Time of Concentration Estimated Using Some Methods and Application in The Ciliwung Watershed, Jakarta
Agung Wahyudi Biantoro1*, SI Wahyudi2, Moh. Faiqun Ni’am2, AG Mahardika3
1 Engineering Departments, Universitas Mercu Buana, Jakarta, Indonesia
2 Engineering Departments, Sultan Agung Islamic University, Semarang, Indonesia
3 Civil Departments, STT Mandala, Bandung, Indonesia
*Corresponding Author: [email protected] Accepted: 15 June 2022 | Published: 30 June 2022
DOI:https://doi.org/10.55057/ijarei.2022.4.2.3
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Abstract: Flood is one of the disasters that must be faced in the lowlands with poor drainage system, when there is high rainfall. Rainfall in the that upstream River can affect water level downstream River. Calculation of river discharge and forecast from that time that arrival from flood can Help prepare population to face that disaster. That method used in this lesson is a quantitative method with analysis from rainfall and planned flood repatriate. Then, time of concentration (TC) before calculated use that time traveling method, That Experience Resource Conservation Serve (NRCS) and Kirpich. That results is compared to with HEC RAS and observation method. That research location is in Ciliwung watershed, with tc calculation of Katulampa (Bogor) to MT. Haryono (Jakarta). That results show that results from design flood discharge using the HSS Nakayasu method for Q2 of 161.04 m3/s, Q5 of 187.07 m3/s, Q 10 of 215.08 m3/s, Q25 of 263.11m3/s, Q50 of 308.91m3/s and Q100 of 364.34m3/s. Time of concentration using the Time Travel method from Katulampa to MT. Haryono 12 hours 32 minutes, while using the NRCS method is 12 hours and 31 minutes while the Kirpich method is 12 hours 52 minutes. That time of concentration using observations is about 13 hour and using the HEC RAS is 14 hours. The closest method to calculate the time of concentration under actual conditions is the Kirpich method.
Keywords: Time concentration, NRC, Kirpich, Time travel, HEC RAS
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1. Introduction
Rainfall and high river water discharge in the upstream are indicators of the possibility of flooding in the downstream area. Various efforts have been made by stakeholders to calculate the time of arrival of water from upstream to downstream so that people affected by floods can immediately prepare themselves or carry out evacuations.
Disaster flood is disaster nature happens in room and time development according to meteorology and watershed dynamics, so that incident flood could influence different region Mechanism physical this introduce connection spatial -temporal between notes flood and loss in different locations during the period time certain must considered consider evaluation effective from collective risk flood (Serinaldi & Kilsby, 2017).
Singh, et.al (2021) mentions existence limitations in Settings zoning flood and lack of countermeasures flood make disaster flood in the country develop the more severe (Singh,
Soni, Kumar, Pasupuleti, & Govind, 2021). Effort subtraction impact flood conducted with various method of them with build system detection early flood, create a flood model as well as develop system drainage. Integration between GIS programs and HEC RAS can help modeled flooding on the Hammerhead River in Northern Morocco. Utilization of both application is helping to make flood mapping and give information about speed water flow, high water level and depth on various type configuration stream (Azouagh, El Bardai, Hilal, &
Stitou el Messari, 2018).
Calculation of the design flood discharge can help interested parties to carry out flood control.
One of the design flood discharge calculation methods used is the Nakayasyu synthetic unit hydrograph (HSS) method (Akbar & Bhaskara, 2020). Calculation flood discharge design could become economic reference for estimate tall water level and wide puddle flood in the future come.
Prediction of flood arrival times can help communities affected by flood- to deal with and mitigate disasters. The difference in the results of the various methods requires anticipation of the factors that can affect the time of arrival of the flood. Often the time of the arrival of floods in the affected area does not match the predictions made in the flood early detection system.
This study tries to find a method that produces the flood arrival time that is closest to the actual condition.
2. Literature Review
Frequent floods doesn't happen in Jakarta only occur moment rain down in the region Mother city, but occur because rain down in the region upstream river. Enhancement tall water level at the dam Katulampa (Bogor) to be ones of flood alert indicator in the Jakarta area. Bulk intensity rain in the area upstream can increase tall water level and water discharge in the area downstream. Based on the Indonesian National Standard (SNI) 2415:2016, what is meant by flood discharge is the maximum discharge from a river or channel whose amount is based on / related to the return period. Meanwhile, rainfall intensity is the height of rainfall in a certain period expressed in mm/hour. The return period is the interval of repetition of an event (exceeded) in a certain period of time (T).
Akbar and Bhaskara (2020) investigated the planned flood discharge in the Parangjoho watershed using the HSS Nakayasu and HSS Soil Conservation Service (SCS) methods. The return period used is up to 200 years. Ishadi, et al (2020) used a kinematic method based on a geographic information system (GIS) in Sangkrah Village, Surakarta (Ishadi, Hadiani, &
Suryandari, 2018). The flood forecast uses 5, 10, 25, 50, 100 and Q3 daily return periods. The results of this study indicate that the planned flood discharge for the 5 year return period (Q5) is 37,295 m3 /second, has the potential to produce a maximum water level of 1,702 m and an inundation area of 9,290,346 m2. The discharge at the 10 year return period (Q10) is 41.4855 m3/second which has the potential to produce a maximum water level of 1,876 m and an inundation area of 10,083,676 m2.
The discharge at the 25 year return period (Q25) is 44,475 m3/second, has the potential to produce a maximum water level of 1,970 m and an inundation area of 10,314.6 m2. The discharge at the 50 year return period (Q50) is 49,224 m3/second, has the potential to produce a maximum water level of 2,117 m and an inundation area of 10,352,767 m2. The discharge at the return period of 100 years (Q100) is 52,204 m3/second, has the potential to produce a maximum water level of 2,207 m and an inundation area of 10,473,183 m2. The maximum
planned flood discharge due to a maximum of 3 days of annual rain that occurred in 2007 was 101.73 m3/second, potentially resulting in a maximum water level of 3,601 m and an inundation area of 12,880,043 m2.
The research of Dia, et.al (2021) shows that flood disturbances cause an increase in the speed of public transport, changes in transit routes, a decrease in travel speed, which causes travel delays and loss of job accessibility . This creates huge economic costs for local commuters about $1.2 million daily and hinders the establishment of an integrated city-wide labor market.
In addition, we reveal considerable socio-spatial heterogeneity, with low income population groups experiencing the most travel delays and identify important network segments that should be prioritized for resilience interventions.
Seyam, et.al (2017) The results show that the time lag is inversely proportional to the intensity of rainfall in a moderate strength relationship. The moderate relationship can be explained by the high complexity and the interaction of other variables that affect the time lag. This approach has the potential to be used in many hydrological applications in the future, particularly those related to surface water hydrology and integrated watershed management (Seyam, 2017).
Siregar, et.al (2020) The results show that the canal only accommodates a 5 years flood return period, not 10 years. The drainage network system consists of minor drainage and major drainage (river) which can be simulated to reduce runoff. Approach is influenced by flow direction and roughness. This parameter is a vital point to set the peak flood travel time. By redesigning and updating the channel capacity can reduce overflow over the node (crossroads) (Siregar et al., 2020). The research of Biantoro, et.al (2022) shows that measuring flood discharge can help provide early information about the possibility of flooding (Biantoro, Wahyudi, Niam, & Mahardika, 2022).
3. Methodology
This study uses quantitative methods by using the calculation of flood discharge analysis, rainfall intensity and flood arrival time. The calculation of flood discharge is used to estimate the occurrence of floods in the future as well as inundation that may occur in the affected area.
The calculation of the intensity of rain is used to estimate the time of the arrival of the flood.
The flood arrival time method used in this study is the time travel method, the velocity method and the Kirpich method. Analysis of flood arrival times also utilizes HEC RAS software.
Sample on study this using bulk data rain in the region Katulampa and MT. Haryono during period 2011-2020. Besides bulk data rain, data used is the elevation data and length of discharge. This research is included in the type of quantitative research by taking secondary data. Secondary data was obtained through the website of the Central Statistics Agency (BPS) and the Ministry of Public Works (PU).
The variables in this study are: (a) Area (m2): The area of the watershed according to Dixon and Easter, the area of the watershed is the area of the topography restricted by back Hill dan bulk rain be equipped by something system river, (b) Time Arrive Flood : Concentration time or when the flood arrives is wrong one chart urgent in do calculation flood discharge especially in use formula rational, debit flood calculated with intensity average rain during time arrive flood, (c) Water level (High water level) : is distance Among tall bottom water level until to surface, (d) Prediction flood: estimate time happening calculated flood in unit time minute or hours, and (e) Flood discharge design calculated with use HSS Nakayasu method.
Time arrive flood calculated with use three method that is time travel method, method speed and method Kirpich ( https://bpsdm.pu.go.id/ ).
1. The time travel method formula : Tt = 𝑙
3600𝑉
2. The velocity method (NRCS) formula : Tt = 0.007 (𝑛𝑙)
0.8 ( 𝑃2 )0.5 𝑆0.4
3. Kirpich method : Tc = 0.0195 (𝐿
√𝑆 )0.77 minute Where :
Tt = Travel Time (hours)
l = length of river or channel (m) P2 = Maximum daily rainfall 2 years n = coefficient of surface type S = Land slope (%)
Furthermore, to analyze the prediction of flood inundation at certain points, QGIS and HEC RAS (Hydraulic Engineering Center, River Analysis System) programs are used. The data needed are water level profile, watershed flow conditions and limits, water level elevation, critical depth, normal depth, discharge curve, digital elevation model (DEM), 2 dimensional flow area, Manning coefficient value, simulation boundary conditions for unsteady flow.
discharge flood hydrograph data and water level. The results obtained are in the form of model simulations and visualization of flood inundation mapping.
4. Conclusion
Concentration time (tc) can also called time arrive flood, until moment this amount equality empirical available for used as formula in calculation concentration time. Concentration time need bulk data rain, distance between locations rain with possible location experience flood or puddle (Danacova, Szolgay, & Vyleta, 2015). Besides that, height also could influence time needed for reach something points. Rainfall in the region upstream River could influence tall water level and water discharge in the area downstream (Rustinsyah, Prasetyo, & Adib, 2021).
Results flood discharge calculation plan could be viewed on Figure 1.
.
Figure 1: Nakayasyu method design flood hydrograph
Based on Figure 1, the flood discharge plan has mark big increase. This thing show that future will be appeared flood discharge that occurred in MT. Haryono Region will the bigger and more inundated increasingly wide area. Results prediction this can used for map region inundation, so can minimize impact flooding in the community in the future come.
Besides map wide puddle and predict flood discharge plan time come on when 2, 5, 10, 25, 50 and 100 years anniversary study this also make prediction time arrive flood in MT. Haryono area, when occur rain bulk with intensity high in the region Katulampa. Calculation results for flood time arrival using time travel method generates time go of 11.92 hours or 12 hours 32 minutes. It means when Katulampa region experience heavy rain on at 06.00 WIB, then can predicted flood discharge will arrived at MT. Haryono on 18:32.
Table 1: Duration from Katulampa (Bogor) to MT Haryono (Jakarta), Time travel methode
No Station S (%) V(m/s) I (m) Time Travel (h)
1 Katulampa to Kampung Kelapa
0.15 2.19 30,700 3.89 2 Kampung Kelapa to Depok 0.15 2.05 10,690 1.45
3 Depok to MT. Haryono 0.02 1.9 39,230 5.74
4 MT. Haryono to Manggarai 0.02 2.6 7,890 0.84 5 Katulampa to MT. Haryono 0.02 2.6 88,510 11.92
Duration 12 h 32 min.
The calculation results of the flood time arrival using velocity method resulted in a travel time of 11 hours 31 minutes. This means that if the Katulampa area experiences heavy rain at 06.00 WIB, it can be predicted that the flood discharge will reach MT. Haryono area at 17.31 WIB.
Table 2: Duration time from Katulampa to MT. Haryono area, Velocity method
No Station S (%) n I (m) P2 (mm) Travel time (h) 1 Katulampa to Kampung Kelapa 0.15 0.41 30,700 95 2.92 2 Kampung Kelapa to Depok 0.15 0.41 10,690 105 1.20 3 Depok to MT. Haryono 0.02 0.41 39,230 110 7.40 4 MT. Haryono to Manggarai 0.02 0.17 7,890 180 0.79 5 Katulampa to MT. Haryono 0.02 0.41 88,510 130 12.31
Duration 12 h 31 min.
Another method used to calculate flood arrival time is the Kirpich method. The results of the calculation of the flood travel time using the Kirpich method resulted in a travel time of 12 hours 56 minutes. This means that if there is heavy rain in the Katulampa area at 18.00, then the flood discharge is expected to reach MT. Haryono at 18:56. When compared with the prediction results using the HEC RAS application, the travel time required by the flood to arrive in the MT area. Haryono is for 14 hours. With results the so there is difference about 10.2%
between speed data time arrive flood method Kirpich manual calculations with method simulation HEC RAS, for location Katulampa to MT Haryono, Jakarta. Using HEC RAS app can make flood discharge prediction and time arrive flood on period repeat 2, 5, 10, 25 and 50 years.
Figure 2: MT. Haryono Depth of Flood Map 2 Years Return Period
Figure 3: MT. Haryono Depth of Flood Map 25 Years Return Period
The flood map shown in Figure 2 is a flood velocity prediction map created using HEC RAS software for a 25 years return period. The complete results of flood prediction using HEC RAS software are shown in Table 3 below.
Table 3: Floods Discharge and Time of concentration at MT. Haryono Area, Ciliwung River, using Hec Ras.
No Flood Discharge
Time of concentration (hours)
Maximum height
Maximum depth
Speed Area
m3/s m m m/sec km2
1. Q-2nd 14.34 +12.12 4.02 2.05 0,315
2. Q-5th 13.34 +12.31 4.22 2.19 0.320
3. Q-10th 12.49 +12,53 4.45 2.33 0,333
4. Q-25th 11.57 +12.84 4.84 3.19 0,456
5. Q-50th 11.25 +13.15 5.15 3.72 0,483
Based on results testing use HEC RAS app is known that on period time 2 years birthday come flood still 14 hours 34 minutes with maximum level depth of 4.02 meters with area of inundation reach 0.315 Km2. When see results prediction of the next 10 years, so seen moment that arrive flood the heavier with increase depth, so also speed water flow and wide resulting inundation. On 50th return period, time flood no up to 12 hours with level depth maximum reach 5.15 meters. Water discharge reaches speed 3.72 m/sec and area of inundation reach 0.482 Km.
Fang, et.al (2008) used watershed parameters to develop the calculation of concentration time (Fang, Thompson, Cleveland, Pradhan, & Malla, 2008). Three different methods were used to estimate the concentration time. Three different methods: the automated method using digital elevation models and geographic information system software, the manual method with watershed delineation, and the manual method without watershed delineation. Tc estimated from five empirical equations using three sets of watershed parameters is compared and analyzed. Tc estimated using watershed parameters developed by the three methods is
qualitatively similar and has average relative differences ranging from 6.4 to −16.9%.
Differences between manual and automatic-based watershed characteristics are considered minor sources of error in relation to other uncertainties inherent in time parameter estimation.
Average relative differences of Tc estimated using different empirical equations with the same set of watershed parameters range from 38 to 207% (absolute average differences range from 3.0 to 2.8h) and are much larger than differences estimated using three sets of watershed parameters. Kirpich and Haktanir–Sezen methods provide reliable estimates of mean values of Tc variations.
The results of this study are in line with research conducted by Akbar and Bhaskara (2020) using the HSS Nakayasu and HSS Soil Conservation Service (SCS) methods to predict flood discharge up to a 200 year return period. As a result, future flood discharges will be even greater, therefore efforts are needed to reduce the impact of future floods. The results of this study are also in line with the research of Ishadi, et al (2020) who used the kinematic method based on geographic information systems (GIS) in Sangkrah Village, Surakarta. The research resulted in flood discharge predictions in Sangkrah village as well as flood-affected maps using GIS.
The results of the research by Seyam, et.al (2017) show that the time lag is inversely proportional to the intensity of rainfall in a moderate strength relationship. The moderate relationship can be explained by the high complexity and the interaction of other variables that affect the time lag. This approach has the potential to be used in many hydrological applications in the future, particularly those related to surface water hydrology and integrated watershed management (Prihartanto & Ganesha, 2019). Other research that is in line is the research of Siregar, et.al (2020) which proves that the results of flood predictions can be used to reduce flood risk, namely by making simulations to reduce runoff.
Results study show that with use method Nakayasu could estimate amount of flood discharge future plans come. Results study show that results flood discharge design use HSS Nakayasu method for Q2 of 161.04 m3/s , Q5 of 187.07 m3/s, Q10 of 215.08 m3/s , Q25 of 263.11 m3/s, Q50
of 308.91 m3/s and Q100 of 364.34 m3/s . Time arrive flood with use Time Travel method from Katulampa to MT. Haryono is for 12 hours 32 minutes, while those using Velocity method is for 12.31 hours while with method Kirpich is for 12 hours 52 minutes Time calculation arrive flood with utilise HEC RAS app is for 14 hours. Based on the results of the calculation of the arrival time of the flood, the calculation results that are closest to the actual conditions in the field are calculations using the Kirpich method, where the actual condition of the flood arrival time is in the range of 13 hours.
References
Akbar, R. A., & Bhaskara, A. (2020). Analisis Debit Banjir Rancangan Pada Daerah Aliran Sungai Celeng Dengan Metode Hidrograf Satuan Sintetik ( Hss ) Nakayasu Dan Soil Conservation Service Analysis of Design Flood Discharge in Celeng River Flow Area Using Synthetic Unit Hydographic Method (. Reviews in Civil Engineering, 4(2), 54–61.
Azouagh, A., El Bardai, R., Hilal, I., & Stitou el Messari, J. (2018). Integration of GIS and HEC-RAS in Floods Modeling of Martil River (Northern Morocco). European Scientific Journal, ESJ, 14(12), 130. https://doi.org/10.19044/esj.2018.v14n12p130
Biantoro, A. W., Wahyudi, S. I., Niam, M. F., & Mahardika, A. G. (2022). Analysis of Ciliwung river flood debit and city flood anticipation using floods early detection system (FEDS). IOP Conference Series: Earth and Environmental Science, 955(1).
https://doi.org/10.1088/1755-1315/955/1/012011
Danacova, M., Szolgay, J., & Vyleta, R. (2015). Estimation of The Relationship Between The Travel Time of Flood Peaks and Peak Discharge on The Poprad River by Multilinear Flood Routing. International Journal of New Technology and Research, 1(6), 263656.
Fang, X., Thompson, D. B., Cleveland, T. G., Pradhan, P., & Malla, R. (2008). Time of Concentration Estimated Using Watershed Paramaters Determined by Automated and Manual Methods. Journal of Irrigation and Drainage Engineering, 134(2).
https://doi.org/https://doi.org/10.1061/(ASCE)0733-9437(2008)134:2(202)
Ishadi, N. K., Hadiani, R. R. R., & Suryandari, E. S. (2018). Penelusuran Banjir Berdasarkan Analisis Metode Kinematik Berbasis Sistem Informasi Geografis (Sig) Di Kelurahan Sangkrah, Surakarta. Matriks Teknik Sipil, 6(3), 530–541.
https://doi.org/10.20961/mateksi.v6i3.36562
Prihartanto, & Ganesha, D. (2019). Flood Time Arrival Estimation based on Empirical Analysis of Recorded Data of Flood EWS in Bekasi City. Jurnal Sains Dan Teknologi Mitigasi Bencana, 14(1), 8–15.
Putera, A. P., & Toruan, K. L. (2016). Rancang Bangun Alat Pengukur Suhu, Kelembaban Dan Tekanan Udara Portable Berbasis Mikrokontroler Atmega16. Jurnal Meteorologi Klimatologi Dan Geofisika, 3(2), 42–50.
Rustinsyah, R., Prasetyo, R. A., & Adib, M. (2021). Social capital for flood disaster management: Case study of flooding in a village of Bengawan Solo Riverbank, Tuban, East Java Province. International Journal of Disaster Risk Reduction, 52(November 2020), 101963. https://doi.org/10.1016/j.ijdrr.2020.101963
Serinaldi, F., & Kilsby, C. G. (2017). A Blueprint for Full Collective Flood Risk Estimation:
Demonstration for European River Flooding. Risk Analysis, 37(10), 1958–1976.
https://doi.org/10.1111/risa.12747
Seyam, et al. (2017). Analysis of rainfall intensity impact on the lag time estimation in tropical humid rivers. International Journal of ADVANCED AND APPLIED SCIENCES, 4(10), 15–19. https://doi.org/10.21833/ijaas.2017.010.003
Singh, R. K., Soni, A., Kumar, S., Pasupuleti, S., & Govind, V. (2021). Zonation of flood prone areas by an integrated framework of a hydrodynamic model and ANN. Water Science and Technology: Water Supply, 21(1), 80–97. https://doi.org/10.2166/ws.2020.252
Siregar, R. I., Nursyamsi, N., Indrawan, I., Sembiring, R. A., Karolina, R., & Dewi, R. A.
(2020). An Approach of Travel Time of Flood Peaks and Runoff Model towards Low Impact Development. Simetrikal: Journal of Engineering and Technology, 2(1), 1–12.
https://doi.org/10.32734/jet.v2i1.2631
