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Article Preprint. Publication available at https://doi.org/10.1016/j.jclepro.2019.118242
© 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
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Integrated Water Resources Management 1 and Modeling: A Case Study of Bow River 2 Basin, Canada 3
4
Kai Wang1,2,3, Evan G. R. Davies1*, Junguo Liu2,3 5
1Department of Civil and Environmental Engineering, University of Alberta, 9211 – 116 St NW, Edmonton 6 Alberta, Canada, T6G 1H9 7 2School of Environmental Science and Engineering, Southern University of Science and Technology, 8 Shenzhen, China, 518055 9 3State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution 10 Control, School of Environmental Science and Engineering, Southern University of Science and 11 Technology, Shenzhen, China; 12 * Corresponding author, E. Davies, evan.davies@ualberta.ca, Phone: 1-780-492-5134 13
Abstract: This paper introduces a comprehensive modeling framework for Integrated Water Resources 14
Management (IWRM). The model incorporates water demands, allocation, and uses under various climate, 15
population, and economic scenarios as well as management strategies. Model outputs include crop yields, 16
municipal water use, power generation, recreational reservoir uses, and environmental flows. Further, a 17
novel indicator integrates many key model outputs for social, economic, and environmental assessment 18
based on the basin-scale water balance, and provides a comprehensive and clear representation of basin 19
conditions to improve the understanding of IWRM trade-offs. When applied to the Bow River Basin, 20
Canada, as a practical example, this Bow River Integrated Model (BRIM) revealed through a set of 21
modeling scenarios that (1) only the industrial demands exceeded the water license in the high-water 22
demand and reference scenarios due to population growth and economic development; (2) increased cooling 23
tower shares for thermal power plants was the most effective policy in managing industrial demand; and 24
(3) the new indicator reflected both basin-scale conditions and sectoral water stresses. Finally, model results 25
from three gaming scenarios showed socio-economic and environmental trade-offs clearly, providing users 26
with guidance in developing a comprehensive view of basin-scale water security, and hands-on experience 27
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to support IWRM. Therefore, the modeling framework and novel indicator can improve an understanding 28
of IWRM concepts and strategic trade-offs in efforts towards basin water sustainability. 29
Keywords: River basin management; IWRM; decision support tool; simulation gaming; system dynamics; 30
Bow River basin 31
1. Introduction 32
As an essential resource both for the environment and for socio-economic development, water is under 33
increasing pressure from population and economic growth, uneven resource distribution, and climate 34
change (Liu et al., 2017). Currently, approximately 4 billion people (or two-thirds of the global population) 35
live under severe water-scarce conditions at least one month of the year (Mekonnen and Hoekstra, 2016). 36
Water management to address such scarcity has the central theme of securing various human demands, 37
including environmental requirements, for long-term sustainability (UN-Water, 2015). However, 38
traditional water management, a sectoral approach that focuses on single water use sectors, such as 39
agriculture or municipalities, is insufficient to address conflicting demands and usually leads to 40
unsustainable resource use. Therefore, the UN proposed the implementation of Integrated Water Resources 41
Management (IWRM), which “promotes the coordinated development and management of water, land and 42
related resources in order to maximize economic and social welfare in an equitable manner without 43
compromising the sustainability of vital ecosystems and the environment” (Global Water Partnership 44
(GWP), 2000). 45
Increasing awareness and application of IWRM has produced significant improvements in water resources 46
management, and an “integrated” approach has been adopted in various water resources modeling efforts 47
to support IWRM. Further, both water supply and demand have been incorporated to some degree into 48
demand projections and hydrologic modeling. Recent examples include water demand projections under 49
scenarios of socio-economic and climate change with WEAP (Asghar et al. 2019), and attribution of climate 50
change and human (water withdrawal) effects on streamflow changes with SWAT (Yan et al. 2018). Surface 51
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and groundwater interactions have been modeled to manage better potentially intense competition for 52
limited fresh water. Suárez et al. (2014) and Sanzana et al. (2019) investigated surface and groundwater 53
systems to reduce water stress and assess the hydrological impacts of urbanization and irrigation, 54
respectively. Integration of water quality and quantity has also improved the validity of water resource 55
evaluations for decision support, since although it may be available, polluted water is not appropriate for 56
all uses. Buitrago et al. (2016) integrated a water quality model with water balance, hydrological and 57
hydrodynamic models in a web-based tool for operational decision making and IWRM. Liu et al. (2016) 58
assessed water scarcity comprehensively by integrating water quantity and quality, while explicitly 59
considering environmental flow requirements. Further, multiple spatial scales from upstream and 60
downstream, to regional, national, and transboundary have been the focus of water resources modeling and 61
management for sustainable water allocation and use, especially for water stress from drought or under 62
flooding conditions (Degefu et al., 2019; Busscher et al., 2019). Social, economic, and environmental 63
factors have been modeled to provide comprehensive support for water management decisions associated 64
with social equity (e.g. safety for domestic water supply, Bouckaert et al., 2018; Jensen and Nair, 2019), 65
economic efficiency (e.g. cost recovery, Schnegg and Kiaka, 2019; Langarudi et al., 2019), and ecosystem 66
sustainability (e.g. environment and river flow, Volenzo and Odiyo, 2018; Edeson, 2018). System dynamics, 67
Bayesian networks, coupled component models, agent-based models, and knowledge-based models are 68
commonly used approaches in planning and decision-making processes for IWRM. See Kelly et al. (2013) 69
for general examples of integrated environmental assessment and management, and Badham et al. (2019) 70
for guidance on the phases and steps that comprise the process for IWRM modeling practices. 71
Progress in financing, infrastructure development, and management has been weaker, according to the UN-72
Water assessment (UNEP, 2012). Significant challenges to IWRM include the division of river basins into 73
multiple jurisdictions that are not managed based on hydrologic characteristics, and a lack of practical 74
experience in integrated management (Ait-Kadi, 2014). The UN 2030 Agenda, and especially the 75
Sustainable Development Goals (SDGs), serve as a wake-up call for IWRM to deliver results on the ground 76
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that improve water security across different sectors and scales. Recommendations to fulfil the potential of 77
IWRM to deliver concrete outcomes include (1) facilitation of social learning supported by data, and 78
learning-by-doing, especially in terms of trade-offs inherent in water management decisions, and (2) 79
platforms that bring stakeholders together for collaborative decision-making (World Water Council, 2018). 80
In terms of the first recommendation, it is important to evaluate water system conditions quantitatively, 81
since specific values can provide stakeholders with a shared vision as well as information to improve their 82
understanding of water resource systems. Indicators can help to provide such a quantitative evaluation. 83
However, most water scarcity indicators are developed to provide understanding of physical conditions (e.g. 84
supply, demand, and their relationships) rather than more comprehensive information. Classical examples 85
include the Falkenmark Indicator (Falkenmark et al., 1989), the withdrawal to availability ratio (WTA, 86
Raskin et al., 1997), the cumulative abstraction to demand ratio (CAD, Hanasaki et al., 2008), the 87
sustainability index (SI, Xu et al., 2002), and the water footprint-based scarcity index (Hoekstra et al., 2012). 88
Integrated approaches are rarely applied in scarcity indicator development (Liu et al., 2017). Exceptions 89
include efforts by the International Water Management Institute (IWMI) to evaluate water scarcity from 90
both physical and economic aspects (Seckler et al., 1998), and Liu et al. (2016), who made a first attempt 91
to integrate water quantity, quality, and environmental flow (QQE) in a water scarcity assessment. 92
Further, a simulation gaming approach may contribute to both recommendations by bringing multiple 93
stakeholders together for consensus building as well as providing hands-on experience of collaborative 94
decision-making (Aubert et al., 2019; Bassi et al., 2015). Common approaches include board games, role-95
playing, online debates, scenario building, and computer simulation models (Hoekstra, 2012). In particular, 96
computer simulation models have been used as decision support tools for gaming, and provide results that 97
show trade-offs of water management decisions, improve stakeholder communication, motivate creativity, 98
promote understanding, and provide hands-on management experience (Van der Wal et al., 2016; Bassi et 99
al., 2015). Recently, the use of gaming in combination with simulation modeling, called “serious games”, 100
has received attention for water system planning and management – see examples provided by Savic et al. 101
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(2016). However, most simulation models used for water resources games focus on specific water sectors 102
such as urban demands (e.g. SimCity (D'artista and Hellweger, 2007) and WATERSTORY (Bassi et al., 103
2015)) agricultural water uses (e.g. Irrigania (Seibert and Vis, 2012)), and groundwater salinization (e.g. 104
SAHYSMOD (Inam et al., 2017)), which omit important interactions among various water use sectors. 105
This paper presents a simulation gaming model that can be used for IWRM, and applies it to the Bow River 106
Basin, Canada, as a practical example. Named the Bow River Integrated Model (BRIM), it simulates 107
sectoral water allocations and uses, and key socio-economic and environmental factors, to support IWRM-108
focused gaming. The BRIM is intended for use as a decision support tool that (1) permits a rapid simulation 109
of comprehensive, long-term IWRM-based policies and technical alternatives for management and 110
conservation, (2) improves broad understanding of basin-scale security conditions through representation 111
of the key water use sectors, (3) illustrates water sustainability trade-offs using a novel index that integrates 112
IWRM-relevant concerns – social, economic, and environmental dimensions, and (4) provides an 113
experimental environment to gain IWRM experience through simulation gaming. The BRIM can also be 114
adapted for IWRM-based management in other river basins through relatively straightforward 115
modifications made with a spreadsheet program. 116
The following sections introduce the research area, the Bow River Basin (Section 2), followed by the 117
research methodology (Section 3). Then Section 4 describes the BRIM in terms of model structure and data 118
sources (Section 4.1), model outputs (Section 4.2), and model validation (4.3). Section 5 demonstrates and 119
discusses the model results of possible applications. Finally, Section 6 provides conclusions. 120
2. Study Area 121
The Bow River Basin in Southern Alberta, Canada (Fig. 1a), has a semi-arid climate with 400-500 mm of 122
precipitation and approximately 400 mm of evapotranspiration annually (BRBC, 2010; Government of 123
Alberta, 2013). About 80% of the Bow River water source is snowmelt from the Rocky Mountains, with 124
the remainder from rain, groundwater, and glacial melt (BRBC, 2010). Approximately 46% of its natural 125
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annual flow (with 98.7% from surface water) is allocated to agricultural, municipal, and industrial 126
withdrawals (Fig. 1b); further, the 1969 Master Agreement on Apportionment (Prairie Provinces Water 127
Board, 2015) requires Alberta to pass on 50% of this flow to the downstream provinces of Saskatchewan 128
and Manitoba. In 2006, the South Saskatchewan River basin, of which the Bow River is a part, was closed 129
to new water allocations (Province of Alberta, 2007). Finally, recent studies of potential climate change 130
impacts on the Bow River Basin project increased temperature, uncertain precipitation, and decreased 131
streamflow, which all result in a greater probability of lower water availability into the future (Dibike et al., 132
2016; Jiang et al., 2015; Tanzeeba and Gan, 2012). 133
Insert Figure 1 here 134 135 Thus, the Bow River Basin is facing growing challenges in balancing growing water demands with a limited 136
and uncertain water supply. In the dry years of 2000 and 2001, with averages of 2600 million m3 annual 137
flow, the total irrigation diversions were more than 90% of the basin irrigation license of 1689 million m3 138
(AAF, 2016). In the largest city in the basin, Calgary, the municipal consumption has averaged 72% of the 139
peak-day production capacity over the past decade primarily because of high population growth, which was 140
the highest among all Canadian cities over the past five years (Statistics Canada, 2016). Poor water quality 141
and low river-flows that do not meet environmental water demands are more frequent, especially in dry 142
years and close to the basin mouth (BRPRC, 2010). Finally, in addition to its effects on basin water 143
availability, climate change may increase irrigation and municipal water demands, reduce hydro-power 144
generation, and result in less environmental flow (AI-EES and WaterSMART, 2013a; AMEC, 2009; 145
Natural Resources Canada, 2007). 146
Water management of the Bow Basin is shared among public and private organizations that focus on 147
different water use sectors (BRBC, 2005; Ali and Klein, 2014). They have taken various actions to manage 148
the risk of water scarcity, including improving on-farm water efficiency (Ammar et al., 2014), municipal 149
water conservation (City of Calgary, 2010), environmental water protection (Alberta Environment, 2006), 150
and climate change planning (AI-EES and WaterSMART, 2013b). However, there is a lack of management 151
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approaches to integrating all their concerns, such as a holistic analysis of management policy impacts and 152
their trade-offs over the long term, and communication and collaboration among different stakeholder 153
groups to ensure basin-scale water sustainability in the context of IWRM. To address this gap, the BRIM 154
can be used in the fashion of a “serious water management game” (Savic et al., 2016) to provide an 155
experimental environment for gaining IWRM experience. 156
3. Modeling Methodology: System Dynamics 157
System dynamics (SD; Forrester, 1961; Sterman, 2000) has been used widely to model complex systems 158
for decision-support purposes. It produces “causal-descriptive” models that can be used to project future 159
conditions, based on a representation of system structure in terms of stock and flow dynamics, material and 160
informational delays and nonlinear feedbacks. Winz et al. (2009) and Mirchi et al. (2012) provide further 161
details and examples of SD applications to water resources management. 162
System dynamics can “facilitate recognition of interactions among disparate but interconnected subsystems” 163
that drive the larger system's dynamic behavior, and can identify and improve understanding of their root 164
causes (Mirchi et al., 2012: 2423). Its ability to capture interactions among subsystems that can lead to 165
complex dynamics in the whole system (Davies and Simonovic, 2011; Yang et al., 2014) makes it useful 166
for integrating physical processes, socio-economic, and environmental systems to support IWRM at the 167
river-basin scale (Rusca et al., 2012). 168
Finally, SD models can be developed relatively quickly, run fast (usually within a second per simulation), 169
are typically straightforward to modify and understand, and provide clear and accessible simulation results 170
(Winz et al., 2009). Thus, SD is often used to improve understanding and learning, to promote public 171
education and participation, and to assess the effectiveness of alternative policies comprehensively and 172
inexpensively through alternative scenario building, sensitivity analysis, and gaming approaches (Mirchi et 173
al., 2012; Savic et al., 2016; Alessi and Kopainsky, 2015). 174
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4. The Bow River Integrated Model (BRIM) 175
The BRIM is a system dynamics-based lumped model that can quantify and communicate the effects of 176
water management strategies at an annual scale in the Bow River Basin. Covering the time period from 177
1996 to 2040, the BRIM includes the main water use sectors in the basin: agricultural, municipal, industrial, 178
environmental, and recreational water uses, as well as the water supply (see the names in bold type in Fig. 179
2). These sectors are connected through water allocations and other water, land, technical, and financial 180
management policies. Based on selections of these policies (inputs), the model simulates sectoral water 181
demands, allocates water supply to satisfy the demands, and finally generates water consumption and return 182
flows. Further, based on the water balance, the BRIM simulates socio-economic and environmental outputs 183
– many of which are also indicators of basin-scale sustainability. The model results then represent basin-184
scale social, economic, and environmental conditions related to the main water sectors both in the short- (1 185
or 2 year) and longer terms (>10 years). Finally, the BRIM can operate as a serious game, with a pause after 186
each time-step that allows players to change their management strategies based on current model variable 187
and indicator values; this policy adjustment process is represented in Fig. 2 as a “feedback”, which is 188
intended to improve player understanding of the impacts of human action (water management) on the 189
natural water system and their potential trade-offs. 190
Insert Figure 2 here 191
4.1. Model Structures and Data Sources 192
Several model structures in the BRIM – the water supply, population, municipal, agricultural, 193
environmental, and recreational sectors – are adapted from the Invitational Drought Tournament (IDT) 194
Model. Please see Wang and Davies (2015) for further details about the structures of these sectors. Data 195
used to adjust the model for the Bow River Basin are described in the Supplementary Material. 196
The BRIM includes a new, detailed industrial water sector based on the North American Industry 197
Classification System (NAICS, Statistics Canada 2012), which was developed to provide a consistent 198
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framework for the collection, analysis, and dissemination of industrial statistics, and is widely used by 199
policy analysts, researchers, business, and the public (see Table S1). Further, a novel integrated basin water 200
sustainability index (IBWSI) that considers key social, economic, and environmental model outputs into a 201
single index for IWRM purposes reveals management trade-offs, estimates basin-scale water sustainability, 202
and aims to support decision-making. These changes to the model make it more useful for basin-scale water 203
management, allow it to provide clearer evaluations of water resources conditions, and demonstrate its 204
potential value for adaptation to other river basins. 205
The industrial water sector in the BRIM simulates industrial water demand, allocation, and use for the 206
power generation, oil and gas extraction, mining, and manufacturing industries of the Bow River Basin – 207
see Fig.4. In the following discussion, each sector is described with a general model structure including 208
outputs, key variables and their dynamics, and policies; the data sources for model initialization are 209
provided in the Supplementary Material. 210
Insert Figure 3 here 211
The thermal power generation sector simulates power plant water demands for cooling purposes, which 212
vary with the electricity generation technology and cooling system types and their shares. In the Bow River 213
Basin, natural gas-fired power plants are cooled by once-through, cooling tower, and cooling pond systems 214
– see Table S2 for the water efficiencies and system shares. Water demand for thermal power generation is 215
simulated based on annual electricity demand and withdrawal efficiency (Fig. 3). The electricity demand 216
increases over time with population and economic growth (represented by “thermal power generation 217
change rate”) and, if not satisfied under water-stress conditions, can result in a “thermal power generation 218
deficit”, which represents one effect of water shortages on basin water sustainability. Further, the water 219
withdrawal efficiency is averaged based on cooling system shares, which are affected by a policy called 220
“cooling system changes” that is available in the gaming mode. Finally, the required water withdrawal is 221
calculated from the minimum of the current water demand and water allocation. Note that the water 222
withdrawals of other sectors are determined in the same way as in the thermal power sector. 223
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The hydropower sector simulates annual generation, water consumption, and the relative profitability of 224
production (Fig. 3), based on a simplified annual streamflow to hydropower generation relationship. The 225
volume of water consumed by evaporative losses, which are affected by climate and reservoir 226
characteristics such as the surface area, depends on the water consumption rate (m3/MWh). Losses in profit 227
result from a management policy used in the gaming mode, “hydro reservoir drawdown”, to mitigate 228
downstream water stress (usually during summer seasons and typically used to maintain environmental 229
requirements). Although such summer reservoir releases do not necessarily affect total annual hydropower 230
generation, these variables are intended to represent their possible effects on profitability that result from a 231
lower power pool price in Alberta in summer than in winter (TransAlta, 2014). Reservoir releases also 232
reduce the recreational value of reservoir parks (Ledwaba, 2011). 233
The mining water sector simulates annual water demand, use, and mining production (Fig. 3). Mining in 234
the Bow River Basin produces primarily non-metallic minerals – limestone that is used to produce cement, 235
hydrated lime, and pulverized limestone (Natural Resources Canada, 2014). These three products are used 236
here to estimate the limestone production based on a weighted percentage (Table S3). Mining water demand 237
is simulated based on annual limestone production and water use efficiency, with the annual production 238
demand assumed to be the plant production capacity, and water efficiency assumed to be constant. Then 239
water withdrawal is used to calculate the mining production. In normal years, the water demand is satisfied; 240
however, in water-scarce years, production may be reduced, as represented by the “fractional mining 241
production reduction”. 242
The oil and gas extraction sector simulates annual oil and gas production using parallel model structures, 243
one for oil and one for natural gas, as well as the associated water demand and use. The oil extraction 244
subsector, described here as an example, was developed based on the Hubbert peak theory (Hubbert, 1956) 245
and the Fossil2 model developed by Naill (1992) – see Fig. 3. Production is determined from (1) established 246
reserves, (2) extraction capacity, and (3) capacity utilization, and is subdivided into conventional and 247
hydraulic fracking production based on an adjustable “fraction of fracking production” ratio. The 248
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established reserve is modeled as a stock with discovery increasing and extraction decreasing its value, 249
while the extraction capacity depends on the number of wells and average well production, also modeled 250
as stocks. Finally, capacity utilization is variable, and is determined by price (modeled as a lookup function) 251
and water stress values. 252
Water demands for oil and natural gas, which are similar to other industrial sectors and are not shown in 253
Fig. 3, are simulated based on the production of each resource and its corresponding water use efficiency, 254
which is affected by the extraction method. The water withdrawal is determined by the water allocation 255
priority; it can also be adjusted over time by a policy that permits “alternative water sources utilization”. 256
Under water-scarce conditions, the water withdrawal may not satisfy demands due to a limited water 257
allocation. The available supply is then allocated to oil and gas extractions by their relative values ($/m3 258
water withdrawal), as determined from the resource market price, production cost, and water use efficiency. 259
Finally, the manufacturing sector calculates the profit for three manufacturing categories (high, medium, 260
and low water value-added ratios, in $/m3 of water intake), and the corresponding water demand and use 261
values. These three categories represent profit losses for a reduction in water access (Martz et al., 2007) for 262
the specific industry types in each category. The maximum profit for each manufacturing category is driven 263
by the corresponding manufacturing GDP in Calgary. The annual manufacturing water demand is 264
calculated from the combination of maximum profit ($) with the value-added ratio ($/m3 water intake), 265
which can change over time through technological improvements. Then manufacturing water withdrawal 266
is allocated to each of the three categories according to their value-added ratios, with the allocated volume 267
used to calculate the profit. Under water stress conditions – when the actual profit may be lower than the 268
maximum profit, represented as a fractional profit reduction – the resulting decrease in profitability drives 269
a gradual conversion from lower to higher value-added ratio groups. 270
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4.2. Model Outputs 271
The BRIM includes agricultural, municipal, industrial, and environmental water sectors. The model 272
simulates their water demands based on various climatic, economic, and demographic scenarios, as well as 273
each sector’s actual water use (which may be less than the demand) according to water supply and 274
management policies. Further, based on the water use values, socio-economic outputs are calculated, such 275
as crop yields, power generation, oil and gas extraction, recreational profits, and environmental flows. 276
Following recommendations on indicators for integrated management, such as the inclusion of 277
environmental flows and social and economic factors (Liu et al., 2016; Damkjaer and Taylor, 2017), model 278
outputs also include a new basin-scale water sustainability index that incorporates key model variables from 279
each water-use sector commonly used for IWRM assessment (see Fig. 4). This index, called the Integrated 280
Basin Water Sustainability Index (IBWSI), is intended to reveal water stress through the water balance 281
(supply and demand), as the other indicators do, but also to provide greater detail on the social, economic, 282
and environmental components of concern for IWRM and to improve the multi-dimensional understanding 283
of human (management policy) impacts on the natural water system and its feedbacks on the system state 284
to improve management. This focus on cross-sectoral water demands and human-nature interventions 285
differentiates IBWSI from the other common indices such as WTA and the Falkenmark Indicator. 286
Insert Figure 4 here 287
The “security” values used for the social component is defined as the supply reliability under water stress. 288
They are calculated using actual sector water use or power generation divided by the sectoral water or 289
power demand (under conditions of no water stress). The “profit ratios” for the economic component are 290
used to reveal the fraction of the maximum profit achievable under water stress, and are calculated as the 291
ratio of each variable’s actual profit to its maximum profit (without water stress). Finally, the “requirement” 292
values of the environmental component are defined as the percentages of specific water requirements that 293
have been met and are represented by the ratios of actual river flows to the conservation or downstream 294
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requirement flows. Each component value is calculated as the average of its related variable values, and the 295
IBWSI is represented by the geometrical distance between the point of component value (c1, c2, c3) and the 296
point of best value (1, 1, 1) in a three-dimensional (social, economic, and environmental) space, see 297
equation (1). IBWSI values of 0 correspond to sustainable conditions, values between 0 and 0.2 represent 298
low water stress, and values higher than 0.2 indicate unsustainable conditions. Further, to aid comparison, 299
four common indices are modified (represented with *) so that values higher than “0” represent 300
unsustainable conditions, based on their original thresholds. See equations (2)-(5). 301
𝐼𝐵𝑊𝑆𝐼𝑐1
(1)
𝑊𝑇𝐴∗0 𝑊𝑇𝐴0.4
𝑊𝑇𝐴0.4 𝑊𝑇𝐴0.4 (2)
𝑆𝐼∗0 𝑆𝐼0.2
0.2 𝑆𝐼 𝑆𝐼0.2 (3)
𝑄𝑄𝐸∗0 𝑄𝑄𝐸 1
𝑄𝑄𝐸 1 𝑄𝑄𝐸 1 (4)
𝐹𝑎𝑙𝑘𝑒𝑛𝑚𝑎𝑟𝑘∗
0 𝐹𝑎𝑙𝑘𝑒𝑛𝑚𝑎𝑟𝑘1000
1000 𝐹𝑎𝑙𝑘𝑒𝑛𝑚𝑎𝑟𝑘
1000 𝐹𝑎𝑙𝑘𝑒𝑛𝑚𝑎𝑟𝑘1000 (5)
In its conventional form, WTA is defined as annual basin-scale withdrawals divided by the renewable 302
resources, with values higher than 0.4 representing high water-stress conditions (Raskin et al., 1997). SI 303
divides the water surplus by the water supply in a given region, and ranges in value from zero, which is 304
unsustainable, to values greater than 0.2, which are no-stress conditions (Xu et al., 2002). QQEquantity is the 305
ratio of blue water footprint to availability; the basin is under water stress conditions when the value is 306
higher than 1 (Liu et al., 2016). The Falkenmark Indicator proposes less than 1000 m3/person/year as the 307
threshold of water scarcity (Falkenmark et al., 1989). 308
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4.3 Model Validation 309
The BRIM model structures were developed and parameters were set based on existing models and 310
published data to ensure that mathematical equations and variable values represented corresponding real-311
world systems adequately. The model was also subjected to several extreme-condition tests to ensure that 312
it generated reasonable results even for significant changes in input. Further, Monte Carlo simulations were 313
also used to evaluate the uncertainty of a variety of model equations and parameters. Fig. 5 shows the results 314
of model validation, comparing model behavior with historical data and trends for the Bow River Basin in 315
terms of social, economic, and environmental variables at an annual scale. The coefficient of determination 316
(R2) and normalized root mean square error (NRMSE) were used to evaluate the magnitude of variance 317
explained by the model compared with the total actual variance, and the percentage of differences (between 318
actual and simulated values) in the mean actual value. In general, the model outputs explained most of the 319
actual data with acceptable errors. 320
Insert Figure 5 here 321
5. Results 322
This section provides model results for sample simulations and a set of gaming simulations based on three 323
possible water demand scenarios. These scenarios are intended to illustrate a wide range of plausible socio-324
economic, climate, and management conditions into the future in the Bow River Basin; they also provide 325
an indication of the capabilities of the BRIM as a gaming decision-support tool, and could form the basis 326
of comparisons against gaming scenarios. 327
5.1. Scenario Setup 328
Three water demand scenarios – high water demand (HWD), reference (REF), and low water demand 329
(LWD) – were prepared with different population growth rates, climate change and economic conditions, 330
and water management policy adoptions, as shown in Table 1. The population growth rates were set to 331
values of 2.3% and 3.1%, which are -10% and +20% changes to the historical average growth rate of 2.6% 332
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per year (Government of Alberta, 2015); the low growth rate (2.3%) was observed in 2010, while the high 333
growth rate (3.1%) matches a high population projection from the Government of Alberta (2015). 334
Representative climate scenarios were developed based on recent studies (Jiang et al., 2015; Rood et al., 335
2016; Tanzeeba and Gan, 2012) that have projected 10%-20% increases in temperature, uncertain 336
precipitation, and decreases in streamflow corresponding to higher evapotranspiration during spring and 337
summer seasons in Alberta. The overall impact was projected to increase the irrigation requirement (AMEC, 338
2009), simulated by reducing precipitation values in the BRIM. Where available, values for the economic 339
scenarios of LWD, REF, and HWD were set according to the minimum, average, and maximum historical 340
values, respectively. Management policy selections were based on historical management actions in the 341
Bow River Basin. Please see the Supplementary Material for the details of the historical data used in the 342
scenarios. Note that where values were not available from historical statistics, 10%-30% changes relative 343
to reference values were used to generate plausible ranges of future conditions. 344
Table 1: Simulation scenario configurations 345 Scenarios and their configurations HWD REF LWD
Population Population growth rate 3.1% 2.6% 2.3%
Climate Temperature change rate 20% 0 0
Streamflow change rate -20% 0 0
Precipitation change rate -20% 0 0
Economy
Annual irrigation land expansion rate 0.6% 0.25% 0.25%
Annual power generation increase rate 4% 2% 2%
Annual mining production increase rate 1% 0 0
Change in mfg. GDP increase rate 10% 0 0
2040 oil and gas price, $/bbl, $/m3 92, 0.4 66, 0.23 66, 0.23
Agricultural policy
Annual irrigation efficiency increase rate 0 0.45% 0.5%
Crop mix changes (e.g. forage area change) 10% 0 -10%
Irrigation reservoir drawdown, million m3 0 0 0
Livestock rate reduction (beef, pigs), million 0 0 0.17, 0.03
Municipal policy
Conservation programs (outdoor), lpcd 0 2 6
Leak management for conservation 0 50% 60%
Economic incentive (increase of low-flow appliance adoptions) 0 0.3% 0.5%
Greywater reuse (except for kitchen uses) 0 0 30%
Xeriscaping (outdoor water conservation) 0 0 30%
Industrial policy Cooling system changes a 0 0 10%
Hydraulic fracturing utilization rate change b 20% 0 -10%
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Saline water usage rate c 0 0 10%
Mfg. value-added ratio improvement rate d 0 0 10%
Environmental policy Hydro reservoir drawdown, million m3 0 0 0
a Changing cooling tower share rates of thermal power plant in 2018 balanced by a corresponding reduction of once-through and 346 cooling pond shares respectively; b Changing percentage of oil and gas production using fracturing method each year; c Using given 347 percentage of demand as saline water each year; d Improving manufacturing water use efficiency (measured by value-added ratio, 348 $/m3 water intake) by given percentage each year. 349
Three aspects of model behavior, termed model investigations, and a set of gaming results are discussed 350
below, based on these three scenarios: 351
Investigation 1 determines basin-scale and sectoral water withdrawal demands of the three 352
scenarios, and compares them against licensed water allocations in the Bow River Basin to 353
investigate the sustainability of water use. 354
Investigation 2 explores how industrial demands can be affected by five industrial water 355
management policies – i) 10% decrease in the hydraulic fracking utilization rate, ii) 10% use of 356
saline groundwater, iii) 10% increase in manufacturing water efficiency, iv) 10% increase in 357
cooling tower share, v) combined industrial policies including i), ii), iii), and iv) – and compares 358
them against the highest water stress scenario, HWD; 359
Investigation 3 compares the IBWSI against modifications of four common indices – WTA*, SI*, 360
QQEquantity*, and the Falkenmark* Indicator – for the scenario with the highest water stress, HWD. 361
The CAD indicator is not calculated because some of the variable values required are not simulated; 362
and, 363
Investigation 4 illustrates effects on the IBWSI and its component values of three water 364
management gaming scenarios by operating the BRIM as a game – which pauses after each annual 365
time-step to allow players to change their management strategies based on current model variable 366
and indicator values. 367
For investigation 4, we developed three gaming scenarios – G1, G2, and G3 – to illustrate efforts to 368
mitigate water stress impacts based on the highest water stress scenario, HWD, with emphasis on 369
environmental, economic, and social aspects, respectively. G1 satisfied environmental demands by 370
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rationing municipal allocations to 434 MCM, 421 MCM, and 421 MCM (license is 491 MCM) and 371
drawing down the reservoir by 15 MCM, 72 MCM, and 72 MCM in 2038, 2039, and 2040, respectively. 372
G2 aimed to mitigate the economic impacts of water stress on the industrial sector by improving the 373
manufacturing value-added ratio by 2% each year after 2028, rationed municipal water allocation as 374
G1, and rationed environmental water allocation by satisfying non-environment demands first. Finally, 375
G3 satisfied thermal power generation demands by rationing other industrial sectors’ allocations (e.g. 376
oil and gas) after 2032, and to ration the environmental water allocation in the same manner as in G2. 377
5.2. Scenarios Results 378
In Investigation 1, the basin-scale water withdrawals in all three scenarios were within existing water 379
licenses for the entire simulation period (Fig. 6a). Note that inter-annual fluctuations resulted from 380
variations in irrigation demands (approximately 80% of the total demand), which were caused by the 381
prescribed precipitation patterns. Thus, the irrigation demands, which were around 500 million m3 in 2030, 382
were much lower than projected values (AMEC, 2009), which were around 1400 million m3, due to the 383
high precipitation levels applied in the BRIM. In the same year, the BRIM projected municipal demands 384
(205, 304, 369) and industrial demands (26.5, 40, 44) of the LWD, REF, and HWD scenarios were quite 385
close to the AMEC projections, which were 307 and 33 million m3. 386
In 2035, the irrigation demand reached a maximum of 85% of the license in the HWD scenario (Fig. 6a), 387
and the irrigation demand was lower than the licensed volume (Fig. 6b) even as climate change increased 388
it by 20%-30%. In the same scenario, municipal water demand approached 95% of its license by 2040 (Fig. 389
6c). Finally, industrial demands exceeded the water license in 2028 and then 2036 in the HWD and REF 390
scenarios, respectively (Fig. 6d); the increasing demand resulted mainly from the growth of power 391
generation and manufacturing GDP. Comparison of the simulated water demands with the licensed 392
allocations at both basin and sectoral scales permits an exploration of sectoral water security under a wide 393
range of conditions, demonstrates potential approaches to avoid water shortages, and illustrates potential 394
outcomes for sectoral users when demands exceed licenses. Further, the different water security conditions 395
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18
at the basin and sectoral scales also reveal the importance of integrating all water use sectors for assessment, 396
as one water use sector experiences water stress (Fig. 6d) while the overall basin is water secure (Fig. 6a). 397
Insert Figure 6 here 398
Industrial demands for the five management scenarios of Investigation 2 are shown in Fig. 7, along with 399
the HWD scenario. The model clearly illustrated the impacts of different policies. Policies 2i) and 2ii) had 400
a very limited impact on the demand compared to the HWD scenario, with less than a 1% reduction in 401
demand. The reason is simple: these two policies only reduce the water requirements for oil and gas 402
extraction, which represent only 4% of the industrial demand in 2018. Note, however, that reductions in 403
water demands for oil and gas production may result in better surface water quality (Jensen, 2008), which 404
is not simulated in the BRIM. Policy 2iii) decreased the total industrial demand by 8% through reducing 405
the manufacturing water demand by 25% from 2023 onwards, after a five-year (2018-2022) implementation 406
period. Suitable water conservation policies and technologies for the manufacturing sector could include, 407
for example, dry cleaning processes for food and beverage manufacturing and water reuse for chemical and 408
machinery manufacturing (Fabricators and Manufacturers Association, 2013). Policy 2iv) decreased the 409
total industrial demand by 25% through a 42% reduction in power plant water demands from 2027 onwards 410
(ten years was assumed necessary to complete this policy). To increase cooling tower shares from 80% to 411
90% through 5% reductions in once-through and cooling pond shares would be expensive and time-412
consuming (Kablouti, 2015), which is the main trade-off of this policy. Finally, the combined policy 2v) 413
was clearly the most effective in reducing total industrial demand, and was the only scenario that kept 414
demand within the current water license (44 million m3) over the course of the simulation. This sort of 415
comparison of effectiveness also provides support for water management policy development and aids 416
IWRM assessments, despite the omission of some trade-offs, such as application costs, in the current 417
version. Note that site-specific policy impacts can be easily modified for different basins through changing 418
values stored in a spreadsheet. 419
Insert Figure 7 here 420
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Results of the IBWSI and four modified indices for Investigation 3 are shown in Fig. 8. Only the WTA* 421
and SI* indices showed significant water stress in 2019 and 2020, primarily because they do not count return 422
flows as available water. Further, the water stress conditions revealed by WTA* (2033-2040), SI* (2033-423
2040), Falkenmark* Indicator (2037-2040), and QQEquantity* (2038-2040) resulted from a climate-change-424
induced supply reduction and an increase in demand with population growth and economic development. 425
In contrast, the IBWSI revealed unsustainable conditions earlier than the other indicators, as it incorporates 426
water conditions for the whole basin as well as for sectoral users; thus, the water stress in the industrial 427
sector that began in 2028 increased the overall IBWSI value as the economic component of the index fell. 428
Further, the IBWSI represents water sustainability not only through the water balance, but also through 429
basin-scale economic, social, and environmental conditions. Therefore, the three subcomponent values 430
integrated into the IBWSI provide a comprehensive overview – and capture the multi-faceted nature – of 431
basin-scale security, and can be used to assist IWRM assessments and practices in achieving basin water 432
sustainability. In comparison with the other indicators, which are straightforward, easy to understand and 433
produce aggregate values for the whole river basin, the more complicated IBWSI reflects both an overall 434
basin-scale and sectoral water stresses, and is not as volatile as the other indicators because of the integration 435
of multiple factors in the calculation; however, it requires more detailed information. 436
Insert Figure 8 here 437
Investigation 4 showed the performance of the three gaming scenarios from 2025 to 2040. The IBWSI and 438
subcomponent index values revealed clear differences among the gaming scenarios and demonstrated the 439
effects of policy trade-offs in IWRM (Fig. 9). Clearly, all scenarios had increasing IBWSI values after 2028, 440
since the industrial demand began to exceed its license in the HWD scenario (Fig. 6d), and the resulting 441
water deficit reduced industrial profits in subsequent years. Therefore, G2 had the lowest values after 2028 442
since the manufacturing water management policy it implemented reduced the industrial water demands, 443
which then mitigated the potential losses in industrial profits (Fig. 9c) and reductions in power generation 444
(Fig. 9d). After 2038, to mitigate severe water stress in the industrial sector, G2 rationed environmental and 445
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municipal allocations, which decreased environmental and social indices (Fig. 9b and 9d) and generated a 446
further increase in the IBWSI value. G3 had the highest IBWSI values, since it tried to satisfy social (e.g. 447
municipal use and power generation) demands under water stress condition (Fig. 9d) by rationing other 448
industrial sectors and environmental demands without any conservation management, which then reduced 449
the environmental and industrial profit (economic) indices significantly (see Fig. 9b and 9c). Note that the 450
increased environmental indices of G2 and G3 after 2039 (Fig. 9b) were due to increased precipitation. G1 451
satisfied environmental demands in severe water stress years (particularly after 2037) by rationing 452
municipal allocation and drawdown reservoir storage, which gave this scenario the highest environmental 453
index during these years. However, such management also generated low economic and social index values 454
(Fig. 9c and 9d). 455
IBWSI integrates social, economic, and environmental considerations to support IWRM, as recommended 456
for water governance by the scientific community. However, it is difficult to identify the specific trends or 457
trade-offs that drive changes in the indicator value, since it integrates large numbers of variables together. 458
For example, Pires et al. (2017) investigated 170 water indicators for IWRM and highlighted 24 that cover 459
the majority of the social, economic, environmental, and institutional aspects. However, the use of a single 460
indicator obscures the interactions and trade-offs among these integrated components. Instead, the “serious 461
game” is an effective tool to understand the causes of changes in IBWSI through user interaction with the 462
model – in a learn-by-doing process – and thus to promote a better understanding of trade-offs in IWRM. 463
Overall, the gaming results showed the trade-offs of different approaches clearly, which could help model 464
users to assess basin-scale water security comprehensively and to improve their understanding of the role 465
of IWRM management options in increasing basin-scale water sustainability, especially under water scarce 466
conditions. More importantly, such a gaming approach is also useful to improve the mutual understanding 467
among multiple stakeholders, a critical step in promoting IWRM application to ensure that all stakeholders 468
can participate effectively in conflict resolution efforts and in building consensus. 469
Insert Figure 9 here 470
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6. Conclusions 471
This paper introduced a flexible and transferable framework that can be used for IWRM to promote 472
understanding and quantification of IWRM principles at the river-basin scale. Called the Bow River 473
Integrated Model (BRIM), it was applied above for both traditional simulation and simulation gaming 474
scenarios. Model structures were modified from the IDT Model with calibration to the Bow River Basin, 475
Canada; further, a new industrial sector and an integrated basin water sustainability index (IBWSI) were 476
introduced. The BRIM simulates water balance and socio-economic factors of the five main basin-scale 477
water uses that are relevant to IWRM-based policies and technical alternatives, and illustrates the evolution 478
of the basin-scale system under various population, economic, and climate uncertainties. The current 479
version of the BRIM does not consider policy application costs, which will be added in future research to 480
better support decision making. 481
Understanding the trade-offs of water management policies plays a key role in achieving overall social, 482
economic, and environmental sustainability. Indicators permit a quantitative evaluation of river basin 483
conditions to guide more comprehensive and efficient management, and aid a comparison of different 484
policy performance criteria. The new IBWSI provides a comprehensive and clear illustration of basin-scale 485
and cross-sectoral scale sustainability in the context of IWRM, and incorporates aspects of the basin-scale 486
water balance as well as social, economic, and environmental conditions. In comparison with other water 487
stress indicators, IBWSI provides greater insight into the causes of unsustainable conditions and is less 488
volatile, but requires more detailed information, which means that its calculation is unfeasible where 489
variable values are unavailable in a region. Further, the choice of variables used to calculate IBWSI may 490
differ by river basin, and institutional criteria are excluded at present. 491
Integration is widely recommended in indicator development for water scarcity assessment. However, using 492
integrated indicators to support decision making for IWRM is difficult because of the complexity of river 493
basin systems. Here, we argue that simulation gaming can be an effective approach to improve the 494
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understanding of the management trade-offs in a complex system, and could better support the development 495
of IWRM based policies. Thus, the powerful role of “serious games” in supporting IWRM should be further 496
investigated in future research. 497
Finally, although the Bow River Basin was the focus of this research, the BRIM can also be applied with 498
limited modification to other basins that face similar water management issues, and it has the potential to 499
support basin water management and planning, drought management, water policy assessing and 500
development, public and student education and engagement, as well as serious water management games 501
in the context of IWRM. 502
Acknowledgments 503
The authors thank A. Boulton-Chaykowski, C. Osuji, and S. Tanzeeba, for their interest and help, and for 504
the data they provided. This research was supported by the Alberta Land Institute at the University of 505
Alberta, Canada; the National Natural Science Foundation of China (41625001; 41571002) and the 506
Strategic Priority Research Program of Chinese Academy of Science (Grant No. DA20060402). 507
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700
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701
Fig. 1. (a) Bow River Basin map (BRBC, 2005) and (b) basin water allocations (BRBC, 2016). 702
(a) (b)
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703 Fig. 2. General structure of the BRIM (yellow: sample management policies, red: sample model outputs). 704
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705 Fig. 3. Basic structure of industrial water sector (green: inputs; yellow: policies; red: outputs; < >: shadow 706 variables as copies of existing variables). 707
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708 Fig. 4. Framework of IBWSI. 709
Irrigation water security
Municipal water security
Power security
Agricultural profit ratio
Power generation profit ratio
Mining profit ratio
Oil and gas profit ratio
Manufacturing profit ratio
Recreational profit ratio
Water conservation requirement
Social (c
1
)
Economic (c
2
)
Environmental (c
3
)
IBWSI
Variable Component
Downstream requirement
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710 Fig. 5. Model simulated results and estimated actual data. 711
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712 Fig. 6. Water withdrawal demand and license of (a) basin scale, (b) irrigation, (c) municipal, and (d) 713 industrial sectors. 714
3000
2250
1500
750
02016 2020 2024 2028 2032 2036 2040
70
52.5
35
17.5
02016 2020 2024 2028 2032 2036 2040
2000
1500
1000
500
02016 2020 2024 2028 2032 2036 2040
600
450
300
150
02016 2020 2024 2028 2032 2036 2040
(a) (b)
(c) (d)
million m3
water demand and license (10
6
m
3
)
water demand and license (10
6
m
3
)
water demand and license (10
6
m
3
)
water demand and license (10
6
m
3
)
HWD water demand
Licensed withdrawal
LWD w ater demand
REF water demand
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715 Fig. 7. Industrial water withdrawal demand. 716
industrial water demand (106m3)
70
57.5
45
32.5
20 2016 2020 2024 2028 2032 2036 2040
Time (Year)
2i) fracking utilization rate -10%
2ii) saline water usage +10%
2iii) mfg. water efficiency +10%
2iv) cooling tower shares +10%
2v) combined industiral policies
base case
HWD
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717 Fig. 8. Basin water indicators under HWD scenario. 718
indicator values
IBWSI
Falkenmark*
WTA*
SI*
QQEquantity*
0.5
0.375
0.25
0.125
0
2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040
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719 Fig. 9. Three gaming aspects performances of (a) IBWSI, (b) environmental index, (c) economic index, 720 and (d) social index under HWD scenario. 721
environmental index
economic index IBWSI index
2025 2028 2031 2034 2037 2040
2
02520282031203420372040
2025 2028 2031 2034 2037 2040
2025 2028 2031 2034 2037 2040
G1
G2 G3
social index
(a) (b)
(c) (d)
0.5
0.375
0.25
0.125
0
1
0.975
0.95
0.925
0.9
1
0.875
0.75
0.625
0.5
1
0.95
0.9
0.85
0.8
