The assessment of flood hazard in Pondok Keumuning village of Langsa City, Aceh due to heavy rain in 2020

Langsa faces the recurring threat of floods every year, primarily caused by high rainfall resulting in surface runoff in the Langsa Watershed. However, there is uncertainty regarding the specific causes of flooding in the city. This research discusses the flood hazards generated by Langsa River due to high rainfall in Keumuning Village, Langsa City, in 2020. The data utilized in this study includes daily rainfall data obtained from PTPN I Langsa, specifically from the Kebun Lama (KLM), Kebun Baru (KBR), and Tualang Sawit (TWS) stations. Additionally, observed water level data is collected from the Keumuning water gauge station operated by the Aceh Irrigation Agency. Since there is no river discharge record at the Keumuning water gauge station, the Nreca method is used to analyze the direct runoff from the Langsa Watershed due to rainfall, linking it to the water level at Keumuning. Historical flood event data is compiled from the Regional Disaster Management Agency of Langsa City, online media sources, and interviews with flood-affected communities. The research findings indicate that floods in Keumuning Village, Langsa, are triggered by high rainfall, leading to increased river discharge as a result of heavy rainfall at the TWS station (located upstream in the Langsa Watershed) with a 2-year return period. This can result in flooding for the villages around the Langsa River. Similarly, suppose rainfall reaches a 2-year return period at the KLM and KBR stations situated within the middle and downstream of the Langsa Watershed. In that case, it poses a flood threat to the upstream villages along the estuary branches. However, further studies are necessary to assess compound floods triggered by tidal water levels, rainfall, and river discharge in the vicinity of these estuary branches.


Introduction
The Langsa River flows through the center of Langsa City, which is a densely populated and developing city in the province of Aceh, Indonesia.This river contributes to the infrastructure development of that area such as fresh water supply, flood control, transportation, and potential irrigation.The downstream part of Langsa River is developed as a tourism and business area, featuring the most significant mangrove tourism in Asia, an airstrip, and the Kuala Langsa Port.Several national and urban roads connecting Aceh and North Sumatra provinces cross the Langsa River.Therefore, the Langsa River is one of the most strategic rivers for development in the province of Aceh.However, several annual floodprone areas along the Langsa River generate significant disturbance to the sustainability development of those areas.These flood-prone prone are located in flat areas that could be found in both the 1314 (2024) 012100 IOP Publishing doi:10.1088/1755-1315/1314/1/012100 2 downstream and the upstream of Langsa River [1,2].Meanwhile, several slum housing areas have been growing up along the downstream of Langsa's Riverbank in the last two decades.Some of that area are influenced by tides so floods are generated not only by excess rainfall but also by high tide (rob) so the combination of both generators could yield the highest flood.The upper part of Langsa River is developed as the built environment (urban area, business center, water supply, farmland, landmark building, etc.) where flood-vulnerable areas could be found in several flat areas along the upper part of Langsa River Stream.Those flat areas could be natural lowlands or wetland areas generated by low drainage capacity.As indicated by several previous studies, such flood area condition is comparable to the reported flood area in Bandung Basin [3][4][5][6][7].
Based on the recorded flood event, several previous studies have indicated an increasing flood event in both flood-vulnerable areas, the downstream and upper part of Langsa River, due to the land use change and urbanization [8][9][10].The flood problem in the downstream part of Langsa River is comparable to the flood problem in the downstream part of Ciliwung River Jakarta City [11][12][13][14].Meanwhile, the Flood problem in the upper part of Langsa River is comparable to the flood problem in the upper part of Citarum River, West Java, and downstream part of Bengawan Solo River, East Java [4][5][6][7]15].Kusuma et al [13], Formanek et al [11], and dan Farid et al [12], conclude that as with most of the flood management in the urban area of Indonesia, the flood management in Jakarta faces erosion, sedimentation, and land use change problems.The land use change in the upper part of the Ciliwung River tends to increase the rate of not only its extreme discharge but also its erosion and sedimentation.Several previous studies demonstrate very well the correlation of land use change to the increasing extreme discharge as shown by Papagiannaki [15] dan Kuntoro et al [5].Febi et al [14] discussed how to enhance the resiliency of the community of Jakarta to reduce the risk generated by the influence of climate change on increasing the extreme discharge of the Ciliwung River.Meanwhile, the lack of field data, on rainfall and discharge distribution, remains the main concern in getting eligible results of climate change influence on the extreme discharge of Ciliwung as indicated by Farahnaz et al [16].Based on neural network methods Gunawan et al. [17] demonstrate the application mathematical model for river sediment load estimation, however, this model requires a record data measurement that is not applicable to Ciliwung River data.
Suprayogi et al [18] conclude that land subsidence is one of the most important parameters that should be addressed in decreasing flood risk in Jakarta where freshwater availability for the local people is the key solution.Furthermore, several reservoirs are developed in the upper part of the Ciliwung River and one estuary dam is proposed in Jakarta Bay.The upstream reservoir is developed not only to increase the freshwater availability during the dry season but also to reduce the extreme discharge during the wet/rainy season as indicated by Kusuma et al [19].However, Sengara et al [20] concluded that as an archipelago country located in a very active tectonic plate and volcano, Indonesia has a very high probability of earthquake hazards that will generate a high risk of dam failure of any reservoir in its surrounding area.This dam break will generate collateral hazard to its downstream area that usually becomes a favor of urban area development.The sudden release of a large water mass from the reservoir might cause massive destruction either in life or property particularly in that urban area as indicated by [21][22][23][24][25]. Sarminingsih et al [4] discussed the influence of the increasing flood depth on the increasing flood disaster risk.Meanwhile, Jonathan et al [26] developed a machine learning model to correlate satellite data to flood level for supporting flood hazard prediction.Yatsrib et al. [27] discuss flood risk reduction efforts in the Cengkareng District located in North Jakarta, where it is concluded that tidal influence is important for obtaining more accurate flood predictions in the area.Sand and Gravel Mining are one of the main industries in the Bandung Plain, Abfertiawan et al [7] describe the significant impact of mining activities on the extreme flow discharge of the Ukud River, which results in similar problems in the Langsa River.The Langsa River Basin has experienced considerable degradation in the upstream part of the basin, which is potentially susceptible to river sedimentation, as indicated by [28].Bennett et al [29] explain that triggers for coastal flooding in Jakarta are caused by coastal storms, tidal waves, and river discharge, known as combined flooding.If these triggers occur simultaneously, they can increase the threat of flooding in the coastal areas of Jakarta Bay, which is also applicable to the situation in Langsa City located on the eastern coast of Aceh.
The purpose of this study is to determine the relationship between flood-triggering characteristics and the historical flood events in Langsa City during 2020.daily rainfall data, and daily river discharge data.The flood-triggering data will be compared to the history of flood events in the affected villages, allowing for the interpretation of the factors that contribute to floods in the face of the flood threats that have occurred in Langsa City.However, this study is still in its preliminary stage, and further research is needed to assess the probability of flood triggers and to establish effective and efficient mitigation measures in Langsa City.This will serve as a basis for future flood risk assessments.

Study Area Location
This research focuses on Langsa City, located in the Langsa watershed with coordinates of 4°32'19.92"-4°32'34.99"north latitude and 97°58'56.07"-98°3'48.49"east longitude.Langsa City is one of the 18 coastal cities in Aceh Province, consisting of three river basins (Birem Puntong River Basin, Langsa River Basin, and Manyak Payed River Basin).The city has a total area of 262.41 km2 and is comprised of 66 villages, with the Langsa River Basin being the most dominant, covering an area of 161.85 km2 and containing 62 villages [30,31].The downstream area of the Langsa River Basin borders the Malacca Strait through three estuary branches, namely Laubanie Estuary, Matang Estuary, and Kuala Langsa Estuary, As shown in Figure 1.The majority of Langsa City is located at low elevations, with an altitude of fewer than 13 meters, and the population growth rate in 2020 was recorded at 2.17 percent per year [31].

Figure 1. Adminitrasion Village of Langsa City in Langsa Watershed
Administratively, the city of Langsa is bounded by the eastern part, directly bordering Aceh Tamiang Regency, while the western and southern parts are bordered by East Aceh Regency.

Data and Material
The data used in this study are rainfall data obtained from PTPN I Langsa and Aceh Irrigation Agency at 3 stations (Sta Kebun Baru (KBR), Kebun Lama (KLM), and Tualang Sawit (TWS)) from 2010 to 2020 in daily data as shown in Figure 2. Historical data on flood events in Langsa City from 2020 were obtained from the Regional Disaster Management Agency of Langsa City, online media, and interviews with flood-affected communities.

Figure 2. Annual Maximum Daily Rainfall Data
Figure 2 illustrates the maximum daily rainfall conditions with the highest rainfall recorded at station KLM reaching 124 mm in 2017, station KBR reaching 107 mm in 2010, and station TWS reaching 120 mm in 2019.This data is used to predict planned rainfall for a return period of 2 to 100 years, serving as the basis for determining the effective rainfall distribution in generating hourly rainfall, thus enabling the projection of direct river discharge during different periods in the Langsa River Basin based on land cover, as shown in Figure 3.
The most dominant distribution of land cover/use in the Langsa watershed is forest covering an area of 71.25 km2 upstream, downstream of the watershed is dominated by aquaculture ponds covering an area of 43.35 km2 and mangroves of 24.82 km2 and settlements covering an area of 22.41 km2 which are spread out in the middle of langsa city.Bathymetry data for the Langsa River were obtained from the Aceh Irrigation Agency.
The planned rainfall distribution is used as a threshold for rainfall that triggers surface runoff, leading to river discharge.This helps determine the dominant rainfall magnitude that serves as the main trigger for floods in Langsa City, particularly during specific return periods that pose a flood threat.This information is crucial for evaluating flood risks and implementing necessary mitigation measures to address floods occurring within that time interval.

Methods
Floods occur when triggering factors such as rainfall and river discharge work together.The research process generally includes a literature review, data collection, and correlation analysis of flood triggers by combining daily river discharge and daily rainfall, water level observations, and the height of the riverbank in Keumuning village.The flowchart of the discussion can be seen in Figure 4.

Figure 4. Flow chart of study
Analysis of the frequency distribution by dividing rainfall using the Thiessen polygon method as was done in research [9] and analyzing the distribution of return period rainfall using the normal distribution, normal log, Pearson III log, and Gumbel methods.Furthermore, an analysis of rainfall-runoff was conducted using the Synthetic Unit Hydrograph (SUH) technique to generate flood discharge hydrographs with a 2-year return period [32].The methods employed included the Soil Conservation Service Curve Number (SCS CN), Nakayashu, Snyder, ITB I, ITB II, and HEC HMS.To perform this analysis, various parameters such as lag time, curve numbers (CN), basin area, percentage of impervious area, rainfall, and other hydrological parameters were required.The output of this analysis consists of planned flood discharge hydrographs that can be applied in the study area [33].
In this study, the estimation of base flow and surface runoff was performed through synthetic streamflow simulation using the Nreca model.Since there were no streamflow observations available at the Keumuning gauge station, the Nreca model's parameter values were manually selected based on the assumptions and characteristics of the watershed, as described in the research [34].The parameter selection process, including Moisture Storage, Nominal, PSUB, Begin Stor GW, and GWF, was conducted iteratively until a high correlation value between rainfall and runoff, close to 1, was achieved.
Specifically, the nominal parameter (100 + Cx) represents the soil's capacity index in the catchment area, where a value of C < 0.2 was used to account for seasonal rainfall conditions, and x denotes the average annual rainfall derived from Thiessen daily rainfall data.The groundwater storage capacity was evaluated by comparing the values between January and December, and if the difference exceeded 200 cm, the model needed to be reevaluated.The GWF coefficient, representing the characteristics of continuous reliable flow, was set to 0.3 (0.2 ≤ GWF < 0.5), and the PSUB coefficient, which accounts for the soil storage filling ratio at depths of 0-2 meters, had a constraint of 0.5 < PSUB ≤ 0.9.For large permeable aquifers, a value of 0.7 was used.
To determine the potential evapotranspiration in this study, the modified Penman method [22] was employed.However, since there were no available evaporation measurement stations within the study area, data from the nearest BMKG stations, namely BMKG Malikussaleh (North Aceh, Aceh Province) and BMKG I (Medan, North Sumatra), were used for temperature, relative humidity, sunshine duration, and wind speed parameters.In this study, the instantaneous flow velocity was measured by considering the difference in water level.The aim was to obtain a rating curve graph that illustrates the relationship between river discharge and water level at the Keumuning water gauge station from 2015 to 2020.Additionally, the height of the river bank was used as a threshold to determine the overflow limit that could cause flood threats.Furthermore, visualization was performed through a graph that shows the relationship between synthetic discharge using the Nreca model, rainfall, and water level based on historical flood events in 2020 in the village of Keumuning from the Water Agency of Aceh.Consequently, it can be determined that the flood threats occurring in Keumuning are influenced by tidal activities downstream of the Langsa River during rainfall and runoff from the Langsa Watershed.

Results and Discussion
Rainfall distribution analysis using regular, log normal, log Pearson III, and Gumbel distribution method based on the goodness of fit statistics of the frequency distribution method chosen is Gumbel frequency distribution of rainfall return period, as in Figure 5

Figure 5. Distribution frequency of rainfall
The bankfull capacity of the Langsa River was estimated based on the measurement of instantaneous flow velocity at a cross-section of the river at the Keumuning water gauge station.The cross-section had a base width of 9.1 m (Figure 6), a bankfull water level (WL) of 3.57 m, a river slope of 0.00022, and the flow velocity (V) was obtained from the regression equation shown in Figure 7.As a result, the river discharge under bank full capacity conditions was determined to be 55 m 3 /s.Bankfull capacity according to [32] is a return period of 2.33 years which is close to 50.01 m3/s (SUH SCS-HMS) with an area of the Keumuning sub-watershed of 93.59 km 2 and a river length of 34.24 km as shown in Figure 8. Bankful capacity is used to see the condition of the river discharge during a flood in the city of Langsa.The daily river discharge data from the water agency of the provincial government obtained from the Keumuning water gauge station from 2015 to 2020 indicates the highest recorded discharge on December 05, 2020, reaching 272.62 m3/s (Gambar 8), with water level (H) of 7 meters.The comparison between the river discharge and water level height of the Langsa River from 2015 to 2020 is shown in Figure 8.The rating curve data is used to validate the Langsa River discharge based on the results obtained from the synthetic unit hydrograph for a return period of 2.33 years [32].This validation process involves comparing the estimated discharge values obtained from the unit hydrograph analysis with the corresponding values derived from the rating curve.By comparing these two datasets, the accuracy and reliability of the unit hydrograph method can be evaluated, ensuring the validity of the estimated river discharge for the given return period.

Figure 9. SUH 2 years return period
Figure 9 illustrates the bank total capacity of Langsa River based on a 2-year return period and the peak discharge using the synthetic unit hydrograph (SUH) approach with the SCS-HMS method.The obtained peak discharge at the Keumuning water gauge station is 50.01 m 3 /s.Additionally, the bank full capacity discharge of Langsa River is estimated using the regression equation derived from the instantaneous flow velocity measurements.The equation is V = 0.2259h + 0.1249 (Figure 7).In Figure 8, the cross-sectional area of the river (A) is determined with a channel base width of 9.1 m, and the full condition has a flow depth of 3.57 m.The difference in elevation between the full condition flow elevation (11.135 m) and the riverbed elevation (7.562 m) indicates a river slope (m) of 2.12.Utilizing this information, the cross-sectional area of channel (A) is calculated to be approximately 59.01 m 2 , and the Langsa River discharge at Keumuning during full condition is 55 m 3 /s.This estimation closely aligns with the synthetic unit hydrograph (SUH) for a 2-year return period.The flood events in Langsa City in 2020 due to increased Langsa River discharge occurred Twice (Figure 11).The simulation of surface runoff in the Nreca Rainfall-Runoff model was conducted due to the lack of directly measured discharge at the Keumuning water gauge station.The simulation results revealed a strong correlation (91.70%) between rainfall events and the simulated Nreca River discharge in the Sungai Langsa Watershed.This correlation was derived from daily rainfall data (Thiessen Method from 3 Sta rainfall) and potential evapotranspiration over the period 2009 to 2020.However, direct runoff showed a weak relationship with daily river discharge based on water levels at the Keumuning gauge station, as indicated by a correlation coefficient of 21.20%, as shown in Figure 10.This suggests that an increase in surface runoff from the watershed does not lead to a significant rise in water levels at the Keumuning gauge station during flood events (Figure 10).From 2015 to 2020, Keumuning Village in the city of Langsa experienced 10 instances of flood threats with water levels exceeding the river's bank elevation, as indicated in Figure 8. 6 flood threats occurred in the year 2020 as described in Figure 12.One of these threats happened on November 5-6, 2020, and another on December 4-5, 2020.
On the flood event of November 5, 2020 (Figure 11a), the rainfall in the Keumuning area, calculated using the Thiessen method, was 58 mm (the highest recorded rainfall at the TWS station was 88 mm, KLM: 31 mm, KBR: 72 mm on November 4, 2020), while the surrounding areas received 39 mm of rainfall (the highest recorded rainfall at the TWS station was 30 mm, KLM: 44, KBR: 42 on November 4, 2020).These conditions resulted in the water level reaching 13.56 m, as measured at the Keumuning water monitoring post, exceeding the river's bank elevation, which is only 11.14 m.Surface runoff based on the Nreca model reached 45.17 m 3 /s and 74.40 m 3 /s, starting one day before the occurrence of the flood.13 (a) visualize the flood threat that occurred on November 5, 2020, with a recorded water level elevation of 13.56 meters, resulting in a floodwater height of 0 -50 cm (Low) covering an area of 30.13 hectares and > 50 -100 cm (Medium) covering an area of 37.59 hectares.Subsequently, on December 6, 2020, the flood threat escalated with a measured water level elevation of 14.56 meters, leading to an expansion of the flood extent.The floodwater height was recorded as 0 -50 cm (Low) covering an area of 30.74 hectares, > 50 -100 cm (Medium) covering an area of 30.12 hectares, and > 50 -100 cm (High) covering an area of 37.59 hectares.The data on the extent of flood inundation was collected and compiled by the Department of Public Works and Housing of Langsa City, the Regional Disaster Management Agency, and the affected local community.The flood threat in Langsa City, which caused water levels to rise in the Keumuning River during November and December 2020, was influenced by rainfall at the TWS station.During that period, recorded rainfall reached 88 mm and 110 mm, exceeding the 2-year planned rainfall.This rainfall occurred one day before and during the flood threat in the upstream Langsa River Basin.
Apart from Keumuning village, there are 9 other villages downstream of the river that experience floods every year.Flood triggers in the downstream area are not solely caused by river discharge but also result from the interaction between high rainfall and tidal water entering through the Langsa River estuary.Floods in Keumuning village occur when there is significant rainfall in the upstream Langsa River Basin, as recorded at the TWS station, whenever the rainfall approaches or exceeds the 2-year recurrence period, posing a threat to Keumuning village and other villages along the Langsa River.
Furthermore, there are recorded instances of flood-affected villages in the downstream area.These floods are caused by a combination of tidal water and heavy rainfall in the downstream area when the rainfall approaches or exceeds the 2-year recurrence period, even if there are no recorded rainfall observations in the upstream Langsa River Basin.In conclusion, the flood threat in the Langsa River is primarily caused by the high rainfall in the upstream Langsa River Basin (TWS station), impacting Keumuning village in Langsa City, Aceh, as well as other villages downstream along the Langsa River.

Conclusions
Flood Threat in Keumuning Village, Langsa City, occurs during periods of heavy rainfall in the upstream area of the Langsa River Basin.This high precipitation leads to an increase in surface runoff, particularly if the rainfall approaches or exceeds the 2-year return period rainfall in the upstream Langsa River Basin, as recorded at the TWS Rain Gauge Station.Whenever a flood threat emerges in Keumuning Village, its impacts are felt by downstream villages situated around the Langsa River.The rainfall events that pose a flood threat typically occur one or two days before the actual flooding.This is because heavy rainfall from two days prior has already saturated the ground, rendering it incapable of absorbing more water.Consequently, subsequent rains are more likely to become surface runoff, leading to flooding.The flood threats on November 5th and December 5th, 2020, illustrate how prior water discharge can influence the water level on the following days, mainly if additional rainfall occurs.
Furthermore, Langsa City faces flood threats caused by tidal surges in the downstream area of the basin, especially when combined with rainfall that surpasses the 2-year return period rainfall in the downstream region.This phenomenon is commonly referred to as combined floods.To address this situation effectively, further studies are needed, involving 1D and 2D simulations to visualize the impacts of combined floods in Langsa City.The objective is to provide recommendations for effective flood control measures in the future.

Figure 7 .
Figure 7.The regression of instantaneous flow velocity at Keumuning water gauge station.

Figure 11 .
Figure 11.Relationship of Rainfall, Discharge, and Highest Tides in 2020 (a) Flood Event 5 Nov 2020 and (b) Flood Event 5 Dec 2020