Abstract

An effective urban drainage system is the primary key to overcoming the risk of flooding due to high rainfall intensity. Tempe District, Wajo Regency, South Sulawesi, is one of the areas facing severe problems in its drainage system. The flooding in this area is caused by the inability of conventional drainage systems to handle high surface water runoff, especially during periods of intense rain. This research introduces a new approach to sustainable drainage systems by applying the infiltration well technique. The analysis was carried out by considering the infiltration well parameters, such as a depth of 3 m, a radius of 0.6 m, and a soil permeability of 5 x 10^-5 m/s. This infiltration well was implemented in the upstream subcatchment area of the Sengkang City primary channel. The results of the analysis show that the use of infiltration wells can produce a significant positive impact in reducing flood risk. In a five-year return period, the flood volume can be reduced by 12.15 x 10³ m³; in a 10-year return period, the volume reduction reaches 13.16 x 10³ m³. Although the effectiveness of infiltration wells is limited to 9% and 6% for each return period, this approach significantly contributes to minimizing the impact of flooding in affected areas. Hopefully, these findings can contribute to planning and managing urban water systems, especially in areas vulnerable to flood risk.

1 Introduction

Urban drainage systems significantly contribute to sustainable development and increase living space by balancing various opportunities and challenges that influence urban design (Chen et al. 2021; Seyedashraf et al. 2021; Chapman and Hall 2022). Urban drainage systems aim to mimic natural drainage by adopting techniques to treat surface water runoff that causes flooding (Lee et al. 2016; de Fraiture et al. 2017). The process can involve collection, storage, and cleaning before allowing it to return to the environment. The approach currently being developed is a sustainable drainage system (Zhou 2014; Ferrans et al. 2022; Shkaruba et al. 2021; Kapetas and Fenner 2020). Solutions to reduce the risk of runoff are different from solutions to conventional drainage problems (Sagala et al. 2022).

Conventional drainage management is a monofunctional solution using a conveyor system aiming to collect and drain water from the catchment as quickly as possible. At the same time, the Sustainable Urban Drainage System (SUDS) is a multifunctional solution using natural processes that aims to collect and maintain water in the catchment area for as long as possible. (Lähde et al. 2019; Dung et al. 2021; Oladunjoye et al. 2022). SUDS is designed to deal with excess water, which can potentially cause flooding in urban areas.

Tempe District is one of the urban areas in the Sengkang City area, South Sulawesi Province, which experiences flooding almost every year due to problems in the drainage system. This area experiences flooding due to high rainfall intensity, which is not proportional to the drainage system's capacity (Ali et al. 2017). The average population growth rate in the Tempe sub-district also reached 0.91% per year, exceeding the district average of 0.33%. It impacts the population density in the sub-district and directly influences changes in land use in the Sengkang city area. The impact experienced by residents is an increase in flood events 5 to 6 times a year with inundation heights between 0.5 to 1 m (Idris et al. 2022).

By integrating three aspects, which include a sustainable urban drainage system (Sustainable Urban Drainage System), green-blue infrastructure, and the realization of Sustainable Design Goal 06, management can be implemented that prioritizes the restoration of natural functions and regulation of surface flow volumes. The strategy is to apply rainwater harvesting methods (La Rosa and Pappalardo 2020; Adem Esmail and Suleiman 2020; Jiménez Ariza et al. 2019; Langeveld et al. 2022). Rainwater harvesting can be used for drinking water directly, accommodated by reservoirs, lakes, lakes infiltrated by absorption reservoirs, biopores, and absorption wells, and flowing through natural channels and artificial channels (Arfandi et al. 2022). Rainwater harvesting with modern techniques can be done using ponds, reservoirs above or below ground, or a combination of both (Bitterman et al. 2016; Duguna and Januszkiewicz 2019). Rainwater harvesting with reservoirs built on the surface can collect rainwater that falls on the roof, channeled through gutters (Silvia et al. 2021). This method is profitable because this reservoir can support the basic need for clean water during the rainy season (Maryono 2015). Apart from land, rainwater can also be collected using infiltration wells (Sherlina and Annisa 2022; Tamelan et al. 2020; Ariyani et al. 2021; Pranoto et al. 2022).

Infiltration wells allow surface runoff to flow underground (Arafat 2008). The design of rainwater harvesting with infiltration wells requires several considerations, namely the covering surface area (land area, roof area, or other pavement), rain characteristics (rain intensity, rain time, rain discharge), soil permeability coefficient, groundwater level (Kusumastuti et al. 2017; Chui and Trinh 2016). The benefits of infiltration wells are that the quantity of free groundwater can be preserved, the free groundwater level remains stable, the area of standing water can be minimized, and the dimensions of the drainage network can be minimized. In addition, groundwater quality is improved, land subsidence is prevented, and saltwater intrusion in coastal areas is avoided. However, it should be remembered that the performance of infiltration wells is very dependent on permeability (Mardiah et al. 2018). Infiltration wells were initiated in Indonesia by Sunjoto and developed by several other researchers (Arafat 2008; Mardiah et al. 2018; Iriani et al.2013; Azis et al. 2016).

The objective of this research is to evaluate the effectiveness of infiltration wells in reducing surface runoff and flooding in urban drainage systems. This study specifically investigates the impact of infiltration well implementation on flood volume and inundation extent under different rainfall return periods. A case study is conducted in Sengkang City, Indonesia, focusing on Tempe District, to analyze the hydrological and hydraulic effects of infiltration well application using one-dimensional modeling. This research proposes a simulation-based approach to assess the role of infiltration wells in mitigating urban flooding. The plan involves hydrological modeling of runoff scenarios with and without infiltration wells for 5-year and 10-year return period rainfall events. The analysis also includes overflow volumes, water pooling points, and changes in inundation area to provide a comprehensive understanding of the intervention’s performance. The outcomes of this study are expected to offer data-driven insights and policy references for sustainable urban drainage planning in similar contexts.

This study is subject to several limitations. It does not account for turbulence and sediment transport in surface water runoff, both of which can significantly influence infiltration performance, especially under flood conditions  (Teshukov 2007; Ngatcha and Nkonga 2023; Ngatcha et al. 2024). Turbulent flow, especially in high-intensity rainfall events, can alter sediment dynamics and cause discrepancies between modeled and in-situ infiltration well performance (Ngatcha 2024). Furthermore, differences in soil properties and external environmental factors may lead to variations in runoff response, which are not explicitly addressed in the model used. Future studies are encouraged to incorporate these aspects using advanced hydrodynamic and sediment transport modeling tools.

2 Materials and Methodology

2.1 Research location

This research was conducted in Sengkang City, or more precisely, Tempe District, Wajo Regency, South Sulawesi, Indonesia. Sengkang City is located at 30° 39′ 00″ S – 40° 16′ 00″ S and 119° 53′ 00″ E – 120° 27′ 00″ E. Sengkang City has an area of 38.27 km² consisting of 16 villages. More details of the research location can be seen in Figure 1.

Figure 1 (a) Topographic and elevation map of Tempe District with administrative boundaries; (b) Regional context of the study area within Indonesia; and (c) Location of Tempe District on the island of Sulawesi.

2.2 Data collection

This research uses rainfall information from the Tropical Rainfall Measuring Mission (TRMM) satellite from 1998 to 2021. This rainfall data was obtained from the National Institute of Aeronautics and Space (LAPAN). Apart from that, a map of soil types in the Tempe District was used as supporting data, and this map was obtained from the Wajo City Regional Research and Development Agency (Balitbangda) government office. The combination of rainfall data from satellite sources and information about soil types in the region provides a strong foundation for further analysis of drainage systems and flood risk mitigation.

2.3 Hydrological analysis

The hydrological analysis is carried out to obtain rainfall intensity, calculate planned discharge in an area, and determine infiltration well planning. The following are the steps taken in hydrological analysis:

Rainfall analysis

Rainfall analysis is carried out to assess the planned flood discharge in actual conditions. In this context, several commonly used types of frequency distributions, such as Normal, Gumbel, Log Normal, and Log Pearson III distributions, were evaluated (Mustamin et al. 2021). The process of determining the planned flood discharge is carried out by selecting the best distribution method identified through frequency analysis. This evaluation includes 2 statistical tests, namely the Kolmogorov-Smirnov test and the Chi-square test, to assess the suitability of each distribution to the observed rainfall data. By applying these criteria, the distribution that best fits the data is selected as the basis for calculating the rainfall plan. This approach ensures more accurate and efficient calculations, providing a solid basis for estimating flood discharge that may occur in real situations.

Rain intensity analysis

The Mononobe equation is used to convert rainfall into intensity because the rainfall data used is daily rainfall data (Karamma and Pallu 2018; Suleman et al. 2021).

(1)

Where:

I	=	rain intensity (mm/hour),
R24	=	depth of rain (mm), and
t	=	rain duration (hour).

The results of the rainfall intensity analysis are used as the primary data in the analysis of infiltration wells.

Determination of permeability coefficient

The permeability coefficient measures the soil's ability to facilitate water flow through soil pores. In this research, permeability coefficient determination was carried out to obtain these values based on soil type data. These permeability coefficient values are then used as critical parameters in calculating water infiltration volumes, providing essential information for further understanding the potential for water infiltration into the soil.

Dimensions and requirements of infiltration wells

After obtaining data from hydrological analysis and calculating the permeability coefficient, the next step is to plan the dimensions of the infiltration well. Theoretically, the optimum contribution of infiltration wells can be calculated using the 1988 Sunjoto equation as follows (Sunjoto 2008):

(2)

Where:

H(t)	=	depth of the infiltration well (m),
F	=	geometric factor (m),
Q	=	incoming water discharge (m3/s),
t	=	drainage time (seconds),
k	=	soil permeability coefficient (m/s), and
r	=	radius of the well (m).

Preparation of simulation models using the SWMM application

The principle of SWMM one-dimensional modeling is carried out using the model concept and is logically carried out in the following stages (Idris et al. 2022):

1. Rain runoff or inundation height in each subcatchment is calculated as follows:

(3)

Where:

D1	=	water depth after rain (mm),
Dt	=	water depth in the sub-basin at time t (mm), and
Rt	=	rain intensity at time interval t (mm/hour).

Infiltration Rate (I_t) can be calculated using the Horton equation or the Curve Number (Karamma et al. 2022; Mustamin et al. 2023). For the Horton equation, the following equation is used:

(4)

(5)

Where:

lt	=	infiltration rate at a given time (mm/hours),
fc	=	steady-state infiltration rate (mm/hours),
f0	=	initial infiltration rate (mm/hours),
k	=	decay constant for infiltration rate (per time unit),
t	=	time (hours),
D2	=	depth of water after infiltration (mm), and
D1	=	initial depth of water before infiltration (mm).

The Horton equation (l_t) describes the exponential decline in infiltration rate over time until it stabilizes at f_c. The value of D₂ represents the remaining water depth after infiltration, calculated as the initial depth (D₁) minus the cumulative infiltration rate (l_t).

2. Outflow discharge from subcatchment surface runoff is calculated using the following Manning equation:

(6)

(7)

Where:

ν	=	velocity (m/s),
n	=	Manning coefficient,
S	=	slope of the land,
B	=	width of the land/length of the drainage (m), and
Q	=	discharge (m3/s).

3. The water level in the subcatchment area from the influence of rain, infiltration and outflow is calculated using the following equation:

(8)

Where:

Dt+∆t	=	water depth at time t+∆t (mm),
D2	=	depth of water after infiltration (mm),
Q	=	discharge (m³/s),
A	=	subcatchment area (m²), and
∆t	=	time interval (s).

This equation quantifies the change in water depth within the subcatchment area as a result of outflow discharge (Q) over a given period (Δt). It allows for the estimation of water level reduction in the area over time.

4. The first to fourth stages are also calculated for all subcatchments that have been defined.
5.  The water discharge entering the channel (conduit) is calculated by adding the discharge from the land/subwatershed (Q_oi) with a debit from the upstream of the channel (Q_gi) is calculated by the following equation:

(9)

6. Calculation of changes in water level as a result of increasing discharge in the channel is calculated using the following equation:

(10)

Where:

Y1	=	water level at the end of the time interval Δt (m),
Yt	=	water level at time t (m), and
Ag	=	average cross sectional flow area (m²).

7. The Manning equation is used to calculate channel outflow discharge with the following equation:

(11)

(12)

Where:

R	=	hydraulic radius of the channel (m),
S	=	slope of the channel, and
Ac	=	cross-sectional area of the channel at Y1 (m2).

8. Calculation of the water depth in the channel due to the total inflow and outflow that occurs in the channel is calculated using the following continuity equation:

(13)

9. The sixth to ninth stages are used to calculate all the channels that have been defined.

3 Results and Discussion

3.1 Soil type and groundwater level

The research area comprises the Walanae formation, consisting of sandstone interbedded with siltstone, tufa, marl, claystone, conglomerate, and limestone (Sukamto and Supriyatna 1982). This finding aligns with other research results, which show that soil permeability in Sengkang City is around 5 x 10^-5 m/s (Idris 2023). Based on the categorization of soil types in Table 1, the soil types can be classified as Clean Sands with permeability coefficients (k) ranging from 10^-4 - 10^-6 m/s and the suitability for the construction of infiltration ponds of this soil type is in the good to moderate class (ABG 2024).

Table 1 Soil permeability.

Soil type	Description	k (m/s)	Suitability
Cobbles and boulders	Permeability may be greater as flow may be turbulent	1	Excellent
Gravels	Uniformly graded coarse aggregate with zero fines and minimal sand	10-1 – 10-2	Very good
Gravel sand mixtures	Clean, well graded, with minimal fines (e.g. crushed stone or ‘Type 3’ road aggregate)	10-3 – 10-4	Good
Clean sands	Sands with low silt or clay content	10-4 – 10-6	Good to moderate
Silt mixtures	Mixtures of sand, silt and clay (topsoil is typically in this category)	10-6 – 10-10	Moderate to poor
Clays	Pure clays	1010 – 10-12	Practically impermeable
Artificial	Bituminous mixtures, cement stabilized soil, geosynthetic liners	< 10-12	Practically impermeable

This type of soil is more easily permeable to water than clay. So, the absorption wells can be planned, but the absorption time will be longer.

The groundwater level elevation of Tempe District, as depicted in Figure 2, shows a level range of between 80 to 150 meters from the ground surface. According to Vienastra and Sari (2023), groundwater depth is categorized into three classes: shallow (less than 7 meters), moderate (7–15 meters), and deep (greater than 15 meters) (Vienastra and Sari 2023). In this context, the groundwater level in Tempe District, ranging from 80 to 150 meters, is far beyond the "deep" category. This significant depth indicates very limited accessibility for shallow wells. Therefore, constructing infiltration wells is a suitable option, allowing water to be absorbed into the ground to increase the groundwater storage capacity in the area.

Figure 2 Wajo regency groundwater distribution map (Suhardi 2020).

3.2 Rainfall intensity analysis

There are four methods used in analyzing rainfall intensity: Normal Distribution, Log Normal, Gumbel, and Log Pearson III. The initial stage in this analysis is to test the suitability of rainfall data distribution with Chi-square and Kolmogorov-Smirnov tests.

Table 2 Rainfall data distribution suitability test.

No	Method	Suitability test
		Chi-square		Smirnov–Kolmogorov
		Count value (qualified)	Limit value	Count value (qualified)	Limit value
1	Gumbel	1	5.991	0.1143	0.4090
2	Log Pearson III	1	5.991	0.0945	0.4090
3	Normal	3	5.991	0.1823	0.4090
4	Log Normal	3	5.991	0.1154	0.4090

Based on Table 2, the Chi Square test and Smirnov-Kolmogorov test show that the distribution of daily rainfall follows the Log Pearson III distribution with the smallest calculated value compared to other methods, so the Log Pearson III distribution method was selected for the frequency analysis of rainfall plans based on the available data. In this context, calculations using the Log Pearson III distribution were carried out for a return period of 5 years, producing a planned rainfall value of 116.11 mm, and for a return period of 10 years, producing a value of 139.04 mm, as recorded in Table 3.

Table 3 Planned rain calculation.

Return period (T)	Probability (P) (%)	Skewness Coefficient (Cs)	Frequency Factor (G)	Logarithm of rain data (Log X)	Rainfall depth (X) (mm)
2	50	1.2701	-0.2055	1.9471	88.53
5	20	1.2701	0.7225	2.0649	116.11
10	10	1.2701	1.3389	2.1431	139.04
20	5	1.2701	1.9743	2.2238	167.41
25	4	1.2701	2.1014	2.2399	173.75
50	2	1.2701	2.6540	2.3101	204.20
100	1	1.2701	3.1918	2.3783	238.96
1000	0.1	1.2701	4.9216	2.5979	396.20

The term "planned rainfall" refers to the estimated amount of rainfall expected to occur within a specific return period based on statistical analysis of historical rainfall data. Unlike observed rainfall, which is the actual amount of rainfall recorded during a specific event, or average rainfall, which represents the mean rainfall over a longer period, planned rainfall serves as a design parameter for infrastructure planning and flood risk mitigation. It is often used interchangeably with "design rainfall" and provides a critical basis for designing systems such as drainage networks, infiltration wells, and flood control measures, ensuring they can handle anticipated rainfall events within the chosen return periods.

This method was chosen as it provides an accurate basis for estimating planned rainfall within a specific period, which is essential information in flood risk planning and mitigation.

Next is the calculation of hourly rainfall intensity. The duration of rain in Indonesia based on the observation that the centralized rain occurs no more than 7 hours (Thessalonika and Fauzi 2018), so the maximum rain intensity used in this study is the duration of 6 hours a day. Using the Mononobe formula, the hourly rainfall intensity will be calculated. The results are shown in Table 4.

Table 4 Hourly rainfall intensity distribution values.

No.	Rainfall time (hours)	Rain distribution (%)
1	1	55.03%
2	2	14.30%
3	3	10.03%
4	4	7.99%
5	5	6.75%
6	6	5.90%

The hourly distribution values are then used in the calculation of net rainfall with a 5-year and 10-year return period in Figure 3.

Figure 3 Rainfall intensity at 5- and 10-year return period.

The selection of 5- and 10-year return periods is adjusted to the typology of the study area which is categorized as a medium-sized city with an area of approximately 500 hectares. According to the Minister of Public Works and Housing Regulation No. 12/PRT/M/2014 of 2014 on the Implementation of Urban Drainage Systems, for medium-sized cities with an area greater than 500 hectares, return periods of 5 to 10 years are generally used for drainage system planning and flood risk mitigation measures. This classification ensures that the return period selected is appropriate for the study area, considering the scale of the urban infrastructure and the associated level of risk. By following these guidelines, planned rainfall estimates provide a solid basis for planning and mitigating flood risk in the area.

3.3 Calculation of flood events with return periods of 5 and 10 years

Modeling was carried out using one-dimensional modeling with the SWMM 5.2 application. based on input data from data collection on the characteristics of the research site conditions. The physical character of the Sengkang City drainage system consists of 4 primary channels that all lead to the city center and then flow out to the water body, the Cendranae River, as shown in Figure 4.

Figure 4 Map of subcatchments, and junctions.

Measurement of channel dimensions was carried out by direct field measurement to obtain existing channel dimensions according to the existing channel path map (conduit) with a length of 32,601 m secondary channels and 8,773 m primary channels. The analyzed drainage network consists of open channels and closed channels at road crossings. Hydraulic parameters used in the modeling include Manning's roughness coefficient, with a value of n = 0.015 for concrete-lined channels and n = 0.035 for natural channels. For channel elevations ranging from 47.345 msl – 6.8 msl.

Figures 5 and 6 provide important information on the performance of the drainage system under the 5- and 10-year rainfall intensity scenarios. The highlighted critical paths (in red and yellow) and water runoff points indicate areas where drainage infrastructure is insufficient to handle water runoff, potentially leading to localized flooding. These results underscore the urgency of implementing targeted interventions, such as increased capacity in primary channels or additional retention ponds, to mitigate flood risks. Furthermore, the visualization also helps in prioritizing areas that require infrastructure improvements and informs urban flood management strategies, ensuring resilience to future rainfall events.

Figure 5 Simulation map with 5-year return period.

Figure 6 Simulation map with 10-year return period.

Based on the results of one-dimensional modeling using rainfall events with a 5-year return period, critical paths were found in several channels, especially primary channels A, B, C, and D, which are visually marked in red. The analysis showed the potential for overflowing water at the nodes, with a total volume of flood runoff reaching 131.21 x 10³ m³ and inundation detected at 45 points for the 5-year return period and 204.59 x 10³ m³ and inundation detected at 58 points for the 10-year return period. This volume of flood runoff signifies a considerable risk to infrastructure and communities in the area. The results of running simulations of inundation points are detected (yellow to red), this is in line with flooding events that occur in the field. Based on the comparison of the results of interviews with the community and the simulation results show only 2 points that are not depicted as overflowing by the SWMM application. The verification results show an average deviation of 6.66%, the deviation is still below <10% and is acceptable. These findings illustrate a significant level of flood risk in the context of 5 and 10-year return period rainfall intensity and highlight areas that require further attention in flood planning and mitigation.

3.4 Urban flood management design

Determination of coefficients

In this study, flood mitigation is planned by implementing infiltration ponds in the upstream area of the primary channel at the study site. The choice of focusing on the upstream area is based on strategic considerations to reduce the volume of water runoff before it reaches the downstream area. This approach aims to suppress peak flow rates and reduce pressure on downstream channel infrastructure, thus helping to mitigate flood risk overall.

Residential areas dominate the study site, so the soil coefficient was taken as 0.6, and the roof flow coefficient was chosen as 0.7 based on the data in Table 5. This reflects the dominance of built-up areas with high levels of imperviousness, which require effective interventions such as infiltration ponds to manage stormwater runoff.

Based on the categorization of soil types in Table 1, the permeability values indicate Clean Sands soils that are in the Good to Moderate class. Therefore, the soil permeability value was set at 5 x 10^-5 m/s in this study. While a more distributed approach across the catchment could be an ideal solution, the prioritization of upstream areas in this study reflects a practical and strategic first step, especially considering the limited resources and need for urgent intervention. Future research is expected to expand the scope to evaluate the combined effects of mitigation strategies in upstream and downstream areas to create a more comprehensive approach.

Table 5 Value of flow coefficient (C) on various soils and types of watersheds.

Type of drainage area	Condition	C
1. Grass	Flat sand soil, 2%	0.05 – 0.10
	Average sand soil, 2 - 7%	0.10 – 0.15
	Steep sand soil, 7%	0.15 – 0.20
	Flat clay soil, 2%	0.13 – 0.17
	Average clay soil, 2 - 7%	0.18 – 0.22
	Clay soil with steep slope, 7%	0.25 – 0.35
2. Business	Old Town area	0.75 – 0.95
	Periphery	0.50 – 0.70
3. Residential	"Single family" area	0.30 – 0.50
	Separate "multi-units"	0.40 – 0.60
	Closed "multi-unit"	0.60 – 0.75
	“Sub-urban”	0.25 – 0.40
	Region house apartment	0.20 – 0.70
4. Industry	Lightweight region	0.60 – 0.80
	Heavy region	0.60 – 0.90
5. Landscaping, graveyard		0.10 – 0.25
6. Playground		0.20 – 0.35
7. Railway yard		0.20 – 0.40
8. Unworked areas		0.10 – 0.30
9. Street	Paved	0.70 – 0.95
	Concrete	0.80 – 0.95
	Stone	0.70 – 0.95
10. To walk and ride a horse		0.75 – 0.85
11. Roof		0.75 – 0.95

3.5 Planning the number of infiltration wells

Infiltration wells are infiltration hole structures deliberately created to collect some of the rainwater to facilitate infiltration into the ground. The results of the initial simulation show that several subcatchments experienced significant water runoff and impacted the primary channels of Sengkang City. To overcome this, research was carried out by examining the subcatchment in detail, and a plan was designed to install infiltration wells at intervals of every 1,000 m². The location of the planned infiltration ponds can be seen in Figure 7 with the calculations documented in Table 6.

Table 6 Planning data on the number of absorption wells per subcatchment area.

No.	Subcatchment	Total area	Impact	Interval	Number of infiltration wells
		m2	Go to	m2
1	SUBC1	269,798	River A	1000	270
2	SUBC2	672,502	River A	1000	673
3	SUBC9	508,982	Out Fall	1000	509
4	SUBC18	229,651	River B	1000	230
5	SUBC19	291,304	River B	1000	291
6	SUBC64	325,251	River C	1000	325
7	SUBC65	113,994	River C	1000	114
8	SUBC66	84,235	River D	1000	84

Figure 7 Subcatchment infiltration well plan.

3.6 Calculation of infiltration discharge

In this stage, the contribution of water absorption is calculated by adding infiltration wells in each subcatchment area. The optimum contribution of infiltration wells is calculated using the Sunjoto equation, with planning for infiltration wells that have a depth of 3 m, an infiltration well radius of 0.6 m (see Figure 8), an infiltration duration of 3600 seconds, and a soil permeability of 5 x 10^-5 m/s. The discharge that can be absorbed by one infiltration well can be found in Table 7.

Table 7 Q calculation results for infiltration wells.

H	r	Fk	Fkt	πr2	(a/b)	Exp (-(c))	1-(d)	Q
(m)	(m)		a	b	c	d		(m3/s)
3	0.6	0.000165	0.594	1.1304	0.525478	0.5912728	0.409	0.0012

Referring to the calculation results in Table 7, information is obtained regarding the discharge that can be absorbed due to the implementation of infiltration wells in each subcatchment area. Details regarding absorbed discharge per subcatchment area are then presented in Table 8. This data provides an overview of the contribution of absorption wells in handling water runoff in each subcatchment area, providing an essential basis for further understanding the effectiveness of planned flood mitigation.

Table 8 Table of absorbed discharge calculations per subcatchment area.

No	Subcatchment	Number of infiltration wells	Q Absorbed (m3/s)	Total Absorbed (m3/s)	Total Absorbed (L/s)
1	SUBC1	270	0.0012	0.327	326.991
2	SUBC2	673	0.0012	0.815	815.055
3	SUBC9	509	0.0012	0.616	616.438
4	SUBC18	230	0.0012	0.279	278.548
5	SUBC19	291	0.0012	0.352	352.423
6	SUBC64	325	0.0012	0.394	393.600
7	SUBC65	114	0.0012	0.138	138.063
8	SUBC66	84	0.0012	0.102	101.730

Figure 8 Typical infiltration well plan (cm).

3.7 Calculation of reduced flood volume

The discharge that has been successfully absorbed comes from the results of calculations as shown in Table 6 and is then input into the SWMM 5.2 application through a trial-and-error approach in the subcatchment. This approach involves adjusting the percentage of non-permeable surface (% impervious) until the data recorded in Table 6 shows a reduction in runoff discharge. After that, a simulation is carried out by running the application for a return period of 5 years and 10 years. The simulation results show a decrease in the total flood volume, as recorded in Table 9. This finding reflects the effectiveness of the mitigation implemented in reducing flood risk in the study area.

Table 9 Reduction calculation results.

No	Description	Total Flood Volume (103 m3)
		5-yr return period	10-yr return period
1	Total initial flood volume	131.21	204.59
	Total flood volume with infiltration wells (m3)	119.06	191.43
	Effectiveness	12.15	13.16
	%	9%	6%
2	Number of nodes flooding	45	58
	Number of flooding nodes with infiltration ponds	44	57
	Node flooding reduced	1	1

Based on the data in Table 7, the research results show that the implementation of infiltration wells has positively impacted reducing the total flood volume in both return periods observed. The initial total flood volume of 131.21 x 10³ m³ at the 5-year return period and 204.59 x 10³ m³ at the 10-year return period was successfully reduced to 119.06 x 10³ m³ and 191.43 x 10³ m³, respectively, after the implementation of infiltration wells. The effectiveness of the infiltration wells can be seen in the reduction in total flood volume, namely 12.15 x 10³ m³ (9%) at the 5-year return period, and 13.16 x 10³ m³ (6%) at the 10-year return period. It significantly contributes to reducing the impact of flooding in the study area. Apart from that, attention can also be paid to the number of flooding nodes, where implementing infiltration wells reduced the number of flooding nodes from 45 to 44 at the 5-year return period, and from 58 to 57 at the 10-year return period. Overall, the use of infiltration wells showed a consistent reduction in the level of affected nodes, illustrating its efficiency in mitigating flood risk at the study site.

4 Conclusion

Flooding is a severe problem faced by Sengkang City. One-dimensional simulations have provided an understanding of existing flood conditions in the region. The total overflow reached 131.21 x 10³ m³ during the 5-year return period, with 45 puddle points formed. Meanwhile, the overflow increases to 204.59 x 10³ m³ for the 10-year return period with 58 puddle points. This data indicates the significant impact of flooding on the population and infrastructure of Sengkang City.

Harvesting rainwater using infiltration wells in the upstream subcatchment of the Sengkang City primary channel has proven effective in reducing flood volume. Rainwater harvesting reduced flood volume by 12.15 x 10³ m³ (5-year return period) and 13.16 x 10³ m³ (10-year return period), with respective effectiveness of 9% and 6%. Even though the percentage is relatively low, this step provides positive benefits in reducing the impact of flooding in the area.

These findings are in line with previous studies that emphasize the role of nature-based solutions such as infiltration wells in enhancing urban flood resilience (La Rosa and Pappalardo 2020; Zhou 2014) . In comparison to studies conducted in larger metropolitan settings, the effectiveness values observed in Sengkang may appear modest, but they demonstrate significant applicability in medium-density urban areas with limited drainage infrastructure.

Nevertheless, the study acknowledges limitations, particularly in the absence of extensive field validation due to the reliance on secondary data. Future research should integrate spatial validation through remote sensing or field measurement to improve model accuracy and strengthen policy implications.

Flood management in Sengkang City requires a more comprehensive approach. Apart from harvesting rainwater through infiltration wells, improvements and expansion of drainage channels and sluice control systems must be considered. Developing better infrastructure and increasing public awareness about rainwater management and flood control are essential factors in reducing flood risk.

In the long term, reducing flood risk in Sengkang City must be a priority. It involves investing in better drainage systems, including improving drainage infrastructure and increasing channel capacity to accommodate larger overflows. Public education about the importance of rainwater management and flood control must also be improved. Collaboration between government, communities, and relevant stakeholders is essential for effective flood management in Sengkang City.

By contextualizing the simulation results with broader sustainable urban drainage frameworks, the study contributes to an evolving discourse on urban water management in flood-prone areas. The lessons learned from this case can serve as references for similar cities in Indonesia and other developing regions.

By considering existing flood conditions, the effectiveness of rainwater harvesting, and the need for a comprehensive approach and flood risk reduction efforts, these measures can help Sengkang City face the flood challenge more effectively. Governments and local communities must work together to implement these solutions to create safer and more resilient environments against future flooding.

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