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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
viii
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
ix
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
1
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
32
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
34
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