Incidence Of Flooding Research Articles

Check dams on China's Loess Plateau: An overview

AbstractCheck dams are widely constructed on China's Loess Plateau, which had a total number of 58,776 by the end of 2019. Great achievements in check dam construction have been gained regarding the economic and environmental impacts. This study reviews the remarkable benefits of check dams on the land reclamation and environmental improvement on the Loess Plateau, and sediment reduction entering the Yellow River. However, the flood incidents on check dams have been frequently reported for the past decades, which has attracted more attention in the context of climate change and extreme rainfall events recently. Advances in the flood migration techniques achieved by the research group led by the first author have been highlighted to migrate the breach risk of check dams due to floods. The “family tree method” has been proposed to determine the survival status and critical rainfall threshold of each check dam in the complicated dam system. An updated dam breach flood evaluation framework and the corresponding numerical algorithm (i.e., DB‐IWHR) have been developed. Moreover, innovative types of water‐release facilities for check dams, including geobag stepped spillway and prestressed concrete cylinder pipe in the underlying conduit, have been proposed and developed. Finally, the perspectives concerning the check dam construction on the Loess Plateau have been put forward.

Assessment of Smallholder Farmers’ Responses to Flood Incidence in the Oti River Sub-Basin

This study aimed to assess the responses of smallholder farmers to incidence of floods in the Oti river sub-basin. The findings provide useful information for enhancing the adaptive capacity of smallholder farmers to respond to flood hazards and improve their resilience and disaster preparedness in the study area. This study used the simple random sampling procedure to select 399 farmers and descriptive statistics was used to identify and examine response strategies adopted by smallholder farmers. The results of the study showed that livelihood of smallholder farmers were greatly impacted by factors such as: Distance to River, Elevation of Communities below the River, Increase in Rainfall, Lack of Early Warning Systems and Lack of Emergency Response Systems. The results further showed that the farmers responded to flood incidence through: Building of Raised Beds, Crop Diversification, Livestock Relocation, the use of Conservation farming practices.The majority (87%) of the farmers stated that they were vulnerable to the impacts of floods in the study area. The findings of this study enhanced understanding on access to relevant and timely information such as an early warning to farmers during the start of the farming/ rainy season to support their adaptive responses to floods.

Impact of climatic variabilities and extreme incidences on the physical environment, public health, and people’s livelihoods in Ethiopia

Ethiopia’s vulnerability to climate change is exacerbated by high poverty rates, rapid population growth, increasing prevalence of vector-borne diseases, and heavy reliance on rain-fed agriculture. This narrative review aims to compile existing data on the impacts of climate extremes on the physical environment, public health, and livelihoods in Ethiopia, thereby highlighting the significance of this region for such a study. Data were sourced from peer-reviewed journal articles from databases like PubMed, Scopus, and Web of Science, as well as reports and other unpublished documents. Results show that Ethiopia is facing increasing frequency, severity, duration, and timing of climate-related extreme events. Key challenges include environmental degradation, reduced crop yields, recurring floods, droughts, famines, increased heat waves, and spread of infectious diseases. Average daily rainfall is projected to decrease from 2.04 mm (1961–1990) to 1.97 mm (2070–2099), indicating a worsening climate trend. Moreover, the average annual temperature has risen by 1.3°C since 1960, at a rate of 0.28°C per decade. Flood records indicate a sharp rise, with 274 flood incidents recorded in 2020, causing extensive damage, including an annual soil loss of 1 billion tons in the Ethiopian highlands, reducing land productivity by 2.2% annually. Droughts from 1964 to 2023 affected 96.5 million people, reduced GDP by 4%, decreased agricultural output by 12%, and increased inflation rates by 15%. The regions of Afar, Somali, Gambella, and Benshangul Gumuz exhibit extreme vulnerability to health impacts due to rising temperatures. Addressing climate extremes is critical to mitigate their adverse effects on Ethiopia’s environment, public health, and livelihoods.

Analysis of Flood Incidents in Manado City due to Rainfall Fluctuations (Case Study of the Tondano Watershed), Indonesia

Background Fluctuating rainfall intensity refers to the fluctuations or variations in the pattern of rainfall intensity over time in a certain area. This encompasses changes in intensity, duration, frequency, and spatial distribution of rainfall intensity occurring within a specific time frame. Objective This study investigates the fluctuation of rainfall and its relationship with flood occurrences in Manado City, particularly in the Tondano Watershed Area (DAS Tondano). Methods Average and maximum rainfall data are utilized to analyze recurring flood patterns. Results The analysis indicates that average rainfall intensity > 21.79 mm and cumulative rainfall > 52.64 mm, maximum rainfall intensity > 64 mm, and cumulative rainfall > 99.10 mm serve as threshold points for the onset of floods in Manado City. Conclusion Recommendations are made for continuous rainfall data collection in the Tondano Watershed area, especially Manado City, to support more effective flood control planning. The conclusions drawn from this analysis provide valuable insights into understanding and addressing flood risks in the region.

Water Level and Flow Detection System: An IoT-Based Flood Monitoring Application

In Malaysia, floods have become increasingly prevalent, causing significant damage to various aspects of life including infrastructure, property and the economy. The risk to human lives is particularly high in low-lying areas and areas with poor drainage systems. Water contamination during floods can also lead to health issues and the spread of waterborne diseases. The psychological impact of floods should not be underestimated, as individuals may experience a range of emotions such as frustration, sadness, fear and anger. To address these challenges, this study focuses on developing a flood alert system using Internet of Things (IoT) technology. By utilizing ESP32 microcontroller, flow sensor, float sensors, water flow as well as water level can be accurately measured and users can be alerted through the Blynk application. The system ensures accuracy in measuring flow rates, with recorded values ranging from 2.404 L/min to 2.444 L/min. This device can measure water flow and water level which will be generated through ESP32 microcontroller and notifying the user via the Blynk application as for the IoT part. This project has been carried out in indoor experiments to evaluate the efficiency of the float and flow sensors, respectively. Through indoor experiments, it has been demonstrated that this system is capable of effectively monitoring and alerting users to potential flood incidents, helping to safeguard lives and property.

Enhancing flood forecasting and warning precision through multi-task deep learning approaches

ABSTRACT This study proposes a multi-task deep learning model for simultaneous prediction of time-series water levels and flood risk thresholds, aiming to enhance flood forecasting precision. Using AutoKeras, single-task and multi-task models were optimised to predict water levels 10–360 min ahead based on 720 min of prior data. The multi-task model consistently outperformed the single-task model across multiple evaluation metrics, including correlation coefficients, RMSE, Nash–Sutcliffe efficiency, and Kling–Gupta efficiency scores. Real-time prediction tests on actual rainfall events further validated the multi-task model's improved accuracy and applicability in operational flood forecasting. The study demonstrates significant progress in flood prediction methodologies, offering a more comprehensive approach to forecasting and categorising flood incidents.

Microbiological quality of drinking water from three flood-prone communities along the Odaw River in Accra, Ghana

ABSTRACT Access to safe drinking water is considered a fundamental human right, yet, in most developing countries, this is not the case, as microbial contamination of drinking water is commonly responsible for the transmission of many waterborne diseases, including cholera, typhoid fever, diarrhoea, and dysentery. The study aimed to assess the microbiological quality of drinking water from three communities along the Odaw River in Accra, known for their poor waste-disposal practices and incidents of flooding. Water samples were collected from community tap water and locally manufactured sachet-water from three flood-prone communities along the Odaw River, namely, Alajo, Avenor, and Agbogbloshie, and analysed for the presence of indicator organisms: total coliforms, faecal coliforms, and Escherichia coli (E. coli). All brands of sachet water sampled from the three communities had no microbial contamination. Conversely, most community tap water showed contamination with indicator organisms that exceeded the international standard of 0 per 100 mL for potable water. The presence of faecal coliforms and E. coli in community-shared tap water is an indication of poor water quality and may present a risk for waterborne disease outbreaks among households and communities.

Flood Incidence and Sustainable Development Planning Implications in Santchou, Cameroon

While studies on the incidence of flooding exist in several parts of Cameroon, very little empirical evidence has been reported for highly dynamic and urbanizing landscapes in Cameroon. Yet, micro level evidence on flooding and their development planning implications are required to advance theory building in the frame of development planning and hazard mitigation in rapidly urbanizing contexts. This paper bridges the knowledge gap by using the case of Santchou, a highly dynamic landscape, to: (a) analyse the spatio-temporal variations in flood incidence, (b) determine the drivers of flood occurrence, and (c) examine their sustainable development planning implications. To achieve these objectives, a representative sample of 141 households was conducted in five communities within the study area. This was complemented by key informant interviews (n=8) and field observations. Data was collected using the Kobo Collect tool. The collected data was analysed descriptively and presented as tables and bar charts, with the aid of the Statistical Package for Social Sciences (SPSS version 25). Cartographic data was analysed spatially and presented using maps. Based on the findings, the following conclusions were derived: Firstly, flood occurrence vary spatially in terms of severity, with very high intensity in the south east; this decreases towards the north west and western sections of the study area. Flood occurrence also vary over time with the peak of occurrence observed in the month of July and August which corresponds to the peak of the rainy season. Secondly, the most significant effects of floods include; the destruction of physical installations such as houses, household items. Floods equally affect human activities through the destruction of crops, livestock and the disruption of transport circulation. Furthermore, flood effects are less severe in Santchou Ville and Albatross neighborhoods compared to neighborhoods such as; Madagascar, Bessouck and Kassala Farm. Thirdly, while planning approaches exist at household, neighborhood and municipality levels, the most effective involves the use of sandbags and the cleaning of water channels. This study recommends that, sustainable development planning measures currently implemented at the household, neighborhood and municipal levels should be encouraged by the state and civil society organizations such as NGOs. Additionally, sustainable development planning measures should be implemented alongside proactive measures such as; flood alert signals, and the resettlement of inhabitants to safer areas. Future studies should assess the effectiveness of flood management approaches in the study area.

Measuring rising heat and flood risk along the belt-and-road initiative

China’s global infrastructure financing flagship, the Belt and Road Initiative (BRI) encompasses countries hosting over 60% of the global population and one-third of worldwide GDP. It is based mainly on long-term loans that will mature decades into the future, and timely repayments are only possible if they remain commercially viable. But despite its vast global scope, little is known about the climate risks that could imperil the operations of BRI projects over the next few decades, and, consequently, threaten their long-term sustainability. We narrow this gap by estimating the impacts of future climate change on 217 BRI projects across 70 countries and 9 sectors in two dimensions. First, the effects of increased heat stress on human physical work capacity are calculated using the wet bulb globe temperature and an assessment of the workload for each selected BRI project. Second, the potential structural damages from more frequent flooding incidents are measured by return period (RP), where a shorter RP signals heightened risk. Both have direct impacts on human productivity and infrastructural integrity, which are essential to maintaining the operational viability and financial stability of BRI projects. We compared projected changes on both measures for the mid- and late-twentieth centuries (2041–2060 and 2081–2100) to the historical baseline (1981–2010). We found that BRI projects face escalating vulnerability to climatic risks on both counts. The results underscore a broad variance across different future carbon emission scenarios measured under the Shared Socioeconomic Pathways (including SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). BRI’s aggregated climatic risks are substantially elevated under a high carbon emission scenario compared to a low emission scenario. By the end of the twentieth century, labor workability under SSP3-7.0 (31%) could potentially decrease three times more compared to SSP1-2.6 (10%). Under an intermediate-emission scenario (SSP2-4.5), floods with a historical return period of 10 years could have a return period of 5 years in the future. Significantly hampering the utilization and economic return generation potential of infrastructure projects. In addition, regional geography contributes to risk heterogeneity, with 100-year floods occurring every 15 years in South Asia and every 24 years in Sub-Saharan Africa. Such climate risk implications, potentially overlooked by development financiers, represent significant risks to the sustenance of the BRI, estimated to be worth $1 trillion.

Situational Analysis of Groundwater Resources in Kenyan Drylands, Case study of Turkana County

Recent climate changes have increased the incidences of severe droughts and floods, which have increased the vulnerability of pastoralists in the Kenyan drylands. Thus, there is a need to carry out a situational analysis of groundwater as it is the main source of water which provides baseline information useful for planning Analysis of results indicates that Turkana County is dominated by shallow wells and boreholes with the shallowest wells located along the seasonal rivers and Lake Turkana. The borehole depth was found to range between 20 to 200m. Most of the boreholes located near Lake Turkana were shallower compared to those far away from the lake with a depth ranging between 0 to 52m. Most of the boreholes had a low yield that ranged between 1.2 to 4.6m3/hr be attributed to the basement rocks which are prevalent in the county and are classified as poor aquifers. The water points were mainly dense in urban areas, which was mainly the central region where the water demand was high and this was attributed to the high population. The groundwater levels occurred mostly at 13m indicating that Turkana County has mostly shallow aquifers that predominantly occurred along the river valleys and at the edge of the volcanic deposits. The groundwater quality is mainly saline as most of the boreholes had high TDS, EC and chloride levels. The presence of high fluoride levels indicates the presence of high volcanic rocks that have high fluoride ions that are largely prevalent in the study region. The Sulphate, Nitrite and Nitrate Levels in all the water samples analyzed were below the EU, WHO and KEBs standards indicating the low anthropogenic activities carried out in the drylands as most farmers are pastoralists. The most prevalent cation was sodium in some of the boreholes indicating why the groundwater was saline.

Enhancing natural disaster image classification: an ensemble learning approach with inception and CNN models

The core problem of this research is the rapid and accurate classification of natural disasters, which is essential for effective response and mitigation strategies. Existing detection methods are often time-consuming and costly. The purpose of this research is to introduce an innovative approach to the multi-class classification of natural disasters using image data from a Kaggle dataset encompassing Cyclone, Wildfire, Flood, and Earthquake incidents. The method used is an ensemble learning model that combines the strengths of the InceptionV3 model and a custom Convolutional Neural Network (CNN). The result of this study is an ensemble model that achieves a commendable accuracy of 92.79%, surpassing individual models and demonstrating the efficacy of combining diverse features extracted by InceptionV3 and CNN architectures. Additionally, a standalone CNN model is implemented, achieving an accuracy of 88.76%. The research concludes that the ensemble model’s superior performance makes it a valuable tool for the multi-class classification of natural disaster images.

Formal Modelling and Model Checking of a Flood Monitoring and Rescue System: A Case Study of Safety-Critical System

The flood incidents are becoming more often and severe, thus extreme events require efficient and effective means of controlling and saving lives and property. The reason for this paper is to use Formal Modelling and Model Checking to analyse a new safety critical Flood Monitoring and Rescue System (FMRS) that shall form the basis for the efficient response to floods. Employing the TLA+ analysis, which outlines the FMRS’s dynamic behavior and operational specifications comprehensively. It is important to stress that in our work we address one of the most exciting directions of applying formal methods for the first time in collaboration with real-world safety-critical system designers and offer a powerful and transparent systematic approach to verifying safety-critical systems’ correctness, safety, and reliability. The TLA+ specifications are very carefully designed to represent multiple aspects of the FMRS, such as sensor systems, communication interfaces, as well as the rescue activity itself. To this end, we use model checking methodologies in order to assess the system’s compliance with the required safety properties, including timely detection of floods, correct delivery of data, and synchronization of rescue operations. The performed model checking demonstrates the presence of essential information about the system’s potential failure and weaknesses, which can be used for FMRS architecture improvement and development. Thus, this case shows that the best use of formal methods exist not as ad hoc methods for resolving some issues in the development of safety-critical systems, but a structured template that could be applied in other domains where high degree of assurance in the reliability of a system is needed. Besides the novel method for the better future of the field of formal verification, the proposal also sketches functional relevance of integrating the effective and efficient approaches for monitoring floods and emergency rescue operations in real-world contexts.

Assessing the Efficiency of Fully Two-Dimensional Hydraulic HEC-RAS Models in Rivers of Cyprus

Floods are among the most widespread and recurrent natural disasters globally. In the European region, climate change leads to an increase in the incidence and intensity of flooding. For effective management of the phenomenon, the European Union instituted Directive 2007/60/EC for the assessment and management of flood risks in order to reduce the negative consequences of flooding on human health, economic activities, the environment, and cultural heritage. Cyprus, as a member of the European Union, had to comply with the provisions of the directive. Within the second implementation of the directive, combined 1D/2D hydraulic models were conducted. These data served as a benchmark for the present research, in which the differences in the inundated area, depths, and simulation time are investigated using a full 2D hydraulic simulation. The present research examines two Areas of Potentially Significant Flood Risk, one in an urban and one in a rural area. Overall, the proposed 2D methodology was found to represent inundated areas to a good extent with almost zero deviation in comparison to the 1D/2D method. This study demonstrated the adequacy of the 2D hydraulic simulation method, which offers greater flexibility in modeling a variety of hydraulic scenarios, enabling planning and flood risk management that is vital for protecting communities, infrastructure and the environment from the devastating impacts of floods.

Vulnerability, Resilience and Adaptation of Lagos Coastal Communities to Flooding

Lagos has been identified as one of the 50 cities most vulnerable to extreme sea levels. The state also ranked 30th among 136 port cities in terms of population exposure to flooding under a past climate scenario (2005) and 15th under a future climate scenario (2070s). The state faces significant environmental strains as a result of its geo-economic status. Some of the problems are wetland loss, pollution, population pressure, restricted access to drinkable water, and flooding. All these factors have contributed to the instability of Lagos ecosystems, but the impact of flooding is particularly significant because Lagos is surrounded by aquatic ecosystems and its low elevation and topography increase its susceptibility to flooding. The method adopted in this review involved the use of PICO (Population, Intervention, Control, and Outcomes) criteria to synthesize the research questions and objectives. Thereafter, PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines was employed for the study selection criteria, search strategies and data extraction methods. A broad search strategy involving databases (Google scholar, Science Direct), pertinent keywords and search filters was used to identify relevant articles and minimize selection bias. After the search, PICO criteria was again used to select the studies to be considered in the review. The selected text were downloaded and the essential scientific information were extracted and analyzed in the study. The impacts of flooding are numerous. Flooding is a key factors that has prevented Africa’s growing urban population from escaping poverty, and it also impedes the achievement of some SDGs. This is because many African cities lack the resources and infrastructures needed to withstand extreme weather conditions. Surviving in flood-prone cities like Lagos is a daunting task because flood affects livelihood, human health, and can even cause death. Specifically, the 2020 flood incidence in Nigeria affected about 40% of the local government areas, and 97% of the states, displacing over 120,000 persons and killing 68 persons aside from properties and farmlands destroyed. Women and children are the most vulnerable. The adoption of advanced flood risk management strategies could help in flood containment and management in the state.

Integrated flood hazard assessment using multi-criteria analysis and geospatial modeling

Floods are one of the most prevalent disasters worldwide, including in the capital city of Indonesia. Multi-criteria decision analysis is combined with Geographic Information Systems to produce a mapping of flood hazard areas. The weighting for each parameter is based on six criteria: rainfall, slope gradient, topography, soil type, land cover, and distance from rivers. The flood hazard map is validated using inundation data from the Regional Disaster Management Agency for the years 2015 and 2020. From the general analysis, it can be determined that the parameter most influencing floods is rainfall with a weight of 0.270, followed by slope gradient at 0.164, topography at 0.124, soil type at 0.096, land cover at 0.190, and distance from rivers at 0.155. Therefore, through mapping using QGIS, it is revealed that in 2002, highly flood hazard areas comprised 20.99% of the total Ciliwung Watershed area, which increased to 24.31% in 2020. The validation of the flood hazard map was conducted by recording the coordinate points of flood incidents in 2015 and 2020, revealing that the affected areas within the Ciliwung Watershed occurred in high to very high vulnerability zones. This research demonstrates that flood events in the study area occurred in high to very high flood hazard zones. The results of this study are considered valuable and important for providing accurate information to local governments to develop cost-effective and efficient strategies in dealing with potential flood hazards.

SHAP-powered insights into spatiotemporal effects: Unlocking explainable Bayesian-neural-network urban flood forecasting

Given the increased incidence of pluvial floods due to climate change and urbanization, the demand for highly efficient and accurate modeling within urban drainage systems has intensified, making machine learning and deep learning techniques increasingly popular. Nonetheless, these data-driven approaches face challenges in adequately capturing and interpreting dynamic process-evolving features, especially the spatiotemporal effects emanating from manholes during urban waterlogging events. To address these issues, this study proposes a general framework that extracts the spatiotemporal effects using the dynamic spatial Durbin model, integrates such effects with four machine learning models (i.e., artificial neural network, Bayesian neural network (BNN), light gradient boosting machine, and long short-term memory network), and clarifies decision-making processes of the best model by employing the Shapley Additive Explanations (SHAP) method. The results indicate that (1) the BNN with spatiotemporal effects (BNNST) not only outperforms other benchmark models but also provides accurate forecasts with quantifiable uncertainties; (2) compared to the original model, spatiotemporal effects enhance the models’ understanding of urban flooding dynamics, thereby improving predictive precision; (3) spatiotemporal effects comprise roughly 14 % of the contributions to the BNNST’s output, as interpreted by SHAP-based explanations; (4) incorporating interpretability into the BNN technique underscores the trustworthiness of the explanations at varying confidence levels, thereby deepening the understanding of decision-making processes.

Estimation of the economic impact caused by flooding events in dwellings in Sinaloa, Mexico

In Sinaloa, Mexico, rainfall is intense and can cause floodings in rural and urban areas. Although the risks cannot disappear entirely, deficient urban planning, poor drainage system, and accumulation of waste increase the vulnerability of the population, generating inequality derived from urban conditions. Through 160 surveys applied to dwellers in three of the most important cities of Sinaloa: Los Mochis, Mazatlán, and Culiacán, this work evaluates the losses suffered per household as a result of a flood incident. Losses of at least $70 256 Mexican pesos (3800 USD considering 18.00MXN/USD currency exchange in 09/06/23) per home were estimated, in addition to a significant devaluation of the properties. The study concludes that a socio-environmental justice approach should be adopted to ensure equal vulnerability reduction for all families, instead of the current approach of compensation and transfers which solves nothing in the long-term.

Constructing a county-level environmental events dataset for China during the Ming and Qing dynasties (1368–1911)

This article builds on China’s rich recorded history to present a newly constructed dataset of environmental events at the county-level for the period 1368 to 1911. Essential to the dataset construction is the use of recently developed AI techniques, including new optical character recognition, lexical analysis based on machine learning, and word frequency analysis. The result is that environmental information was extracted from different sources, comprised of structured collections of printed historical records, and then combined and parameterized to construct a county-level environmental dataset that includes information on the incidence of cold spells, droughts, floods, and wind events. The dataset is validated and Geographic Information Systems are used to manage and present the spatially disaggregated data. The new data are expected to benefit future researchers looking to study historical environmental extreme events, or examine their impact on other aspects of Chinese economy and society.

Assessment of flood incidents, damages, and mitigation efforts in Danglag, Consolacion, Cebu, Philippines

Assessment of flood incidents, damages, and mitigation efforts in Danglag, Consolacion, Cebu, Philippines

Anomaly Detection in Weather Phenomena: News and Numerical Data-Driven Insights into the Climate Change in Romania’s Historical Regions

The extreme phenomena have been increased recently in frequency and intensity causing numerous damage that cannot be neglected by residents, local authorities and social media. More European countries are experiencing a growing number of occurrences, such as floods, powerful winds, storms, icing, and unusual temperature fluctuations. Particularly, the year 2023 has recorded the highest temperatures in the history of humanity. In this research, we compile a dataset that combines news reports with numerical data pertaining to weather conditions and air quality at the historical region level in Romania. We examine the news and recorded data spanning the years from 2009 to 2023 using anomaly detection and clustering techniques to compare the results. Specifically, we employ Isolation Forest and Autoencoders to identify anomalies within the data that are further clustered to analyse the detection process. We explore the occurrence frequency and duration of daily simultaneous extreme weather events over the years, conducting statistical tests like the Mann–Kendall test to discern trends in the extreme phenomena. The findings reveal statistically significant increasing trends in the incidence of heatwaves, storms and floods. When we set the Mean Squared Error (MSE) threshold to 95%, both methods detect nearly 16% of the anomalies, and this figure rises to over 25% when the MSE threshold is set to 90%. An analysis of anomalies at the regional level indicates that most anomalies are detected in the Transylvania and Muntenia regions, while the Banat region experiences the lowest level of anomalies.