An integrated model of climate change with watershed-based hydrologic-reservoir
water dynamics and its application in water resources assessment
Lanhai Li
whatIf? Technologies Inc.
338 Somerset Street West, Suite 3
Ottawa, Ontario K2P 0J9
Canada
Abstract
This study tries to investigate potential impacts of climate change on hydrologic
processes and reliability of water resources in the Northern American Prairie watershed
where snowmelt is a main water source. System Dynamics is employed as an effective
methodology to organize and integrate existing information available on climate change
scenarios, watershed hydrologic processes, reservoir water dynamics and water resource
assessment system. The Second Generation Coupled Global Climate Model (CGCM2) is
selected to generate the climate change scenarios with daily climate change data for
hydrologic modeling. Watershed-based hydrologic and reservoir water dynamics
modeling focus on dynamics processes of both streamflow formation within the
watershed driven by climatic parameters, and the reservoir water dynamics under
reservoir operation rules. The hydrologic component of the model reflects the phenomena
of dynamic change in vegetation canopy and soil physical state as active temperature
changes in the winter. The reservoir water component of the model explains the relations
among water volume, the reservoir operation rules and the demands from different
stakeholders, while the reliability measure describes the effectiveness of present reservoir
operation rules to meet the demands on drought protection, water secure and flood
control. Simulation results demonstrate that an increase in temperature in the prairie may
bring more high-peak-streamflow occurrences and more water resources in terms of
annual total streamflow. Reliability assessment indicates that current reservoir operation
rules can provide a high reliability in drought protection and flood control under two
selected climate change scenarios. More water resources generated from climate variation
and change imply an potential shifting of current high priority of reservoir operating rules
from drought protection to water secure aspects.
1. Introduction
Potential impacts of climate change on hydrology include changes in the
hydrologic cycle (Houghton et al., 1990) and the availability of water resources (Brent
and Yu, 1999). More significant impact may be expected on hydrological processes in the
Northern American Prairie region where the main contribution to flooding is coming
from the snowmelt. Snowpack accumulation and melt are regarded as important sources
of runoff and contribute significantly to the cause of floods in North America (World
Meteorological Organization, 1970; Gray and Male, 1981). Climate change may
potentially influence flood starting time and the magnitude of flood peak as well as the
performance of flood protection facilities. In North American prairie river basins,
snowfall in winter accounts for about 27% of annual precipitation. Snowpack
accumulated in the winter starts to melt in the spring and become major sources of
streamflow, which may result in floods in association with rain events. The well-known
causal parameters producing floods in Canadian prairie include: (a) soil moisture at
freeze-up time (previous autumn); (b) total winter precipitation; (c) rate of snowmelt; (d)
spring rain amount; and (e) timing factor (Warkentin, 1999). Therefore, temperature and
precipitation are the two major variables that affect the above five parameters.
Considerable efforts have been made to investigate the impacts of climate change
on hydrological processes, water resources and the assessment of flood protection
facilities. System simulation has been believed to be a powerful methodology linking
climate change to hydrology for predicting the streamflow and assessing the performance
of reservoir operations under climate changes. Climate change parameters can be
generated by the paleoclimate analogue, the recent climate analogue and the general
circulation modeling (GCM) (Simonovic and Li, 2004). The methods for predicting the
streaflow include statistical approaches (Thomas and Megahan, 1998), time series
techniques (Jakeman and Hornberger, 1993), complex runoff models (Bobba and Lam,
1990; Arp and Yin, 1992; Kite et al., 1994) and Neural networks (Hsu et al., 1995;
Lealand et al., 1999; Ehrman et al., 2000). Ahmad and Simonovic (2000) simulated the
flood damage under different reservoir management strategies. An integration of the
climate change model and hydrological model with reservoir operation assessment model
will provides a solid basis for assessing the potential impacts of climate change on
reservoir operation performance. Based on the analysis of limitations and constraints of
existing hydrological models for prairie conditions, Li and Simonovic (2002) developed a
hydrological model using System Dynamics Principles to represent the prairie
hydrological process. As a key part of a regional dynamic hydroclimatologic assessment
model (DYHAM) developed by Simonovic and Li (2003), the hydrological model (Li
and Simonovic, 2002) was combined with climate change scenarios and flood protection
assessment model to assess the reliability of a complex urban flood protection system.
The streamflows were calculated and predicted by the hydrological model under real time
and climate change conditions, then fed to flood protection system assessment model.
Reasonable allocation of water resources by reservoir operation plays an
important role in meeting the requirements of sustainable water resources and mitigating
the adverse impact of climate variations and changes. In the regional dynamic
hydroclimatologic assessment model (Simonovic and Li, 2003), the reservoir was taken
as a flood protection facility instead of a component of watershed-based hydrological
system. Too low winter temperature for the prairie area predicted by HadCM3 which was
developed at the Hadley Centre, Bracknell, U.K. may increase the uncertainty of
prediction. This study tries to view the reservoir as a component of watershed-based
hydrologic and water resource management system, and integrate a watershed-based
hydrologic model with a reservoir model to assess the performance of reservoir operation
in prairie under climate change scenarios generated by CGCM2 which was developed at
the Canadian Centre for Climate Modeling and Analysis. The paper first introduces the
assessment methodology, then applies the methodology for a case study from North
American prairie region, finally ends with discussions and conclusions.
2. Methodology
The methodology developed by Simonovic and Li (2003) for assessing the longterm impact of climate change on an integrated urban flood protection system in the Red
River Basin in Canada includes three steps: (1) development of the climate change
scenarios; (2) modeling of the hydrologic processes; and (3) development and application
of the system performance assessment model. In order to assess the impacts of climate
change on long-term watershed water resources, this study modifies the original
methodology and integrates a watershed-based hydrologic model with a reservoir water
dynamics model. The model links climate change scenarios to reservoir water resources
assessment through hydrologic processes taking place in the watershed. This new
methodology for long-term impacts of climate change includes: climate parameter
generation from real observation or the climate change scenarios, watershed-based
hydrological and reservoir operation model, and assessment of reservoir water resources
under given reservoir operation rules (Figure 1). Real time mode offers opportunities for
calibrating parameters and verifying the modeling results, while simulation mode makes
scenario design and long-term assessment possible.
Real time mode
Simulation mode
Real time
observations
Climate change
senario geneation
Temperature
Precipitation
Hydrologic model
Reservoir water
dynamics model
Watershed-based
hydrologic and
reservoir model
Reservoir water resources
assessment
Fig. 1. Schematic diagram of the model integrating climate change, hydrologic process
and reservoir water resource assessment
2.1 Climate Change Scenarios Generation
There are different methods to generate climate parameters, such as the
paleoclimate analogue, the recent climate analogue and the general circulation modeling
(GCM). The development of Global Circulation Models (GCM) over the past 20 years
has provided a tool for simulating past, current and future climate and the evolution of the
atmosphere in response to external forcing mechanisms. Although there are several
scenarios have been generated by the GCMs from The Intergovernmental Panel on
Climate Change (IPCC), but daily temperature and precipitation are not readily available
for all scenarios by some GCMs. In order to construct comparable scenarios and generate
precipitation and temperature data, the Canadian GCM Version 2 (CGCM2) developed at
the Canadian Centre for Climate Modelling and Analysis (CCCma), British Columbia,
CANADA, is used to generate climate change scenarios. CGCM2 is a fully coupled
model, linking the atmosphere, ocean and land surface. The atmosphere is modeled on a
grid of points approximately 400 km X 400 km over the entire earth’s surface. It
simulates the exchanges of energy and moisture among grid cells at an hourly time step.
It also simulates these exchanges among 10 vertical levels in the atmosphere and 29
vertical levels in the ocean at each grid point. It has a simplified land surface scheme that
also simulates the fluxes of energy and moisture between the atmosphere and land
surface. CGCM2 produces output for 25 climatic variables including various measures of
temperature, pressure, wind speed, precipitation and humidity. The model has been used
to simulate both past climate (Kim et al. 2002) and future climate for the IPCC analyses
out to 2100 (Flato and Boer 2001). Future climate is simulated through running different
emissions scenarios, each reflecting unique assumptions about driving forces on
emissions such as demographics, socio-economic development and technological change.
The IPCC analysis was based on a “medium” assumption of future CO2 emissions,
known as the IS92a scenario, i.e. effective CO2 concentration increasing at 1% per year
after 1990. CGCM2 has more recently been run under a number of emissions scenarios
taken from the IPCC Special Report on Emissions Scenarios (SRES), which provide a
range of some 40 emission scenarios varying in their assumptions about technological
development, fossil fuel use and social cohesiveness. Two scenarios: SRES A2 and SRES
B2 are used for the assessment of water resources and reservoir operation risk in this
study because they are able to provide daily climate parameters. The A2 scenario
envisions population growth to 15 billion by the year 2100 and rather slow economic and
technological development. It projects slightly lower GHG emissions than the IS92a
scenario, but also slightly lower aersol loadings, such that the warming response differs
little from that of the earlier scenario. The B2 scenario envisions slower population
growth (10.4 billion by 2100) with a more rapidly evolving economy and more emphasis
on environmental protection. It therefore produces lower emissions and less future
warming. Detail discussion on climate change results from the A2 and B2 scenarios can
be found in the IPCC Third Assessment Report (IPCC, 2001).
2.2 Development of watershed-based hydrologic and reservoir water
dynamics Model
In order to reflect the dynamic characteristics of hydrologic system, the System
Dynamics, a feedback-based theory to study the relations between system structure and
behaviour, is used to model watershed-based hydrologic behaviour and reservoir water
dynamics. A feedback system with a closed-causal-loop structure brings results from its
past actions of the system back to control its own future behaviour. A negative feedback
loop in the system seeks a gaol or brings the system to equilibrium, while a positive
feedback loop generates a growth process which causes the system to diverge or move
away from the goal or equilibrium. Based on the dynamic processes of the hydrologic
cycle occurring in a watershed, Li and Simonovic (2002) developed a hydrologic model
using system dynamics approach to explore hydrological processes in the Red River
Basin where the main contribution to flooding is coming from the snowmelt. Based on
the comparison with other hydrological models, the model developed by Li and
Simonovic (2002) can perform much more accurate results (Elshorbagy et al., 2005).
Ahmad and Simonovic (2000) developed a reservoir operation model using system
dynamics theory for assessing the flood damage under different reservoir management
strategies. This study integrated above two models together, and developed a new
dynamic hypothesis which links the watershed hydrological structure, as well as the
climate factors, to the streamflow generation and the change of water volume in the
reservoir (Figure 2). The signs in the figure, ‘+’ and ‘-’, represent the positive or negative
relationships between the first variable and the next one. The figure indicates that the
streamflow is determined by the interaction among climatic factors, vegetation, physical
properties of soil, and soil moisture saturation. The snow is accumulated in the winter.
Snowmelt water and/or rainfall in the spring will partially be intercepted by vegetation,
and some infiltrated into the soil. Surplus water after interception and infiltration together
with interflows from surface soil and subsoil and baseflow from groundwater is routed as
streamflow flowing into the reservoir. The water in the reservoir may flow out to meet
the demands of both different stakeholders and/or alleviation of draught or flood.
Precipitations as snowfall or rainfall positively bring external water into the watershed
hydrologic-reservoir system, while processes: water interception, infiltration, percolation,
evapotranspiration and release from reservoir etc., become negative feedback loops to
control the moisture storage in canopy, surface soil and subsoil, groundwater and
reservoir (Figure 2).
The essential dynamics of streamflow formation is captured by a vertical water
balance using five tanks representing snow, interception, surface, subsurface and
groundwater storage, while the change of reservoir water volume is determined by its
inflow from generated streamflow and existing reservoir water storage as well as its
outflows. On the mass conservative balance, the stocks and flows in a watershed can be
mathematically expressed as follows:
Temperature
Snowfall
Snow storage
+
Vegetation canopy
+
+
Snowmelt
+
+
_ Interception
capacity
(-)
+
Soil defrost Canopy
storage +
Rainfall
+
Active temperature +
accumulation
+
available Water
+
+
water Interception
_
+
Water for infiltration
+
_ Infiltration
capacity
surface soil
(-)
water saturation
+
+
water infiltration
+
(-)
+
Evapotranspiration
+
to subsoil+
(-)
_
_
+
(-)
+
+ +
Streamflow
+
+
+
reservoir
water level
desired
water level
reservoir
_
+ storage
_
+
+
water level
difference
(-)
-
+
upper stream
flooding
(-)
+
release
_
subsoil
water saturation
Groundwater
storage
+
+
_
+
Percolation
to groundwater +
reseroir area
water loss
Overland flow
catchment
(-)
area
subsoil +
interflow
Subsoil storage
(-)
+
+
surface soil _
Surface soil
storage
+
_
(-)
interflow
surface soil
+
(-)
+
water saturation
+
Soil
defrost
Percolation
_
_
_
+
+
demands of
different users
(-)
Base flow
+
+
downstream
flow
downstream
flooding
+
Figure 2. Basic dynamic hypothesis for watershed-based hydrologic-reservoir water
dynamics
dS1
 PSF  RSM
dt
(1)
dS 2
 RCI  REC
dt
(2)
dS 3
 RI  RE1  RF 1  RP1
dt
(3)
dS 4
 RP1  RE 2  RF 2  RP 2
dt
(4)
dS 5
 R P 2  R BF
dt
(5)
dSR
 Q  Rout  Rloss
dt
(6)
Rof  ( RSM  Pr )  RCI  RI
(7)
R  Rof  RF 1  RF 2  RBF
(8)
Q  SMTH 3( R * A, t d , Qi ) * r
(9)
Where S1, S2, S3, S4 and S5 represent the water storages (cm) in snowpack,
canopy, upper soil layer, subsoil layer and groundwater, respectively; PSF is precipitation
water (cm/day) in term of the snowfall; PSM is snowmelt water (cm/day); RCI stands for
canopy interception rate (cm/day); REC, RE1 and RE2 are water losses (cm/day) due to
evaporation in canopy and evapo-transpiration in upper soil and subsoil layers; R I is the
rate of upper soil infiltration (cm/day); RF1 and RF2 are interflow rate (cm/day) in upper
soil and subsoil layers; RP1 and RP2 are percolation rate (cm/day) in upper soil and subsoil
layers; RBF stands for baseflow (cm/day), Rof (cm/day) is the overland flow, R (cm/day)
is total water available for routing as streamflow, Q (m3/s) is the streamflow responding
to R with a travel time delay in which a third-order exponential smooth function (HPS,
1997) is used, A is catchment’s area (km2), r is unit conversion coefficient, td represents
average delay time (day) and Qi stands for initial streamflow (cm km2/day). SR is the
water storage in the reservoir (m3), Rloss is water loss rate due to evaporation and leakage
from reservoir (m3/day) and Rout is flow-out rate from reservoir (m3/day).
The calculations for the flow rates among the stocks are based on Li and
Simonovic (2002). Outflow rate from a reservoir depends on the selected reservoir
operating rules. In order to represent the seasonal variation of canopy interception
capacity and the change of soil physical states, Li and Simonovic (2002) states that
vegetation canopy grows exponentially with accumulation of active temperature (>0°C),
and that soil water storage and infiltration during soil frozen season are affected by the
period of time during which the temperature remains above and below the active
temperature. This phenomenon results in soil defrosting and refreezing with an
exponential process with accumulation of active temperature. The soil refreezes again if
temperature drops below 0 °C for a number of days. The active temperature accumulation
will be lost and starts again from zero. Accordingly, the influence of active temperature
accumulation on the canopy size and soil physical state can be written as

cc
[(  T ) / TC max ]

Ctc  

1

(T / T
) ci
Cti   I Im ax
1

 T
TI  
 0
if
if
if
 N
N   0
 0
if
if
1 if
N0  
0 if
T 0
T 0
T
 TC max
(10)
 TC max
if
TI  TIm ax
if
TI  TIm ax
T  0 and
if
T
N  Nn
N  Nn
T 0
T 0
(11)
(12)
(13)
(14)
Where Ctc and Cti are the influence of temperature on the canopy size and soil
physical state (dimensionless), cc is an exponential coefficient of active temperature
accumulation on the canopy growth, and TCmax is the maximum active temperature
accumulation point at which canopy storage reaches maximum, TImax is a maximum TI
point at which surface soil is fully defrosted (°C), ci is an exponent for describing the
influence of TI on soil defrosting (dimensionless), N is the number of continuous days
with temperature below active point (days), Nn is a maximum N after which TI will be
lost and surface soil will refreeze again, N0 is a logical variable to identify the day in
which temperature is higher or lower than the active temperature.
2.3 Water Resources assessment
The purposes of reservoir operation are to meet the various demands on water
from flood control, recreation and water supply for residential life and industrial
production. The main objective of this study is to investigate the potential impact of
climate change on water resources availability under existing reservoir operation rules. A
reliability criterion is commonly used to assess reservoir system performance in water
resource practice (Hashimoto et al., 1982; Klemes, 1985; Moy et al., 1986; Kaczmarek,
1990, Burn and Simonovic, 1996). Reliability (R) is defined as the probability of success:
R = Prob (S)
(15)
Where S is defined in various ways for the different reservoir purposes, but
generally corresponds to meeting desired demands. The practice form of the reliability
measure used in this study is given as the probability of the reservoir being in a reference
state and defined as:
R
1 T
 zt
T t 1
(16a)
zt  1
x t  S
(16b)
zt  0
x t  F
(16c)
Where R is the reliability; zt is the state of the reservoir system in the time interval
t; S is the satisfactory state; F is the failure state; and T is the duration of operating period
(day). For the purpose of system performance assessment, the yearly reliability (within a
year) and the total reliability (calculated over the simulation horizon of 100 years) are
calculated.
2.4 Model implementation and calibration
The GCM for constructing comparable scenarios and generating precipitation and
temperature data are from the CGCM2 which generates two climate change scenarios: A2
and B2, for watershed-based hydrologic and reservoir modeling. The watershed-based
hydrologic and reservoir model was developed and implemented using the STELLA II
development tool (High Performance Systems, 1997). STELLA II provides a modeling
environment for the modeler to define the objects and the functional relationships by
using the basic building blocks including stocks, flows, converters and connectors. Stocks
are used to represent storage, which can be changed with flows. Flows are defined and
regulated by converters. Converters are used to store algebraic relationships, define
external input to the model and hold values for constants. Connectors serve to indicate
the cause-effect relations among the model elements. The model is represented by
differential and difference equations that can be solved with either Euler’s or RungeKutta method.
The calibration process of the model includes the determination of model
parameters and initial values for all state variables. Input data set for the hydrologic
model use includes all calibrated parameters, daily temperature, daily precipitation and a
set of initial values for the state variables. Parameters are selected from a range of
feasible reference values, then tested in the model, and adjusted until satisfactory
agreement between predicted and observed variables is obtained. Original hydrological
model has been calibrated and verified by the data in 1979, 1995, 1996 and 1997 from the
Red River Basin, shared by Canada and the USA (Li and Simonovic, 2002). The results
show that the simulated streamflow reflects the variation in temperature and precipitation
as well as the moisture interaction between the surface, subsurface and the groundwater
storages. Sensitivity analysis indicates that parameters related to the surface soil storage
and the temperature are not sensitive to the selected-parameter variations with ±10%,
while the statistical comparison using coefficient of efficiency, coefficient of
determination and square of the residual mass curve coefficient reveals that the
simulation error is unsystematic and random, and the model can well reproduce the
observed flood starting time, peak and the flood duration. The model can provide a longterm prediction of streamflow under different climate change scenarios on daily basis.
The main output includes simulated (and observed in calibration and verification stages)
discharge at the reservoir which is fed to calculate reservoir storage and release. The
reservoir water dynamics model has been quantitatively calibrated and tested by Ahmad
and Simonovic (2000) on the basis of the operation rules to control flow out rate of water
from the reservoir.
3. Application of watershed-based hydrologic and reservoir water
dynamics model
3.1 Description of the study area
The Assiniboine River Basin and Shellmouth reservoir in prairie Canada are taken
as an example study (Figure 3). The Assiniboine River originates out of the Porcupine
Hills, northwest of Preeceville in eastern Saskatchewan. A major tributary, the Whitesand
River, rises out of the Beaver Hills, northwest of Yorkton and joins the Assiniboine River
at the town of Kamsack. The River drains the area from eastern part of Saskatchewan to
the western part of Manitoba, and meets the Qu’Appelle River approximately 70
kilometers downstream from Shellmouth Dam. The Basin has a drainage area of 21,000
km2, of which 79 percent is located in Saskatchewan. Topographically, the Assiniboine
River basin is gently to moderately undulating with higher relief evident in the Northeast
portion. Climatologically, the basin is continental sub-humid characterized by long, cold
winter and short, warm summer. Based on the climate data for the ecoregions within the
Upper Assiniboine River Watershed study area, mean annual precipitation ranges from
440 to 500 mm, 27% of which is snow, while mean annual temperature ranges from 0.6
to 2.8°C (Smith et al., 1998). The frost-free season varies from 90-110 days. The
streamflow in the basin is highly variable on daily basis. During spring, water levels on
the Assiniboine River reach peak due to snowmelt, and rapidly decline to a base level.
About 63% of annual total flow is contributed by the mouths of April and May, while
only 3% by December to March. Variation of year-to-year flows is also high due to
climate variations.
The Shellmouth Dam created in 1970 is approximately 1319 m long and 19.8 m
high zoned earth-fill embankment. A gated concrete conduit with discharge capacity of
198.2 m3/s on the east abutment and a concrete chute spillway on the west abutment
control water outflows from the dam (Water Resources Branch 1992). The reservoir is 56
km in length, 1.28 km in average width and covers a surface area of 61 km2 when it is
full. The elevation of top of the dam is 435 m above mean sea level with a dead storage
elevation of 417 m. The minimum water level controlled by the conduit is 422.5 m. The
spillway crest elevation is 12.32 m higher, at 429.32 m. The volume of inactive pool
below the conduit invert elevation is 12.3 x 106 m3. The difference between volume of
reservoir at active storage (370 x 106 m3) and crest level of natural spillway (477 x 106
m3) is flood storage capacity of reservoir, i.e. 107 x 106 m3. Current operating rules
specify that the reservoir should be brought to 185 x 106 m3 at about water level 423.98 m
by March 31 to accommodate floods and a reservoir volume of 370 x 106 m3 at about
427.5 m is a goal during the summer months. Maximum reservoir outflows from the
conduit and spillway are limited to 42.5 m3/s to prevent flooding downstream and the
outflow must be greater than 0.71 m3/s to avoid damage to fish and aquatic life in the
river system (Water Resources Branch 1995).
Figure 3. Study area and locations
3.2 Results
3.2.1 Climate parameters from CGCM2
Daily temperature and precipitation data generated by CGCM2 are used to
represent climate variability and change and to predict streamflow values at Shellmouth
reservoir. Data was extracted from one grid located at approximately 101.25 °W,
50.10°N for the Assiniboine River basin. Figure 4 shows the trend of simulated annual
temperature from two scenarios of CGCM2 for next 100 years. The results indicate that
annual mean temperature fluctuates year by year, but it tends to increase in two scenarios
in next 100 years. Comparison shows that scenario A2 generates higher annual average
temperature than scenario B2 does (figure 4). Annual average temperature may reach
10°C in scenario A2, and about 9°C in scenario B2 in 2090s. Average daily temperature
for next 100 years will reach 6.7°C in scenario A2 and 5.9°C in scenario B2, which are
higher 4.1°C and 3.3°C than average historical daily temperature which is about 2.6°C
observed at MOOSOMIN Station within the basin1. Figure 5 shows the comparison of the
average monthly temperature calculated from 100 years of simulated data. The result is
clearly showing that Scenario A2 generates higher temperature than scenario B2 does
around a year. The difference in average daily temperature from Feb. to Apr. is as high as
1.5°C. Higher temperature in both winter and early spring may influence snow
accumulation and snowmelt timing, which will results in a change in the streamflow and
the flood patterns including flood starting time and peak values.
Figure 4. The trend of annual mean temperature generated by CGCM2
Figure 5. Mean monthly temperature simulated using CGCM2 model
Simulated annual precipitation is shown in Figure 6. Simulated precipitations
from two scenarios vary year by year, but they exceed the historical average (511.4 mm)
at MOOSOMIN station in most years. The average annual precipitations over the 1001
http://www.climate.weatheroffice.ec.gc.ca/climateData/canada_e.html
year simulation horizon generated from Scenarios A2 and B2 are 604.7mm and
588.0mm, respectively. They are higher 18.3% and 15.2% than historical annual average
at MOOSOMIN station. Annual precipitation ranges from 385.3mm to 1158.2 mm in
Scenario A2 and from 405.1 mm to 858.2 mm in Scenario B2, which are greater than the
historical range from 226 mm to 734 mm. Historical extreme daily precipitation was
observed with 121.9mm, while simulated extreme daily precipitation from scenario A2
and B2 are 91.9mm and 132.4mm, respectively. The results show that the global
warming will bring more precipitation to this region, and that Scenario A2 will bring a
greater variation in annual precipitation.
Figure 6. The trend of annual precipitation generated by CGCM2
3.2.2 The assessment of water resources and reservoir operation
3.2.2.1 Streamflows
A long-term assessment of streamflow includes peak streamflow within each year
and annual total streamflow generated by two scenarios. Figure 7 presents the streamflow
in the basin for next 100 years under two climate change scenarios. The results show little
difference in peak streamflow magnitude between two scenarios when the peak values
are less than 100 m3/s, but there exists a clear difference in the cases that the peak values
are greater than 100 m3/s (table 1). Scenario B2 generates more years with the peak
values from 100 m3/s to 200 m3/s than Scenarios A2 does. In the cases with higher peak
streamflows, Scenario A2 generates 38% of peak streamflow values being greater than
200 m3/s for next 100 years, while Scenario B2 only does 26%. Compared to historical
highest peak streamflow value with 661 m3/s in 1995, simulations of both Scenario A2
and Scenario B2 from 2001 to 2100 generate two times peak streamflows greater than
historical record in 1995. The highest peak streamflows generated from two scenarios are
745.1 m3/s in Scenario A2 and 713.0 m3/s in Scenario B2, which does not show a
significant difference. The results show that climate change may result in more often
peak streamflows greater than historical record, and that Scenario A2 will generate more
years than Scenario B2 does with peak streamflows over 200 m3/s.
7a. Simulated streamflow from the Scenario A2
7a. Simulated streamflow from the Scenario B2
Figure 7. Comparison of simulated streamflow from CGCM2
Table 1 Distribution of simulated peak flows for next 100 years (%)
Peak flow ranges (m3/s)
Scenarios
<100
100 - 200
200-300
300-400
>400
>1995 peak flow
A2
34
28
17
14
7
2
B2
33
41
12
3
11
2
Annual total streamflows generated from two scenarios are shown in Figure 8.
Annual total streamflows fluctuate over years, but around 80% of annual total
streamflows generated for next 100 years are greater than historical average. Average
annual total streamflows from Scenario A2 and Scenario B2 are higher than 45.1% and
35.2% than historical average, respectively. Simulation of two scenarios from 2001 to
2100 results in two times total annual streamflows greater than historical record in 1995.
Statistical characteristics and distribution of annual total streamflows from two
scenarios are shown in Table 2. Scenario A2 can generate higher the maximum,
minimum and mean annual total streamflows than scenario B2 can do. The maximum,
minimum and mean annual total streamflows generated by the scenario A2 are higher for
4.1%, 7.8% and 6.8% than those generated by scenario B2. Table 2 also reveals the
difference in monthly distribution of mean annual total streamflow from two scenarios.
The results show that Scenario A2 generates more streamflows than scenario B2 does in
spring from Jan. to April, while Scenario B2 generates more streamflows than scenario
B2 does in summer from May to Jul. A change in monthly distribution of annual total
streamflow is mainly caused by higher mean temperature in spring generated from
Scenario A2. Higher temperature in spring from Scenario A2 results in two phenomena:
more precipitation falling in form of rainfall, and earlier and faster snowmelt. Both
phenomena will directly or indirectly contributes to an increase in streamflow. The higher
temperature as a consequence of climatic change may result in an earlier peak streamflow
starting time in this region. The results indicates that climate change not only influence
the annual total streamflows, also change its monthly distribution pattern within a year,
which will provide important guidelines for reasonable allocation of water resources
within a year or between years. The difference in monthly distribution of annual total
streamflows within a year from two scenarios proves that the model is able to catch the
watershed hydrological processes and the relation between temperature and snowmelt.
Figure 8 shows the annual total streamflow for next 100 years.
Table 2. Statistical characteristics and distribution of simulated annual total streamflows
Monthly distribution of annual streamflows (%)
Scenarios
Max
Min
Mean Jan Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov Dec
A2
18980.4
3142.7 7241.6 5.11
5.52
7.61 19.74 25.26 17.26
6.64
2.58
2.96
2.52
1.74
3.08
B2
18206.9
2899.2 6746.2 4.70
4.17
4.89 14.38 30.21 19.59
8.15
2.87
3.92
2.35
1.75
3.02
3.2.2.2 Reliability of reservoir operation: water resources and flood
This study concentrated on the impact of climate change on the performance
characteristics of the Shellmouth Reservoir as operated for seasonal water allocation and
flood control under current operational rules. Water levels in the reservoir were applied to
measure the satisfactions of reservoir operations. The reference criteria used to address
the water resource state are based on the reservoir winter target level, summer target level
and spillway crest level, and classified as four status (table 3): scarcity (water level less
than winter target), normal (water level between winter target and summer target),
abundance (water level greater than summer target, but less than spillway crest level),
and Flood (water level greater than spillway crest level).
Table 3. The reference criteria used to address the water resource state
Water resource
classifications
Water level criteria (m)
Scarcity
Normal
< 423.98
423.98 to 427.50
Abundance
427.50 to 429.32
Flood
> 429.32
Figure 9 shows the probabilities of water resource status at the Shellmouth
reservoir and their distribution for next 100 years under two climate change scenarios.
The plots shown in Figure 9 highlight that the variability in the reservoir performance can
be expected under two scenarios applied. The reservoir water level stays between normal
and abundance level in the most simulation years under two scenarios conditions. The
scarcity level most takes place in first half simulation period, while it may only occur in a
few years in the last 50 simulation years. Reservoir water level also reaches the spillway
crest level in some simulation years. A comparison of reservoir operation performance
from two scenarios demonstrates that Scenarios A2 generates higher abundance and
flooding possibilities as well as less scarcity possibilities than B2 does. There are 31
years in which reservoir water level generated from Scenarios A2 can reach flood level,
but only 21 years from Scenario B2. On the other hand, compared to 35 scarcity years
from Scenario B2, only 30 scarcity years comes from Scenario A2. Meanwhile, a wider
variance of abundance and flooding possibilities also can be observed from Scenario A2,
especially in last decades of simulation period. This trend is consistent to peak
streamflows and annual total streamflows discussed in section 3.2.2.1.
As discussed above, reliability measures are designed to assess the ability of the
reservoir for drought protection and flood control through seasonal water allocation. For
the purpose of differentiation from scarcity and flood, the probabilities of normal and
abundance are added together for calculating water secure reliability because of the
reasons that normal water level represents the demand on recreation in summer and
abundance water level strands for flood water storage during raining seasons. A
quantitative comparison of summary statistics for reservoir operation performance from
two scenarios for next 100 years is shown in table 4. The information in Table 4 reveals
that the reservoir functions high reliabilities with an average values from 94.91% to
98.26% for different purposes under two climate change scenarios. The highest
reliabilities are associated with flood control and scarcity protection, while the lowest
ones are related to the satisfaction of secure water level. The high reliability for flood
control and scarcity protection reflects the fact that the reservoir was initially designed to
protect the reaches of reservoir downstream from flooding and drought through
reasonable seasonal water allocation, and the priority of the current reservoir operation
policy is also based on this purpose. The low water secure reliability may not be so
critical because it may just reduce the possibilities for human recreation, but will not
influence water supply for the downstream users. Compared to Scenario B2, Scenario A2
generates higher reliability for drought protection and less reliability for flood control and
recreation under current reservoir operation rules. Higher annual total streamflow from
Scenario A2 is responsible for these results. However, the range of reliability from
scenario B2 is greater than that from, scenario A2.
9a. Annual accumulated probability distribution under the Scenario A2
9a. Annual accumulated probability distribution under the Scenario B2
Figure 9. Annual accumulated probability distribution of water resource for next 100
years under two climate change scenarios.
According to the information shown in Figure 9 and table 4, a high variability in
water resource reliability can be expected under both climate change scenarios. However,
two scenarios generated a very similar trend, i.e. scarcity probability declines and normal
and abundance probability increases, which means reliability for protecting drought
increases. The result occurs due to an increase in annual total streamflow as a response to
climate change scenarios in last simulation decades. The result brings some potential
room for re-evaluating the current operating rules for this system. Potential benefits may
be obtained from reducing the current high priority on drought protection and increasing
the priority on water secure aspects, such as water supply and recreational purposes,
because occurrence frequency of scarcity conditions are expected less under future
climate change conditions. This adjustment may reduce the range of water secure
reliability, and finally increase water secure reliability.
Table 4. Summary of reservoir reliability measures (%)
Secure
Scarcity
Scenarios
Mean
A2
B2
A2
Flooding
B2
A2
B2
97.34
96.95
94.91
95.21
97.57
98.26
4.71
4.96
5.95
6.45
4.28
3.93
Max
100.00
100.00
100.00
100.00
100.00
100.00
Min
84.93
82.74
79.73
65.75
83.29
83.01
Standard deviation
4. Discussions and conclusions
Decisions regarding reservoir operations are especially important for seasonally
balanced water supplies and the protection of reservoir downstream from drought or
flooding in the North American prairie where snow accumulation and snowmelt act as
important water source for streamflow formation. Global warming may bring a more
uncertain change in temperature and precipitation which may seriously influence the
hydrologic processes and water supply. In order to investigate the potential impact of
climate change on the hydrologic processes and water resources in the region, this study
applies system dynamics as an effective methodology to organize and integrate existing
information available on climate change scenarios, watershed hydrologic processes,
reservoir operation and water resource assessment system. The global climate model
CGCM2 is applied to generate the climate change scenarios which provides a clear
picture of climate change in a large scale with daily climate change data for hydrologic
modeling. Watershed-based hydrologic and water volume dynamics modeling focus on
the dynamics process of both streamflow formation within the watershed driven by
climatic parameters, and the reservoir water volume change under reservoir operation
rules. The selected hydrologic model explains the interactions among the surface and
subsurface storage, and reflects the phenomena of dynamic change in vegetation canopy
and soil physical state as active temperature changes in the winter as well as the
contribution of snowpack accumulation and snowmelt to streamflow. The reservoir water
volume dynamics modeling follows the reservoir operation rules which are based on the
demands from the different stakeholders. The reliability measure describes the
effectiveness of present reservoir operation rules to meet the demands on drought
protection, water secure and flood control. The results indicate that fast global population
growth with rather slow economic and technological development may accelerate an
increase in the temperature in the prairie region, which may bring more high-peakstreamflow occurrences and more water resources in terms of annual total streamflow in
the region. Reliability assessment demonstrates that current reservoir operation rules can
provide a high reliability in drought protection and flood control for the reservoir
downstream under two selected climate change scenarios. More water resources from
climate variation and change imply a shifting of current high priority of reservoir
operating rules from drought protection to water secure aspects. Different stakeholder
may benefit more from this priority adjustment.
Watershed-based hydrological-reservoir water dynamics model focuses on longterm impact of climate change on water resources reliability. It provides a bridge to link
the climate change to watershed water resources assessment. Future studies may extend
the model to reflect the spatial variation of climate and watershed soil and topographic
characteristics for the purpose of increasing the model’s predicting ability. In order to
reduce the risk from future flood events, it is also recommended to study the performance
of current reservoir operation rules in terms of vulnerability and resiliency.
Acknowledgements
The authors would like to thank Environment Canada for providing downloadable
online Climate Data, watershed Hydrometric measurement data and predicted daily
climate change data in the study area, which made this study possible.
References
Ahmad, S. and S. Simonovic, 2000. ‘System Dynamics of Reservoir Operations for
Flood Management’, Journal of Computing in Civil Engineering, 14(3):190-198.
Amin Elshorbagy, Antarpreet Jutla, Lee Barbour, and Jim Kells Can. J. Civ. Eng. 32:
144–158 (2005)
Arp PA, Yin X. 1992. Prediction water fluxes throughout forests from monthly
precipitation mean monthly air temperature records. Canadian Journal of Forest
Research 22: 864-877.
Bobba AG, Lam DCL. 1990. Hydrological modeling of acidified Canadian watersheds.
Ecological Modeling 50: 5-32.
Brent, F. and Z. Yu, 1999. ‘An Evaluation of Two Hydrologic models for Climate
Change Scenarios’. Journal of the American Water Resources Association,
35(6):1351-1363.
Burn D. H., and S. P. Simonovic, 1996. Sensitivity of reservoir operation performance to
climatic change. Water Resources Management, 10:463-478.
Ehrman JM, Higuchi K, Clair TA. 2000. Backcasting to test the use of neural networks
for predicting runoff in Canadian rivers. Canadian Water Resources Journals 25:
(30): 279-291.
Flato, G.M., and G.J. Boer, 2001. Warming Asymmetry in Climate Change Simulations,
Geophysical Research Letters, 28, 195-198
Gray D M, Male DH. 1981. Handbook of Snow. Pergamon Press, New York.
Hashimoto, T., Stedinger, J. R. and Loucks, D. P. 1982. Reliability, resiliency and
vulnerability criteria for water resources systems performance evaluation, Water
Resources Research. 18(1), 14-20.
High Performance Systems, Inc. 1997. Technical Documentation--STELLA, 255pp.
Houghton J T, Jenkins G J, and Ephraums J J (eds), 1990. Climate Change, the IPCC
Scientific Assessment. Cambridge University Press.
Hsu KL, Gupta HV, Sorooshian S. 1995. Artificial neural network modelling of the
rainfall-runoff process. Water Resources Bulletin 25: 483-490.
IPCC, 2001: Climate Change 2001. The Scientific Basis. J.T. Houghton et al. (eds.),
Cambridge University Press, 881pp.
Jakeman AJ, Hornberger GM. 1993. How much complexity is warranted in a rainfallrunoff model? Water Resources Research 29: 2637-2649
Kite GW, Dalton A, Dion K. 1994. Simulation of streamflow in a macroscale watershed
using general circulation model data. Water Resources Research 30: 1547-1559.
Klemes, V. 1985. Sensitivity of water resources systems to climate variations. World
Climate Problem Report. WCP-98, World Meteorological Organization, pp.115.
Kontzamanis-Graumann-Smith-MacMillan Inc. (KGS Group), 2000. Flood Protection for
Winnipeg, Part III - Prefeasibility Study. Winnipeg, Manitoba.
L. Li, S. P. SIMONOVIC. 2002. System Dynamics Model for Predicting Floods from
Snowmelt in North American Prairie Watersheds. Hydrological Processes, 16,
2645-2666.
Lealand CM, Born SH, Simonovic SP. 1999. Short-term streamflow forecasting using
artificial neural network. Journal of Hydrology 214: 32-48.
Moy, W.S., Cohon, J. L. and ReVelle, C. S. 1986. A programming model for the analysis
of the reliability, resiliency and vulnerability of a water supply reservoir. Water
Resources Research, 22: 489-498.
S. P. SIMONOVIC, L. LI. 2003. Methodology for Assessment of Climate Change
Impacts on Large-Scale Flood Protection System. Water Resour. Plann. Manage.,
ASCE 129(5), 361-372.
S. P. SIMONOVIC, L. LI. 2004. Sensitivity of the Red River Basin Flood Protection
System to Climate Variability and Change. Water Resources Management 18, 89110.
S.-J. Kim, G. Flato, G. Boer, N. McFarlane 2002. A coupled climate model simulation of
the Last Glacial Maximum, Part 1: transient multi-decadal response Volume 19,
Numbers 5-6, Pages:515 – 537
Smith, R.E., G.F. Veldhuis, G.F. Mills, R.G. Eilers, W.R. Fraser, G.W. Lelyk. 1998.
Terrestrial Ecozones, Ecoregions, and Ecodistricts, An Ecological Stratification of
Manitoba’s Natural Landscapes. Technical Bulletin 98-9E. Land Resource Unit,
Brandon Research Centre, Research Branch, Agriculture and Agri-Food Canada,
Winnipeg, Manitoba.
Thomas RB, Megahan WF. 1998. Peak flow responses to clear-cutting and roads in small
and large basins, Western Cascades, Oregon: a second opinion. Water Resources
Research 34:3393-3403.
Warkentin AA. 1999. Red River at Winnipeg – Hydrometeorological Parameter
Generated Floods for Design Purposes. Report of Manitoba Water Resources,
Winnipeg, Canada, 32.
Water Resources Branch 1992 Water Resources Branch, (1992). Shellmouth reservoir
study: preliminary study of spillway control gates. Manitoba Department of Natural
Resources, Canada.
Water Resources Branch, 1995. Assiniboine river flooding & operation of Shellmouth
dam. Report of the Shellmouth Flood Review Committee, Manitoba Department of
Natural Resources, Canada.
World Meteorological Organization. 1970. Guides To Hydrometeorological Practices.
WMO-No. 168. TP.82, Geneva, Switzerland.