Occurrence Of Hazards Research Articles

Overview of Circular Structures of Various Origins and Sizes in Egypt as a Contribution to Natural Hazard Data Mining Based on Remote Sensing Data and Geoinformation Systems (GIS) Analysis

This study is focused on the detection and typification of circular features with different sizes, origins, and states of erosion as well as on their surrounding tectonic setting, as well as on their impact on the environment and on the occurrence of natural hazards based on different satellite data of Egypt. Sentinel 2, Landsat and ASTER images and Sentinel 1- and ALOS L-band Phased Array Synthetic Aperture Radar (PALSAR)-radar data make it possible to identify larger ring structures as well as smaller circular features like maars or sinkholes in karst areas. Evaluations of the various satellite data contribute to the systematic and standardized inventory ring structures, most of which are related to magmatic intrusions. Such an inventory is a prerequisite for hazard preparedness and should be part of natural hazard data mining integrated into a Geo Information System (GIS). Mapping traces of volcanic activities (craters, maars, cones) is essential as a contribution to land use planning. By gathering the data and integrating the knowledge of the different ring structures in standardized GIS data base one of the many steps towards hazard preparedness and adapted land use planning can be achieved. Circular features are often buried by aeolian and fluvial sediments and become only visible on radar images or on Landsat or ASTER RGB images combining thermal bands. Larger ring structures have not only an influence on groundwater flow but also on geodynamic activity (earthquakes and related secondary effects).

Electromagnetic danger of electric and hybrid vehicles

Problem statement. In electric and hybrid transport, passengers sit very close to the electrical system of considerable power, usually for a considerable time. People are concerned about the level of frequency, intensity of electromagnetic radiation, and the time spent in transport. The relatively high currents achieved in these systems and the short distances between power devices and passengers mean that the latter can be exposed to the corresponding magnetic fields. This means that there is a need to assess the electromagnetic environment inside these vehicles before they are put on the market. When designing electric and hybrid vehicles and their components, it is necessary to take into account the hazards of magnetic field exposure to drivers, passengers and the environment. The aim is to study the effect of electromagnetic radiation on people and improve the efficiency of vehicle operation by assessing the electromagnetic safety of electric and hybrid vehicles at the stage of their operation, as well as to improve methods for assessing electromagnetic radiation depending on operating conditions. Methodology. Methods are a systematic approach to research, protection against electromagnetic radiation in transport, conducting measurements of electromagnetic radiation in electric and hybrid transport, finding out the spatially strong inhomogeneity of the magnetic field and building 3D topology using numerous uniformly distributed magnetic field sensors in the vehicle interior, i.e. by a differential phase-gradient method for the study of ultra-low frequency amplifiers from 0.001 - 100 Hz of geomagnetic variations, a method of testing magnetic fields. This method allows using three three-component magnetic sensors arranged in a triangle at a short distance from each other (magnetic gradiometer) to construct a vector of gradients and phase velocities of magnetic variations for any of the three components of magnetic fields. As a result, the phase-gradient method has found its application for measuring magnetic fields in electric and hybrid vehicles, as well as for detecting the topology of magnetic fields, taking into account their extreme spatial heterogeneity, the sensors in the cabin should be located quite densely, i.e., the number of sensors should be at least 10-20 (evenly at a distance of ten centimeters at floor level, at head level of drivers and passengers, etc.). Scientific novelty: the study of electrical equipment in electric and hybrid transport has shown that the source of electromagnetic fields, which have a strong temporal and spatial heterogeneity in the frequency range from 0 to hundreds of megahertz, that they are a superposition of fields from many sources (from electric motors, traction batteries, live elements, various electrical equipment (starting and braking resistance, group switches, rheostat units, electrical circuits for ventilation, lighting, heating, etc.). Practical relevance. The problems of determining the moment of occurrence of electromagnetic hazards in electric and hybrid vehicles are considered. It is established that electronic devices on board are usually higher in frequency than magnetic fields, which is associated with changes in traffic modes. Safety measures against electromagnetic radiation in electric and hybrid modes of transport are presented.

The application of gray system-variable fuzzy sets coupling theory on the susceptibility assessment of debris flow hazards

The occurrence of debris flow hazards has significant destructive effects people’s safety and the safety of their property, so the risk assessment of debris flow hazards has great significance. The maximum amount of debris flow at one time (X1), the reserves of loose substance (X2), the watershed area (X3), the length of the ditch (X4), the relative height difference (X5), the gradient of side slope (X6), the longitudinal slope of main ditch (X7), the length ratio of replenishment section (X8), the daily maximum rainfall (X9), and Faults developed in the watershed (X10) are selected as the evaluation index at first. Then the gray system-variable fuzzy sets coupling model is introduced; secondly, the relative membership degree matrix about the fuzzy variable sets is established, and the weight coefficients considering the uncertainty of assessment indices are determined based on the entropy weight method; finally, the susceptibility level of debris flow hazards is determined using the mean ranking feature value. The conclusion is drawn that the results obtained based on the coupling technique are consistent with the current specification; its accuracy arrives at 83%, and the method is feasible for the susceptibility level assessment of debris flow hazards. Compared to the traditional variable fuzzy sets model, it avoids the uncertainty of magnitude in the standard analysis, it can ensure the objectivity and accuracy of the calculation result, and its calculation process is simple and efficient. In total, the findings of the proposed model provide an alternate way to assess the susceptibility level of debris flow hazards and improve the evaluation accuracy in the future.

Knowledge, perceptions and practices on healthcare waste management and associated occupational health hazards among healthcare professionals in the Colombo District, Sri Lanka: a cross-sectional study.

Proper Healthcare Waste (HW) management is directly influenced by the knowledge and attitudes of Healthcare Professionals (HCP). However, studies that characterize the knowledge and practices of HCP on HW management are limited in Sri Lanka. This study was conducted to characterize the knowledge, perceptions and practices of HCP on the management of HW and to determine the risk factors influencing HW related occupational health hazards in the Colombo District of Sri Lanka. A total of 407 HCP were recruited as the study population from selected hospitals in the Colombo District. Information on socio-demographic factors, knowledge, attitudes and practices on HW management were gathered using an interviewer-administrated questionnaire. The Binary Logistic Regression (BLR) was used to determine the socio-economic risk factors associated with the occurrence of HW related health issues among the respondents. The majority of respondents were characterized with a high knowledge level (76.9%) and positive attitudes (53.8%) on HW management. Incineration (82.6%) was recognized as the most widely used HW treatment method. Personal Protective Equipment (PPE) was used at a satisfactory level (85.5%), while liquid waste treatment was limited (57.5%). The occupational designation, level of training received in HW management, professional experience, vaccination status for tetanus, degree of knowledge and attitudes on HW management were recognized as significant risk factors (p < 0.05) associated with the occurrence of HW related occupational hazards. Even though, the treatment of HW was satisfactory, strengthening the existing mechanisms for monitoring of HW management, provisioning more resources and organizing training and awareness programmes on HW management for HCP are recommended.

Analysis of Rainfall Characteristics Caused by Urban Geological Disasters: Evidence from Wuhan

The research indicates that geological disasters are primarily influenced by weather and climatic conditions, in addition to geological stability factors . Typically, the prediction and forecasting of geological disasters are based on geological conditions, combined with rainfall forecasts. Geological conditions serve as the foundation for the occurrence of geological hazards, often necessitating geological hazard zoning within the corresponding region through disaster investigations and associated calculations. This zoning is further analyzed to predict the risk of geological disaster occurrence based on factors such as prior rainfall frequency, rainfall intensity, and rain type characteristics.

Bioreceptivity of unused building materials: effects on microbial colonisation and diversity—a Polish–Indian study

The colonisation of building materials by microorganisms is the first step in terms of a subsequent biodeterioration and an occurrence of potential building-associated health hazards. Contrary to many studies to date, focusing mainly on old buildings with symptoms of biocorrosion, this research focuses on the bioreceptivity of selected unused building materials and the factors determining the intensity of their colonisation. The experiments were carried out in parallel in India and Poland, with the use of indigenous microflora from a given region. They revealed the similar dynamics of the microbial multiplication in concrete and plasterboard and the extreme intensity of the process: after only 10 weeks under favorable conditions, the number of bacteria and fungi reached 107 CFU/g of dry weight of the material. The stimulation of microbial growth by individual environmental factors was slightly different in Indian and Polish studies: in India, the strongest stimulating factor was the periodic increase in humidity, while in Poland, the highest intensification of growth was observed in samples enriched with organic matter. Mould biodiversity turned out to be higher in India, although some types of fungi common to both regions were isolated.

Surface defect detection of industrial components based on vision

Early and effective surface defect detection in industrial components can avoid the occurrence of serious safety hazards. Since most industrial component surfaces have tiny defects with high similarity to the detection background, there are often issues of missed or false detections when defects are detected, leading to low detection accuracy. To deal with the aforementioned issue, this essay suggests a high-precision detection model for surface defects in industrial components based on the YOLOv5 algorithm. First, the original spatial pyramid pooling (SPPF) is innovated by proposing the SPPFKCSPC module, which improves the network's capacity for feature extraction from targets at different scales and fuses multiscale features better. Then, C3 is combined with SPPFKCSPC and replaces the C3 module of the backbone network, which improves feature expression and enhances the receptive field of the network. Finally, the coordinate attention mechanism (CA) has been embedded into the YOLOv5 neck network, and the bounding box regression loss function of the algorithm is improved to EIOU, not only improving the precision of the target localization and recognition model but also enhancing the overall network performance. Based on the public datasets NEU-DET and PV-Multi-Defect, multiple sets of experiments were conducted using innovative algorithms. On the NEU-DET dataset, we got a mean average accuracy (mAP) of 88.3%, which is 7.2% greater than the original approach. On the PV-Multi-Defect dataset, the mAP value reached 97.5%, an improvement of 1.5%. As shown by the experimental data, the detection results significantly improved.

Open gridded climate datasets can help investigating the relation between meteorological anomalies and geomorphic hazards in mountainous areas

The initiation of geomorphic hazards in mountainous environments is highly susceptible to temperature and precipitation forcing. To assess future changes in these hazards, statistical methods linking meteorological anomalies with the occurrence of past hazards can be employed. These methods are currently trained using long records from in-situ meteorological stations. This requires large efforts for data access, collection and homogenisation, which severely limit the scalability of these approaches. Here, we use a consolidated statistical method and a vast catalogue of geomorphic hazards occurred across the Alpine range to show that open-access datasets of gridded temperature and satellite-based precipitation estimates can be used as surrogates of in-situ observations. Further, we find that satellite data can capture precipitation anomalies leading to debris flows that are missed by in-situ observations.

Reclassifying historical disasters: From single to multi-hazards

Multi-hazard events, characterized by the simultaneous, cascading, or cumulative occurrence of multiple natural hazards, pose a significant threat to human lives and assets. This is primarily due to the cumulative and cascading effects arising from the interplay of various natural hazards across space and time. However, their identification is challenging, which is attributable to the complex nature of natural hazard interactions and the limited availability of multi-hazard observations. This study presents an approach for identifying multi-hazard events during the past 123 years (1900–2023) using the EM-DAT global disaster database. Leveraging the ‘associated hazard’ information in EM-DAT, multi-hazard events are detected and assessed in relation to their frequency, impact on human lives and assets, and reporting trends. The interactions between various combinations of natural hazard pairs are explored, reclassifying them into four categories: preconditioned/triggering, multivariate, temporally compounding, and spatially compounding multi-hazard events. The results show, globally, approximately 19 % of the 16,535 disasters recorded in EM-DAT can be classified as multi-hazard events. However, the multi-hazard events recorded in EM-DAT are disproportionately responsible for nearly 59 % of the estimated global economic losses. Conversely, single hazard events resulted in higher fatalities compared to multi-hazard events. The largest proportion of multi-hazard events are associated with floods, storms, and earthquakes. Landslides emerge as the predominant secondary hazards within multi-hazard pairs, primarily triggered by floods, storms, and earthquakes, with the majority of multi-hazard events exhibiting preconditioned/triggering and multivariate characteristics. There is a higher prevalence of multi-hazard events in Asia and North America, whilst temporal overlaps of multiple hazards predominate in Europe. These results can be used to increase the integration of multi-hazard thinking in risk assessments, emergency management response plans and mitigation policies at both national and international levels.

Electromagnetic radiation from electrically driven vehicles

The problem of electromagnetic radiation of vehicles exists and worries people, because it is not clear what is the safe level of frequency, intensity, time spent by drivers and passengers in vehicles, etc. The article is devoted to the disclosure of the problems of adverse effects of electromagnetic fields (EMF) and electromagnetic radiation (EMR) of modern hybrid and electric vehicles on drivers, passengers, pedestrians and the environment. The purpose of the study was to investigate the process of occurrence, propagation and impact of negative functions of electromagnetic radiation on biological objects, their propagation speed, flicker, wavelength and frequency, and radiation devices in electric and hybrid vehicles. The methodology of the article is based on the results of research by scientists from around the world and our own research and experiments, the results of which are presented in this article. The results of the scientific work are reviewed from researchers on this topic and their own tests are described The authors point out the need to measure electromagnetic radiation at the stages of their operation, repair and manufacture, since these vehicles running on electric traction generate magnetic fields that can violate electromagnetic safety. This is because electric vehicles have power plants, sensors, control, information and communication systems. Electric currents flowing through electric motors, power circuits, and batteries while driving generate magnetic fields (MFs) in the low frequency ranges (ultra low frequency (ULF), 0.001 - 10 Hz; extremely low frequency (ELF), 10 - 300 Hz).Higher harmonics of the electric field in electric vehicles and hybrid vehicles are generated by various electronic devices on board, information and communication systems. For example, in hybrid vehicles, magnetic pulses of up to 5 kHz are generated when the internal combustion engine is switched to electric mode. In addition, all types of cars generate a low-frequency pulsating magnetic field when the steel wheel disks rotate. The frequency f of this field is determined by the wheel speed and is usually f 20 Hz, but the spectrum also contains harmonics. The scientific novelty is that for the first time the concept of modeling and optimization of electromagnetic hazard assessment of electric vehicles and hybrid vehicles is presented, which has a unified approach to the study of the magnetic field regardless of the structure and design schemes of their power plants at the stage of operation; for the first time methods for assessing important parameters of the magnetic field of electric vehicles and hybrid vehicles at the stage of operation are developed based on the concept of optimal control of the power plant depending on the operating conditions. The practical significance is the originality, which lies in the fact that the authors have considered the problems of determining the moment of occurrence of traffic hazards in various road and transport situations and the technical hazards of various types of electric vehicles and hybrid vehicles.

Nonlinear causal relationships between urbanization and extreme climate events in China

China has undergone rapid urbanization since 1980s, which could in return alter the occurrences of regional weather and sequential hazards, posing challenges to sustainable development of human society. However, casual relationships between urbanization and climate extremes are still poorly understood. In this study, causal relationships between urbanization and extreme temperature/precipitation are quantified using a nonlinear Granger causality framework. Causal hotspots are observed not only in major urban agglomerations but well as in less urbanized areas. For both temperature and precipitation extremes, the impact of urbanization could last within 5 years (mainly 4 years).The causality varies greatly with different climate background, but the contribution of urbanization to extreme temperature and precipitation exhibits a high level of consistency in individual city. Urbanization has profoundly causal relationships with nighttime extreme heat with higher morbidity and mortality to urban residents. Averagely, urbanization causally accounts for almost 36.9% to temperature extremes. On the other hand, urbanization has higher casual contribution to total precipitation. The inland regions are more likely to be affected by urbanization than coastal regions due to weak monsoon influence. On average, urbanization causally accounts for almost 39.7% to precipitation indices. This study innovatively reveals the significant causal relationships between urbanization and extreme temperature/precipitation changing patterns of China, providing valuable insights for developing sustainable cities in the future.

MCDM-Based Wildfire Risk Assessment: A Case Study on the State of Arizona

The increasing frequency of wildfires has posed significant challenges to communities worldwide. The effectiveness of all aspects of disaster management depends on a credible estimation of the prevailing risk. Risk, the product of a hazard’s likelihood and its potential consequences, encompasses the probability of hazard occurrence, the exposure of assets to these hazards, existing vulnerabilities that amplify the consequences, and the capacity to manage, mitigate, and recover from their consequences. This paper employs the multiple criteria decision-making (MCDM) framework, which produces reliable results and allows for the customization of the relative importance of factors based on expert opinions. Utilizing the AROMAN algorithm, the study ranks counties in the state of Arizona according to their wildfire risk, drawing upon 25 factors categorized into expected annual loss, community resilience, and social vulnerability. A sensitivity analysis demonstrates the stability of the results when model parameters are altered, reinforcing the robustness of this approach in disaster risk assessment. While the paper primarily focuses on enhancing the safety of human communities in the context of wildfires, it highlights the versatility of the methodology, which can be applied to other natural hazards and accommodate more subjective risk and safety assessments.

Time-Frequency Response of Acoustic Emission and Its Multi-Fractal Analysis for Rocks with Different Brittleness under Uniaxial Compression

The occurrence of rock burst hazards is closely related to the brittleness of rocks. Current research has paid less attention to the in-depth relationship between rock brittleness and acoustic emission (AE) signal characteristics and precursor information caused by rock fracture. Therefore, in order to further improve the accuracy of the AE monitoring of rockburst hazards, uniaxial compression tests were carried out and AE were monitored for rocks with different brittleness (yellow sandstone, white sandstone, marble, and limestone) in this paper. The relationship between the mechanical properties and the time-frequency characteristics of the AE was analyzed. In addition, the multi-fractal theory was introduced to further deconstruct and mine the AE signals, and the multi-fractal characteristics of AE from rocks with different brittleness were investigated. The results show that the stronger the brittleness of the rock, the higher the main frequency and main frequency amplitude of the AE. Brittleness is positively correlated with the multi-fractal parameter Δα (uniformity of data distribution) and negatively correlated with Δf (frequency difference between large and small data). In addition, the dynamics of Δα and Δf provide new indicators for AE monitoring of rock stability, and their abrupt changes can be regarded as precursors of failure. The weaker the brittleness of the rock, the earlier the failure precursor is and the more significant it is. This has potential engineering application value, which can help identify rockburst precursors and take timely protective measures to ensure engineering safety.

Hurricane risk assessment in a multi-hazard context for Dominica in the Caribbean

Hurricanes can trigger widespread landslides and flooding creating compound hazards and multiple risks for vulnerable populations. An example is the island of Dominica in the Caribbean, where the population lives predominantly along the coast close to sea level and is subject to storm surge, with steep topography rising behind, with a propensity for landslides and flash river flooding. The simultaneous occurrence of the multiple hazards amplifies their impacts and couples with physical and social vulnerabilities to threaten lives, livelihoods, and the environment. Neglecting compound hazards underestimates overall risk. Using a whole island macroscale, (level-I) analysis, susceptibility scenarios for hurricanes, triggered landslides, and floods were developed by incorporating physical process parameters. The susceptibilities were combined with vulnerability indicators to map spatial patterns of hurricane multi-risks in Dominica. The analysis adopted a coupled approach involving the frequency ratio (FR), analytic hierarchy process (AHP), and geographic information system (GIS). Detailed hazard modelling was done at selected sites (level-II), incorporating storm surge estimates, landslide runout simulations, and steady flow analysis for floods. High-resolution terrain data and simulation models, the Rapid Mass Movement Simulation (RAMMS) and the hydrologic engineering center’s river analysis system (HEC-RAS), were employed. Ground validation confirmed reasonable agreement between projected and observed scenarios across different spatial scales. Following the United Nations Office for disaster risk reduction (UNDRR) call for the inclusion of local, traditional, and indigenous knowledge, feedback, and expert opinion to improve understanding of disaster risk, 17 interviews with local experts and 4 participatory workshops with residents were conducted, and findings were incorporated into the analysis, so as to gain insights into risk perceptions. The study’s outcomes encompass projections and quantification of hurricane compound hazards, vulnerabilities, accumulated risks, and an understanding of local priorities. These findings will inform decision-making processes for risk mitigation choices and community actions by providing a new framework for multi-hazard risk assessment that is easy to implement in combining different data forms.

Inventories of natural hazards in under-reported regions: a multi-method insight from a tropical mountainous landscape

ABSTRACT Impacts of natural hazards are on an increase globally. To mitigate these impacts, sound disaster risk reduction strategies must rely on comprehensive inventory of natural hazards. However, to date, many regions worldwide still suffer from a dearth of information. The goal of our research is to assess how, with limited means and several methods, environments that are typically under-researched can be covered with accurate information on which further research aiming at understanding and mitigating disaster risk could be set out. We focus on the Kigezi highlands, an under-reported and populated tropical region of Uganda (Africa) known to be impacted by natural hazards. We conducted a multi-method multi-temporal inventory over the entire region using satellite image analysis, exploration of literature and archives, and field surveys. We also set up a network of 15 geo-observers to collect accurate space-time data on seven natural hazards. Our multi-method inventory generated a database comprising over 4000 natural hazard occurrences with frequent natural and anthropogenic interactions. Our results indicate that Kigezi highlands is a hotspot for natural hazards associated with extensive risks, whose numbers are underestimated. We therefore highlight the importance of working with a multiple-method approach in characterizing natural hazards and their interactions.

An empirical flood fatality model for Italy using random forest algorithm

Due to the increasing occurrence of natural hazards, such as floods, a significant number of lives are lost each year worldwide. The risk of experiencing catastrophic losses from flooding is exacerbated due to the changing climate and increasing anthropogenic activities. Consequently, predicting the conditions leading to fatalities is crucial in the assessment of flood risk. However, the existing modeling capabilities in this field are limited, emphasizing the critical need for the development of such tools. Here, we show that the occurrence of flood fatalities can be estimated using a random forest (RF) algorithm applied to nine explanatory variables characterizing each fatality. Furthermore, by converting the RF model outcomes into a user-friendly tool, it is possible to predict the probability of the occurrence of flood-related fatalities, based on variables describing hazard intensity and the environmental and sociodemographic conditions contributing to such events. Our results represent an initial attempt towards a predictive model of flood fatalities in the Italian context. They reveal the key factors that together influence flood fatalities, enabling the prediction of such occurrences. These findings can serve as a foundational framework for quantitatively assessing the risk to the population from such events and as a valuable resource for identifying strategies to mitigate flood risk.

The Failure Risk Analysis of the Water Supply Network

The primary objective of this work is to introduce a novel approach that modifies the method for analyzing and assessing the risk of water supply network failure. The approach aligns with recommendations from the World Health Organization and the European Union regarding the reliability and safety of water supply to consumers. The presented method for assessing the risk in the water distribution subsystem was based on the vulnerability identifying method (VIM) and involves the determination of the vulnerability index (VI). The VIM vulnerability factors considered encompass the failure rate, chemical stability of water, and issues related to water corrosion properties in water distribution subsystems. The obtained risk assessment includes parameters such as the probability of hazard occurrence, the consequences of these hazards, and vulnerability to them. This concept was evaluated using real operational data from the water distribution subsystem. The estimated risk level, under the given operating conditions, indicates its acceptability.

Insights into the Movement and Diffusion Accumulation Characteristics of a Catastrophic Rock Avalanche Debris—A Case Study

In this study, the 1991 rock avalanche, in Touzhai, Zhaotong, Yunnan, China, was considered the study object. The investigation of the landslide accumulation body revealed that the Touzhai rock avalanche accumulation body has the characteristics of wide gradation and poor sorting. A combination of field investigations, indoor and outdoor experiments, and numerical simulations were used to invert the occurrence and spreading range of rock avalanche-debris flow hazards. To invert and analyze its dynamics and the crushing process, a three-dimensional discrete element modeling was performed on the real terrain data. Simulation results showed that the movement time of the numerically simulated Touzhai rock avalanche was approximately 200 s. After 50 s of movement, the peak velocity reached 32 m/s, and the velocity gradually decayed after the sliding mass rubbed violently against the valley floor and collided with the mountain. Due to the meandering nature of the gully, the sliding mass makes its way down the gully and constantly collides with the mountain, making particles appear to climb, with some particles being blocked by the valley. After 150 s of movement, the average velocity rate decreased substantially, and the landslide-avalanche debris reached the mouth of the trench. After 200 s of movement, the average sliding velocity tends to 0 m/s, where the avalanche debris tends to stop and accumulate. When the rock avalanche movement reaches the mouth of the gully, the avalanche debris spreads to the sides as it is no longer bounded by the hills on either side of the narrow gully, eventually forming a ‘trumpet-shaped’ accumulation, and the granular flow simulation matched the findings of the landslide site accumulation.

Characterizing CONUS-wide spatio-temporal changes in daily precipitation, flow, and variability of extremes

The hydrological cycle strongly relies on the spatial and temporal variability of precipitation, which has been considerably affected by climate change. Focusing on CONUS, this research extended the analysis by evaluating the variability of daily concentrations of various components of the hydrological cycle in terms of the Gini index and its potential effects on floods and droughts by integrating historical data with an existing database of extreme events. Differences were found in the geographical location of significant correlations among the hydrological variables investigated, implying that the impacts of variability are neither spatially nor temporally uniform. The work also showed the importance of selecting thresholds of precipitation and flow to define “extreme” events. We identified a precipitation deficit threshold at the 5th percentile and an extreme precipitation threshold at the 95th percentile as the most representative. Moreover, for flood, the 95th percentile and for hydrological drought, the 5th percentile were the most representative. Spatially the study showed that, the concentration of precipitation deficit and evapotranspiration had a major impact on the occurrence of hydrological hazards, such as droughts and floods.

Flood frequency and flood forecasting analysis of Krishna basin Andhra Pradesh

Floods are occurrences of natural hazards, frequent during the year in many rivers across the globe. Every year, several rivers in India are vulnerable to flooding, causing loss of property and life. Krishna is one of the major rivers in India which is vulnerable to flooding in every monsoon. In this study, flood analysis was conducted for the year 2019. Rainfall data from AWS was used to estimate discharge levels in dams during the monsoon of 2019. It was observed that moderate rainfall occurred in the months of August and September, corresponding to low rainfall and that extreme flooding occurred in the same month. Compared to the flood inundation map of the satellite, it shows a close relationship with the flood map of 2019 and the affected area. The study shows that proper analysis of rainfall will be helpful in predicting downstream floods. To evaluate the flood control situation with appropriate data management in the Krishna basin, the usage of flood water is strong. Such types of studies would help to provide reliable and prompt flood forecasts and advance warning to redirect the main river flow to small canals, which will help to mitigate, excavate and remediate flooding in any area.