Prediction of flood sensitivity based on Logistic Regression, eXtreme Gradient Boosting, and Random Forest modeling methods.

Floods are one of the most destructive disasters that cause loss of life and property worldwide every year. In this study, the aim was to find the best-performing model in flood sensitivity assessment and analyze key characteristic factors, the spatial pattern of flood sensitivity was evaluated using three machine learning (ML) models: Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). Suqian City in Jiangsu Province was selected as the study area, and a random sample dataset of historical flood points was constructed. Fifteen different meteorological, hydrological, and geographical spatial variables were considered in the flood sensitivity assessment, 12 variables were selected based on the multi-collinearity study. Among the results of comparing the selected ML models, the RF method had the highest AUC value, accuracy, and comprehensive evaluation effect, and is a reliable and effective flood risk assessment model. As the main output of this study, the flood sensitivity map is divided into five categories, ranging from very low to very high sensitivity. Using the RF model (i.e., the highest accuracy of the model), the high-risk area covers about 44% of the study area, mainly concentrated in the central, eastern, and southern parts of the old city area.

Hydraulic modeling of the Periyar River using HEC-RAS: unraveling flow dynamics.

The Periyar River, a vital component of Kerala's ecosystem in India, serves as a lifeline supporting agriculture, hydropower generation, and ecological equilibrium. This study adopts a multifaceted approach to address critical challenges in the Periyar basin, with a primary focus on flood mitigation due to the region's susceptibility to devastating floods. Covering a length of 67.85 km, the study intricately segments the Periyar River into distinct reaches for a comprehensive steady flow analysis, considering factors such as seasonal monsoon fluctuations, diverse catchment topography, and human-induced alterations. Utilizing advanced modeling techniques, particularly HEC-RAS software, the study effectively predicts and simulates shifts in hydraulic behavior. The results, including velocity plots and cross-sectional maps, offer accurate insights into critical parameters, enabling the identification of areas with high velocity occurrence. This information proves instrumental in making informed decisions for the construction of river restoration structures, crucial for mitigating the impact of floods. The study's findings contribute valuable tools for future forecasting and sustainable management of the Periyar River, addressing the complex interplay of natural and anthropogenic factors.

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.

Health Consequences Management in a Multi-Hazard Context: A Systematic Review of the Coincidence of Flood and the COVID-19 Pandemic.

OBJECTIVES: The co-occurring flood and coronavirus disease (COVID-19) increase the consequences for health and life. This study examined the strategies to manage the health consequences of the co-occurring flood and COVID-19, with a specific focus on these 2 challenges. METHODS: This review included all the studies published in peer-reviewed journals between January 1980 and June 2021. Several electronic databases were searched, including Scopus, Web of Science, and PubMed. Mixed Methods Appraisal Tools (MMT), version 2018, assessed the articles retrieved through a comprehensive and systematic literature search. Descriptive and thematic analyses were carried out to derive strategies for managing the health consequences of the simultaneous flood and COVID-19. RESULTS: Among 4271 identified articles, 10 were eligible for inclusion. In total, 199 strategies were identified in this review for managing the multi-hazard health consequences of flooding and COVID-19, which were classified into 9 categories and 25 subcategories. The categories included policy making and decision making, coordination, risk communication, logistics, planning, preparedness measures, response measures, social and humanitarian support, and actions of local communities and non-governmental organizations. CONCLUSIONS: Managing a multi-hazard and reducing its health consequences requires various actions. Flood management must be needed, and flood-affected people and their health should be protected.

Machine learning-based potential loss assessment of maize and rice production due to flash flood in Himachal Pradesh, India.

Flash floods in mountainous regions like the Himalayas are considered to be common natural calamities. Their consequences often are more dangerous than any flood event in the plains. These hazards not only put human lives at threat but also cause economic deflation due to the loss of lands, properties, and agricultural production. Hence, assessing the impact of such hazards in the existing agricultural system is of utmost importance to understand the probable crop loss. In this paper, we studied the efficiency of the remotely sensed microwave data to map the croplands affected by the flash flood that occurred in July 2023 in Himachal Pradesh, a mountainous state in the Indian Himalayan Region. The Una, Hamirpur, Kangra, and Sirmaur districts were identified as the most affected areas, with about 9%, 6%, 5.74%, and 3.61% of the respective districts' total geographical area under flood. Further, four machine learning algorithms (random forest, support vector regressor, k-nearest neighbor, and extreme gradient boosting) were evaluated to forecast maize and rice crop production and potential loss during the Kharif season in 2023. A regression algorithm with ten predictor variables consisting of the cropland area, two vegetation indices, and seven climatic parameters was applied to forecast the maize and rice production in the state. Amongst the four algorithms, random forest showed outstanding performance compared to others. The random forest regressor estimated the production of maize and rice with R2 more than 0.8 in most districts. The mean absolute error and the root mean squared error obtained from the random forest regressor were also minimal compared to the others. The maximum production loss of maize is estimated for Solan (54.13%), followed by Una (11.06%), and of rice in Kangra (19.1%), Una (18.8%) and Kinnaur (18.5%) districts. This indicated the utility of the proposed approach for a quick in-season forecast on crop production loss due to climatic hazards.

Dispersal and connectivity in increasingly extreme climatic conditions.

While climate change has been shown to impact several life-history traits of wild-living animal populations, little is known about its effects on dispersal and connectivity. Here, we capitalize on the highly variable flooding regime of the Okavango Delta to investigate the impacts of changing environmental conditions on the dispersal and connectivity of the endangered African wild dog (Lycaon pictus). Based on remote sensed flood extents observed over 20 years, we derive two extreme flood scenarios: a minimum and a maximum flood extent, representative of very dry and very wet environmental periods. These conditions are akin to those anticipated under increased climatic variability, as it is expected under climate change. Using a movement model parameterized with GPS data from dispersing individuals, we simulate 12,000 individual dispersal trajectories across the ecosystem under both scenarios and investigate patterns of connectivity. Across the entire ecosystem, surface water coverage during maximum flood extent reduces dispersal success (i.e., the propensity of individuals to disperse between adjacent subpopulations) by 12% and increases dispersal durations by 17%. Locally, however, dispersal success diminishes by as much as 78%. Depending on the flood extent, alternative dispersal corridors emerge, some of which in the immediate vicinity of human-dominated landscapes. Notably, under maximum flood extent, the number of dispersing trajectories moving into human-dominated landscapes decreases by 41% at the Okavango Delta's inflow, but increases by 126% at the Delta's distal end. This may drive the amplification of human-wildlife conflict. While predicting the impacts of climate change on environmental conditions on the ground remains challenging, our results highlight that environmental change may have significant consequences for dispersal patterns and connectivity, and ultimately, population viability. Acknowledging and anticipating such impacts will be key to effective conservation strategies and to preserve vital dispersal corridors in light of climate change and other human-related landscape alterations.

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.

Application of multi-criteria decision-making on low-impact development practice selection for the Kinyerezi River sub-catchments in Dar es Salaam, Tanzania.

The impermeable areas in catchments are proportional to peak flows that result in floods in river reaches where the flow-carrying capacity is inadequate. The high rate of urbanization witnessed in the Kinyerezi River catchment in Dar es Salaam city has been noted to contribute to floods and siltation in the Msimbazi River. The Low-Impact Development (LID) practices that includes bio-retention (BR) ponds, rain barrels (RBs), green roofs (GRs), etc. can be utilized to mitigate portion of the surface runoff. This study aims to propose suitable LID practices and their sizes for mitigating runoff floods in the Kinyerezi River catchment using the Multi-Criteria Decision-Making (MCDM) approach. The results indicated that the BR and RBs were ranked high in capturing the surface runoff while the sediment control fences were observed to be the best in reducing sediments flowing into the BR. The proposed BR ponds were greater than 800 m2 with 1.2 m depth while RB sizes for Kinyerezi and Kisungu secondary schools and Kinyerezi and Kifuru primary schools were 2,730; 2,748; 1,385; and 1,020 m3, respectively. The BR ponds and RBs are capable of promoting water-demanding economic activities such as horticulture, gardening, car washing while reducing the school expenses and runoff generation.

Transcriptome analysis of antioxidant system response in Styrax tonkinensis seedlings under flood-drought abrupt alternation.

BACKGROUND: Styrax tonkinensis (Pierre) Craib ex Hartwich faces challenges in expanding in the south provinces of Yangtze River region due to climate extremes like flood-drought abrupt alternation (FDAA) caused by global warming. Low tolerance to waterlogging and drought restricts its growth in this area. To study its antioxidant system and molecular response related to the peroxisome pathway under FDAA, we conducted experiments on two-year-old seedlings, measuring growth indexes, reactive oxygen species content, antioxidant enzyme activity, and analyzing transcriptomes under FDAA and drought (DT) conditions. RESULTS: The physiological results indicated a reduction in water content in roots, stems, and leaves under FDAA conditions. The most significant water loss, amounting to 15.53% was observed in the leaves. Also, ROS accumulation was predominantly observed in leaves rather than roots. Through transcriptome analysis, we assembled a total of 1,111,088 unigenes (with a total length of 1,111,628,179 bp). Generally, SOD1 and CAT genes in S. tonkinensis seedlings were up-regulated to scavenge ROS. Conversely, the MPV17 gene exhibited contrasting reaction with up-regulation in leaves and down-regulation in roots, leading to increased ROS accumulation in leaves. CHS and F3H were down-regulated, which did not play an essential role in scavenging ROS. Moreover, the down-regulation of PYL, CPK and CALM genes in leaves may not contribute to stomatal closure, thereby causing continuous water loss through transpiration. Whereas, the decreased root vigor during the waterlogging phase and up-regulated CPK and CALM in roots posed obstacles to water absorption by roots. Additionally, the DEGs related to energy metabolism, including LHCA and LHCB, were negatively regulated. CONCLUSIONS: The ROS generation triggered by MPV17 genes was not the main reason for the eventual mortality of the plant. Instead, plant mortality may be attributed to water loss during the waterlogging phase, decreased root water uptake capacity, and continued water loss during the subsequent drought period. This study establishes a scientific foundation for comprehending the morphological, physiological, and molecular facts of S. tonkinensis under FDAA conditions.

Exploring Phosphate Solubilizing Bacterial Communities in Rhizospheres of Native and Exotic Forage Grasses in Alkaline-Sodic Soils of the Flooding Pampa.

The flooding pampa is one of the most important cattle-raising regions in Argentina. In this region, natural pastures are dominated by low-productivity native grass species, which are the main feed for livestock. In this context, previous studies in the region with the subtropical exotic grass Panicum coloratum highlight it as a promising species to improve pasture productivity. Cultivable phosphate solubilizing bacteria (PSB) communities associated to native (Sporobolus indicus) and exotic (Panicum coloratum) forage grasses adapted to alkaline-sodic soils of the flooding pampa were analyzed. PSB represented 2-14% of cultivable rhizobacteria and Box-PCR fingerprinting revealed a high genetic diversity in both rhizospheres. Taxonomic identification by MALDI-TOF showed that PSB populations of P. coloratum and S. indicus rhizospheres are dominated by the phylum Proteobacteria (92,51% and 96,60% respectively) and to a lesser extent (< 10%), by the phyla Actinobacteria and Firmicutes. At the genus level, both PSB populations were dominated by Enterobacter and Pseudomonas. Siderophore production, nitrogen fixation, and indoleacetic acid production were detected in a variety of PSB genera of both plant species. A higher proportion of siderophore and IAA producers were associated to P. coloratum than S. indicus, probably reflecting a greater dependence of the exotic species on rhizospheric microorganisms to satisfy its nutritional requirements in the soils of the flooding pampa. This work provides a novel knowledge about functional groups of bacteria associated to plants given that there are no previous reports dedicated to the characterization of PSB rhizosphere communities of S indicus and P coloratum. Finally, it should be noted that the collection obtained in this study can be useful for the development of bioinputs that allow reducing the use of chemical fertilizers, providing sustainability to pasture production systems for livestock.

Sub1 QTL confers submergence tolerance in rice through nitro-oxidative regulation and phytohormonal signaling.

Constant change in global climate has become the most important limiting factor to crop productivity. Asymmetrical precipitations are causing recurrent flood events around the world. Submergence is one of the most detrimental abiotic stresses for sustainable rice production in the rainfed ecosystems of Southeast Asia. Therefore, the development of submergence-tolerant rice is an essential requirement to encounter food security. Submergence tolerance in rice is governed by the major quantitative trait locus (QTL) designated as Submergence1 (Sub1) near the centromere of chromosome 9. The introduction of the Sub1 in high-yielding rice varieties producing near-isogenic lines (NILs) has shown extreme submergence tolerance. The present study aimed to understand the responses of rice genotype IR64 and its Sub1 NIL IR64 Sub1 following one week of complete submergence treatment. Submergence imposed severe nitro-oxidative stress in both the rice genotypes, consequently disrupting the cellular redox homeostasis. In this study, IR64 exhibited higher NADPH oxidase activity accompanied by increased reactive oxygen species, reactive nitrogen species, and malondialdehyde buildups and cell death under submergence. Higher accumulations of 1-Aminocyclopropane-1-carboxylic acid, gibberellic acid, and Indole-3-acetic acid were also observed in IR64 which accelerated the plant growth and root cortical aerenchyma development following submergence. In contrast, IR64 Sub1 had enhanced submergence tolerance associated with an improved antioxidant defense system with sustainable morpho-physiological activities and restricted root aerenchyma formation. The comprehensive analyses of the responses of rice genotypes with contrasting submergence tolerance may demonstrate the intricacies of rice under complete submergence and may potentially contribute to improving stress resilience by advancing our understanding of the mechanisms of submergence tolerance in rice.

The Changes of Amino-Acid Metabolism between Wheat and Rice during Early Growth under Flooding Stress.

Floods induce hypoxic stress and reduce wheat growth. On the other hand, rice is a semi-aquatic plant and usually grows even when partially submerged. To clarify the dynamic differences in the cellular mechanism between rice and wheat under flooding stress, morphological and biochemical analyses were performed. Although the growth of wheat in the early stage was significantly suppressed due to flooding stress, rice was hardly affected. Amino-acid analysis revealed significant changes in amino acids involved in the gamma-aminobutyric acid (GABA) shunt and anaerobic/aerobic metabolism. Flood stress significantly increased the contents of GABA and glutamate in wheat compared with rice, though the abundances of glutamate decarboxylase and succinyl semialdehyde dehydrogenase did not change. The abundance of alcohol dehydrogenase and pyruvate carboxylase increased in wheat and rice, respectively. The contents of aspartic acid and pyruvic acid increased in rice root but remained unchanged in wheat; however, the abundance of aspartate aminotransferase increased in wheat root. These results suggest that flooding stress significantly inhibits wheat growth through upregulating amino-acid metabolism and increasing the alcohol-fermentation system compared to rice. When plant growth is inhibited by flooding stress and the aerobic-metabolic system is activated, GABA content increases.

Analysing the outbreaks of leptospirosis after floods in Kerala, India.

A growing number of studies have linked the incidence of leptospirosis with the occurrence of flood events. Nevertheless, the interaction between flood and leptospirosis has not been extensively studied to understand the influence of flood attributes in inducing new cases. This study reviews leptospirosis cases in relation to multiple flood occurrences in Kerala, India. Leptospirosis data were obtained for three years: 2017 (non-flood year) and two years with flooding-2018 (heavy flooding) and 2019 (moderate flooding). We considered the severity of flood events using the discharge, duration and extent of each flooding event and compared them with the leptospirosis cases. The distribution of cases regarding flood discharge and duration was assessed through descriptive and spatiotemporal analyses, respectively. Furthermore, cluster analyses and spatial regression were completed to ascertain the relationship between flood extent and the postflood cases. This study found that postflood cases of leptospirosis can be associated with flood events in space and time. The total cases in both 2018 and 2019 increased in the post-flood phase, with the increase in 2018 being more evident. Unlike the 2019 flood, the flood of 2018 is a significant spatial indicator for postflood cases. Our study shows that flooding leads to an increase in leptospirosis cases, and there is stronger evidence for increased leptospirosis cases after a heavy flood event than after a moderate flooding event. Flood duration may be the most important factor in determining the increase in leptospirosis infections.

Biodegradable microplastics pose greater risks than conventional microplastics to soil properties, microbial community and plant growth, especially under flooded conditions.

Biodegradable plastics (bio-plastics) are often viewed as viable option for mitigating plastic pollution. Nevertheless, the information regarding the potential risks of microplastics (MPs) released from bio-plastics in soil, particularly in flooded soils, is lacking. Here, our objective was to investigate the effect of polylactic acid MPs (PLA-MPs) and polyethylene MPs (PE-MPs) on soil properties, microbial community and plant growth under both non-flooded and flooded conditions. Our results demonstrated that PLA-MPs dramatically increased soil labile carbon (C) content and altered its composition and chemodiversity. The enrichment of labile C stimulated microbial N immobilization, resulting in a depletion of soil mineral nitrogen (N). This specialized environment created by PLA-MPs further filtered out specific microbial species, resulting in a low diversity and simplified microbial community. PLA-MPs caused an increase in denitrifiers (Noviherbaspirillum and Clostridium sensu stricto) and a decrease in nitrifiers (Nitrospira, MND1, and Ellin6067), potentially exacerbating the mineral N deficiency. The mineral N deficit caused by PLA-MPs inhibited wheatgrass growth. Conversely, PE-MPs had less effect on soil ecosystems, including soil properties, microbial community and wheatgrass growth. Overall, our study emphasizes that PLA-MPs cause more adverse effect on the ecosystem than PE-MPs in the short term, and that flooded conditions exacerbate and prolong these adverse effects. These results offer valuable insights for evaluating the potential threats of bio-MPs in both uplands and wetlands.

Rainfall analysis and deriving design storm for hydrological modelling of Koyna dam catchment area in Maharashtra state of India.

Rainfall is a key hydro meteorological variable. Climate change is disrupting the hydrological cycle and altering the usual cycle of rainfall, which frequently results in long-lasting storms with significant rainfall. A first step in hydrologic design of project is to determine the design storm or rainfall events to be used. For deriving design storm, researchers concluded that instead of using generalized readily available curves or maps, it is better to estimate design storm based on site specific historical rainfall data. The objective of the study is to analyze the rainfall data in the koyna watershed area in order to evaluate the design storm, which will be further used as an input data for HEC-HMS event based hydrological modelling of flood peak attenuation of design storm flow at koyna dam during extreme rainfall event. In this study, 40 years (1982-2021) of rainfall data from 8 rain gauge stations in Koyna Dam Catchment area is used initially for performing trend analysis through statistical and graphical techniques and then for Isopluvial analysis. The Sen's slope test and the Mann-Kendall test are the statistical techniques employed, and Innovative Trend Analysis is the graphical technique used. IDF approach is used for deriving design storm, and using Gumbel's frequency distribution method Isohyetal maps, IDF tables and curves are prepared for 2,10,25,50,75 and 100 year return periods and 6,12,24,48 and 96 h durations. Results obtained from statistical and graphical trend analysis of annual rainfall series are consistent. No statistically significant trend in annual rainfall series is observed, however there is rising and falling trend was observed in annual as well as monthly rainfall series. From the results of design storm study, the design storm hyetograph of 10 years return period and 96 h duration is selected, which gives the rainfall intensity of 10.88 mm/h for the koyna catchment. There are various dams nearby koyna catchment, The Isohyet maps, IDF curves and table output available from this study can be more reliably used during planning and design of hydraulic structure for other areas near by koyna catchment.

Medical needs during the Kumamoto heavy rain 2020: analysis from emergency medical teams' responses.

BACKGROUND: Rainfall-induced floods represented 70% of the disasters in Japan from 1985 to 2018 and caused various health problems. To improve preparedness and preventive measures, more information is needed on the health problems caused by heavy rain. However, it has proven challenging to collect health data surrounding disasters due to various inhibiting factors such as environmental hazards and logistical constraints. In response to the Kumamoto Heavy Rain 2020, Emergency Medical Teams (EMTs) used J-SPEED (Japan-Surveillance in Post Extreme Emergencies and Disasters) as a daily reporting tool, collecting patient data and sending it to an EMTCC (EMT Coordination Cell) during the response. We performed a descriptive epidemiological analysis using J-SPEED data to better understand the health problems arising from the Kumamoto Heavy Rain 2020 in Japan. METHODS: During the Kumamoto Heavy Rain 2020 from July 5 to July 31, 2020, 79 EMTs used the J-SPEED form to submit daily reports to the EMTCC on the number and types of health problems they treated. We analyzed the 207 daily reports, categorizing the data by age, gender, and time period. RESULTS: Among the 816 reported consultations, women accounted for 51% and men accounted for 49%. The majority of patients were elderly (62.1%), followed by adults (32.8%), and children (5%). The most common health issues included treatment interruption (12.4%), hypertension (12.0%), wounds (10.8%), minor trauma (9.6%), and disaster-related stress symptoms (7.4%). Consultations followed six phases during the disaster response, with the highest occurrence during the hyperacute and acute phases. Directly disaster-related events comprised 13.9% of consultations, indirectly related events comprised 52.0%, and unrelated events comprised 34.0%. As the response phases progressed, the proportions of directly and indirectly related events decreased while that of unrelated events increased. CONCLUSION: By harnessing data captured by J-SPEED, this research demonstrates the feasibility of collecting, quantifying, and analyzing data using a uniform format. Comparison of the present findings with those of two previous analyses of J-SPEED data from other disaster scenarios that varied in time, location, and/or disaster type showcases the potential to use analysis of past experiences to advancing knowledge on disaster medicine and disaster public health.

UAV-based modelling of vegetation recovery under extreme habitat stresses in the water level fluctuation zone of the Three Gorges Reservoir, China.

Impoundment of the Three Gorges Reservoir on the upper Yangtze River has remarkably altered hydrological regime within the dammed reaches, triggering structural and functional changes of the riparian ecosystem. Up to date, how vegetation recovers in response to compound habitat stresses in the water level fluctuation zone remains inexplicitly understood. In this study, plant above-ground biomass (AGB) in a selected water level fluctuation zone was quantified to depict its spatial and temporal pattern using unmanned aerial vehicle (UAV)-derived multispectral images and screened empirical models. The contributions of multiple habitat stressors in governing vegetation recovery dynamics along the environmental gradient were further explored. Screened random forest models indicated relatively higher accuracy in AGB estimation, with R2 being 0.68, 0.79 and 0.62 during the sprouting, growth, and mature periods, respectively. AGB displayed a significant linear increasing trend along the elevational gradient during the sprouting and early growth period, while it showed an inverted U-shaped pattern during late growth and mature period. Flooding duration, magnitude and timing were found to exert greater negative effects on plant sprouting and biomass accumulation and acted as decisive factors in governing the elevation-dependent pattern of AGB. Localized spatial variations in AGB were modulated by other stressors such as sediment burial, soil erosion, soil moisture and nutrient content. Occurrence of episodic summer floods and vegetation distribution were responsible for an inverted U-shaped pattern of AGB during the late growth and mature period. Generally, AGB reached its peak in August, thereafter an obvious decline by an unprecedent dry-hot climatic event. The water level fluctuations with cumulative flooding effects exerted substantial control on AGB temporal dynamics, while climatic condition played a secondary role. Herein, further restorative efforts need to be directed to screening suitable species, maintaining favorable soil condition, and improving vegetation pattern to balance the many trade-offs.