*Corresponding author: Civil Engineering Department, Engineering Faculty, University of Lampung, 35145, Indonesia E-mail address: [email protected] (Riki Chandra Wijaya)
doi: https://doi.org/10.21776/ub.pengairan.2023.014.02.8 Received: 22-05-2023; Revised: 31-08-2023; Accepted: 04-11-2023
P-ISSN: 2086-1761 | E-ISSN: 2477-6068 © 2023 [email protected]. All rights reserved. 171
Vol. 14 No. 02 (2023)
Jurnal Teknik Pengairan: Journal of Water Resources Engineering
Journal homepage: https://jurnalpengairan.ub.ac.id/index.php/jtp
Original research article
Digital Analysis Method for Predicting Flood in A River of Lhoksukon Aceh North Indonesia
Riki Chandra Wijaya*
Civil Engineering Department, Engineering Faculty, University of Lampung, 35145, Indonesia A R T I C L E I N F O A B S T R A C T
Keywords:
Digital Method;
Flood;
Lhoksukon Aceh Utara
The major floods that occurred in 2022 in Lhoksukon, North Aceh, Indonesia, had a severe impact on infrastructure damage and loss of life, making it necessary to analyze and predict its impact. In this regard, digital methods can effectively analyze and predict the impact of such natural disasters. The digital method is processing digital data through software to produce the desired information. Based on the results of the digital analysis, it has been found that the flood discharge in the Lhoksukon area of North Aceh is around 1848 m3/second with a flow velocity of about 3.484 m/second. This high flow velocity can cause significant damage to the infrastructure in the affected areas. The flood height in the river area ranges from 9.545 m from the riverbed, which is a cause for concern. The increase in water level will cause an expansion of the inundation area in the river area, which can lead to further damage. Therefore, the results of this study can be used to develop effective flood mitigation strategies and infrastructure planning in the affected areas. Additionally, the study's findings can be used to develop early warning systems to alert people in the affected areas to evacuate before the floods reach dangerous levels.
1. Introduction
Flood problems have become a major problem at this time due to increased infrastructure development and population in various regions in Indonesia. Changes in land use have caused fewer catchment areas and increased surface water volume. The development of the watershed area is the main factor in increasing the flow of surface water [1]. This is the main cause of flooding. One of the areas in Indonesia currently affected by flooding is the city of Lhoksukon.
Lhoksukon, North Aceh, is an area that is often affected by floods. In 2022, floods occurred and submerged most areas in Lhoksukon, North Aceh. Not only physical losses but also non-physical ones related to the impact of this massive flood.
Floods have submerged around 113 villages in Lhoksukon.
Around 21,209 people were displaced by this flood [2].
Losses due to flooding can certainly be minimized with the latest information or flood disaster mitigation. The benefits of flood disaster mitigation will be able to provide information to the public about areas that are potentially affected by floods [2]. With this information, it is hoped that the community will be more alert and adaptable to future flood events. This vigilance will emphasize the impact of flooding so that losses due to flooding will be reduced [3], [4].
To mitigate floods, a hydrological analysis is needed in flood-affected areas [5]. This analysis can also be used to
make flood prediction maps and depths in the analyzed area.
Various methods in flood analysis have been developed in recent years. The methods range from statistical analysis to software development. Among them is the use of regression analysis in predicting peak flood discharge [6], integration between the drainage management system and other infrastructure will be able to control flooding in urban areas [7], and the use of HEC RAS software can produce predictions from flooding to sedimentation in river bodies [8]. The Lhoksukon area, North Aceh, can be used as a location for hydrological analysis to obtain predictions of existing flood inundation and flood depth for various periods or return periods of rain events [9]. For this reason, in this study, a hydrological analysis will be carried out to produce predictions of flood inundation and flood depth in parts of the Lhoksukon area, North Aceh. This research is one of the latest studies conducted during the flood disaster in the city of Lhoksukon in 2022.
2. Method
Some basic data needs to be completed in this research.
The research location is at latitude 5°2'29.00" N and longitude 97°19'12.80"E. Some of the data collected in this study include rainfall data for the last ten years from 2011 to 2021 for daily rainfall data in Malikussaleh Station latitude: 5°13'43.24"N
172 and longitude: 96°56'51.19"E. Related to the analyzed location, land elevation data in the watershed area, river length data, and land use data. This research's data sources are from BMKG Online, Indonesian Geospatial, Bappeda Aceh Utara, and Google Earth. Some of these data were obtained from both secondary and primary.
Based on the flow chart in Figure 1, the data calibration process is carried out in the hydrological analysis process. If the discharge resulting from the analysis calculation has a significant difference from the actual discharge at the same time, ranging from more than 20%, then the hydrology must be improved again. However, if smaller than that, the research can be continued at the hydraulics analysis stage. In the initial step of the collected rainfall data, the process of calculating the average rainfall is carried out. The process of calculating the average rainfall can be done using the arithmetic method. The use of this method is due to the availability of data for only one meteorological station at the location. The equations of the arithmetic method are as follows [10].
𝑷̅ =𝑷𝟏+𝑷𝟐+⋯+𝑷𝑵
𝑵 =𝟏
𝑵∑𝑵𝒊=𝟏𝑷𝒊 (1) P is average rainfall (mm), P1 is rainfall -1 (mm), P2 is rainfall - 2 (mm), PN is rainfall -N (mm), and N is the sum of rainfall data (mm). Soil Conservation Service (SCS) Curve Number (CN) is the rainfall calculation model as a function of the cumulative amount of rain, the outermost layer of soil, land use, and evaporation on the land. The SCS method was used in this study to determine how much the initial loss of rainwater can be estimated from the existing land types at the location. This method already has a set value that can be seen based on the standard of rainwater loss from the observation table. SCS methods use the following equation [10] :
𝑃𝑒=(𝑃−𝐼𝑎)2
𝑃−𝐼𝑎+𝑆 (2) Pe is accumulated excess rain (mm), P is the height of accumulated rain at t-time (mm), Ia is initial abstraction (lost initial) (mm), and S is maximum potential storage (mm).
Based on the analysis reports from many small experiments, SCS was developed as an empirical relationship from Ia and S [10] :
Ia = 0.2 S
Then, the cumulative excess at that time was:
𝑃𝑒=(𝑃−0.2)2
𝑃−0.8𝑆 (3) Based on the average rainfall data, it is possible to determine the return period rainfall using the Gumbel Method. Based on the determination of the method used, there are several alternative methods: the Gumbel Method, the Pearson Log type III Method, and the Rational Method. Several statistical calculations were carried out to determine the standard deviation, skewness, and kurtosis values. This is done to see how much deviation occurs in each distribution so that the method that is closest to normal is known from all the processed data distributions. This calculation was determined using the following equation:
The average value is formulated by [11]:
𝑥̅ = ∑𝑥
𝑛
(4) x is the average value (rainfall data unit is mm), and n is the sum data (rainfall data unit is mm).
Table 1. Confidence coefficient (c) Gumbel Methods
C (%) 50 68 80 90 95 99
f(c) 0.674 1.00 1.282 1.645 1.96 2.58
The Deviation standard is formulated by:
𝑠𝑡𝑑(𝑥) = √(𝑥− 𝑥̅)2
𝑛−1 (5) Std (x) is the deviation standard (rainfall data unit is mm), 𝑥̅
is the average value (rainfall data unit is mm), and N is the sum data. The skewness coefficient is formulated by [12] :
𝐶𝑠 = 𝑛∑(𝑥− 𝑥̅)3
(𝑛−1)(𝑛−2)(𝑠𝑡𝑑(𝑥))3
(6) Cs is the skewness coefficient, Std (x) is deviation standard (rainfall data unit is mm), 𝑥̅ is average value (rainfall data unit is mm), and n is the sum data. The kurtosis coefficient is formulated by :
𝐶𝑘 = 𝑛2∑(𝑥− 𝑥̅)4
(𝑛−1)(𝑛−2)(𝑛−3)(𝑠𝑡𝑑(𝑥))4 (7) Ck is kurtosis coefficient, Std(x) is deviation standard (rainfall data unit is mm), x ̅ is average value (rainfall data unit is mm), and n is sum data. Based on the results of the characteristics of the frequency distribution and the characteristics of the use of the function, it can be determined that the method used in this analysis is the Gumbel Method.
In a method, there are limitations in making predictions from a distribution. In this case, the Gumbel Method has distribution limits which can be determined using the following equation [5], [6], [13].
𝑋1/2= 𝑋𝑇± 𝑓(𝑐)𝑆𝑒 (8) Where f(c) can be determined based on the following table c (confidence coefficient) [14].
Based on Table 1 above, f(c) of the Gumbel Method can be determined based on the confidence coefficient. Se is Error value = 𝑏𝜎𝑛−1
√𝑁, 𝑏 𝑖𝑠 √1 + 1.3𝐾 + 1.1𝐾2 , K is Frequent Factor, σ(n-1) is Deviation Standard (mm), and N is Number of samples. Determination of hourly rain is carried out to determine the planned return period rainfall every few hours.
In this study, the hourly rainfall was determined for 5 hours.
The distribution of rainfall is taken for 5 hours based on the value of the distribution factor, which is defined as the assumption of rain for 5 hours on one rainy day. However, the rainy season can be chosen according to the researchers' predictions in determining it. The equations used to determine hourly rain are [5] :
𝑅𝑡= 𝑅0(5
𝑇)2/3
(9) 𝑅𝑡 is the average rainfall from the beginning to the T-hour (mm), 𝑅0=𝑅24
5 (mm), T is rain time from the beginning to the T (hour), and 𝑅24 is daily effective rain (net rain causing runoff) (mm). Furthermore, to determine the amount of rainfall at hour T, the following equation can be used:
𝑅𝑇= 𝑡. 𝑅𝑡− (𝑡 − 1)𝑅(𝑡−1)
(10) 𝑅𝑇 is The amount of rainfall at hour T (mm), t is Rain time from start to hour to T (hour), 𝑅𝑡 is Average rainfall from start to hour T (mm), and 𝑅(𝑡−1) is Average rainfall from start to hour (t-1) (hour). Concentration time is the time required by the river flow from the most extreme position in the watershed under consideration to the outlet. The time of concentration can be calculated if the length of the river and
173 the average slope of the river bed are known. In this study, the Kirpich equation can be used to calculate the concentration time, namely [14]:
𝑡𝑐= 0.0078𝐿0.77𝑆−0.385 (11) 𝑡𝑐is Time of concentration (minute), L is Length of reach (m), and S is The average slope of the riverbed. To determine the relationship between the two variables can be used correlation analysis method. Correlation analysis can be known by the coefficient of determination, which can be determined using the equation [15]:
𝑟2=∑(𝑦̂−𝑦̅)∑(𝑦−𝑦̅)22 (12) (𝑦̂ − 𝑦̅) is explained deviation (Unit adjusts variable), and (𝑦 − 𝑦̅) is the difference of each data to the average value (Unit adjusts the variable). To determine the value of the correlation coefficient, the equation can be used:
𝑟 = ±√𝑟2 (13)
3. Result and Discussion
Rainfall in 2021 shows rainfall height fluctuations; these fluctuations can be shown in the following Figure 1.
Based on Figures 1 and 2, it can be seen that the maximum rain height in 2021 will be 165.2 mm. The maximum hourly rainfall from 2011 to 2022 in Figure 2 is 23.07 mm. The 2021 rainfall data is used for model calibration, while the 10-year rainfall data is used to predict the return period flood. This high rainfall will affect the quantity of river flow discharge on the Lhoksukon River. Based on the rainfall height, river discharge can be calculated using HEC HMS software [16], [17]. The specified number of sub basins is three sub basins.
The length of the river is around 65 km with a total watershed area of around 983.41 km2 and an average slope of around 0.018. Each sub-basin is assigned an area based on geographic characteristics. Based on the results of the analysis, the area of each sub-basin is determined, as shown in the following Table 2.
Based on the Table 2, the area of each subbasin, the run- off loss due to infiltration is also determined using the initial loss method. It was determined that the initial loss of each subbasin was 10 mm by using Equation 2. The time of water flow in each concentrated subbasin has a value as calculated using the Kirprich equation.
Figure 1. Rainfall data in 2021 Lhoksukon, North Aceh, Indonesia
Table 2. Area of Sub Basins Subbasin Area (km2)
1 564.8200
2 69.1700
3 349.4200
Figure 2. Rainfall data hourly from 2011 to 2022
Table 3. Surface runoff concentration time in each subbasin Subbasin Tc (Minute) Tc (Hour)
1 334.5164 5.5753
2 326.3651 5.4394
3 336.0080 5.6001
Based on the Table 3, the average time of a concentrated flow is around 5.54 hours. As for the river, it takes a concentrated time of around 0.96 hours and a late time of around 0.58 hours. The initial loss of each subbasin can be determined based on the land cover type for each character.
The characteristics of land use types affect the absorption rate of rainwater. The following shows the land use map of the study area.
Based on Figure 3, it can be seen that the majority of land use in the surrounding area is rice fields. This shows that the infiltration area is still relatively moderate for hydrological conditions. Apart from that, the type of soil in the river body also affects river flow velocity. This influence is indicated by the level of surface roughness known as the roughness coefficient [18]. Initial loss and impervious can be defined in the following table.
Based on Table 4 above, the impervious level in each subbasin is determined from the existing land use conditions.
Soil types and land conditions determine how much water absorption occurs when it rains. The hydrological model developed in this study from river network conditions is shown in the following Figure 4.
Based on Figure 4 above, the watershed shape of the river in Lhoksukon can be seen. By using this hydrological model, an estimate of surface runoff discharge can be determined.
Based on the results of the hydrological model analysis, it is known that the surface runoff discharge is shown in the following graph in Figure 5.
The model calibration process is carried out by comparing the model output discharge data to the existing discharge data simultaneously. Based on the measurement discharge data, it is found that the comparison graph between the model discharge and the current discharge is as follows.
174 Figure 3. Land cover area of Lhoksukon
Figure 4. River network
Based on Figure 6, it can be seen that the peak discharge during 2021 is 855.9 m3/s. The discharge will be simulated hydraulically using HEC RAS 2D. A model calibration process was carried out before carrying out the hydraulics simulation process using HEC RAS.
Based on the graph in Figure 7, it can be seen that the model discharge line is close to the existing discharge line.
The difference value of each data can be known using the Pearson correlation test method. Correlation analysis using
statistical analysis methods is usually indicated by the value of the Pearson correlation coefficient. The Pearson correlation coefficient is between 0 and 1. The closer to number one, the closer the relationship between the two variables being compared is closer or stronger. Based on this method, it can be seen how big the relationship between the existing discharge data and the model discharge is. The calculation results are shown in the graph below.
Based on Figure 7, the graph above shows a collection of points that are close together; this shows that the relationship between the two variables is very good.
Table 4. Initial loss of subbasin Subbasin Ia (mm) Impervious
(%)
1 25 50
2 15 25
3 20 40
Figure 5. Flow in the River Lhoksukon, North Aceh, Indonesia
Figure 6. Calibration of digital models in the River Lhoksukon, North Aceh, Indonesia
Figure 7. Pearson correlation from calibration models
175 The correlation coefficient value based on the calculation results is 0.995. This correlation value is classified as very strong because it is close to the maximum correlation coefficient value, which is one. Based on the statistical test results, it can be concluded that the model results can be used for the next calculation stage.
The process of making a hydraulics model using HEC RAS 2D first determines the elevation map of the study location. The elevation map was obtained through the DEMNAS data source, available on the website https://tanahair.indonesia.go.id/demnas. The data is then processed with image processing software to determine the tin data format. Based on the processing results, the form of tin data is obtained, as shown in the following Figure 7.
Based on Figure 8, it can be seen the geometry of the river and the characteristics of the slope of the land. Then, discharge data for one year at the location was inputted based on the results of hydrological calculations. The discharge curve is shown in the following Figure 9.
Based on Figure 9, The graph above shows debit fluctuations for one year. The maximum discharge value based on the results of this calculation is 1028 m3/s.
Figure 8. Elevation digital map
Figure 9. Discharge curve (m3/s)
The simulation results are shown in two conditions: a flood discharge condition without baseflow of around 319.5 m3/s and a discharge condition with a baseflow of about 1028 m3/s. Baseflow values taken in this study range from 600 m3/s.
This is based on the existing discharge value at the study location. The simulation results show the area of inundation that occurs, as shown in Figure 10.
Based on the simulation results at the discharge condition of 315 m3/s, the water level elevation is around 6.160 m from the bottom of the river. Meanwhile, the velocity of the water flow is about 1848 m/s. Simulation conditions with baseflow discharge is shown in Figure 12 and Figure 13.
Figure 10. Gradation of water depth Q = 315 m3/s
Figure 11. Gradation of flow velocity Q = 315 m3/s
Figure 12. Water depth gradation Q = 1848 m3/s
176 Figure 13. Flow velocity gradation Q = 1848 m3/s
The depth of the flood water level elevation at discharge conditions of 1848 m3/s ranges from 9.545 m from the bottom of the river. Meanwhile, the flood flow velocity is around 3.484 m/s. This depth will cause an expansion of the flood inundation area and high erosion in the river area. Increased velocity of river water flow will provide the potential for high erosion of the river body [19]. These results show that the river's capacity is no longer able to deliver the river flow discharge when it rains. This is shown in Figure 10 and Figure 11. The 315 m3/s rain discharge indicates the flow is almost full [20]. Hal ini dapat disebabkan oleh sungai yang sudah mendangkal karena sedimentasi yang tinggi.
Therefore, it is necessary to plan for flood control in affected areas in extreme conditions like this.
4. Conclusion
The flood event of 2022 had a devastating impact because there were so many losses due to levee collapses due to excessive river discharge. Based on this event, it is necessary to predict the peak discharge of the river flow in Lhoksukon, which is useful in planning embankments and preventing widespread flooding in the future. For this reason, this research is needed to provide information on the predicted impact of flooding on the river in Lhoksukon. In the research area in Lhoksukon, it can be seen that the condition of the land is still relatively densely populated. This densely populated area is close to a river, so the impact of flooding will be even greater. Based on the results of the analysis of this study, the height of the flood reached 9.5 m from the riverbed. River flow velocity ranges from 3.48 m/s at maximum flood discharge conditions based on base flow.
With the flood discharge, it can be seen that there is a flood pool on the side of the river border. Besides flood inundation, the level of potential for erosion and sedimentation in river bodies is very high when the flood is at its maximum. This is indicated by the high velocity of the river flow. Therefore, residents around the riverbanks should be careful about being hit by a sudden flood discharge. This will have a negative impact on soil erosion, infrastructure such as bridges, and the potential for casualties.
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