in some years

lack_of_cooperation

Lack of cooperation reduces system performance

(reduction in benefits 0..1) 0.01 0.2 normal_0_1

47


Lower

bound

Upper

bound

Distrib-

ution*

illegal_abstractions

Illegal abstractions reduce system performance (reduction


poor_maintenance_operation

Poor maintenance and operation reduce system

performance (reduction 0..1) 0.01 0.1 normal_0_1 *Distributions used in this simulation are normal, normal_0_1 (only values between 0 and 1 allowed), pos_normal (only positive values allowed) and constant

(user defined but not variable)

48

Cooperation scenario


Lower

bound

Upper

bound

Distrib-

ution* general variables

n_years number of years to run simulation 30 30 constant

cost_CV coefficients for introducing annual variation 10 15 pos_normal

benefit_CV coefficients for introducing annual variation 10 20 pos_normal

general_CV coefficients for introducing annual variation 10 15 pos_normal

general benefit valuation

value_of_surviving_infant How much should a avoided infant death be valued at? 200 100,000 pos_normal

value_of_disease_treatment How much does a treatment for water-borned disease cost? 3 300 pos_normal

future_water_price_m3_Habaswein

Price of a m3 of water in the project in Habaswein with the


future_water_price_m3_Wajir

Price of a m3 of water in the project in Wajir with the


future_water_price_m3_pipeline_com

munities

Price of a m3 of water in the project in along the pipeline

with the project 1 10 correlated

present_water_price_m3_Habaswein

Price of a m3 of water in the project in Habaswein at

present 5 20 normal

present_water_price_m3_Wajir Price of a m3 of water in the project in Wajir at present 5 20 normal

present_water_price_m3_pipeline_com

munities

Price of a m3 of water in the project in along the pipeline at

present 5 20 normal

Wajir_water_users Number of water users in Wajir 80,000 300,000 correlated

Habaswein_water_users Number of water users in Habaswein 20,000 100,000 correlated

Pipeline_communities_water_users

Number of water users in the communities along the

pipeline 1,500 50,000 correlated

Wajir_present_water_purchase_per_pe

rson_day_l Water purchase per person per day in Wajir at present 10 20 pos_normal

Habaswein_present_water_purchase_p

er_person_day_l Water purchase per person per day in Habaswein at present 10 20 pos_normal

Pipeline_communities_present_water_

purchase_per_person_day_l


the pipeline at present 10 20 pos_normal

Wajir_future_water_purchase_per_per Water purchase per person per day in Wajir with the 20 80 pos_normal

49


Lower

bound

Upper

bound

Distrib-

ution* son_day_l project

Habaswein_future_water_purchase_pe

r_person_day_l

Water purchase per person per day in Habaswein with the


Pipeline_communities_future_water_p

urchase_per_person_day_l


the pipeline with the project 20 80 pos_normal

water_revenue_trend Water cost change trend (integrates price and #users; in %) 2 5 pos_normal

water_tax_rate Tax rate on water sales 0.01 0.08 pos_normal

GOK_ORIO_initial_investment Initial investment 40 million 70 million normal

Payment for Ecosystem Services

PES_rate

Share of water company revenue used for Payments for

Ecosystem services 0.01 0.02 pos_normal

PES_share_Wajir Share for Wajir 0 0 pos_normal

PES_share_Habaswein Share for Habaswein 0.5 0.8 pos_normal

PES_share_pipeline_communities Share for pipeline communities 0 0.1 pos_normal

PES_share_downstream Share for upstream 0 0 pos_normal

PES_share_upstream Share for downstream 0 0.3 pos_normal

WAJIR

Costs for Wajir

Wajir_initial_investment Initial investment 0 0 constant

Wajir_running_costs_metering Running costs /metering 0 0 constant

Wajir_salaries Salaries 0 0 constant

Wajir_repairs Repairs 0 0 constant

Wajir_aquifer_monitoring Costs for aquifer monitoring 0 0 constant

Wajir_infrastructure_maintenance Infrastructure maintenance 0 0 constant

Wajir_pipeline_security Pipeline security 0 0 constant

Benefits for Wajir

Wajir_additional_surviving_infants Number of additional surviving infants 200 1,200 pos_normal

Wajir_disease_treatments_saved Number of disease treatments that are no longer necessary 20,000 200,000 pos_normal

Wajir_higher_productivity_benefits Benefits through higher productivity 200,000 12,500,000 pos_normal

50


Lower

bound

Upper

bound

Distrib-

ution*

Wajir_revenue_from_more_demand_f

or_local_products_services



Wajir_reduced_brain_drain_benefits Benefits of reduced brain drain, fewer people leaving town 1 2,000,000 pos_normal

Wajir_higher_investment_benefits Benefits through higher investment 100,000 2,500,000 pos_normal

Wajir_job_creation_benefits Benefits through job creation 100,000 2,000,000 pos_normal

Wajir_reduced_water_treatment_costs Reduced water treatment costs 1 100,000 pos_normal

Wajir_sanitation_benefits Benefits from better sanitation (not captured above) 1 1,000,000 pos_normal

Wajir_reduced_reliance_on_shallow_

wells_benefits Benefits from reduced reliance on shallow wells 200,000 2,000,000 pos_normal

Wajir_water_during_dry_season_drou

ght_benefits


season 200,000 2,000,000 pos_normal

Wajir_livelihood_improvement Other livelihood benefits 1 1,000,000 pos_normal

Wajir_revenue_from_water_sale Revenue from water sales 0 0 constant

Wajir_discount_rate Discount rate applied to estimates for Wajir 8 13 pos_normal

HABASWEIN

Costs for Habaswein

Habaswein_initial_investment Initial investment 0 0 constant

Habaswein_running_costs_metering Running costs /metering 0 0 constant

Habaswein_salaries Salaries 0 0 constant

Habaswein_repairs Repairs 0 0 constant

Habaswein_aquifer_monitoring Costs for aquifer monitoring 0 0 constant

Habaswein_infrastructure_maintenanc

e Infrastructure maintenance 0 0 constant

Habaswein_pipeline_security Pipeline security 0 0 constant

Benefits for Habaswein

Habaswein_additional_surviving_infan

ts Number of additional surviving infants 20 480 pos_normal

Habaswein_disease_treatments_saved Number of disease treatments that are no longer necessary 2,000 40,000 pos_normal

Habaswein_higher_productivity_benef

its Benefits through higher productivity 100,000 4,000,000 pos_normal

51


Lower

bound

Upper

bound

Distrib-

ution*

Habaswein_revenue_from_more_dema

nd_for_local_products_services



Habaswein_reduced_brain_drain_bene

fits Benefits of reduced brain drain, fewer people leaving town 1 600,000 pos_normal

Habaswein_higher_investment_benefit

s Benefits through higher investment 10,000 600,000 pos_normal

Habaswein_job_creation_benefits Benefits through job creation 50,000 500,000 pos_normal

Habaswein_reduced_water_treatment_

costs Reduced water treatment costs 1 50,000 pos_normal

Habaswein_sanitation_benefits Benefits from better sanitation (not captured above) 1 500,000 pos_normal

Habaswein_reduced_reliance_on_shall

ow_wells_benefits Benefits from reduced reliance on shallow wells 1 10,000 pos_normal

Habaswein_water_during_dry_season_

drought_benefits


season 1 10,000 pos_normal

Habaswein_livelihood_improvement Other livelihood benefits 1 400,000 pos_normal

Habaswein_revenue_from_water_sale Revenue from water sales 0 0 constant

Habaswein_discount_rate Discount rate applied to estimates for Habaswein 8 13 pos_normal

PIPELINE COMMUNITIES

Costs for pipeline communities

Pipeline_communities_initial_investm

ent Initial investment 0 0 constant

Pipeline_communities_running_costs_

metering Running costs /metering 0 0 constant

Pipeline_communities_salaries Salaries 0 0 constant

Pipeline_communities_repairs Repairs 0 0 constant

Pipeline_communities_aquifer_monito

ring Costs for aquifer monitoring 0 0 constant

Pipeline_communities_infrastructure_

maintenance Infrastructure maintenance 0 0 constant

Pipeline_communities_pipeline_securit

y Pipeline security 0 0 constant

52


Lower

bound

Upper

bound

Distrib-

ution* Benefits for pipeline communities

Pipeline_communities_additional_surv

iving_infants Number of additional surviving infants 3 288 pos_normal

Pipeline_communities_disease_treatme

nts_saved Number of disease treatments that are no longer necessary 500 25,000 pos_normal

Pipeline_communities_higher_producti

vity_benefits Benefits through higher productivity 10,000 500,000 pos_normal


more_demand_for_local_products_ser

vices



Pipeline_communities_reduced_brain_

drain_benefits Benefits of reduced brain drain, fewer people leaving town 1 200,000 pos_normal

Pipeline_communities_higher_investm

ent_benefits Benefits through higher investment 1 200,000 pos_normal

Pipeline_communities_job_creation_b

enefits Benefits through job creation 1 200,000 pos_normal

Pipeline_communities_reduced_water_

treatment_costs Reduced water treatment costs 1,000 30,000 pos_normal

Pipeline_communities_sanitation_bene

fits Benefits from better sanitation (not captured above) 1 50,000 pos_normal

Pipeline_communities_reduced_relianc

e_on_shallow_wells_benefits Benefits from reduced reliance on shallow wells 1 20,000 pos_normal

Pipeline_communities_water_during_d

ry_season_drought_benefits


season 10,000 500,000 pos_normal

Pipeline_communities_livelihood_imp

rovement Other livelihood benefits 10,000 50,000 pos_normal


water_sale Revenue from water sales 0 0 constant

Pipeline_communities_discount_rate

Discount rate applied to estimates for the pipeline

communities 8 13 pos_normal

WATER COMPANY

Costs for water company

53


Lower

bound

Upper

bound

Distrib-

ution* Water_company_initial_investment Initial investment 1 5,000,000 pos_normal

Water_company_running_costs_meteri

ng Running costs /metering 100,000 2,000,000 pos_normal

Water_company_salaries Salaries 1,000,000 4,000,000 pos_normal

Water_company_repairs Repairs 100,000 1,000,000 pos_normal

Water_company_aquifer_monitoring Costs for aquifer monitoring 200,000 1,000,000 pos_normal

Water_company_infrastructure_mainte

nance Infrastructure maintenance 1,000,000 3,500,000 pos_normal

Water_company_pipeline_security Pipeline security 70,000 175,000 pos_normal

waste_of_water_costs Costs incurred through waste of water 1 300,000 pos_normal

wildlife_protection_and_conservation_

costs Costs for wildlife conservation 1 500,000 pos_normal

Benefits for water company

Water_company_additional_surviving

_infants Number of additional surviving infants 0 0 constant

Water_company_disease_treatments_s

aved Number of disease treatments that are no longer necessary 0 0 constant

Water_company_higher_productivity_

benefits Benefits through higher productivity 0 0 constant

Water_company_revenue_from_more_

demand_for_local_products_services


products 0 0 constant

Water_company_reduced_brain_drain

_benefits Benefits of reduced brain drain, fewer people leaving town 0 0 constant

Water_company_higher_investment_b

enefits Benefits through higher investment 0 0 constant

Water_company_job_creation_benefits Benefits through job creation 0 0 constant

Water_company_reduced_water_treat

ment_costs Reduced water treatment costs 0 0 constant

Water_company_sanitation_benefits Benefits from better sanitation (not captured above) 0 0 constant

Water_company_reduced_reliance_on

_shallow_wells_benefits Benefits from reduced reliance on shallow wells 0 0 constant

54


Lower

bound

Upper

bound

Distrib-

ution*

Water_company_water_during_dry_se

ason_drought_benefits


season 0 0 constant

Water_company_livelihood_improvem

ent Other livelihood benefits 0 0 constant

Water_company_discount_rate Discount rate applied to estimates for the water company 5 10 pos_normal

DOWNSTREAM USERS AND ECOSYSTEMS

Costs for downstream

downstream_environmental_impact Total valuation of environmental impacts per year 1 5,000,000 pos_normal

Benefits for downstream

downstream_other_benefits Other downstream benefits 1 50,000 pos_normal

downstream_discount_rate

Discount rate applied to estimates for downstream users

and ecosystems 8 13 normal

UPSTREAM USERS AND ECOSYSTEMS

Costs for upstream

upstream_environmental_impact Total valuation of environmental impacts per year 1 30,000 pos_normal

Benefits for upstream

upstream_other_benefits Other upstream benefits 1 30,000 pos_normal

upstream_discount_rate

Discount rate applied to estimates for upstream users and

ecosystems 8 13 normal

RISK FACTORS

Other developments

chance_dam_development

Chance of big dam projects that affect water supply to


chance_oil_development

Chance of oil development that affects water supply to


Factors that cause the project to fail instantly

negative_feasibility_report Risk of negative feasibility report (chance 0..1) 0 0 constant

low_water_yield Risk that water yield is too low (chance 0..1) 0.01 0.05 normal_0_1

inadequate_benefit_sharing Risk of inadequate benefit sharing (chance 0..1) 0 0 constant

political_interference Risk of political interference (chance 0..1) 0 0 constant

55


Lower

bound

Upper

bound

Distrib-

ution* Factors that cause the project to fail later

drying_wells_base

Risk of drying wells under current conditions, no dams and

no oil (chance per year 0..1) 0.005 0.01 normal_0_1

drying_wells_dams

Added risk of drying wells by dam developments (increase


drying_wells_oil

Added risk of drying wells by oil developments (increase


increased_salinity_base Risk of excessive salinity (chance per year 0..1) 0.01 0.02 normal_0_1

increased_salinity_dams

Added risk of excessive salinity by dam developments


increased_salinity_oil

Added risk of excessive salinity by oil developments


Factors that cause project to fail in some years

maintenance_problems

Maintenance problems cause benefits to fail in some years


pipeline_failure

Pipeline failure causes benefits to fail in some years


water_price_too_high

High water price causes benefits to fail in some years


regional_conflict

Regional conflict causes benefits to fail in some years


Factors that reduce performance in all years

poor_project_design

Poor project design reduces system performance (reduction


Factors that reduce performance in some years

lack_of_cooperation

Lack of cooperation reduces system performance

(reduction in benefits 0..1) 0 0 constant

illegal_abstractions

Illegal abstractions reduce system performance (reduction


poor_maintenance_operation

Poor maintenance and operation reduce system

performance (reduction 0..1) 0.01 0.1 normal_0_1 *Distributions used in this simulation are normal, normal_0_1 (only values between 0 and 1 allowed), pos_normal (only positive values allowed) and constant

(user defined but not variable not variable)

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