Analysis of factors in community willingness to manage floods in East Java during the pandemic.

Urbanisation is an important aspect of environmental management and requires special attention, as half of the world's population lives in urban areas. As the urban population grows, this poses a number of challenges, particularly related to environmental issues and floods. Indonesia, a developing nation and the fourth most populous globally, with over 264 million inhabitants, faces significant environmental and flood concerns, notably exacerbated by the coronavirus disease 2019 (COVID-19) pandemic. These issues are poised to exert a profound and enduring influence on the populace. East Java province, one of the densely populated provinces in Indonesia, experiences complex environmental problems, floods, and air pollution. Therefore, this research identified the factors that influenced the community's willingness to participate in environmental conservation and flood management in East Java province during the COVID-19 pandemic. This research employed a mixed-method approach, combined qualitative and quantitative methods, and utilised the triangular relationship theory. The result showed six predominant factors within the population of East Java that significantly affected the environmental conditions and the frequency of flood events in the area. Contribution: Floods in the city during the pandemic caused concern for those affected by the disaster and the implementation of activities adjusted government policies. For sustainability, the urban environment in Indonesia is working hard to anticipate flooding in cities. Apart from that, the government, private sector, community leaders, and the media also play an important role.

Infectious Diarrhea Risks as a Public Health Emergency in Floods; a Systematic Review and Meta-Analysis.

Introduction: Infectious diarrhea, a significant global health challenge, is exacerbated by flooding, a consequence of climate change and environmental disruption. This comprehensive study aims to quantify the association between flooding events and the incidence of infectious diarrhea, considering diverse demographic, environmental, and pathogen-specific factors. Methods: In this systematic review and meta-analysis, adhering to PROSPERO protocol (CRD42024498899), we evaluated observational studies from January 2000 to December 2023. The analysis incorporated global data from PubMed, Scopus, Embase, Web of Science, and ProQuest, focusing on the relative risk (RR) of diarrhea post-flooding. The study encompassed diverse variables like age, sex, pathogen type, environmental context, and statistical modeling approaches. Results: The meta-analysis, involving 42 high-quality studies, revealed a substantial increase (RR = 1.40, 95% CI [1.29-1.52]) in the incidence of diarrhea following floods. Notably, bacterial and parasitic diarrheas demonstrated higher RRs (1.82 and 1.35, respectively) compared to viral etiologies (RR = 1.15). A significant sex disparity was observed, with women exhibiting a higher susceptibility (RR = 1.55) than men (RR = 1.35). Adults (over 15 years) faced a greater risk than younger individuals, highlighting age-dependent vulnerability. Conclusion: This extensive analysis confirms a significant correlation between flood events and increased infectious diarrhea risk, varying across pathogens and demographic groups. The findings highlight an urgent need for tailored public health interventions in flood-prone areas, focusing on enhanced sanitation, disease surveillance, and targeted education to mitigate this elevated risk. Our study underscores the critical importance of integrating flood-related health risks into global public health planning and climate change adaptation strategies.

Associated impact mechanism of heavy rain and floods in the middle and lower reaches of the Yangtze river basin based on ocean-atmosphere anomaly patterns.

Heavy rainfall and flooding disasters are increasing due to global warming. A clear understanding of the mechanism of heavy rain and floods is the basic premise of disaster risk management. However, most previous studies emphasized more on the single anomalous signal from the average state in the whole season, which may neglect the combined influence of multiple signals in the ocean-atmosphere and differential characteristics of anomalous signals at different periods. Here, our study aimed to reveal the possible influence mechanism of heavy rain and floods in the middle and lower reaches of the Yangtze River Basin (MLRYRB) by systematically analyzing the monthly-scale and daily-scale ocean-atmosphere anomaly patterns in the preceding periods of heavy rainfall and flooding events. The results showed that heavy rainfall and flooding events were highly likely to occur in the region one month after El Niño decayed, with the flooding intensity in June having the negative correlation with the sea ice concentration anomaly in the Arctic with a lag of about 5 months (150 days). Besides, North Atlantic Oscillation, Western Pacific subtropical high, blocking, East Asian subtropical westerly jet, and the water vapor fluxes from the Arabian Sea and western Pacific Ocean could be used as the anomalous signals inducing heavy rain and floods. The daily-scale conceptual model inducing heavy rainfall and flooding events was built based on the patterns of all anomalous signals, which detailed the possible impact mechanism of heavy rain and floods in the region. By making targeted forecasts of anomalous signals and using this information in water resources planning and management based on climate mechanisms, it will have a significant impact on water management in the country.

Performance and uncertainty assessment of green roofs for urban flood reduction in a high-density catchment in Ahmedabad, India.

Urban flooding poses a significant challenge to the rapidly growing Indian cities. Low-impact development strategies such as green roofs have shown the potential to reduce urban flooding. However, their performance assessment significantly varies across different studies. Therefore, the study's primary objective is to evaluate green roofs in the Indian context. For this evaluation, the green roofs are assessed based on building-level implementation scenarios for a high-density urban area in India for 25%,50%, and 75% application rates and different rainfall intensities (2,3 and 4-h duration and 2,5,10 and 25-year frequencies). Secondly, to probe the variations in the green roof performance across studies, uncertainty contributions to the runoff reduction from different parameters are quantified. The results show that green roofs can reduce up to 62% of flood volume and 24% of runoff. However, they are reasonably effective only beyond 25% application rates. Further, rainfall intensity contributes the most to the uncertainty of runoff reduction from green roofs. This uncertainty assessment implies that localized evaluation of green roofs depending on local rainfall conditions is required for city-wide policy planning. The study has a significant contribution to building confidence in the ability of green roofs to reduce urban floods in the context of developing countries like India.

An assessment of priorities in handling climate change impacts on infrastructures.

Climate change (CC) will likely significantly impact the world's infrastructure significantly. Rising temperatures, increased precipitation, and rising sea levels are all likely to stress critical infrastructures (CI). Rising temperatures can lead to infrastructure damage from extreme heat events. This can cause roads and bridges to buckle or crack, leading to costly repairs and potential traffic disruptions. In addition, heat waves can damage vital electrical infrastructure, leading to widespread power outages. In light of this context, this article reports on a study which examined the connections and impacts of CC on infrastructure. The study employed a mixed-method approach, combining bibliometric analysis for the period 1997-2022 with a series of relevant case studies from the five continents to offer insight into the impact of CC on infrastructure. The article fills a research gap in respect of assessments of the extent to which climate change (CC) negative influences the infrastructure, with a special focus on developing countries. It also showcases CI projects and adaptation measures being currently deployed, to address CC. The results show that the current infrastructure is vulnerable to CC. The selected case studies on CI adaptation show that in developing and industrialised countries, there is a perceived need to understand better the connections and potential impacts of CC on critical areas such as transport, settlements, and coastal infrastructure. In order to protect infrastructure from CC impacts, governments need to invest in measures such as flood control, early warning systems, and improved building codes. Additionally, they need to work to reduce greenhouse gas emissions more actively, which are the primary cause of CC.

Global systematical and comprehensive overview of mountainous flood risk under climate change and human activities.

Snow-covered mountainous regions are crucial for the hydrologic cycle. Any changes in the cryosphere are critical and directly impact the hydrologic cycle and socio-environment of the downstream. It is likely to occur more extreme events of precipitations, raising the risk of flooding worldwide. Glacier melting is increasing, thus the formation of the moraine-dammed lake called glacial lake, whose outburst may be a catastrophic disaster. Due to steep topography, flash floods with high energy can sweep away infrastructure, electric power stations, property, and livelihood and even change the channel morphology, hence the whole environment. In this article, we present the causes of flooding in mountainous regions and historical trends of mountainous flooding and its management policies. Carbon emission is a driver to increase the temperature of the globe and which is triggering the flash floods in mountainous regions is illustrated using data from different sources. The discussion section includes how technology helps to achieve a climate-resilient environment. Understanding river morphology, mapping and monitoring risks, and simulating essential natural processes are necessary for reducing the cascading hazards in the mountains. There is still a gap in modern data collection techniques in mountainous regions. More advanced technology for regional and global collaborations, climate change adaption, and public awareness can build the climate resilience cryosphere.

Improving flood and drought management in transboundary Upper Jhelum Basin-South Asia.

The two contrasting extremes of the hydrological spectrum have substantial and far-reaching impacts on a wide range of sectors including water resources, agriculture and food security, energy, infrastructure, and ecosystem. The compounding factors of climate change, burgeoning population, and rapid economic development create unprecedented challenges in devising effective and sustainable strategies to cope with these natural disasters and minimize their devastating impacts. This study identifies the geographical areas that are prone to meteorological wet-dry extreme events, as drivers of hydrologic floods and droughts, and their temporal compounding in the transboundary Upper Jhelum Basin-South Asia. Additionally, the study provides a comprehensive overview of the existing and proposed water development projects, their coping capacities, and potential impacts that may be positive or negative in hydrologic, social, economic and environment terms. Extensive review and data analysis revealed that the both Pakistan and India, along with state governments, have implemented a significant number of water-related projects across the basin, however significant progress towards achieving their stated goals remains elusive. Currently, Pakistan operates 15 runoff river type hydropower plants, and an additional 11 similar projects are under construction. In contrast, Indian administrative Kashmir has 10 such plants in operation and 4 under construction. The primary factors that impede the realization of expected benefits from these projects are geography, high flow variability across seasons, climate change, insufficient planning, geopolitical disputes, lack of transboundary cooperation, financial limitations, and reservoir operation. Based on these factors, the present study suggests some alternative water management measures that offer flexibility, cost-effectiveness, accessibility, and a low environmental impact. These solutions include implementation of sub-surface floodwater harvesting system in the southwest of the basin, where extreme wet and dry events occur in close succession, augmentation of existing hydropower reservoirs with floating photovoltaic technology, and non-structural measures, including early warning systems, ecosystems-based adaptation, and green infrastructure interventions such as restoring headwater forests, reclaiming floodplains, and wetlands. This will result in reduced flood and drought impacts at local and downstream areas and reduce the reliance of local communities on forest wood.

Floods and cause-specific mortality in the UK: a nested case-control study.

BACKGROUND: Floods are the most frequent weather-related disaster, causing significant health impacts worldwide. Limited studies have examined the long-term consequences of flooding exposure. METHODS: Flood data were retrieved from the Dartmouth Flood Observatory and linked with health data from 499,487 UK Biobank participants. To calculate the annual cumulative flooding exposure, we multiplied the duration and severity of each flood event and then summed these values for each year. We conducted a nested case-control analysis to evaluate the long-term effect of flooding exposure on all-cause and cause-specific mortality. Each case was matched with eight controls. Flooding exposure was modelled using a distributed lag non-linear model to capture its nonlinear and lagged effects. RESULTS: The risk of all-cause mortality increased by 6.7% (odds ratio (OR): 1.067, 95% confidence interval (CI): 1.063-1.071) for every unit increase in flood index after confounders had been controlled for. The mortality risk from neurological and mental diseases was negligible in the current year, but strongest in the lag years 3 and 4. By contrast, the risk of mortality from suicide was the strongest in the current year (OR: 1.018, 95% CI: 1.008-1.028), and attenuated to lag year 5. Participants with higher levels of education and household income had a higher estimated risk of death from most causes whereas the risk of suicide-related mortality was higher among participants who were obese, had lower household income, engaged in less physical activity, were non-moderate alcohol consumers, and those living in more deprived areas. CONCLUSIONS: Long-term exposure to floods is associated with an increased risk of mortality. The health consequences of flooding exposure would vary across different periods after the event, with different profiles of vulnerable populations identified for different causes of death. These findings contribute to a better understanding of the long-term impacts of flooding exposure.

Assessment of coping capacity of public health facilities with health emergency situations during an event of flood in Mumbai.

BACKGROUND: Floods had been a repeated phenomenon in India, causing considerable losses to properties, life and infrastructure, and public utilities. Floods are found to be a common natural disaster occurring not only in developing countries but also in developed nations. Hospital preparedness against disaster could help in the management of the surge of patients in an effective manner. This study aims to assess the capacity of public health facilities for coping up with health emergency situations during an event of flood in Mumbai. MATERIALS AND METHODS: A cross-sectional study was conducted among 38 public health facilities in Mumbai. A purposive sampling technique was used for the selection of the health facilities. Administrative staff, medical officers, and pharmacy in charges were included in the study. RESULTS: The study revealed that surge capacities in hospitals were adequate as they had additional beds for monsoon-related diseases for the preceding flood situation. There was a triage policy, and the staff were trained on the same. Procurement of drugs was easy because of a good networking system between hospitals. Due to this networking system, patients were also transferred to the nearest healthcare facility in a short span of time. CONCLUSION: The response during an event of a flood in the health facilities of Mumbai city was good as they have regular training sessions to be prepared for the emergency situations during monsoon as they have repeated exposure to floods.

Community-based screening for post-traumatic stress disorder among flood victims - A cross-sectional study from Kerala, India.

Background: Natural disasters like floods have various physical and psychological effects on victims. Post-traumatic stress disorder (PTSD) is a condition that arises as a delayed reaction to extraordinarily threatening or catastrophic situations. Aim: The objective was to screen for PTSD and associated factors among victims residing in the flood-affected areas of Kerala, India. Methodology: A community-based screening for PTSD was done among 600 flood victims residing in three selected districts (Pathanamthitta, Alleppey, and Kottayam) of Kerala. A trauma screening questionnaire was used to screen for PTSD. Results: More than 90% of them had to stay in relief camps during floods. More than 80% had damage to houses, followed by loss of domestic animals and vehicle damage. Among participants, 298 (49.7%) screened positive for PTSD 3 months post disaster. No previous history of flooding, odds ratio (OR) = 8.6 [confidence interval (CI) 5.7-13.1]; younger age, OR = 1.41 (CI 1-1.9); higher family income, OR = 4.2 (CI 2.5-6.8); education, OR = 1.4 (CI 1-2.1); flood-related morbidity, OR = 8.8 (CI 5.3-14.8); and death of a family member, OR = 3.4 (CI 1.2-9.3), were the factors that were found to be significantly associated with stress among respondents. Conclusion: Almost 50% of flood victims were screened positive for PTSD. This study's findings reiterate the need to provide psychological support as a priority along with other disaster control measures.

Non-cholera Vibrio spp. invasive infections in the summer following May 2023 flood disaster in Romagna, Italy: a case series.

Non-cholera Vibrio spp. includes ubiquitous organisms living in aquatic environments. Their occurrence is associated with global warming and meteorological disasters. In May 2023 the Romagna region, Italy, was affected by severe floods. In the following 15 weeks we observed 5 patients with invasive infections caused by V. vulnificus (3/5) and V. harveyi (2/5). All patients (median age 77 years) had medical comorbidities and shared exposure to seawater. Two patients needed surgery; 2 died. In conclusion, we observed an increased burden of Vibrio spp. invasive infections after May 2023 floods, affecting old patients with predisposing medical conditions.

Combining multisensor images and social network data to assess the area flooded by a hurricane event.

In this study, multisensor remote sensing datasets were used to characterize the land use and land covers (LULC) flooded by Hurricane Willa which made landfall on October 24, 2018. The landscape characterization was done using an unsupervised K-means algorithm of a cloud-free Sentinel-2 MultiSpectral Instrument (MSI) image, acquired during the dry season before Hurricane Willa. A flood map was derived using the histogram thresholding technique over a Synthetic Aperture Radar (SAR) Sentinel-1 C-band and combined with a flood map derived from a Sentinel-2 MSI image. Both, the Sentinel-1 and Sentinel-2 images were obtained after Willa landfall. While the LULC map reached an accuracy of 92%, validated using data collected during field surveys, the flood map achieved 90% overall accuracy, validated using locations extracted from social network data, that were manually georeferenced. The agriculture class was the dominant land use (about 2,624 km2), followed by deciduous forest (1,591 km2) and sub-perennial forest (1,317 km2). About 1,608 km2 represents the permanent wetlands (mangrove, salt marsh, lagoon and estuaries, and littoral classes), but only 489 km2 of this area belongs to aquatic surfaces (lagoons and estuaries). The flooded area was 1,225 km2, with the agricultural class as the most impacted (735 km2). Our analysis detected the saltmarsh class occupied 541 km2in the LULC map, and around 328 km2 were flooded during Hurricane Willa. Since the water flow receded relatively quickly, obtaining representative imagery to assess the flood event was a challenge. Still, the high overall accuracies obtained in this study allow us to assume that the outputs are reliable and can be used in the implementation of effective strategies for the protection, restoration, and management of wetlands. In addition, they will improve the capacity of local governments and residents of Marismas Nacionales to make informed decisions for the protection of vulnerable areas to the different threats derived from climate change.

Spatiotemporal variation of hydrological connectivity and its threshold effects on flood dynamics: An examination in the arid and semi-arid regions, China.

Hydrological connectivity, a critical indicator of underlying surface changes, plays a pivotal role in the generation and evolution of floods. This study focuses on examining hydrological connectivity and its significant impact on flood dynamics. The Hekou-Longmen section (HL) is used as a case area because of its frequent flooding, which is typical of arid and semi-arid zone. By employing the modified hydrological connectivity index (IC), this study evaluated the hydrological connectivity and examined its spatiotemporal variation of the HL. Based on 1131 Annual Maximum Instantaneous Streamflow (AMS) data of 21 sub-basins in the HL, a panel threshold regression model was used to reveal threshold effect of IC on flood dynamics. The results showed that the annual mean IC showed a decreasing trend, with spatial variation dominated by significant decreases and no change. Furthermore, it was found that the magnitude of the effect of extreme precipitation (EP) on AMS increased with increasing IC thresholds. The threshold effect of EP on AMS were found to exist during the 1990s, 2000s, and 2010s, with thresholds of 2.84, 3.27, and 3.37, respectively. This research established a quantitative framework for comprehensively evaluating the impact of underlying surface changes on flood, providing important reference for the study of flood mechanisms in similar arid and semi-arid regions globally.

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.

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.

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.

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.

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.

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.

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.