Published)
Chapter 5 Meeting Environmental Flow Requirements in a Changing World using
5.3 Scenario Design: Changing Worlds
Figure 5.1a shows that the inflow hydrograph is a function of climate and land use (level of development). In the future, this is likely to change and produce different inflow hydrographs. The concept behind the THC approach is that because it adapts the outflow hydrograph based on the real-time storage level, it is able to match any desired target hydrograph in real-time and hence adapts to changes in inflow hydrographs. To demonstrate that the THC approach can
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adapt to future land use and climate changes, its ability to achieve the same environmental flow target hydrograph is tested for the following three worlds:
Current World, Future World: 2050, and Future World: 2090. The characteristics of these future worlds are summarized in Table 5.1 and explained below.
The pre-determined environmental flow target hydrograph for a “pre- development” lot with 0% impervious area was set based on the recommendations in Australian Runoff Quality (Wong, 2006), in which it is suggested that a 50% annual exceedance probability (AEP) is a sustainable discharge criterion for environmental purposes. The temporal pattern for the target hydrograph was obtained by using an example rainfall temporal pattern (pattern 8) from Australian Rainfall and Runoff 2019 (Ball et al., 2016) for the selected location and AEP. This hydrograph was used as the target hydrograph for all of the three plausible future worlds detailed below.
5.3.1 Current World
In the “Current World”, catchment conditions have changed from pre- development conditions due to medium-density development. This results in significant land-use change, increasing the impervious fraction from 0% to 50%, and hence the resulting runoff hydrograph. However, the climate is the same as for pre-development conditions.
5.3.2 Future World 2050
In “Future World 2050”, development has increased from medium-density (50%
impervious) to high-density (90% impervious). In addition, there has been a 2.4°C increase in extreme annual average temperature (Moise et al., 2015), resulting in an overall increase of 12% in rainfall intensity (Ball et al., 2016) and an increase in the peakiness of the rainfall temporal patterns (i.e., more intense peak rainfall and less rainfall during other periods) (Wasko and Sharma (2015) as a result of climate change in accordance with RCP 8.5 (Dale et al., 2015).
5.3.3 Future World 2090
In “Future World 2090”, development remains at high-density (90%
impervious), but there has been a further change in climatic conditions in
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accordance with RCP 8.5. Specifically, there has been a 5.1°C increase in extreme annual average temperature (Moise et al., 2015), resulting in an overall increase of 28% in rainfall intensity (Ball et al., 2016) and an increase in the peakiness of the rainfall temporal patterns (i.e., even more intense peak rainfall and even less rainfall during other periods) (Wasko and Sharma (2015).
Table 5.1 Experimental Configurations Environmental
Flow Target
Current World
Future World: 2050
Future World: 2090 Land-use
Type
Pre-development (0% impervious)
Medium density development
(50%
impervious)
High Density Development
(90%
impervious)
High Density Development
(90%
impervious) Increase in
Temperature
Current Current 2.4°C 5.1°C
Increase in Rainfall Intensity
Current Current 12% 28%
Rainfall Temporal
Pattern
Pattern 8 Pattern 8 Pattern 8 altered to have slightly higher peaks
Pattern 8 altered to
have significantly higher peaks 5.4 Methodology
5.4.1 Case Study
The case study is a typical allotment-scale catchment located near Darwin, Australia. The case study system consists of a single stormwater storage that is connected to the impervious roof area of a 400 m² allotment, which changes for the various scenarios described in Section 5.3. Details of the case study are summarised in Table 5.2.
Table 5.2 Case Study System Characteristics
Catchment Property Value Storage
Property
Value
Location Darwin No. of
Storages
1
Catchment Size (m²) 400 Storage
Height (m) 2 Impervious Area (%) Depends on land-use
type (see Table 5.1)
Storage Volume
(m³)
10
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Initial Loss* (mm) 15 (pervious)
1 (impervious) Orifice Diameter
(mm)
90 Continuing Loss* (mm/h) 4 (pervious)
0 (impervious)
Orifice Discharge Coefficient
0.65
*Based on the information provided in Australian Rainfall and Runoff (2019) 5.4.2 Simulation Approach
The simulation framework shown in Figure 5.1b is used to test the THC approach on the case study catchment. As part of the approach, the control time step (Δt) and target hydrographs are pre-determined, where the control time step (Δt) is defined as the time interval suitable for adjusting the storage orifice opening percentage (Ot). For the first time step (t = control time step (Δt)), the storage level information (Ht) is measured in real-time, and the target flow (Qtarget,t) for this time step is retrieved from the target hydrograph for the same time. Next, the storage orifice opening percentage (Ot) is calculated using the orifice equation (equation 5.1). This will ensure the outflow of the stormwater storages (Qout,t) closely approximates the target flow (Qtarget,t) for this time step.
For the next time step (t = 2Δt), the target flow (Qtarget,t) and the orifice opening percentage (Ot) are updated using the same procedure, and this simulation cycle continues until the end of the rainfall event. By applying this simulation framework, the outflow of the stormwater storages (Qout,t) will closely approximate the target flow (Qtarget,t), which is derived from the target hydrograph, for all time steps.
The above approach is implemented with the stormwater simulation model EPA SWMM (v5.1.012, Stormwater Management Model) (Gironás et al., 2010) and its Python package PySWMM (v0.5.1, Python Wrapper for Stormwater Management Model) (McDonnell et al., 2020). EPA SWMM simulates urban hydrology quantity and quality, and PySWMM is able to automatically update the inputs of the SWMM model in accordance with the THC approach.
Combining these two packages has proven to successfully simulate the real- time control of stormwater storages in previous studies (Liang et al., 2019, Liang et al., 2021).
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The control timestep (Δt, Figure 5.1b) was set at 1 minute, as this provides a good balance between algorithm performance, which improves with a reduction in Δt, and practicality. Updating the orifice opening at 1 min intervals can be achieved easily in practice by using suitable computer-controlled electrical actuators that are connected to the orifice system to adjust its opening/closing (Cui et al., 2017, Ma et al., 2021).
5.4.3 Performance Assessment
Relative Mean Absolute Error (RMAE) is used to assess the performance of the THC approach. This corresponds to the average absolute difference between the target outflows and the actual outflows from the THC approach, scaled by the mean target outflow, calculated as follows:
Relative Mean Absolute Error (%) =
1
𝑛∑𝑛𝑖=1|𝑄𝑜𝑢𝑡,𝑡−𝑄𝑡𝑎𝑟𝑔𝑒𝑡,𝑡|
1
𝑛∑𝑛𝑖=1𝑄𝑡𝑎𝑟𝑔𝑒𝑡,𝑡