3. Results and discussion
3.1 Single Event Simulation
Hypothetical extensive green roofs (as mentioned in Section 2) are used
for Bi (i = 1, 2, …, 5). When choosing
different Green roofs or Bioretention cells as LID type in SWMM, the
simulation shows slight differences in the amount of lid drainage and
final storage obtained by but the surface runoff is consistent (Tables
S1 and S2). We used the predicted runoff from 2-yr, 10-yr, and 100-yr
precipitation events to evaluate the potential for surface runoff
reduction.
3.1.1 Sensitivity Analysis
The results of sensitivity analysis show that the saturated hydraulic
conductivity is the most sensitive parameter for evaporation and surface
runoff; wilting point is the most sensitive parameter affecting the
initial LID storage; and soil field capacity is the most sensitive
parameter affecting the final storage capacity (Table 4). Based on the
sensitivities of the 5 parameters and main objective of this study to
mitigate urban flooding by GRS, a combination that minimizes surface
runoff was selected (Table 5). The following analyses use these
substrate properties, since these parameters produced the best overall
result from the sensitivity test.
3.1.2 Runoff Retention of GRS
Fig. (5) shows the comparison of simulated runoff at the condition of
the TRS and GRS. The key features are summarized in Table 6. Results
indicated that the GRS significantly performs better than the TRS in
reducing surface runoff. The GRS has a most significant influence on
reducing urban runoff for the 2-year precipitation event. The reduction
in total runoff volume by the GRS decreases as precipitation intensity
increases: 42%, 34%, and 27% reductions for 2-yr, 10-yr, and 100-yr
precipitation events, respectively. Meanwhile, we find that runoff
coefficient (Q/P) of the GRS increases from 57.41% to 72.19% when the
precipitation intensity changes from 2-yr storm to 100-yr storm (Table
6), which indicated that the retention capacity of the GRS decreases
with the increase of the precipitation intensity.
It is of great importance to effectively reduce the peak surface runoff
since it often accompanied by maximum erosions and local flooding (Li
and Babcock, 2014). Our simulation results show that the GRS could give
a much higher reduction for the peak flow than the TRS in the 2-yr
precipitation event. For larger precipitation events (i.e., 10-year and
100-year precipitation events), however, this effect is reduced
significantly (Table 6). Additionally, we found that the GRS has little
ability to postpone runoff peak timing (less than 1 minute) in all the
assumed precipitation events (Fig. 5).
Because
ET
is negligible in the single event simulation, the volume of retained
stormwater is the difference between precipitation and discharged water.
For the GRS, the ratios of retained to precipitation volumes under 2-yr,
10-yr, and 100-yr precipitation events are 75%, 55%, and 41%,
respectively. This result is consistent with some previous studies
(Carpenter and Kaluvakolanu, 2011; Carter and Rasmussen, 2006;
Fassman-Beck et al., 2013; Getter et al., 2007; VanWoert et al., 2005)
and indicates that there are limitations for runoff reduction by green
roofs in heavy precipitation events.
3.1.3 Mitigation Conditions of Flooding Nodes and Overloaded
Conduits of CSS / SWS in
GRS
The ability of the GRS to mitigate urban flooding can be showed from the
amount of floods discharge generated after storm events of different
intensities (Fig. 6). In the 2-yr precipitation event, neither the GRS
nor the TRS generate flooding (thus results were not shown in Fig. 6).
In the 10- and 100-yr precipitation events, however, the flooding
volumes of the GRS are 82% and 28% less than those of the TRS,
respectively. In addition, the peak flooding flow in the case of the GRS
is significantly less than that of the TRS (Fig. 6) and the peak
flooding flow is 72%, and 19% lower than those of the TRS in the 10-yr
and 100-yr precipitation events, respectively. Moreover, the drainage
condition of CSS / SWS in GRS is much better than that of TRS. According
to the simulations from the SWMM, in the 10-yr precipitation event,
there are one flooding node (J4) and one overload conduit (C4) in the
CSS / SWS of TRS (Figs. 7a and c). As shown in the Fig.7c, the GRS
reduces the capacity of conduit (C4) to less than 1 (1 means overloaded)
and the duration and volume of flooding node (J4) in the GRS are much
less than that of the TRS. In the 100-yr precipitation event, there are
three flooding nodes (J2, J3, J4) and two overloaded conduits (C3, C4)
in the TRS (Figs. 7b and d). The GRS, even in such a high intensity,
still reduces the flood risk well with only one overloaded pipeline (C4)
and three flooding nodes. Although there are still three flooding nodes
(J2, J3, J4) in the GRS case in the 100-year events, they are
significantly mitigated.
3.2 Continuous simulations
In the continuous simulations (31-year), the change in surface runoff is
consistent with that of rainfall with June as the peak month of surface
runoff (Fig. 8). There is a relatively high average evaporation between
April and September in the GRS case. In August and September, the
rainfall is relatively low (Fig. 9). In addition to reducing runoff, ET
can reduce urban temperatures, especially during warm months
(Tabares-Velasco, et al., 2012). The surface runoff in the GRS case
accounts for 47% of annual precipitation, while that in the TRS case
accounts for 90% (Table 7).
Green roofs can be beneficial to urban environment by reducing surface
runoff through ET (Li and Babcock, 2014). The pore space in the soil
layer of green roofs, which provides space for retention, can hold water
by capillary forces until water is lost via ET. Therefore, ET amount can
be quantified by analyzing the moisture loss in green roofs (Li and
Babcock, 2014). The evaporation amounts in the GRS and TRS cases are
very close (Fig. 10) and account for 11.6% and 10.2% of annual
precipitation, respectively. The reason could be that the evaporation
values are calculated based on water balance in the SWMM, without
considering the actual dynamic process. The output of PET (Potential
Evapotranspiration) in the SWMM, which account for 25.2% of the annual
precipitation, is calculated by Hargreaves method (Fig. 10). The RET
calculated based on the Penman-Monteith method and Pan Evaporation
method, account for 38.6%, 37.7% total precipitation respectively. The
AET calculated by the Advection-Aridity method and Granger-Gray method,
however, account for 44.9% and 36.7% of total precipitation,
respectively (Fig. 10). The ET values calculated based on the
Penman-Monteith method, Pan Evaporation method, and Granger-Gray method
are very close. They are a little smaller than the ET obtained from the
Advection-Aridity method and much higher than the PET result of the
Hargreaves method. Therefore, the ratio of annual ET value in the GRS to
total precipitation should range from 36.7% to 38.6%, which represents
the rainwater retention potential of green roofs in MLRYR.
Besides the rainwater retention potential of the GRS, the evaporation
curves in Fig. 10 also illustrate the difference in evaporation patterns
between the GRS and TRS. Although the ratio of evaporation in the GRS to
that in the TRS is just 1.14:1, the timings of evaporation from GRS and
TRS are quite different. In the TRS, evaporation and precipitation are
concurrent. The evaporation curve is intermittent consistent with rain
event timing. In the GRS, however, the rain water could be retained and
evaporated between precipitation events. This result has a flaw because
the evaporation result of SWMM is based on the principle of water
balance. Since the TRS cannot retain water, precipitation and
evaporation occur simultaneously in the time series outputs of SWMM to
achieve a balance. However, in real-world the relative humidity should
be very high and limit evaporation during precipitation periods.
Although there exists a drawback of the output data, the general trend
of the continuous evaporation curve of the GRS implies more continuous,
and likely larger, cumulative heat dissipation from the building. It
thereby has potential to mitigate the urban heat island (UHI) effect.
Similar results were also shown in previous researches that have
evaluated green roofs’ thermal benefits and suggested that GRS can
reduce the UHI effect through changing roof albedo, decreasing heat
transfer into buildings, decreasing long-wave radiation from leaves,
evapotranspiration, and heat storage by plants (Kwok, Wong, and Lau
2013; Tabares-Velasco, et al., 2012; Coutts, et al., 2013). Thus,
thermal benefits could be another attractive function of green roofs if
applied at the urban scale.
3.3 Performance of the IGRS
We also evaluated whether the IGRS, a combined system of green roof and
rooftop disconnection, is more effective at mitigating urban flooding
and groundwater depletion. In the single event simulation, flooding only
occurs in the cases of 10-yr and 100-yr precipitation events. The
flooding volumes in the cases of GRS and IGRS are very close (Figs. 11a
- 11f, Table 8). In the 10-yr precipitation event, with the increase of
greenbelt from 1m to 3m, the flood volume in the IGRS case is 2.09% -
2.77% less than that in the GRS. As in the 100-yr precipitation event,
with the increase of greenbelt, the flood volume in the IGRS is 0.8% -
1.0% less than that in the GRS. When compared with the TRS, the
flooding volume in the GRS and IGRS are 67.69% - 71.10% and 30.13% -
33.22% (depending on green belt width) less than those of the TRS for
the 10- and 100-yr precipitation events, respectively. In terms of the
drainage condition of CSS / SWS, the flooding nodes (Figs. 12a - 12f)
and overloaded conduits (Figs. 13a - 13f) in the IGRS are also
consistent with the conditions in the GRS. As shown in the Figs. 12a-12f
and Figs. 13a-13f, both the IGRS and the GRS can mitigate the flooding
nodes and reduce the overloaded time of conduits when compared with the
TRS, but the capacity of flood detention is slightly affected by the
width of greenbelt. Results of runoff volume indicated that the
differences in runoff volume and runoff coefficient (Q/P) between GRS
and IGRS (Table 9) are insignificant. However, the simulated peak flows
in the GRS case are 16.18% - 16.95%, 25.07% - 25.90% and 28.83% -
29.46% (depending on green belt width) lower than those in the IGRS
case for the 2-yr, 10-yr, and 100-yr precipitation events, respectively
(Table 9).
In the continuous simulation, evaporation, surface runoff, and
infiltration are three key parameters to evaluate the effect of the
systems on retaining stormwater. In the same precipitation event,
evaporation in the IGRS is 0.4% more than that in the GRS (Table 10).
Although the surface runoff in the case of GRS is 20-27% less than that
in the IGRS case, the overflowing water from GRS is drained into the CSS
/ SWS, which increases the burden on the CSS / SWS and does not recharge
groundwater. In the IGRS case, however, the overflowing water was
diverted from green roofs to infiltrate in greenbelts and recharge
groundwater, which could thereby reduce the burden on the CSS / SWS. As
a result, infiltration is the most important measure of the IGRS
effectiveness. The proportions of infiltrated water from annual
precipitation are shown in 3 categories based on the belt widths (i.e.,
1 m, 2 m, and 3 m; See Fig. S2). For 1 m, 2 m, and 3 m greenbelts, the
infiltration of IGRS accounted for 10%, 16%, and 19% of annual
precipitation respectively, while that of GRS was only 0.36%, 0.73%,
and 1.09%. With the 3 different green belt widths, IGRS would recharge
to groundwater with total runoff volumes of 2126, 3273, and 4033
m3 ha-1 yr-1 more
than with GRS.
The XIFS is a representative of the whole city because of the
homogeneous climate condition (precipitation, solar radiation, wind,
etc.) and the consistent planning standard for building density and
floor area ratio according to the Urban Planning of Nanchang City.
Therefore, we can project the simulated results from this study site to
most areas of the city if not all. Assuming that the IGRS is widely used
in the city, the total estimated runoff that can recharge groundwater
would be beneficial to mitigate urban drought and groundwater depletion
problems in the city. According to the comparison of infiltration ratio
between GRS and IGRS (Fig. S3), although not as good as GRS in
controlling surface runoff, the IGRS can substantially enhance
groundwater recharge. In the whole city, the GRS and IGRS can be used in
different areas according to their own characteristics. Particularly,
the GRS can be adopted in the areas where CSS / SWS capacity is
insufficient to cope with large peak runoff because the peak runoff of
GRS is 16-29% less than that of IGRS and can be used as an effective
way to reduce surface runoff. In places where the capacity of CSS / SWS
is large enough to deal with surface runoff, the IGRS can be used as an
innovative way to make full use of the rich rainwater resources in
Nanchang to recharge groundwater since urban flooding is not a threat in
these places. The cities in the MLRYR are generally densely built areas
that have a large potential to be retrofitted with GRS and IGRS.