metadata: authors: 0: author: Kalra,Nidhi Rajiv
1: author: Groves,David Geoffrey
2: author: Bonzanigo,Laura
3: author: Molina Perez,Edmundo
4: author: Ramos,Cayo
5: author: Brandon,Carter J.
6: author: Rodriguez Cabanillas,Iván
count: Peru
docna: 0: docna: Robust decision-making in the water sector:
            a strategy for implementing Lima’s long-term water resources
            master plan
docty: Policy Research Working Paper
subtopic: Science of Climate Change,Climate Change and Health,Climate Change and Environment,Water and Food Supply,Water and Human Health,Hydrology,International Trade and Trade Rules,Water Supply and Sanitation Economics,Small Private Water Supply Providers,Town Water Supply and Sanitation
teratopic: Water Supply and Sanitation,Water Resources,Environment
abstracts: cdata!: How can water resource agencies make
            smart investments to ensure long-term water reliability when
            the future is fraught with deep climate and economic
            uncertainty? This study helped SEDAPAL, the water utility
            serving Lima, Peru, answer this question by drawing on state
            of the art methods for decision making under deep
            uncertainty. These methods provide techniques for evaluating
            the performance of a water system over a wide range of
            plausible futures and then developing strategies that are
            robust across these futures. Rather than weighting futures
            probabilistically to define an optimal strategy, these
            methodologies identify the vulnerabilities of a system and
            then evaluate the key trade-offs among different adaptive
            strategies. Through extensive iteration and collaboration
            with SEDAPAL, the study used these methods to define an
            investment strategy that is robust, ensuring water
            reliability across as wide a range of future conditions as
            possible while also being economically efficient. First,on
            completion, the study helped SEDAPAL realize that not all
            projects included in the Master Plan were necessary to
            achieve water reliability, and the utility could save 25
            percent (more than $600 million) in investment costs.
            Second, the study helped focus future efforts on demand-side
            management, pricing, and soft infrastructure, a refocusing
            that is difficult to achieve in traditional utility
            companies. Third, the study helped SEDAPAL gain the support
            of regulatory and budget agencies through the careful
            analysis of alternatives. Fourth, the study allowed the
            utility to postpone lower priority investments, and to
            analyze future options based on climate and demand
            information that simply is not available now.
geo_reg_key: 119228,517191
repnme: repnme: Robust decision-making in the water sector:
            a strategy for implementing Lima’s long-term water resources
            master plan
display_title: Robust decision-making in the water
            sector: a strategy for implementing Lima’s long-term water
            resources master plan
listing_relative_url: /research/2015/10/25153429/robust-decision-making-water-sector-strategy-implementing-lima’s-long-term-water-resources-master-plan
topicv3: Water m1326522 137,Water Demand Management m1326746 1064,Climate Change m1326829 567,Trade m1326924 698,Water Supply m1326961 138
publishtoextweb_dt: 2015-12-14T00:00:00Z
historic_topic: Water Supply and Sanitation,Water Resources,Environment
no_of_pages: 47
author_keys: 0: author_key: 1245653
1: author_key: 2067954
2: author_key: 1255963
3: author_key: 99583
model_extractions: WPS7439 Policy Research Working Paper 7439 Robust Decision-Making in the Water Sector A Strategy for Implementing Lima’s Long-Term Water Resources Master Plan _Nidhi Kalra David G. Groves Laura Bonzanigo Edmundo Molina Perez Cayo Ramos Carter Brandon Iván Rodriguez Cabanillas_ Climate Change Group Office of the Chief Economist October 2015
Policy Research Working Paper 7439

# **Abstract**

How can water resource agencies make smart investments to ensure long-term water reliability when the future is fraught with deep climate and economic uncertainty? This study helped SEDAPAL, the water utility serving Lima, Peru, answer this question by drawing on state of the art methods for decision making under deep uncertainty. These methods provide techniques for evaluating the performance of a water system over a wide range of plausible futures and then developing strategies that are robust across these futures. Rather than weighting futures probabilistically to define an optimal strategy, these methodologies identify the vulnerabilities of a system and then evaluate the key trade-offs among different adaptive strategies. Through extensive iteration and collaboration with SEDAPAL, the study used these methods to define an investment strategy that is robust, ensuring water reliability across as wide a range of future conditions as possible while also being economically efficient. First,on completion, the study helped SEDAPAL realize that not all projects included in the Master Plan were necessary to achieve water reliability, and the utility could save 25 percent (more than $600 million) in investment costs. Second, the study helped focus future efforts on demand-side management, pricing, and soft infrastructure, a refocusing that is difficult to achieve in traditional utility companies. Third, the study helped SEDAPAL gain the support of regulatory and budget agencies through the careful analysis of alternatives. Fourth, the study allowed the utility to postpone lower priority investments, and to analyze future options based on climate and demand information that simply is not available now.

This paper is a product of the Office of the Chief Economist, Climate Change Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at lbonzanigo@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Robust Decision-Making in the Water <u>Sector</u>

A Strategy for Implementing Lima’s Long-Term Water Resources Master Plan

## **Authors:**

**Nidhi Kalra**<sup>**1**</sup> **, David G. Groves**<sup>**1**</sup> **, Laura Bonzanigo**<sup>**2**</sup> **, Edmundo Molina Perez**<sup>**1**</sup> **, Cayo Ramos**<sup>**3**</sup> **, Carter Brandon**<sup>**2**</sup> **, and Iván Rodriguez Cabanillas**<sup>**4**</sup>

1. Evolving Logic, Inc. 2. The World Bank

3. Universidad La Molina, Lima, Perú 4. SEDAPAL

**JEL** : D81, H54, O21, O22

**Keywords** : long-lived infrastructure investments, water supply, decision making under uncertainty, prioritization
# Acknowledgments

The task team leader and authors thank the many people who have made this project possible and contributed to its success. These include: Juan Carlos Barandiaran, Cesar Bedon Nunura, Daniel Benvenutto, Yolanda Andia Cardenas, Josue Cespedes Alarcon, Omar Concepcion Yanac, Marisa Escobar, Marianne Fay, Rut Sigrun Arteaga Gil, Sue Goldmark, Ivo Imparato, Eduardo Ismodes, Lizmara Kirchner, Mara Elena La Rosa, Iris Marmanillo, Gabriela Puente-Arnao, Ana Rosa Quispe C., Diego Rodriguez, Manfred Schuetze, Ankur Shah, Marco Vargas Medina, Griselle Felicita Vega, Violeta Wagner, and David Yates.
# 1 Introduction

Lima, the capital of Peru, faces major water-related challenges. Home to approximately 9.8 million people (INEI, 2013), it is the fifth largest metropolitan area in Latin America. With average precipitation of just 6 mm per year (WMO, 2015), it is also the second largest desert city in the world. A rapidly growing population with approximately one million underserved urban poor, current water shortages, competition for water between sectors, and climate change impacts may leave the region under perpetual water stress.

SEDAPAL, Lima’s water utility, provides water to most of the metropolitan region, which includes the adjacent port district of Callao of approximately one million people (INEI, 2013). Nearly all of SEDAPAL’s supplies are drawn from the Chillón, Rimac, and Lurin river basins in the region. Recognizing the urgency of Lima’s water situation, SEDAPAL has developed an aggressive multi-billion dollar Master Plan, which includes 12 major infrastructural investment projects that SEDAPAL proposes to implement between now and 2040 at a cost of US $2.3billion. SEDAPAL is further considering two additional water supply projects for a cost of US $400 million. As Figure 1-1 shows, these 14 investments are a mix of reservoirs, water treatment plants, desalination plants, and tunnels to transfer water between watersheds. Together, the investments are designed to meet the 30% increase in water demand that SEDAPAL projects for the coming decades. Lima’s demand for water is distributed across four regions:

- Central Lima, which has 85% of demand and is in the Rimac basin;

- Northern Lima and Callao, which has 7% of demand and is in the Chillón basin;

- Eastern Lima, which has 6% of the city’s demand and is also in the Rimac basin;

- Southern Lima, which has 2% of the city’s demand and is in the Lurin basin.

In 2014, SEDAPAL submitted its Master Plan to national regulators for approval. In mid2015, SEDAPAL obtained the Master Plan’s approval including management goals, rate formula, and tariff structures for the regulatory 5-year period 2015-2020.

1
<!-- Start of picture text --> ¥ , wy) at \ at<br>be ie Pun Run Res/Chillon WTP. Tisha<br>4 :<br>: é'<br>A1<br>Demand Regions Jacaybamba Res<br>ae Contra LimaP is Chancay 4<br>[i] Res / Huaral WTP ‘ ©<br>Eastem Lima © os, Gasacancha Res<br>Northem Lima and Callao T S Antonio Res / Chillon WTP :<br>[i Southem Lima Chancay GWo @ Autisha Res /Lurigancho WTP:<br>som. © ©<br>Ventanilla Desal) 2%. _ la Pomacocha Res/Huachipa WIP<br>* Desalination Plant oo » “Sc<br>| Groundwater at: *<br>& Reservoir =, }<br>@& Reservoir and Water Treatment Plant neg 7 A<br>@— TransmissionReservoir and Water and Water Treatment Treatment PlantPlant (trans Andean) aMF“os i Limaroe Sur Desal<br>>) Water Treatment Plant Canete Trans | WTP<br><!-- End of picture text -->
changes are uncertain—rainfall may increase or decrease—but droughts may also become more severe and more common (IPCC, 2014). The combined effects on streamflow in the basins that serve Lima are highly uncertain.

These uncertainties raise important questions for decision makers concerned with water reliability in Lima. Are the capital projects in the Master Plan sufficient to ensure reliability in the face of deeply uncertain future climate change and demand? On the other hand, are all proposed projects necessary to achieving reliability? Many projects are challenging to implement—how should considerations of project feasibility shape the city’s investment strategy? Ultimately, how should projects be prioritized? Which should be implemented now, which can be delayed until they are necessary, and what specific indicators would trigger their implementation?

This study draws upon state-of-the-art methods for decision making under deep uncertainty (DMU) to give SEDAPAL and decision makers in Lima answers to these pressing questions. It draws upon several methodologies including Robust Decision Making, Decision Scaling, and Adaptive Pathways, to prioritize the investments in SEDAPAL’s Master Plan. Together these methods help define an investment strategy that is _robust_ , ensuring water reliability across as wide a range of future conditions as possible while also being economically efficient. This strategy has two key characteristics of a robust plan:

1. It is _no-regret_ . It identifies investments and projects that are useful no matter what the future brings;

2. It is _adaptive_ . It guides decision makers on how to implement future investments and projects as climate, demand, and other future conditions evolve.

The strategy is defined in a decision tree as shown in Figure 1-2. It consists of a set of near-term, no-regret investments that SEDAPAL can embark upon now; signposts of specific project feasibility, streamflow, and demand conditions SEDAPAL should monitor in the medium and long term; and sets of deferred projects that SEDAPAL should implement if the signposts are triggered. Section 7 describes the strategy and decision tree in detail.

Importantly, the strategy prioritizes among the 14 supply-side investments. Yet efficiency and demand management will play a key role in ensuring water reliability in Lima, particularly as they may be more cost effective than some of the supply-side investments. At the time of this project, SEDAPAL sought to focus its analysis on the Master Plan projects as it concurrently explored ideas for demand side and efficiency options. Therefore, while this analysis does not explicitly evaluate efficiency and demand management actions, it highlights the need for such actions by testing the limits of the water reliability that can be achieved with the proposed projects. In Section 8, we recommend evaluating demand and efficiency actions alongside supply-side investments in a follow-on analysis to develop an integrated water resource management plan.

3
<!-- Start of picture text --> Near-Term ! Mid-Term Long-Term<br>; ans |<br>: ! Long-Term !<br>: No-Regret :<br>: ; 1 Investments<br>; ir #4 on , '<br>} Mid-Term; I| : | Long-Term I 1" 1<br>' ' No-Regret ; ‘4 Signposts |! 1<br><!-- End of picture text -->

<!-- Start of picture text --> 1 Investments ! 4 i<br>1 ' ‘4<br>| : | ! !<br>Near-term, ' ' ' i i<br>No-regret Mid-Term ! I ' ‘ ' 1<br>investments Signpost 1 : 4 ; :<br>:':I]1<br>1Poy ' :<br><!-- End of picture text -->

<!-- Start of picture text --> '11<br>'<br>i ! 4 ! ;<br>,:14111<br>11<br>' '<br>;1I<br>‘1 1<br>: ae<br><!-- End of picture text -->
assessment of how they can help SEDAPAL achieve water reliability efficiently. Section 7 presents a robust, adaptive decision tree of no-regret projects based on those results. The final section summarizes key findings, next steps, and future applications. In addition, Appendix A accompanies Section 4, providing details on the demand and streamflow uncertainties.

# 2 Methodology

Water resource planners are increasingly turning to decision making under uncertainty (DMU) methods to address climate change and other uncertainties in their long-term plans (Brown, 2010; Groves _et al._ , 2008). Central to these approaches is the recognition that it is not possible to determine the likelihoods of plausible future states of the world in order to identify “optimal” water management strategies. The most appropriate set of water management investments may differ significantly depending on what the future holds. Therefore, developing an optimal strategy and then exploring performance sensitivities does not provide the necessary information to determine a prudent course of action. Instead, the goal must be to identify “robust” strategies—those that will perform satisfactorily across the wide range of possible futures (Groves _et al._ , 2014b).

Robust water management strategies generally consist of a portfolio of individual management decisions, such as new infrastructure, operational procedures, or water use efficiency programs. The implementation of the portfolio is not predefined, but rather is adaptive and adjusts in response to new information about future conditions. As there are generally many different ways to achieve water management objectives, evaluating tradeoffs among the different options and portfolios is critical to the final decision making and achieving stakeholder support for policies.

## 2.1 Methods for Robust, Adaptive Water Management

Developing robust, adaptive water management strategies requires new analytic techniques. Water agencies must consider more than just the historical hydrologic record when projecting future hydrologic conditions. For systems that are supply limited, evaluating a range of demand projections is also essential to understanding the full range of possible water management needs. Other factors important to the design and performance of a water management system (for instance, water regulations or investment effectiveness) are also highly uncertain and should be explored.

DMU methods provide techniques for evaluating the performance of a water system over a wide range of plausible futures and then developing strategies that are robust across these futures (Kalra _et al._ , 2014). Rather than weighting futures probabilistically to define an optimal strategy, DMU methods identify the vulnerabilities of an agency or utility’s system and then evaluate the key trade-offs among different adaptive strategies. Through iteration, often with extensive and direct participation of decision makers and stakeholders, a robust, adaptive strategy is identified. Such a strategy defines a set of 5
near-term investments, signposts—conditions that would trigger new actions or adjustments—and deferred actions for possible future implementation.

Early applications of these methods to water planning include Robust Decision Making (Groves and Lempert, 2007; Lempert _et al._ , 2003) (RDM) studies in Southern California (Groves _et al._ , 2008, 2014a; Lempert and Groves, 2010) and the U.K. (Dessai and Hulme, 2007). These applications illustrated techniques for generating and evaluating a large ensemble of futures, identifying the key vulnerabilities of current management through a process called “scenario discovery” (Bryant and Lempert, 2010), and then highlighting key trade-offs among the most robust strategies. The Southern California application also introduced rudimentary adaptivity into the robust strategies based on implementing additional management actions if the gap between water demand and available supply grew too small. Recent other applications include two pilot studies for the Water Resources Foundation (Groves _et al._ , 2014b), an evaluation of the Integrated Resources Plan of the Metropolitan Water District of Southern California (Groves _et al._ , 2014a), an application to flood risk mitigation in Vietnam (Lempert _et al._ , 2013a), and for the U.S. Bureau of Reclamation’s Colorado River Basin Study (Bureau of Reclamation, 2012; Groves _et al._ , 2013).

Concurrently, a related methodology has been developed called Adaptation Pathways (Walker _et al._ , 2001), which focuses on defining a comprehensive set of alternative sequences of decisions and identifying the conditions that could guide decision makers along the most appropriate sequence. Haasnoot et al. (2013) demonstrates this approach for flood control in the Netherlands, which includes novel visualizations of different sequences of investments, called “pathways.” Another related methodology, called Decision Scaling (Brown _et al._ , 2012), has been described and applied in the Great Lakes region (Brown _et al._ , 2011) and the Niger River Basin (Brown, 2011). Decision Scaling provides techniques for extensively exploring plausible climate conditions to define the key climate-related thresholds that distinguish different robust strategies.

The World Bank is supporting other projects organized around DMU in several developing countries. These projects range from hydropower investments in Nepal and Africa (Cervigni _et al._ , 2015), urban flood management in Sri Lanka, and transport network’s vulnerability in Peru and Ecuador. A conceptual framework has also recently been developed by the Water Global Practice to guide project planners through the application of DMU techniques to climate change risk assessment and risk management.

These methods recognize the importance of decision maker and stakeholder involvement in the decision analysis. For example, to help SEDAPAL planners gain a deeper appreciation for “deep uncertainty”, the study team facilitated a “serious game” (Mendler de Suarez _et al._ , 2012) called “Decision for the Decade”.<sup>1</sup> The study team then developed participatory, interactive decision support tools to evaluate simulation results and develop portfolios of water management investments. The tools developed are similar to those recently used to support the development of Louisiana’s 2012 Coastal

> 1Information on this “serious game” can be found on the web at: http://www.greengrowthknowledge.org/page/decision-making-under-uncertainty-testing-methodologiesreal-world

6
<!-- Start of picture text --> “Robust Decision Making”<br>1. Decision “Decision<br>Structuring Scaling’<br>A. Tradeoff a 2. Case<br>Analysis a omens Generation<br><!-- End of picture text --> <!-- Start of picture text --> “Adaptive<br>Pathways”<br>3. Vulnerability<br>Analysis “Interactive, Analytic<br>Decision Support”<br>Robust<br>; Descriptions of key<br>strategies vulnerabilities<br><!-- End of picture text -->
Master Plan to evaluate, and to specify the performance metrics that would be used to assess the Plan’s robustness. Based on this information, the team developed a water management model to simulate the performance of SEDAPAL’s system under different futures and levels of investment. Sections 3 and 4 provide details from this step.

Our analysis first defined the baseline vulnerabilities, described in Section 5. We evaluated the performance of SEDAPAL’s current water management system in 2040 across 300 different plausible future scenarios of future supplies and demand using the water management model (Step 2, Figure 2-1). We then assimilated these results into a decision support tool and identified the key vulnerabilities—that is, the key uncertain drivers that would lead SEDAPAL’s plan to not achieve its goals (Step 3, Figure 2-1).

We then returned to Step 1 and developed a large set of portfolios of projects--all variations of the SEDAPAL Master Plan. To do this, we evaluated the performance of each individual project across the 300 futures and then used an interactive, analytical decision support tool to define optimal portfolios for each of the 300 futures. By evaluating the vulnerabilities of each portfolio, we honed in on a small set of different portfolios that would achieve SEDAPAL’s goals under the wide range of different futures. This portfolio analysis is described in section 6.

Our last analytic step was to develop a single, adaptive strategy for SEDAPAL. By analyzing simulation results of each portfolio, we identified the key ingredients of a robust, adaptive strategy:

- Near-term no-regret projects—project included in all portfolios,

- Signposts—uncertain conditions that should trigger additional SEDAPAL investment,

- Deferred actions—additional investments to implement under different future conditions, and

- Remaining vulnerabilities—conditions in which any implementation sequence of Master Plan projects would not meet SEDAPAL’s goals.

This step is described in section 7.

# 3 Engagement with SEDAPAL and Stakeholders

Following best practices in decision support, this project embedded analytics in an intensive and structured participatory process with SEDAPAL and other stakeholders. It began in December 2013 and the analysis was completed in March 2015, with this final report published in the subsequent months. Figure 3-1 shows activities and milestones.

This project kicked off with a multi-day workshop at SEDAPAL in December 2013 to build a shared understanding of the problem and cultivate relationships between stakeholders and analysts. At the workshop, we collaboratively scoped the analysis (Step 1 in Figure 2-1) and elicited the key analytical elements discussed in Section 4. We launched data gathering needed to build the WEAP model. We also held meetings to keep other stakeholders abreast of the new study, including Peru’s National Water 8
<!-- Start of picture text --> Kickoff Workshop 1 Intermediate Workshop2 Intermediate Workshop 3 Final Workshop 4<br>Modeling and Vulnerability Planning Tool and Developing Robust Deliversbles<br>Simulations Analysis Portfolio Development No-Regret, Adatptive Plans PORVETERNOS<br>Dec 13 Feb 14 Apr 14 Jun 14 Aug 14 Oct 14 Dec 14 Feb 15 Apr 15 Jun 15<br><!-- End of picture text -->
- **Policy levers (L)** are actions that decision makers want to consider, in this case the components of SEDAPAL’s Master Plan and related investments;

- **Exogenous uncertainties (X)** are factors like climate change and demand that are outside the control of decision makers but that may affect the ability of actions to achieve decision makers’ goals;

- **Metrics (M)** are the performance standards used to evaluate whether or not a choice of policy levers achieves decision makers’ goals, e.g. water reliability; and

- **Relationships (R)** , generally represented by simulation models, define how the policy levers perform, as measured by the metrics, under the various uncertainties.

The XLRM matrix developed for this study is summarized in Table 4-1. This section continues to describe the policy levers (L) under consideration, the metrics (M) used to judge the effectiveness of those policies, the uncertainties (X), and lastly the models (R) that quantify the relationships among the factors.

**Table 4-1: XLRM framework of key factors in this analysis**

|**Uncertainties (X)**|**Policy Levers (L)**|
|---|---|
|●<br>Future water demand<br>●<br>Future stream flow<br>●<br>Project feasibility|●<br>12 projects in SEDAPAL’s Master Plan<br>●<br>2 additional projects<br>●<br>Budget for infrastructure<br>●<br>(Efficiency and demand management)|
|**Models (R)**|**Metrics (M)**|
|●<br>WEAP Model<br>●<br>Interactive, analytic decision<br>support tool|●<br>90th percentile of monthly met demand, as a<br>percent of total demand<br>●<br>Cost of plan|

# 4.1 Policy Levers (L)

SEDAPAL’s Master Plan consists of twelve investment projects; a mix of dams, tunnels, water treatment plants, desalination plants, and tunnel transfers. SEDAPAL is also considering two additional investment projects—Cañete transfer/water treatment plant (WTP) and Chancay Reservoir—that were considered important to evaluate but not part of this plan. Table 4-2summarizes the key characteristics of these projects. The first column identifies the shorthand name we use for each project throughout this paper, the second summarizes the key elements of each project, the third notes the amount of water the project is intended to supply, and the fourth is the estimated cost of each project.

10
**Table 4-2: Master Plan and related projects evaluated in this analysis**

|**Project Name**|**Project Description**|**Additional Designed Supply By**<br>**Region and Season**|**Cost**<br>**(M**<br>**USD)**|
|---|---|---|---|
|Atarjea WTP|Expansion of Atarjea<br>WTP|2 m<sup>3</sup>/s to Central Lima all year<br>round<br>|1.6|
|Autisha Res /<br>Lurigancho WTP|Autisha Reservoir and<br>S. J. Lurigancho WTP|1.2 m<sup>3</sup>/s to Central Lima in the dry<br>season only|21.9|
|Cañete Trans /<br>WTP*|Water transfer from<br>river Cañete, South of<br>Lima, with a WTP in<br>Lurin|3.5 m<sup>3</sup>/s to Southern Lima (70%)<br>and 1.5 m<sup>3</sup>/s to Central Lima (30%)<br>all year round|320|
|Casacancha Res|Casacancha Reservoir|1.8 m<sup>3</sup>/s to Central Lima in the dry<br>season only|25|
|Chancay GW*|Groundwater extraction<br>from the lower<br>watershed of the river<br>|<br>1.5 m<sup>3</sup>/s to Northern Lima and<br>Callao in the dry season only|62|
||Chancay|||
|Chancay Res /<br>Huaral WTP|<br>Chancay Reservoir and<br>Huaral WTP|2 m<sup>3</sup>/s to Northern Lima and Callao<br>all year round|274|
|Chosica WTP /|Chosica WTP and|0.6 m<sup>3</sup>/s to Central Lima (40%) and<br>|97|
|Graton Tunnel|enlargement of the<br>Graton tunnel|0.9 m<sup>3</sup>/s to Eastern Lima (60%) all<br>year round||
|Lima Sur Desal|Lima Sur desalination<br>plant|0.4 m<sup>3</sup>/s to Southern Lima all year<br>round|110|
|Jacaybamba Res|Jacaybamba Reservoir|1.4 m<sup>3</sup>/s to Northern Lima and<br>Callao in the dry season only|145|
|Lurin WTP|Lurin WTP|0.4 m<sup>3</sup>/s to Southern Lima in the<br>wet season only|25|
|Pomacocha Res /<br>Huachipa WTP|Pomacocha Reservoir,<br>expansion of Huachipa<br>WTP, and Trans-<br>Andean Tunnel #2|4.5 m<sup>3</sup>/s to Central Lima (90%) and<br>0.5 m<sup>3</sup>/s to Southern Lima (10%) in<br>the dry season only|767|
|Pun Run Res /<br>Chillón WTP|Pun Run Reservoir,<br>Trans-Andean Tunnel<br>#2, and expansion of<br>Chillón WTP|1.9 m<sup>3</sup>/s to Northern Lima and<br>Callao (80%) and 0.5 m<sup>3</sup>/s to<br>Central Lima (20%) all year round|200|
|S. Antonio Res /<br>Chillón WTP|San Antonio Escondido<br>Reservoir and<br>Expansion of Chillón<br>WTP|1.1 m<sup>3</sup>/s to Northern Lima and<br>Callao (80%) and 0.3 m<sup>3</sup>/s to<br>Central Lima (20%) in the dry<br>season; 0.8 m<sup>3</sup>/s to Northern Lima<br>and Callao (80%) and 0.2 m<sup>3</sup>/s to<br>Central Lima (20%) in the wet<br>season|230|
|Ventanilla Desal|Ventanilla desalination<br>plant|1.5 m<sup>3</sup>/s to Northern Lima and<br>Callao allyear round|400|

_*Cañete Trans/WTP and Chancay GW are not part of SEDAPAL’s Master Plan but have been suggested as important investments to consider during the course of this analysis._ 11
# 4.2 Metrics (M)

In designing its Master Plan, SEDAPAL was principally concerned with achieving water reliability as cost effectively as possible. For this analysis, SEDAPAL defines water reliability as meeting 90% of demand 90% of the time, measured monthly. In other words, water reliability is achieved if the 90th percentile of monthly met demand is at least 90%. This led to the use of two primary metrics used to compare portfolios:

1. The 90th percentile of monthly met demand (and whether this exceeds 90%), and

2. The cost of the plan (e.g. portfolio of investments).

We also evaluated the supply that would be provided to each of Lima’s demand regions to estimate the cost effectiveness of individual components of Lima’s Master Plan.

# 4.3 Uncertainties (X)

We assess three key uncertainties that challenge SEDAPAL’s ability to achieve water reliability:

1. **The level of demand in the city** . Demand will be shaped principally by future population growth and per-capita water use. This varies in different districts. Overall, Lima and Callao currently have a demand of 855 Mm<sup>3</sup> for a population of 9.5M. Official projections of 2040 population range from 11.5M to 15.5M and per-capita water consumption from approximately 170 to 240 l/p/day, giving a range of future demand of 730 Mm<sup>3</sup> per year to 1,790 Mm<sup>3</sup> per year.<sup>2</sup>

2. **Future stream flows** . In the wet season, 90% of Lima’s water is drawn from the Rimac, Chillón, and Lurin Rivers. The remaining 10% comes from groundwater. In contrast, in the dry season 20% comes from groundwater sources. Climate change and other factors may alter water flows, but downscaled climate projections and rainfall-runoff model are not yet available for these river basins. We explored how the system responds to potential changes in monthly stream flows proportional to the minimum (-60%) and maximum (+90%) changes in monthly precipitation, as projected in the IPCC’s Fourth Assessment Report (AR4 – see Figure 4-1).<sup>3</sup> This approach is consistent with the principles of Decision Scaling, which seek to assess the sensitivity of investments to climate (Brown _et al._ , 2012).

2 This approach provides a broad range of total future water demand in Lima in order to help establish limits of the Master Plan in ensuring water reliability. For this purpose, it is not necessary to differentiate domestic, commercial, agricultural, and industrial water demand. (The latter three are much smaller than municipal demands.) A future study that includes efficiency, reuse, and demand management policies should make these distinctions, however, because different policies are appropriate for each sector. For instance, recycled water may be appropriate for non-domestic uses.

3 The IPCC’s 5th Assessment Report was not available during the time this study was conducted. Appendix A provides details on how we used these changes to develop futures.

12
<!-- Start of picture text --> 50<br>a : H =<br>=<br>6a :‘<br>c;<br># O° es =<br>2'<br>a::t 7'<br>70<br>Annual Wet Season Dry Season<br>(Dec-Feb) (Mar-Nov)<br><!-- End of picture text -->
<!-- Start of picture text --> 60% e °° ° ° © 0%r) of ° . Ps °<br>©.6 e e ~ © ¢ © @<br>@ e %e —* : - ° °<br>r e e e<br>40% jee ots o° as ° . °<br>2 e.° e ° ° - 1. e °<br>s be % * ~@,* e4* ° TM<br>ES 20% e° °e ®e ° es e e° oO e e : © e e ®<br>5 e * e °. 4.e¢ © e<br>a e e oO © @<br>c e oO @ 'e<br>° ee e? ad<br>2 Current Change in 0% e e ® ° e<br>8 0% ; sts -<br><!-- End of picture text --> <!-- Start of picture text --> £© ° e se ° e| e e ° c «4 %<br>2 -20% bd e ° e e @<br>£ A © © e fe %e ° °<br>o e e = e, ° e e 2 ry e<br>e Oy ? e e ‘ e ee ry<br>40% od ° ® ° a<br>® e ee %@ . ee e e é<br>ob<br>@ e © ee ° ° °<br>ee e eo s.<br>60% ° e e oe = bed ee ."e<br>Current Demand: 855 Mm3 SEDAPAL 2040 Demand Projection(1,125 Mm3)<br>700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800<br>Total Demand (Mm3)<br><!-- End of picture text -->
for infrastructure elements such as reservoirs, canals, and hydropower projects (Huber-Lee _et al._ , 2006; Purkey _et al._ , 2007; Yates _et al._ , 2005a, 2005b, 2008). Additionally, it has watershed rainfall-runoff modeling capabilities that allow all portions of the water infrastructure and demand to be dynamically nested within the underlying hydrological processes. This functionality allows the modeler to analyze how specific configurations of infrastructure, operating rules, and operational priorities will affect water uses as diverse as instream flows, irrigated agriculture, and municipal water supply under the umbrella of input weather data and physical watershed conditions. This integration of watershed hydrology with a water systems planning model makes WEAP ideally suited to study the potential impacts of climate change and other uncertainties internal to watersheds.

The SEDAPAL WEAP model represents Lima’s system through a series of demand and supply nodes, connected via transmission links representing either natural streams or engineered canals. The water demand in the Metropolitan area is represented by a demand node for each of Lima’s four main regions: Central Lima, Eastern Lima, Southern Lima, and Northern Lima and Callao. The model contains all existing water infrastructure, including five reservoirs in the Alto Mantaro, two groundwater basins, and all canals and transfer tunnels of the Marca I, II, and V,<sup>5</sup> including a trans-Andean tunnel. The model also contains the existing water treatment plants of Atarjea and Chillón. Additionally, each of the 14 proposed Master Plan projects can be modeled individually or in different combinations.

The WEAP model takes as input (i) monthly hydrological series, (ii) the depth, levels, and operations of the reservoirs, and their evaporation, (iii) the design capacity of all investments, and (iv) different demand scenarios, divided per demand region. Besides water supply to the different areas of the city, the model also reports monthly water levels in the reservoirs, groundwater extraction levels, and unmet demand. Figure 4-3. shows a screenshot of the schematic of SEDAPAL’s water management system within WEAP.

This is SEDAPAL’s first model of the water system supplying Lima Callao, and they will continue to use it for their planning after this project. Before the development of this model, SEDAPAL’s project prioritization was based primarily on experts’ considerations and the additional supply sought in different parts of the city. SEDAPAL did not have tools that allowed for an integrated and dynamic analysis of different projects. SEDAPAL’s continuous involvement in the model’s development helped both build confidence in the model and its results, and increase the likelihood of a successful technology transfer of the model to SEDAPAL for its future analyses.

We developed a code to evaluate the WEAP model on the Amazon cloud, allowing the simultaneous running of up to a hundred cases at once. First, we ran the baseline (i.e., without Master Plan) under the 300 water supply and demand futures we created, then the baseline plus one individual project at the time, and finally, the first selection of portfolios to identify which ones performed better across these futures.

> 5 The three Marcas indicate the sequenced construction of the investments: Marca I began in 1960s, Marca II at the end of 1990s, and Marca III-V in 2000s.

15
<!-- Start of picture text --> WW. WEAP: Topologial0Dic_baseline<br>Area Edit View Schematic General Advanced Help<br>-<br>. Be ¥ i\ — RD iver G4)sion (24) i‘es ; 7<br>Schematic. A|! ReservoirGroundwater (14) (5) Se e<br>| ® Other Supply (2) -<br>M1@ Demand Site (6)<br>E:[=== = || @ CatchmentRunoff/infiltration 7<‘(26 Sead4nm t¢@ eaez<br>1 — Transmission Link (22) Sy ; Ae<br>¥|@ WastewaterTreatment Plant M " ph<br>‘| mm Run of River Hydro<br>a 1 — Return Flow (4) D>.<br>V1 ® Flow Requirement (5) Fig ON a<br>V\ » Streamflow Gauge (6) E Tout °<br>l=) [1398760786443 “ ( oT .<br>ald | Country ‘ “Vi<br>|— Major Rivers Sef ad Rc<br>MMMB¥\— riosLagunasmarca primariosiii cuenca _7 Af KK wyWF yeGhee sin = \y<br>MD marcav a P P re» Se)or<br>MI) marca ii Se 7 , ‘:<br>MID marca iv S. gf Z. NAGE<br>wee ‘i<br>iS | hale, "<br>eS: . Y/'<br><!-- End of picture text -->
feasibility specifications. The decision support tool then reports the cumulative effects of each optimal portfolio and supports the comparisons of projects selected for each portfolio. The analytical figures in this paper are screenshots from the tool. It is available online at https://goo.gl/BRojPW.

# 5 Baseline Vulnerability

We present the results of this study by stepping through a series of questions about the proposed investments and trade-offs among portfolios. In this section, we assess the performance of the baseline vulnerabilities of the current system and the Master Plan. In section 6, we generate and evaluate portfolios of projects under different budgets. In section 7, we use these portfolios to develop a decision tree that guides SEDAPAL through an adaptive set of no-regret investments.

## 5.1 Does Lima’s Current System Achieve Water Reliability?

Lima’s current water demand is approximately 855 Mm<sup>3</sup> . Using WEAP to evaluate the performance of the current system under current and historical flow conditions and current demand reveals that SEDAPAL’s existing system just achieves water reliability. Lima meets 90.4% of the demand 90 percent of the time. (Recall that water reliability is achieved when the 90th percentile of monthly met demand is above 90%.)

## 5.2 Could SEDAPAL Achieve Future Water Reliability Without Further Investment?

It is useful to assess whether Lima could achieve water reliability in the future without the proposed projects. This analysis reveals the need for future investments.

We assessed whether SEDAPAL could achieve future water reliability by evaluating the performance of the current system in WEAP in each of the 300 plausible demand and streamflow futures.

Without the proposed projects, SEDAPAL cannot achieve water reliability in 2040 if demand exceeds approximately 920 Mm<sup>3</sup> , regardless of future flows. This is somewhat more than current levels of demand but much less than SEDAPAL’s projection for 2040 demand of 1,125 Mm<sup>3</sup> and far less than the highest plausible demand of 1,800 Mm<sup>3</sup> . If future flows decrease, SEDAPAL may not be able to ensure water reliability even if future demand were less than today’s demand. These results confirm SEDAPAL’s need to undertake major investments in either new water supply or efficiency and demand management to achieve water reliability for Lima.

Figure 5-1 shows the results. Each mark represents a future. The size of each mark indicates the 90th percentile of monthly met demand—smaller marks indicate lower met demand and hence worse performance. Marks are also colored differently—blue circles 17
<!-- Start of picture text --> 90th Percentile of<br>O (% ofTotal Demand)<br>40% @e O ok?b O O Q, > ® O 40.0060.00<br>; @od oP O O ©) 80.00<br>Es O Yo O OO © )t00.00<br>E O Oa Po<br>8 20% e O O fe) ae O O5<br>7):2 () °8 [OoOS — BPoo fone)©<br>:¢"a @ 0 6 Go oY OO 8 6B °PMogfe) i)<br>2 -20% O On~O @ (e)<br>5 QB O O (e)<br>8 O@Bp Ss iP O O QO<br>40% o| O 0 O42 Ke)<br>-60% aS Po& ©O | |° 0 00igO O ® 6 ROO &2fe) 6<br>i) O ° Qe }<br>Current Demand: 855Mm3 SEDAPAL 2040 Demand Projection: 1,125 Mm3<br>700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800<br>Total Demand (Mm3)<br><!-- End of picture text -->
<!-- Start of picture text --> 60% e e O Monthy MetDoma)<br>eo |. °° oe? 0856,O om°° ota eman<br>40% r ) @ e@ ® @) © 60.00<br>> @ O ©) 80.00<br>— 14 ry O O O O (_ ) 100.00<br>i 20% ots e *e eee) O Oo oO<br>jn i} fe = B00 GX<br>> 0S Pala g 0 00 REED<br>5 © eo Mo? (80<br>FA @ CO O<br>E -20% oe ® TR O @<br>" P04 bm ae<br>-40% ® ® 8 Ql O O Oo O ve 0 oO.O<br>eo @®@ Dp. °O O<br>60% @ on Om o om "<br>e ee 5 0 O% Qe ep Q fe)<br>Current Demand: 855Mm3 SEDAPAL 2040 Demand Projection: 1,125 Mm3<br>700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800<br>Total Demand (Mm3)<br><!-- End of picture text -->
# 6 Portfolio Development and Analysis

Implementing all 14 projects can ensure water reliability in many but not all plausible futures. In some cases, however, a smaller set of projects may be sufficient (e.g., if demand or streamflow are favorable), necessary (e.g., if some projects are too difficult to implement), or preferable (e.g., for cost savings). Developing and evaluating portfolios of projects—i.e., combinations of the 14 individual projects—can help SEDAPAL understand trade-offs between performance and cost and reveal how to prioritize among the full set of potential investments.

## 6.1 How Can We Develop Portfolios For A Robust Implementation of the Master Plan?

In this project, we use two smaller budgets—75%, and 50%—to develop and assess the performance of smaller portfolios. For these two budgets and the full budget case, we identify the portfolios that achieve the greatest water reliability under different project feasibility, demand, and streamflow conditions. These portfolios form the basis of a decision tree that guides no-regret, adaptive investment decision making for SEDAPAL.

We use the decision support tool to first identify optimal portfolios for each future given budgetary constraints. To do this, the tool maximizes the amount of water that would be supplied cumulatively by the projects in a given portfolio, for a given budget. This approach generally selects the projects that are most cost effective—those that deliver the most supply for a given level of investment.

As described in Figure 4-3., each project is designed to supply a particular amount of water to different areas of the city and in different seasons. However, the actual supply delivered may be less than designed if the future is relatively dry and there is insufficient water available, or if the future has relatively low demand and not all of the available supply needs to be delivered. We estimate these future-specific effects by simulating in WEAP each project individually in each future. Figure 6-1 shows box plots of each project’s monthly supply deliveries across the 300 futures for all of Lima, costs, and costeffectiveness. It shows that Atarjea WTP is more cost effective than other projects by several orders of magnitude, given its large deliveries and low cost. It also shows that Atarjea WTP, Autisha Res/Lurigancho WTP, Casacancha Res, and Pun Run Res/Chillón WTP have negative deliveries to the city in some futures. This is because multiple parts of the system draw upon the same limited sources of water. In some futures, these four projects may divert water from other parts of the system that would have had greater supply deliveries.

20
<!-- Start of picture text --> Project<br>Atarjeawte = |_| 1.6M +g t—<br>OM 5M 10M 15M 20M OM 500M 1,000M 0 1 2 3<br>Supply Delivered (m3) Project Cost (US $) Supply Delivered/ Cost (m3/$)<br>Lurganenowre Hill | 22m | __—$_ et<br>CancteTrans/wre |} ——{ = =—<br>CasacanchaRes _— {HH | 25m ———<br>Chancay GW eH | Ca aS —<br>cmattn! AH = bi<br><!-- End of picture text -->

<!-- Start of picture text --> JacaybambaRes _—«ff co lH<br>Lima Sur Desal Hl By 110m HI<br>Lurinwre | J2sm HI<br>PomacochaHuachipaRes wre/ = 1a|] ee Him<br>Pcnienwre ttt HE 20 ttt<br>S. AntonioRes /<br>Chillonwre = E a mH<br>Ventanila Desa! = fH ne WH<br>OM 5M 10M 15M 20M OM 500M 1,000M 0.00 0.05<br>Supply Delivered (m3) Project Cost (US $) Supply Delivered/ Cost (m3/$)<br><!-- End of picture text -->

<!-- Start of picture text --> 25" 75h<br>percentile percentile<br>| 4<br>t t t<br>Min Median Max<br><!-- End of picture text -->
<!-- Start of picture text --> Project Future With More Streamflow and More Demand Future With Less Streamflow and Lower Demand<br>Autisha Res / Demand Region<br>Lurigancho WTP BB Southom Lima<br>ome iorsBB Eastom Lima<br>Casacancha Res j | Hl Cortrai Lima<br>Chancay Res /<br>Huaral WTP<br>Chosica WTP /<br>Graton Tunnel<br><!-- End of picture text -->

<!-- Start of picture text --> Jacaybamba Res | |<br>Lima Sur Desal a |<br>Lurin WTP | |<br>Pomacocha Res /<br>Huachipa WTP<br>Pun Run Res /<br>Chillon WTP<br>S. Antonio Res /<br>Chillon WTP<br>Ventanilla Desal LCL]<br>-2M OM 2M 4M 6M 8M 10M 12M 14M = -2M OM 2M 4M 6M 8M 10M 12M 14M<br>Supply Delivered (Mm3) Supply Delivered (Mm3)<br><!-- End of picture text -->
* x + x Oo

<!-- Start of picture text --> 60% o o¢ —cee ¢ & 8 5 08 88 O<br>40% ~ 4» % or%e 0 + a)<br>o%,¢ : es o* o Y O<br>6 o ¥o O one)<br>jn FF, 523° FSO %<br>; 7 a x On xe SP o oe B®<br><!-- End of picture text --> <!-- Start of picture text --> 4© .20% o% o%? o CP xe 0 e)fe) @ ro<br>° e008& “Sars e O = 3 0°<br>om % 9% FOS 04° 068° °o,0<br>ete VV" oO og D 6 ee,<br>60% oe? 5 @ -o ors *<br>$ a * O 02% an<br>700 800 900 1000 1100 1 1400 1500 1600 1700 1800<br><!-- End of picture text -->
<!-- Start of picture text --> ——- | — (|-<br>100% Budget, Full wa<br>Project Feasibility<br><!-- End of picture text -->

<!-- Start of picture text --> 3<br>75% Budget, Full F i<br>Project Feasibility 5<br><<br>2<br>2<br>s<br>50% Budget, Full é ‘ns<br>Project Feasibility 5<br>=<br>40 60 80 100 0 1 2<br>90th Percentile of Monthly Met Demand (% of Total) Cost of Portfolio (USS, Billions)<br><!-- End of picture text -->
<!-- Start of picture text --> Project 0 4 §<br>X Unavailable Status in Portfolio a ¢§~ Pse 2~ ~= Fa4 = PySa ra]= Pr~ 3s<br>D@@ Available butAvailable and Excluded Included Ezs° pegee=aze$eee oF=w5=e2 =34°& 3>§5 rm82a2ao asece4eeSe$2 8358> é6e£5 5£ao 4=$8szesEs 5 rde0¢3c&® A==5es23Sec 6==s=6<br><£ <i35s °= °2 cs)= rs}= 5 oo£& Ss$ .3 3S azros aSs 5 aoap >5<br>100% Budget, Full Project<br>> Feasibility<br><!-- End of picture text --> <!-- Start of picture text --> =<br>a<br>5<br>75% Budget, Full Project<br>Feasibility<br>oa£<br>4<br>50% Budget, Full Project<br>Feasibility<br><!-- End of picture text -->
<!-- Start of picture text --> 60% Bi #e * *<br>x<br>= x * d * #<br>wo 40% * ha wx<br>3 * padet x x<br>2 x<br>: 20% % Nye* x% % x x x<br>i i Rex x 2%<br>: 0% a * * *%e% R<br><!-- End of picture text --> <!-- Start of picture text --> & ed % %<br>3 2% % *xX x %<br>§ * *<br>é x * x<br>* RX MR x<br>* x. Xx * * x<br> Bx x<br>60% a - *. 3 Pei* * yt x ¥ *<br>Current Demand: 855Mm3 SEDAPAL 2040 Demand Projection: 1,125 Mm3<br>700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800<br>Total Demand (Mm3)<br><!-- End of picture text --> i” x
<!-- Start of picture text --> Project a -5 =c<br>X Status in Portfolio 7 5 2 2 ¢ _ = 4 i -<br>Unavailable 5 ~ md ~ ~s ind S e a ~ Fy 3<br>DO@@ AvailableAvailable and but Excluded Included a5$CJ Pstt£a$9oD n=32Fy.£ 2a3$& =3>3< é3?3= peEE2¢egt2s sE=s 6a$5« a5c 288beeG5 e«3¢2 fa=25of5e2 5=ac&<br>'< <3$5§&§6 3& #2re) #252 286&6 s§$ 3EE §3 éz§8 &@:SE <2«as >§<br>100% Budget, Full Project<br>2 Feasibility<br>2<br>a<br>3<br>% 75% Budget, Full Project<br>$ Feasibility<br>¢<br>a<br><!-- End of picture text --> <!-- Start of picture text --> &<br>50% Budget, Full Project<br>Feasibility<br>2 100% Budget, Limited Project<br>a Feasibility<br>a<br>Ss<br>uo<br>u<br>3<br>eca Feasibility75% Budget, Limited Project<br>3<br>=<br>§ 50% Budget, Limited Project<br>Feasibility<br><!-- End of picture text -->
<!-- Start of picture text --> No Project |_| __ 0.00<br>urcoe |§ {| TT.<br>Project Feasibility<br>Project Feasibility :<br><!-- End of picture text -->

<!-- Start of picture text --> Project Feasibility<br>ct<br>100% Budget, Limited $<br>cs)<br><<br>75% Budget, Limited<br>3<br>a<br>50% Budget, Limited 5<br>40 60 80 100 0 1 2<br>90th Percentile of Monthly Met Demand (% of Total) Cost of Portfolio (US$, Billions)<br><!-- End of picture text -->
<!-- Start of picture text --> 9 x<br>x x<br>60% x xy TM 3 Zz BsF<br>x * %, %<br>o 40% * *X% a #* % wx> 4 x<br>3 % x FH, x adi<br>§ 20% * = R Noe % x %<br>¢ * R x &<br>. % % x xx “alk ¥%<br>Sa x x x % WEE *<br><!-- End of picture text --> <!-- Start of picture text --> . 9% x*| *% * x wy %<br>& -20% % % 4% 3¢ %,<br>5 x x” x *<br>5 % H*x 2% * a xx oy» *<br>40% % Ry & Ry * * *. *<br>-60%‘ x *"* we = x[OKOK x Regs %x ge x<br>Current Demand: 855Mm3 SEDAPAL 2040 Demand Projection: 1,125 Mm3<br>700 800 900 1000 ©1100 «=©61200 = 1300 1400 1500 1600 1700 1800<br>Total Demand (Mm3)<br><!-- End of picture text -->

Ww x
<!-- Start of picture text --> som | Region’ ~—|_—«|’_—«éRRegion2 Region 4<br>|| 50%,Budget 75%, and 100% | | 50% Budgetand Reliability cannot<br>or and Limited | Full Project be achieved<br>| Project Feasibility | Feasibility<br>i ‘a Full Project Feasibility<br><!-- End of picture text -->

<!-- Start of picture text --> &<br>£ ‘ ‘<br>ee<br>40% H<br>|current Demand: 855Mm3 |SEDAPAL 2040 Demand Projection: 1.125 Mm3<br>700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800<br>Total Demand (Mm3)<br><!-- End of picture text -->
<!-- Start of picture text --> a<br>5=<br>Project Status in Portfolio a 5 g z _ 4 os Ps & > a<br>X Unavailable =f = @ — 4 3 ef > $ a<br>O a 2o € 2 = e s 3 2 . ES 2 co é<br>Available but Excluded 5 ge e § © « ES 5 2 e a £ « 2 5 a<br>@@ Available and Included 3 ee[ey % § e & sc Z 5 ss § S< ==<br>3S £6 2 ri] 8 9 2s 4 er « 25 §<br>>8 3285 2§ 88 $f5 SE5a 28= &$ §&8 £§ §8$s SEa <2Ss =e§<br>< <5 o ° S 5= 66 5 er 3 ez &F sd ><br>=<br>33 75% Budget, Full Project<br><!-- End of picture text --> <!-- Start of picture text --> u<br>ar}<br>A°<br>a 50% Budget, Full Project<br>5 Feasibility<br>uw<br>oxi=6e=25 50% Budget, Limited— Project<br>E2¢ Feasibility<br>ae $<br><!-- End of picture text -->
<!-- Start of picture text --> Near-Term Mid-Term Long-Term<br>Mid-Term, Full Feasibility 50% Budget, Full Project Feasibility<br>* Cajiete Trans/WTP — Ane additional projects)<br>* Casacancha Res Climate and | __ 7 . a<br>* Chosica Res/Graton WTP Demand? = ago ca<br>i) >. Chancay Res/Hural WTP<br>Lt * Lima Sur Desal<br>~ + Jacaybamba Res<br>SS<br><!-- End of picture text -->

<!-- Start of picture text --> Feasibility? + Chancay GW<br>* Chancay Res/Hural WTP<br>* Near Term ++ Jacaybamba ResLima Sur Desal<br>Atarjea WTP & * Additional actions beyond Master Plan<br>+ Lurin WTP %<br>* Pomacocha o<br>Res/Huachipa Climat d<br>WTP imate an<br>Demand?<br>' | 80% Budget, Limited Project Feasibility<br>Mid-T Limited Feasibili = (No additional projects)<br><!-- End of picture text -->

<!-- Start of picture text -->
* Chancay GW —<br>* Chancay Res/Huaral WTP jj<br>* Lima Sur Desal i pgp New+ Additional actions beyondactions beyond Mater Master Plan Plan<br>Legend Feasibility undetermined wena” wns<br>Implement K<br>teres* MH) Ful feasibility énrier |<br>© projects Monitor uncertainties b Limited. feasibilitya demandless demandmore<br><!-- End of picture text -->
may exceed this. Therefore, it is very possible that the future may fall outside of Region 1. In this case, SEDAPAL will need to develop additional options to achieve water reliability because the 75% and 100% budgets with limited project feasibility do not confer additional benefit. The uncertainty range outside Region 1 represents the remaining vulnerability for SEDAPAL’s Master Plan and related projects if Cañete Trans/WTP and Chosica Res/Graton Tunnel are not possible.

This sequence of actions is the most robust way for SEDAPAL to implement its Master Plan and related projects when future demand, streamflow, and project feasibility are deeply uncertain.

# 8 Discussion and Conclusions

SEDAPAL, Lima’s water utility, has developed an aggressive US $2.7 billion Master Plan to improve long-term water reliability in the face of climate change and growing demand. This study draws upon state-of-the-art methods for decision making under deep uncertainty (DMU) to help SEDAPAL implement its Master Plan and related projects robustly, using an adaptive strategy of no-regret options.

## 8.1 Key Analytical Findings and Recommendations for SEDAPAL

This analysis helps answer several pressing questions about SEDAPAL’s Master Plan. First, is the Master Plan sufficient to ensure reliability in the face of deeply uncertain future climate change and demand? The analysis shows that the Master Plan and related investments can ensure reliability in many futures, including to levels of demand that SEDAPAL forecasts, provided that climate change or other drivers do not decrease streamflow significantly. However, it cannot ensure reliability in many plausible futures where demand is higher than projected, streamflow declines, or implementation of critical projects is not possible or delayed.

_On the other hand, are all proposed projects necessary to achieving reliability?_ The analysis shows that SEDAPAL can achieve the very same degree of water reliability more efficiently by implementing only ten of the fourteen projects. This results in a 25% cost savings. In more slightly favorable demand and streamflow conditions, a set of six projects costing 50% of the full budget can achieve reliability.

_Many projects are challenging to implement—how should considerations of project feasibility shape the city’s investment strategy?_ SEDAPAL identified three projects that may be more challenging to implement than the others. Our analysis shows that two of these three projects are critical to ensuring long-term water reliability. Without them, SEDAPAL may only be able to achieve reliability if future demand is less than projected, unless climate change very significantly increases streamflow. Therefore, early efforts to ensure timely implementation of these difficult projects are warranted.

_Ultimately, how should projects be prioritized? Which should be implemented now, which can be delayed until they are necessary, and what specific indicators would trigger their implementation?_ We have developed decision tree for SEDAPAL Master Plan implementation, as shown in Figure 7-3. It consists of a set of near-term, no-regret 33
investments that SEDAPAL can embark upon now; signposts of specific project feasibility, streamflow, and demand conditions SEDAPAL should monitor in the medium and long term; and sets of deferred projects that SEDAPAL should implement if the signposts are triggered. Framed in this way, the decision tree describes steps that can help SEDAPAL implement its Master Plan and related projects robustly:

- In the near term, SEDAPAL can embark upon three no-regret projects at a cost of US $794 million: the Atarjea water treatment plant, the Lurin water treatment plant, and the combined Pomacocha reservoir/Huachipa II water treatment plant. These projects are necessary for ensuring water reliability in the future regardless of project feasibility, streamflow, and demand conditions.

- In the midterm, SEDAPAL should take steps to enable the implementation of the Cañete groundwater project and the combination Chosica water treatment plant/Graton tunnel. These two projects are critical for meeting water reliability in many plausible futures but are also more difficult to implement. If they cannot be implemented, SEDAPAL may not be able to ensure water reliability at its projected levels of demand.

- SEDAPAL should implement the remaining projects adaptively, using the decision tree of no-regret, robust projects for guidance.

- Lastly, SEDAPAL should develop strategies that reduce the need for new water supplies, given that the Master Plan and related projects cannot ensure reliability under all plausible futures. Potential actions include demand management, increasing system efficiency and reducing losses, and developing non-traditional water sources such as reuse of treated wastewater for non-potable purposes (landscape, irrigation, commercial and industrial needs).

# 8.2 A Follow-On Analysis Could Help SEDAPAL Develop An Integrated Portfolio With Nearer Term Priorities

This project analyzes 14 traditional supply-side infrastructure investment projects for SEDAPAL. It reveals that supply-side investments alone are not enough and that efficiency, demand management, and non-traditional water supply projects may be necessary for robustly achieving long-term water reliability. In some cases, such investments may be more economically efficient than the proposed infrastructureintensive projects, may serve different regions of Lima that are otherwise underserved, or may offer additional flexibility if they have short lead-times and can be implemented quickly as needed. SEDAPAL recently submitted a collection of such projects to SUNASS, Lima’s water supply and sanitation regulatory authority.

A subsequent analysis of investments based on the methodology used in this work would help SEDAPAL prioritize among those investments as well. This would produce an integrated and efficient portfolio of traditional and non-traditional water supplies, efficiency measures, and demand management actions. Among its benefits, an integrated strategy offers more flexibility and responsiveness in the face of uncertainty.

This project focuses on water reliability by 2040, the horizon of SEDAPAL’s Master Plan. Yet SEDAPAL’s water reliability in the nearer term is also uncertain—the city is 34
growing fast and changing rapidly. Simultaneously, some projects may take many years to implement; others may take decades. It would be useful to prioritize further the noregret projects based on their impact on nearer-term water reliability and their implementation timeframes. This would help SEDAPAL achieve near-term water reliability goals and determine with greater precision when it must begin implementing each project to ensure it will be ready to supply water when it is needed.

# 8.3 SEDAPAL Could Use DMU Approaches In Other Planning Efforts

The future may bring severe multi-year droughts to Lima such as those experienced in other parts of the world. SEDAPAL could use DMU methodologies to update its multi-year drought management plan to complement its Master Plan implementation strategy, but with a focus on shorter-term actions. Such a drought management plan would involve a mix of short-term actions (such as water restrictions and using tailored operating rules for reservoirs and other infrastructure), mid-term actions (such as water banking and encouraging efficient end-use technologies), and long-term actions such as those assessed in this study. It would also involve a mix of near-term uncertainties (such as drought length, severity, and adoption of policies) as well as long-term uncertainties (such as climate change and demand).

Water reliability is intimately linked with water quality, as water pollution can limit the amount of water available for drinking, agriculture, and other uses. Lima’s water supply is vulnerable to a variety of pollution sources—upstream agricultural runoff, urban runoff, mining, and the waste streams of many different industries. This analysis highlights projects and water sources that are critical for water reliability, thus indicating where water quality is also of highest importance. Using the water quality functionalities built in to WEAP, or coupling water quality models to WEAP, SEDAPAL could expand this study to consider water quality issues. Specifically, SEDAPAL could use DMU methods to further understand what activities most threaten water quality and where, and develop a strategy of investment projects, operation rules, and regulatory policies that would help reduce pollution and mitigate the effects of pollution. Such a strategy could form the basis for collaboration with industry, regulators, and stakeholders concerned with water quality.

# 8.4 Lessons Learned

RDM and related DMU methods represent a new way of thinking about how nearterm actions can best manage future risks. Analysts are generally trained in predictive thinking and the decision makers they inform often expect predictive quantitative information. DMU methods answer a fundamentally different question. Rather than ask, “what will happen?” they allows analysts and decision makers to ask, “What should we do today to most effectively manage the full range of events that might happen?” This project holds two key lessons for future applications of DMU, which we discuss in turn.

35
# _8.4.1 The Value of Methodological Impartiality and Flexibility_

In our experience, analysts (including consultants and researchers from academia) approach a problem with a particular methodology and set of techniques in mind from the outset. It is often the one in which they are most skilled, whether it be benefit-cost analysis, scenario planning, or a particular DMU method. Many requests for project analyses (e.g., requests for proposals) encourage this implicitly by asking analysts to articulate a detailed technical approach to a problem based on just a short description and before meeting stakeholders.

This way of thinking can be limiting both for the project and for the analysts undertaking it. It can implicitly and explicitly limit analysts’ opportunity to draw upon the best methodology or methodologies for the problem, and hamper the evolution of the methodological approach as the project proceeds. The implicit limitation arises because little time and few resources are built into the project to experiment with methodologies and techniques. The explicit limitation arises because progress is often benchmarked against a scope of work that details a methodological approach that was developed before the project began. The result is that analysts may not draw upon the methods that are best suited to a problem.

This project had few preconceived expectations and restrictions, and we believe this contributed significantly to our being able to draw upon multiple methodologies and evolve the approach as the project went on. While we, too, began with the intent of using a particular approach (Robust Decision Making), we found mid-course that incorporating thinking from Decision Scaling, Adaptive Pathways, and interactive, adaptive decision support would serve SEDAPAL better than our original methodological framing alone. The flexibility and exploratory nature of this project allowed us to adapt to the changing needs of the project. Moreover, these changing needs became clear from an evolution in thinking on the parts of both the analytical team and SEDAPAL, as the former better understood the planning problem and the latter better understood the power of DMU methods. We urge future projects and analyses to embrace this approach still further by being more agnostic to the specific DMU technique, embedding methodological exploration and development into the project budget and timeline, and seeking opportunities for drawing upon multiple methodologies.

- _8.4.2 The Value of Participatory Analysis and the Stakeholder’s Commitment to Innovation_

In many projects, technical teams of external consultants and researchers perform an analysis for a client or stakeholder (i.e., SEDAPAL). The technical team produces analysis and findings and the stakeholder receives them. This allocation of roles runs the risk that the analysis will not truly meet stakeholders’ needs, as the literature on decision support notes (National Research Council, 2009). The analysis may not answer the questions that the stakeholder is asking; the stakeholder may not buy into the methodological process or findings; or the stakeholder may not be able to take intellectual ownership of methodology, tools, and outcomes.

SEDAPAL committed to this project and to the process of “deliberation with analysis” that is recognized as best practices for climate-related decision making 36
(National Research Council, 2009). First, it committed its technical staff to be a core part of the analytical team. They brought their expertise on Lima’s water needs and resources, SEDAPAL’s system, the regulatory and policy context in which they make decisions, and an understanding of the goals and aims of their Master Plan. The analytical team met weekly – sometimes daily – by phone. SEDAPAL’s staff validated the data, models, and findings at each step of the analysis. Their involvement has ensured that the analysis asks and answers the right questions, that the results are important and practical for SEDAPAL, and that SEDAPAL seeks to take ownership of the methods and tools developed in the analysis and employ them in their future planning activities.

Second, and perhaps even more importantly, SEDAPAL was willing to experiment with a new approach to long-term planning and embark on a complex analysis that differed greatly from approaches it used in the past. In embracing innovation, SEDAPAL also embraced the possibility that the analysis would produce complex or controversial findings that it would have to explain to stakeholders, including regulators and national authorities. Because of SEDAPAL’s boldness, we were able to ask and answer difficult questions about its Master Plan in the pursuit of long-term water reliability for Lima.

## 8.5 Conclusions

This project helps SEDAPAL understand its Master Plan more fully. It enables SEDAPAL to assess climate change threats without first needing to predict the future climate. It helps SEDAPAL identify projects that are particularly important for achieving water reliability. And, it reveals the strengths and vulnerabilities of its Master Plan concisely—as a specific set of conditions in which the Master Plan and related projects can achieve water reliability and in which additional actions may be necessary. It also helps SEDAPAL implement its Master Plan robustly by identifying near-term, no-regret projects that it can embark upon now, while pursuing additional actions adaptively as future conditions evolve.

# 9 References

Anandhi A, Frei A, Pierson DC, Schneiderman EM, Zion MS, Lounsbury D, Matonse AH. 2011. Examination of change factor methodologies for climate change impact assessment. _Water Resources Research_ **47** (3): n/a–n/a. DOI: 10.1029/2010WR009104.

Brown C. 2010. The end of reliability. _Journal of Water Resources Planning and Management_ **136** (2): 143–145.

Brown C. 2011. Decision-Scaling for Robust Planning and Policy under Climate Uncertainty. _World Resources Report Uncertainty Series_ . Washington DC.

Brown C, Ghile Y, Laverty M, Li K. 2012. Decision scaling: Linking bottom-up vulnerability analysis with climate projections in the water sector. _Water Resources Research_ **48** (9): n/a–n/a. DOI: 10.1029/2011WR011212.

37
Brown C, Werick W, Leger W, Fay D. 2011. A Decision-Analytic Approach to Managing Climate Risks: Application to the Upper Great Lakes. _JAWRA Journal of the American Water Resources Association_ **47** (3): 524–534. DOI: 10.1111/j.17521688.2011.00552.x.

Bryant BP, Lempert RJ. 2010. Thinking inside the box: A participatory, computerassisted approach to scenario discovery. _Technological Forecasting and Social Change_ . Elsevier Inc. **77** (1): 34–49. DOI: 10.1016/j.techfore.2009.08.002.

Bureau of Reclamation. 2012. _Colorado River Basin Water Supply and Demand Study: Study Report_ . .

Cervigni R, Liden R, Neumann JE, Strzepek KM. 2015. _Enhancing the Climate Resilience of Africa’s Infrastructure: The Power and Water Sectors_ . World Bank: Washington, D.C. DOI: 10.1596/978-1-4648-0466-3.

Coastal Protection and Restoration Authority (CPRA). 2012. _Louisiana’s Comprehensive Master Plan for a Sustainable Coast_ . Coastal Protection and Restoration Authority of Louisiana: Baton Rouge, Louisiana.

Dessai S, Hulme M. 2007. Assessing the Robustness of Adaptation Decisions to Climate Change Uncertainties: A Case Study on Water Resources Management in the East of England. _Global Environmental Change_ **17** (1): 59–72.

Groves DG, Bloom E, Lempert RJ, Fischbach JR, Nevills J, Goshi B. 2014a. Developing Key Indicators for Adaptive Water Planning. _Journal of Water Resources Planning and Management_ . American Society of Civil Engineers (10.1061/(ASCE)WR.19435452.0000471).

Groves DG, Fischbach JR, Bloom E, Knopman D, Keefe R. 2013. _Adapting to a Changing Colorado River_ . RAND Corporation: Santa Monica, CA.

Groves DG, Fischbach JR, Kalra N, Molina-Perez E, Yates DN, Purkey DR, Fencl A, Mehta V, Wright B, Pyke G. 2014b. _Developing Robust Strategies for Climate Change and Other Risks: A Water Utility Framework_ . Denver, CO.

Groves DG, Fischbach JR, Knopman D, Johnson DR, Giglio K. 2014c. _Strengthening Coastal Planning: How Coastal Regions Could Benefit from Louisiana’s Planning and Analysis Framework_ . Santa Monica, CA.

Groves DG, Knopman D, Lempert RJ, Berry SH, Wainfan L. 2008. Presenting Uncertainty About Climate Change to Water-Resource Managers. RAND Corporation.

Groves DG, Lempert RJ. 2007. A new analytic method for finding policy-relevant scenarios. _Global Environmental Change_ **17** (1): 73–85. DOI: 10.1016/j.gloenvcha.2006.11.006.

Groves DG, Sharon C. 2013. Planning Tool to Support Planning the Future of Coastal Louisiana. _Journal of Coastal Research_ . The Coastal Education and Research Foundation **67** : 147–161. DOI: 10.2112/SI_67_10.

Haasnoot M, Kwakkel JH, Walker WE, ter Maat J. 2013. Dynamic adaptive policy pathways: A method for crafting robust decisions for a deeply uncertain world. _Global Environmental Change_ **23** (2): 485–498.

38
Huber-Lee A, Swartz C, Sieber J, Goldstein J, Purkey D, Young C, Soderstrom E, Henderson J, Raucher R. 2006. _Decision Support System for Sustainable Water Supply Planning_ . Awwa Research Foundation: Denver, CO.

INEI. 2001. _Estimaciones y proyecciones de poblacion, 1950-2050, urbana-rural 19702025_ . .

INEI. 2013. _Estado de la Población Peruana 2013_ . .

IPCC. 2014. _Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change_ . Cambridge University Press: Cambridge UK and New York NY.

Joyce B, Purkey D, Yates D, Groves DG, Draper A. 2010. _Integrated Scenario Analysis for the 2009 California Water Plan Update, Volume 4_ . California Department of Water Resources: Sacramento, CA.

Kalra N, Hallegatte S, Lempert R, Brown C, Fozzard A, Gill S, Shah A. 2014. _Agreeing on robust decisions : new processes for decision making under deep uncertainty, Volume 1_ . Washington, D.C.

Lempert RJ, Groves DG. 2010. Identifying and evaluating robust adaptive policy responses to climate change for water management agencies in the American west. _Technological Forecasting and Social Change_ . Elsevier Inc. **77** (6): 960–974. DOI: 10.1016/j.techfore.2010.04.007.

Lempert RJ, Kalra N, Peyraud S, Mao Z, Tan SB, Cira D, Lotsch A. 2013a. Ensuring Robust Flood Risk Management in Ho Chi Minh City. The World Bank.

Lempert RJ, Popper SW, Bankes SC. 2003. _Shaping the Next One Hundred Years: New Methods for Quantitative, Long-Term Policy Analysis_ . RAND Corporation, MR-1626RPC: Santa Monica, Calif.

Lempert RJ, Popper SW, Groves DG, Kalra N, Fischbach JR, Bankes SC, Bryant BP, Collins MT, Keller K, Hackbarth A, Dixon L, LaTourrette T, Reville RT, Hall JW, Mijere C, McInerney DJ. 2013b. _Making Good Decisions Without Predictions_ . RAND Corporation.

McKay MD, Beckman RJ, Conover WJ. 1979. Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code. _Technometrics_ . Taylor & Francis **21** (2): 239–245. DOI: 10.1080/00401706.1979.10489755.

Mendler de Suarez J, Suarez P, Bachofen C, Fortugno N, Goentzel J, Gonçalves P, Grist N, Macklin C, Pfeifer K, Schweizer S, Van Aalst M, Virji H. 2012. _Games for a New Climate: Experiencing the Complexity of Future Risks_ . The Frederick S. Pardee Center for the Study of the Longer-Range Future, Boston University.

National Research Council. 2009. _Informing Decisions in a Changing Climate_ . Panel on Strategies and Methods for Climate-Related Decision Support, Committee on the Human Dimensions of Climate Change, Division of Behavioral and Social Sciences and Education: Washington, DC.

39
Plöger J. 2012. Gated barriadas: Responses to urban insecurity in marginal settlements in Lima, Peru. _Singapore Journal of Tropical Geography_ **33** (2): 212–225.

Purkey D, Huber-Lee A, David Y, Hanemann M, Herrod JS. 2007. Integrating a Climate Change Assessment Tool into Stakeholder Driven Water Management Decision-making Processes in California. _Water Resources Management_ **21** : 315–329.

Vince G. 2010. Out of the mist. _Science_ **330** (6005): 750–751. DOI: 10.1126/science.330.6005.750.

Walker WE, Rahman SA, Cave J. 2001. Adaptive policies, policy analysis, and policymaking. _European Journal of Operational Research_ **128** : 282–289.

WMO. 2015. _World Weather Information Service – Lima_ . .

Yates D, Galbraith H, Purkey D, Huber-Lee A, Sieber J, West J, Herrod-Julius S, Joyce B. 2008. Climate warming, water storage, and Chinook salmon in California’s Sacramento Valley. _Climatic Change_ **91** (3/4): 335–350. DOI: 10.1007/s10584-008-94278.

Yates D, Purkey D, Sieber J, Huber-Lee A, Galbraith H. 2005a. WEAP21—A Demand-, Priority-, and Preference-Driven Water Planning Model: Part 2: Aiding Freshwater Ecosystem Service Evaluation. _Water International_ . Routledge **30** (4): 501–512. DOI: 10.1080/02508060508691894.

Yates D, Sieber J, Purkey D, Huber-Lee A. 2005b. WEAP21—A Demand-, Priority-, and Preference-Driven Water Planning Model: Part 1: Model Characteristics. _Water International_ . Routledge **30** (4): 487–500. DOI: 10.1080/02508060508691893.

40
# Appendix A. Creating Demand and Streamflow Futures

We statistically generated 300 demand and streamflow futures, in addition to a future representing no change from today (855 Mm3 of demand and historical streamflows). A future is defined by a specific combination of values, one for each of the uncertainties. These futures are not predictions, and we do not assign any likelihood to their occurrence. Rather, we use them to stress test and better understand the behavior of the Master Plan. Using WEAP, we determined the 90th percentile of monthly met demand of each individual project and of the portfolios in each of these 300 futures.

In this analysis, to create the 300 futures we use Latin Hypercube Sampling (LHS). LHS is a randomized experimental design based on the higher dimensional generalization of a Latin Square. Our experience suggests LHC is more useful for multi-scenario analyses than other standard sampling methods, such Monte Carlo or full-factorial, because it provides the most complete exploration of the model's behavior over the input space for the fewest number of points in sample (Saltelli et al, 2000; Groves et al., 2014). To generate the futures, LHS

1. Takes as input the minimum and maximum values for each of the _u_ uncertainties and the number _n_ of futures desired,

2. Divides each uncertainty range into _n_ values at equally spaced intervals, and 3. Randomly generates _n_ futures such that each of the _n_ values for an uncertainty is included in exactly one future, and no future contains more than one value from the _n_ th interval.

To create these futures, we first need to identify appropriate ranges for the demand and streamflow uncertainties considered. We discuss these in turn.

## Generating Demand Futures

We used four sources of data of population and water use projections for the Lima Metropolitan Region: two from SEDAPAL (SEDAPAL, 2014),<sup>11</sup> one from Peru’s National Institute of Statistics and Informatics (INEA<sup>12</sup> ), and one from a study by the German Federal Enterprise for International Cooperation for SEDAPAL. Each source projected (i) the total population in Lima in 2040 and (ii) demand per capita, which includes system losses. We multiplied each demand per capita projection by each population projection, as illustrated in Table A-1. We defined the ranges by multiplying the minimum and maximum population projections with the minimum and maximum demand per capita, respectively. From this method, the estimated minimum and maximum are 730 Mm3 and 1790 Mm3, respectively (red values in Table A-1). For each of the 300 futures, we assume that the ratio of regional demand to total demand remained

> 11 Personal communication with SEDAPAL, January 2014.

> 12 http://www.inei.gob.pe/

41
constant. We also assume that demand remains constant throughout the year and therefore divide annual demand into twelve roughly equal monthly demands.<sup>13</sup>

**Table A-1:Ranges for plausible demand futures (m3/year), Total Lima, 2040**

|||||**Popul**<br>|**ation**||
|---|---|---|---|---|---|---|
||||**SEDAPAL**|**5-Year**<br>**Plan**|**INEI**|**GIZ**|
|||**x**|**_12.5M_**|**_13.3M_**|**_11.7M_**|**_17.3M_**|
|**Water**|**SEDAPAL**|**_83_**|1,031M|1,101M|966M|1,425M|
|**Consumption**<br>**(m3/p/year)**|**5-Year Plan**|**_84_**|1,049M|1,120M|982M|1,450M|
||**INEI**|**_63_**|787M|840M|**737M**|1,088M|
||**GIZ - high **|**_102_**|1,282M|1,370M|1,201M|**1,773M**|

# Generating Streamflow Futures

In contrast to demand, there is significant variation in intra-annual streamflow due to seasonal variation in precipitation at the headwaters of each of these basins in the Andes (Vera _et al._ , 2006). Because of its extensive reservoir storage capacity, however, the system is largely insensitive to month-to-month changes in streamflow and much more sensitive to changes in seasonal in streamflow. Therefore, we represent each streamflow future with two values, one for the dry season (March-November) and one for the wet season (December-February).

Our method of estimating future streamflow is to use a delta change factor to adjust historical monthly flows, consistent with the principles of Decision Scaling (Brown, 2011). Under this method, we apply a range of percent changes to historical precipitation data to obtain projections of future precipitation. The precipitation data is then used in a rainfall-runoff model to estimate future streamflow.

We specify the range of delta factors to exceed that from downscaled climate projections. We use estimates of downscaled precipitation changes from global climate models and historical streamflow data from SEDAPAL. Figure 4-1 summarizes the changes in annual and seasonal precipitation projected by 15 climate models in 3 emissions scenarios. We use the minimum and maximum changes as the ranges for an LHS-generated set of monthly precipitation deltas. That is, each of the 300 futures contains 12 unique values of monthly change factors. Without a rainfall-runoff model, we

> 13 In many parts of the world, variations in temperature and precipitation within a year drive intraannual variations in demand. Demand increases in hotter or drier periods and decreases in cooler or wetter periods. Lima has little variation in intra-annual demand because temperature is roughly constant throughout the year and precipitation is negligible. This is consistent with SEDAPAL’s observations of historical demand.

42
assume a 1:1 relationship between precipitation and runoff and apply these change factors directly to the 45-year monthly historical streamflow record. This results in 300 different sequences of 540 months of streamflow. We use these months of streamflow to emulate interannual variability at a single point in time, and not as a transient sequence of streamflow across time. That is, we treat all 540 months of data as samples from a single future climate regime in 2040, not as a 45-year sequence of streamflow from 2015 to 2060.

# Assessing Project And Portfolio Performance With Futures

We run each project and portfolio in 300 futures to test its performance under different uncertain demand and streamflow conditions. To evaluate a project or portfolio in a single future, WEAP calculates the monthly met demand as a percent of total demand in each of the 540 months. We post process these results to calculate our key metric—the 90th percentile of monthly met demand across the 540 months.

A key objective of the analysis is to summarize the conditions under which a portfolio achieves water reliability. While there are three exogenous demand and streamflow variables in each future, we describe performance in terms of demand and dry season streamflow because the system as currently envisioned is sensitive to changes in dry season streamflow but not wet season streamflow. This is because, in the wet season, SEDAPAL prioritizes filling of reservoirs to meet upcoming dry season demand, and streamflow in the wet season often exceeds reservoir capacity. Therefore most changes in wet-season streamflow do not affect water deliveries. In contrast, in the dry season, changes in streamflow can have immediate and direct impact on water deliveries and met demand. Changes in how SEDAPAL operates its system could change this sensitivity.

43