ANALYSIS OF
CISANGGARUNG RIVER FLOOD IN SIDARESMI VILLAGE, PABEDILAN DISTRICT, CIREBON
REGENCY USING HEC-RAS SOFTWARE
Dimas
Prasetyo1, Mochamad Aby Fachrie Algadry2, Nurdiyanto3
Universitas Swadaya Gunung Jati,
Indonesia
[email protected]1, [email protected]2,
[email protected]3
Abstract
The eastern part of Cirebon Regency is an
area that is prone to flooding because it is included in the Cisanggarung
Watershed (DAS). The Cisanggarung watershed area has a high level of flood
vulnerability, with the flood-prone area reaching around 3,479 hectares. This
study aims to analyze flooding in Sidaresmi Village by employing the HSS
Nakayasu method to calculate the flood discharge levels. This approach
facilitates the estimation of peak flood discharges for a range of return
periods, including Q2 or 2-year, Q5 or 5-year, Q10 or 10-year, Q25 or 25-year,
Q-50 or 50-year, Q100 or 100-year. Subsequently, these discharge values are
utilized in flood modeling conducted with HEC-RAS software. The modeling
results using HEC-RAS software show that the Cisanggarung River in Sidaresmi
Village does not have sufficient capacity to accommodate the flood discharge
that occurs. With flood discharges exceeding the river's capacity, the risk of
overflowing water becomes very high, which can result in flooding in
surrounding areas. These findings highlight the importance of mitigation
measures, such as river infrastructure improvements, to reduce the impact of
flooding in the area.
Keywords: Micro-Hydro; Dependable Flow; Thiessen Polygon; Crossflow Turbine
Corresponding:
Nurdiyanto
E-mail: [email protected]
Introduction
Floods are one of
the most widespread and devastating natural disasters globally, affecting
millions of lives, causing major economic losses, and posing significant
environmental challenges. The World Bank (2020) highlighted that more than 1.47
billion people worldwide are vulnerable to flooding, with damages estimated to
exceed $650 billion annually. Urbanization, climate change, and deforestation
exacerbate flood risks by altering natural hydrological patterns and increasing
runoff. Advanced flood modeling and prediction technologies are critical in
addressing these challenges (Dyhouse, 2003).
With its tropical
climate and archipelagic geography, Indonesia is highly vulnerable to flooding.
Cirebon Regency, especially Sidaresmi Village in Pabedilan District, frequently
experiences flooding due to the overflowing Cisanggarung River. Local reports
indicate that the village experiences repeated inundation during the rainy
season, disrupting agricultural activities and endangering people�s
livelihoods. Despite many structural measures, such as embankments and drainage
systems, flooding continues due to inadequate flood management strategies (Wigati & Soedarsono, 2016);
(Prayogo et al., 2023).
Traditional flood
management approaches, while helpful, are often reactive and need more
predictive capacity. Hydraulic modeling software such as the Hydrologic
Engineering Center's River Analysis System (HEC-RAS) offers a robust solution
by simulating river hydraulics, predicting flood extents, and evaluating
mitigation strategies. Recent studies have highlighted the effectiveness of
HEC-RAS in a variety of hydrological contexts, from urban areas such as Jakarta
(Pratama, 2020) to rural watersheds such as the
Tabanio watershed in Kalimantan (Prayogo et al., 2023).
�Although HEC-RAS has been widely applied in
flood studies across Indonesia, its application in Sidaresmi Village still
needs to be explored. Previous studies have focused on larger river systems or
urban catchments, often neglecting smaller yet equally vulnerable rural areas.
This study addresses this gap by applying HEC-RAS to analyze the flood dynamics
of the Cisanggarung River in Sidaresmi Village. By combining stable and
unstable flow analysis, this study aims to comprehensively understand flood
behavior and inform local mitigation measures (Brunner, 1994); (Iswardoyo & Satria, 2023).
The main objective
of this study is to analyze the flood pattern of the Cisanggarung River in
Sidaresmi Village using HEC-RAS software. Specifically, this study seeks to
Simulate flood scenarios under various hydrological conditions using HEC-RAS
5.0.7. Identify flood-prone areas and evaluate the effectiveness of existing
mitigation measures. Provide actionable recommendations to improve flood
management in Sidaresmi Village.
This study focuses
on applying HEC-RAS to model the flood dynamics of the Cisanggarung River,
considering stable and unstable flow conditions. This analysis emphasizes
essential parameters such as water surface profile, floodplain area, and
hydraulic structure. Data from local hydrological records, topographic surveys,
and previous flood events form the basis of simulation input (Al Amin et al., 2018; Fajar, 2022).
The findings of this
study have significant implications for flood management and planning in
Sidaresmi Village and similar rural settings. Policy Implications The results
will guide local governments in designing targeted flood mitigation policies,
including constructing retention basins and embankment enhancements. Technical
Implications: By demonstrating the utility of HEC-RAS, this study encourages
the adoption of advanced hydraulic modeling tools in regional flood management
practices. Community Implications: This study fosters community resilience by
identifying vulnerable zones and informing early warning systems and evacuation
plans (Rolobessy et al., 2024; Rizki, 2021).
HEC-RAS is a widely
used hydraulic modeling software developed by the US Army Corps of Engineers.
It enables the simulation of river hydraulics, including steady and unsteady
flow, sediment transport, and water quality analysis (Brunner, 1997). Studies demonstrate its
versatility in modeling various hydrological conditions, from floodplain
analysis (Dyhouse, 2003) to numerical simulation of
permanent and variable flow regimes (Vill�n B�jar, 2014).
Application of
HEC-RAS in Flood Studies: Several studies highlight the efficacy of HEC-RAS in
flood management: Urban Context, Pratama, et al. (2020) used HEC-RAS to simulate
flood distribution in Jakarta, identify critical inundation zones, and inform
infrastructure planning. Rural Watershed, Prayogo et al. (2023) applied HEC-RAS in a
watershed.
Research
Method
The method used in
this research is quantitative, an empirical approach that focuses on
collecting, analyzing, and presenting data in the form of numbers. This method
is applied to obtain accurate results by relying on statistical calculations.
In this
investigation, the dataset comprised both primary and secondary data sources.
The primary data were directly elicited through firsthand observation of the
research subject. Meanwhile, secondary data was collected from various sources
relevant to the research, such as rainfall data from BBWS Cimanuk Cisanggarung
and PUPR, DEMNAS maps, and land cover maps downloaded from the website Tan
Hair. Indonesia.go.id and a literature review from previous studies.
This research was
conducted in the Cisanggarung River, Sidaresmi Village, Pabedilan Subdistrict,
Cirebon Regency, West Java Province. This location was chosen because it often
experiences flooding, making it very relevant to analyze in the context of flood
risk management and mitigation efforts.
Rainfall
Analysis
Flood analysis in
Sidaresmi Village, Pabedilan Sub-district, Cirebon Regency using Thiessen
Polygon to calculate the distribution of maximum rainfall for the last 10 years
(2013-2022) from 3 rainfall stations: Darma Station, Jatiseeng Station, and
Cikeusik Station.
One method that can be used in rainfall
analysis is the Thiessen polygon method. The Thiessen Polygon approach assigns
a weight to each station, representing the adjacent territory. It posits within
a watershed that the precipitation in a specific area corresponds to that
recorded at the closest station, allowing the station's rainfall data to
represent that region (Nurdiyanto, 2019).
Where :
�
Frequency
Analysis
Frequency analysis
aims to assess the severity of extreme events and ascertain their recurrence
intervals or return periods using a probability distribution model (Nadia et al., 2019). Among the probability
distributions frequently employed are the Normal, Gumbel, Log Normal, and Log
Pearson Type III distributions.
To identify the most
suitable distribution model for hydrological data, it is essential to evaluate
the fit based on the statistical characteristics of the empirical data. This
evaluation necessitates examining whether the statistical properties of the data,
such as the coefficient of kurtosis (CK), mean, coefficient of skewness (CS),
standard deviation, and CV, or the coefficient of variation align with the
prerequisites of the respective probability distributions.
Table 1. Determination of data distribution
type
|
Type of Distribution
|
Terms Statistical Parameters
|
Statistical parameters of
observation data
|
Description
|
|
Normal
|
Cs ≈� 0
|
-0,8615
|
Unqualified
|
|
Ck ≈ 3
|
4,4346
|
Unqualified
|
|
Log-Normal
|
Cs =  �+ 3.Cv
|
-0,8615
|
Unqualified
|
|
Ck = 
|
4,4346
|
Unqualified
|
|
Gumbel
|
Cs = 1,14
|
-1,0803
|
Unqualified
|
|
Ck = 5,4
|
5,0521
|
Unqualified
|
|
Log
Pearson III
|
If there is no suitable statistical parameter
|
-
|
Qualified
|
Source: Calculation Result
Log Pearson
Type III Frequency Distribution
The Pearson Log III
method uses the logarithmic value to compute return period rainfall. The
determination of return period rainfall using the Log Pearson III distribution
method is contingent upon the K value derived from the Reduction Frequency
Table of the Pearson Log III method (Kementrian PUPR, 2018).
Mean:
X̅ = 
Standard Deviation:
Sd = 
Skewness Coefficient:
Cs = 
Rainfall
Plan log Pearson III method:
Xt = Log (X̅ + Ktr x Sd)
Probability
Distribution Test
Several methods are
often used to perform distribution fit tests. Two of the most common are the
Chi-Square test and the Smirnov-Kolmogorov test.
The objective of the
Chi-Square test is to ascertain the adequacy of a selected distribution in
representing the statistical distribution of the analyzed data sample. The
analysis is deemed valid when the computed chi-square value exceeds the
critical chi-square threshold.
Description:
K = Number of Classes
n = The amount of data
DK = K - (P+1)
Description:
DK = Degrees of freedom
P = For normal and binominal distributions (2) and Gumbel distributions
(1)
Ei = n/Cs
Description:
Ei = sum of theoretical values in subgroup I
n = number of subgroups
�= 
Description:
= calculated chi-square parameter
Oi = number of observation values in subgroup I
The
Smirnov-Kolmogorov fit test, frequently called a non-parametric fit test,
operates independently of any specific distribution function. The following
outlines the procedure for its implementation:
-
Sort the data (Xi)
from largest to smallest
-
Determine the
empirical probability of each data after sorting P(Xi) with the Weibull
formula:�
P(Xi) = i/(n+1)
Notes:
n = number of data
i = data sequence number
-
Determine the
reduction variable
F(t) = 
Description:
F(t) = reduction variable
Xi = i-th data
X̅ = mean value
S = standard deviation
-
Determine the
theoretical probability of each sorted data P' (Xi) based on the selected
probability distribution. Theoretical probability = 1 - area under the standard
curve corresponding to the f(t) value.
-
From the two
probability values, determine the difference between the observed probability
and the theoretical probability.
D = maximum
(P'(Xi) - P(Xi))
-
Determine whether
the D value is smaller than the critical D value (Do)
Flood
Discharge Analysis
The Nakayasu HSS
method is commonly utilized to analyze anticipated flood discharges. Nakayasu
conducted extensive research on unit hydrographs across multiple river systems
in Japan. The outcomes of these studies have been encapsulated into specific
equations and procedural steps for calculation. The requisite data for these
computations include 24-hour rainfall (R24) measured in millimeters, river
length (L) in kilometers, catchment area (A) in square kilometers, and the
distribution of adequate hourly rainfall.
Qp =� 
�
Description:
Qp = peak flood discharge (m3/det),
Re = rainfall unit (mm),
Tp = grace time from the beginning of the rain to the peak of the flood
(hour),
T0,3 = time required by the decrease in discharge from the peak to 30% of
the peak discharge
A = catchment area to the outlet,
C = conveyance coefficient.
To determine Tp and T0,3, the following formula approach is used:
Tp = tg +
0.8 tr
T0.3 =
α tg
tg represents the time lag, defined as the interval in hours between
rainfall and the peak flood discharge.
Rivers with channel length L > 15 km:
tg = 0.4 +
0.058 L
Rivers with a channel length of L < 15 km:
tg = 0.21
L^0.7
Notes:
Tr = rainfall time unit (hour)
α = watershed characteristic coefficient
-
On an upward curve (0
< t < Tp)
Qt = Qp 
Notes:
Q(t) = runoff
before finding peak discharge (m3),
t = time
(hour).
-
On the downward curve,
Pada Kurva Turun
(Tp < t < Tp + T0,3)
Q = Qp x 
(Tp + T0,3 < t < Tp + T0,3 + 1,5 T0,3)
Q = Qp x 
(t > Tp + T0,3 + 1,5 T0,3)
Q = Qp x 
ArcGIS
ArcGIS is a robust
platform for constructing and deploying Geographic Information Systems (GIS).
It enables disseminating geographic data to a global audience across diverse
disciplines. This platform comprises an array of tools and software that
furnish comprehensive GIS functionalities.
Coordinated
application interfaces on this platform facilitate a wide range of GIS tasks,
from the most basic to the most complex, including visualization, spatial
processing, mapping, management, data editing, and geographic analysis (Zhang, 2021).
GIS demonstrates its utility and efficiency by facilitating integrated spatial
and attribute data analysis, including hydrology, land use, topography, and
population demographics. This data is processed and analyzed to extract the
specific information needed for various applications (Zardari et al., 2019).
HEC-RAS
Initiated in the
early 1990s by the Hydrologic Engineering Center, HEC-RAS represents the
inaugural application within a suite of Windows-based software tailored for
hydrologic engineering. Officially launched in July 1995, HEC-RAS has emerged
as HEC's premier software, supplanting HEC-2 with its ability to execute
calculations for determining water surface profiles (Alzahrani, 2017).
HEC-RAS has garnered
widespread utilization for river flow modeling, extending to analyses of
floodplains, sediment transport, dam breaches, and water quality. Historically
prominent for its one-dimensional (1D) river hydraulic modeling, HEC-RAS has
developed a significant two-dimensional (2D) modeling capability, responding to
the growing need for precise floodplain mapping and flood hazard evaluations.
The 2D functionality of HEC-RAS enables the modeling of expansive and intricate
river systems, facilitating the simulation of water movements across both
horizontal and vertical dimensions (Ansori et al., 2023).
RESULT
AND DISCUSSION
Rainfall
Analysis
Flood analysis in Sidaresmi Village, Pabedilan Sub-district, Cirebon
Regency, using Thiessen Polygon to calculate the distribution of maximum
rainfall for the last 10 years (2013-2022) from 3 rainfall stations: Darma
Station, Jatiseeng Station, and Cikeusik Station. This method was chosen because the Cisanggarung watershed has a
relatively medium area, between 500 and 5000 km�.
Figure
1. Thiessen Polygon Map
The following is the Area (A) of each rain station
area:
Darma Station: 359.015 km�
Cikeusik Station: 468.243 km�
Jatiseeng Station: 170.966 km�
Table 2. Thiessen Polygon Rainfall Calculation
|
Year
|
Station
|
Average Rainfall �(Xmax)
|
|
Darma
|
Cikeusik
|
Jatiseeng
|
|
X (mm)
|
X.A
|
X (mm)
|
X.A
|
X (mm)
|
X.A
|
|
2013
|
104
|
37337,56
|
90
|
42141,87
|
103
|
17609,498
|
97,26
|
|
2014
|
128
|
45953,92
|
92
|
43078,356
|
92
|
15728,872
|
104,95
|
|
2015
|
103
|
36978,545
|
97
|
45419,571
|
92
|
15728,872
|
98,30
|
|
2016
|
136
|
48826,04
|
90
|
42141,87
|
115
|
19661,09
|
110,83
|
|
2017
|
137
|
49185,055
|
110
|
51506,73
|
77
|
13164,382
|
114,06
|
|
2018
|
108
|
38773,62
|
117
|
54784,431
|
72
|
12309,552
|
106,06
|
|
2019
|
107
|
38414,605
|
126
|
58998,618
|
87
|
14874,042
|
112,49
|
|
2020
|
96
|
34465,44
|
103
|
48229,029
|
86
|
14703,076
|
97,57
|
|
2021
|
100
|
35901,5
|
131
|
61339,833
|
107
|
18293,362
|
115,74
|
|
2022
|
75
|
26926,125
|
85
|
39800,655
|
95
|
16241,77
|
83,12
|
Source: Calculation Result
Log Pearson Type III Frequency Distribution
Table 3. Smirnov-Kolmogorov Calculation Results
|
No
|
Tahun
|
Xmax (mm)
|
Xi (mm)
|
Log Xi
|
Log
(Xi-Xt)
|
Log
(Xi-Xt)�
|
Log
(Xi-Xt)�
|
|
1
|
2013
|
97,26
|
115,74
|
2,0635
|
0,0482
|
0,0023
|
0,0001
|
|
2
|
2014
|
104,95
|
114,06
|
2,0571
|
0,0419
|
0,0018
|
0,0001
|
|
3
|
2015
|
98,30
|
112,49
|
2,0511
|
0,0359
|
0,0013
|
0,0000
|
|
4
|
2016
|
110,83
|
110,83
|
2,0446
|
0,0294
|
0,0009
|
0,0000
|
|
5
|
2017
|
114,06
|
106,06
|
2,0255
|
0,0103
|
0,0001
|
0,0000
|
|
6
|
2018
|
106,06
|
104,95
|
2,0210
|
0,0057
|
0,0000
|
0,0000
|
|
7
|
2019
|
112,49
|
98,30
|
1,9926
|
-0,0227
|
0,0005
|
0,0000
|
|
8
|
2020
|
97,57
|
97,57
|
1,9893
|
-0,0259
|
0,0007
|
0,0000
|
|
9
|
2021
|
115,74
|
97,26
|
1,9879
|
-0,0273
|
0,0007
|
0,0000
|
|
10
|
2022
|
83,12
|
83,12
|
1,9197
|
-0,0956
|
0,0091
|
-0,0009
|
|
Total
|
Σ
|
1040,37
|
20,1524
|
0,0000
|
0,0174
|
-0,0007
|
|
Amount of Data
|
n
|
10
|
|
Average
|
Ṝ
|
104,04
|
2,0152
|
0,0000
|
0,0017
|
-0,0001
|
|
Standard Deviation
|
S
|
0,0440
|
|
Coef. Asymmetry
|
Cs
|
-1,1
|
Source: Calculation Result
Table 4. Rainfall Calculation for return periods of 2, 5, 10, 25, and 100
years
|
Tr (Year)
|
KTr
|
XTr (mm)
|
|
100
|
1,519
|
120,80
|
|
50
|
1,436
|
119,79
|
|
25
|
1,324
|
118,44
|
|
10
|
1,107
|
115,87
|
|
5
|
0,848
|
112,87
|
|
2
|
0,180
|
105,47
|
Source:
Calculation Result
Probability Distribution Test
-
Chi-square
Table
5. Rainfall
Return Period Rating
|
No
|
Year
|
Xmax (mm)
|
Xi (mm)
|
Log Xi
|
|
1
|
2013
|
97,26
|
115,74
|
2,06348
|
|
2
|
2014
|
104,95
|
114,06
|
2,05713
|
|
3
|
2015
|
98,30
|
112,49
|
2,05110
|
|
4
|
2016
|
110,83
|
110,83
|
2,04464
|
|
5
|
2017
|
114,06
|
106,06
|
2,02554
|
|
6
|
2018
|
106,06
|
104,95
|
2,02097
|
|
7
|
2019
|
112,49
|
98,30
|
1,99256
|
|
8
|
2020
|
97,57
|
97,57
|
1,98932
|
|
9
|
2021
|
115,74
|
97,26
|
1,98794
|
|
10
|
2022
|
83,12
|
83,12
|
1,91969
|
|
Total
|
Σ
|
1040,37
|
20,15
|
Source: Calculation Result
Calculating
the number of classes (K)
Calculating
degrees of freedom (Dk)
Table
6. Chi-Square Calculation Results
|
Class
|
Limit Value of Each Class
|
Ef
|
Of
|
((Ef-Of)^2)/Ef
|
|
1
|
|
>
|
110,34
|
2,5
|
4
|
0,9
|
|
2
|
105,44
|
-
|
110,34
|
2,5
|
1
|
0,9
|
|
3
|
86,814
|
-
|
105,44
|
2,5
|
4
|
0,9
|
|
4
|
|
<
|
86,81
|
2,5
|
1
|
0,9
|
|
Total
|
10
|
10
|
3,6
|
Source: Calculation Result
From the
table above, it is known that the calculated chi-squared value is 3.6. As for
the critical value with degrees of freedom (DK) = 1.322 and degrees of
confidence ɑ = 5%, the critical chi value is 4.534. Thus, the calculated
chi value is smaller than the critical chi value, so the log Pearson type III
probability distribution is acceptable for analyzing rainfall data.
-
Smirnov-Kolgomorov
The
following are the results of the Smirnov-Kolmogorov test calculation using the
Log Pearson III distribution.
Table
7. Smirnov-Kolmogorov Calculation Results
|
No
|
Rainfall
|
Log Xi
|
P(xi)
|
f(t)
|
P'(Xi)
|
ΔP
|
|
1
|
115,74
|
2,06
|
0,09
|
1,10
|
0,11
|
0,01
|
|
2
|
114,06
|
2,06
|
0,18
|
0,95
|
0,16
|
0,02
|
|
3
|
112,49
|
2,05
|
0,27
|
0,81
|
0,22
|
0,06
|
|
4
|
110,83
|
2,04
|
0,36
|
0,67
|
0,28
|
0,08
|
|
5
|
106,06
|
2,03
|
0,45
|
0,23
|
0,47
|
0,02
|
|
6
|
104,95
|
2,02
|
0,55
|
0,13
|
0,51
|
0,04
|
|
7
|
98,30
|
1,99
|
0,64
|
-0,52
|
0,62
|
0,02
|
|
8
|
97,57
|
1,99
|
0,73
|
-0,59
|
0,63
|
0,10
|
|
9
|
97,26
|
1,99
|
0,82
|
-0,62
|
0,64
|
0,18
|
|
10
|
83,12
|
1,92
|
0,91
|
-2,17
|
0,90
|
0,01
|
|
|
|
|
|
D
|
0,18
|
Source: Calculation Result
From the
table above, it can be seen that the maximum calculated D value is 0.18. With
the number of data 10 and 5% confidence degree, the critical D value is 0.41.
Thus, the calculated maximum D value is smaller than the essential D value, so
the log Pearson type III probability distribution is acceptable for analyzing
rainfall data.
Flood Discharge Analysis
The following are the steps in analyzing the HSS
Nakayasu plan flood discharge:
1) Rain intensity calculation
I = 
�
Description:
Table
8. Rain Intensity Calculation
|
T (Minute)
|
Tc (Hour)
|
Periods
|
|
2
|
5
|
10
|
25
|
50
|
100
|
|
5
|
0,0833
|
191,66
|
205,09
|
210,54
|
215,23
|
217,67
|
219,51
|
|
10
|
0,1667
|
120,74
|
129,20
|
132,63
|
135,58
|
137,12
|
138,28
|
|
20
|
0,3333
|
76,06
|
81,39
|
83,55
|
85,41
|
86,38
|
87,11
|
|
40
|
0,6667
|
47,91
|
51,27
|
52,64
|
53,81
|
54,42
|
54,88
|
|
60
|
1
|
36,57
|
39,13
|
40,17
|
41,06
|
41,53
|
41,88
|
|
90
|
1,5
|
27,90
|
29,86
|
30,65
|
31,34
|
31,69
|
31,96
|
|
120
|
2
|
23,03
|
24,65
|
25,30
|
25,87
|
26,16
|
26,38
|
|
150
|
2,5
|
19,85
|
21,24
|
21,81
|
22,29
|
22,55
|
22,74
|
|
180
|
3
|
17,58
|
18,81
|
19,31
|
19,74
|
19,96
|
20,13
|
|
210
|
3,5
|
15,86
|
16,97
|
17,43
|
17,81
|
18,01
|
18,17
|
|
240
|
4
|
14,51
|
15,53
|
15,94
|
16,30
|
16,48
|
16,62
|
|
270
|
4,5
|
13,42
|
14,36
|
14,74
|
15,06
|
15,24
|
15,36
|
|
300
|
5
|
12,51
|
13,38
|
13,74
|
14,04
|
14,20
|
14,32
|
|
360
|
6
|
11,07
|
11,85
|
12,17
|
12,44
|
12,58
|
12,68
|
|
390
|
6,5
|
10,50
|
11,23
|
11,53
|
11,79
|
11,92
|
12,02
|
|
420
|
7
|
9,99
|
10,69
|
10,98
|
11,22
|
11,35
|
11,44
|
|
450
|
7,5
|
9,54
|
10,21
|
10,48
|
10,72
|
10,84
|
10,93
|
|
480
|
8
|
9,14
|
9,78
|
10,04
|
10,27
|
10,38
|
10,47
|
|
510
|
8,5
|
8,78
|
9,39
|
9,64
|
9,86
|
9,97
|
10,06
|
|
540
|
9
|
8,45
|
9,04
|
9,28
|
9,49
|
9,60
|
9,68
|
Source: Calculation Result
2) Calculating Hourly Rain Distribution
Hourly rainfall distribution is planned by
estimating the rainfall duration and intensity, which changes every hour. The
hourly rainfall distribution analysis is calculated based on the rainfall
intensity in one day, with a total rainfall duration of 6 hours.
Rt
= t x I - (t - 1) x I(t - 1)
Description:
Rt =
rainfall height at hour t (mm)
I = rainfall
intensity at hour t (mm)
I(t - 1) =
rainfall intensity before hour t (mm)
T = time of
concentration (hour)
Table
9. Hourly
rainfall distribution calculation
|
2-Year Return Period
|
5-Year Return Period
|
10-Year Return Period
|
|
Hours -
|
Hours -
|
Hours -
|
|
1
|
I
|
36,57
|
mm/hour
|
1
|
I
|
39,13
|
mm/hour
|
1
|
I
|
40,17
|
mm/hour
|
|
Rt
|
36,565
|
mm
|
Rt
|
39,13
|
mm
|
Rt
|
40,17
|
Mm
|
|
2
|
I
|
23,03
|
mm/hour
|
2
|
I
|
24,65
|
mm/hour
|
2
|
I
|
25,30
|
mm/hour
|
|
Rt
|
9,504
|
mm
|
Rt
|
10,17
|
mm
|
Rt
|
10,44
|
Mm
|
|
3
|
I
|
17,58
|
mm/hour
|
3
|
I
|
18,81
|
mm/hour
|
3
|
I
|
19,31
|
mm/hour
|
|
Rt
|
6,6669
|
mm
|
Rt
|
7,13
|
mm
|
Rt
|
7,32
|
Mm
|
|
4
|
I
|
14,51
|
mm/hour
|
4
|
I
|
15,53
|
mm/hour
|
4
|
I
|
15,94
|
mm/hour
|
|
Rt
|
5,3075
|
Mm
|
Rt
|
5,68
|
mm
|
Rt
|
5,83
|
Mm
|
|
5
|
I
|
12,51
|
mm/hour
|
5
|
I
|
13,38
|
mm/hour
|
5
|
I
|
13,74
|
mm/hour
|
|
Rt
|
4,482
|
Mm
|
Rt
|
4,80
|
mm
|
Rt
|
4,92
|
Mm
|
|
6
|
I
|
11,07
|
mm/hour
|
6
|
I
|
11,85
|
mm/hour
|
6
|
I
|
12,17
|
mm/hour
|
|
Rt
|
3,9178
|
Mm
|
Rt
|
4,19
|
mm
|
Rt
|
4,30
|
Mm
|
|
25-Year Return Period
|
50-Year Return Period
|
100-Year Return Period
|
|
Hours -
|
Hours -
|
Hours -
|
|
1
|
I
|
41,06
|
mm/hour
|
1
|
I
|
41,53
|
mm/hour
|
1
|
I
|
41,88
|
mm/hour
|
|
Rt
|
41,06
|
mm
|
Rt
|
41,53
|
mm
|
Rt
|
41,88
|
Mm
|
|
2
|
I
|
25,87
|
mm/hour
|
2
|
I
|
26,16
|
mm/hour
|
2
|
I
|
26,38
|
mm/hour
|
|
Rt
|
10,67
|
mm
|
Rt
|
10,79
|
mm
|
Rt
|
10,89
|
Mm
|
|
3
|
I
|
19,74
|
mm/hour
|
3
|
I
|
19,96
|
mm/hour
|
3
|
I
|
20,13
|
mm/hour
|
|
Rt
|
7,49
|
mm
|
Rt
|
7,57
|
mm
|
Rt
|
7,64
|
Mm
|
|
4
|
I
|
16,30
|
mm/hour
|
4
|
I
|
16,48
|
mm/hour
|
4
|
I
|
16,62
|
mm/hour
|
|
Rt
|
5,96
|
mm
|
Rt
|
6,03
|
mm
|
Rt
|
6,08
|
Mm
|
|
5
|
I
|
14,04
|
mm/hour
|
5
|
I
|
14,20
|
mm/hour
|
5
|
I
|
14,32
|
mm/hour
|
|
Rt
|
5,03
|
mm
|
Rt
|
5,09
|
mm
|
Rt
|
5,13
|
Mm
|
|
6
|
I
|
12,44
|
mm/hour
|
6
|
I
|
12,58
|
mm/hour
|
6
|
I
|
12,68
|
mm/hour
|
|
Rt
|
4,40
|
mm
|
Rt
|
4,45
|
mm
|
Rt
|
4,49
|
Mm
|
Source: Calculation Result
3) Flow Coefficient
Land surface conditions or land use influence the conveyance coefficient
(C).
Table
10. Flow coefficient
result
|
Area
Categories
|
A (km2)
|
C
|
AC
|
|
Settlement
|
89,522
|
0,7
|
62,67
|
|
Paddy fields
|
302,42
|
0,7
|
211,69
|
|
River
|
10,237
|
0,5
|
5,12
|
|
Industrial Area
|
0,081
|
0,8
|
0,06
|
|
Cultivated land
|
365,15
|
0,45
|
164,32
|
|
Forest
|
188,28
|
0,5
|
94,14
|
|
Total
|
955,69
|
|
443,86
|
Source: Calculation Result
From the table above, we can find the value
of the flow coefficient with the following equation:
C =
C =
�= 0,46
4) Effective rainfall
Here is the
equation for calculating adequate rainfall;
Re
= Rt x C
Description:
Re =
adequate rain (mm)
Rt = hourly
rainfall distribution (mm)
C = flow
coefficient
Figure
2. Effective
rainfall calculation
Source: Calculation Result
5) Base Flow Calculation
Base flow is water that originates below the ground
surface and flows back into the river. It ensures that the river continues to
flow from one rainfall event to the next.
Here is the
equation for calculating base flow:
QB
= 0.4751 x A^(0.6444) x D^(0.9430)
QB = 0.4751
x 955.688^(0.6444 ) x 0.7^(0.9430 )
QB = 3.02
m�/s
Description:�
QB = base
flow (m3/s)
A =
watershed area (km3)
D = drain
network density (km/km2)
D = L/A
L = river
length (km)
6) Nakayasu calculation
In the
calculation of HSS, Nakayasu required watershed parameter characteristics,
namely:
Watershed
area (A) = 955.688 km2
Main river
length (L) = 62.5 km
-
Calculating Time lag
(tg)
tg is the
time lag, which is the time between rainfall and peak flood discharge.
tg
= 0.4 + 0.058 L
tg = 0.4 +
0.058 x 62.5
tg = 4.03
hours
-
Calculating rain
duration (tr)
tr
= 0.75 x tg
tr = 0.75 x
4.03
tr = 3.02
hours
-
Calculating the peak
flood time (tp)
TP is the
time from the rain's beginning to the flood's peak.
Tp
= tg + 0.8 tr
Tp = 4.03 +
0.8 3.02
Tp = 6.44
hours
-
Calculating
watershed characteristic coefficient (ɑ)
ɑ = 
ɑ
= 
ɑ
= 1,83 Hours
-
Flood peak reduction
up to 30% (T_0.3)
�= ɑ x tg
�= 1,83 x 4,03
�= 7,53 hours
-
Calculating Nakayasu
peak discharge
Qp =� �
Qp =� 
�
Qp = 13,29 m�/s
-
Calculating rising
and falling curves
Rising
curves (0 < t < Tp)
(0 < t < 6,44)
Qa = Qp (
)
Table
12. Rising
curve calculation
