Flood Research Articles

ANALISIS POLA DISTRIBUSI CURAH HUJAN LEBAT DOMINAN DI DAERAH ALIRAN SUNGAI (DAS) CITANDUY HULU

Rainfall plays an important role in hydrological and environmental planning. Meteorological conditions affect rainfall through its intensity, amount, and duration. However, heavy rainfall can cause significant damage and loss. Research was conducted in the Upper Citanduy watershed, West Java, using rainfall data from 8 stations over a range of 5–14 years. The study took a heavy rainfall event threshold of ≥ 50 mm in 24 hours. The research aims to analyze the dominant heavy rainfall patterns in the Upper Citandyu watershed. Empirical analysis used the Mononobe Method and Alternating Block Method (ABM), while rainfall distribution was analyzed with Inverse Distance Weighted (IDW) using Geographic Information Systems. The dominant heavy rainfall duration in the Upper Citanduy watershed occurred over 4–6 hours. Rainfall distribution analysis showed a pattern that was not influenced by the elevation of the rainfall station. The analysis results show that the Mononobe Method is closer to the dominant observed heavy rainfall distribution for 4 hours, while ABM is closer to the durations of 5 and 6 hours. The suitability of the distribution pattern was measured using Correlation (r), Root Mean Squared Error (RMSE), and Nash-Sutcliffe Efficiency (NSE) criteria, with Mononobe providing better results than ABM method. This research contributes to the understanding of rainfall patterns that can form the basis for analyzing designed rainfall intensity as a step in flood disaster mitigation in the Upper Citanduy watershed.Keywords: heavy rainfall, rainfall distribution pattern, rainfall intensity, upper Citanduy watershed, geographic information system

Spatio-Temporal Variation in Pluvial Flash Flood Risk in the Lhasa River Basin, 1991–2020

The analysis of temporal and spatial variability in risk has garnered significant research attention, particularly regarding flash flood disasters in the context of warming and wetting conditions on the Qinghai–Tibetan Plateau. Focusing on the Lhasa River basin, this study develops a framework that integrates geographic information systems and a combined subjective–objective weighting approach to comprehensively assess flash flood risk despite limited observations. This paper investigates the distribution patterns of hazard, vulnerability, and the integrated risk of pluvial flash floods; demonstrates the reliability of the assessment results; and provides mitigation recommendations for disaster risk management at the county level. The results showed a trend in increasing flash flood risk in recent decades compared to the 1990s. Moreover, very-high- and high-risk areas were concentrated in downstream regions with frequent precipitation extremes and anthropogenic activity. From 1991 to 2020, the high to very high-risk areas gradually expanded from central Lhasa to neighbouring counties. This study contributes valuable insights into flash flood risk assessment cand mapping, which are crucial in terms of the protection of life and property in the plateau basin.

Adaptation and Mitigation Model for Flood Disaster Resilience in West Malangke District, North Luwu Regency, Indonesia

Adaptation and Mitigation Model for Flood Disaster Resilience in West Malangke District, North Luwu Regency, Indonesia

Design and Construction Of A River Water Level Monitoring System Based On IOT (Internet Of Things)

The prolonged rainy season due to extreme weather changes that occur in various regions in Indonesia means that people must be more alert to flood disasters. Many losses occurred due to flood disasters. Therefore, an effort is needed to minimize these losses, namely by creating a river level monitoring system based on IoT (Internet of Things). This river water level detection system uses an ultrasonic sensor (water level sensor), NodeMCU ESP8266 to read data and send it via the telegram application and a buzzer which functions to make a sound when the sensor detects water and the potential for flooding. The system will work automatically when water touches the sensor and sends flood detection status information via the telegram application. This system can be implemented in flood-prone areas. With this monitoring system, river water levels can be monitored and the risk of flooding can be reduced.

Assessment of the extent of community preparedness to flood risk in Jigawa State, Nigeria

Jigawa State faces annual flood disasters that have resulted in significant loss of life and property, putting many communities at risk. This study aim to evaluate the preparedness for flood risk in Jigawa State, Nigeria. A sample of 666 respondents was selected for a self-administered questionnaire, with 601 fully completed. The collected data were analyzed descriptively to determine the preparedness index. (PI). From the result of the findings, a majority (77.7%) of the respondents agreed that the community is aware of flood risks. There was also a provision of a neighborhood directory (97.8%), local shelter (75.9%) and emergency contacts (55.4%) during floods. The PI (92.6%, 98.6% 84.1% and 85.1% respectively), are above the 65% threshold and hence considered ‘prepared’ in this regard; but, ‘somewhat prepared’ (PI of 61.7%) regarding awareness of flood disaster risk management strategies; ‘and unprepared’ (PI of 55.5%, 50.5%, 50.5%, 50.8% and 54.3% respectively) in terms of community organization of public education and training of flood risk reduction (66.6%); engagement on rehearsals/emergency drills (51.6%); good community-based warning system (51.4%); and a standalone system for property protection measures during floods (52.4%); as well as an active recovery plan (62.9%). It was recommended that proper embankment protection and water regulation outlets be established. Additionally, real-time flood forecasting and effective early warning systems should be developed. Communities should be assessed to identify flood risk zones before construction activities begin, or alternatively, resettlement to safer areas should be considered for those living in flood-prone regions.

Critical analysis of the emerging flood disaster resilience assessment indicators

Purpose The world is reeling from the effects of climate change with increased extreme precipitation. Flooding is amongst the most recurring and devastating natural hazards, impacting human lives and causing severe economic damage. This paper aims to conduct a systematic review to critically analyse the most reported and emerging flood disaster resilience indicators. Design/methodology/approach A total of 35 papers were selected through a systematic process using both Web of Science and Scopus databases. The selected literature was subjected to a thorough thematic content analysis. Findings From the review, 77 emerging flood disaster resilience assessment indicators were identified. Furthermore, based on the individual meanings and relationships of the derived indicators, they were further categorized into six groups, namely, physical, institutional, social, psychological, ecology and economic. More also, it was identified that most of the selected publications have used objective resilience measurement approaches as opposed to subjective resilience measurement approaches. Originality/value The generated list of flood disaster resilience indicators will provide insights into the capacities which can be improved to enhance the overall resilience to flood disasters in communities.

Spatiotemporal variations of global precipitation concentration and potential links to flood-drought events in past 70 years

The evaluation of precipitation events is crucial for predicting severe droughts and floods, particularly in the context of global warming. This study presents a comprehensive spatiotemporal analysis of the global precipitation concentration index (CI) from 1950 to 2020, comparing CI with nine extreme precipitation indices to assess its applicability. The world map was divided into three distinct regions related to flood-drought events using the standardized precipitation index (SPI). Key findings include: 1) CI exhibited significant spatial heterogeneity, with elevated values in seasonally warm coastlines and arid regions. Seasonal changes in CI were not synchronized, particularly lower in Northern Hemisphere winters, while the Southern Hemisphere displayed minor seasonal variation, alongside a long-term growth trend in approximately 36.5 % of the global area. 2) CI's spatiotemporal variations helped delineating regional precipitation patterns, revealing a strong correlation (R2 = 0.87) with the extreme precipitation index R95c, but a negligible relationship with total precipitation (P) (R2 = −0.05). This facilitated the global partitioning into three regions: Humidity Amplification Region (I), Dryness Intensification Region (II), and Climate Fluctuation Region (III). 3) Flood-drought events were potentially linked to variations in CI and P, where In Region I experienced increased flood risk due to rising CI and P, Region II faced heightened drought risk from rising CI and declining P, and Region III showed opposing effects on floods due to reduced CI and increased P, with P variability significantly influencing flood frequency. 4) The main atmospheric circulation factors varied by region, typically including the Antarctic Oscillation (AAO), Atlantic Multi-decadal Oscillation (AMO) and Arctic Oscillation (AO), with AAO being most significant. This research underscores the intricate relationships between climatic indices and hydrological extremes, emphasizing the challenges posed by global warming, atmospheric circulation changes, and data uncertainties in assessing drought and flood disasters.

Construction of Disaster-aware Communities through Local Wisdom and Social Media

The social construction process runs simultaneously. The utilization of local wisdom and social media is the result of the community construction process. Disaster-aware communities can utilize local wisdom and social media to overcome flood disasters. This research aims to analyze the construction of a disaster-aware community in Wonoasri Village, including the use of local wisdom and social media to overcome disasters. This research uses the construction theory of Berger and Luckmann. The research method used was a qualitative method with a phenomenological approach. Purpose sampling techniques were used to collect research informants. Data collection through observation, interviews, FGD, and documentation. The validity of the data using triangulation and data analysis using interactive models. The research results show that the social construction process runs simultaneously. The social construction process of the community starts from community experience, which shapes community knowledge about floods, the formation of Destana, and the transfer of knowledge, to the utilization of local wisdom and social media to overcome floods in Wonoasri Village.

Research on flood warning model based on dimensionality reduction and integrated neural network

Flood disasters have long-term impacts on human societies and ecosystems, and population growth and urbanization lead to changes in surface conditions that increase the frequency and magnitude of floods. Therefore, understanding and predicting flood probability and constructing a flood warning mechanism are urgent problems. In this paper, a flood warning mechanism based on the probability of flood occurrence is proposed to construct a high-performance and highly interpretable model in a step-by-step manner using machine learning and deep learning methods. First, the correlation level is elucidated by the PCA dimensionality reduction method. Secondly, the K-means clustering method is used to replace the qcut binning method, and the classification model is built by combining the multidimensional data point distribution and classified by polynomial logistic regression. Finally, the integrated neural network model is constructed, the visual model performance is used for full feature optimization, and the performance of the classification model is optimized to achieve the flood prediction effect through the mean square error as the combination of the weights of the integrated model.

Hydrological streamflow modeling at the inlet of Gilgel Gibe III dam of Ethiopia using soil and water assessment tool

AbstractComprehensive approach is a pre‐requisite to improve the estimation of variability in streamflow data for designing water supply systems, generating hydropower, determining environmental flows, allocating water, and conducting pollution studies that need a comprehensive approach. There is a need to model streamflow data to develop effective water policy plans when sufficient data are unavailable. This study aims at assessing the accuracy and applicability of the Soil and Water Assessment Tool (SWAT) model in predicting streamflow at the inlet of the Gilgel Gibe III dam watershed in Ethiopia. Historical meteorological data from nine available stations between 1987 and 2017 were used to predict the streamflow. The SWAT model was calibrated and validated for two different time periods (1997–1999 and 2003–2004) using SWATCUP program and SUFI‐2 technique. Calibration and validation were based on monthly observed discharges at the Abelti and Gojeb stations. The results showed acceptable values of R2, Nash–Sutcliffe modeling efficiency (NSE), and percent bias (PBIAS). At the Abelti station and Gojeb station, the calibration and validation results were 0.82, 0.79, and −10.1%; 0.88, 0.84, and −14.4%; and 0.86, 0.79, and 14.1% for calibration and 0.88, 0.8, and 11.1% for validation, respectively. The prediction uncertainty of 95% was consistent with the observed discharge. The mean annual runoff was 440.08 m3/s, demonstrating the model's effectiveness in simulating streamflow at the dam's inlet. The calibrated and validated model can be used to study the effects of climate and land use changes; analyze water quality and sediment yield; and inform future water policy plans, dam construction planning, and flood disaster risk management.

Geospatial analysis of alarmingly increasing flood vulnerability and disaster risk within the northeast himalaya region of India

Geoenvironmetally the eastern part of the Himalaya region is highly vulnerable to flood and other natural disasters as it consists of fragmented and tectonically active geology and geomorphology, very high monsoon rainfall (>360 cm) and subsequent runoff, rugged hilly terrain with high ranges of elevation and slope, dense drainage density etc. Other hand, the unplanned developmental activities keep going and scaling up this vulnerability and risk to floods and other disasters. Addressing this burning issue, a geospatial technology-based case study of the Kohima district, Nagaland state (India), is presented here. The geospatial "technology-based" analyses employed in the study are thematic GIS mapping of flood hazard, vulnerability and risk controlling factors; and performing overlay operation using the AHP model in GIS software to generate spatio-temporal map layers of flood vulnerability and disaster risk. Results reveal that the region is under a high rate of monsoon climate change (increasing temperature, rainfall, rainy days, rainfall events and flood events with an annual rate of 0.35 %, 1.12 %, 0.36 %, 2.67 % and 4 % respectively), land use degradation (increasing built-up area with 0.60 %, annual rate decreasing forest, shrubs and water bodies with 0.80 % accumulated annual rates respectively) and demographic changes (increasing urban as well and rural population density with 0.53–2.10 % and 0.55–2.14 % respectively). Accumulated impacts of climate change, land use degradation and demographic changes causing an increase in flood hazard, vulnerability and disaster risk. Flood hazard zones and vulnerability zones extending with 0.50 % (4.89 km2) and 0.84 % (8.16 km2) annual rates respectively, subsequently the flood risk zones categorized as a moderate, high and very high potential risk, have been spreading out with a yearly rate of 0.07 % (0.65 km2), 0.13 % (1.31 km2) and 0.03 % (0.33 km2) respectively. It decreases the area under low and very low-risk zones by 0.17 % (1.63 km2) and 0.07 % (0.65 km2) annual rates respectively. Following up on these annual rates, the spatial distribution of flood hazard, vulnerability and risk zones for the next decade (2031–2040) have also been projected, revealing alarming situations, if flood disaster risk reduction (F-DRR) measures were not implemented in timely. It is strongly believed that the proposed study will be very useful for district-level planners and administrators to implement sustainable development planning, for the scientific fraternity to enhance their research work in the field of flood disaster management and for individuals for their safety in terms of life and property.

“Filling the void”: the diversified grassroots disaster narratives of the 2023 riverine flood in Z City, China

PurposeDisaster narratives constitute a crucial and unique element in disaster understanding, recovery and communication. Along with facing various severe disasters, China’s dominant official disaster narratives have played an indispensable role in promoting post-disaster social solidarity. However, the consequences of “weak” official disaster narratives remain unknown.Design/methodology/approachIn 2023, during a riverine flood in northern China’s Z City, diversified grassroots disaster narratives emerged, “filling the void” when the official narratives were comparatively “weak”. This paper uses this riverine flood in Z City as an uncommon case to analyze the official-grassroots disaster narrative interaction. During fieldwork, first-hand and second-hand data from in-depth interviews, on-site visits and public information were collected.FindingsBased on fieldwork, this paper first introduced the “weak” official disaster narratives in Z City. Second, this paper organized and classified these grassroots disaster narratives into three interconnected stages: the flood’s causes, government and social responses, and recovery. In “filling the void,” “sacrifice” and “betrayed” sensemaking are detected and functions of these diversified grassroots disaster narratives become clear.Originality/valueOfficial and grassroots disaster narratives are not simply competing, but complementary. As a unique soft lens to interpret disasters, this paper further discussed grassroots disaster narratives’ underlying connotations and potential impacts on social resilience building. Also, this paper provides insights into the disaster narrative dynamics, especially in China’s political and cultural context.

An Analysis of Policy Frameworks on Business Formulization and Disaster Management to Mitigate Flood Loss and Damage Among Informal Businesses in Sri Lanka

The 27th Conference of the Parties to the United Nations Framework Convention on Climate Change in Egypt made a historical decision on disaster loss and damage for vulnerable countries. Even though parties agreed to salvage vulnerable nations with generous support, a fundamental question remains as to the extent to which these vulnerable countries can accurately identify the most vulnerable communities/areas. In Sri Lanka, informal businesses are particularly vulnerable to floods, but their informal status has limited the extent to which they could receive flood protection, including disaster-resilient infrastructure developments. The main objective of this paper, therefore, is to identify challenges that informal businesses have experienced in dealing with flood disaster risks. In doing so, we analyzed government policies and laws that are related to business formalization and disaster management. We also conducted interviews with key informants to verify our data. Our analysis found that the Sri Lankan government requires informal business owners to follow complicated rules to register their businesses. For these owners, who are not highly educated, these processes and fear of high tax rates discouraged registering their businesses. The central government tends to prioritize flood mitigation actions for formalized business areas. Informal businesses are not usually covered by flood insurance and compensation. In conclusion, we emphasize the need to establish widely available legal and administrative support for informal businesses to register. Adopting business continuity plans (BCPs) and keeping standardized business records also help businesses minimize flood loss and damage.

Strengthening of the hydrological cycle in the Lake Chad Basin under current climate change

Central Sahel is affected by a reinforcement of rainfall since the beginning of 1990s. This increase in rainfall is affected by high inter-annual variability and is characterized by extreme rain events causing floods of unprecedented magnitude. However, few studies have been carried out on these extreme events. Moreover, with current climate change expected to strengthen the hydrological cycle, we don’t know if these events could become more frequent. Here, we report the hydrological changes that currently occur in the Lake Chad basin. Based on ground observations and satellite data, we focused on the 2022 flood event, demonstrating that it was the most important event from the last 60 years, comparable to what occurred during the last wet period between the 1950s and the 1960s. We showed that under this precipitation regime and if warming is not regulated at a global scale, the return period of the 2022 major riverine flood is expected to be between 2 and 5 years. By using modelling experiments, our study also suggested that in the next decade, future flow rates of the main rivers draining the Lake Chad basin could reach the values observed in the 1950s. These results strongly suggest anticipating water management in a context of poor infrastructural development.

Rainfall Prediction using Backpropagation Method and GIS for Disaster Mitigation Mapping

Frequent weather or climate changes, coupled with the topography of Cirebon and Majalengka which are mostly lowlands and close to the sea, raise concerns that flood disasters can occur if mitigation is not done early. This research aims to predict and map rainfall and runoff discharge as a flood mitigation effort in the Cirebon and Majalengka areas. The method used in this research is the Backpropagation Method, which resembles the way the neuron system works in the human brain to learn patterns, optimize weights and biases, and reduce the error rate (mean square error) so that the predicted value is closer to the actual value. The results showed that Cirebon was predicted to have the highest rainfall of 2471 mm in 2048, based on prediction data from BMKG Penggung Station, while Majalengka was predicted to have the highest rainfall of 2922 mm in 2048, based on prediction data from BMKG Kertajati Station. The largest runoff discharge in the Cirebon region occurred in Susukan District (51.96 km²) with a discharge of 68.648 m³/second (Q25), and in the Majalengka region occurred in Lemahsugih District (78.64 km²) with a discharge of 104.16 m³/second (Q25). The implications of this research include mitigation efforts that can be carried out, such as reducing development above drainage channels, reforestation, maintaining open areas, reducing land use change, conservation of rivers and catchments, construction of flood embankments, and issuance of related regulations.

Feasibility of Emergency Flood Traffic Road Damage Assessment by Integrating Remote Sensing Images and Social Media Information

In the context of global climate change, the frequency of sudden natural disasters is increasing. Assessing traffic road damage post-disaster is crucial for emergency decision-making and disaster management. Traditional ground observation methods for evaluating traffic road damage are limited by the timeliness and coverage of data updates. Relying solely on these methods does not adequately support rapid assessment and emergency management during extreme natural disasters. Social media, a major source of big data, can effectively address these limitations by providing more timely and comprehensive disaster information. Motivated by this, we utilized multi-source heterogeneous data to assess the damage to traffic roads under extreme conditions and established a new framework for evaluating traffic roads in cities prone to flood disasters caused by rainstorms. The approach involves several steps: First, the surface area affected by precipitation is extracted using a threshold method constrained by confidence intervals derived from microwave remote sensing images. Second, disaster information is collected from the Sina Weibo platform, where social media information is screened and cleaned. A quantification table for road traffic loss assessment was defined, and a social media disaster information classification model combining text convolutional neural networks and attention mechanisms (TextCNN-Attention disaster information classification) was proposed. Finally, traffic road information on social media is matched with basic geographic data, the classification of traffic road disaster risk levels is visualized, and the assessment of traffic road disaster levels is completed based on multi-source heterogeneous data. Using the “7.20” rainstorm event in Henan Province as an example, this research categorizes the disaster’s impact on traffic roads into five levels—particularly severe, severe, moderate, mild, and minimal—as derived from remote sensing image monitoring and social media information analysis. The evaluation framework for flood disaster traffic roads based on multi-source heterogeneous data provides important data support and methodological support for enhancing disaster management capabilities and systems.

Near Real-Time Responsive Flood Event Representation: An Open-Source Interactive Web Application Architecture

Abstract. The adoption of accessible Digital Twin technology in flood applications has been impeded by a notable lack of practical examples. Arguably, the most distinguishing feature of Digital Twin technology is the integration of near real-time data analytics through IoT sensor connectivity, which has remained underutilised in flood applications and therefore indicates a critical research gap. This project endeavoured to develop a comprehensive and replicable open-source architecture framework, specifically tailored for near real-time responsive flood event representation. The resultant interactive web application integrated near real-time river height and rain fall data streams and performed on-demand data analytics. Key results include the establishment of a back-end spatial database, a coupled physical city and digital space model, and a functional holistic front-end user interface. Additionally, this research aligned with the Gemini Principles by emphasising data interoperability and maintaining an information feedback loop. The study also aimed to configure near real-time IoT sensor connections, implement event triggers, and deliver interactive visualisations in an easily accessible format. The research addressed key questions surrounding the effective integration of IoT sensor data, identification of crucial flood indicators and parameters, and rapid quantification of flood event impacts. Ultimately, this research project demonstrates how Digital Twin technology can swiftly provide decision-makers with crucial insights during flood disaster events.

An event logic graph for geographic environment observation planning in disaster chain monitoring

Effective geographic environment observation planning is the key to obtain disaster monitoring and warning information. The previous researches can only make observation plans for a single disaster at some specific stages. They are difficult to apply to the dynamic evolution of the disaster chain. Timely and comprehensive geographic environment observation planning is urgently needed to provide high-value monitoring data for the identification and response of secondary disaster chains. Event logic graph (ELG) shows great potential in evolutionary law expression and chain event reasoning. Therefore, this study proposed an observation ELG (OELG), in which events and their logical relationships are modeled as nodes and edges to express the occurrence and development motivation of observation events. The disaster chain observation planning can be transformed into the reasoning of potential continuous observation events. Subsequently, an OELG-based geographic environment observation planning framework was proposed, which realizes the construction, instantiation, and plan reasoning of OELG. The observation planning experiment was carried out taking the flood disaster chain that occurred in Beijing, China and Nordrhein-Westfalen, Germany as examples. The results show that OELG can generate disaster chain observation plan more timely, comprehensively, and continuously than other models, thus providing support for disaster chain risk monitoring and emergency response.

Hydro-informer: a deep learning model for accurate water level and flood predictions

AbstractThis study aims to develop an advanced deep learning model, Hydro-Informer, for accurate water level and flood predictions, emphasizing extreme event forecasting. Utilizing a comprehensive dataset from the Slovak Hydrometeorological Institute SHMI (2008–2020), which includes precipitation, water level, and discharge data, the model was trained using a ladder technique with a custom loss function to enhance focus on extreme values. The architecture integrates Recurrent and Convolutional Neural Networks (RNN, CNN), and Multi-Head Attention layers. Hydro-Informer achieved significant performance, with a Coefficient of Determination (R2) of 0.88, effectively predicting extreme water levels 12 h in advance in a river environment free from human regulation and structures. The model’s strong performance in identifying extreme events highlights its potential for enhancing flood management and disaster preparedness. By integrating with diverse data sources, the model can be used to develop a well-functioning warning system to mitigate flood impacts. This work proposes a novel architecture suitable for locations without water regulation structures.

Design and Development of a Prototype Water Level Control System for Early Flood Detection Based on The Internet of Things: a Case Study of Kambaniru Dam

Flooding can occur due to overflowing water during the rainy season, therefore early detection of the water level in dams as water runoff processing structures is needed. When heavy rain occurs it can result in flood disasters that are detrimental to communities, dam infrastructure and agricultural land. This research aims to monitor water levels online as initial information about impending flood disasters. Monitoring using Internet of Things (IoT) based technology is intended to obtain water level information in real time. In this device the HC-SR04 ultrasonic sensor is used as a water level reader, a waterflow sensor as a water discharge reader and a servo motor as a sluice gate controller. The results of this research are a prototype of a water level detection device that can provide water level information and can control sluice gates. automatically and can be controlled manually or remotely via the latest notifications in the Blynk application using the MQTT protocol. This research was carried out by taking information based on the results of direct observations and interviews with officers at Kambaniru Dam, Lambanapu Village, Kambera District. In this way, the prototype of this detection tool will be easy to use as initial information about the possibility of flooding.