Journal of Natural Disaster Science, Volume 39，Number 2，2018，pp49-61

Numerical Assessment of On-Site Storage Facilities to Mitigate Pluvial
Inundation Damage in Urban Area
Kenji KAWAIKE* and Hajime NAKAGAWA**Disaster Prevention Research Institute, Kyoto University(Received: Oct. 18, 2018 Accepted: Dec. 12, 2018)

AbstractOn-site storage facilities are an effective countermeasure for mitigating the damage caused bypluvial inundations in urban areas. In this study, we slightly modified our previously-developed
integrated simulation model for pluvial inundation, and applied it to an actual urban area. We
quantitatively evaluated the mitigation effects of assumed on-site storage facilities, and foundthat as a single strategy, on-site storage facilities with a practical scale of storage capacity could
not provide sufficient mitigation. We recommend installing both on-site and off-site storage
facilities widely distributed in a catchment area.
Keywords: urban inundation, torrential rainfall, sewerage system, on-site storage facilities

1. INTRODUCTION
In Japan, inundation disasters frequently occur, particularly in urban areas. These disasters arecaused by local torrential rainfall. Stormwater drainage systems of sewer pipe networks areintended to play an important role in preventing such inundation disasters. However, when therainfall intensity exceeds the capacity of the drainage system, the system overflows and sewerage
is discharged on the surface. In some cases, this inundation is caused by a loss of inherent
infiltration and retention functions in the catchment area, due to its development and cover withimpermeable surface materials. Therefore, the construction of stormwater storage facilities torestore these functions to a catchment area is an effective countermeasure against pluvial
inundation. There are two types of storage facilities: off-site and on-site. In off-site storage, suchas retarding basins and regulating ponds, stormwater propagating away from remote locations isstored via rivers or sewer pipes. In contrast, in on-site storage facilities (for instance, small tanks
installed in homes, school yards, or playgrounds), stormwater is immediately collected andstored on-site. However, because of the high construction costs and the fact that the ability ofthese on-site facilities to mitigate damage during inundation disasters is unclear, adequate storage
facilities have not been installed in urban areas.
Numerical simulations are an effective tool for analyzing the mitigation effects of on-sitestorage facilities. Many simulation models for urban pluvial inundation have been developed andapplied to actual urban areas (e.g. Chen et al., 2007; Leandro et al., 2009). Most of these models
consist of a two-dimensional (2D) model for surface inundation, a one-dimensional (1D) model
for sewer pipe flow, and an interaction model connecting surface inundation and pipe flow.
However, most of those studies were aimed at developing simulation models and reproducing
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past rainfall event phenomena, and the developed models have rarely been used to estimate themitigation effects of countermeasures.
Our research group has been developing an integrated simulation model of urban
inundation, consisting of a 2D model, a 1D model, and an interaction model using weir andorifice equations (Lee et al., 2013; Bazin et al., 2014). The model has been applied to actual
urban areas in Japan using detailed topography and pipe network data (Lee et al., 2016; Kawaike
et al., 2016). The developed model has been used to evaluate the mitigation effects of off-sitestorage facilities in simulations applied to Osaka (Ko et al., 2018).
In this study, we performed several simulations using the assumption that on-sitestormwater storage facilities are installed at single-family housing and apartment housing, etc., toassess whether such facilities can effectively mitigate against inundation damage. Thus, thepurpose of this study is to quantitatively evaluate the effects of on-site storage facilities against
pluvial inundation damage.

2. NUMERICAL MODEL
2.1 Integrated model for pluvial inundation
To simulate inundation flow in this study, we employed our pluvial inundation model, which
consists of a 2D model of overland inundation flow, a 1D model of sewer pipe flow, and aninteraction model based on the weir and orifice equations (Lee et al., 2013; Lee et al., 2016).
This model assumes that overland stormwater flows either drain into sewer pipes or overflow
sewer pipes through storm drains at every street mesh.
2.2 Treatment of buildings
In our previous model, computational meshes in the building category had sufficiently highelevations, and the stormwater present on that mesh flowed by gravity across the ground surface
according to the elevation difference, without any special treatment. Additionally, the highelevations prevented the building interiors from flooding.
In this study, we improve the treatment of buildings in the model. For the meshelevations in the building category, we adopted elevation data calculated from a 5-m resolution
digital elevation map published by the Geospatial Information Authority of Japan. We assumed
that depending on the proportion of the building roof area, part of the stormwater present on
those meshes flows directly into the sewer pipes via the shortest distance from the building meshcentroid. Here, we need to consider the arrival time from the building to the pipe. In this study,
we assume that the propagating velocity of the stormwater is 1.0 m/s. The stormwater laterally
flows into the pipe with a time lag derived from the propagating distance and assumed
propagating velocity.
Building interiors are not completely water-proof. In actual events, inundation water
intrudes into buildings. In this study, we introduced intrusion parameter β at the boundaries
between the building meshes and other meshes. Intrusion parameterβis defined as the opening
ratio of possible length for inundation water exchange to the mesh boundary length, which
regulates the flow exchange between streets and residential blocks. The inflow discharged into
the building is restricted by multiplying the intrusion parameter (β) by the flow flux. The
adopted value of intrusion parameterβ is described later.
The treatment of buildings in this study is illustrated in Fig. 1.
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Journal of Natural Disaster Science, Volume 39，Number 2，2018，pp49-61

Fig. 1 Treatment of buildings

3. APPLICATION TO AN ACTUAL URBAN AREA
3.1 Target area
The target of this study is a sewerage catchment area in Osaka City: Nakahama Treatment Area.
Figure 2 shows the target area and its location. Nakahama Treatment Area is located on theeastern side of Uemachi high-land, which has an area of 18.1 km2 and a population of 300,000.
Nakahama Treatment Area was selected because its delineation is relatively easy ––– surroundedby Uemachi high-land, Neya River, and Hiranogawa Diversion Channel. We have applied ourmodel to this area several times (Lee et al., 2016; Ko et al., 2018).

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Fig. 2 Target area
This target area was divided into 109,034 unstructured triangular meshes. The mean sizeof those meshes is approximately 20 m. However, in some regions, much finer meshes 2 m insize are adopted. Those computational meshes are categorized as single-family housing,apartment housing, offices, schools, playgrounds, streets, and others. Figures 3 and 4 show thesurface elevations and categories of the computational meshes, respectively.

single-family housing
apartment housing
officeschoolplaygroundstreetothersFig. 3 Surface elevation

Fig. 4 Mesh categories
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Journal of Natural Disaster Science, Volume 39，Number 2，2018，pp49-61

Sewer pipes with diameters greater than 600 mm and the manholes connected to thosepipes are adopted in the simulation. In this study we considered 4,445 sewer pipes and 4,317
manholes. In Osaka City, because of low elevation of the residential area, compulsory drainage
from the downstream end of the sewer pipe network with pumps is necessary. Five pumping
stations are located in the target area, and their drainage effects are considered in the simulation.
However, the water level of the external rivers is neglected. Figure 5 shows the sewer pipes,
manholes, and pumping stations used in this study. Overall, the sewer pipes flow from south tonorth, and their outlets and pumping stations are concentrated in the northern part of the target
area.

1

2

3

7

4

8

5

Sewer pipe
ManholePumping station
Water level gauge

96

Fig. 5 Sewer pipes, manholes, and pumping stations
3.2 Intrusion parameterIn the simulation, the value of intrusion parameter β (described in 2.2) must be determined.
However, it is very difficult to convert the detailed building configuration data into an intrusion
parameter at each mesh boundary. Therefore, as we describe in this section, we uniformly set the
intrusion parameter to four different values (β = 0.0, 0.001, 0.01, and 0.1) and compared theresults. Figure 6 (a) – (d) shows the maximum inundation depth with different intrusion
parameter values. We use rainfall data observed at five rain gauges around the target area on
August 25, 2013. During this rainfall event, 27.5 mm rainfall was observed at Osaka Regional
Headquarters, JMA, which broke their highest record of 10-minute precipitation and resulted in
more than 500 houses being inundated. The hourly and daily precipitations were 49.0 mm and88.5 mm, respectively. For β = 0.0 (Fig. 6 (a)), the inundated area is restricted along the streets,and high water depths are found. On the other hand, for β = 0.001, 0.01, and 0.1 (Fig. 6 (b), (c),and (d)), inundation occurs in almost the same places as the case of β= 0.0, and the inundated
area is slightly larger with lower water depths. However, we did not observe significant
differences in water depths among these three cases.

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K.KAWAIKE, H.NAKAGAWA(a) β = 0.0(b) β = 0.1(c) β = 0.01(d) β = 0.001

Fig. 6 Maximum inundation depth using different intrusion parameters
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Because there is no validation data for overland inundation in this area, we cannot
determine the value of β quantitatively here. For the next section, intrusion parameter β is set
to 0.01 as the most probable value of the opening ratio in a highly urbanized area. In thesimulations of the next section, intrusion parameter β will affect inundation flow on theoverland surface after the stormwater is cut off at the on-site storage.
3.3 Validation of sewer pipe model
The water levels at the nine manholes indicated in Fig. 5 were measured during the short-term
rainfall event on August 25, 2013. Figure 7 shows a comparison between the measured andcalculated water levels at the nine manholes. We found that the water level peak and fluctuating
trends could be calculated at many manholes, although some numerical instabilities and waterlevel overestimations were found at several of the manholes. The difference of the initial waterlevel and overestimation might be caused by some problems of replication of the rainfall
distribution or the actual pipe connection on the simulation data. Numerical fluctuations arefound at Water Level Gauges 1, 2, and 3 affected by compulsory water removal at the pumping
stations and the slot model applied to the sewerage system simulation. Consequently, thesimulation results of this model are acceptable for urban pluvial inundation.

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Elevation [m] (O.P.)

obs1

obs2

obs3

obs4

obs5

obs6

obs7

obs8

obs9

sim1

sim2

sim3

sim4

sim5

sim6

sim7

sim8

sim9

Fig. 7 Comparison between observed and simulated water levels at nine manholes
4. MITIGATION EFFECTS OF ON-SITE STORAGE FACILITIES
4.1 Simulation conditions
Osaka City experienced short-term torrential rainfall events in 2011, 2012, and 2013. Pluvial
inundation occurred in some locations in our target area, the Nakahama Treatment Area. In thisstudy, as countermeasures against such pluvial inundation, we assumed that small-scale on-sitestorage facilities were installed in the target area.
According to the mesh categories indicated in Fig. 4, the following storage capacities are
assumed in each mesh. The standard values for possible storage area and maximum storage
depth are described in the official guidelines (Association for Rainwater Storage and Infiltration
Technology, 2007).

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Single-family housing
The roof area occupies 90% of the mesh area, and one single-family housing accounts for 70 m2.
A storage capacity of 1 m3 is attached to each individual house. The stormwater catchment area
is equivalent to the roof area.
Apartment housing
The roof area occupies 60% of the mesh area, and the remaining 40% is used for parking lots, etc.
Assuming a sufficiently large amount of storage capacity, all of the stormwater present on theroof is stored. In other areas (parking lot, etc.), storage on the ground surface is considered; thepossible storage mesh area of 84% and a maximum storage depth of 10 cm. The catchment areaof the stormwater is equivalent to the parking lot area.
Offices
The roof area occupies 60% of the mesh area, and all of the stormwater present on the roof isstored. Storage on the ground surface is not considered here, which means that the stormwater
present in the other 40% of the area is not stored, and flows on the surface as inundation water.
Schools
The school building roof area occupies 30% of the mesh area, and the remaining 70% is used forthe school yard. All of the stormwater present on the school building roof is stored. Storage on
the ground surface is considered for the school yard; a possible storage mesh area of 40% and amaximum storage depth of 30 cm. The stormwater catchment area is equivalent to the school
yard area.
Playgrounds
The ground surface provides a possible storage mesh area of 60% and a maximum storage depthof 20 cm. The stormwater catchment area is equivalent to the entire mesh area.
In the simulation, the above proportions of the roof area and surface area are assumed ineach computational mesh. The rainfall is given on each mesh, with a runoff ratio of 0.9, andsome part of the rainfall amount is stored in the storage facilities, depending on the residualstorage capacity of each mesh. If the mesh has no residual storage capacity, the rainfall is givenon the surface as the inundation water.
We set the following three cases for storage capacities: no storage facilities (Case 0), thecapacity of storage facilities (Case 1), and the case where only the single-family housing meshes
were provided with five-fold capacity (Case 2). The storage capacity of each category issummarized in Table 1. Case 1 can be regarded as a feasible goal for implementing on-sitestorage facilities. Case 2 would be difficult to implement using only on-site storage facilities, but
can be considered to be a goal for total storage capacity, including alterative off-site storage
facilities used to significantly mitigate pluvial inundation damage.

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Table 1 Storage capacity allocated to each mesh category (unit: m3)
Case 0Case 1Case 2Single-family housing
087,309436,543
Apartment housing
029,57729,577
Office000School072,25372,253
Playground0
42,04242,042
Total
0231,181
580,415(The roof storage capacity is not included except for individual houses)

The rainfall used in this simulation is a central peaked hyetograph with a return period of
10 years. The rainfall duration time is set as 2 h, because torrential rainfall was concentrated in aperiod of only 1 or 2 h for the events observed in Osaka City on August 27, 2011; August 18,
2012; and August 25, 2013. Figure 8 shows the hyetograph used in this simulation, derived fromthe following rainfall intensity equation (1) for Osaka City.
999

𝑟𝑟 = 2/3
𝑡𝑡

(1)

+4.81

The total rainfall amounts for 1 and 2 h are 49.6 mm and 68.6 mm, respectively.

[mm/10 min.][min.]

Fig. 8 Hyetograph of the model precipitation

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Decreasing ratio of thesewer pipe peak water
depth from Case 0

Difference ofwater depth [m](b) Maximum water depth difference
between Case 0 and Case 1(a) Maximum water depth of Case 0(c) Maximum water depth of Case 2(d) Maximum water depth difference
between Case 0 and Case 2

Fig. 9 The maximum water depth of Case 0 and mitigation effects of on-site storage facilities
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4.2 Mitigation effects of on-site storage facilities against pluvial inundation
Figure 9 (a) shows the maximum water depth for Case 0. The inundated areas are widely spreadbecause the rainfall intensity used here is almost equivalent to that observed on August 25, 2013,
which exceeded the sewerage system capacity. Along streets with low elevations, several areas
experienced water depths greater than 0.5 m maximum. Figure 9 (b) shows the difference inmaximum water depths between the case with no storage capacity (Case 0) and the case withstorage capacity (Case 1). Based on these differences, the mitigation effects of on-site storage
facilities were small, although small decreases in water depths were found in a very few numberof areas. Figure 9 (b) also shows the decreasing ratio of sewer pipe peak water depth from thatof Case 0. Additionally, the figure shows that the decreasing effects of sewer pipe water depth
are also small. In this simulation, while the storage at large apartment complexes, offices,
schools, and playgrounds still remained vacant capacity, the storage capacity of single-family
housing was full of stormwater at an early stage. Therefore, the storage capacity shown in Table
1 is not sufficient for the amount of precipitation modeled, and the total capacity should be
increased, particularly for the single-family housing, which occupying the majority of the target
area.
With reference to a similar numerical study by Nakagawa et al. (2004), although thesimulation conditions such as the rainfall pattern, the land use, and the capacity of assumed onsite storage facilities are different from those in our simulation, they also concluded that the
mitigation effects of storage facilities are limited if the assumed storage capacity is not sufficientfor the total rainfall amount. However, even in those cases, the inundated area with a depth of
more than 20 cm decreased to some extent from the case with no storage, which is different fromour results and requires further investigation.
Figure 9 (c) and (d) show the maximum water depth for Case 2 and the difference inmaximum water depth between the case without storage capacity (Case 0) and the case with fivefold storage capacity in the single-family housing meshes (Case 2), respectively. From thesefigures, we can observe that the water depths decrease significantly, particularly in areas withlow elevations and relatively high water depths (as in Case 0). The areas in which the water
depths decreased are much more widely found in this simulation than those of Ko et al., (2018)
who implemented a single large storage tank with a capacity of 50,000 m3, attached to a sewer
pipe. From Fig. 9 (d), we can see that the water depths in the sewer pipes decrease in many
locations from the upstream to the downstream area, which might help to decrease overland
inundation depths. Consequently, if the storage capacity is widely and sufficiently distributed in
the catchment area, inflow discharges into the sewerage system are decreased, as are inundation
depths on the ground surface.

5. CONCLUSIONS
Using an integrated model for pluvial inundation, we aimed to quantitatively evaluate themitigation effects of assumed on-site storage facilities. We found that the practical on-sitestorage capacity for a single-family home (1 m3) does not provide sufficient mitigation.
Additionally, the effects of storage at large apartment complexes, offices, schools, andplaygrounds are limited for model precipitation with a 10-year return period. Overland
inundation depths decreased significantly when five-fold storage volume was provided to singlefamily housing (single-family housing occupied most of the target area). The effects of such on60

Journal of Natural Disaster Science, Volume 39， Number 2， 2018， pp49-61

site storage facilities, which were widely and sufficiently distributed in the overall catchment
area, were found in many places. In contrast, the effects of off-site storage were locally foundjust around the facility. In the next step of our research, we will investigate the mitigation effects
provided by the simultaneous installation of on-site and off-site facilities in a target area, andtheir ability to prevent pluvial inundation damage.

Acknowledgement
This research was supported by a grant (17CTAP-C095650-03) from the Infrastructure andTransportation Technology Promotion Program funded by the Ministry of Land, Infrastructure
and Transport of the Korean government.

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