Integrated Water Resources Modelling of the Upper Thames River Basin
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18th Canadian Hydrotechnical Conference
18ième congrès spécialisé hydrotechnique canadienne
Challenges for Water Resources Engineering in a Changing World
Winnipeg, Manitoba, August 22 – 24, 2007 / 22 – 24 août 2007
Integrated Water Resources Modelling of the Upper Thames River
Basin
P. Prodanovic1 and S. P. Simonovic1
1
Department of Civil and Environmental Engineering, The University of Western Ontario, London, ON
Abstract: The model presented in this paper consists of a continuous hydrologic (physical) model
component dynamically coupled with a socio-economic model component of the Upper Thames River
basin, located in south-western Ontario, Canada. Two components of the model are linked with a number
of feedback loops, implying that physical characteristics both affect, and are affected by, the socio-
economic activities they are embedded in. For example, land use change affects infiltration and runoff
characteristics of the regional hydrology, which impacts water availability thereby limiting future
development and expansion. Feedback loops between physical and socio-economic domains are
identified as being responsible for creating the systemic structure that determines the overall behaviour of
the basin. Climatic change vulnerability and risk are also assessed with the above model, taking into
consideration climatic, physical and socio-economic characteristics of the region. A number of different
simulation scenarios are explored, from which key management guidelines are identified.
1. Introduction
The water resources community has long realized that its complex problems are best approached using
the systems approach, where problems are seen in an integrated or a holistic way. One of the main
premises of this approach is that no problem can be studied in isolation, but must be considered in the
entire context it is embedded in. Today, this means using interdisciplinary, multi-stakeholder teams in a
coordinated fashion with the aim of achieving a common vision for the future. The systems approach to
water management has been able to combine socio-economic aspects with the more familiar, physical
perspectives. The concept of integrated water resources management emerged as a result of this
realization in addition to the growing complexity of water resources systems, and its management
challenges. Although there are many definitions of the concept available, this study adopts the one
provided by the Global Water Partnership GWP (2000): “Integrated water resources management is a
process which promotes the coordinated development and management of water, land and its related
resources, in order to maximize the resultant economic and social welfare in an equitable manner without
compromising the sustainability of vital ecosystems.” For the Canadian perspective on integrated water
resources management, the reader is referred to the work by (Mitchell, 2006).
An original methodology to make the integrated water resources management process operational is
presented in this paper. The key characteristics of complex water resources system management are
captured, including: feedback based system structure, representation of detailed physical and socio-
economic processes, spatial and temporal variability, and the multi-stakeholder participation context.
Emphasis is also placed on long term planning. In this methodology, physical processes (described with a
continuous hydrologic model) are dynamically linked to socio-economic activities (captured with a system
dynamics simulation model), each influencing, and being influenced by the other. The systems approach
1is used to integrate, in a single computer simulation model, processes that have previously been treated
separately (i.e., physical changes in a hydrologic system and socio-economic changes such as
population growth, rate of land and business development, etc). The methodology presented for
integrated water resources management not only provides helpful means for implementing the concept,
but also aids in a broader understanding of the problem by all involved parties (especially those with
conflicting viewpoints). Insight into the behaviour of the entire system (i.e. hydrologic, climatic, socio-
economic) generates knowledge that can contribute to better and more effective water management. The
rest of this paper explains the methodology and illustrates its application. We first describe the research
approach and the model, and then illustrate the approach through the application to the Upper Thames
River basin, ending with conclusions.
2. Methodology
To practically implement the idea of integrated water resources management requires the use of a
systems approach and simulation strategy. A systems approach is broadly defined as a viewpoint (or a
paradigm) that looks at problems in an integrated/holistic manner, where the physical characteristics of
the problem (i.e. hydrologic, hydrologic, water quality, sediment) are intertwined with socio-economic
processes (i.e. population growth, land development, business activity, recreation, etc). Physical and
socio-economic processes occurring in the river basin should not be studied separately as a basin
response depends on the interaction of the physical and socio-economic processes.
One way to implement the systems approach is through a system dynamics simulation strategy
(Forrester, 1961). The system dynamics method is grounded in feedback control theory, with its
fundamental building block being the feedback loop. Feedback is defined as a process whose output is
returned to the input to further regulate its output. It is also defined as an action (i.e. an outflow from a
reservoir) that affects a system state (i.e. the volume of a reservoir), which then additionally regulates the
action (i.e. the outflow). The main premise of system dynamics is that the complexity of system structure,
captured by multiple feedbacks among system elements, determines the system's behaviour.
The strength of system dynamics methodology is in using the concept of feedback to mathematically
represent processes of integrated water resources management. It also provides means for capturing
physical, environmental, socio-economic and political processes into a single model. Multiple stakeholder
participation is an essential step in determining the model structure and identifying different input
scenarios and/or management policy options to be evaluated.
There have been several applications of system dynamics simulation in water resources (Matthias and
Frederick. 1994; Simonovic et al., 1997; Simonovic and Fahmy, 1999; Saysel et al., 2002; Fernández and
Selma, 2004; Sehlke and Jacobson, 2005; among others). Even though all of the studies use system
dynamics to address problems of integrated water resources management, they all share one weakness -
simpler representation of the physical processes in order to include their interactions with socio-economic
processes occurring in the basin. Quite often, as a result, stakeholders familiar with detailed, rigorous
physical models do not clearly see the benefits of system dynamics simulation. The goal of this study,
therefore, is to use well established models of physical (i.e. hydrologic) processes, and integrate them
(via feedback) with the socio-economic models through system dynamics. In this way, the detail and
clarity of well established physical models is preserved and strengthened with the description of socio-
economic processes of importance for the river basin. In our opinion this is a framework that can be used
in an efficient implementation of the principles of integrated water resources management.
3. Upper Thames River Basin Case Study
The modelling framework introduced the previous section was applied to the Upper Thames River basin,
located in south-western Ontario, Canada. The integrated water resources model was created for the
purpose of studying impacts of climatic change on the hydrologic-socio-economic system of the area.
Changes in timing, magnitude and frequency of hydrologic extremes (floods and droughts) is considered,
2as well as the most important socio-economic variables (i.e. population growth, land development,
business activity, water use).
Most of the Upper Thames River basin is currently occupied by agricultural land (80%), while forest cover
and urban uses take about 10% each. The basin area is approximately 3,500 km2, and its population is
approximately 420,000 (of which 350,000 are residents of the City of London). The region receives
approximately 1,000 mm of precipitation per annum, while the yearly average discharge of the Thames
River (273 km long, eventually emptying into the Lake St. Clair) is 35.9 m3 /s.
Strong development pressure in urban and rural communities, together with potential negative
consequences of the changing climatic signal, have the possibility to affect hydrologic extremes (floods
and droughts) and socio-economic activities (such as population growth, development, business
opportunities) in the basin. Changing climate, manifesting itself through changes in timing, regularity,
magnitude, and frequency of hydrologic extremes may force basin managers to revise and/or update their
water management guidelines. Existing water management infrastructure (i.e. dams, dykes, sewage and
drainage systems, ponds, etc) may have to be retrofitted, or replaced. This could potentially place
tremendous strain on the area due to the very high intensity of development occurring in parts of the
basin (particularly in north London).
3.1 Integrated Water Resources Model of the Upper Thames River Basin
The integrated model was created for the analysis of climatic and socio-economic changes in the Upper
Thames River basin. It consists of two major components - hydrologic and socio-economic, linked with a
number of feedback loops. The following is a description of model components and their linkages.
3.1.1 Continuous Hydrologic Model Component
The continuous hydrologic model component used in this study is based on the continuous version of the
HEC-HMS model (USACE, 2000). Previous hydrologic studies (Cunderlik and Simonovic, 2007) applied
this model to the Upper Thames River basin, providing a calibrated model for use in this study. This
model component mathematically represents hydrologic processes operating within the watershed
boundaries. The processes captured by the hydrologic component include: climatic input (temperature,
precipitation), snow accumulation and melt, losses, surface transform, baseflow, and hydrologic
river/reservoir routing.
The hydrologic model component (Figure 1) works by taking temperature and precipitation as input
(obtained from an external weather generator model, described by Sharif et al., 2007) and running it
through the snow accumulation/melt module. Based on the degree-day method, precipitation (adjusted for
snow accumulation and/or melt) is used as an input into the losses module. The losses module is a
detailed water balance representation based on the soil moisture accounting algorithm. It uses canopy,
surface, soil (upper and tension zone) and two ground water layers to represent movement of moisture in
the land based hydrologic cycle.
The output from the losses module includes ground water recharge, evapotranspiration, surface excess
and baseflow (lateral outflow from each ground water layer). Surface excess (essentially precipitation
minus the losses) is used as an input into the transform module, where it is converted to surface runoff by
convoluting a unit hydrograph. Surface runoff and baseflow are then combined to produce direct runoff,
which is supplied to a routing module. The routing in this work was performed using a modified Puls
method, and is applied to both, river and reservoir routing, finally producing streamflow. The hydrologic
model component operates on a six hour time step. Additional information on the hydrologic model
component is provided in Cunderlik and Simonovic (2007).
3Figure 1: Hydrologic Model Modules
3.1.2. Socio-Economic Model Component
The socio-economic model component was developed using the system dynamics simulation approach,
and contains three spatial units (corresponding to areas belonging to each of the three counties in the
basin). These spatial units are selected for the description of regional economic activity. The systemic
structure of each unit in the model is identical, as socio-economic characteristics in each are similar. The
modeling sectors included in the socio-economic component include: urban and rural population, urban
and rural business activity, housing and land use.
Figure 2 shows the structure of the socio-economic model component, and is briefly described as follows.
The dynamics of the population sector (further divided into urban and rural) depends on availability of
adequate housing, jobs and water resources. This means that if housing, jobs or water (individually or in
combination) become scarce, the population growth will not be sustained. Similar reasoning applies to the
business activity sectors (also divided into urban and rural). Its viability depends on the labour force
provided by the population sectors (a fraction of the population represents labour force), availability of
land (obtained from the land use sector), and availability of water. The dynamics of the housing sector are
contingent on land available for housing (land zoned for residential uses), as well as the demand for
housing (obtained from the population sector).
The land use sector is the most complex part of the socio-economic model component, and includes the
dynamics of land rezoning for four different land use types: forest cover, agricultural, residential and
business lands. Land rezoning is assumed to occur in response to pressures generated by different
communities. For example, if a region's economy is booming and housing is starting to become scarce,
pressure from the residential community will eventually lead municipal officials to rezone additional land
to residential uses. Similar logic is used for rezoning of forest cover to agricultural land, and/or between
agricultural land and residential/business uses. Lastly, when additional agricultural land is not available,
competition builds between residential and business land use types. Then, based on pressures from
residential and business communities, land can be rezoned to try and match needs with availability.
4Additional details regarding the socio-economic model component presented here are available in a
report by Prodanovic and Simonovic (FIDS, 2003).
Figure 2: Socio-Economic Model Component Architecture
3.1.3. Feedback Linkages
The main contribution of this work is in the explicit consideration of feedback links between the hydrologic
and socio-economic model components. These are necessary as each component is responsible for the
functioning of the other. Feedback loops between the two model components are shown in a causal loop
diagram in Figure 3, and are discussed below.
The left part of the causal loop represents the structure of the continuous hydrologic component, while the
right part shows a few selected variables of the socio-economic component. From precipitation and direct
runoff information (supplied by the hydrologic component), drought levels are computed based on the
Ontario low water response rules. Drought levels are then used to estimate water use in the basin. More
intensive drought conditions lead to reduced water use efforts by all water users. Ground water recharge,
obtained from the hydrologic model component, is a variable that also influences water use. Stakeholder
consultations revealed that authorities will never allow more ground water to be used than is currently
recharged, therefore making water availability equivalent to recharge. Water use affects the possibility for
economic growth and expansion, which further modifies decisions for development of urban land. Flood
damage (obtained from direct runoff) affects population and business sectors. An assumption is made
that as flood damage increases, businesses and residents will initiate efforts to repair and/or rebuild their
damaged assets.
The socio-economic component also provides a number of inputs for the hydrologic model component.
The growth and prosperity of population and business sectors demands additional development, which
increases the amount of urban area. More urban development implies that more surfaces, once covered
with forest or open fields, become partially impervious, therefore reducing surface storage capacity in the
hydrologic model component, leading to more excess runoff during precipitation events. Water that could
have infiltrated now enters into the rivers quickly, increasing peak flow and with it flood damage. More
urban land in the basin also implies less vegetation, which means less evapotranspiration. Reduced
vegetation cover also reduces infiltration capacity, as tree roots no longer provide additional paths for
water to infiltrate. Reduced infiltration capacity therefore, eventually leads to lower ground water recharge
and thus lower ground water availability.
5Figure 3: Feedbacks between hydrologic and socio-economic components
3.2. Scenario Analysis
To illustrate the application of the model a number of scenarios, defined in terms of climatic (temperature
and precipitation) and socio-economic inputs (policy options) were developed. Each scenario is one
combination of climatic and socio-economic inputs.
3.2.1. Climatic Inputs
The climatic input is provided to the hydrologic model component by an external weather generator model
(Sharif et al., 2007). The weather generator is a simulation model that synthetically generates climate
information for a region based on both, locally observed data and outputs of the detailed Global
Circulation Models. Three climate inputs considered in this project, and include historic (or base case),
and two climate change inputs.
The historic base case input uses locally observed daily climate data for 1964-2001, together with various
perturbation and reshuffling algorithms that generate an arbitrary long period of synthetic data (including
events not previously observed) for fifteen stations in the basin. Inputs B21 and B11, on the other hand,
are generated using the same base case data, and from outputs of CCSRNIES and CSIROM2kB Global
Circulation Models (http://www.cics.uvic.ca). Input B21 is based on a future where rainfall increases in
6timing and magnitude and is thus used to evaluate the basin response to flooding. Input B11 on the other
hand, is used to represent a future where dry spells and droughts become more frequent.
3.2.2. Socio-Economic Policy Inputs
A number of different socio-economic inputs were considered, only two of which are presented here. The
reader is referred to the report by Prodanovic and Simonovic (FIDS, 2003) for additional information.
The base case (or no change) socio-economic input corresponds to current management practices
adopted in the Upper Thames River basin. The second socio-economic policy tested is one of water
conservation. A simulation was set that assumed area water users agree to reduce their per capita water
consumption by 30% when compared to the base case scenario.
4.0. Results
4.1. Hydrologic Output
The hydrologic output is presented in Figure 4, and is a result of post-processing flow data produced by
the stream gauge at Byron (western edge of London). Frequency analysis of the hydrologic output was
performed with Log Pearson III and Gumbel distributions for daily peak flood flows; Weibull distribution
was used on seven and thirty day average flows for droughts.
Figure 4: Effect of altered climate on floods and droughts in London
Analysis of possible adverse changes in frequency of peak flows can be also performed by using Figure
4. For example, if the top right graph is selected (showing the Log Pearson III fit) the peak flow of 714
7m3/s for the historic scenario has a return period of 100 years, while the same flow under wet climate B21
will occur with a return period of only 30 years. Such a finding implies that incidents of flooding in London
will occur more frequently in the future. This is true not only for large floods, but for more regular floods as
well.
A similar calculation can be performed for drought conditions, which will remain more or less the same
under the dry climate scenario B11 as shown in Figure 4.
4.2. Socio-Economic Output
During the course of this study one important and unanticipated finding became readily apparent. After
numerous simulation scenarios were performed, the following finding consistently emerged: socio-
economic policies have the potential to change to a greater degree the physical and socio-economic
landscape of the region than climate change can by itself. That conclusion was reached after analyzing
socio-economic outputs as a result of altered climates (i.e., climate input was changed while keeping
fixed socio-economic conditions).
Figure 5 shows the evolution of the state variables within urban and rural sectors of the socio-economic
model component. The interesting point revealed by the graphs is that after some two to three decades,
growth of businesses, farm units and population stops. Similarly, Figure 6 shows the results of two socio-
economic inputs for a number of land and water use variables.
Figure 5: Middlesex urban and rural conditions under RedWat
8Figure 6: Middlesex land and water conditions under RedWat
The observed behaviour appears to be counter-intuitive. This is because it challenges the widely
accepted view that strict conservation measures benefit the area by reducing total water use. Immediately
after the implementation of the conservation policy, total water use is indeed reduced. However, since the
area now has greater initial water availability (people and businesses are using less), the restriction on
future growth based on water availability is relaxed (as more water is now available). This means that
higher growth rates in the regional economy, and in the population can be sustained, thus amounting to
more growth than in the base case scenario. Eventually, the equilibrium state is reached with higher
levels of population, businesses, farm units and houses, while the area's overall water use remains the
same in comparison to the base case.
5.0. Conclusions
A systemic way of thinking, coupled with system dynamics simulation modelling offers an innovative
operational framework for implementation of integrated water resources management principles. This
framework was demonstrated to be useful in studying impacts of climate change on water resources
systems and their management. Integrated water resources modelling implemented using system
dynamics simulation has the ability to capture the complexity of both physical (i.e. hydrologic, climatic)
and socio-economic (i.e. population, land and water use) systems, as well as their interactions. The
9flexibility of using scenarios, together with an ability to check sensitivity of different management
strategies give the methodology an added practical benefit. The modelling framework can also help
uncover key leverage points, where small changes (often found in unexpected places) can bring
substantive positive and encouraging effects. The structure and flow of information between the physical
and socio-economic subsystems must be studied with a clear understanding that a system structure
(which socio-economic institutions in part generate) regulates the overall system behaviour.
An integrated water resources management model consisting of a detailed hydrologic component has
been coupled (via feedback) to a socio-economic component (built using system dynamics) for the Upper
Thames River basin. Flood and drought characteristics due to changes in the climatic signal have been
assessed, as well as consequences of two socio-economic policies. Conclusions reached imply that
flooding will have increasingly serious consequences in the future as a result of climate change, while
drought conditions are expected to remain at the same level. Socio-economic policies (of which two were
presented) indicated that simple, one-sided initiatives (i.e., strict water reduction) may not turn out to be
better in the long term than the status quo. What is needed, are policies that evaluate different
management options in combination with changing physical conditions. Then, and only then, can the
model reveal policy options needed for the long term sustainability of the basin.
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