SYSTEMS APPROACH TO THE ASSESSMENT OF CLIMATIC CHANGE IN A SMALL
RIVER BASIN
Predrag Prodanovic1 and Slobodan P. Simonovic1
1
Department of Civil and Environmental Engineering, University of Western Ontario,
London, Ontario, Canada, N6A 5B9; email: pprodano@uwo.ca
Abstract: The focus of this study is on the mathematical modeling support for
implementation of the inverse approach to climatic change impact assessment in small river
basins. The model used in the assessment of risk and vulnerability to changing climatic
conditions is described in this paper. The model is a combination of a sophisticated system
dynamics model linked to a continuous hydrologic model of a river basin. The system
dynamics model represents the socio-economic components such as urban and rural
population, housing, business activity, land and water use. The hydrologic model is a
seventeen parameter semi-distributed water balance model. The combined model links the
socio-economic aspects of a river basin (such as changing land use patterns) to physical
processes (such as potential evapotranspiration and direct runoff). The physical processes
(through direct runoff and precipitation) are linked to flood damage and drought levels, which
then directly influence the population and business sectors of the socio-economic
component. A number of such key feedback relationships are discussed in the paper. The
application of the model is illustrated with a case study of a small river basin (3,500 km 2) in
southwestern Ontario, Canada.
Key words: Continuous Hydrologic Modelling, System Dynamics, Systems Thinking,
Climate Change, Risk, Vulnerability
1.
Introduction
Climatic change impact and vulnerability assessments have received much attention
in the past two decades because of a growing realization that climate is changing as a result
of human activities (IPCC, 2001). Burning fossil fuels and changing land use patterns have
lead to an increase in atmospheric concentration of carbon dioxide (and other greenhouse
gases), thus resulting in a steadily increasing mean global surface temperature.
Documented evidence exists that increased global surface temperature intensifies the
hydrological cycle, and therefore induces changes in the frequency and magnitude of
hydrologic extremes such as floods and droughts.
Traditional ways of studying the climatic change in river basins is by downscaling
output from global circulation models, and using them as inputs in the hydrologic models. A
number of studies have implemented such methodologies, and thus estimated impacts of
changed climate (Coulibaly and Dibike, 2004; Palmer et al., 2004). However, a number of
uncertainties are inherent to this approach. Firstly, the global models have temporal scales
that are sometimes incompatible with temporal scales of river basins. For example, the
rainfall-runoff modelling requires data with relatively short time steps (daily and/or hourly); the
global models however, are only able to produce monthly outputs with high degree of
confidence (Cunderlik and Simonovic, 2006). Temporal downscaling of monthly global output
must therefore be employed, and shorter duration events be estimated, thus compounding
uncertainty. Secondly, spacial scales of global models can also be incompatible with spacial
scales of river basins. The global models typically have resolutions between 3-5 deg in
latitude and 5-10 deg. in the earth’s longitude, and are thus significantly larger than many
river basins. Such coarse resolution is thus inadequate in the representation of many
relevant smaller scale river basin phenomena.
The traditional approach is plagued by uncertainties in spacio-temporal downscaling
from global to local scales. Furthermore, in the study by Coulibaly and Dibike (2004), the
authors report significant differences between different downscaling methods (i.e., some
downscaling techniques would produce an increase, while others a decrease in mean annual
flow for the same global input). Therefore the choice of downscaling techniques can drive the
outcome of the analysis, and thus mask the true system behaviour under the conditions of
altered climate. As a result the end-users (water management authorities, government policy
makers and stakeholders) became sceptical of results of climate change impact analysis.
The inverse approach, originally developed by Cunderlik and Simonovic (2004b,
2006) takes an alternate (or bottom-up) route to climate change impact assessment. Its main
strength is that it focuses on users of water resources systems, and keeps them involved in
the process. The inverse analysis includes the following steps (modified from Cunderlik and
Simonovic, 2006):
1. The critical hydrologic exposures (such as floods and droughts) leading to failures of
the water resources systems are first identified, together with their risk levels. The
end-users are involved in this stage, as they are most familiar with particulars of the
water resources system in question.
2. The critical hydrologic exposures of the previous step are transformed into their
corresponding critical meteorological conditions (such as extreme precipitation,
sudden warming, sustained dry spells) with a hydrologic model. Relationships
between critical hydrologic exposures and their corresponding meteorological
conditions for each location of interest are formulated. They capture specific
management practices of the end-users (such as flood/water control, environmental
and watershed planning, source water protection, etc.).
3. A weather generator is used next to simulate critical meteorological conditions of
present and future climates. Meteorological data is synthetically generated for an
arbitrary long period that is statistically similar to the observed historical record. The
generated data is of high spacio-temporal resolution, as required by the hydrologic
model. Altered climate scenarios are then generated that are conditioned upon the
historical data (such as increased or decreased precipitation, warmer or wetter
springs, etc) and linked to large scale global circulation model outputs.
4. Using an ensemble of weather generator scenarios, relationships similar to those of
step 2 are obtained for each scenario considered. Such information can provide an
estimate of change in the frequency of critical exposures.
5. The last step in the inverse approach requires an application of the integrated
assessment model that captures behaviour of both physical and socio-economic
systems, and thus evaluates risks and vulnerability to changing climatic conditions
within the study area.
The focus of this paper is on the development of a model for use in the last step of
the inverse approach. In order to adequately describe the physical and socio-economic
processes operating within a basin, various links between the two must be established. The
links sought are of the feedback type, where a state of the system is used to formulate a
decision rule, which then alters the state of the system itself. Feedback links between
hydrologic and socio-economic processes of a region are relatively straightforward. For
example, an adequate water supply of an area depends on the availability of water within a
basin (via aquifers or surface storage). As an area expands, the total demand for water
increases, while the availability of water decreases (as lake and ground water levels are
lowered). As lake and ground water levels change, so does the regional hydrology, thus
completing a feedback loop.
Although relatively straightforward, this kind of (feedback) thinking is seldom used in
practice, where linear or sequential reasoning is most often employed. In Canada, cities and
municipalities in preparing their master plans take exogenous population projections,
compute system demand based on growth rates, and compare it against known sources of
supply. This kind of reasoning misses key feedback relationships that in reality govern the
behaviour of the entire system.
A paradigm that uses feedback loops as its fundamental building block is referred as
systems thinking (Richardson, 1991; Senge, 1990), and is adopted in this study. In its
application, the boundaries of classical modelling (either hydrologic or socio-economic) are
extended, thus forcing the user to postulate and test hypothesis regarding how a physical
process affects, and is affected by, a social process. In doing this, the user will be able to see
implications of policies and decisions that otherwise wouldn’t be considered. Use of systems
thinking is not only instrumental in the assessment of risk and vulnerability of anticipated
future conditions, but brings to the user useful knowledge upon which decisions can be
made.
A number of research papers have recently appeared that use systems thinking and
the concept of feedback to water resources management problems. The research by Li and
Simonovic (2002) demonstrates one of the first applications of system dynamics simulation
and systems thinking to studying hydrologic processes in North American prairie watersheds.
The work of Saysel et al. (2002) develops a system dynamics model for the purpose of
studying agricultural development in Turkey. The model consists of sectors representing
water resources, land use, demography and agricultural pollution. Stave (2003) on the other
hand, uses a very simple model to bring about public understanding of water management
options in Las Vegas, Nevada. Problem definition similar to Saysel et al. (2002) is adopted
by Fernández and Selma (2004), who studied water scarcity of irrigated landscapes in Spain.
Sehlke and Jacobson (2005) proposed a model to investigate the utility of system dynamics
approach in modelling large complex hydrologic systems and their interaction between the
surface and ground water in the western United States.
Even though the above modelling efforts have been prepared for different purposes,
all share one common feature: they study water resources management problems using the
framework of systems thinking and feedback. Nearly all employ some sort of interactions
between physical and socio-economic realms. However, all of the models share another
commonality: they are not widely accepted by end-users, practitioners and policy makers. As
a result, the models rarely get used (other than by the authors themselves). If such models
are constructed with the participation of the end-users, utilizing tools they are already familiar
with, the outlook can be more promising. This is what the current study is set out to do.
The rest of the paper is organized as follows: Section 2 provides a basic sketch of the
modelling methodology. It describes the model’s system structure, the data used, and the
key feedback loops. The application of the methodology is outlined in Section 3, together
with outputs of simulated system behaviour. Concluding remarks are given in Section 4.
2.
Methodology
The model used in the assessment of risk and vulnerability to climatic change is a
combination of two different models, linked with a number of feedback loops. The combined
model consists of a continuous hydrologic model, coupled with a system dynamics model of
the study area. Each is described next.
Continuous hydrologic modelling provides a way to simulate the long term behaviour
of a basin. All such models found in practice (Bennett, 1998; Johanson et al., 1980;
Leavesley et al., 1983) are based on the principles of water balance; they divide an area
studied into a number of conceptual buckets (canopy, surface, soil and ground water) and
estimate the flows between them (infiltration, percolation, runoff). The input to such models
usually includes precipitation, temperature and sometimes solar radiation, and their outputs
are flows (surface excess, baseflow, direct runoff, evapotranspiration, ground water
recharge, etc.). In essence, continuous the hydrologic models are rainfall-runoff models that
take into account the long term soil moisture balance. In contrast, event based models are
those with simplified account of the moisture balance (sometimes representing losses by a
simple function or a coefficient), and are therefore used for determination of basin response
from single storm events (Bedient and Huber, 1988, p. 313).
System dynamics is a methodology grounded in feedback control theory (Forrester,
1961). Its main premise is that of interconnectedness of system components; in other words,
it seeks to uncover interconnections between different parts of a larger whole. The
methodology emphasizes a long-term aggregate continuous view point, meaning that actions
resulting from major decisions do not take place instantly, but rather gradually adjust over
time. The strength of system dynamics lies in using mathematical models to formulate, test,
and re-formulate dynamic hypothesis of a real world problem, especially when social and
physical interactions are present. The system dynamics modelling can help the user gain
insight into situations of dynamic complexity, and possibly reveal causes of policy resistance
(Sterman, 2000).
2.1. Model Description
The model developed as part of this research is a combination of a continuous
hydrologic model and a system dynamics model. The continuous hydrologic framework used
in the study was one developed by the United States Army Corps of Engineers (Bennett,
1998; USACE, 2000)—Hydrologic Modelling Center Hydrologic Modelling System (HECHMS). The model was previously applied, callibrated and verified for the study area in the
work by Cunderlik and Simonovic (2004a, 2005). The input data used in the model was
produced by a weather generator developed by Burn and Sharif (2004). The weather
generator model was used to create an ensemble of climate scenarios based on statistical
properties of historical data, and selected data from the global circulation model outputs.
Time series of precipitation, maximum and minimum temperatures were synthetically
generated for each scenario, and used as exogenous input into the hydrologic component of
the model.
The system dynamics model consists of several sectors representing the most
relevant socio-economic activities within the basin. The model sectors include urban and
rural demographics, housing, business activity, land and water use, and is an extension of
the work of Alfeld and Graham (1976). The level of detail of only one such sector (urban
business activity) is shown as a stock and flow map in Figure 1; note that italicized variables
represent information obtained from other model sectors, not shown in the map.
Figure 1: Stock and flow map of the urban business activity sector
The urban business activity sector consists of four feedback loops: availability of
business land, labour, and (surface and ground) water. Each of these imposes a limit to
growth of business activity, meaning that if one limit is reached, growth stops. The first loop
is the land availability loop, and represents the dynamics of land occupancy by businesses.
Each business unit occupies an average amount of land, which aggregates (when all
business units are taken into account) to a total business land occupied. The availability of
business land is a ratio of business land occupied to business land available (determined
from the land use sector, not shown here), which then determines an investment rate in
future business. For example, if initial land occupancy is low, and if conditions are favourable
(in terms of plentiful labour force, and availability of resources such as water), the investment
rate will increase, and the number of businesses will grow. However, as more and more land
gets occupied, it becomes difficult for business units to acquire land and move to the area.
This is because “convenient access by truck or railroad is essential to merchandisers and
manufacturers, and a variety of other businesses ... who must consider availability of
customer and office parking”, among other things (Alfeld and Graham, 1976, p. 179). Similar
reasoning is employed in the formulation of the labour availability loop.
In terms of water availability loops, use to availability ratios are found by computing
business water use, and dividing them by the available water (either from ground or surface
sources). If the use to availability ratios are low (meaning businesses are using much less
water than is available), business investment can proceed provided other conditions are
favourable. However, as the use to availability ratios grow, less and less water is available
for new businesses, thus slowly imposing a limit to growth. In cases like this, the local
governments usually cease to issue additional permits for industrial and commercial uses of
water, which in turn halts additional business investment.
2.2. Combined Model Schematic and Key Feedback Loops
A number of feedback loops are used to link the hydrologic component of the model
(left side in Figure 2) with the socio-economic component (right side in Figure 2). For
example, the hydrologic model computes a value of the ground water recharge, which the
population and business sectors use. The activities of these sectors generate the increases
in largely impervious urban land. Increasing urban land contributes to surface excess and
direct runoff, thus reducing ground water recharge. In order to assemble the feedback links
between the socio-economic component (i.e., urban land) and the hydrologic component, a
number of assumptions are introduced. One such assumption is the relationship between
urban land and the time of concentration, a part of the routing component in the hydrologic
model. Another link is between the urban land and the level of imperviousness. The feedback
relationships are shown in Figure 3.
Figure 2: Upper Thames System Model Structure
Figure 3: Assumed feedback relationships
The rationale for the shape of assumed relationships is the following. As more and
more of basins’s land becomes urbanized, the time of concentration decreases. This is
because increasing urbanization alters the path of runoff to basin’s outlet. The urban land
also has a direct effect on the level of imperviousness (a measure of how much rainfall
becomes surface runoff, and does not infiltrate into the soil). A simple linear relationship is
formulated for the purposes of this study. Of course, other shapes could easily be tested
using a sensitivity analysis.
Similar logic is used to formulate links between the amount of vegetation cover (forest
and agricultural land) and potential evapotranspiration. For example, as more and more of
the basin’s area become urbanized, its vegetation cover decreases due to rezoning of
forested and agricultural lands. As the vegetation cover decreases, so do the rates of
potential evapotranspiration, which over time tend to increase the surface runoff. An
additional link exists between the drought level and water use. The level of drought is
computed according to local drought definitions (OLWR, 2003). Generally, when an area
experiences conditions of drought, the local water management agency announces this
information to the public, and asks for a reduction of non-essential water uses (such as
washing cars, or watering lawns). As drought conditions worsen, additional water use
restrictions may take place.
3.
Case Study
The model developed has been applied to the Upper Thames River basin, located in
southwestern Ontario. The basin’s total drainage area is about 3,500 km2, and its population
is slightly less than half a million. 80% of the basin’s land area is classified as agricultural,
10% as urban, and 10% as forest. The major urban center in the basin is the City of London
(population of 350,000), which has experienced much growth in recent times. Majority of
urban growth has been taking place along the Thames River and its smaller tributaries. Since
the watershed is located in a highly developed part of the province of Ontario, the region
constantly experiences increasing pressures from both urban and rural land uses.
The hydrologic model developed by Cunderlik and Simonovic (2004a, 2005) consists
of a thirty two sub catchments (or spacial units), twenty one river reaches, and three major
reservoirs. The precipitation and temperature input data is obtained from the weather
generator, developed by Burn and Sharif (2004) for a number of climate scenarios. Sub
catchment losses are computed via the Soil Moisture Accounting algorithm (Bennett, 1998),
while hydrologic routing is employed for the river reaches and reservoirs via the Modified
Puls method (USACE, 2000). The hydrological model operates on a time step of six hours.
The structure of the system dynamics model follows the description given in Section
2. Only three spacial units are considered, one for each county in the basin—Middlesex,
Oxford and Perth. Three spacial units are selected partly because the socio-economic data
for smaller spacial units were not available. Each spacial unit of the model has an identical
structure, but is populated with different data. The system dynamics model operates on a
monthly time step.
The two models are linked via feedback as described in the previous section. Monthly
average flows are used as inputs into the socio-economic component, which after processing
provides the relevant information to the hydrologic component once per month (i.e., the
parameters of the hydrologic model change on a monthly basis). This means that hydrologic
parameters such as potential evapotranspiration, percent of imperviousness and time of
concentration are updated once per month, even though the hydrologic model component
operates on a six hour time step.
3.1. Simulation Output
The combined model is simulated for a 300 year period, for four different weather
generator inputs: (i) historically identical scenario (or base case), (ii) an increasing and (iii)
decreasing precipitation and (iv) increasing temperature scenario. Daily annual maximum
flows are selected and statistically analysed as indicators of vulnerability to flooding. Seven
day annual minimum flows are chosen as indicators of droughts. The resultant statistical
analysis plots for one of the stream gauges are shown in Figures 4-5.
Figure 4: Frequency of daily annual maximum flows at Byron
Figure 5: Frequency of 7 day annual minimum flows at Byron
In terms of floods, we can observe that a flow of, say 600 m3/s has a return period of
about 100 yrs for the historically identical case, and an 18 yr return period for the increasing
precipitation case. This result is somewhat expected---an increasing precipitation tends to
decrease the return period of flows. However, we also observe an unexpected result: peak
flows past a return period of about 30 yrs tend to increase for a decrease in precipitation
scenario when compared to the historical or base case. This is indeed counterintuitive, and it
implies that it is possible for the peak flow to increase, even if precipitation decreases. This
result can be partially explained by continuously increasing urbanization, which gradually
increases peak flows. The numerous feedback processes operating in the combined model
are responsible for producing counter-intuitive behaviour such as this. It is worthwhile
mentioning that classical studies which consider steady state hydrologic models (i.e., ones
that do not change as social and physical conditions of the basin change) are unlikely to
produce behaviour such as this. Regarding low flows, we see more of an orderly result.
Under the increased precipitation scenario, low flows generally increase, while for the
increased temperature and reduced precipitation scenario, low flows mostly worsen.
The outputs of the system dynamics model are shown as time series plots. The
variables selected are ground water recharge, business activities (in terms of business units),
and business ground water use, shown in Figures 6-8.
Figure 6: Simulated ground water recharge
Figure 7: Simulated Business Units
Figure 8: Simulated business ground water use
Rapid development for the first fifty years of simulation tends to reduce ground water
recharge significantly. However, it is interesting to note that after the intensive development,
ground water recharge springs back up (although short of its pre-development level). The
increasing precipitation scenario shows the highest recharge values, while the reduced
precipitation scenario the lowest.
Area’s business activity (shown in Figure 7) tend to grow for the first forty years of
simulation, although at a declining rate. In essence, the business activities are limited by
water availability (see Figure 8) some twenty years after the beginning of the simulation, and
are therefore unable to increase further. We see that nearly all scenarios equilibrate to
roughly the same final value; we also see that decreased precipitation scenario shows the
lowest peak value when compared to other scenarios considered. With the difference
between the historic and the changed climate scenarios, we can observe the impact of
climatic change on area’s business activities.
4.
Conclusions
This paper presents a framework for assessment of climatic change impacts, risk and
vulnerability for small watersheds. The inverse approach (an alternative to downscaling of
global circulation model outputs) is applied. A continuous hydrologic model making use of the
latest available knowledge of hydrologic processes is coupled with a system dynamics model
representing socio-economic processes of the study area.
The benefits of the selected approach are mainly in the improved knowledge and
understanding of the major drivers of change in the basin. A number of insights are identified,
especially those regarding the magnitude of vulnerability and risk that a changed climate
could potentially impose.
Acknowledgements
The research described in this paper was supported by grants from the Canadian
Foundation for Climatic and Atmospheric Sciences (CFCAS), and Natural Sciences and
Engineering Research Council (NSERC) to whom the authors are grateful. The authors
would also like to thank Dr. Juraj Čunderlík, Dr. Donald H. Burn, and Mr. Mark Helsten for
their assistance during the course of the research.
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