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Mitigating climate change in city of Tshwane

Table 2. Seasonal and annual daily temperatures in the City of Tshwane from 1961 to 1990
Temperature (°C) Winter Spring Summer Autumn Average
Minimum 3.9 12.5 16.1 10.7 10.80
Maximum 20.5 26.5 28.2 24.6 24.90
Average 12.2 19.5 22.2 17.6 17.85
Source: City of Tshwane . 2
model’s starting state and apply selected rules to Table 3. Parameters of the drainage system (with
simulate the evolution of the system over time. 45 and without policy implementation)
A combination of SD and DES was used in this Drainage Drainage system Drainage system
study. The SD method is best known for its high level parameter without policy with policy
of abstraction, requiring few details, and is typically intervention intervention
deterministic with continuous time modeling. Its Height (mm) −1,800 −3,600
coupled first-order differential equations are solved Width (mm) −1,800 3,800
by numerical methods. In contrast, DES focuses on
systems where a sequence of operations or tasks needs Designed flow 3 36.0
3
to be performed. 44,45 rate (m /sec)
The choice to combine SD and DES arose from
the fact that SD offers an opportunity to precisely were used to predict the behavior over a 10-year period
evaluate and model the impact of climate change on under the SD modeling framework.
road and stormwater infrastructure. On the other hand, The methodology focuses on the development of two
DES can be useful in the development and testing of drainage system models: one based on the “business-as-
new policies, as it helps understand how changes in usual” approach (i.e., without policy implementation)
systems (such as variation in weather conditions) and the other incorporating dynamic factors, such as
can affect overall outcomes (such as the impact on forecasted changes in environmental policies. These
road and stormwater infrastructure, as well as the models are compared to the present drainage network
proposed mitigation strategies). DES is a powerful and design, which relies solely on historical data. The
comprehensive paradigm capable of modeling almost performances of the drainage systems are evaluated
any system that changes over time through events, by comparing their designed flow rate values to the
provided these changes can be reasonably approximated simulated flow rate values (expressed as percentages)
within the events or important occurrences. Thus, the to determine whether the systems operate within the
abstract simulation used in SD is more suited for long- expected range of design values, particularly in relation
term, strategic modeling and simulation. 44,45 Hence, the to flood occurrences, as assessed by the DES. Table 3
combination of SD and DES was found to be suitable presents the parameters of the drainage system (with
for investigating and representing the impact of climate and without policy implementation) used for the DES.
change on road and stormwater infrastructure, including The DES was conducted in four steps:
the proposed mitigation strategies. The integrated (i) Observation of the system’s real dynamics. The
approach enables the simulation of climate-related model’s input parameters comprise the following: the
events, such as temperature changes, rainfall, and GHG rainfall dataset (derived from Table 1), the
emissions that occur at specific times. Meanwhile, temperature dataset (derived from Table 2), and
SD enables these activities to be tracked continuously the drainage system’s parameters (Table 3). The
over time. DES focuses on understanding the changes model’s outputs include the GHG effect, GDP loss,
in the state and events, while SD tracks the activities number of extreme events, and drainage system
to capture the system’s state at different points in time performance. The performance of the drainage
without any gaps. systems was evaluated by comparing their designed
The combined use of DES and SD modeling was flow rates with the simulated values to determine
carried out sequentially. DES was used to simulate the lifespan and quality of roads and stormwater in
the behavior of the City of Tshwane’s stormwater the model.
infrastructure, representing two scenarios (with and (ii) Modeling the relationship between GDP loss and
without policy implementation). Thereafter, the models GHG emissions. The relationship between GDP

Volume 22 Issue 2 (2025) 191 doi: 10.36922/AJWEP025080049