Abstract
This study aims to analyze the distribution of floods in the Bila River and its countermeasures in reducing the impact that occurred in Sidrap Regency. The study performs hydrological analysis using the Bila watershed rainfall data, calculates the planned flood discharge using the HSS Soil Conservation Service (SCS) model, and simulates the flood flow profile using the HEC-RAS 2D numerical model. It is found that the Q20 flood discharge of the Bila and the Bulucenrana Rivers entered the Bila River downstream calculated from the HSS SCS analysis are 738.60 and 779.50 m3/s, respectively. The overflow of the Bila River flood affects nine villages, namely Kalola Village (0.06 km2), Sogi Village (0.01 km2), Kalosi Alau Village (0.32 km2), Kampale Village (0.11 km2), Salomalori Village (0.42 km2), Tanru Tedong Village (2.12 km2), Kalosi Village (0.91 km2), Salobukkang Village (1.70 km2), and Taccimpo Village (4.01 km2). It is proposed that the best solution to deal with the issue is by introducing an eco-drainage system, namely by constructing a retention pond with a maximum storage volume of 3.81 million m3 or with a normal storage of 2.4 million m3. The existence of a retention pond can reduce the inundated area around 8.28 km2 or 85.71%.
HIGHLIGHTS
The distribution of floods in the Bila River was investigated.
The Bila watershed rainfall data were used to simulate the flood flow profile.
The finding of this study provides a solution to overcome the flood issue in Sidrap Regency.
The study suggests for constructing a retention pond with a maximum storage volume of 3.81 million m3.
Retention pond can reduce the inundated area around 8.28 km2 or 85.71%.
INTRODUCTION
Calamities of all kinds, including natural disasters, are common in Indonesia (Soemabrata et al. 2018). Natural disasters are natural phenomena that can cause environmental damage and destruction which in turn can cause loss of life, loss of property, and the destruction of infrastructures that have been built so far. Natural disasters that occur as a result of excessive exploitation of land, forest, and water natural resources as well as due to changes in global weather or climate have resulted in an increase of critical land that is susceptible to floods, droughts, landslides, forest, land fires, erosion, and sedimentation. One of the disasters that often occur is flood (Suryana et al. 2018; Sholihah et al. 2020).
Floods are a rather regular type of natural disaster in Indonesia, especially during the rainy season, as nearly every city in Indonesia experiences flooding (Halim 2018). The problem has not yet been resolved, despite the fact that this calamity virtually invariably occurs every year. With time, the flood's frequency, magnitude, depth, and duration all tend to increase (Surapto 2011; Hijriah 2013; Surminski 2014; Moftakhari et al. 2018).
According to Torti (2012), flooding can have very negative effects on people, including loss of life and property destruction. As a result, the flooding problem in Indonesia needs to be resolved right away. To reduce the risk of damage and losses due to flooding, flood prevention needs to be implemented in a real way (FAG 2018). Studying the river's current circumstances and its flood characteristics is one option for resolving the issue. By doing so, one can identify which river segments are at risk of flooding and create the best strategy possible based on hydraulic feasibility (Nanlohy et al. 2009; Khalil et al. 2020; Serra-Llobet et al. 2022).
Area development to meet various needs such as housing, agriculture, trade, industry, offices, roads, and others from year to year is increasing as a result of population growth and the development of its activities. This causes a decrease in environmental quality including a decrease in the quality of watersheds that cause losses. The most obvious losses are drought in the dry season and floods in the rainy season (Pambudi 2019; Syafri et al. 2020; Purwanto 2021).
This condition also occurs in the Bila River, marked by events around the Bila River in the form of reduced river capacity, increased flood discharge, and overflow of the Bila River and its tributaries which resulted in damage to public facilities, rice fields, gardens, and residential areas. This is exacerbated by the scouring of the river flow which causes damage to the river banks and threatens important facilities in the vicinity (Wahyuddin Qadri et al. 2016; Staddal et al. 2017).
Almost every year, the river is affected by floods, but the worst floods occurred on 9 and 13 June 2019. It was reported that three sub-districts in Sidrap Regency, South Sulawesi, were submerged due to the inundation of the Bila River (Tanru Tedong). The sub-districts that were inundated were Dua Pitue (eight villages), Pitu Riawa (six villages), and Pitu Riase (two villages). The main cause of this flood is that some of the embankments on the banks of the river are broken and also the high intensity of rainfall (BNPB 2022).
In order to stop the flood in the Bila River and minimize damages, flood catastrophe mitigation is required as anticipation program. Mitigation is defined as a sequence of actions taken to lower the risk of disasters, including structural improvements, public awareness campaigns, and the development of response capabilities (non-structural) (Pancasilawan 2020). By managing flooded areas with the aid of dams, weirs, river embankments, riverbed dredging, river normalization, etc., structural efforts can be made concrete. While non-structural efforts are made to map potential risks and risks of flooding, provide socialization and early warning rescues, make spatial arrangements so that environmentally unfriendly land use in flood-prone areas and water catchment areas can be controlled, and offer to counsel to the community, especially those who live in flood-prone areas (El Anshori 2020; Havinga 2020; Iskandar et al. 2021; Nugraheni et al. 2022).
Following the research background, the goal of this study is to examine the distribution of floods in the Bila River area and flood mitigation to lessen the impact of flood disasters. This information can later be used by managers to improve flood control structures and by residents of floodplain areas to be vigilant during the rainy season.
MATERIALS AND METHODOLOGY
The data used in this research include
- (1)
The Tropical Rainfall Measuring Mission (TRMM) rainfall data were obtained from the National Institute of Aeronautics and Space for 1998–2020. There were a total of five rainfall stations used in this study, which were dispersed throughout the Research Watershed region.
- (2)
Soil type data used is FAO – UNESCO Soil Map of World in 2007.
- (3)
Land usage data used is the Land Usage Map of BIG (Geospatial Information Agency) in 2022.
- (4)
For flood tracking, river topography data along 2 km is combined with DEM (Digital Elevation Model) with 2.7 ArcSecond resolution from BIG.
The unit hydrograph technique is used as one of the important foundations of a method for estimating the flood hydrograph of a design rainfall. Synthetic unit hydrograph methods that are currently commonly used in Indonesia include the Snyder-SCS, Snyder-Alexeyev, Nakayasu, GAMA-1, HSS-αβγ, and Limantara methods (Natakusumah et al. 2011; Habibi S A et al. 2021). In making flood hydrographs for rivers where there are no or very few flood hydrograph observations, it is necessary to look for the characteristics or parameters of the catchment area first, for example, the time to peak hydrograph (time to peak magnitude), bed width, area, slope, the length of the longest channel, the runoff coefficient, and so on (Habibi S A et al. 2021; Mustamin et al. 2021). Usually, we use synthetic hydrographs that have been developed in other countries, the parameters of which must be adjusted to the characteristics of the catchment under consideration. The synthetic hydrograph used in this modeling is SUH SCS Curve Number (HEC-HMS) following the results of previous studies at the same location, indicating that this method is the closest to field conditions (Suleman et al. 2021).
The Soil Conservation Service (SCS) synthetic unit hydrograph was developed by the U.S. Department of Agriculture (USDA SCS). The SCS synthetic unit hydrograph method is used to calculate the magnitude of the planned flood discharge associated with land use in a watershed. This hydrograph is the same as the rational method which has a component of the magnitude of the influence of land use and soil type expressed in a value, namely the Curve Number (Deby R 2021). Hydraulics modeling using HEC-RAS software, with 2D Unsteady Flow equations. HEC-RAS is software that has been widely applied for flood distribution mapping and has obtained quite good results with large-resolution DEM data (Ongdas et al. 2020; Afifah R et al. 2021; Pamungkas R C et al. 2021; Yakti B P et al. 2021; Karamma et al. 2022). To describe the inundation in the Bila River, researchers used Floodplain Mapping in the RAS Mapper application which can directly describe the resulting inundation. To make the simulation results more informative, the simulation results are then processed in the ArcGIS application to become a map.
Alternative countermeasures are carried out when the capacity of the river is insufficient. The alternative is in the form of eco-drainage, namely retention ponds. The application of eco-drain can be done in several ways. Retention methods are divided into two types, namely ‘off-site retention’, for example making ponds or reservoirs, and ‘on-site retention’, for example, retention on building roofs, parks, parking lots, open fields, and yards. The method of ‘infiltration’ is by making artificial recharge in certain areas in the form of infiltration wells, infiltration ditches, infiltration areas, and water-permeable pavements (Karamma et al. 2021). However, this study uses the off-site retention method which is related to the handling of the Bila watershed.
RESULTS AND DISCUSSION
Design flood discharge analysis
The stages in the design flood discharge analysis begin with an analysis of the maximum rainfall in the last 23 years at five rain posts that are located or represent rainfall conditions in the study location. The rainfall data is then used in calculating regional rainfall at the research location. The results of the analysis of rainfall become the basic data used in the calculation of the design flood discharge. The results of regional rainfall calculations in the Bila and Bulucenrana watersheds can be seen in Tables 1 and 2.
No. . | Years . | Rainfall maximum . | Regional rain . | ||||
---|---|---|---|---|---|---|---|
Post 1 . | Post 2 . | Post 3 . | Post 4 . | Post 5 . | |||
Coefficient . | 0.15 . | 0.64 . | 0.08 . | 0.08 . | 0.06 . | ||
1 | 1998 | 82 | 84 | 78 | 91 | 78 | 83.59 |
2 | 1999 | 113 | 109 | 103 | 95 | 100 | 107.37 |
3 | 2000 | 56 | 72 | 81 | 75 | 75 | 71.08 |
4 | 2001 | 99 | 122 | 98 | 112 | 102 | 114.58 |
5 | 2002 | 75 | 79 | 70 | 83 | 68 | 77.17 |
6 | 2003 | 118 | 144 | 200 | 173 | 224 | 151.45 |
7 | 2004 | 102 | 104 | 100 | 87 | 103 | 101.68 |
8 | 2005 | 103 | 97 | 142 | 141 | 164 | 109.18 |
9 | 2006 | 95 | 68 | 83 | 70 | 91 | 74.77 |
10 | 2007 | 88 | 95 | 98 | 102 | 102 | 95.01 |
11 | 2008 | 88 | 86 | 101 | 80 | 87 | 87.32 |
12 | 2009 | 82 | 115 | 85 | 82 | 82 | 103.08 |
13 | 2010 | 76 | 74 | 91 | 81 | 84 | 76.89 |
14 | 2011 | 63 | 60 | 69 | 79 | 68 | 63.09 |
15 | 2012 | 97 | 163 | 120 | 114 | 83 | 141.62 |
16 | 2013 | 67 | 96 | 93 | 101 | 84 | 91.36 |
17 | 2014 | 72 | 70 | 73 | 76 | 76 | 71.10 |
18 | 2015 | 61 | 61 | 54 | 65 | 49 | 59.87 |
19 | 2016 | 69 | 72 | 78 | 76 | 80 | 72.98 |
20 | 2017 | 96 | 122 | 108 | 86 | 79 | 111.96 |
21 | 2018 | 80 | 67 | 87 | 70 | 79 | 71.57 |
22 | 2019 | 69 | 72 | 84 | 68 | 67 | 72.02 |
23 | 2020 | 57 | 104 | 98 | 96 | 118 | 97.21 |
No. . | Years . | Rainfall maximum . | Regional rain . | ||||
---|---|---|---|---|---|---|---|
Post 1 . | Post 2 . | Post 3 . | Post 4 . | Post 5 . | |||
Coefficient . | 0.15 . | 0.64 . | 0.08 . | 0.08 . | 0.06 . | ||
1 | 1998 | 82 | 84 | 78 | 91 | 78 | 83.59 |
2 | 1999 | 113 | 109 | 103 | 95 | 100 | 107.37 |
3 | 2000 | 56 | 72 | 81 | 75 | 75 | 71.08 |
4 | 2001 | 99 | 122 | 98 | 112 | 102 | 114.58 |
5 | 2002 | 75 | 79 | 70 | 83 | 68 | 77.17 |
6 | 2003 | 118 | 144 | 200 | 173 | 224 | 151.45 |
7 | 2004 | 102 | 104 | 100 | 87 | 103 | 101.68 |
8 | 2005 | 103 | 97 | 142 | 141 | 164 | 109.18 |
9 | 2006 | 95 | 68 | 83 | 70 | 91 | 74.77 |
10 | 2007 | 88 | 95 | 98 | 102 | 102 | 95.01 |
11 | 2008 | 88 | 86 | 101 | 80 | 87 | 87.32 |
12 | 2009 | 82 | 115 | 85 | 82 | 82 | 103.08 |
13 | 2010 | 76 | 74 | 91 | 81 | 84 | 76.89 |
14 | 2011 | 63 | 60 | 69 | 79 | 68 | 63.09 |
15 | 2012 | 97 | 163 | 120 | 114 | 83 | 141.62 |
16 | 2013 | 67 | 96 | 93 | 101 | 84 | 91.36 |
17 | 2014 | 72 | 70 | 73 | 76 | 76 | 71.10 |
18 | 2015 | 61 | 61 | 54 | 65 | 49 | 59.87 |
19 | 2016 | 69 | 72 | 78 | 76 | 80 | 72.98 |
20 | 2017 | 96 | 122 | 108 | 86 | 79 | 111.96 |
21 | 2018 | 80 | 67 | 87 | 70 | 79 | 71.57 |
22 | 2019 | 69 | 72 | 84 | 68 | 67 | 72.02 |
23 | 2020 | 57 | 104 | 98 | 96 | 118 | 97.21 |
No. . | Years . | Rainfall maximum . | Regional rain . | |||
---|---|---|---|---|---|---|
Post 1 . | Post 2 . | Post 4 . | Post 5 . | |||
Coefficient . | 0.73 . | 0.00 . | 0.23 . | 0.04 . | ||
1 | 1998 | 82 | 84 | 91 | 78 | 83.75 |
2 | 1999 | 113 | 109 | 95 | 100 | 108.62 |
3 | 2000 | 56 | 72 | 75 | 75 | 60.81 |
4 | 2001 | 99 | 122 | 112 | 102 | 101.93 |
5 | 2002 | 75 | 79 | 83 | 68 | 76.49 |
6 | 2003 | 118 | 144 | 173 | 224 | 134.31 |
7 | 2004 | 102 | 104 | 87 | 103 | 98.56 |
8 | 2005 | 103 | 97 | 141 | 164 | 114.10 |
9 | 2006 | 95 | 68 | 70 | 91 | 89.23 |
10 | 2007 | 88 | 95 | 102 | 102 | 91.80 |
11 | 2008 | 88 | 86 | 80 | 87 | 85.98 |
12 | 2009 | 82 | 115 | 82 | 82 | 81.61 |
13 | 2010 | 76 | 74 | 81 | 84 | 77.34 |
14 | 2011 | 63 | 60 | 79 | 68 | 66.95 |
15 | 2012 | 97 | 163 | 114 | 83 | 100.58 |
16 | 2013 | 67 | 96 | 101 | 84 | 75.74 |
17 | 2014 | 72 | 70 | 76 | 76 | 73.36 |
18 | 2015 | 61 | 61 | 65 | 49 | 61.61 |
19 | 2016 | 69 | 72 | 76 | 80 | 71.16 |
20 | 2017 | 96 | 122 | 86 | 79 | 93.01 |
21 | 2018 | 80 | 67 | 70 | 79 | 77.49 |
22 | 2019 | 69 | 72 | 68 | 67 | 68.38 |
23 | 2020 | 57 | 104 | 96 | 118 | 68.18 |
No. . | Years . | Rainfall maximum . | Regional rain . | |||
---|---|---|---|---|---|---|
Post 1 . | Post 2 . | Post 4 . | Post 5 . | |||
Coefficient . | 0.73 . | 0.00 . | 0.23 . | 0.04 . | ||
1 | 1998 | 82 | 84 | 91 | 78 | 83.75 |
2 | 1999 | 113 | 109 | 95 | 100 | 108.62 |
3 | 2000 | 56 | 72 | 75 | 75 | 60.81 |
4 | 2001 | 99 | 122 | 112 | 102 | 101.93 |
5 | 2002 | 75 | 79 | 83 | 68 | 76.49 |
6 | 2003 | 118 | 144 | 173 | 224 | 134.31 |
7 | 2004 | 102 | 104 | 87 | 103 | 98.56 |
8 | 2005 | 103 | 97 | 141 | 164 | 114.10 |
9 | 2006 | 95 | 68 | 70 | 91 | 89.23 |
10 | 2007 | 88 | 95 | 102 | 102 | 91.80 |
11 | 2008 | 88 | 86 | 80 | 87 | 85.98 |
12 | 2009 | 82 | 115 | 82 | 82 | 81.61 |
13 | 2010 | 76 | 74 | 81 | 84 | 77.34 |
14 | 2011 | 63 | 60 | 79 | 68 | 66.95 |
15 | 2012 | 97 | 163 | 114 | 83 | 100.58 |
16 | 2013 | 67 | 96 | 101 | 84 | 75.74 |
17 | 2014 | 72 | 70 | 76 | 76 | 73.36 |
18 | 2015 | 61 | 61 | 65 | 49 | 61.61 |
19 | 2016 | 69 | 72 | 76 | 80 | 71.16 |
20 | 2017 | 96 | 122 | 86 | 79 | 93.01 |
21 | 2018 | 80 | 67 | 70 | 79 | 77.49 |
22 | 2019 | 69 | 72 | 68 | 67 | 68.38 |
23 | 2020 | 57 | 104 | 96 | 118 | 68.18 |
From the results of these calculations, it can be concluded that the Bulucenrana River is a river that provides the largest volume of water that enters the downstream Bila River and also has great potential to experience overflow or flooding along its flow.
Existing condition hydraulic analysis
Based on the results of mapping the flood-prone areas of the Bila River in Sidrap Regency, it can be seen that the performance of the existing conditions is not able to accommodate the existing flood discharge. The flood-affected areas based on village boundaries in Sidrap Regency can be seen in Table 3. Table 3 shows that the village most affected by the overflow of the Bila River is Taccimpo Village with an area of 4.01 km2.
No. . | Locations . | Areas (km2) . |
---|---|---|
1 | Desa Kalola | 0.06 |
2 | Desa Sogi | 0.01 |
3 | Desa Kalosi Alau | 0.32 |
4 | Desa Kampale | 0.11 |
5 | Kelurahan Salomalori | 0.42 |
6 | Kelurahan Tanru Tedong | 2.12 |
7 | Desa Kalosi | 0.91 |
8 | Desa Salobukkang | 1.70 |
9 | Desa Taccimpo | 4.01 |
Total | 9.66 |
No. . | Locations . | Areas (km2) . |
---|---|---|
1 | Desa Kalola | 0.06 |
2 | Desa Sogi | 0.01 |
3 | Desa Kalosi Alau | 0.32 |
4 | Desa Kampale | 0.11 |
5 | Kelurahan Salomalori | 0.42 |
6 | Kelurahan Tanru Tedong | 2.12 |
7 | Desa Kalosi | 0.91 |
8 | Desa Salobukkang | 1.70 |
9 | Desa Taccimpo | 4.01 |
Total | 9.66 |
Flood management with retention basins
Looking at the results of the flood simulation and the results of the field survey, it can be concluded that the main cause of flooding in the Bila River is the inability of the river to accommodate the flood discharge from two rivers, namely the Bulucenrana River and the Bila River, so it is very necessary to revitalize the Bila River with an eco-drain, namely a retention basin.
The main consideration in determining the location of the retention pond in this study is in the upstream section by utilizing the old river channel to accommodate flood discharge and can also reduce flood discharge leading to the river estuary, namely Lake Tempe, given the complexity of flooding in the lake.
No. . | Locations . | Flood-affected areas before retention basins (km2) . | Flood-affected areas after retention basins (km2) . | Difference (km2) . |
---|---|---|---|---|
1 | Desa Kalola | 0.06 | – | 0.06 |
2 | Desa Sogi | 0.01 | – | 0.01 |
3 | Desa Kalosi Alau | 0.32 | – | 0.32 |
4 | Desa Kampale | 0.11 | 0.11 | – |
5 | Kelurahan Salomalori | 0.42 | – | 0.42 |
6 | Kelurahan Tanru Tedong | 2.12 | 0.13 | 1.99 |
7 | Desa Kalosi | 0.91 | 0.18 | 0.73 |
8 | Desa Salobukkang | 1.70 | 0.96 | 0.74 |
9 | Desa Taccimpo | 4.01 | – | 4.01 |
Total | 9.66 | 1.38 | 8.28 |
No. . | Locations . | Flood-affected areas before retention basins (km2) . | Flood-affected areas after retention basins (km2) . | Difference (km2) . |
---|---|---|---|---|
1 | Desa Kalola | 0.06 | – | 0.06 |
2 | Desa Sogi | 0.01 | – | 0.01 |
3 | Desa Kalosi Alau | 0.32 | – | 0.32 |
4 | Desa Kampale | 0.11 | 0.11 | – |
5 | Kelurahan Salomalori | 0.42 | – | 0.42 |
6 | Kelurahan Tanru Tedong | 2.12 | 0.13 | 1.99 |
7 | Desa Kalosi | 0.91 | 0.18 | 0.73 |
8 | Desa Salobukkang | 1.70 | 0.96 | 0.74 |
9 | Desa Taccimpo | 4.01 | – | 4.01 |
Total | 9.66 | 1.38 | 8.28 |
CONCLUSION
Based on the results of the research conducted, it can be concluded that the flood discharge from the HSS SCS analysis that enters the Bila Hilir River consists of the Bila and Bulucenrana Rivers with a flood discharge of Q20 = 738.60 m3/s and Q20 = 779.50 m3/s, respectively. The overflow of the Bila River flood affected nine villages, namely Kalola Village (0.06 km2), Sogi Village (0.01 km2), Kalosi Alau Village (0.32 km2), Kampale Village (0.11 km2), Salomalori Village (0.42 km2), Tanru Tedong Village (2.12 km2), Kalosi Village (0.91 km2), Salobukkang Village (1.70 km2), and Taccimpo Village (4.01 km2). So that the total area affected is 9.66 km2. The solution to addressing the Bila River flood is with an environmentally sound drainage system, namely by planning to build a retention pond with a maximum storage volume of 3.81 million m3 and a normal storage of 2.4 million m3 which can reduce around 8.28 km2 or 85.71% of the area inundation prior to the presence of a retention pond.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.