Historical Flood Events Research Articles

Mitigating flood risk at Shah Alam, Malaysia for sustainable development

The natural environment and infrastructure are severely damaged by floods, which are becoming frequent occurrences. It may not be able to stop floods given the impending climate change and variability of the weather. Yet mapping flood risk can help with both mitigation and prevention of flooding. The Shah Alam experiences flash floods yearly, which is why it was chosen. An interactive approach to decision-making under multi-criteria decision analysis (MCDA), the analytical hierarchy process (AHP), is used. The flood risk zone is divided into five categories very low-risk (32.38%), low-risk (22.25%), moderate-risk (23.17%), high-risk (18.87%), and very high-risk (3.33%) respectively. Based on the data gathered for this study, the economic losses are identified, low-risk areas (RM 2091), moderate-risk areas (RM 2343), and high-risk areas (RM 2728). The historical flood events occur in the Seksyen 36, 18, 17, and most of the southern part of Shah Alam, Malaysia. Rainfall and the Klang River have the biggest effects on these regions, with water drainage systems being critical in the Shah Alam area to reduce the risk of floods. When analysing flood risks in the Shah Alam region and throughout the nation, important stakeholders both state and non-state actors can benefit greatly from the flood susceptibility map provided by this study.

Detection of flood trends and drivers in the Taihu Basin, China

Study regionTaihu Basin, China Study focusFloods threaten humans, the environment, economic activity, and infrastructure. In this study, a new trend test and flood-frequency methods were adopted to detect extreme floods and their distributions based on flood-event identification. To fully understand the phased process of the influence of human activities on extreme hydrological processes, 12 copula functions were employed creatively in combined static and dynamic time-varying correlation aspects between extreme precipitation and floods. New hydrological insights for the regionAlthough both significant and insignificant increasing trends of the annual maximum water level in all three hydrological districts were examined, the periodic oscillations of all the stations were similar. Thus, it was significant to fully detect the periodical variation of floods. Extreme floods occurred mainly in the 1990s, as measured by frequency estimates. Generally, the nonstationary response relationship between heavy rain and an extreme water level was gradually strengthened; that is, a certain magnitude precipitation seemed to induce a greater-intensity flood event as time passed. Through the identification of historical flood events and the analysis of the rise and fall processes of floods, we found that the main reason for variation in the response relationship was the increase in the water level before the rising stage, rather than the water level rising in the Taihu Basin. Our study findings further existing knowledge on the regional flood-control design standard and can ensure the coexistence of humans and water systems in the future.

Historic Flood Events and Current Flood Hazard in Ulaanbaatar City, Central Mongolia

Climate change will increase the frequency of extreme weather events, alter rainfall patterns, and exacerbate flood disasters in Ulaanbaatar City. Here we combine aerial and satellite imagery with cadastral data, to scrutinize the historical trajectory of rainfall patterns and flood disasters in Ulaanbaatar over the past six decades. The study focusses on the causative factors behind historical floods, current flood conditions, the geographical distribution of floods, land ownership in floodprone areas, and the spatial allocation of fences and buildings based on social conditions. Over the last 60 years, Ulaanbaatar received a total of 16,780 mm of precipitation, with a staggering 80.5% of this total occurring during the summer season. Over this period, the city has endured about ten significant flood disasters. The most severe and destructive events occurred in 1966, 1982, 1994, 2003, 2009, and 2023 as river basins and mountain flash floods. These flood events claimed at least 220 lives, affected around 46,000 households, and caused economic losses of ca. 3.3 million U.S. dollars. Our study identifies several flood hazard areas along the Tolgoit, Selbe, Uliastai, and Tuul River valleys, which define a flood buffer zone extending 200 m from their banks, encompassing 59 khoroos of 7 districts in Ulaanbaatar. There are 27,970 fences and 12,887 buildings in the 200 m buffer zone, which is 66.5% of all fence unit area, and 46.3% of the total building, situated within the identified flood risk areas. In response to these findings, we emphasize the urgent need for comprehensive long-term strategy for sustainable flood management based on disaster resilence.

Spatial variability of rainfall: deciphering flood characteristics and model precision

ABSTRACT This study investigates the intricate role of spatial variability in rainfall (SVR) concerning flood characteristics and its impact on refining flood prediction models. A spatial variability index was used to classify rainfall events into two categories: spatially homogeneous (Class A) and heterogeneous (Class B). The analysis of historical flood events suggests that the SVR influences flood peaks. This research introduces a novel approach to assess SVR’s role in calibrating hydrological models, subsequently improving model selection. By separately calibrating Class A and B events within both lumped and distributed models, the models yield superior results compared to the conventional approach. For the catchments considered, the lumped models demonstrated heightened performance for Class A events, while the distributed models outperformed in Class B events. This study underscores not only the influence of SVR on flood dynamics but also the efficacy of event-based classification in refining hydrological models for superior flood prediction accuracy.

Flood risk assessment of the Garita River in the urban zone of San Luis Potosí City, by hydrodynamic modeling

Rapid and uncontrolled urban growth and land use changes in watersheds worldwide have led to increased surface runoff within metropolitan areas, coupled with climate change, creating a risk for residents during the rainy season. The city of San Luis Potosí is no exception to this phenomenon. One affected watercourse is the Garita Stream, which flows inside the city near urbanization. It is essential to analyze the effects of urban sprawl on this stream based on historical precipitation data for the town. Hydrological and topographical information were required to conduct this research. The hydrological study of the basin involved analyzing the region’s geomorphology and historical climatological data. For the stream’s topography, aerial photogrammetry using an unmanned aerial Vehicle (UAV) and Global Navigation Satellite System (GNSS) equipment was employed to conduct topographic surveys in the area. To find out when the Garita stream would overflow and which areas are most likely to flood, numerical modeling was done using 1D, 2D, and 3D programs like SWMM5 (Storm Water Management Model), HEC-RAS (Hydrologic Engineering Center’s River Analysis System), and EDFC Explorer (Environmental Fluid Dynamics Code). These models simulated different return periods and their correlation with current flooding events recorded in the area, thereby further proposing solutions to mitigate overflow issues. By conducting these simulations and analyzing the results, solutions can be suggested to address the overflow problems in the area based on historical flood events at various return periods caused by the Garita Stream.

Impact of climate change on future flood susceptibility projections under shared socioeconomic pathway scenarios in South Asia using artificial intelligence algorithms

This study investigated the impact of climate change on flood susceptibility in six South Asian countries Afghanistan, Bangladesh, Bhutan, Bharat (India), Nepal, and Pakistan—under two distinct Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6 and SSP5-5.8, for 2041–2060 and 2081–2100. To predict flood susceptibility, we employed three artificial intelligence (AI) algorithms: the K-nearest neighbor (KNN), conditional inference random forest (CIRF), and regularized random forest (RRF). Predictions were based on data from 2452 historical flood events, alongside climatic variables measured over monthly, seasonal, and annual timeframes. The innovative aspect of this research is the emphasis on using climatic variables across these progressively condensed timeframes, specifically addressing eight precipitation factors. The performance evaluation, employing the area under the receiver operating characteristic curve (AUC) metric, identified the RRF model as the most accurate, with the highest AUC of 0.94 during the testing phase, followed by the CIRF (AUC = 0.91) and the KNN (AUC = 0.86). An analysis of variable importance highlighted the substantial role of certain climatic factors, namely precipitation in the warmest quarter, annual precipitation, and precipitation during the wettest month, in the modeling of flood susceptibility in South Asia. The resultant flood susceptibility maps demonstrated the influence of climate change scenarios on susceptibility classifications, signalling a dynamic landscape of flood-prone areas over time. The findings revealed variable trends under different climate change scenarios and periods, with marked differences in the percentage of areas classified as having high and very high flood susceptibility. Overall, this study advances our understanding of how climate change affects flood susceptibility in South Asia and offers an essential tool for assessing and managing flood risks in the region.

Altered metapopulation dynamics in a headwater specialist in geomorphically dynamic catchments

Abstract Human activities have widely disrupted the spatial ecological processes critical for population and ecosystem integrity. Local effects of altered sediment regimes in rivers are well explored, but how they impact connectivity at a catchment scale is less explored. In this paper we document evidence for altered metapopulation dynamics for a headwater specialist, blackspotted topminnow (Fundulus olivaceus) in catchments with geomorphically dynamic river mainstems. We quantified decade‐scale patterns of fluvial geomorphic activity via planform analysis of historical and recent aerial imagery, and patterns of gene flow in F. olivaceus via analysis of single nucleotide polymorphisms, identified by genotype‐by‐sequencing. Mainstem streams narrowed and increased in sinuosity, probably in recovery from historic flood events. Substantial variability in response indicated underlying differences due to differential disturbance history and catchment sensitivity to change. Multiple analytical approaches all found geography to be a major factor in genetic structuring among catchments. Metrics of genetic differentiation and heterozygosity for F. olivaceus within catchments were related to the degree of geomorphic change inferred from a multivariate composite metric. Catchments with more geomorphic change tended to have more population structuring. Five populations assigned to adjacent catchments. Analysis of heterozygosity in presumed donor, recipient, and mis‐assigned catchments indicated founder effects in recipient catchments and probable subsequent allopatric recolonisation following local extirpations. Our results demonstrate complex nonlocal effects of channel evolution on metapopulation dynamics in a pool‐dwelling non‐lithophilous headwater specialist. Our integrative approach allows strong insight into deeper effects of geomorphic processes on aquatic ecosystems. F. olivaceus is ecologically dissimilar to taxa traditionally anticipated to respond to sediment‐related disturbances and demonstrates how broader channel morphological change can affect habitats beyond sedimentation of the stream bed.

Social disparities in flood exposure and associations with the urban environment in 44,698 neighborhoods in 276 cities in eight Latin American countries.

Climate change is expected to greatly increase exposure to flooding, particularly in urban populations in low- and middle-income countries. We examined within-city social disparities in exposure to flooding in 276 Latin American cities and associated features of the neighborhood urban environment. We used a spatially granular dataset of historical flood events from 2000 to 2018 to describe neighborhood flooding within cities across eight Latin American countries (Argentina, Brazil, Chile, Colombia, Costa Rica, Guatemala, Mexico, and Panama). We estimated the percentage of the population living in flooded neighborhoods, described social disparities in flooding based on neighborhood educational attainment, and compared the magnitude of disparities across and within cities. We used multilevel models to examine how city- and neighborhood-level factors are related to neighborhood flooding. We examined 44,698 neighborhoods in 276 cities from eight countries with a total of 223 million residents and 117 distinct flood events from 2000-2018. One in four residents in neighborhoods in the lowest education quintile lived in neighborhoods with flooding, compared to one in 20 residents of the highest neighborhood education quintile. Greater neighborhood flooding was associated with lower neighborhood-level educational attainment and with neighborhoods that were coastal, less dense (population or intersection), further from the city center, greener, and had steeper slopes. There was no association between city-level educational attainment and flooding. There are large social disparities in neighborhood flooding within Latin American cities. Residents of areas with lower education attainment face substantially higher risks of flooding. Policymakers must prioritize flood adaptation and recovery efforts in neighborhoods with lower socioeconomic position.

A new approach to flood susceptibility analysis of urbanised alluvial fans: the case of Bursa City (Türkiye)

AbstractFloods stand out as one of the most significant disasters impacting human life, causing widespread economic and social damage across the globe. Numerous research studies have concentrated on comprehending the contributing factors of flooding. Despite the prevalence of morphometry-based basin flood susceptibility analyses in existing literature, a comprehensive examination that encompasses anthropogenic features in alluvial fans is notably lacking. This study aims to evaluate the flood susceptibility of alluvial fans and their catchments in urbanised areas, integrating individual and collective basin/fan (B/F) morphometry and land use characteristics with flood inventory data. The study area selected for this investigation is the basin and fan systems situated on the northern slope of the Uludağ Massif (2543 m), the highest point in the Marmara Region, northwestern Türkiye. Twelve basin morphometric parameters were applied to 5 m resolution Digital Elevation Model data, while six fan morphometric and anthropogenic parameters were applied to alluvial fans. In addition, the results were assessed using the Normalised Morphometric Flood Index method to mitigate subjectivity in result ranking. According to the integrated basin/fan flood susceptibility results obtained through bivariate analysis, B/F6 exhibits a very high susceptibility, while B/F1, B/F3, B/F2, B/F5, B/F4, and B11/F8 demonstrate high susceptibility. B7, 8, 9, 10/F7 display a moderate potential for generating floods. These findings align significantly with historical flood events in the basin/fan area.

Application of Numerical Modeling and GIS for Simulating Inundation Under Dam Failure Scenarios

Abstract Dam failure and unsafe dam reservoirs cause extremely serious flooding and affecting the environment and ecosystem in the downstream area. The study applied numerical modelling (MIKE 11, MIKE 21, MIKE FLOOD) combined with GIS technology to simulate flooding due to the collapse of Dong Be reservoir, Thanh Hoa, Vietnam, with flood scenarios of frequency P=1.5% and P=0.5%. The study calibrated and validated the MIKE FLOOD model for two historic flood events in 2010 and 2017. Assessment results show that they were relatively consistent with flood trace survey data. Inundation results in two scenarios show that the inundation area with the P=0.5% scenario tends to increase compared to the P=1.5% scenario. The evaluation of simulation results for flooding under two scenarios, when overlaid with administrative, population, and construction layers, will help assess the impact of dam failure and establish its consequences.

Predicting Flood Event Class Using a Novel Class Membership Function and Hydrological Modeling

AbstractPredicting flood event classes aids in the comprehensive investigation of flood behavior dynamics and supports flood early warning and emergency plan development. Existing studies have mainly focused on historical flood event classification and the prediction of flood hydrographs or certain metrics (e.g., magnitude and timing) but have not focused on predicting flood event classes. Our study proposes a new approach for predicting flood event classes based on the class membership functions of flood regime metrics and hydrological modeling. The approach is validated using 1446 unimpacted flood events in 68 headstream catchments widely distributed across China. The new approach performs well, with class hit rates of 68.3% ± 0.4% for all events; 65.8% ± 0.6%, 56.8% ± 0.9%, and 69.5% ± 0.9% for the small, moderate and high spike flood event classes, respectively; and 82.5% ± 1.2% and 75.4% ± 1.1% for the moderate and high dumpy flood event classes, respectively. Furthermore, it performs better in the basins of northern China than in those of southern China, particularly for the small spike flood event class in the Songliao and Yellow River Basins, with hit rates of 80.0% ± 3.2% and 78.8% ± 3.2%, respectively. Our results indicate that the new approach will help improve the prediction performance of flood events and their corresponding classes, and provide deep insights into the comprehensive dynamic patterns of flood events for early warning and control management.

Incorporating historical flood events in type-based statistics

Flood statistics form the basis of many hydrologic designs. However, short observation periods can lead to a high degree of uncertainty in the quantile estimates, especially for extreme floods. One way to expand the information used in the statistics and, in particular, to include other exceptional floods in the sample is to take historical floods into account. The use of these floods, which are usually reconstructed from chronicles or flood marks, has long been established for classic flood frequency analysis. However, the fact that historical floods can also have different origins and are therefore not statistically identically distributed has so far been ignored. To avoid this violation of the statistical assumptions, we show how historical floods, subdivided according to their genesis, can be taken into account in type-based flood statistics. For this purpose, the classical partial probability weighted moment (PPWM) method is extended for the type-based mixture model of partial duration series (TMPS). The result is compared to a Markov chain Monte Carlo method in order to compare the differences between the two methods. Type-based statistics can also be used to attribute the different influences of historical floods on the quantile estimate. An example shows that floods associated with long-lasting precipitation in particular are overestimated in short samples, while heavy rainfall floods are comparatively well represented. The results show that the consideration of historical events in type-based statistics not only allows a more balanced view of extreme floods, but also enables the influences of these to be attributed.

Application of Parallel Computing on Hybrid Inundation Model, Case Study: Chiayi County Flood 23-24 August 2018

A robust 2D inundation model is needed to support flood warning systems in urban areas. The conventional 2D hydrodynamic model uses shallow water equations as the governing equation and is computationally expensive. Although the models have benefited from parallel computation techniques, some issues remain. As an alternative, many flood models have been developed using different approaches, such as Cellular Automata (CA), DEM-based (DBM), and data-driven models. The hybrid inundation model (HIM) was developed by combining the CA-DBM concepts. The purpose of this study is to implement the parallel computation technique to increase the efficiency of HIM. The model performance was evaluated using the historical flood event in Chiayi County, Taiwan. Results showed that there is no significant difference between HIM and TUFLOW in terms of flood depth estimation, even though TUFLOW included the drainage system within the analysis. These results proved that the drainage system was not working during the event. HIM and TUFLOW give underestimated flood depth prediction compared to the observed data. The main reason because the observed data was obtained from local community testimonies. Hence, there might be many uncertainties in the observed data value. Finally, the parallelization process successfully decreased the computation time of the original HIM. The computation was decreased from 450 to 11 minutes depending on the number of cores used in the simulation.

FOREWARNS: development and multifaceted verification of enhanced regional-scale surface water flood forecasts

Abstract. Surface water flooding (SWF) is a severe hazard associated with extreme convective rainfall, whose spatial and temporal sparsity belie the significant impacts it has on populations and infrastructure. Forecasting the intense convective rainfall that causes most SWF on the temporal and spatial scales required for effective flood forecasting remains extremely challenging. National-scale flood forecasts are currently issued for the UK and are well regarded amongst flood responders, but there is a need for complementary enhanced regional information. Here we present a novel SWF-forecasting method, FOREWARNS (Flood fOREcasts for Surface WAter at a RegioNal Scale), that aims to fill this gap in forecast provision. FOREWARNS compares reasonable worst-case rainfall from a neighbourhood-processed, convection-permitting ensemble forecast system against pre-simulated flood scenarios, issuing a categorical forecast of SWF severity. We report findings from a workshop structured around three historical flood events in Northern England, in which forecast users indicated they found the forecasts helpful and would use FOREWARNS to complement national guidance for action planning in advance of anticipated events. We also present results from objective verification of forecasts for 82 recorded flood events in Northern England from 2013–2022, as well as 725 daily forecasts spanning 2019–2022, using a combination of flood records and precipitation proxies. We demonstrate that FOREWARNS offers good skill in forecasting SWF risk, with high spatial hit rates and low temporal false alarm rates, confirming that user confidence is justified and that FOREWARNS would be suitable for meeting the user requirements of an enhanced operational forecast.

Threshold-based flood early warning in an urbanizing catchment through multi-source data integration: Satellite and citizen science contribution

An effective flood early warning system is vital to take action to save lives and protect properties in urban areas which are increasingly prone to flooding. Despite substantial progress in flood early warning systems, limited available and accessible data often impede their advancement and reliability. Engaging communities affected by flooding can help address data and information gaps in flood early warning systems, facilitated by appropriate methods. This study developed and evaluated a flood threshold combination method to support a community-based flood early warning system in the Akaki catchment, home to Addis Ababa, the capital city of Ethiopia. Various flood threshold combinations were formulated, calibrated and validated by integrating multiple sources of data: rainfall, antecedent precipitation index estimates, Sentinel-1 Synthetic Aperture Radar satellite time series of flood extent, long-term simulated streamflow, citizen science data, river water level and three days lead-time numerical weather prediction rainfall forecast. During validation, the rainfall and river water level threshold combination outperformed other threshold combinations with probability of detection, false alarm ratio, and critical success index estimates of 0.74, 0.18 and 0.63, respectively. The flood threshold combination showed high detection performance for most flooding conditions. Flood forecasts with a 1-day lead-time exhibited a high likelihood in detecting historical severe flood events. The study provides a tested methodology for selecting suitable flood threshold-combinations, enhance the engagement of citizen scientists in a community–based flood early warning system in urban communities.

Performance analysis of the lisflood hydrological model in a flood event in the madeira river basin

The largest recorded flood of the Madeira River (largest tributary of the Amazon River) occurred in 2014, remaining above the flood level for 90 days, reaching the maximum level on March 30, at 19.69 m. Among the various tools capable of assisting in monitoring and forecasting floods are hydrological models, such as LisFlood, a distributed hydrological model of the rainfall-runoff type. Thus, the objective of this research was to evaluate the performance of the LisFlood model for maximum flows in the Madeira River basin. There were a total of 7 river gauge stations calibrated and subsequently validated, distributed across 5 sub-basins. The calibration process was carried out using a multi-objective method, applying the NSGA II as an optimization algorithm, with the model's performance being evaluated by the NSE and KGE metrics. The calibration and validation results demonstrated, in general, that the LisFlood model performed well between the simulated and observed flow values for the Madeira River basin. The average of the 07 stations analyzed was 0.81 for KGE and 0.69 for NSE, for the calibration process. In validation, the average metrics were 0.78 in KGE and 0.67 in NSE. In relation to the historic flood event that occurred in 2014, it can be seen that the model followed the maximum flow peak observed at the Porto Velho station, with a difference in the simulated flow of 17% lower than the observed flow, for the year 2014, demonstrating good efficiency of LisFlood in simulating maximum flow. Thus, the study demonstrated that the application of the LisFlood model in large basins is effective in simulating maximum flows, satisfactorily simulating extreme flood peaks.

Amplified Extreme Floods and Shifting Flood Mechanisms in the Delaware River Basin in Future Climates

AbstractHistorical records in the Delaware River Basin reveal complex and spatially diverse flood generating mechanisms influenced by the region's mountains‐to‐plains gradients. This study focuses on predicting future flood hazards and understanding the underlying drivers of changes across the region. Using a process‐based hydrological model, we analyzed the hydrometeorological condition of each historical and future flood event. For each event, at the subbasin scale, we identified the dominant flood generating mechanism, including snowmelt, rain‐on‐snow, short‐duration rain, and long‐duration rain. The rain‐induced floods are further categorized based on the soil's Antecedent Moisture Condition (AMC) before the event, whether dry, normal, or wet. Our historical analysis suggests that rain‐on‐snow is the primary flood mechanism of the Upper Basin. Although most frequent, the magnitude of rain‐on‐snow floods is often less severe than short rain floods. In contrast, historical floods in the Lower Basin are primarily caused by short rain under normal AMC. Given the uncertainties in climate projections, we used an ensemble of future climate scenarios for flood projections. Despite variations in regional climate projections, coherent perspectives emerge: the region will shift toward a warmer, wetter climate, with a projected intensification of extreme floods. The Upper Basin is projected to experience a marked decrease in rain‐on‐snow floods, but a substantial increase in short rain floods with wet AMC. The largest increase in flood magnitude will be driven by short rains with wet AMC in the Upper Basin and by short rains with normal AMC in the Lower Basin.

Climate Change Impact Assessment and Disaster Risk Financing Strategies in Mali: A Comprehensive Analysis of Drought and Flood Events

Climate change impact increasingly led to humanitarian assistance increase and needed. To better address climate change impacts mitigation, a suitable financing instrument is essential to facilitate government, humanitarian, and other stakeholders' finance mobilization. However, few studies have been done on disaster risk profiling to guide decision-makers in their choices. Disaster risk profile analysis has been conducted in Mali to facilitate financial resources mobilization and climate finance instrument choice by identifying Historical drought and flood events. To do so, published papers; and some international institution websites dealing with climate hazard events such as Reliefweb, Hazard/risk, climate information services, Relief Web; CRED- EM-DAT; World Bank Climate Knowledge Portal, World Bank- UNDRR -ThinkHazard, WFP ARC/GeoNode/VAM/DataViz addressing Mali country have been assimilated for evidence accumulation and synthesis and presented in a database. Furthermore, national statistics and national reports on hazards have been also reviewed. Before starting the reading exercise, a reading/analytical framework has been elaborated. In Mali the year 1984 to 2019, 21 flood events occurred in Mali and the most exposed regions to floods are Koulikoro, Bamako and Gao. From 1969 to 2020, 21 drought events were registered in in Mali and the most affected regions are Koulikoro, Mopti, Gao, Kayes. The time return period of drought has been estimated to 3 years while the time return of flood has been estimated to be about 2 years. Drought events are less frequent than flood events, however, drought events affect more population than floods. Also, Also, the yearly response cost for drought events is USD million 277.46 with an average US USD 204.37 cost per affected population. For the flood, even the yearly response cost is USD 11.107 million With USD 261.82 per affected population. Macro-insurance and CAT bonds are more suitable disaster risk financing instruments and are recommended to better address drought events while Anticipatory action and government contingency funds are more suitable climate disaster risk financing instruments to better address flood events in Mali.

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.

Flood Modelling of Premulung River, Bengawan Solo

Premulung River or commonly known as Kali Premulung is one of many branches of Bengawan Solo River in its upstream area. This river pass through one of the most historical cities in Central Java, Surakarta. The overcapacity of this river leads to flood event that has a negative impact on humans. The purpose of this research is to analyze the Premulung River capacity and simulate the flood caused by rainfall design. The hydrological matter was analyzed using Hydrognomon and HEC-HMS while flood modelling was analyzed using HEC-RAS software one- and two-dimension (1D & 2D) simulation. Model calibrations were carried out based on historical flood events (depth, duration, and area of inundation) and local interview due to data limitation. Based on the simulation, the flood modelling shows that the current capacity of Premulung River cannot accommodate its peak discharge for two (Q2) and twenty years (Q20) return period flood. There are two main spots identified flooded due to Q2 flood with depth varies from 40 to 80 cm and duration from 4 hour to 7 hour. For Q20 flood, there are also two same spots identified flooded with depth varies from 1.2 m to 1.8 m and duration from 6 hour to 9 hour. The result of this study can be a reference for flood dike design in the future which still require further detailed investigation.