IJAR Indonesian Journal of Advanced Research 2986-0768 Formosa Publisher 10.55927/ijar.v4i7.14965 Capacity Analysis of Downstream Cijangkelok River Using HEC-RAS Software Anggista Dhea Department of Civil Engineering, Faculty of Engineering, University of Swadaya Gunung Jati, Indonesia Zahra Farach Alifia Department of Civil Engineering, Faculty of Engineering, University of Swadaya Gunung Jati, Indonesia Mariani Nadia Department of Civil Engineering, Faculty of Engineering, University of Swadaya Gunung Jati, Indonesia Winasis Akbar Department of Civil Engineering, Faculty of Engineering, University of Swadaya Gunung Jati, Indonesia winasis1977@gmail.com 29 07 2025 13 05 2025 27 06 2025 29 07 2025 4 7 1543 1558

This study aims to analyze the capacity of the Cijangkelok River downstream in facing the planned flood discharge, with a hydraulic modeling approach using HEC-RAS software. Maximum daily rainfall data from five rainfall stations during the 2014-2023 period was used as the basis for calculating flood discharge using the Rational method and Nakayasu Synthetic Unit Hydrograph (HSS). The calculation results showed that the peak flood discharge for the 10, 25, and 50-year return periods reached 599.71 m³/det, 648.91 m³/det, and 682.82 m³/det using the Rational method, and 835.01 m³/det, 888.68 m³/det, and 922.90 m³/det using the Nakayasu HSS method, respectively. Flow simulations with HEC-RAS indicated that some river cross sections, especially at STA 27+950, 28+050, and 28+100, were unable to accommodate the maximum discharge, causing runoff outside the channel. This finding indicates that the capacity of the existing channel is not adequate to safely channelize flood flows. The results of this study are expected to serve as a technical reference in river normalization planning and more targeted and sustainable flood mitigation efforts.

 Cijangkelok River River Bearing Capacity HEC-RAS Flood Discharge Hydrological Analysis http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License. INTRODUCTION

Water is one of the sources of life for all living things, but when in very large quantities, water can also be a problem for life. One form of problem that can be caused by a very large volume of water is flooding.

Flooding is a natural disaster that has a major impact on human survival. This disaster always comes during the rainy season. Floods often occur in lowlands or in areas around rivers. Floods occur due to several factors, namely human factors and natural factors. From human factors, floods occur due to massive deforestation, the change of infiltration areas into residential areas, poor maintenance of drainage systems and the frequent dumping of garbage in rivers out of place. Meanwhile, natural factors are caused by high rainfall intensity and sedimentation along the river.

The flood phenomenon that occurs around the Cijangkelok River is caused by several factors. Naturally, the high rainfall in this area, especially in the wet months of September to February, often increases the river water discharge to exceed its capacity. In addition, sedimentation that occurs in the riverbed due to soil erosion worsens the situation by reducing the river's flow capacity. On the other hand, human activities such as conversion of catchment land into settlements, deforestation in upstream areas, dumping of waste into the river, and inadequate drainage systems exacerbate the flood risk in this region.

In response to these problems, a comprehensive analysis is needed to understand the capacity of the Cijangkelok River and design measures to reduce the risk of flooding.

Cijangkelok River and design effective mitigation measures. One technical approach that can be used is through hydraulic analysis using the Hydrologic Engineering Center's River Analysis System (HEC-RAS) software.

HEC-RAS is software designed to simulate water flow in open channels, both in one-dimensional and two-dimensional flow conditions.

The use of HEC-RAS in analyzing the capacity of the Cijangkelok River is very important to provide a scientific basis for decision making. With simulations based on accurate information, the recommendations produced can be more targeted and efficient in efforts to reduce flood risk.

In addition, this analysis is also in line with the need for adaptation to climate change which has an impact on rainfall patterns and river flow intensity. With HEC-RAS-based modeling, future scenarios can be projected, so that planned mitigation measures are more long-term oriented.

 LITERATURE REVIEW River

In SNI 8066: 2015, it is stated that the definition of a river is a natural and / or artificial waterway or container in the form of a water flow network and the water in it, starting from the upstream to the estuary, with the right and left bounded by a border line (PP No. 38 of 2011). A unit of water system area that is formed naturally, where water will flow through rivers and tributaries is called a watershed (DAS). A river is a large, elongated stream of water that flows continuously from upstream to downstream. Rivers must be protected and

preserved, their functions and utilization improved, and their destructive power to the environment controlled (Kurniawan, 2012).

 River Bearing Capacity

The wet cross section is the cross section of the water flow, while the wet perimeter is defined as the length of the side of the river cross section that is in contact with water (SNI 2830: 2008).

River storage capacity is one of the important parameters in hydraulic analysis and water resources management, especially in the context of flood control. According to the Department of Public Works (2006), river storage capacity is defined as the maximum discharge that can be carried by a river without causing runoff outside the channel. In other words, this capacity determines the limit of the river's ability to safely channel water from upstream to downstream.

According to Subekti (2015), in his research on evaluating river storage capacity in urban areas, it is known that the imbalance between incoming flood discharge and river capacity is often the main cause of flooding. Therefore, evaluating the capacity is very important in flood control planning.

In practice, capacity analysis is often conducted using hydraulic simulation with software such as HEC-RAS. This application is used to calculate the water level profile based on a specific discharge and the physical characteristics of the river, thus enabling the evaluation of potential water runoff (US Army Corps of Engineers, 2016).

 Hydrological Cycle

According to CD Soemarto (1995), Hydrology is the science that explains the presence and movement of water in our nature, which includes various forms of water, which involves changes in its changes between liquid, solid, and gas states in the atmosphere, above and below the ground surface. According to Sri Harto (1990), in general, hydrology is intended as a science concerning water problems.

The hydrological cycle is the process of water circulation on earth that takes place continuously through the stages of evaporation, transpiration, condensation, precipitation, infiltration, and surface flow. This process serves as a natural mechanism in maintaining water balance in the atmosphere, land and oceans (Arsyad, 2010). Water that evaporates from the surface of the sea, lakes, and vegetation (evapotranspiration) rises into the atmosphere and forms clouds through the condensation process. Furthermore, water vapor falls back to the earth's surface in the form of rain or snow (precipitation), then infiltrates into the soil or flows on the surface as runoff (Chow et al., 1988).

This cycle is classified into three forms, namely: (1) short cycles, where water vapor from the ocean falls directly back into the ocean as rain; (2) medium cycles, where water vapor moves over land before undergoing precipitation and returning to the ocean via rivers; and (3) long cycles, which involve the formation of snow or glaciers, then melting and flowing into the ocean (Ward & Robinson, 2000).

Understanding the dynamics of the hydrological cycle is essential in hydrological studies, especially in the context of water resources management, flood prediction, and river basin infrastructure planning (Asdak, 2007).

 Discharge

Discharge is defined as the volume of water flowing through a river/open channel cross section per unit time (SNI 8066:2015). According to (Triadmodjo 2008), river flow discharge is the amount of water that passes through the cross section of the river per unit time, which is usually expressed in cubic meters per second (m³/s).

According to (Chow et al., 1988), discharge is the amount or rate of water flow through a cross section of a river or channel in a certain unit of time. In general, discharge is expressed in units of cubic meters per second (m³/s) and is calculated as the product of the wet cross-sectional area and water flow velocity. Factors that influence the size of the discharge include rainfall, watershed characteristics, soil type, land use, and vegetation cover conditions (Asdak, 2007). Changes in land use, such as urbanization and deforestation, can increase peak discharge and accelerate the response of surface flow to rainfall, which impacts

the potential for flooding in downstream areas (Ward & Robinson, 2000).

Discharge measurements can be made directly in the field using tools such as current meters or through indirect approaches using empirical methods and hydrological modeling. One of the commonly used indirect approaches is the synthetic unit hydrograph (HSS) method, such as Nakayasu, Snyder, and GAMA I (Triatmodjo, 2008).

 Rainfall Analysis

Rainfall is one of the meteorological parameters that greatly affects water availability, hydrological systems, and the potential for natural disasters such as floods and landslides. Understanding the characteristics of rainfall, both spatially and temporally, is important in planning and managing water resources. According to (Setiawan et al. 2020), uneven rainfall distribution can cause an imbalance between water supply and demand in an area, especially in areas with complex topography and land use.

In the context of statistical analysis, rainfall data is often analyzed using descriptive statistical methods, homogeneity tests, and probabilistic methods to determine extreme rainfall patterns. Trend and variability analysis of rainfall is also widely done to assess long-term climate change. Research by (Nugroho and Fitriani 2019) shows that the Mann-Kendall method and Sen's slope estimator are quite effective in identifying rainfall trends in the tropics.

In addition, climate classifications based on rainfall data, such as the Oldeman and Schmidt-Ferguson methods, are commonly used in the agricultural sector to determine appropriate cropping patterns. Research by (Arifin et al. 2021) confirms that rainfall-based climate classification is able to provide an overview of seasonal periodization relevant to agricultural activities in Indonesia.

The study of extreme rainfall is also important in disaster mitigation. According to (Yuliana and Prasetyo 2022), extreme rainfall events often cannot be predicted by annual averages alone, so extreme hydrological approaches such as frequency analysis using Gumbel or Log Pearson Type III distributions are needed.

 HEC-RAS

HEC-RAS is a software developed by the Hydrologic Engineering Center (HEC) of the U.S. Army Corps of Engineers to simulate steady and unsteady flow of one-dimensional rivers. It allows users to analyze the water level profile, flow velocity, and energy distribution along a river cross section based on available topographic and hydrological data (Brunner, 2016).

In various studies, HEC-RAS has been widely used to evaluate river storage capacity, design flood control systems, and assess the impact of land use changes on flow conditions. According to Pratama and Wahyuni 2020), HEC- RAS simulation is able to model flood conditions based on discharge input from hydrological analysis and produce accurate inundation maps when combined with elevation data from GIS.

The implementation of HEC-RAS also allows users to examine the effect of artificial structures such as bridges, embankments, and weirs on river flow. In a study by Sari et al. (2021), the use of HEC-RAS showed that the presence of inadequate bridges can cause narrowing of the effective cross-section of the flow and increase the potential for overflow during peak discharge.

On the other hand, validation of the HEC-RAS model is a crucial part in ensuring the accuracy of the simulation results. The process of calibration and verification against actual water level data is standard in this modeling. As stated by Nurhasanah and Damanik (2019), accuracy in inputting geometry and hydrology parameters greatly affects the output results, so detailed and accurate field data is required.

Figure 1. Thinking Framework

 METHODOLOGY

Figure 2. Framework of Thought

The basic theory underlying this research is functionalist theory, which suggests that each element in a system or structure has a specific function that contributes to the sustainability of the system. In this research, a quantitative method is used. This approach aims to measure and analyze phenomena using data in the form of numbers and statistics. Some methods commonly applied in the quantitative approach include surveys, experiments, and statistical analysis.

 RESEARCH RESULTS Determination of Watershed

Watershed processing results can be seen in the following figure:

Figure 3. Cijangkelok Watershed Map

 Maximum Rainfall

The following will present a table recapitulating the maximum rainfall data of the Cijangkelok River rain station.

Table 1. Recapitulation of maximum rainfall data
Year Rainfall Data
Cibingbin Malahayu Cibendung Cikeusik Jatiseeng
2014 161 114 106 119 28
2015 72 199 97 182 52
2016 70 81 87 120 86
2017 169 79 130 65 60
2018 237 136 119 110 74
2019 169 187 124 126 68
2020 103 74 166 103 70
2021 79 147 121 131 42
2022 82 126 100 85 80
2023 93 158 132 87 51
Area Rain

To be processed, point rain must be converted into area rain. There are several methods that can be used to analyze rainfall areas, but in this study the method used is the Polygon Thiessen method.

Figure 4. Polygon Thiessen results

𝑘𝑜𝑒𝑓. 𝑡ℎ𝑖𝑒𝑠𝑒𝑛 =

𝐴𝑖

𝐴

0,56

=

88,06

= 0,006

Table 2. Watershed area affecting 5 STAs
No Station Name Rain Area Represented (Ai) km2 Thiessen coefficient Ai/A (Fk) Percentage/Weight (%)
1 Malahayu 0,56 0,006 0,64
2 Cibendung 50,00 0,568 56,78
3 Cibingbin 22,20 0,252 25,21
4 Cikeusik 6,90 0,078 7,84
5 Jatiseeng 8,40 0,095 9,54
Total (A) 88,06 1,000 100,00

Table 3. Rainfall Calculation Results of Thissen Polgon Method
No Year Date Max Rainfall with the Same Occurrence Watershed Rainfall
Cibingbin Malahayu Cibendung Cikeusik Jatiseeng
Coefficient 0.25 0.01 0.57 0.08 0.10 Rainfall Area
19-Feb-14 161 10 17 0 0 50,30 62,17
1 2014 07-Dec-14 0 114 0 14 0 1,82
20-Dec-14 0 0 106 0 18 62,17
06-Mar-14 15 20 7 119 42 21,01
09-Mar-14 23 23 10 27 63 19,75
20-Jan-15 72 1 97 63 28 80,84
24-Jan-15 23 199 0 4 9 8,18
2 2015 25-Jan-15 17 2 97 63 28 80,84 80,84
15-Nov-15 20 5 0 182 0 22,06
06-Mar-15 23 23 11 25 63 20,16
28-Nov-16 70 9 0 72 5 23,82
3 2016 28-Nov-16 13 81 12 0 0 10,61 57,55
21-Nov-16 33 10 87 0 0 57,55
10-May-16 0 8 4 120 84 13,74
16-Nov-16 14 4 0 0 103 13,38
28-Dec-17 169 0 0 0 0 42,61 73,91
4 21-Feb-17 21 79 0 0 0 5,89
2017 09-Feb-17 0 0 130 0 0 73,91
22-Mar-17 0 4 0 65 0 5,12
20-Jan-17 0 16 18 34 60 18,71
23-Feb-18 237 10 0 2 0 59,97
12-Dec-18 0 136 0 0 0 0,82 70,35
5 2018 26-Feb-18 0 0 119 0 12 70,35
26-Jan-18 0 6 12 110 0 15,47
16-Feb-18 60 3 22 0 77 34,98
29-Dec-19 169 16 0 42 22 48,10
01-Dec-19 81 187 9 0 0 10,72
6 2019 11-Feb-19 40 16 124 22 9 83,17 83,17
01-May-19 0 3 8 126 1 14,53
03-Mar-19 30 3 12 69 67 28,10
10-Feb-20 103 8 14 10 17 46,86
7 2020 27-Feb-20 56 74 14 56 17 20,08 94,39
29-Jan-20 0 7 166 0 1 94,39
15-Apr-20 103 8 14 103 51 46,90
24-Nov-20 12 8 13 12 70 19,60
01-Apr-21 79 10 0 0 0 20,02
8 2021 20-Mar-21 0 147 27 0 0 10,27 78,76
27-Mar-21 31 0 121 25 3 78,76
19-Feb-21 6 10 5 131 97 21,11
13-Apr-21 8 12 10 29 42 16,14
12-Apr-22 82 1 17 28 2 26,81 61,35
9 2022 27-Dec-22 1 126 105 5 3 61,35
17-Feb-22 0 10 100 9 0 57,55
21-Apr-22 0 10 4 85 95 18,06
27-Feb-22 0 8 6 65 80 18,06
20-Mar-23 93 28 8 11 0 27,11
10 2023 12-Jan-23 15 158 0 0 0 5,54 50,09
18-Jan-23 58 27 132 1 2 50,09
02-May-23 59 4 10 87 37 30,37
Average 123,500 130,100 118,200 112,800 81,200 0,81 75,259
Std. Dev 68,007 44,132 22,508 31,902 17,067 12,456
Co. Variance 0,551 0,339 0,190 0,284 0,210 0,166
Co. Kurtosis 3,913 2,945 5,070 5,558 2,864 2,910
Co. Skewness -0,343 0,170 0,803 0,512 0,216 0,027
Frequency Analysis

Hydrological factors are not only determined by their mean values, but also by the distribution of their values, which can be greater or less than the mean. The extent of this spread-or dispersion-can be measured by parameters such as mean value, standard deviation (Sx), coefficient of variation (Cv), slope coefficient (Cs), and kurtosis coefficient (Ck).

Standard Deviation (S)

√∑(𝑥𝑖 − 𝑥)2

Sx =

n − 1

Coefficient of Variation (Cv) Cv = 𝑠𝑥

𝑎𝑣𝑒𝑟𝑎𝑔𝑒

Coefficient of Skewness (Cs)

n × (xi − 𝑥)3

𝑐𝑠 = (𝑛 − 1) × (𝑛 − 2) × 𝑠𝑥3

Kurtosis Coefficient (Ck)

𝑛2 × (xi − 𝑥)4

𝑐𝑘 = (n − 1) × (n − 2)(n − 3) × 𝑆𝑥4

Table 4. Frequency analysis recapitulation
No. Distribution Type Requirement Calculation Result Conclusion
1 Gumbel Cs $\leq$ 1.1306 0.0269 Meets
Ck $\leq$ 5.40 2.9104 Meets
2 Log Normal Cs Cv4+3Cv = 0.5011 Does not meet
Ck = Cv8 + 6Cv6 + 15Cv4 +16Cv2 + 3 3.4497 Does not meet
3 Normal Cs = 0 0.0269 Does not meet
Ck = 3 2.9104 Not Meets
4 Log Person Type III Other than the above value/flexible Meets
Rain Distribution with Gumbel

The method used to determine the amount of rainfall in this method is usually used to analyze surface runoff and flood frequency in a watershed. The

data generated using this method is in the form of annual maximum period rainfall data.

𝑦𝑡 − 𝑦𝑛

𝐾 =

𝑠𝑥

Rmax = average + (K X Sx)

Table 5. Gumbel Recapitulation
P. Recurrence (Year) Yt K Rmax (mm)
10 2,2502 1,8481 98
25 3,1985 2,8468 111
50 3,9019 3,5875 120
Chi-Square

The methods used to test the alignment of the distribution analytically are using the Chi-Square and Sminov Kolmogorov methods graphically.

Table 6. Chi-Square Test Results
Class Interval Oi Ei (Ei - Oi) (Ei - Oi)2 (Ei - Oi)2/Ei
1 P < 64,5 1 2 1 1 0,5
2 64,53 < P < 71,3 1 2 1 1 0,5
3 71,3 < P < 77,5 4 2 -2 4 2
4 77,5 < P < 85,6 3 2 -1 1 0,5
5 P > 85,6 1 2 1 1 0,5
Total 10 10 0 8 4

Table 7. Recapitulation of Chi-Square Test Results
Probability Distribution calculated c2 c2cr c2 ? c2cr Description
Gumbel 2 5,9910 c2 < c2cr Accepted
Normal 1 5,9910 c2 < c2cr Accepted
Log Normal 4 5,9910 c2 < c2cr Accepted
Log Pearson Type 3 4 5,9910 c2 < c2cr Accepted
Smirnov Kolmogorov

The rain distribution suitability test was conducted to determine the suitability between the calculation method and the rain data.

Table 8. Kolmogorov Smirnov Test Results
No xi P(Xi) f(t) P*(xi) DP
1 94,39 0,09 1,54 0,17 0,08
2 90,09 0,18 1,19 0,21 0,03
3 83,17 0,27 0,64 0,24 0,03
4 80,84 0,36 0,45 0,26 0,11
5 78,76 0,45 0,28 0,46 0,01
6 73,91 0,55 -0,11 0,49 0,05
7 70,35 0,64 -0,39 0,62 0,02
8 62,17 0,73 -1,05 0,64 0,09
9 61,35 0,82 -1,12 0,70 0,12
10 57,55 0,91 -1,42 0,98 0,07
max 0,12

Table 9. Recapitulation of Smirnov Kolmogorov Test Results
Probability Distribution D Pmax D Pcritical DPmax < DPcr Description
Gumbel 0,120 0,4090 DPmax > DPcr Accepted
Normal 0,153 0,4090 DPmax > DPcr Accepted
Log Normal 0,185 0,4090 DPmax > DPcr Accepted
Log Pearson Type 3 0,840 0,4090 DPmax < DPcr Not Accepted
Flood Discharge Analysis

The calculation of runoff water discharge is carried out with the following rational formula: "Q Max = 0.278 x C x I x A"

Table 10: Rainfall Distribution
Return Period Method USED
Gumbel Normal Log Normal Log Pearson
2 75,2593 75,2593 74,3187 74,3187 75,2593
5 85,7227 85,7227 85,5913 85,6201 85,7227
10 91,2035 91,2035 92,1629 92,1939 92,1939
20 95,6878 95,6878 97,9132 98,4546 98,4546
25 97,0580 97,0580 99,7408 99,7575 99,7575
50 100,7949 100,7949 104,9003 104,9709 104,9709
100 104,2827 104,2827 109,9565 109,8826 109,9565

The calculation of runoff water discharge is carried out with the following rational formula: "Q Max = 0.278 x C x I x A"

2

Intensity formula (I) = 𝑅10 24 3

× ( )

24 𝑡

Table 11. Rain Intensity Recapitulation
Return Period I 10 I 25 I 50
Discharge (m3/det) 31 33 35

Table 12. Recapitulation of Discharge Return Periods
Return Period Q 10 Q 25 Q 50
Discharge (m3/det) 599,709 648,910 682,822
HSS Nakayasu

The method for analyzing the calculation of Cijangkelok river flow discharge is HSS-Nakayasu

Q(max) = 𝐴×𝑅 3,6×(0,3×𝑇𝑝)+𝑇0,3

Table 13. HSS - Nakayasu results
Time (Hour) Q10 Q25 Q50
0,00 0 0 0
0,42 441,3551 469,8296 498,3041
1,00 283,8422 302,1546 320,4671
2,00 133,3267 141,9284 150,5301
2,01 132,4065 140,9489 149,4912
3,00 80,3873 85,56445 90,75018
4,00 48,56997 51,70351 54,83706
4,40 39,72196 42,28466 44,84737
5,00 31,65582 33,69813 35,74044
6,00 21,69569 23,09541 24,49514
7,00 14,8694 15,82872 16,78803
8,00 10,19092 10,8484 11,50588
9,00 6,984471 7,435082 7,885693
10,00 4,786891 5,095722 5,404554
11,00 3,280753 3,492414 3,704076
12,00 2,248503 2,393568 2,538633
13,00 1,541039 1,64046 1,739882
14,00 1,056169 1,124309 1,192449
15,00 0,723858 0,770559 0,817259
16,00 0,496105 0,528112 0,560118
17,00 0,340011 0,361948 0,383884
18,00 0,233031 0,248065 0,263099
19,00 0,159711 0,170014 0,180318
20,00 0,10946 0,116521 0,123583
21,00 0,075019 0,079859 0,084699
22,00 0,051415 0,054733 0,05805
23,00 0,035238 0,037512 0,039785
24,00 0,024151 0,025709 0,027267

Figure 5. Nakayasu HSS Return Period Q10, Q25, Q50

 HEC-RAS

River flow modeling in HEC-RAS is based on topographic measurement data in the field where river flow data from upstream to downstream are digitized in HEC-RAS then enter the cross section data in the cross section window as shown in Figure 5.

Figure 6. Cijangkelok River Flow Modeling

Extreme depth occurs at STA 28 + 100. Where the river water overflows due to the condition of the cross section that is not goo+d enough.

Figure 7. section at STA 28 + 100

The following is a cross-sectional image of the HEC-RAS results that exceed the embankment on the cijang kelok river:

Figure 8. STA 27+950 Figure 9. STA 28+000

Figure 10. STA 28+050

The following are cross-sectional images of HEC-RAS results that do not exceed the embankment on the cijangkelok river:

Figure 11. STA 27+600 Figure 12. STA 27+700

Figure 13. STA 27+800 Figure 14. STA 27+900

 DISCUSSION

Based on the results of the Cijangkelok River capacity research analysis, it shows that several river cross sections are unable to accommodate the 50-year return period flood discharge. The cross sections at STA 27+950, STA 28+050, and STA 28+100 experience overflow because the water level exceeds the existing embankment or riverbank. This condition indicates that the capacity of the existing

cross section is not sufficient to channel the peak discharge. In contrast, the cross sections at STA 27+600, 27+700, 27+800 and 27+900 are still able to accommodate flows up to Q50 discharge, indicating variations in geometric conditions and local capacity along the river.

 CONCLUSIONS AND RECOMMENDATIONS

From the results of the research mentioned for hydrological calculations using 5 rain stations namely STA Cibendung, STA Cibingbin, STA Cikeusik, STA Malahayu, and STA Jatiseeng. The calculation resulted in the following return period discharge: Q10 = 599.7091 m3/det, Q25 = 648.9097 m3/det, and Q50 = 682.8221 m3/det.

The calculation of the Hydrological analysis used is the rational method and runoff calculation using the Nakayasu HSS, the rational method is used because the watershed area is less than 100km2 and the Nakayasu HSS method is chosen because the Nakayasu HSS is the main choice in Indonesia because its empirical coefficients, such as peak discharge coefficients and time coefficients, have been calibrated and validated widely with data from watersheds in Indonesia and the Asian region in general. This makes it more suitable and provides more accurate results for local hydrological conditions than other HSSs that may be developed in very different climates or geographies (eg HSS SCS developed in the United States).

From the results of research and modeling using HEC-RAS, it can be concluded that the downstream Cijangkelok River has an inadequate capacity to cope with the planned flood discharge for the 50-year return periods. This is concerning, considering river planning uses a lower return period discharge, namely Q10, as the benchmark for normal river capacity.

This is evidenced by the inundation or overflow of water in several river cross sections, especially when the maximum discharge occurs. In the analysis of the Cijangkelok River cross section in the HEC-RAS program, there is some runoff through the embankment, such as at STA 27+950, STA 28+850, and 28+100.

 ADVANCED RESEARCH

Further research is recommended to use a two-dimensional (2D) model or a combination of 1D-2D in HEC-RAS to obtain more detailed inundation simulation results, especially in residential areas. Integration with GIS data and satellite images is also required for spatial mapping of affected areas. In addition, it is necessary to calibrate the hydraulic model using actual water level data to improve simulation accuracy. Further research could also include evaluating flood control scenarios, such as river widening, levee construction and retention ponds. Social and economic aspects analysis is also added to strengthen technical recommendations oriented towards community-based mitigation.

 ACKNOWLEDGMENTS

We as the authors would like to express our deepest gratitude to the supervisors who have provided guidance, direction, and motivation during the

process of preparing this research. We would also like to thank the Cirebon District Public Works and Spatial Planning Office for the assistance of data and information that was very helpful in conducting the research. Our gratitude also goes to all lecturers and staff at the Civil Engineering Study Program of Swadaya Gunung Jati University for the knowledge and facilities provided during the study period. We would also like to thank our family and friends who have provided moral support and encouragement so that this research can be completed properly.

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