Empowering real-time flood impact assessment through the integration of machine learning and Google Earth Engine: a comprehensive approach.

Floods cause substantial losses to life and property, especially in flood-prone regions like northwestern Bangladesh. Timely and precise evaluation of flood impacts is critical for effective flood management and decision-making. This research demonstrates an integrated approach utilizing machine learning and Google Earth Engine to enable real-time flood assessment. Synthetic aperture radar (SAR) data from Sentinel-1 and the Google Earth Engine platform were employed to generate near real-time flood maps of the 2020 flood in Kurigram and Lalmonirhat. An automatic thresholding technique quantified flooded areas. For land use/land cover (LULC) analysis, Sentinel-2's high resolution and machine learning models like artificial neural networks (ANN), random forests (RF) and support vector machines (SVM) were leveraged. ANN delivered the best LULC mapping with 0.94 accuracy based on metrics like accuracy, kappa, mean F1 score, mean sensitivity, mean specificity, mean positive predictive value, mean negative value, mean precision, mean recall, mean detection rate and mean balanced accuracy. Results showed over 600,000 people exposed at peak inundation in July-about 17% of the population. The machine learning-enabled LULC maps reliably identified vulnerable areas to prioritize flood management. Over half of croplands flooded in July. This research demonstrates the potential of integrating SAR, machine learning and cloud computing to empower authorities through real-time monitoring and accurate LULC mapping essential for effective flood response. The proposed comprehensive methodology can assist stakeholders in developing data-driven flood management strategies to reduce impacts.

Monitoring dynamics of Kyagar Glacier surge and repeated draining of Ice-dammed lake using multi-source remote sensing.

Glacier surges, a primary factor contributing to various glacial hazards, has long captivated the attention of the global glaciological community. This study delves into the dynamics of Kyagar Glacier surging and the associated drainage features of its Ice-dammed lake, employing high temporal resolution optical imagery. Our findings indicate that the surge on Kyagar Glacier began in late spring and early summer of 2014 and concluded during the summer of 2016. This surge resulted in the transfer of 0.321 ± 0.012 km3 of glacier mass from the reservoir zone to the receiving zone, leading to the formation of an ice-dammed lake at the glacier's terminus. The lake experienced five outbursts between 2015 and 2019, with the largest discharge occurring in 2017. And the maximum water depth during this period was 112 ± 11 m, resulting in a water storage volume of (158.37 ± 28.32) × 106 m3. On the other hand, our analysis of the relationship between glacier surface velocity and albedo, coupled with an examination of subglacial dynamics, revealed that increased precipitation during the active phase of the Kyagar Glacier results in accumulation of mass in the upper glacier. This accumulation induces changes in basal shear stress, triggering the glacier's transition into an unstable state. Consequently, glacier deformation rates escalate, surface crevasses proliferate, potentially providing conduits for surface meltwater to infiltrate the glacier bed. This, in turn, leaded to elevated basal water pressure, initiating glacier sliding. Furthermore, we postulated that the repetitive drainage of Kyagar Ice-dammed lake was primarily influenced by the opening and closing of subglacial drainage pathways and variations in inflow volumes. Future endeavors necessitate rigorous field observations to enhance glacier surge simulations, deepening our comprehension of glacier surge mechanisms and mitigating the impact of associated glacial hazards.

Spatio-temporal detection of agricultural disaster vulnerability in the world and implications for developing climate-resilient agriculture.

Drought and floods seriously affect agriculture across the world. It is of great importance to lower down the agricultural vulnerability to disasters to build climate-resilient agriculture. The paper aims to investigate the spatio-temporal changes of agricultural vulnerability to drought and floods in the world in the period 2003-2019. Research results show that (1) the agricultural vulnerability to drought and floods is at a low level across the globe owning to the dual effects of decreasing exposure and increasing adaptability; (2) the northern parts of United States, northeastern parts of China, and the border between Russia and Kazakhstan are identified as most vulnerable areas to drought and floods; and (3) spatio-temporal mismatch of precipitation is the main factor to cause floods and drought while better adaption is beneficial to minimize the adverse effects of disasters. Based on analysis on the drivers and spatial patterns of drought and floods risk in all dimensions, tailored measures and policies are put forwards to make adaptive strategies of agriculture to climate change.

Effects of the catastrophic 2020 Yangtze River seasonal floods on microcystins and environmental conditions in Three Gorges Reservoir Area, China.

Introduction: During July and August 2020, Three Gorges Reservoir Area (TGRA) suffered from catastrophic seasonal floods. Floods changed environmental conditions and caused increase in concentration of microcystins (MCs) which is a common and potent cyanotoxin. However, the effects and seasonal variations of MCs, cyanobacteria, and environmental conditions in TGRA after the 2020 Yangtze River extreme seasonal floods remain largely unclear, and relevant studies are lacking in the literature. Methods: A total of 12 representative sampling sites were selected to perform concentration measurement of relevant water quality objectives and MCs in the representative area of the TGRA. The sampling period was from July 2020 to October 2021, which included the flood period. Organic membrane filters were used to perform the DNA extraction and analyses of the 16S rRNA microbiome sequencing data. Results: Results showed the seasonal floods result in significant increases in the mean values of microcystin-RR (MCRR), microcystin-YR (MCYR), and microcystin-LR (MCLR) concentration and some water quality objectives (i.e., turbidity) in the hinterland of TGRA compared with that in non-flood periods (p < 0.05). The mean values of some water quality objectives (i.e., total nitrogen (TN), total phosphorus (TP), total dissolved phosphorus (TDP), and turbidity), MC concentration (i.e., MCRR, MCYR, and MCLR), and cyanobacteria abundance (i.e., Cyanobium_PCC-6307 and Planktothrix_NIVA-CYA_15) displayed clear tendency of increasing in summer and autumn and decreasing in winter and spring in the hinterland of TGRA. Discussions: The results suggest that seasonal floods lead to changes in MC concentration and environmental conditions in the hinterland of TGRA. Moreover, the increase in temperature leads to changes in water quality objectives, which may cause water eutrophication. In turn, water eutrophication results in the increase in cyanobacteria abundance and MC concentration. In particular, the increased MC concentration may further contribute to adverse effects on human health.

Prolonged drought periods over the last four decades increase flood intensity in southern Africa.

In semi-arid sub-Saharan Africa, climate change and the intensification of human activities have altered the hydrological balance and modified the recurrence of extreme hydroclimatic events, such as droughts and floods. The geomorphological heterogeneity of river catchments across the region, the variable human pressure, and the lack of continuous hydroclimatic data preclude the definition of proper mitigation strategies, with a direct effect on the sustainability of rural communities. Here, for the first time in Africa, we characterize hydrological extreme events using a multidisciplinary approach that includes sedimentary data from dams. We focus on the Limpopo River basin to evaluate which factors control flood magnitude since the 1970. Extreme flood events were identified across the basin in 1988-89, 1995-96, 1999-2000, 2003-04, 2010-11, 2013-14 and 2016-17. The statistical analysis of sedimentary flood records revealed a dramatic increase in their magnitude over the studied period. A positive correlation between maximum river flow and antecedent prolonged drought conditions was found in South Africa and Mozambique. Most importantly, since 1980, we observed the likely decoupling of extreme floods from the magnitude of La Niña events, suggesting that the natural interannual variability driven by El Niño-Southern Oscillation (ENSO) has been disrupted by climate changes and human activities.

Applying machine learning in the investigation of the link between the high-velocity streams of charged solar particles and precipitation-induced floods.

This study explores a possible link between solar activity and floods caused by precipitation. For this purpose, discrete blocks of data for 89 separate flood events in Europe in the period 2009-2018 were used. Solar activity parameters with a time lag of 0-11 days were used as input data of the model, while precipitation data in the 12 days preceding the flood were used as output data. The level of randomness of the input and output time series was determined by correlation analysis, while the potential causal relationship was established by applying machine learning classification predictive modeling. A total of 25 distinct machine-learning algorithms and four model ensembles were applied. It was shown that in 81% of cases, the designed model could explain the occurrence or absence of precipitation-induced floods 9 days in advance. Differential proton flux in the 0.068-0.115 MeV and integral proton flux > 2.5 MeV were found to be the most important factors for forecasting precipitation-induced floods. The study confirmed that machine learning is a valuable technique for establishing nonlinear relationships between solar activity parameters and the onset of floods induced by precipitation.

Model-based estimation of seasonal transport of macro-plastics in a marine protected area.

Management of plastic litter in Marine Protected Areas (MPAs) is expensive but crucial to avoid harms to critical environments. In the present work, an open-source numerical modelling chain is proposed to estimate the seasonal pathways and fates of macro-plastics, and hence support the effective planning and implementation of sea and beach cleaning operations. The proposed approach is applied to the nearshore region that includes the MPA of Capo Milazzo (Italy). A sensitivity analysis on the influence of tides, wind, waves and river floods over the year indicates that seasonality only slightly affects the location and extension of the macro-plastic accumulation zones, and that beach cleaning operations should be performed in autumn. Instead, the influence of rivers on plastic litter distribution is crucial for the optimal planning of cleaning interventions in the coastal area.

Adolescent mental distress in the wake of climate disasters.

Objective: Retrospective exposure to a higher number and prolonged duration of climate-related disasters could be positively associated with adolescent mental distress. Methods: Person-level data came from 38,616 high-school students residing in 22 urban public-school districts in 14 states (U.S. Youth Risk Behavior Survey, 2019). Each district's federally declared climate-related catastrophes (severe storms, floods, wildfire, etc.) came from the Federal Emergency Management Agency. Logistic regression models estimated the adjusted odds ratios (aOR) of adolescent mental distress (MD, using survey responses feeling prolonged sadness/ hopelessness and short sleep duration) according to disaster events and days during three exposure periods (past 2-, 5-, 10-years); adjusted for age, gender, race/ethnicity, socio-economic disadvantage, feeling unsafe at school, district area size, district poverty, and region. Results: Over 10 years, the median number of disaster events was 3 and total disaster days was 64. Adolescents experiencing the highest number of disaster days (top quartile vs. less) had 25% higher odds of MD when exposed within the past 2-years (aOR 1.25 [95% CI 1.14, 1.38]), and 20% higher odds of MD when exposed within the past 5-years (aOR 1.20 95% CI 1.07, 1.35). The odds of MD were not statistically associated with exposure periods that extended to 10 years, nor disaster events (instead of disaster days, all p-values > 0.1). Conclusions: Severe weather will become more frequent and last longer with human-induced climate warming. More studies like this are needed to understand the broad range of adverse effects and enhance planning and preparedness including preparing for worsening mental health among adolescents.

The impact of tropical cyclones and water conservancy projects on island's flash floods.

The analysis of the influencing factors of flash floods, one of the most destructive natural disasters, is the basis of scientific disaster prevention and mitigation. There is little research considering the influence of tropical cyclones (TCs) and water conservancy projects on flash floods, which cannot be ignored in the island areas where flash floods often occur due to the complex influence of various factors. In this study, under the pressure-state-response framework (PSR framework), the factors affecting the distribution of flash floods on Hainan Island, China, from 1970 to 2010 were quantitatively analyzed by using the geographical detector method. By dividing the time period, give full play to the advantages of the PSR framework and show the evolution process of various factors. Different from inland areas, extreme precipitation and tropical cyclones play a major role in the spatial distribution of flash floods on Hainan Island, China, and the driving force of tropical cyclones is 1.1 times that of extreme precipitation on average. Medium-sized reservoirs play the greatest role in the prevention of flash floods on Hainan Island, and their driving forces reach 0.38 times of extreme precipitation on average, followed by large-sized reservoirs and small-sized reservoirs. Large-sized reservoirs are limited in quantity and have limited effectiveness in preventing flash floods on Hainan Island. Therefore, in the forecasting and risk management of flash flood in the island area, more attention should be paid to the impact of extreme precipitation and TCs, and the role of medium-sized reservoir should be fully exerted.

Nowcast flood predictions in the Amazon watershed based on the remotely sensed rainfall product PDIRnow and artificial neural networks.

The aim of this study was to develop artificial neural network (ANN) models to predict floods in the Branco River, Amazon basin. The input data for the models included the river levels and the average rainfall within the drainage area of the basin, which was estimated from the remotely sensed rainfall product PDIRnow. The hourly water level data used in the study were recorded by fluviometric telemetric stations belonging to the National Agency of Water. The multilayer perceptron was used as the neural framework of the ANNs, and the number of neurons in each layer of the model was determined via optimization with the SCE-UA algorithm. Most of the fitted ANN models showed Nash-Sutcliffe efficiency index values greater than 0.9. It is possible to conclude that the ANNs are effective for predicting the flood levels of the Branco River, with horizons of 6, 12 and 24 h; thus, constituting a viable option for use in river-flood warning systems in the Amazon basin. For the forecast with a 24-h horizon, it is essential to include the average rainfall of the basin that accumulated over the last 48 h as input data into the ANNs, along with the levels measured by the streamflow stations. The indirect rainfall estimates provided by PDIRnow are an excellent alternative as input data for ANN models used to predict floods and constitute a viable solution for regions where the density of rain gauge stations is low, as is the case in the Amazon basin.

Climate shocks' impact on agricultural income and household food security in Bangladesh: An implication of the food insecurity experience scale.

Floods and extreme rainfall are common climatic phenomena in Bangladesh, and farm households are more susceptible to such shocks. This paper assesses the impact of climate shocks on agricultural income and food security of farm households in Bangladesh using an extensive nationally representative dataset from the Bangladesh Integrated Household Survey 2018-19, including 5604 sample rural households in 64 districts. However, this research considered 24 districts, representing 2131 sample farm households, by developing an exogenous climate shock indicator based on data from the Yearbook of Agricultural Statistics of Bangladesh 2018. Empirical findings on the grounds of simultaneous quantile regression reveal that climate shocks substantially lower agricultural income in the study regions. However, the presence of prime-age women (15-49) in the home, the male-headed family, farmland, and livestock ownership of the household are the decisive factors that safeguard agricultural income. Applying the Food Insecurity Experience Scale (FIES), descriptive statistics disclose that most farm households suffer at various food insecurity levels (considerably moderate, noticeably mild, and tiny severe), while the rest are at the food security level. The key finding regarding ordered probit regression uncovers that climate shocks significantly increase household food insecurity (at different levels of FIES). In other words, cropland damage due to floods and extreme rainfall reduces the food security of farm households in the study districts. On the other hand, increased farm size and educated households are profoundly protected against food insecurity. This study, therefore, recommends that raising livestock can complement agricultural income, and enhancing education would ensure households' food security in the climate-exposed areas of Bangladesh.

Prevalence and determinants of post-traumatic stress disorder five months after the 2019 huge flooding in Iran.

BACKGROUND: Despite the high occurrence of floods in Iran, its psychological consequences have been less discussed. The present paper addresses the prevalence of Post-traumatic Stress Disorder (PTSD) and its determinants among the affected adults by the huge flood of 2019. METHODS: An analytical cross-sectional study was conducted through household face-to-face surveys in August and September 2019. Individuals who were affected by floods and were at least 16 years old were randomly selected from three provinces in Iran: Lorestan and Khuzestan in the west and southwest, and Golestan in the northeast. The questionnaire of demographic and flood related variables in addition to the Impact of Event Scale-Revised (IES-R) were utilized to collect the data. We applied a complex sample analysis to describe the prevalence of PTSD and logistic regression analyses to find its determinants. RESULTS: Out of the 2,305 individuals approached for surveys, 1,671 (72.5%) adults affected by the floods participated in the study. The majority of participants were housewives, married, had either no formal education or primary education, and resided in rural areas. The prevalence of PTSD in the participants was 24.8% (CI 95%: 20.7-28.8%) and was significantly higher in Lorestan province (39.7%, P < 0.001). Determinants of PTSD, were unemployment (adjusted odds ratio [AOR] = 3.53, CI 95%: 1.38-9.00), primary (AOR = 2.44, CI 95%: 1.10-5.41) or high school (AOR = 2.35, CI 95%: 1.25-4.40) education (vs. university), a history of mental disorders (AOR = 2.36, CI 95%: 1.22-4.58), high damage to assets (AOR = 2.29, CI 95%: 1.40-3.75), limited access to health care services after the flood (AOR = 1.95, CI 95%: 1.20-3.19), not receiving compensation for flood damage (AOR = 1.94, CI 95%: 1.01-3.83), high wealth index (AOR = 1.90, CI 95%: 1.23-2.93), and flooded house with a height of more than one meter (AOR = 1.66, CI 95%: 1.02-2.76). CONCLUSION: Results show a notable prevalence of PTSD, especially in Lorestan province, among adults affected by floods. Determinants of PTSD include unemployment, lower education, psychiatric history, extensive property damage, limited post-flood healthcare access, lack of compensation, and increased flood exposure. We recommend adopting an inclusive screening approach for high-risk groups and developing appropriate therapeutic and supportive interventions.

Assessment of mould remediation in a healthcare setting following extensive flooding.

BACKGROUND: A new hospital building was close to completion when a large pipe carrying clean water broke, causing extensive flooding. AIM: To determine the flood-associated fungal risk to susceptible patients who would use that building. METHODS: Though standard flood remediation by the builders was relatively straightforward, there was no model for specialist assessment of patient risk due to the flood-associated mould growth. As levels of background airborne fungal spores can be expected to vary significantly over time, we could not use absolute levels to indicate either an excess of airborne fungal spores or successful remediation. Therefore it was decided to use weekly settle plates, exposed at the same time in flooded (test) and equivalent non-flooded (control) areas to compensate for variations in background levels. Flood-related risk was estimated by the ratio between fungal colonies on the test and control sets of settle plates, rather than absolute number. FINDINGS: Whereas the physical flood remediation, including the use of 'anti-fungal' treatments, was completed in three weeks post flooding, fungal contamination in flooded areas took 38 weeks to return to control levels and remained so for a further six weeks of observation. CONCLUSION: By the use of this method, we were able to assure the absence of flood-associated fungal risk to susceptible patients who would use that building. We recommend that infection prevention and control teams consider using this approach should they be faced with similar situations.

A study on siting of emergency shelters for dam failure floods considering population distribution and weather effects.

In recent years, dam failures have occurred frequently because of extreme weather, posing a significant threat to downstream residents. The establishment of emergency shelters is crucial for reducing casualties. The selection of suitable shelters depends on key information such as the number and distribution of affected people, and the effective capacity and accessibility of the shelters. However, previous studies on siting shelters did not fully consider population distribution differences at a finer scale. This limitation hinders the accuracy of estimating the number of affected people. In addition, most studies ignored the impact of extreme rainfall on the effective capacity and accessibility of shelters, leading to a low applicability of the shelter selection results. Therefore, in this study, land-use and land-cover change (LUCC) and nighttime lighting data were used to simulate population distribution and determine the number and distribution of affected people. Qualified candidate shelters were obtained based on screening criteria, and their effective capacity and accessibility information under different weather conditions were quantified. Considering factors such as population transfer efficiency, construction cost and shelter capacity constraints, a multi-objective siting model was established and solved using the non-dominated sorting genetic algorithm II (NSGA- II) to obtain the final siting scheme. The method was applied to the Dafangying Reservoir, and the results showed the following: (1) The overall mean relative error (MRE) of the population in the 35 downstream streets was 11.16 %, with good fitting accuracy. The simulation results truly reflect the population distribution. (2) Normal weather screening generated 352 qualified candidate shelters, whereas extreme rainfall weather screening generated 266 candidate shelters. (3) Based on the population distribution and weather factors, four scenarios were set up, with 63, 106, 73, and 131 shelters selected. These two factors have a significant impact on the selection of shelters and the allocation of evacuees, and should be considered in the event of a dam-failure floods.

Public Health and Health System's Responsiveness During the 2022 Floods in Pakistan: What Needs to Be Done?

OBJECTIVE: In 2022, Pakistan witnessed unprecedented flooding, submerging one-third of the country under-water, ruining millions of houses, taking lives, afflicted injuries, and displacing scores of people. Our study documents not only the public health problems that have arisen due to this natural calamity but also the state of health systems' response. METHODS: We conducted a qualitative study asking key questions around prevalent health problems, health-care seeking, government's response, resource mobilization, and roadmap for the future. We purposively selected 16 key frontline health workers for in-depth interviews. RESULTS: Waterborne and infectious diseases were rampant posing huge public health challenges. Disaster mitigation efforts and relief operations were delayed and not at scale to cover the entire affected population. Moreover, a weak economy, poverty, and insufficient livelihoods compounded the tribulations of floods. Issues of leadership and governance at state level resulted in disorganized efforts and response. CONCLUSIONS: Pakistan is famous for its philanthropy; however, lack of transparency and accountability, the actual benefits seldom reach the beneficiaries. Such climatic disasters necessitate a more holistic approach and a greater responsiveness of the health system. In addition to health services, the state must respond to financial, social, and infrastructural needs of the people suffering from the calamity.

Flood Risk and Preventive Choices: A Framework for Studying Human Behaviors.

The topic of flood phenomena has always been of considerable importance due to the high risks it entails, both in terms of potential economic and social damage and the jeopardizing of human lives themselves. The spread of climate change is making this topic even more relevant. This work aims to contribute to evaluating the role that human factors can play in responding to critical hydrogeological phenomena. In particular, we introduce an agent-based platform for analyzing social behaviors in these critical situations. In our experiments, we simulate a population that is faced with the risk of a potentially catastrophic event. In this scenario, citizens (modeled through cognitive agents) must assess the risk they face by relying on their sources of information and mutual trust, enabling them to respond effectively. Specifically, our contributions include (1) an analysis of some behavioral profiles of citizens and authorities; (2) the identification of the "dissonance between evaluation and action" effect, wherein an individual may behave differently from what their information sources suggest, despite having full trust in them in situations of particular risk; (3) the possibility of using the social structure as a "social risk absorber", enabling support for a higher level of risk. While the results obtained at this level of abstraction are not exhaustive, they identify phenomena that can occur in real-world scenarios and can be useful in defining general guidelines.

Reproductive health challenges during a flood: A qualitative study.

AIM: The study aimed to investigate women's reproductive health challenges during floods. DESIGN: This study is qualitative, specifically employing content analysis with an inductive approach. METHODS: Data were collected through in-depth, semi-structured individual interviews between July and December 2021. The study involved 13 women affected by floods in Golestan province, Aq Qala Township, and also included seven healthcare providers and officials. Before the interviews, informed and written consent was obtained from all participants. The sampling process continued until data saturation was achieved. RESULTS: The analysis of the participants' experiences in this study revealed four main categories of requirements, which were as follows: Maternal and Child Health with four subcategories, Essentials of Women's Health Care with two subcategories, Problems of Relationships with two subcategories, and Aggression and Physical Violence with two subcategories. In conclusion, during floods, women encounter numerous challenges in preserving their reproductive health. Recognizing and understanding these challenges can be instrumental in effectively planning measures to prevent or address them during disasters like floods. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE IMPACT: Every disaster has unique conditions and challenges. The health requirements of individuals impacted by floods differ from those affected by other natural disasters. By identifying the specific reproductive health needs of women affected by floods, midwives and other healthcare providers can enhance their planning efforts, enabling them to better address and fulfil these needs during such critical situations. PATIENT OR PUBLIC CONTRIBUTION: Thirteen women were affected by floods, and seven healthcare providers and officials were interviewed.

Acute human leptospirosis in a Caribbean region of Colombia: From classic to emerging risk factors.

BACKGROUND: Leptospirosis is a zoonosis of worldwide incidence, with a broad spectrum of health risk factors. AIM: The objective was to determine risk factors associated with acute human leptospirosis and to explore predictive variables of risk to human leptospirosis. METHODS: The study was carried out in the Department of Córdoba, in the north of Colombia. We conducted a longitudinal prospective descriptive study with non-probabilistic sampling, which included 339 patients suspected of leptospirosis. Positive cases were confirmed by MAT and PCR. The determination of social and environmental risk factors was done with a survey on epidemiological and environmental variables to establish an association between cases of leptospirosis and risk factors as well as predictive variables. RESULTS: We found 19.8% (67/339) cases of acute leptospirosis, and the seroprevalence was 27.1% (92/339). The most frequent serogroups were Sejroe, Australis, Pomona, Batavie, Pyrogenes and Grippotyphosa. We identified the following risk factors: age between 10 and 19 years (OR = 2.571; 95% CI); pig ownership (OR = 2.019; 95% CI); bathing or recreational activities in lake/lagoon (OR = 3.85; 95% CI) and in dams (OR = 3.0; 95% CI); floodings 30 days before the onset of symptoms (OR = 2.019; 95% CI), and a mean temperature of 28°C (p 0.044; 95%CI). As significant predictor variables, we identified age (10-19 years), bathing or recreational activities in the lake/lagoon, and flooding 30 days before symptoms were again evidenced. This region presents classic risk factors (pig ownership) and emerging environmental risk factors (recreational practice or bathing in a lake/lagoon and flooding 30 days before the onset of symptoms), and demographic factors such as young age (10-19 years). CONCLUSIONS: These factors are also predictors of human cases of acute leptospirosis and provide contextual information on environmental and public health that should be considered for epidemiological surveillance in this endemic area.

Natural disasters: a comprehensive study using EMDAT database 1995-2022.

INTRODUCTION: The frequency, intensity, and geographical reach of natural disasters, fueled in part by factors such as climate change, population growth, and urbanization, have undeniably been escalating concerns around the world. DESIGN AND METHODS: This is a retrospective analysis of natural disasters recorded in the Emergency Events Database from 1995 to 2022. RESULTS: Between 1995 and 2022, 11,360 natural disasters occurred, with a mean of 398 per year. Asia experienced the most disasters (4390) and the highest number of casualties (918,198). Hydrological disasters were the most common subgroup (4969), while geophysical disasters led in terms of deaths (770,644). Biological disasters caused the most injuries (2544), particularly in Africa. CONCLUSION: Recognizing the historical impacts of the various subtypes of natural disasters may help different regions better risk analyze and mitigate the unique risks associated with such events.

Dominant flood types in mountains catchments: Identification and change analysis for the landscape planning.

The classification of floods may be a supporting tool for decision-makers in regard to water management, including flood protection. The main objective of this work is the classification of flood generation mechanisms in 28 catchments of the upper Vistula basin. A significant innovation in this study lies in the utilization of decision trees for flood classification. The methodology has so far been applied in the Alpine region. The analysis reveals that peak daily precipitation in the catchments mainly occurs in summer, particularly from June to August. Maximal daily snowmelt typically happens at the end of winter (March to April) and occasionally in November. Winter peaks are observed in March to April and, in some areas, in November to December, while summer peaks occur in May and, in specific catchments, in October. Higher peak flows for annual floods are noted in March to April and June to August. Most annual floods in the Upper Vistula basin are classified as Rain-on-Snow Floods (RoSFs) or Lowland River Floods (LRFs). LRFs contribute from 19% to almost 72%, while RoSFs range from 18% to 75%. In Season 1 (summer), most seasonal floods are identified as LRFs (51%-100%), with very few as RoSFs (0%-46.9%). In Season 2 (winter), the opposite pattern is observed, with most RoSFs (48.4%-97.9%) and fewer LRFs (0%-20.6%). While there are changes in flood patterns, they are not statistically significant. Conducted studies and obtained results can be useful for the preparation of flood prevention documentation and for flood management in general.