Assessing hydro-climatic uncertainties on hydropower generation

Mémoire

Fatemeh Movahedinia

Maîtrise en génie des eaux Maître ès sciences (M.Sc.)

Québec, Canada

© Fatemeh Movahedinia, 2014

iii

RÉSUMÉ

Dans le cadre de ce travail, nous avons quantifié l’impact des incertitudes hydrologiques et

climatiques sur la production hydroélectrique dans le bassin de la rivière Gatineau. L’approche mise

en œuvre repose sur des simulations climatiques, la modélisation hydrologique et l'optimisation de

l'exploitation des réservoirs.

Les résultats hydrologiques confirment ce que d’autres études ont déjà montré, à savoir un

hydrogramme plus contrasté, marqué par une fonte des neiges plus précoce et un volume de crue

plus important, suivi d’une saison estivale plus sèche que par le passé. En termes de production

d’énergie, cela se traduit par une production attendue d’énergie supérieure mais également plus

variable. Ce gain d’énergie se produit essentiellement à la fin de l’hiver-début du printemps et fait

suite aux précipitations plus importantes sur le bassin au cours de l’hiver. Compte tenu des

caractéristiques physiques du système (capacités de stockage et de turbinage), les modifications

du régime hydrologique entrainent des déversements supplémentaires, essentiellement pendant la

fonte des neiges.

v

ABSTRACT

This research quantifies the impact of hydrological and climatic uncertainties on hydropower

generation in the Gatineau River basin. The proposed approach is based on climate simulations,

hydrological modeling and optimization of reservoir operation.

Hydrological results from this study confirm what other studies have shown, a more mixed

hydrograph marked by earlier snow melting and greater flood volume, followed by drier summers

than in the past. In terms of energy production, this translates into an expected increase in energy

but also in a more variable production. This gain of energy mainly occurs in the late winter-early

spring and follows the higher rainfall in the basin during the winter. Given the physical

characteristics of the system (storage and turbine capacity), changes in the hydrological regime

entail additional spills, mainly during snowmelt.

vii

TABLE OF CONTENTS

RÉSUMÉ ................................................................................................................. iii

ABSTRACT..............................................................................................................v

TABLE OF CONTENTS ......................................................................................... vii

LIST OF TABLES ................................................................................................... ix

LIST OF FIGURES .................................................................................................. xi

ACKNOWLEDGEMENTS ..................................................................................... xiii

Chapter 1: Introduction ..........................................................................................1

1.1. Research Context ......................................................................................................................1

1.2. Research Objectives .................................................................................................................1

1.3. Thesis Outline ............................................................................................................................2

Chapter 2: Review of Studies on Climate Change Impacts on Water Resources and Hydropower ..................................................................................3

2.1. Climate Change Impacts on Hydrological Regime ....................................................................3

2.2. Climate Change Impacts on Hydropower Production ...............................................................4

Chapter 3: Methodology ........................................................................................7

3.1. General Description of the System ............................................................................................7

3.2. Description of Data ....................................................................................................................8

3.3. The Hydrological Model Calibration Procedure ...................................................................... 10

3.4. Hydro-Climatic Projection Chain ............................................................................................ 11

3.5. The Reservoir Optimization Problem ..................................................................................... 11

3.6. Performance Evaluation ......................................................................................................... 13

3.7. Assumptions ........................................................................................................................... 14

Chapter 4: Results and Discussion .................................................................... 15

4.1. Calibration and Validation Performance ................................................................................. 15

4.2. Climate Change Impacts on Climate Variables ...................................................................... 15

4.2.1. Precipitation and Temperature Impact ............................................................................ 15

4.3. Climate Change Impacts on Hydrological Regimes ............................................................... 16

4.3.1. Stream Flow Impact ........................................................................................................ 16

4.4. Climate Change Impacts on the Water Resource System ..................................................... 18

4.4.1. Reservoir Operation Rules .............................................................................................. 19

4.4.2. Energy Generation Impact .............................................................................................. 20

4.4.3. Measures of System Performance With Respect to Energy Generation ........................ 22

4.4.4. Uncertainty of Unproductive Spill Impact ........................................................................ 23

Chapter 5: Conclusions and Recommendations ............................................... 25

5.1. Concluding Remarks .............................................................................................................. 25

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5.2. Recommendations for Future Research................................................................................. 26

REFERENCES ...................................................................................................... 27

ANNEXES ............................................................................................................. 31

Appendix A: Hydrographs .............................................................................................................. 31

Appendix B: Mean Annual Water Storages ................................................................................... 32

Appendix C: Monthly Energy Generations .................................................................................... 32

Appendix D: Unproductive Spills ................................................................................................... 35

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LIST OF TABLES

Table 1: Calibration and validation performance (NSEsqrt [-]) of hydrological models at each subbasin ............................................................................................................................................ 15

xi

LIST OF FIGURES

Figure 1: (a) Map of the Ottawa River drainage basin with the Gatineau River. (b) The Gatineau River watershed (five subbasins). (c) Water resource system schematic ...........................................8

Figure 2: The process of hydrologic projection ....................................................................................9

Figure 3: Twenty hydrological models structure (adapted from Seiller et al., 2012) ......................... 10

Figure 4: Optimization of the reservoir operation problem (adapted from Labadie, 2004) ............... 12

Figure 5: Scatter plot of seasonal changes in precipitation and temperature between future and reference period for the Gatineau River watershed .......................................................................... 16

Figure 6: The total, hydrological models and natural climate of the overall mean flow evolution (between REF and FUT, %) for the Gatineau River watershed ........................................................ 17

Figure 7: The total, model and climate member uncertainty with the constructed member sets from five climatic members (twenty-five sets) for the Gatineau River watershed ..................................... 18

Figure 8: Baskatong reservoir monthly water storage for FUT (2041-2070) and REF (1961-1990) periods for different climate natural variability, including twenty lumped hydrological models ......... 19

Figure 9: The total, hydrological models and natural climate of the overall mean energy generation evolution (between REF and FUT, %) for the Gatineau River watershed ........................................ 20

Figure 10: (a) Box plot and (b) CDF plot of annual energy generation including five climate members per lumped conceptual model (150 values) for the Gatineau system ............................................... 21

Figure 11: The box plot of the average monthly relative changes of energy generation between FUT and REF conditions for Gatineau system for different climate members, each box plot includes twenty lumped conceptual models .................................................................................................... 22

Figure 12: Cumulative Distribution Function of reliability (a) and vulnerability (b) of the entire system regarding the energy generation for FUT and REF projections ........................................................ 23

Figure 13: The box plot (Top) and CDF plot (Bottom) of projected total annual energy spills for the entire system (blue ranges represent FUT and gray ones represent REF) ...................................... 24

Figure 14: Benefit foregone due to spillage losses (annual pattern) for the entire Gatineau system 24

xiii

ACKNOWLEDGEMENTS

I would like to express my gratitude to Professor Amaury Tilmant for his guidance and knowledge

through the period of preparation of the thesis plan and the research itself. I would also like to

extend my appreciation to Professor François Anctil for presenting this opportunity to me and his

continued support.

I am thankful to Professor Geneviève Pelletier for her comments that have definitely improved my

thesis.

Very special thanks to Gregory Seiller for sharing his expertise and knowledge of Matlab

programming. He always responded to my queries that really helped me have better knowledge

about my simulation results.

I extend my gratitude to Dr. Philipp Meier and Dr. Guilherme Fernandes Marques for opening my

mind to the interesting world of research and encouraging me to go ahead.

Thanks also to all the my supportive colleagues and friends, Gregory, Diane, Mabrouk, Youen

Jérôme, Flora, Islem, Anne, Slim, Darwin, Annie-Claude, François, Benoit, Antoine, Sepideh, Sonya

and Nicolas for providing a good atmosphere in our group and for useful discussions.

My greatest appreciation goes to my parents and my siblings who encouraged me in all my

decisions and made my studies possible. Without their support, love and the convictions they

passed on to me, I would perhaps never have made my way into a technical institute and a scientific

degree.

An important thanks goes to my brother Amir for always believing in me, for his endless

encouragement, unwavering support and timely advice. He always reminded me to take deep

breathes at times when I felt like exploding from all the pressure. Thanks to him for questioning me

about my ideas, helping me think rationally and for hearing my problems.

I also place on record, my sense of gratitude to one and all who, directly and indirectly, have lent

their helping hand in this venture.

Fatemeh uebec, Canada

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Chapter 1: Introduction

1.1. Research Context

In the twenty-first century, global climate change has become one the greatest challenges facing

human societies. Rising temperatures and changes in precipitation patterns are expected to be the

result of increased greenhouse gas emissions including carbon dioxide. Because various human

activities depend on water, it is expected that the water sector will play a pivotal role in adaptation

strategies (Harrison and Whittington, 2002).

In the energy sector, the production of hydroelectricity will be affected if the hydrological regime of

the rivers is altered by climate change. Although there are many works devoted to climate change

impacts on hydropower generation, some research questions have so far been little explored,

especially those related to some sources of uncertainty. In the context of climate change, there are

different sources of uncertainty related to climate (global climate simulation, future levels of gas

emissions and natural variability) and to the modeling techniques and approaches used in realizing

the impact study (hydrological model, calibration method, downscaling method and adaptation

strategies). To the best of our knowledge, uncertainties associated with the hydrological model

structure and the climate natural variability have not been extensively studied.

Typically, the modeling of climate change impacts on a given water resources system requires a

large number of phases, each bringing its own uncertainties. Understanding the relative contribution

of each source of uncertainty is therefore a prerequisite to the understanding of climate change

impacts and the design, analysis and implementation of adaptation strategies.

The scope of this research is to explore the potential impacts of climate change by considering the

sensitivity analysis (the choice of hydro-meteorological tools) on reservoir operation, including

hydropower production. More specifically, this work confronts uncertainties related to natural climate

variability and to lumped hydrological model structures in the context of climate change impacts on

a specific water resources system in Quebec.

1.2. Research Objectives

This work investigates possible impacts of climate change on the hydrological regime of the

Gatineau River basin and assesses the relative contribution of the uncertainties that come from the

lumped hydrological model structures and the climate natural variability.

The specific objectives are to:

1. Carry out a hydrological modeling of the Gatineau River basin under a reference and

future periods to assess the alteration of the flow regime due to climate change;

2

2. Explore the uncertainty of the hydrologic processes by repeating (1) for various

hydrological model structures (up to twenty);

3. Explore the uncertainty associated with the natural variability of the climate by repeating

(2) for five climate members provided by Environment Canada; and

4. Assess the impact of climate change on the production of energy using an optimization-

based reservoir operation model of the cascade of power stations in the Gatineau River

basin.

1.3. Thesis Outline

This thesis contains five chapters organized as follows:

Chapter two describes an overview of the literature useful in this area of study.

Chapter three deals with the material and methods adopted for this study. It gives a brief description

of the case study and the data used. Also, this chapter provides a brief description of the sources of

the uncertainty for assessing climate change impacts on hydrological regimes and hydropower

production.

Chapter four discusses the results of the impact of climate change on hydrological regimes as well

as the impacts on hydropower production and reservoir operation. Particularly, analysis of the

potential prediction uncertainties induced by hydrological model structures and climate natural

variability is discussed in this chapter. This chapter highlights the results of hydropower simulations

and the changes on hydropower systems that could be expected in the future by considering

various indicators.

And finally, Chapter five contains a summary of the main results, the conclusions and

recommendations for future research work.

3

Chapter 2: Review of Studies on Climate Change Impacts

on Water Resources and Hydropower

Many studies have analyzed the hydrological impacts of climate change. However, little attention is

paid to the uncertainties associated with the modeling process. Moreover, the number of studies

dealing with the impact of climate change on the operation of water resource systems is also

limited.

2.1. Climate Change Impacts on Hydrological Regime

In recent years, a great effort has been devoted to investigate the impact of climate change on the

hydrological regime in different regions of the world. The projection of future river flows is affected

by different sources of uncertainty in the hydro-climatic modeling chain such as gas emission

scenarios, global climate modeling, downscaling and hydrological modeling.

Seiller and Anctil (2013) investigated the impacts of climate change on the hydrological regime of a

Canadian river addressing the uncertainties that come from lumped hydrological modeling

structures and natural climate variability, illustrated by several members from the same global

model, potential evapotranspiration formulations and snowmelt modules. They found that natural

climate variability is a major source of uncertainty followed by potential evapotranspiration formulas,

hydrological model structures and snow modules.

Minville et al. (2008) applied ten climate projections that were obtained from five general circulation

models (GCMs) and two greenhouse gas emission scenarios (GHGES). They worked on a

Canadian river and they noticed that the largest uncertainty came from GCM related to downscaling

and hydrological modeling.

Ludwig et al. (2009) investigated hydrological model complexity and its response under climate

change by employing the distributed model PROMET, the semi-distributed model HYDROTEL and

a lumped model (HSAMI). Authors mentioned that the levels of complexity of the hydrological

models play a considerable role when evaluating climate change impacts.

Muerth et al. (2012) used a complex model chain consisting of four different global climate models,

downscaled by three different regional climate models, an exchangeable bias correction algorithm,

a separate method to scale RCM outputs to the hydrological model scale and several hydrological

models with different level of complexity to assess the impact of different hydro model concepts

while Kay et al. (2006) compared six different sources of uncertainty: gas emission scenarios, global

climate modeling (GCM), climate downscaling, natural variability (which is disclosed by calculating

GCM runs from slightly modified initial conditions), and hydrological model structures and

parameters.

4

Poulin et al. (2011) investigated model structure uncertainty and parameter equifinality in climate

change studies at a Canadian site. They concluded that model structure uncertainty has more

important role in determining the impact of climate change than parameter uncertainty.

Velázquez et al. (2013) studied the impact of climate change on water resources with the

incorporation of different hydrological models to investigate the uncertainty that arises from

hydrological models for two catchments; one located in Southern Quebec (Canada) and one in

Southern Bavaria (Germany).They used different hydrological indicators: an overall mean flow, the

2-year return period low flow, the 2-year return period high flow and the Julian day of spring-flood at

half volume. They noticed that the choice of model significantly affects the climate change response

of selected hydrological indicators, especially those related to low flows.

Whitfield and Cannon (2000) investigated climatic variation and hydrology in Canada 1976-to 1995.

In general, temperature was warmer in recent periods, especially in the summer and fall and more

pronounced for western and eastern parts of Canada. Southern portions of Ontario, Quebec, Nova

Scotia, and the Yukon showed warmer temperatures in January as well as in June and July.

Regarding precipitation, a decrease will be more widespread in northern Canada and also south of

Canada but there are some exceptions such as minimal decreases in precipitation in Southern

Quebec, Eastern Newfoundland, and southern portions of Southern Ontario. They found

hydrographs with an earlier spring flood, higher winter flow and lower summer flow.

Along the same lines, many earlier studies (Gleick and Chaleki, 1999; McCabe and Wolock, 1999;

Hamlet and Lettenmaier, 1999; Lettenmaier et al., 1992; Lettenmaier and Gan, 1990) confirmed that

the recognized shifts of peak discharge in seasonal runoff are associated with (or caused by)

reduced winter snow accumulation, earlier peak snowmelts, higher winter runoff, higher

evapotranspiration, and thus, lower summer and autumn stream flows.

2.2. Climate Change Impacts on Hydropower Production

Global climate change is expected to have a strong impact on water resources (Intergovernmental

Panel on Climate Change (IPCC), 2007). Hydropower production, depending on river flow, is

sensitive to total runoff (quantity and timing). Therefore, an increase in climate variability even with

no change in the average annual runoff could impact hydropower output and performance.

Canada produces sixty percent of its electricity from hydropower. It is the third largest hydro

generator in the world. Quebec, British Columbia, and Ontario generate the majority of hydroelectric

power in Canada. In Quebec, more than ninety-five percent of electrical generation comes from

hydroelectric sources (EIA, 2010).

Hydropower resource potential depends on factors such as topography, the volume, the variability

and seasonal distribution of runoff. Not only are these regionally and locally determined, but an

5

increase in climate variability, even with no change in average runoff, can lead to reduced

hydropower production unless more reservoir capacities are built and operations are modified to

account for the new hydrology that may result from climate change (Kumar et al., 2011).

IPCC (2007b) and Bates et al. (2008) found both positive and negative regional effects on

hydropower production (on different continents), mainly following the expected changes in river

runoff. For instance, hydropower production in Northern Quebec would likely benefit from greater

precipitation and more open-water conditions, but hydropower plants in Southern Quebec would

likely be affected by lower water levels (Ouranos, 2004). In North America, hydropower production

is known to be sensitive to total runoff, to its timing, and to reservoir levels (Bates et al. 2008).

Minville et al. (2009a) employed one distributed hydrological model and three climate models forced

with SRES A2 green house gas emission scenarios to investigate the management adaptation

potential of a Canadian river in light of climate change. They compared all the changes in three

future projection periods from 2010 to 2099. They analyzed the adaptation of water resource system

management by considering the trends of reservoir levels, hydropower production, power plant

efficiency and spillage. In general, they found that the significant changes in hydropower plants are

linked to changes in hydrological regimes. With regards to the efficiency of power plants, a

reduction in 2050 and 2080 was shown. However, the efficiency increases in 2020. These changes

are statistically significant for power plants in the context of annual mean flow efficiency. Also, they

inferred that changes in the annual and seasonal mean unproductive spills were significant for

nearly all of the future periods. In their next piece of work, Minville et al. (2009b) considered thirty

climate projections including five climate models, two greenhouse gas emission scenarios and three

temporal horizons with one lumped conceptual hydrological model over the same site in Quebec,

Canada. They concluded that the changes in hydrological regimes (annual mean flow) could directly

impact hydropower. But seasonal flow changes show different trends that do not involve the same

trajectory for seasonal hydropower, especially in the spring. They revealed that unproductive spills

increased from upstream to downstream because of low storage capacities in upstream reservoirs

with the increased flow.

Iimi (2007) noticed that there are three main impacts of climate change on hydropower projects.

First, changes in hydrological regimes and hydropower operations have to be reconsidered to take

into account hydrological periodicities or seasonality change. Second, changes in climate variability

may lead to floods or droughts or other extreme climate events. Finally, changing hydrology and

possible extreme events increase the impact of sediment risks and measures. An unexpected

amount of sediment will lower turbine and generator efficiency, resulting in a decline in energy

generated.

Many studies have addressed the effects of climate change on hydropower generation in California,

but such analyses have been largely restricted to large lower-elevation water supply reservoirs

6

(Lund et al. 2003; VanRheenen et al. 2004) or a few individual hydropower systems (Vicuna et al.

2008 and 2009).

Raje and Mujumdar (2010) evaluated climate change impacts on multi-reservoir performances and

adaptive policies for the future. They used three climate scenarios A2, A1B, and B1, and two GCMs:

CGCM2 and MIROC3.2 with two future time slices, 2045-2065 and 2075-2095. They used

stochastic dynamic programming to drive optimal policies in order to maximize reliability with

respect to multi-reservoirs for flooding, hydropower and irrigation. They found that the mean

monthly storage will decrease as a result of the hydrologic impacts of climate change. Climate

changes have negative impacts on mean monthly energy generation, especially for monsoon

month. In this work, four performance indices such as reliability, resiliency, vulnerability and deficit

ratio power were calculated for standard operation policy and stochastic dynamic programming

operation. They concluded that reservoir performance was adversely impacted under climate

change. Madani and Lund (2009) investigated the potential impacts of climate change on high-

elevation hydropower generation in California using the application of the Energy-Based

Hydropower Optimization Model (EBHOM) that is based on energy flows and storage instead of

water volume balances.

Scheafli et al. (2007) evaluated the impact of climate change on hydropower production and

quantified modeling uncertainty by several indicators in the Swiss Alps. They presented their results

through three types of modeling uncertainties such as climate scenario, hydrological, glacier

evolution and management modeling uncertainty. They showed that climate change potentially has

a statistically significant negative impact on system performance.

Carless and Whitehead (2013) studied the impacts of climate change on hydroelectric generation

for a system in Mid Wales (the Plynlimon Flume catchment). They applied the IHACRES approach

with two future periods covering the 2020s (2010-2039) and the 2080s (2070-2099). The climate

change impacts on hydrology show shifts in flow regimes, especially during summer and winter

conditions. In their study, it is noted that despite large changes in seasonal flow, the annual output

of energy generation is almost unchanged due to the loss of energy generation in the summer that

is compensated by increased power generation in winter months. Also, these authors suggested

that planners and developers of hydropower plants might consider changing the size of their plants

to take advantage of higher flows in winter months in future periods.

7

Chapter 3: Methodology

Introduction

The analysis of climate change impacts on hydrological regimes and water resource systems is

carried out by simulating the system behavior for the reference (control) period and for a future

horizon. This simulation can be accomplished in three stages with the aid of three types of models.

First, a climate model is required to simulate the climate variables (future local climate variables).

Then, a hydrological model is used to transform these climate variables into reservoir inflows.

Finally a reservoir management model is employed to simulate the operation of the system using

the inflows generated by the hydrological model.

In addition, to gain key information about the performance of hydrological regimes and water

resource management, Overall Mean Flow (OMF), Reliability and Vulnerability (RV) indicators are

applied in the context of climate change. The analysis of the performance of the hydropower system

relies on energy generation, firm energy, unproductive spills, and reservoir drawdown-refill cycles.

This chapter presents the case study and the water management model developed to analyze

potential climate change impacts on a water resources system.

3.1. General Description of the System

The Gatineau River watershed is located in the southwestern portion of the province of Quebec.

The Gatineau River rises in lakes north of the Baskatong reservoir and flows south to join the

Ottawa River (Figure 1.a). The main river channel length is about 400 kilometers. The watershed’s

area is about 23,700 km², which covers parts of the administrative regions of Abitibi-

Témiscamingue, Lanaudière, Laurentides, Mauricie and Outaouais. The Gatineau River watershed

is subdivided into five subbasins: Baskatong (12540 km²), Cabonga (2201 km²), Maniwaki (5040

km²), Paugan (2700 km²) and Chelsea (1200 km²) from upstream to downstream (Figure 1.b).

Cabonga, is the most challenging for simulation, since lakes and reservoirs occupy a large portion

of the area: the gauging curve used to convert water levels to stream flow can be affected by wind

variations and generate some errors in measurement (Boucher et al., 2011).

The Gatineau River watershed flows are highly regulated by reservoirs, and the highest flows occur

usually in spring due to snow melts. In general, the watershed is characterized by a continental

climate. The climate is warm and humid during the summer, and generally wet, cold and snow

covered in the winter. Still, the climatic variation is significant between the north and the south

regions of the area. The watershed is used mainly for hydropower production. It contains three

hydro power plants that managed by Hydro-Quebec with the respective installed capacity of 50, 219

and 148 MW for the Baskatong, Paugan and Chelsea power plants (Figure 1.c).

8

3.2. Description of Data

Historical data such as hydrological and meteorological data are provided by the Centre d’expertise

hydrique du Québec (CEHQ). Climatic data comes from the Canadian Global Climate Model

(CGCM version 3, IPCC, 2009), fed with the SRES A2 scenario (Nakicenovic et al., 2000). Future

climate projections need to be spatially downscaled from low-resolution GCMs to the watershed

scale (Maurer and Hidalgo, 2010). Data were dynamically downscaled by the Canadian Regional

Climate Model (CRCM version 4.2.3, Christensen et al., 2004; Fowler et al., 2007). Consortium

Ouranos provided downscaled climatic data for the reference simulation (REF, 1961-1990) and

future projection period (FUT, 2041-2070). The climate natural variability is depicted by five climatic

members (A21 to A25). They were bias-corrected to reduce deviations between REF and

observations on precipitation and temperature. Monthly correction factors were computed for each

climatic member on the thirty-year monthly average minimum and maximum temperatures and were

applied on each member in order to keep their respective variance. Precipitation was corrected

using the LOCal Intensity (LOCI) scaling method (Schmidli et al., 2006), adjusting mean monthly

precipitation in terms of frequencies and intensity over thirty years.

Hydro-meteorological Data

The historical hydro-meteorological data such as daily precipitation (mm), maximum and minimum

temperature (˚C), and observed discharge (mm) were available for the Gatineau River watershed.

The historical time series cover years 1969-2005. The Canadian Regional Climate Model (CRCM)

produced reference and future climate data.

Reservoir

Plant

Cabonga

Baskatong

Paugan

Chelsea

b) c) a) Figure 1: (a) Map of the Ottawa River drainage basin with the Gatineau River. (b) The Gatineau River

watershed (five subbasins). (c) Water resource system schematic

9

Precipitation is the primary variable in determining hydrological characteristics and changes in

quantity, timing and intensity that will have an important effect on many aspects of the hydrological

cycle including the alteration of river flows. The balance between water entering the catchment as

precipitation and leaving through evapotranspiration determines the quantity and timing of

catchment runoff. The latter eventually becomes the river flow changes in both precipitation and

PET, which are expected as a result of climate change, and changes in river flow are also

anticipated. Figure 2 schematically shows the procedure of simulations, applying the hydro-climatic

chain.

Conceptual Hydrological Model

Lumped conceptual rainfall runoff models have been widely used in hydrology for many years.

Hydrological models convert climatic inputs into runoff and are used in water resource design and

operation (Lan Anh, 2008). In this study, twenty lumped conceptual hydrological models are used.

Their selection is mainly based on known performance and structural diversity.

Figure3 shows the structural diversity of the twenty lumped hydrological models used in this study.

This Figure embodies the "inputs" (precipitation, melting, and evapotranspiration) and "model

output" (flow rate), as well as different types of "storage" such as surface or interception store (Sf),

soil storage (S), lower soil or root zone storage(Ss), overland flow routing storage (RS), interflow

(delayed) routing storage (RSs), groundwater storage, which can be assimilated in some cases for

a slow routing (N) and main routing storage that can be assimilated to a quick routing (R).The

number of free parameters varies between four and ten, and the number of storage, between two

and seven.

Figure 2: The process of hydrologic projection

Output

PET

Snow Module

Hydrological

Models Calibration/

Simulation

QREF, QFUT

P

T

Futu

re a

nd R

efe

rence

Clim

ate

Va

ria

ble

s

10

Potential Evapotranspiration Formulation and Snow Module

In this study, one snow module and one potential evapotranspiration (PET) formula are considered.

Snow accumulation and melt are simulated with the CemaNeige (N1) snow accounting module

(Valery, 2010) based on the degree-day approach. There are two free parameters in this module:

the melting rate and the snowpack thermal state coefficients. The PET formula selected for this

study is Oudin (E23).It is a radiation-based formula that uses only the temperature as an input.

Investigation of the sensitivity of the hydrological simulation to snow modeling and potential

evapotranspiration formulas is beyond the scope of this work, but they remain sources of

uncertainty in the modeling process.

3.3. The Hydrological Model Calibration Procedure

In this work, the Split Sample Test (SST) is used for calibration and validation procedures. The Split

Sample Test, according to Klemeš ( 986a), is defined as a calibration based on one time period

and a validation, based on another period. We used the period of 1969 to 1988 for calibration and

1988 to 2005 for validation, based on hydrological years.

The automatic optimization algorithm used to calibrate parameter values is the shuffled complex

evolutionary algorithm (SCE-UA) (Duan, 2003). The mean of the square error calculated on the

root-squared flows was selected as an objective function presented as:

√∑ √ √

(1)

Figure 3: Twenty hydrological models structure (adapted from Seiller et al., 2012)

11

Where N is the total number of observations; Qobs is the observed value and Qsim is the simulated

value.

The efficiency of the models for both periods is discussed in terms of the NSEsqrt criterion (Nash

and Sutcliff, 1970), a measure of agreement between observed and simulated values. NSEsqrt

values range from negative infinity to 1, the latter indicating a perfect model simulation, and is

calculated as:

∑ √ √

∑ √ √ ⃑⃑ ⃑⃑ ⃑⃑ ⃑⃑ ⃑⃑ ⃑⃑ ⃑⃑

(2)

3.4. Hydro-Climatic Projection Chain

It is worth underlining that the impacts of climate change on water resources and hydrological

regimes encompassing different sources of uncertainty. In this study, the hydro-climatic chain is

constructed with twenty lumped conceptual models and five climatic members for different

projections along with one PET formula and one snow module for the Gatineau River watershed

with five subbasins. The projections consist in a large number of time series for each subbasin

which lead to 100 (twenty models× five climatic members) projections for the reference period (REF,

1961-1990) and 100 (twenty models× five climatic members) projections for the future period (FUT,

2041-2070).

In addition, the projection results will be transferred to the optimization tools with a total simulation

of 200 runs for FUT and REF in the management model, to investigate the impact of climate change

on hydropower production.

Due to the limited number of climatic members, we considered twenty-five sets (permutation of five

climatic members). The advantage of this series construction is that it takes into account the natural

variability of the climate. In this case, the number of values for the total uncertainty is 500

realizations (twenty models× twenty-five member set).

3.5. The Reservoir Optimization Problem

Reservoir optimization models are common for guiding reservoir operations under different

conditions. Two major approaches exist for optimization, deterministic or stochastic depending on

whether the hydrologic uncertainty is considered or not. An extensive review of available techniques

for optimization and simulation can be found in Labadie (2004).

Hydrologic uncertainty in reservoir optimization can be considered by either explicit (ESO) or implicit

(ISO) stochastic optimization methods (Tickle and Goulter, 1994). ESO integrates probabilistic

descriptions of the input variables (reservoir inflows), thus directly accounting for uncertainty when

optimizing the policies. Instead, ISO evaluates operation policies on a number of equally likely input

12

time series of river discharges, thus indirectly including uncertainty. Theoretically, the operation

policies obtained by applying ISO are valid only for the input time series used. However, compared

to ESO, ISO can be formulated to represent an optimization problem more closely (Karamouz and

Houck, 1987; Rani and Moreira, 2009) and yields lower computational costs (Roefs and Bodin,

1970). In this study, the reservoir operation problem is solved using the ISO approach.

The reservoir operation problem is a sequential decision making problem as illustrated in Figure 4.

Where is the vector of inflows during period ; is n-dimensional set of control or decision

variables during period ; is the vector of volume in storage at the beginning of time period , is

the length of the operational time horizon, and is the cost/benefit of system operation during

period .

This sequential decision-making problem can be solved by trying to maximize (minimize) the sum of

benefits (cost) of the system over T periods:

∑

(3)

Where is a terminal value function and is discount factor for determining the present

values of future benefits (or costs).

The most important constraints are the mass balance equation (Eq.4), the upper and lower bounds

on storage (Eq.5) and on releases (Eq.6):

(4)

(5)

(6)

Where is the system connectivity matrix; is the vector of spills; is the vector of evaporation

losses; is the vector of demands, diversions, or depletions from the system; and are vectors

Figure 4: Optimization of the reservoir operation problem (adapted from Labadie, 2004)

13

with the minimum and maximum storage volumes, respectively; and are vectors with the

minimum and maximum releases, respectively.

Selection of the Optimization Model

To solve the optimization problem (Eq.3-Eq.6), we use the ResPRM model (O’Connell and Harou,

2011). ResPRM (the Prescriptive Reservoir Model) is a reservoir optimization software that can be

used in conjunction with other HEC (the Hydrologic Engineering Center) models. This model uses

deterministic optimization to provide a set of optimal storage allocations and reservoir releases. For

the evaluation of the impact of climate change on hydropower production, we focused on reservoirs

that are currently used to produce power.

This tool is used to optimize the system behavior under the observed climate for the control period

of 1961-1990 and under future climate scenarios for the period of 2041-2070 for five climatic

members and twenty models.

Note that the time-series data used by the model must be in DSS (the Data Storage System) format

which is a database system designed to efficiently store and retrieve scientific data that is typically

sequential. HEC’s Data Storage System (HEC-DSS, 2009) is used for storage and retrieval of the

input and output time-series for this model.

3.6. Performance Evaluation

Two risk criteria, reliability and vulnerability, are used to analyze the performance of the system with

respect to preestablished thresholds (Simonovic and Li, 2004 and Hashimoto et al., 1982).

The main interest of this present application focused on electricity production. A threshold ( )

corresponds to firm energy which is defined when 90% of the energy generation probability

demands is met.

Assuming satisfactory values ( ) in the time series are those equal to or greater than some

threshold , then:

Index signifies a satisfactory or unsatisfactory state of the system. The reliability indicator

can be defined as:

∑

Where, T is the total number of simulated time periods.

(8)

(7)

14

The vulnerability is defined here as the maximal difference between the reference ( ) and the

calculated values of a certain variable of energy generation ( ). Hence, it is computed as:

{

[ ]

3.7. Assumptions

Four key assumptions are made in this study: (1) the operating rules generated by optimization are

adapted to the new hydrological regime; (2) the twenty models cover structural uncertainty; (3) five

climatic members are sufficient to represent the natural climate variability; and (4) by employing the

Implicit stochastic programming the system’s performances are overestimated.

(9)

15

Chapter 4: Results and Discussion

The results that pertain to the entire system are discussed in the body of this chapter. The rest of

the results that refer to more specific aspects (subbasins and power plants) are found in the

Appendix.

4.1. Calibration and Validation Performance

Performance results for calibration and validation of the twenty lumped conceptual models for the

five subbasins are synthesized in Table 1. These results illustrate the difficulty of recognizing a

single hydrological model that offers good performance (based on the structure of the models and

their features). The result for each subbasin is promising for the NSEsqrt coefficient (as discussed

in section 3.3).

Table 1: Calibration and validation performance (NSEsqrt [-]) of hydrological models at each subbasin

Sub-basin Baskatong Paugan Chelsea Maniwaki Cabonga

Model Cal Val Cal Val Cal Val Cal Val Cal Val

Md1 0.76 0.86 0.76 0.78 0.72 0.70 0.66 0.65 0.44 0.48 Md2 0.76 0.82 0.76 0.74 0.72 0.67 0.76 0.76 0.36 0.45 Md3 0.75 0.86 0.75 0.72 0.69 0.60 0.68 0.61 0.44 0.48 Md4 0.69 0.84 0.69 0.65 0.65 0.56 0.61 0.55 0.42 0.46 Md5 0.76 0.87 0.76 0.78 0.74 0.70 0.74 0.79 0.45 0.52 Md6 0.76 0.86 0.76 0.71 0.72 0.62 0.75 0.71 0.38 0.44 Md7 0.75 0.80 0.75 0.72 0.71 0.60 0.69 0.64 0.39 0.43 Md8 0.72 0.75 0.72 0.65 0.70 0.60 0.70 0.66 0.41 0.40 Md9 0.76 0.86 0.76 0.73 0.70 0.61 0.64 0.65 0.43 0.50 Md10 0.71 0.86 0.71 0.78 0.66 0.70 0.61 0.66 0.40 0.51 Md11 0.76 0.86 0.76 0.73 0.74 0.67 0.72 0.73 0.42 0.48 Md12 0.76 0.84 0.76 0.73 0.66 0.63 0.69 0.64 0.35 0.38 Md13 0.77 0.86 0.77 0.77 0.73 0.67 0.76 0.80 0.42 0.45 Md14 0.73 0.83 0.73 0.74 0.70 0.66 0.73 0.73 0.45 0.49 Md15 0.74 0.85 0.74 0.73 0.72 0.63 0.64 0.60 0.42 0.48 Md16 0.77 0.87 0.77 0.76 0.74 0.63 0.70 0.66 0.41 0.50 Md17 0.76 0.85 0.76 0.75 0.73 0.67 0.75 0.80 0.46 0.50 Md18 0.75 0.82 0.75 0.79 0.72 0.67 0.68 0.70 0.44 0.51 Md19 0.76 0.85 0.76 0.80 0.69 0.70 0.66 0.74 0.44 0.51 Md20 0.77 0.86 0.77 0.79 0.74 0.70 0.76 0.78 0.42 0.49

Highest Val Perf

Md16,Md5

Md19

Mds(1,5,10,19,20)

Md17,Md13

Md5

Lowest Val Perf

Md8

Md4,Md8

Md4

Md4

Md12,Md8

4.2. Climate Change Impacts on Climate Variables

4.2.1. Precipitation and Temperature Impact

Seasonal variability of climate data is presented in a scatter plot in Figure 5, for the Gatineau River

basin. This Figure shows the changes (FUT and REF periods) in precipitation as a function of

changes in temperature projected by each climatic member. For the entire system, there is inter-

member variability that is more pronounced in the winter season (December to February, DJF) for

which the increases in temperature are stronger compared to the other seasons. This is

characterized by a larger dispersion on the scatter plots.

In a broad sense, we can see that there will be notably more water in winter from precipitation

increases and less water in summer. In general, for precipitation as well as temperature, the same

16

tendency is expected as the lowest uncertainty between climate natural variability is in summer and

the highest is in winter and autumn.

4.3. Climate Change Impacts on Hydrological Regimes

4.3.1. Stream Flow Impact

Figure 6 illustrates the annual OMF (the interannual average daily flow over a selected period) for

the Gatineau River watershed. A complete set of simulated hydrographs are provided in Appendix

A. The intent of this study is to generate an understanding of the relative change in variable values

from REF and FUT periods, [(OMFFUT-OMFREF/OMFREF ] in percentage (%). The outcomes,

therefore, provide an understanding of the range of the potential consequences of climate change

(the uncertainty of climate natural variability and twenty individual hydrological models) on water

resources.

The uncertainty in Figure 6 for the entire system is constrained between the upper and lower limits

of +40.99% and +4.01%, for a span of 36.98%. For each box and whisker plot, the middle line

represents the median projected parameter, and the top and bottom of the open rectangle (the box)

represents the 25th and 75th percentiles of the projections, respectively. The values of +11.11 and

+21.06% depict the 0.25 and 0.75 quartiles, respectively with an interquartile range of+9.95% and a

median value of+16.18% for the overall uncertainty.

Hereafter in each model uncertainty’s graph, the dashed line shows the mean of each hydrological

model through five climate members. The mean changes are important for Md8 (27.23%), but the

opposite holds with Md3 (10.32%), which has the lowest mean value. The largest uncertainty occurs

with Md08, ranging from 9.11 to 23.33% and the smallest uncertainty, with Md03 which span

reaches 11.34% (between +17.97% and +6.63%). The standard deviation (Std) of the median OMF

Figure 5: Scatter plot of seasonal changes in precipitation and temperature between future and

reference period for the Gatineau River watershed

17

relative change for the hydrological model is 4.46%. The disparity in the climate natural variability

graph for each climate member is low, which indicates low variability of modeling tools (hydrological

model structures). The largest disparity occurs for member#3 with +24.91%. The lowest disparity

comes from member#4 with +13.62%, representing a difference of +11.29% (Std 5.93%).

The results representing the overall mean flow relative changes between REF and FUT confirm that

changes differ greatly from one climatic member to the other and illustrate the significance of

climate natural variability in this context. Therefore, it can be concluded that the uncertainty of

climate natural variability is more important than lumped conceptual hydrological model structures

as depicted by the standard deviation value, which is larger than for the lumped conceptual models.

Comparison of Twenty-five Climatic Member Set Uncertainty in Terms of

Flow

In this section, we investigate the impact of climate change uncertainty based on twenty-five climatic

member sets, which are constructed from five climatic members of scenario SRES A2 and also the

uncertainty that arises from twenty lumped conceptual models. The advantage of this series’

construction is that it takes into account more of the natural variability of the climate and also limits

uncertainty.

Figure 7 illustrates the relative changes of OMF from REF and FUT for each type of tool in the box

plot of OMF total uncertainty (500 values), the box plot of the lumped conceptual models, and the

box plot of climate natural variability (twenty-five sets) for the entire system. In this context, the

interquartile ranges of box plots represent the inner sensitivity to the other modeling tools, and the

median values illustrate the uncertainty by each tool.

Figure 6: The total, hydrological models and natural climate of the overall mean flow evolution

(between REF and FUT, %) for the Gatineau River watershed

18

In this Figure, the global uncertainty (first panel) varies from +40.99% (Md8F3R3) to -1.27%

(Md3F4R5) with a median value of 16.42%, while the interquartile range (IQR) is 9.25% with a

percentile of 20.95% (75th percentile) and 11.65% (25th percentile).

For different hydrological models, the median OMF relative change fluctuates from Md8 to Md3 with

the values of 27.4% and 9.58%, correspondingly. The median change value (17.82%) depicts the

sensitivity of the lumped models with a standard deviation value of 4.33%. The interquartile range of

hydrological models ranges from 10.54% (Md17) to 7.40% (Md10). In this context, Md8 behaves

differently, as identified by Seiller et al. (2012).

From the climate natural variability point of view, the median value changes between F3R3 climate

member set with the value of 24.13% to F4R5 (4.28%) with the standard deviation of 5.25% of the

projected median which is larger than the standard deviation of the hydrological models. From the

five groups of twenty-five members’ sets, the third group shows greater differences (8.56%)

between median values of member sets while the fourth group indicates the lowest deviation

(7.95%) of median. Group one to five refers to climatic members of the reference period considering

the five climatic members of the future period (i.e. group-four refers to the climatic member-four of

the reference period with the five climatic members of the future period). The interquartile range is

curbed between 7.48% (F3R5) and 2.18% (F2R2), expressing lower inner sensitivity. It can be

observed in this diagram that the inner sensitivity of the hydrological models is higher than the

climate natural variability’s inner sensitivity.

4.4. Climate Change Impacts on the Water Resource System

Climate change can induce significant changes in the management of a water resource system,

particularly on uses that are highly dependent on hydrological regimes, such as hydropower

Figure 7: The total, model and climate member uncertainty with the constructed member sets from five

climatic members (twenty-five sets) for the Gatineau River watershed

19

production. This section analyzes the impact of climate change on the Gatineau River system. To

achieve this, an optimization tool (Chapter 3, section 3.5) is used to optimize the operation of the

system for different climatic scenarios for the reference period (1961-1990) and under future climate

projection for the period of 2041-2070. The main objective of this section is to assess the impact of

both climatic and hydrological uncertainties on energy generation.

4.4.1. Reservoir Operation Rules

This section addresses the climate change impacts on reservoir storages. Baskatong, Paugan and

Chelsea are the three hydropower stations with storage (Figure 1). Figure 8 illustrates the

Baskatong reservoir storage resulting from twenty hydrological models with five climatic members.

We can see that the refill phase is shorter for the future period (2041-2070). This is the result of an

earlier spring snowmelt (spring peak shift) due to early runoff. From the end of the summer to

February, results portray a reduction in storage volume in future condition for all climate natural

variability. At the beginning of the high flow season, the storage volumes are similar but the refill

phase is much faster than for the REF period.

The reservoir storage in Paugan (Appendix B) exhibits the same behavior as Baskatong. However,

the variability between the models is more pronounced. To have a better view of this result, we

should work on a shorter time step, which the model was not able to implement.

Water planners and hydropower operators should consider that the operating rules have to be

regionally changed based on the results (individual models) to create adaptive reservoir operation

rules under climate change.

Figure 8: Baskatong reservoir monthly water storage for FUT (2041-2070) and REF (1961-1990)

periods for different climate natural variability, including twenty lumped hydrological models

20

4.4.2. Energy Generation Impact

Figure 9 shows the relative changes in energy generation for the entire Gatineau River system.

Results are given for the five climatic members and the twenty hydrological models. We can see

that the overall uncertainty ranges from +0.97% (Md11Mb4) to +31.35% (Md8Mb3) with a median

value of 13.11%. In other words, there is considerable uncertainty regarding the annual energy

output of the system with an expected annual increase of 10-19%. It should be noted that none of

the scenarios involves a reduction of power output.

The median OMF relative change per lumped model fluctuation confirms the sensitivity to the

lumped model selection. The minimum relative changes across climate members, is for Md3 with

the value of 8.56%. The mean relative change of energy generation across all models is close to

member#1.

The analysis of climate natural variability reveals more variability in relation to lumped conceptual

models. The maximum relative change is provided by member#3 (19.34%). The minimum changes

are provided by member#2 (13.17%). The mean projected for the five climate members varies from

19.76% (member#3) to 9.71% (member#5).

For a robust estimation of energy generation in the context of climate projection and in order to

decrease the uncertainty of climate natural variability, we can put each climate member in thirty

years (150 values) together for all lumped models. Figure 10 shows the box plot (top) and the

cumulative distribution function (CDF) of this series for the reference and future periods.

From Figure 10a, the median total energy generation in the future period per lumped conceptual

model varies from 3.18 (Md8) to 2.853 TWh (Md2). The highest interquartile value is achieved by

Md12 (0.53 TWh).The lowest is achieved by Md1 (0.38 TWh). In the reference period (the left box

plot), the behavior of models is more uniform than the future period. The median values are from

Figure 9: The total, hydrological models and natural climate of the overall mean energy generation

evolution (between REF and FUT, %) for the Gatineau River watershed

21

2.84 (Md4) to 2.52 (Md8) TWh with a difference of 0.31 TWh. The highest and lowest interquartile

range is obtained by Md16 and Md20 and corresponds to 0.52 and 0.33 TWh, respectively.

Figure 10b shows the non-exceedance probability of the optimized annual energy generation over

thirty years for the REF and FUT periods, which have been combined with five climate members

(150 values) for each lumped conceptual model. The blue line bounds the envelope of the future

period and the grayed line with the reference period (Figure 10b). The variability (the width of the

envelope) of twenty models in the future period is larger than in the reference period.

Firm energy is defined as the amount of energy that can be guaranteed 90% of the time. We can

see from Figure 10b that, depending on the scenario, firm energy can range between 2.1 to 2.7

TWh.

In wet years, more than 50% of the energy generation can be achieved between 2.85 and 3.19

TWh. The constant slope of the CDF curve, which implies the uniformity of the density in FUT,

shows a convergence of individual models to 3.5 TWh at the end of this period. In fact, the

alternative view in the CDF plot would suggest that the length of horizontal lines changes rather

quickly (there is a high probability here relative to the energy generation) in the middle range and

ultimately more slowly (the same at the start) in the upper end with large values. The long-left side

tail of the CDF plot in REF is a result of low values that occurs in drought years.

REF

FUT

Figure 10: (a) Box plot and (b) CDF plot of annual energy generation including five climate members

per lumped conceptual model (150 values) for the Gatineau system

22

Monthly Energy Generation

The average monthly energy generation’s relative changes between FUT and REF (%) at each

power plant are presented in Appendix C. Figure 11 shows the monthly energy generation as a

percentage for the entire Gatineau River system.

The first observation is that a more pronounced peak energy generation is captured in winter-spring

months (with increased median values) which are due to hydrological regime impacts under climate

change.

Another observation is that the summer months (the same trend for all members) show lower inner

variability than the other seasons. This study is helpful for water managers to be able to estimate

the projection uncertainty, providing options and inspirations, opening stakeholders’ minds to

potentially make new choices in their management’s decisions and operation policies in response to

a changing climate.

4.4.3. Measures of System Performance With Respect to Energy Generation

The two performance indices (reliability and vulnerability) are computed for total energy generation

of the entire Gatineau River system under climate natural variability and hydrologic variability shown

in Figure 12a and Figure 12b.

In these Figures, the blue line represents future projection and the gray line indicates the reference

period. The results include all mentioned hydrologic models and climate natural variability (100

values) for future and reference periods.

Figure 11: The box plot of the average monthly relative changes of energy generation between FUT

and REF conditions for Gatineau system for different climate members, each box plot includes twenty

lumped conceptual models

23

The reliability (Figure 12a) of the reference period is less than the future condition, which confirms

good system performance under climate change impact. More than 65% of time, the reliability of the

system is 100% in the future period, while in the reference period, the system will not experience a

reliability of 100%. In Figure 12b, future condition is notable for less vulnerability than for the

reference period. As we expected, the vulnerability at each conceptual model is defined when the

reliability of those models are under 100%. In the future projection, more than 70% of the time there

is no vulnerability in the system, whereas for the reference period vulnerability is 390 GWh more

than 50% of the time.

Overall, by considering all identified sources of modeling uncertainties, we can confirm that climate

change will favorably affect the reservoir’s performance in terms of energy generation. The indicator

values for the reference and future periods are strictly different. The reliability and vulnerability

values for the reference period are worse than they are for the future condition. Increased

vulnerability as well as decreased reliability is expected due to projected decreases in inflows at the

reservoirs.

4.4.4. Uncertainty of Unproductive Spill Impact

Figure 13 illustrates the total annual energy spill for the entire system with 150 values (thirty years

by five members). In this Figure, blue plots represent FUT and gray plots represent REF. The bold

CDF plot at each range in FUT and REF indicates the multi-model average of twenty models.

(a)

(b)

Figure 12: Cumulative Distribution Function of reliability (a) and vulnerability (b) of the entire system

regarding the energy generation for FUT and REF projections

24

Appendix D presents the average monthly spill distribution for different climate members compiled

with twenty hydrological models for each power plant and demonstrates which are more involved in

spill production.

The general behavior on the annual scale of the total spill shows an increase in future spills. The

cross signs indicate the outlier of spill data. Increased total annual energy spills are due to the

increased inflows into the reservoir (as discussed in section 4.3). The frequency of the total annual

spill in Figure 13b shows that energy is spilled by the system 65% of times under the future climate

(35% of the time, there is no spill) and 20% of times under the reference climate.

Assuming that the value of energy is (US$50/MWh), Figure 14 indicates that the benefit foregone

can exceed US$10 million of year with an exceedance probability of 5% in the future (blue

envelope).

REF

FUT

Figure 13: The box plot (Top) and CDF plot (Bottom) of projected total annual energy spills for the

entire system (blue ranges represent FUT and gray ones represent REF)

Figure 14: Benefit foregone due to spillage losses (annual pattern) for the entire Gatineau system

25

Chapter 5: Conclusions and Recommendations

5.1. Concluding Remarks

In this study, we analyzed the uncertainties associated with (i) the choice of the hydrological model

structure and (ii) the climate natural variability in the Gatineau River basin. We also proposed a

procedure for the quantitative assessment of the CC impact on the hydropower system in the

Gatineau basin.

The key findings of the study are:

The analysis of the potential impacts of climate change reveals that, from July to

September, the amount of rainfall will be reduced, while the opposite (more precipitation)

will be observed during the rest of the year. The climate projection suggests a temperature

increase over the basin for all seasons. This will affect the snowpack and thus the timing

and extent of spring snowmelt.

The results regarding the OMF changes for the Gatineau watershed indicate that climate

natural uncertainty is more important than the uncertainty derived from the hydrological

structures. We can therefore conclude that climate natural variability plays an important role

in our ability to provide a diagnosis on the impacts of climate change on the hydrologic

regime of a river.

In this study, the HEC-ResPRM model was used to assess the impact of projected climate

change on hydropower production of the Gatineau system. Changes in runoff yield changes

in hydropower generation. As expected, during much of the year (except for the summer

season), energy generation under climate natural variability will increase. Energy

generation during the period of February-May for future climate is also higher than in the

reference period. This is due to the increased peak runoff (warmer temperature, snow

melting and an increase in precipitation) and the limited capacity of the multireservoir

system to accommodate those hydrological changes.

The modified hydrological regime implies that the operating rules should be changed to

maximize the production of electricity. More specifically, since the refill phase starts sooner

and is also faster, the extent and timing of the depletion phase of the reservoir is critical for

the production of energy before and after the spring season. This change is consistent

across all climatic members.

When it comes to firm energy, results show that the reliability of the water system will tend

to increase in the future, with some exceptions for conceptual models under different

climate natural variability. Consequently, the vulnerability of the system will decrease over

time in future projection.

26

The optimization results of the three power plants show more spills in the future conditions

than in the reference period. Water is spilled during spring snowmelt because of the limited

storage capacity of the existing reservoirs. The hydrologic impact of climate change is likely

to result in more spills in the future, annually and seasonally with some exceptions.

Enhanced management and mitigation strategies are required to account for the future

climate influences on hydropower production.

5.2. Recommendations for Future Research

This section aims to suggest areas of future research and different extensions of this study. By

applying the proposed methodology in this study, a decision-making framework may be further

developed and applied to hydropower and water resource system to minimize the damage of

climate change.

The generalization of this conclusion would require application to more sites. However, the

differences in catchment properties (e.g., soil type and topography) can also influence the

uncertainty from the hydrological model structures (e.g., Key et al., 2009). For future work, it is

recommended to use different general circulation models (GCMs), green house gas emission

scenarios (GHGEs), regional climate modeling (RCMs, downscaling methods), water resource

programming models (explicit stochastic programming) , PET formulas, snow modules, hydrological

indicators, other types of hydrological models (physical models), and different future horizons

according to data available. For the sake of completeness of the research, the application of

different GCMs is recommended but as Aronica and Bonaccorso (2013) stressed, different GCMs

often provide inconsistent future scenarios.

Perhaps a single realization of thirty years of climate variability is not enough to consider all of the

existent variability. It is recommended to use a longer data period in this context.

Effective practices could lessen the impact or intensity of climate change on hydropower and some

of the economic effects. Without preventative measures, current practices will lead to annual losses

in hydropower potential and reliability in future climates. A robust methodology and in-depth studies

are required to assess the threat that climate change may pose to the existing installation and

potential hydropower productions. Further study is needed on the changing adaptive policies for

water resource system and modifying power plant infrastructures in order to decrease unproductive

spills and increase hydropower generation.

27

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ANNEXES

Appendix A: Hydrographs

In this section, the impacts of climate natural variability of scenario A2 under the twenty conceptual

hydrological models on the mean interannual daily flow (mm) for the Gatineau River watershed are

investigated. Stream flow uncertainty comes from either the climate natural variability (five

members) or conceptual hydrological modeling as it can be revealed from the 100 simulations and

climate projections. In these Figures, the blue envelope represents the FUT and gray envelope

represents the REF period. The results for the other subbasins look the same. So the Baskatong

subbasin is shown here.

For the Baskatong subbasin (Figure 15) in the reference period, the largest uncertainty takes place

in the peak of the spring flood with a gap of 0.74 mm in the day 121 (between 4.71 and 5.45 mm)

for member#1 and the lowest ones occur during the winter season in the day 45 with the spread of

0.23 mm. As well for member#2 with moving of two days, the largest magnitude of uncertainty

happens in the day 123, between 5.08 and 4.98 mm and the smallest uncertainty of the envelope

happen in day 48 with the spread of 0.23 mm. For members#4, 2, 5 the largest uncertainty falls in

the days 122, 121, 121, which is one or two days earlier compared to the other members. Plots are

organized from member#1 to member#5, consecutively.

Figure 15: Mean interannual daily flow for FUT and REF projections. Example from Baskatong

The largest uncertainty in the future period for the Baskatong subbasin for different members relates

to the spring flood which is advanced 9-14 days corresponding to the reference period.

Early spring peak flows, the decrease of summer flow and increase in winter flows are general

trends compare to the reference periods that are more accentuated at each subbasin. The winter

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flow increases especially in the months of November and December caused by an increase in rainy

precipitation and a decrease in snow pack. The increase of winter precipitation and analogous snow

accumulation with higher temperature intended to an earlier and strongest snowmelt peak in spring

(April month) particularly for all members at each subbasin.

This increased variability compared to the reference period clearly confirms the importance of the

choice of climate natural variability (five members) relative to twenty conceptual hydrological model

structures. These findings on the foundation of mean interannual daily hydrograph containing

hydrological models and climate natural variability reveals that we need some indicators in order to

clearly extract the impact of climate change on water resources.

Appendix B: Mean Annual Water Storages

Paugan (Figure 16) has the same trend as the Baskatong reservoir, but with a greater variability

between the twenty hydrological models. The peak storage is captured in May for all climate natural

variability and approximately earlier, which depicts the earlier snowmelt. During the winter and

autumn seasons (From September to February), the reservoir storage is lower compared to the

reference condition, which is the outcome of the impact of climate change on climate characteristics

and consequently the flow at these seasons. More fluctuations of the models’ behavior for each

climate member induced from the fluctuation of flow for these models.

Figure 16: Paugan reservoir monthly water storage for FUT (2041-2070) and REF (1961-1990) periods

for different climate natural variability, including twenty lumped hydrological models

Appendix C: Monthly Energy Generations

To see the behavior of the power plants under the climatic members, including twenty lumped

models, Figures 17 through 19 illustrate the average monthly energy generation relative changes

between FUT and REF (%) at each power plant.

Figure 17 (Baskatong-Upstream power plant) indicates increasing divergence for April and May

from the reference conditions compared to the rest of the year. For all members, the maximum

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median takes place in April and the minimum one is in October. In Regards to member#1, the

maximum interquartile range happens in November (8.70%) and the minimum takes place in May

(3.14%). The changes in variability (interquartile range) show sensitivity to the selection of lumped

model which is approximately uniform. For member#2, the largest reduction in the interquartile

range happens in November (2.45%) and the maximum of the interquartile range is located in

January (8.3%). February is more sensitive to the range of lumped models and shows the largest

spread from +34.54% to -1.83%. The median change value of +45.03% occurs for member#2,

which is the largest median change value among the other members. The median change value of

member#3 is 73%, which is close to the value of member#2. The maximum and minimum

interquartile range happens in May (8.60%) and September (4.08%), correspondingly. July is less

sensitive to the range of hydrological models and shows a lag between +9.98% and -0.059%.

The maximum and minimum differences between the 25th and 75th percentile for member#4, are

8.6% (November) and 3.6% (July), respectively. In April, the largest spread happens from +41.28%

to +0.79%, which shows the largest amount of uncertainty and seems to be very model specific. For

member#5, the interquartile range varies from +9.34% (May) to +4.67% (Jun), which shows less

inner sensitivity. The extremes of the expected changes vary from +12.7% to -11.25% in February.

Figure 17: The box plot of the average monthly relative changes of energy generation between the FUT

and REF conditions for the Baskatong power plant (upstream power plant) under climate natural

variability. Each box plot includes twenty lumped conceptual models

For Paugan (Figure 18), the relative changes of energy generation for all members (except

member#1) shows the maximum median values in the late winter and the minimum ones changes

between October, July, June and September. The maximum median change happens for

member#3 (34.8%) and the minimum, member#4 (24.2%). The interquartile range experiences

between 10.59% (February) and 3.43% (June) for member#1 and for other members the maximum

interquartile range takes place in April (8.15%), January (10.58%), March (7.95%) and 8.42%

(February) which corresponds to member#2, 3, 4 and 5, which express lower inner sensitivity. The

lowest value of interquartile range is captured by Jun for all members. The largest spread which

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shows the sensitivity of a month to the range of hydrological models varies in the Oct (between

+19.2% and -11.5%), January (between +51.69% and +21.95%), December (between +53.32% and

+11.04%), March (between +39.06% and +9.84%) and September (between +23.53% and -3.10%)

for climatic members, correspondingly.

Figure 18: The box plot of average monthly relative changes of energy generation between FUT and

REF conditions for Paugan power plant under climate natural variability. Each box plot includes twenty

lumped conceptual models

For the Chelsea power plant (Figure 19), member#3 has a larger median change value (36.65%)

that varies from +44.6 (February) to +8.04% (June). This larger median change value is captured in

Feb for all members except member#1 (March) the same as the Paugan power plant behavior. The

extreme of the expected changes (showing more sensitivity to the hydrological models) captured in

May, February, December, November and September respective to members#1 through 5. The

summer month for all members shows lower inner variability than in the other seasons. This study is

helpful for water managers in estimating the projected uncertainties and providing inspirations for

management decisions and operation policies in response to a changing climate.

Figure 19: The box plot of the average monthly relative changes of energy generation between FUT and

REF conditions for Chelsea power plant under climate natural variability. Each box plot includes twenty

lumped conceptual models

35

Appendix D: Unproductive Spills

Figure 20 illustrates the average monthly spills distribution for different climate members compiled

with twenty hydrological models for the Baskatong power plant. In this Figure, each bar shows the

spillage that can be produced per lumped model (in a sequenced order of models, one to twenty).

In general, the marked trend of spillage shows an increase in the future condition for all members

compared to the reference period. More spills can be observed in the winter and spring.

Interestingly, there are some months that spills are non-existent such as December, January and

February for almost all of the members in the reference period. In fact, as it is clear that all the

models will not involve spills in the reference period at each month (bar color), but in the future we

can determine the magnitude of the spill approximately for all of the models. Also, there is a spill

behavior’s malformation for the reference period and rarely can a steady behavior between

members be found.

In May for the future period, the most magnitudes of spill are captured for all of the members.

Member#3 shows more spills throughout the year comparing to the other members and member#4

captures the lowest amount of spills. The changes in spills are related to the increase and reduction

of seasonal and annual flow.

Interestingly, for Paguan (Figure 21), the spill does not exist for members#1 and 2 in the reference

period and also from June to March. In fact, the spill just occurs in April and May. Again, as well as

in Baskatong, member#2 captures more monthly spills and member#4 shows the lowest magnitude

of spills in FUT. The general trend can be observed as an increase of spill in FUT. Under future

condition, the spills take place from Mar to May when inflow to the system reaches the highest

magnitudes. Only member#3 shows spillage in January in future conditions. In the summer, the

average spills do not exist in the future and of course in the reference period for the Paugan power

plant, which is the medial power plant between the Baskatong (upstream reservoir power plant) and

Chelsea. Under different climatic members, the differences and timing of spills points out the

associated importance of inflow and the uncertainty impact of climate natural variability on the

performance of the system.

The spills of the last power plant (Chelsea) are portrayed in Figure 22. Spill is greatest under the all

climatic members due to limited storage and generation capacities compared to the reference

period. During March, April and May in FUT, a lot of water is usually spilled because river flows

exceed the hydraulic capacity of the power plants to generate electricity. For the winter season,

there are an increase in spills due to warmer temperatures, and increases in precipitation and

consequently, runoff increases.

36

For June, July and August some members such as members#1, 2 and 3 propose an increase in

spills compared to the reference period. In REF and FUT, the greatest magnitude of spills occurs in

May.

Figure 20: The mean monthly unproductive spills for different climate members including twenty

models for the climate periods, FUT and REF for the Baskatong power plant

Figure 21: The mean monthly unproductive spills for different climate members including twenty

models for the climate periods, FUT and REF for the Paugan power plant

Figure 22: The mean monthly unproductive spills for different climate members including twenty

models for the climate periods, FUT and REF for the Chelsea power plant