J-PEN Borneo: Journal of Agricultural Sciences Volume VI.
Number 1.
April 3202 Pages: 14-22 YAWAN et al.
Ae Running title is about five words E-ISSN: 2599-2872
P-ISSN: 2549-8150
THE MODELING OF RAINFALL-RUNOFF OF DAS TOJO CENTRAL SULAWESI
USING HEC-HMS SOFTWARE
Teguh Hilmansyah1*.
Aswar Amiruddin2.
Moh Amin3.
Sukardi Nurdin3 1Fakultas Teknik.
Universitas Tadulako.
Jalan Soekarno Hatta No.
KM.
Kelurahan Tondo.
Kecamatan Mantikulore.
Kota Palu.
Provinsi Sulawesi Tengah 2Fakultas Teknik.
Universitas Borneo Tarakan.
Jalan Amal Lama No.
Kecamtan Tarakan Timur.
Kelurahan Pantai Amal.
Kota Tarakan.
Provinsi Kalimantan Utara 3PSDKU Morowali.
Universitas Tadulako.
Desa Bahomante.
Kabupaten Morowali.
Provinsi Sulawesi Tengah *teguhhilmansyah17@gmail.
Submited: 18 February 2023
Accepted: 30 April 2023
ABSTRACT
In the hydrological cycle, the rain falling on the ground surface in a watershed (DAS) then undergoes a process of evaporation, infiltration, and surface runoff.
This conversion of rain into runoff surface can be used as a basis for calculating the discharge of the design flood.
Analyzing flood design is one of the calculations in water resource planning.
River flow discharge is an indicator of the output of a watershed system, especially in the process of converting rainfall into a surface flow.
Flood discharge in a watershed is generally expressed as a hydrograph.
One method that can be used to analyze the conversion of rain into flow is the HEC-HMS The purpose of this research is to determine the design of flood discharge and flow hydrograph of Tojo River using HEC-HMS software.
In this article, the HEC-HMS model components used to analyze hydrographs are SCS CN for the runoff volume model and SCS UH for the direct runoff model.
From the results of modeling using HEC-HMS, the peak flow discharge of the Tojo watershed in Central Sulawesi are 8 m3/s for a 2-year design flood, 239.
8 m3/s for a 5-year design flood, 329.
5 m3/s for 10-year design flood, 5 m3/s for 20-year design flood, 589.
8 m3/s for 50-year design flood and 727.
1 m3/s for 100-year design Furthermore, the results of this study can be used for flood control planning and other water resource Keywords: Flood Design.
Das Tojo.
HEC-HMS
INTRODUCTION
One important aspect in understanding hydrology is an understanding of the drainage system in the Watershed or abbreviated as watershed (DAS) can be interpreted as a land area with ridges as a natural boundary and can stream the rainwater that falls in the system and then flows to a review point through the main river .
DAS is the widening of an area that has contributed to the flow surface in the region .
the book of Applied Hydrology defines watershed (DAS) as an area that is limited by a series of hills or mountains, when rain falls on the area the water flows to a point of review or outlet.
In studying hydrology, we are first introduced to the hydrological cycle, where rain in the hydrological cycle is explained that when rain falls or falls on the surface of a watershed, it will undergo several processes such as evaporation, infiltration, and runoff .
urface runof.
This surface runoff is one indicator of the function of a watershed (DAS), where river flow discharge becomes a measurable parameter of the process of converting rain into the flow.
The process of converting rainfall into flow is very complicated in a river drainage system .
because many factors play a role in determining the characteristics of flow in a watershed, such as rainfall factors and watershed characteristics as a medium for transformation .
Watershed (DAS) characteristics that greatly affect the flow discharge, especially its morphometric characteristics include the size of the watershed (DAS) in this case the area of the watershed (A), the extent to which the main river flows (L), the gradient of the river slope (S) and J-PEN Borneo: Journal of Agricultural Sciences Volume VI.
Number 1.
April 3202 Pages: 14-22 YAWAN et al.
Ae Running title is about five words E-ISSN: 2599-2872
P-ISSN: 2549-8150
The shape and size of the watershed (DAS), topographic conditions, soil type, and vegetation covering a catchment area are the characteristics that most affect the flow conditions in a watershed (DAS) .
The number of factors that play a role in the process of converting rain into flow makes the calculation of flow discharge analytically difficult, so a hydrological model approach is an option for determining river flow discharge in a watershed (DAS) system.
The analysis results of rain-flow modeling can be a basis for a consideration to evaluate the flow conditions in the river of the watershed (DAS) system .
Hydrological modeling has been developed by many software developers.
One of them is HEC-HMS which provides quite good results when analyzing rainfall .
Hydrologic Engineering Center - Hydrological Modeling System or better known as HEC-HMS is software with an open license .
pen sourc.
HEC-HMS is software produced at the U.
Army Corps of Engineers (USACE) engineering center.
HEC-HMS can model complex hydrological systems in a watershed (DAS) through a simplification mechanism of the drainage area system.
Researchers are interested in conducting a study to obtain peak flow discharge values as a result of rainfall-flow modeling analysis using HEC-HMS software in the Tojo watershed of Central Sulawesi.
MATERIALS AND METHODS
The Location of the Research The object studied in this article is the Tojo watershed, which is located in two administrative The Tojo watershed (DAS) is located in the administrative area of Tojo Una-una Regency and the administrative area of North Morowali Regency in Central Sulawesi Province as shown in Figure The characteristics of the Tojo watershed (DAS) which are the main input data in this study are the watershed area and the length of the main river, with a value of 212.
58 km2 Tojo watershed area and 28 km main river length .
Furthermore, these watershed characteristics are used to determine the amount of flow that occurs in the Tojo River using HEC-HMS software.
Figure 1.
Tojo Watershed in Central Sulawesi Province Data Collecting The initial stages of the research began by collecting data that would be used for rainfall-flow analysis using HEC-HMS software.
The data collected and used in the research include:
Daily rainfall data from 2011 to 2020 as the main input data for rainfall flow analysis.
The data of rainfall used is spatial rainfall data obtained through the website:
https://app.
com/climateEngine The data of Land cover is used to know the land use in a watershed (DAS) that serves to calculate the value of Curve Number (CN).
Land cover data used in this research is the land cover data acquired through the website of the Ministry of Environment.
https://sigap.
id/sigap J-PEN Borneo: Journal of Agricultural Sciences Volume VI.
Number 1.
April 3202 Pages: 14-22 YAWAN et al.
Ae Running title is about five words E-ISSN: 2599-2872
P-ISSN: 2549-8150
In addition to using land cover data, to obtain the value of the curve number (CN), soil hydrology group (HSG) data is required.
Soil hydrology group (HSG) is a fundamental component to estimate the conversion of rainfall into flow in a watershed.
The global HSG dataset used in this study has a spatial resolution of 250 m .
Data Analysis The data that has been collected is then grouped based on the data type and analyzed using methods based on the type of each data.
The methods used to analyze the data in this study include:
Frequency analysis, is a method used to analyze rainfall design, namely estimating the depth of rain for a certain rain period .
The frequency analysis stage is also carried out to determine whether a series of data fits a certain data distribution or not .
The initial stage of frequency analysis calculation is to calculate basic statistical parameters such as mean score .
I), standard deviation (S.
, coefficient of variance (C.
, coefficient of skewness (C.
, and kurtosis coefficient (C.
The results of this basic statistical analysis are used to determine the frequency analysis method to be used based on the frequency analysis method on the determination table as shown in table 1.
Table 1.
Parameters for determining the type of rainfall distribution No.
Type of Distribution Term of Condition .
cuycuI A ycy.
= 68,72% .
cuycuI A ycy.
= 68,72% Normal yayaycyc OO 0 yayaycoyco OO 3 yayaycyc = yayaycyc 3 3yayaycyc Log Normal yayaycoyco = yayaycyc 6yayaycyc 6 15yayaycyc 4 16yayaycyc 2 3 yayaycyc = 1,14 Gumbel yayaycoyco = 5,4 Pearson i Log Other than the values mentioned above The equation for each type of distribution follows equation .
through equation .
Normal ycUycUycNycN = ycUycU yayaycNycN ycIycI yayayayayayayayaycNycN = yayayayayayaycUycU yayaycNycN ycIycIycoycoycoycoycoycoycoyco Log Normal, this method is calculated by changing the X score of the data with the logarithmic score of X .
, so that equation .
becomes equation .
Gumbel, the equation used in this method follows equation .
, but the KT frequency factor score is calculated using equation .
ycyc Oeycyc yayaycNycN = ycNycN ycuycu ycIycIycuycu Log Pearson i, the equation for this method follows the log normal distribution equation as shown in equation .
Meanwhile, the KT frequency factor value follows the pearson type i distribution frequency factor whose value varies depending on the coefficient of skewness (C.
and the rainfall recurrence period or probability of occurrence.
The description of each parameter of the above equation as follows:
ycUycUycNycN = Estimated score expected to occur at return period T years ycUycU = average score .
n practice the average score of n sample data is use.
yayaycNycN = frequency factor of the normal distribution ycIycI = standard deviation ycycycNycN = reduce variate for return period T years ycycycuycu = reduce mean for n data sample ycycycuycu = reduce standard deviation for n data sample J-PEN Borneo: Journal of Agricultural Sciences Volume VI.
Number 1.
April 3202 Pages: 14-22 YAWAN et al.
Ae Running title is about five words E-ISSN: 2599-2872
P-ISSN: 2549-8150
After obtaining the data of rainfall design using one of the selected methods, the frequency distribution scores of the data samples are then tested against the chance distribution function using the chi-square or Smirnov Kolmogorov method to determine whether the design rainfall is representative of the frequency distribution or not.
Furthermore, certain return period rainfall is analyzed into hourly distributed rainfall using the mononobe method .
The results of the mononobe rain intensity analysis are then used as input data in the HEC-HMS time series as the hyetograph value of the precipitation gauge.
Next, prepare the data representing subbasin .
parameters such as watershed size, main river length and slope, curve number (CN), initial abstraction (I.
, concentration time .
c ), dan lag time .
lag ).
HEC-HMS
The HEC-HMS tool accommodates almost all of the physical parameters of the watershed in sub-sub models.
The rain-flow simulation process carried out by HEC-HMS is based on the principle of the hydrological cycle, in which rain falling in a catchment water area will experience evaporation, and infiltration and end up at one review point as a discharge.
The accuracy of the rainfall stream analysis results using HEC-HMS software is determined by the available data and the method of analysis determined by the user.
In HEC-HMS software, the amount of flow in a watershed is analyzed using the hydrograph principle.
One of the methods used to calculate hydrographs is the Soil Conservation Services - Curve Number or known as the SCSCN method.
The SCS-CN method will analyze excess rainfall as a function of rain depth (P), soil surface type, and land use using equation .
ycEycEyceyce = .
cEycEOeyayaycayca )2 ycEycEOeyayaycayca ycIycI Where:
ycEycEyceyce = Precipitation excess at time t P = rain depth at time t yayaycayca = Initial Abstraction S = maximum potential retention The score of maximum potential retention (S) and watershed characteristics are associated with curve number (CN) parameter based on equation .
ycIycI = Oe 254 yayayaya Where:
S = maximum potential retention .
CN = curve number Based on some experimental analysis results on small watersheds, the Soil Conservation Service (SCS) developed an empirical relationship between Ia and S as equation .
yayaycayca = 0,2 ycIycI The range of CN scores is between 100 and 30, where the CN value is 100 for waterlogged areas and the CN value is 30 for permeable soils with high infiltration rates .
The CN value of a watershed is calculated based on several parameters such as land use, soil type, vegetation and soil moisture in the land cover.
The CN value of each land use and soil hydrology group can be estimated based on tables published by SCS, and can be seen in the book Applied Hydrology .
CN values for land use types and HSG types can be seen in Table 2.
Table 2.
CN Value For Land Use Or Soil Type Soil Hydrology Group Land cover type Land that has been planted No conservation Grassland J-PEN Borneo: Journal of Agricultural Sciences Volume VI.
Number 1.
April 3202 Pages: 14-22 YAWAN et al.
Ae Running title is about five words Land cover type Poor cover condition Good cover condition Grassland: good condition Forest: - Sparse plants poor cover - good cover Open fields, lawns, gold courses, cemeteries.
Good condition: grass covers 75% or more of the area Medium condition: grass covers 50% - 75% of the area Commercial and business areas .
% Industrial areas .
% Impermeabl.
Residential Extensive % Impermeable 1/8 acre or less 1/4 acre 1/3 acre 1/2 acre 1 acre Parking lot, roof of house/ office/ building, car n the yar.
Pavement road with drainage Gravel road
Dirt road
E-ISSN: 2599-2872
P-ISSN: 2549-8150
Soil Hydrology Group Each watershed is certainly not dominated by one land use and soil type but consists of several land uses or soil types, so the determination of CN values using composite CN can be calculated by equation .
yayayayaycaycaycaycaycaycaycaycaycaycaycaycaycaycaycaycaycayca = Oc yayaycnycn yayayayaycnycn Oc yayaycnycn In this study, the SCS UH model was also used to calculate direct runoff.
The SCS direct runoff model used in HEC-HMS modeling is based on the principle of unit hydrograph calculation.
The SCS direct runoff model also assumed that rainfall occurred evenly throughout the watershed.
obtain an overview of the hydrograph, the peak time is required, for which the SCS method uses equations .
to calculate the peak time .
ycycycayca = yaya0,8 y yayayaya Oe10 10 1900yycUycU 0,5 ycycycoycoycoycoycoyco = 0,6 y ycycycayca Where: t lag = lag time .
tc = concentration time .
L = river length .
Y= average slope of DAS (%).
CN = curve number
RESULTS AND DISCUSSIONS
The rain data used in this study is Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data.
The use of this data has gone through considerations related to the availability of available data including the length of rain data.
The difference in the amount of data analyzed has a significant effect on rainfall estimates at certain rain return periods, this causes significant The deviation of the results of the design rain analysis is getting smaller with the increasing length of rain data used in the analysis .
J-PEN Borneo: Journal of Agricultural Sciences Volume VI.
Number 1.
April 3202 Pages: 14-22 YAWAN et al.
Ae Running title is about five words E-ISSN: 2599-2872
P-ISSN: 2549-8150
CHIRPS rain data has a longer recording duration so it can be utilized in various calculations such as flood analysis .
CHIRPS monthly rain data showed good linearity against Automatic Weather Station (AWS) observation data but produces an overestimated value with a bias of 6% .
To analyze the design flood, the rainfall data used is the maximum rainfall data.
The maximum rainfall data in the Tojo watershed can be seen in Table 3.
Year
JAN
14,94
20,64
2,09
22,01
12,97
16,18
14,83
20,47
14,71
11,15
Table 3.
Monthly maximum rainfall data for 2011-2020 Monthly Maximum Rainfall .
FEB MAR APR MEI JUN JUL
AUG SEP OKT
8,24
18,17 32,14 31,80 94,97 37,51 15,69
56,90 15,22
5,80
11,55 37,48 42,19 44,36 78,35 43,84
38,34 15,89
11,38 39,04 22,49 35,48 46,62 41,90 30,36
35,48 22,36
5,03
14,40 24,06 26,30 42,97 36,41 32,19
13,11 18,72
7,09
10,04 31,19 22,78 40,05 36,10 9,95
10,63 32,69
8,24
18,15 37,37 21,09 45,24 36,30 26,98
32,83 25,19
5,44
15,97 46,94 34,50 65,85 62,63 26,83
72,26 33,59
6,12
14,30 28,75 30,81 36,74 44,35 23,52
16,80 15,59
4,40
18,14 38,70 21,64 52,48 42,50 11,32
52,56 21,65
5,70
13,59 28,90 27,38 76,80 42,83 110,69 84,52 33,15
NOV
10,56
9,31
21,67
12,91
11,49
29,28
13,65
10,67
10,08
12,83
DES
15,99
9,01
8,29
13,98
13,00
8,10
8,38
9,22
9,39
9,82
Source: Analysist Result, 2022 Furthermore, the data were analyzed using basic statistical methods to obtain the mean score (X_T=X I K_T S), standard deviation (S.
, and coefficient of skewness (C.
The calculation results obtained the mean score (X_T=X I K_T S) = 1.
7702, standard deviation (S.
= 0.
1596, and coefficient of skewness (C.
= 0.
Furthermore, from this value, the KT value is determined based on the Log Pearson i distribution table for each return period and the coefficient of skewness (C.
The results of the rainfall distribution analysis for periods of 2, 5, 10, 20, 50, and 100 years using the Log Pearson type i method can be seen in Table 4.
Table 4.
Designated Rain for the method of Log Pearson Type i Return Periode P (%) 0,6913 0,6913 0,6913 0,6913 0,6913 0,6913 -0,1145 0,7909 1,3326 1,8592 2,4028 2,8180 Log X
1,7519
1,8964
1,9829
2,0669
2,1537
2,2199
X .
56,4835 78,7802 96,1319 116,6593 142,4533 165,9322 Sumber: Analysist Result, 2022 The analysis is continued by testing the type of distribution chosen using the chi-square method to determine whether the distribution equation that has been chosen can represent a statistical distribution of the sample being analyzed.
From the results of the chi-square analysis, the calculated chi-square result .
= 1 and the critical chi-square score .
= 5.
991, so that the distribution equation used can represent each sample analyzed.
Furthermore, the results of the rainfall analysis plan are used to calculate the hourly rainfall intensity method using the Mononobe The results of the Mononobe method rain intensity analysis can be seen in table 5 and figure Table 5.
Hourly Rain Intensity Return Period Duration (Hou.
56,483 78,780 96,132 116,659 142,453 165,932 19,582 27,312 33,327 40,444
49,386
57,525
12,336
17,205
20,995
25,478
31,111
36,239
9,414
13,130
16,022
19,443
23,742
27,655
7,771
10,839
13,226
16,050
19,599
22,829
6,697
9,340
11,398
13,831
16,890
19,673
J-PEN Borneo: Journal of Agricultural Sciences Volume VI.
Number 1.
April 3202 Pages: 14-22 YAWAN et al.
Ae Running title is about five words E-ISSN: 2599-2872
P-ISSN: 2549-8150
Return Period Duration (Hou.
56,483 78,780 96,132 116,659 142,453 165,932 5,930 8,271 10,093 12,248
14,957
17,422
5,351
7,464
9,107
11,052
13,496
15,720
4,895
6,828
8,332
10,111
12,346
14,381
Sumber: Analysist Result, 2022 Figure 2.
Graph of rainfall intensity based on rain return period (Analysis result, 2.
The rain intensity values in Table 4 are then compiled in the form of a hyetogpraph using the alternating block method (ABM).
Before analyzing the hydrograph using HEC-HMS, first the analysis of other watershed parameters such as curve number (CN), initial abstraction (I.
and time The watershed parameters used in this study can be seen in Table 6.
The units used in each parameter adjust to the HEC-HMS units.
Table 6.
Values of each input parameter of HEC-HMS in Tojo Watershed Watershed Area 212,58 L .
Y (%) S .
Ia .
91,840 77,63 2,882 14,638 Source : Analysis Result, 2022
Imper
(%)
0,023
Tlag .
419,639 After obtaining the value of each HEC-HMS input parameter, modeling was then conducted.
Modeling starts by inputting the value of each watershed parameter according to Table 5, rainfall time series data .
recipitation gaug.
, meteorological model, and control.
Furthermore, adding simulations for each return period of the design rainfall.
The modeling results show that the design flood discharge for each return period of 2, 5, 10, 20, 50, and 100 years is 133.
8 m3/s, 239.
8 m3/s, 5 m3/s, 442.
5 m3/s, 589.
8 m3/s, 727.
1 m3/s.
The peak of the 2 and 5-year return period flood event occurs at hour 12, while the peak of the 10 to 100-year return period flood event occurs at hour 11 as shown in Figure 3.
J-PEN Borneo: Journal of Agricultural Sciences Volume VI.
Number 1.
April 3202 Pages: 14-22 YAWAN et al.
Ae Running title is about five words E-ISSN: 2599-2872
P-ISSN: 2549-8150
Figure 3.
Hydrograph Flood Design of Tojo watershed using HEC-HMS (Analysis result, 2.
The results of the design flood simulation using HEC-HMS are different from the results of previous research design flood analysis in the same watershed using the HSS ITB-1 method.
The parameters that are assessed in HSS ITB-1 are watershed area (ADAS), main river length (L), and waiting time (T.
analyzed using the Kirpich equation.
HSS area is not dimensionless AHSS .
The vastly different results are due to the parameters analyzed from these two methods being very The most sensitive parameter in the SCS CN method is the waiting time (T.
The waiting time for the SCS CN method is calculated using the equation specified in the HEC-HMS Technical Reference Manual document.
Meanwhile, for the ITB-1 method, the waiting time is analyzed using the Kirpich equation.
CONCLUSION
Based on the modeling results using HEC-HMS, the peak flow discharge of the Tojo watershed in Central Sulawesi Province is 133.
8 m3/s for the 2-year design flood, 239.
8 m3/s for the 5-year design flood, 329.
5 m3/s for the 10-year design flood, 442.
5 m3/s for the 20-year design flood, 589.
m3/s for the 50-year design flood and 727.
1 m3/s for the 100-year design flood.
The peak of the 2and 5-year return period flood occurs at hour 12, while the peak of the 10- to 100-year return period flood occurs at hour 11.
ACKNOWLEDGMENT
The author's gratitude goes to all those who support until this article is published, starting from collecting research data until this article is published.
Thank you to PSDKU Morowali for entrusting the research team to obtain research funds through the DIPA PSDKU Morowali Development Research scheme.
Tadulako University.
REFERENCES