Floods are devastating natural disasters causing significant damage worldwide, especially in southern Latin America, where recurrent river floods lead to severe impacts. This study proposes an innovative flood modelling approach using a naive Bayes classifier to simulate flood extents at a regional scale, incorporating spatial and temporal variability. Using 12 features, including topography, soil properties, precipitation and discharge, the model was trained with multiple flood events, avoiding sampling limitations and evaluating optimal pre-processing strategies for continuous data. The predictive capacity resulted in high performance metrics, with temporal validation accuracy (AC) up to 0.98 and a critical success index (CSI) of 0.58, and spatial validation achieved an AC up to 0.97 and CSI of 0.56, outperforming the hydrodynamic model by 65%. A reduced model with significant features improved computational efficiency and achieved a CSI exceeding 0.60. This practical tool supports flood risk management and enhances resilience in vulnerable regions.

Due to recent rainfall extremes and tropical cyclones that form over the Bay of Bengal during the pre- and post-monsoon seasons, the Nagavali and Vamsadhara basins in India experience frequent floods, causing significant loss of human life and damage to agricultural lands and infrastructure. This study provides an integrated hydrologic and hydraulic modeling system that is based on the Soil and Water Assessment Tool model and the 2-Dimensional Hydrological Engineering Centre-River Analysis System, which simulates floods using Global Forecasting System rainfall forecasts with a 48-h lead time. The integrated model was used to simulate the streamflow, flood area extent, and depth for the historical flood events (i.e., 1991-2018) with peak discharges of 1200 m3/s in the Nagavali basin and 1360 m3/s in the Vamsadhara basin. The integrated model predicted flood inundation depths that were in good agreement with observed inundation depths provided by the Central Water Commission. The inundation maps generated by the integrated modeling system with a 48-h lead time for tropical cyclone Titli demonstrated an accuracy of more than 75%. The insights gained from this study will help the public and government agencies make better decisions and deal with floods.

Hydro-Fluctuation Belt (HFB), a periodically exposed bank area formed by changes in water level fluctuations, is critical for damaging the reservoir wetland landscape and ecological balance. Thus, it is important to explore the mechanism of hydrological conditions on the plant-soil system of the HFB for protection of the reservoir wetland and landscape restoration. Here, we investigated the response of plant community characteristics and soil environment of the HFB of Tonghui River National Wetland Park (China), is a typical reservoir wetland, to the duration of inundation, as well as the correlation between the distribution of dominant plants and soil pH, nutrient contents, and enzyme activity by linear regression and canonical correlation analyses. The results show that as the duration of inundation decreases, the vegetation within the HFB is successional from annual or biennial herbs to perennial herbs and shrubs, with dominant plant species prominent and uneven distribution of species. Soil nutrient contents and enzyme activities of HFB decreased with increasing inundation duration. Dominant species of HFB plant community are related to soil environment, with water content, pH, urease, and available potassium being principle soil environmental factors affecting their distribution. When HFB was inundated for 0-30 days, soil pH was strongly acidic, with available potassium content above 150 mg kg(-1) and higher urease activity, distributed with Arundo donax L., Polygonum perfoliatum L., Alternanthera philoxeroides (Mart.) Griseb., and Daucus carota L. communities. When inundated for 30-80 days, soil pH was acidic, with lower available potassium content (50-150 mg kg(-1)) and urease activity, distributed with Beckmannia syzigachne (Steud.) Fern.+ Polygonum lapathifolium L., Polygonum lapathifolium L., Medicago lupulina L. + Dysphania ambrosioides L. and Leptochloa panicea (Retz.) Ohwi communities. Using the constructed HFB plant-soil correlation model, changes in the wetland soil environment can be quickly judged by the succession of plant dominant species, which provides a simpler method for the monitoring of the soil environment in the reservoir wetland, and is of great significance for the scientific management and reasonable protection of the reservoir-type wetland ecosystem.

Sudden wilt syndrome of chilli, an emerging and destructive disorder, is characterized by an abrupt appearance that has increased in Indo-Gangetic alluvial plains over the past decade. The primary cause associated with the problem is water stagnation that creates hypoxic conditions in the root zone, and the plant mortality is further aggravated by soil-borne fungi Fusarium oxysporum. The effects of sudden wilt on chilli roots were studied morphologically and histologically, using root scanning, optical microscopy and electron microscopy. Significant changes with respect to root length, projected area, number of root tips and root segments, average root diameter as well as estimated volume of roots were observed via root scanning of healthy and diseased root samples. Through optical microscopy of sections of the microtome-cut root junctions displayed normal epidermis in healthy plants, while swollen cells indicated stress in the diseased plants. Cells of laterals and root tips in healthy plants were intact and stained strongly, but because of damaged tissues, cells in diseased plants were macerated and stained weakly. Root tips of healthy plants contained mitotic zones, whereas diseased root tips lacked mitotic zones. Electron microscopy studies revealed that sudden wilt had an adverse impact on xylem diameter, stele diameter, epidermal thickness and cortex thickness as evidenced by significantly lesser values of these parameters. The present study is the first systematic attempt to examine the morphological, histological and ultrastructural changes in chilli in response to sudden wilt syndrome.

Humidity is a basic and crucial meteorological indicator commonly measured in several forms, including specific humidity, relative humidity, and absolute humidity. These different forms can be inter-derived based on the saturation vapor pressure (SVP). In past decades, dozens of formulae have been developed to calculate the SVP with respect to, and in equilibrium with, liquid water and solid ice surfaces, but many prior studies use a single function for all temperature ranges, without considering the distinction between over the liquid water and ice surfaces. These different approaches can result in humidity estimates that may impact our understanding of surface-subsurface thermal-hydrological dynamics in cold regions. In this study, we compared the relative humidity (RH) downloaded and calculated from four data sources in Alaska based on five commonly used SVP formulas. These RHs, along with other meteorological indicators, were then used to drive physics-rich land surface models at a permafrost-affected site. We found that higher values of RH (up to 40 %) were obtained if the SVP was calculated with the over-ice formulation when air temperatures were below freezing, which could lead to a 30 % maximum difference in snow depths. The choice of whether to separately calculate the SVP over an ice surface in winter also produced a significant range (up to 0.2 m) in simulated annual maximum thaw depths. The sensitivity of seasonal thaw depth to the formulation of SVP increases with the rainfall rate and the height of above-ground ponded water, while it diminishes with warmer air temperatures. These results show that RH variations based on the calculation of SVP with or without over-ice calculation meaningfully impact physicallybased predictions of snow depth, sublimation, soil temperature, and active layer thickness. Under particular conditions, when severe flooding (inundation) and cool air temperatures are present, care should be taken to evaluate how humidity data is estimated for land surface and earth system modeling

Extreme climate events such as storms and severe droughts are becoming more frequent under the warming climate. In the tropics, excess rainfall carried by hurricanes causes massive flooding and threatens ecosystems and human society. We assessed recent major floodings on the tropical island of Puerto Rico after Hurricane Maria in 2017 and Hurricane Fiona in 2022, both of which cost billions of dollars damages to the island. We analyzed the Sentinel-1 synthetic aperture radar (SAR) images right after the hurricanes and detected surface inundation extent by applying a random forest classifier. We further explored hurricane rainfall patterns, flow accumulation, and other possible drivers of surface inundation at watershed scale and discussed the limitations. An independent validation dataset on flooding derived from high-resolution aerial images indicated a high classification accuracy with a Kappa statistic of 0.83. The total detected surface inundation amounted to 10,307 ha after Hurricane Maria and 7949 ha after Hurricane Fiona for areas with SAR images available. The inundation patterns are differentiated by the hurricane paths and associated rainfall patterns. We found that flow accumulation estimated from the interpolated Fiona rainfall highly correlated with the ground-observed stream discharges, with a Pearson's correlation coefficient of 0.98. The detected inundation extent was found to depend strongly on hurricane rainfall and topography in lowlands within watersheds. Normal climate, which connects to mean soil moisture, also contributed to the differentiated flooding extent among watersheds. The higher the accumulated Fiona rain and the lower the mean elevation in the flat lowlands, the larger the detected surface flooding extent at the watershed scale. Additionally, the drier the climate, which might indicate drier soils, the smaller the surface flooding areas. The approach used in this study is limited by the penetration capability of C-band SAR; further application of L-band images would expand the detection to flooding under dense vegetation. Detecting flooding by applying machine learning techniques to SAR satellite images provides an effective, efficient, and reliable approach to flood assessment in coastal regions on a large scale, hence helping to guide emergency responses and policy making and to mitigate flooding disasters.

Tropical Cyclone Yaas inflicted substanntial damage on Bhitarkanika National Park (BNP), an eminent wildlife sanctuary housing a vast diversity of flora and fauna, during its occurrence in 2021. The park has experienced a heightened frequency of cyclonic activity in recent years. This study undertakes a comprehensive analysis of the impacts of Tropical Cyclone Yaas on the mangrove forest within BNP, utilizing a broad array of physical, biological, and ecological indices. The assessment method employed in the study encompasses various indicators, such as ecological (Normalized Difference Vegetation Index - NDVI, Enhanced Vegetation Index - EVI, Leaf Area Index - LAI, Normalized Difference Water Index - NDWI, and Normalized Difference Salinity Index - NDSI), biological (Chlorophyll content and Gross Primary Productivity - GPP), and physical (flood monitoring and precipitation) measures. Our findings elucidate the destructive consequences of Cyclone Yaas on the mangrove forest, inflicting significant ecosystem loss attributable to the extreme precipitation and high wind speeds. The biophysical, ecological, and biological indicators reveal profound effects on the local ecosystem, manifested through a decline in vegetation vigor and alterations in soil conditions, notably marked by an increase in salinity.

Coastal wetland soils are frequently underlain by sulfidic materials. Sea level fluctuations can lead to oxidation of sulfidic materials in acid sulfate soils (ASS) and increased acidity which mobilises trace metals when water levels are low, and inundation of coastal wetland soils and reformation of sulfidic materials when water levels are high. We measured the effect of surface water level fluctuations in soils from coastal wetland sites under four different vegetation types: Apium gravedens (AG), Leptospermum lanigerum (LL), Phragmites australis (PA) and Paspalum distichum (PD) on an estuarine floodplain in southern Australia. We assessed effects of fluctuating water levels on reduced inorganic sulfur (RIS) in terms of acid volatile sulfide (AVS), chromium reducible sulfur (CRS) and trace metals (Fe, Al, Mn, Zn, Ni). Intact soil cores were incubated under dry, flooded and wet-dry cycle treatments of 14 days for a total of 56 days. The flooded treatment increased RIS concentrations in most depths in the AG, PA and PD sites. Lower CRS concentrations occurred in all sites in the dry treatment due to oxidation of sulfidic materials when the surface layer was exposed to lower water levels. CRS was positively correlated with SOC in all treatments. The highest net acidity occurred in the dry treatment and lowest occurred in the flooded treatment in most sites. Inundation with seawater caused SO42- reduction and decreased soluble Fe in the PA and PD sites. General decreases in Al, Zn and Ni concentrations in flooded treatments may have been due to adsorption onto colloids or co-precipitation with slight increases in pH. SO42- concentrations decreased in the LL, PA and PD sites in the flooded treatment due to reformation of pyrite. In general, accumulation of RIS in soils under different vegetation types following brackish water inundation varied according to vegetation type, which may be linked to differences in organic material input and particle size distribution. Geochemical characteristics reflected whether oxidation or reduction processes dominated at each site in the wet-dry cycle treatments, with oxidation dominating in the LL and PA sites and reduction dominating in the AG and PD sites. This is likely due to more readily decomposable organic matter forming sulfidic materials during short periods of inundation.

Arctic wetlands play a critical role in the global carbon cycle and are experiencing disproportionate impacts from climate change. Even though Alaska hosts 65% of U.S. wetlands, less than half of the wetlands in Alaska have been mapped by the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) or other high-resolution wetlands protocols. The availability of time series satellite data and the development of machine learning algorithms have enabled the characterization of Arctic wetland inundation dynamics and vegetation types with limited ground data input. In this study, we built a semi-automatic process to generate sub-pixel water fraction (SWF) maps across the Coastal Plain of the Arctic National Wildlife Refuge (ANWR) in Alaska using random forest regression and 139 Sentinel-2 images taken in ice-free seasons from 2016 to 2019. With this, we characterized the seasonal dynamics of wetland inundation and explored their potential usage in determining NWI water regimes. The highest levels of surface water expression were detected in June, resulting from seasonal active layer thaw and snowmelt. Inundation was most variable in riverbeds, lake and pond margins, and depressional wetlands, where water levels fluctuate substantially between dry and wet seasons. NWI water regimes that indicate frequent inundation, such as permanently flooded wetlands, had high SWF values (SWF >= 90%), while those with infrequent inundation, such as temporarily flooded wetlands, had low SWF values (SWF < 10%). Vegetation types were also classified through the synergistic use of a vegetation index, water regimes, synthetic-aperture radar (SAR) data, topographic data, and a random forest classifier. The random forest classification algorithms demonstrated good performance in classifying Arctic wetland vegetation types, with an overall accuracy of 0.87. Compared with NWI data produced in the 1980s, scrub-shrub wetlands appear to have increased from 91 to 258 km(2) over the last three decades, which is the largest percentage change (182%) among all vegetation types. However, additional field data are needed to confirm this shift in vegetation type. This study demonstrates the potential of using time series satellite data and machine learning algorithms in characterizing inundation dynamics and vegetation types of Arctic wetlands. This approach could aid in the creation and maintenance of wetland inventories, including the NWI, in Arctic regions and enable an improved understanding of long-term wetland dynamics.

Coastal inundation causes considerable impacts on communities and economies. Sea level rise due to climate change increases the occurrence of coastal flood events, creating more challenges to coastal societies. Here we intend to draw the understanding of coastal inundation from our early studies, and provide a silhouette of our approaches in assessing climate change impacts as well as developing risk-based climate adaptation. As a result, we impart a distinctive view of the adaption towards the integration of asset design, coastal planning and policy development, which reflect multiscale approaches crossing individual systems to regions and then nation. Having the approaches, we also discussed the constraints that would be faced in adaptation implementation. In this regard, we initially follow the risk approach by illustrating hazards, exposure and vulnerability in relation to coastal inundation, and manifest the impact and risk assessment by considering an urban environment pertinent to built, natural, and socioeconomic systems. We then extend the scope and recommend the general approaches in developing adaptation to coastal inundation under climate change towards ameliorating overall risks, practically, by the reduction in exposure and vulnerability in virtue of the integration of design, planning and polices. In more details, a resilience design is introduced, to effectively enhance the capacity of built assets to resist coastal inundation impact. We then emphasize on the cost-effective adaptation for coastal planning, which delineates the problem of under-adaptation that leaves some potential benefits unrealized or over-adaptation that potentially consumes an excessive amount of resources. Finally, we specifically explore the issues in planning and policies in mitigating climate change risks, and put forward some emerging constraints in adaptation implementation. It suggests further requirements of harmonizing while transforming national policies into the contents aligned with provincial and local governments, communities, and households.