Impact of Resilience Policies on Cape Town's Water-Food Nexus: A System Dynamics Approach

doi:10.21203/rs.3.rs-4642533/v1

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Impact of Resilience Policies on Cape Town's Water-Food Nexus: A System Dynamics Approach

https://doi.org/10.21203/rs.3.rs-4642533/v1

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Background

Climate change is increasingly affecting the supply of resources such as water and food. From 2015 to 2018, Cape Town endured its most severe drought on record. Yet, resource management often occurs in isolation, which contrasts with the holistic perspective provided by the nexus concept that recognizes the interdependence of resource sectors. This study employs system dynamics modelling, to examine the City of Cape Town’s (CoCT) water-food trade-offs and interactions using qualitative and quantitative approaches. It assesses various policies proposed by the CoCT, to improve system resilience and to boost future water supplies, examining their efficacy and potential drawbacks. These policies are tested against future scenarios including population growth and climate change predictions of different severities.

Results

The simulation results show an increase in food demand, which is mainly linked to population growth and a significant decrease in water availability. Without interventions, the CoCT is expected to experience serious water shortages within the 40-year simulation period.

Conclusions

The findings indicate that the CoCT’s strategies will effectively secure adequate water for its expanding population. However, a major concern was found to be the proposed intensification of aquifer exploitation. The model predicts that such an approach could lead to over-abstraction of some aquifers, compromising their sustainability.

water-food nexus

sustainable resource management

policy analysis

systems thinking

water planning

Climate change, population growth, and urbanization present significant challenges for major cities, exerting pressure on vital resources such as water and food. The City of Cape Town (CoCT) experienced a severe drought between 2015 and 2018, leading the water levels of the six major dams supplying water to plunge to critically low levels (City of Cape Town, 2019). Traditionally, resource-related issues have been tackled in isolation, without considering their interconnectedness. The water-energy-food (WEF) nexus, introduced at the 2011 Bonn conference, advocates for a comprehensive and holistic approach to resource management (Hoff, 2011). This nexus facilitates an understanding of these resource systems and examines their interdependencies (as illustrated in Fig. 1). For instance, water is essential in the production of food, irrigation and food processing, while food or food waste can be used to synthesize biofuels. Similarly, energy is indispensable for food processing and transportation, for water conveyance or water treatment and desalination. Additionally, water can be used to generate energy in the form of biogas (Egieya et al., 2017). Holistic strategies applied to the WEF nexus can reveal potential trade-offs and synergies among various strategies. As the 2030 deadline for the United Nation’s Sustainable Development Goals (SDG) approaches, the WEF nexus method is increasingly recognized as a crucial instrument for achieving key objectives such as clean water and sanitation (SDG 6), zero hunger (SDG 2) and affordable and clean energy (SDG 7) (Egieya et al., 2024).

The nexus approach is versatile, and applicable over a range of scales, from global to household scales. On a global scale, Sušnik (2018) explored WEF nexus resource use and gross domestic product (GDP) correlations. At the national level, van den Heuvel et al. (2020) examined the impacts of policies on WEF resources. Many nexus studies are focused on water and use natural boundaries, such as river basins as their system boundaries, for instance, Siderius et al. (2022) assessed water conservation approaches in the Tanzanian Rufiji basin. Administrative boundaries such as provinces or federal states can also define study areas as evidenced by Wu et al. (2021) who used a system dynamic modelling approach to analyze the WEF nexus within the Canadian Saskatchewan province. Another scope commonly used for nexus studies is the urban level. Combining a hydrological model with agent-based modelling, Ding et al. (2021) investigated the effects of water-related policies on the WEF nexus in Cape Town. Moreover, Egieya et al. (2024) utilized system dynamics to make projections for the availability of WEF resources in Cape Town. On the smallest scale, household resource use is studied within the nexus framework, as seen in Hussien et al. (2017).

Criticism of the WEF nexus concept often centered on the lack of real-life results following nexus research (Cairns & Krzywoszynska, 2016), and the lack of a universally agreed-upon nexus concept (Sušnik & Staddon, 2021). According to Brouwer et al. (2020), the variable definition of the WEF nexus is an advantage because it can be expanded or narrowed down as needed.

To effectively address the intricacies of the WEF nexus, various tools and methodologies are used. Although numerous existing tools such as CLEWs (climate, land, energy, and water systems approach) and MuSIASEM (multi-scale integrated analysis of societal ecosystem metabolism) are available, they often face criticism for their lack of user-friendliness, or for their limited applicability to a narrow range of use cases (Dargin et al., 2019). Tools must be chosen based on what is expected from the analysis: some tools are static while others can model dynamic characteristics within a system, some are qualitative, and others are quantitative (Egieya et al., 2022). System dynamics provides a versatile approach to modelling the nexus, as it allows for application at any scale and accommodates both qualitative and quantitative modelling of the problem (Ford, 2009). Furthermore, although most nexus studies primarily focus on the integrated Water-Energy-Food (WEF) nexus, specific components of the nexus, such as the water-food, the water-electricity, or the electricity-food nexus (Gozini et al., 2021; Koppelmäki et al., 2022; Strokal et al., 2019) can be studied.

There is limited existing literature on the Cape Town WEF nexus. Ding et al. (2021) tested the effects of three distinct policies on the nexus where two of the tested policies focused on water tariffs, while the third policy involved supplementary water resources. They conclude that the policy providing additional water supplies showed the most beneficial results, as water tariff increases were likely to negatively impact indigent and lower-income households. In a more recent study, Egieya et al. (2024) utilized system dynamics to predict WEF resource usage within the CoCT over 20 years, providing an overview of the developments in the three nodes. They investigated some predicted impacts of climate change on the system and quantified how the addition of a water supply stream in the form of water reuse could mitigate these impacts.

However, while there is a large body of research on the WEF nexus, and some research specifically on the WEF nexus for Cape Town, in-depth research on the interconnections of water and food resources has not been done. The focus is often either exclusively on water or on the water-energy node, whereas the food aspect of WEF nexus research is often poorly described (Endo et al., 2017). The water-food interaction is a key novelty of this study.

Hence, in this study, the interconnections specifically between food and water in the City of Cape Town (CoCT) are explored, and policies planned to be implemented by the CoCT by 2035 are analyzed using system dynamics. By examining the water-food node of the system, possible benefits and drawbacks of interventions can be better understood.

Study area

As South Africa’s second-largest city, Cape Town was home to approximately 4.6 million residents in 2020 (City of Cape Town, 2020). Figure 2 illustrates the location of the city within the Western Cape Province of the country. Characterized by its temperate climate, Cape Town experiences dry summers and wet winters. The city’s average annual temperature is 16.7^°C with an average yearly precipitation of 515 mm (Petschelt & Hermann, 2013).

Climate change and an expanding population are exacerbating the scarcity of water resources in Cape Town. If the winter rain is insufficient, the city faces challenges in meeting the water needs of the residents. For instance, due to the 2015 to 2018, severe drought the most intense in Cape Town’s recorded history (Egieya et al., 2024), the levels of water in the six major dams which account for 96% of the city’s water supply, dropped to below 13.9% (Webster, 2019). This severe situation necessitated the implementation of stringent water conservation measures. Given the strong interdependence of water and food through irrigation, as well as the water required for food production and processing, the drought had profound repercussions on agriculture and food production in Cape Town and its surrounding areas (Green Cape, 2019). Water scarcity can thus precipitate food scarcity, particularly, in areas where agriculture plays an important role in the local economy, such as Cape Town, where agriculture is vital for food production and a significant source of employment, with 44 000 residents working in this sector in 2020 (City of Cape Town, 2020). A decline in agricultural productivity inevitably leads to job losses. Moreover, resource shortages in the water and food (i.e. agriculture) sectors are further compounded by the nationwide electricity shortage in South Africa, which necessitates regularly scheduled power outages called “load-shedding”. This energy shortfall hampers the provision of other resources, as electricity is essential for water transportation and food production (Ryan, 2022). These interlinked challenges underscore the need for sustainable policies to ensure the future fulfilment of Cape Town’s resource demands.

System dynamics

The WEF nexus is best understood as a system thinking challenge. This perspective is supported by Schlör et al. (2021), who analyzed the nexus employing the system criteria developed by Meadows (2008). Consequently, system dynamics was selected as the most appropriate methodology for describing the Cape Town water-food nexus. For modelling purposes, the chosen software tool is STELLA Architect by isee systems (isee systems, 2023c).

System dynamics modelling is typically conducted in two distinct stages: a qualitative stage and a quantitative stage. The qualitative stage is conducted using causal loop diagrams (CLDs), which help to map and visualize the interactions and feedback loops within the system. The quantitative stage is implemented using stock and flow diagrams (SFDs), which quantitatively represent the accumulations and flows of resources or information. These stages are further elaborated in the subsequent paragraphs of the methods section.

Causal loop diagram of Cape Town’s Water-Food Nexus

The construction of the causal loop diagram (CLD) was based on extensive research into the water-food system and its interconnections. Central to the diagram portrayed in Fig. 3, are variables related to the availability of water and food resources. The water availability variable forms reinforcing loops with water resource elements such as dams and aquifers, implying that more water in the reservoirs equates to greater overall water availability. Conversely, water-utilizing variables like residential water use or food processing establish balancing loops, implying that an increase in water use would lead to a decrease in overall water availability. In Fig. 3, the reinforcing loops are labelled with an “R” while the balancing loops are denoted with a “B”. The nexus between food and water availability is connected primarily through the water used in food production, processing, and crop irrigation. Food availability is significantly influenced by factors such as the number of households, land use and food imports. Moreover, population growth, climate change and land use changes were identified as external variables exerting a substantial impact on the water-food system.

Policy scenarios

To assess the impact of various policies, three distinct scenarios were formulated as illustrated in Table 1: a base case scenario, a business-as-usual scenario (BAU) and a water interventions scenario (WI). The base case scenario depicts the system’s progression over time without incorporating climate change projections serving as a reference point for comparing the outcomes of other scenarios. The business-as-usual scenario integrates climate change forecasts, specifically a gradual increase in average temperatures by 1°C to 1.8°C and a reduction in average precipitation by up to 20% until 2060 (Jack et al., 2022). The water interventions scenario encompasses measures planned by the CoCT aimed at augmenting water availability by 2035, to improve the resilience of the system (City of Cape Town, 2023a). As the frequency and severity of droughts are projected to increase in the future, a drought sub-scenario was included to simulate the effects of a three-year drought within both the BAU and WI scenarios (Orimoloye et al., 2022).

Table 1

List of the scenarios implemented into the stock and flow diagram.
Scenario	Description
base case	- Simulation of the system based on historical data. - Includes population growth based on projections (City of Cape Town, 2020). - Does not include climate change projections or interventions. - Serves as a basis for comparison with other scenarios.
business-as-usual (BAU)	- Two sub-scenarios with different climate change severities: - Increase in average temperatures of 1°C and decrease of precipitation of 5% (best case). - Increase in average temperatures of 1.8°C and decrease of precipitation of 20% (worst case).
water interventions (WI)	- Based on the BAU scenarios. - Include the CoCT’s planned water strategy comprising increased aquifer use, dam augmentation, water reuse and seawater desalination.
drought sub-scenario	- Simulation run of BAU and WI scenario with an included 3-year drought scenario. - The drought sub-scenario sets in after 360 months and reduces the average precipitation by 50% for 36 months.

Stock and Flow Diagram of the WEF Nexus

Building on the insights about qualitative connections within the nexus derived from the CLD, a stock and flow diagram (SFD) was developed. The SFD formulation employed the Euler integration method for the simulation because of its suitability for models that incorporate IF-THEN-ELSE logic and discrete objects (isee systems, 2023a). Through a series of tests, the optimal timestep for the simulation was established to be 0.25 months (i.e.one week), with a total simulation duration of 480 months, or 40 years commencing in January 2023. For clarity, the diagram (Fig. 3) was segmented into five nodes: water, food, population, land, and climate. The interconnections between these nodes are facilitated using “ghosts”, which in STELLA (isee systems, 2023b) are shortcuts to other variables within the system.

In Fig. 4, the allocation of water resources as an SFD in the system is illustrated. At the center of Fig. 4 is the “Available Water” stock, which is the amount of water left after subtracting the water used (demand) from the available water (supply). On the water supply side, most of the water (approximately 96%) comes from the six major dams that serve the city. There are also three groundwater sources (aquifers) used (City of Cape Town, 2023b). Figure 4 also includes water from seawater desalination and reused water, which are part of the plan to increase the resilience of the water supply (WI scenario). On the demand side, there are three main uses of water: agriculture, urban areas, and non-revenue water or water losses (i.e., water that is lost and not paid for) (McKenzie et al., 2012). The urban water demand is categorized according to its users, including commercial, industrial, residential, and government. As different crops and livestock have different water requirements, the agricultural water demand outflow is split between the variety of livestock and crops cultivated in the CoCT and its surrounding agricultural areas.

In Fig. 5, the food node shown in SFD has a similar structure to the water node (i.e., Fig. 4). Here, the focus is on “Food Availability”, which is the amount of food available after considering both food supplied, and food demanded.

The supply side consists of two main sources: food that is imported and food that is produced locally. These sources contribute to the overall food supply in the area.

On the demand side, there are three ways food leaves the system: through exports (food sent to other places), food waste (food that is not consumed or used), and food consumption (food eaten by the local population).

The “Food Availability” stock thus shows the balance between the supply and demand for food. It is important to note that this stock includes an array of foods, both staple and non-staple, to reflect the different eating habits of various population groups. Staple foods include food groups such as eggs, meat, dairy, and grains, whereas non-staple foods comprise mostly fruits (Egieya et al., 2024). The variety indicates a diverse food supply to meet the varied needs and preferences of the population (Odunitan-Wayas et al., 2020).

Since population growth has a strong influence on resource use, it is pertinent to include a population node, as shown in Fig. 6. This node is positioned around a key element called “Population Size”. The “Population Size” stock is like a container that changes based on people coming in and going out. The inflows into the population are, on the one hand, those who join through births or immigration (migrating from elsewhere). The term "outflows" refers to the movement of individuals from the population, either through emigration or death.

An important feature of this population model is that the population size is categorized into different Living Standard Measure (LSM) groups. These groups are based on the average incomes and spending habits of different population groups. By dividing the population into these LSM groups, the model can more closely represent different parts of the population using resources (Ntloedibe et al., 2020). According to the study by Odunitan-Wayas et al. (2018), this categorization aids in understanding and preparing for the various requirements and resource utilization patterns of different segments of society.

There is a limited amount of land available in Cape Town that can be used for different purposes. Because of this, any changes in how land parcels are utilized affect the city’s demand for resources (see Fig. 7). For instance, if agricultural land is converted to residential areas, it will affect the amount of food that Cape Town can produce (Le Roux et al., 2016). The agricultural land is further divided into sections used for different crops and livestock to track their water requirements and properly manage this situation.

To account for the effects of climate change on the system, a climate node (Fig. 8) was included. This sector is crucial because an increase in average temperatures can significantly affect water resources. Specifically, higher temperatures lead to more evaporation of water from dams as well as evapotranspiration (the combined process of water evaporation and plant transpiration) from crops (Nistor et al., 2017). To accurately estimate the effects of these temperature changes, the Food and Agriculture Organization (FAO) suggests using the Penman-Monteith equation (Allen & Pereira, 2006). This equation is a widely recognized method for calculating evapotranspiration. It considers various factors like temperature, humidity, sunlight, and wind speed to provide a more precise understanding of how much water crops need and how much water is lost from reservoirs due to evaporation. For this study since not all the data was available, a simplified version of the Penman equation, developed by Linacre (1977) was employed (Eq. (1) and implemented in the WEF nexus model developed by Egieya et al. (2024). This equation requires fewer climate parameters to estimate potential evapotranspiration.

$$\:{E}_{0}=\frac{500(T+0.006h)/(100-A)+15(T-{T}_{d})}{(80-T)}$$

Where E₀ represents the evaporation rate in mm per day, T is the mean temperature in °C, h the elevation in meters. A denotes the latitude in degrees and T_d the mean dew point in °C.

Data

Most of the data employed in the model is open source, primarily sourced from the CoCT’s open data online portal (City of Cape Town, 2023c). Additional data were collected from government and municipal reports, as well as research papers. In instances where specific data needed for the model were unavailable, assumptions were made using data for the Western Cape Province, or at the national level.

Validation and verification

Once the model was completed, it went through various validation and verification processes to ensure accuracy and reliability. This process involved two main types of validation tests: structural and behavioral (Barlas, 1989). The structural validity analysis comprises tests for the model’s structure, parameter tests, extreme conditions testing, boundary adequacy, and dimensional consistency (Forrester & Senge, 1979). An example of the extreme condition tests can be seen in Fig. 9 Extreme conditions test for the urban water demand variable with negative population growth. The model’s structure was also validated by incorporating the stakeholders’ input. To establish its behavioral validity, the model was tested on behavior anomalies and sensitivity (Forrester & Senge, 1979). Any identified behavioral anomalies at the early stages were corrected. For the sensitivity analysis, different parameters, such as population size and dam levels were chosen to assess the model’s responsiveness to changes. After completing all these tests, the model was used to generate results based on the scenarios described in the Policy Scenarios section. Thus, thorough testing and validation process was crucial in ensuring that the model was both accurate and reliable, providing confidence in its ability to simulate real-world conditions and predict outcomes effectively.

In the context of this study, many results were generated through the combination of different scenarios and sub-scenarios. The following section summarizes a selection of the most significant results gained from the simulation runs. The selected results show the results of population growth and climate change on the Cape Town water-food system. Furthermore, the benefits and trade-offs of the city’s planned water interventions are revealed. The results are presented comparatively to showcase the implications of different scenarios and interventions.

Food availability

The base case simulation, which represents a standard set of conditions without any major changes or interventions, reveals a noticeable decrease in the food availability stock (measured in tons) – approximately 13% for staple foods and 9% for non-staple foods during the simulation period from January 2023 to December 2060. This decline in food availability is closely linked to population growth as depicted in Fig. 10a.

Water availability

Figure 10a offers a comparison of the simulation results for the Available Water stock under three scenarios: the base case, the BAU best case and the BAU worst case scenario. Figure 10a reflects the fluctuations in the water stock levels which are attributed to seasonal variations in precipitation during the summer and winter months in Cape Town. The graph in Fig. 11a distinctly illustrates that as the severity of climate change increases, the city’s water resources start to diminish, particularly noticeable after May 2050 − 330 months into the simulation. Throughout the simulated period, the Available Water stock approaches but does not reach zero under these scenarios, indicating that despite the specific challenges modelled, the city manages to meet its water demands, although water stocks decrease to low levels. During the last decade of the simulation, there is an average of 4 million cubic meters less available water in the system than during the first decade.

In the water interventions (WI) scenario (Fig. 12a), there is a noticeable improvement in the availability of water compared to the business-as-usual scenario: on average, there are 16 million cubic meters more available in the WI scenario, suggesting that the planned interventions have a positive effect on maintaining water levels. Specifically, Fig. 12b displays the combined results of WI scenario and the drought sub-scenario. During this simulation, a three-year drought period is modelled between the months 360 and 396, when the water stock remains relatively stable and does not exhibit any significant decrease.

When considering the aquifer system, the simulation results reveal some important developments. In Fig. 13a, under the base case scenario with the current rate of water extraction, the Cape Flats Aquifer (CFA) is projected to deplete completely after 350 months. In the BAU worst-case scenario, the depletion of the CFA occurs even faster – it runs dry 17 months earlier than in the base case (Fig. 13b). This accelerated depletion highlights the increased strain on the aquifer system in this scenario.

In the WI scenario, the CFA exhausts its resources after 156 months, and there is a significant decline in another major aquifer, the Atlantis-Silverstroom Aquifer. The trend observed in the simulation suggests that under this scenario, this aquifer is also on a path to complete depletion, potentially reaching zero just a few months beyond the end of the simulation period.

Regarding food availability, the model implicitly underscores the role of agriculture in the socio-economic fabric of Cape Town, and the consequent importance of policies and strategies related to food security, as they not only affect the availability of food but also have broader implications for employment and economic stability in the region. The production of food within the city boundaries itself may have only a minor impact on the overall food availability in Cape Town, but agriculture remains a significant sector in terms of employment. Of the approximately 1.6 million residents pursuing formal employment in the city, about 44 000 of them are employed by the agriculture sector, and it can be assumed that agriculture also provides a significant amount of informal employment (City of Cape Town, 2020). The Food and Agriculture Organization of the United Nations (FAO) gives a four-part definition of the term “food security”: the availability of food, access to food, nutrient utilization, and the continuation of the first three components over time (FAO, 2008). An important component in the accessibility of food is economic status (Battersby et al., 2014). Employment is, therefore, a vital factor when it comes to food access.

Within the model, the observed decrease of 13% in staple foods and 9% in non-staple foods in the Food Availability stock over time (Fig. 10) is primarily due to the assumption that food import rates remain constant. In a real-world scenario, it is reasonable to expect that an increase in food demand would lead to higher import rates (Charles & Battersby, 2019). However, import rates will be a function of many variables, including economic and logistic factors which can be difficult to predict (Haysom et al., 2017). Considering a lack of reliable predictions of future changes in food import rates, the model maintains the assumption of constant import rates for simplicity and consistency.

Agriculture in and around the CoCT makes up approximately 30% of the City’s water usage (City of Cape Town, 2023a). It is important to balance this demand with the demands of other water consumers in Cape Town, such as industry and residential water demands. Some of the major dams and aquifers, such as Theewaterskloof Dam provide water for both urban and agricultural consumers (City of Cape Town, 2022). There are, however, no clear policies on the prioritization of water allocation in times of water stress (Matthews, 2021). As urban agriculture plays a major role in the City’s resilience against food insecurity, it is, therefore, important that enough water is allocated to this sector – even in times of water stress (Charles & Battersby, 2019).

As Fig. 11 makes evident, that Cape Town’s increasing population, as well as the expected increase in mean temperatures due to climate change, are going to put the City’s water resources under pressure. The trends observed in the results align with findings from the research conducted by Ding et al. (2021) which employed an agent-based modelling approach. This consistency between different modelling methods reinforces the validity of the conclusions drawn. Particularly noteworthy is the BAU drought scenario presented in Fig. 11b, which underscores Cape Town’s susceptibility to prolonged drought periods. This scenario illustrates the challenges the city could face during multi-year droughts, which are projected to become more frequent and severe (Orimoloye et al., 2022). These findings are significant as they highlight the pressing need for effective water management strategies in Cape Town. The increasing likelihood of extended droughts due to climate change, combined with the pressures of a growing population, presents a substantial challenge to maintaining a sustainable water supply. These factors necessitate proactive planning and the implementation of adaptive measures to mitigate the risks associated with water scarcity and to ensure the long-term resilience of the city’s water resources (City of Cape Town, 2022).

Figure 12, shows that with the inclusion of the City’s water interventions, the “Available Water” stock stabilizes around 20 million cubic meters after approximately 200 months into the simulation. This stability indicates that the strategies, which may include measures like increased water conservation, improved water management practices, or additional water supply sources, are sufficient to sustain the city's water supply even under severe drought conditions. This finding is crucial for water resource planning and management, especially in the context of changing climate patterns and increasing instances of drought due to climate change. It provides valuable insights into the potential effectiveness of proactive water management strategies in ensuring water security.

Despite the apparent success of the water interventions, the modelling results shown in Fig. 13 highlight the vulnerability of the aquifer system under management by the CoCT. The noticeable drop in the Available Water stock is seen in Fig. 12 after 330 months can be understood by examining the aquifer stocks in Fig. 12. This decrease specifically aligns with the depletion of the Cape Flats Aquifer around month 350. The fact that the Available Water stock in the WI scenario does not show a significant decrease during the drought sub-scenario suggests that water management strategies implemented by the CoCT are effective in sustaining water availability. Figure 13, however, reveals a concerning trend: even under the base case scenario, the simulation results show over-extraction of the aquifer. This issue is exacerbated in the BAU scenario due to the impacts of climate change and is most severe in the WI scenario. The increased reliance on groundwater in these intervention strategies leads to heightened exploitation of aquifers (City of Cape Town, 2023a). Over-abstraction of aquifers can lead to several adverse environmental impacts including the lowering of the groundwater table and the intrusion of seawater into freshwater aquifers (Blake et al., 2023). Addressing the over-abstraction of aquifers and sustainable aquifer management is, therefore, crucial (Mauck & Winter, 2021).

The implementation of the drought sub-scenario into the BAU and WI scenarios emphasizes the CoCT’s vulnerability to resource scarcity – especially when multiple stressors such as rapid population growth and droughts coincide. Modelling scenarios with simultaneous adverse events like the depletion of an aquifer and a multi-year drought, therefore, highlights Cape Town’s need for resilience-oriented policies to guarantee the continued future supply of water and food resources.

Limitations

Accurate and relevant input data is crucial for the integrity and reliability of a model. For the model discussed here, most of the data utilized is sourced from open-access platforms. However, there were instances where some of the collected data did not align with other information about the system. In such situations, decisions regarding which data to include were made based on an understanding of the historical behavior of the system. This approach helped in maintaining the consistency and coherence of the model. Moreover, due to the unavailability of certain information about the system in public domains, assumptions were made to fill these gaps. These assumptions were based on the characteristics and behaviors of similar systems, providing a reasonable basis for modelling in the absence of direct data.

One area where data was notably lacking is in the realm of food imports into the city. The scarcity of detailed and specific data in this domain limited the depth and accuracy of the food sector component of the model. More comprehensive and precise data on food imports would enable the model to generate more insightful and meaningful results in this area. Another aspect of the model that relies largely on assumptions is the recharge rate of aquifers. Therefore, the results shown in Fig. 12 should not be treated as accurate future predictions. Nevertheless, these projections show important trends in the use of water resources. According to Forrester (1961), omitting variables crucial to the system's behavior can lead to greater inaccuracies than making educated assumptions to compensate for missing data.. The author underscores the importance of including all relevant variables in a model, even if it means relying on assumptions or estimations, to accurately capture the system's dynamics. Such an approach is essential for building a robust and predictive model that can be effectively used for planning, analysis, and decision-making.

The main objectives of this research were twofold: first, to develop both a qualitative and quantitative system dynamics model focused on the water-food nexus for the City of Cape Town, and secondly to assess the efficacy of different policies proposed by the city to address resource shortages and identifying potential trade-offs.

The reliability and accuracy of the model was ensured through the application of various validation and verification techniques, as recommended in the relevant literature. A key finding from the modelling results, which aligns with expectations, is the increase in resource demands corresponding with population growth. However, the model also underscores a critical need for sustainable resource management, because of the susceptibility of the city to prolonged droughts in the future.

One significant outcome is that the water interventions planned by the CoCT appear to be effective in ensuring adequate water supply to its residents, as per the model’s predictions. Nevertheless, the model reveals certain trade-offs associated with the City’s policies. A notable concern is the possible over-abstraction of two major aquifers, which poses a risk to long-term water sustainability. Furthermore, this finding showcases the importance of highlighting the different nodes of the nexus in more detail, as this trade-off could only be revealed by modelling the individual water resources.

This research, therefore, not only assesses the efficacy of proposed policies but also provides valuable insights into the complexities and challenges of managing the water-food nexus. It emphasizes the importance of considering both the immediate and long-term impacts of policy decisions, especially in the context of environmental sustainability and resource conservation.

Item	Description
BAU	Business-as-usual
CFA	Cape Flats Aquifer
CLEWS	Climate, land, energy, and water systems approach
CLD	Causal loop diagram
CoCT	City of Cape Town
FAO	Food and Agriculture Organization
GDP	Gross domestic product
LSM	living standard measure
MuSIASEM	multi-scale integrated analysis of societal ecosystem metabolism
SDG	Sustainable development goals
SFD	Stock and flow diagram
STELLA	Systems Thinking Experimental Learning Laboratory with Animation
UN	United Nations
WEF	Water-energy-food
WI	Water interventions

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Competing interests

The authors declare no competing interests.

Funding

This work was funded by the South African Water Research Commission (project number C2021/2023 – 00536) and the National Research Foundation (NRF) of South Africa.

Authors’ contributions

VSH built the Cape Town water-food system dynamics model (SDM) and compiled and analyzed the results. YP provided expert knowledge on system dynamics and revised the manuscript. JME provided expert knowledge on the (SDM) and aided in the conception and design of the work acquiring data and revising the manuscript. JG and NJG were involved in the conception and design of the work, revising the manuscript, and provision of research funds. All authors read and approved the final manuscript.

Acknowledgements

The authors are also grateful to Mr. Tshepo Morokong of the Western Cape Department of Agriculture, South Africa for availing the data on the Western Cape Agriculture profile.

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Impact of Resilience Policies on Cape Town's Water-Food Nexus: A System Dynamics Approach

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Causal loop diagram of Cape Town’s Water-Food Nexus

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Stock and Flow Diagram of the WEF Nexus

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