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Flooding is predicted to become more frequent in the coming decades because of global climate change. Recent literature has highlighted the importance of river morphodynamics in controlling flood hazards at the local scale. Abrupt and short-term geomorphic changes can occur after major flood-inducing storms. However, there is still a widespread lack of ability to foresee where and when substantial geomorphic changes will occur, as well as their ramifications for future flood hazards. This study sought to gain an understanding of the implications of major storm events for future flood hazards. For this purpose, we developed self-organizing maps (SOMs) to predict post-storm changes in stage–discharge relationships, based on storm characteristics and watershed properties at 3101 stream gages across the contiguous United States (CONUS). We tested and verified a machine learning (ML) model and its feasibility to (1) highlight the variability of geomorphic responses to flood-inducing storms across various climatic and geomorphologic regions across CONUS and (2) understand the impact of these storms on the stage–discharge relationships at gaged sites as a proxy for changes in flood hazard. The established model allows us to select rivers with stage–discharge relationships that are more prone to change after flood-inducing storms, for which flood recurrence intervals should be revised regularly so that hazard assessment can be up to date with the changing conditions. Results from the model show that, even though post-storm changes in channel conveyance are widespread, the impacts on flood hazard vary across CONUS. The influence of channel conveyance variability on flood risk depends on various hydrologic, geomorphologic, and atmospheric parameters characterizing a particular landscape or storm. The proposed framework can serve as a basis for incorporating channel conveyance adjustments into flood hazard assessment. [ABSTRACT FROM AUTHOR]

Due to global climate change, flooding is predicted to become more frequent in the coming decades. Recent literature has highlighted the importance of river morphodynamics in controlling flood hazards at the local scale. Abrupt and short-term geomorphic changes can occur after major storms. However, our ability to foresee where and when substantial changes will happen is still limited, hindering our understanding of their ramifications on future flood hazards. This study sought to understand the implications of major storm events for future flood hazards. For this purpose, we developed self-organizing maps (SOMs) to predict post-storm changes in stage‐discharge relationships, based on storm characteristics and watershed properties at 3,101 stream gages across the continental United States (CONUS). We tested and verified a machine learning (ML) model and its feasibility for (1) mapping the variability of geomorphic impacts of extreme storm events and (2) representing the effects of these changes on stage‐discharge relationships at gaged sites as a proxy for changes in flood hazard. The established model allows us to select rivers with stage-discharge relationships that are more prone to change after severe storms, for which flood frequency analysis should be revised on a regular basis so that hazard assessment can be up to date with the changing conditions. Results from the model show that, even though post-storm changes in channel conveyance are widespread, the impacts on flood hazard vary across CONUS. The influence of channel conveyance variability on flood risk depends on various parameters characterizing a particular landscape or storm. The proposed framework can serve as a basis for incorporating channel conveyance adjustments into flood hazard assessment. [ABSTRACT FROM AUTHOR]

The exposure of High Mountain Asia (HMA) to disaster risks is heightened by extreme weather conditions and the impacts of climate change. Obtaining knowledge about the long-term response of the landscape to hydroclimatic variations in HMA is paramount, as millions of people are affected by these changes every year. During monsoons, substantial human suffering, and damage to crops and infrastructure in populated communities result from the flooding and debris flow caused by the increase in precipitation extremes each year. Although a few initiatives have undertaken the estimation of flood risk locally, the use of traditional techniques in ungauged basins is, unfortunately, not always possible because of the lack of extensive data required. To address this problem, we present in this study a geomorphologically guided machine learning (ML) approach for mapping flood impacts across HMA. We defined socioeconomic flood impact using the Lifeyears Index (LYI), a systematic index that measures the economic cost and loss of life caused by flooding. This index quantifies the importance of the destruction to infrastructure, capital, and housing in an overall assessment. We trained the proposed model with over 6000 flood events, from 1980 to 2020, and their computed five-year and ten-year LYIs. We used as predictors, (1) the five-year rainfall concentrations (which correlate the magnitude of precipitation events with the time of occurrence) of events retrieved from ERA5 daily data; (2) a geomorphic classifier (flood geomorphic potential) based on hydraulic scaling functions automatically derived from an 8 and 30-meter digital elevation model (DEM) for the region and (3) population. This model proved capable of identifying the hotspots of flood susceptibility on a national scale and showing its variability from 1980 to 2022. The study also highlights the severity of the impacts of hydroclimatic extremes in the entire HMA region. The framework is generic and can be used to derive a wide variety of flood vulnerability and subsequent risk maps in data-scarce regions. [ABSTRACT FROM AUTHOR]

Salinity is an important abiotic stress that limits the productivity of crops growing on the salt affected areas because excess salt concentration in the soil has detrimental effect on growth and development of plants. Beneficial microorganisms having the inimitable characteristics like tolerance to soil salinity, synthesis of plant growth hormones, facilitating nutrient uptake, biocontrol ability and beneficial interaction with plants could be vital to address the problem. An experiment was carried out with the objectives of isolating and characterizing saline tolerant bacteria for utilizing as a tool for bioremediation. Soil samples were collected from three saline affected districts of Bangladesh viz. Khulna, Satkhira and Bhola. The highest bacterial population was found in Satkhira followed by Khulna and the lowest was found in Bhola. Eighteen (18) bacterial isolates viz. BU B1, BU B2, BU B3, BU B4, BU B5, BU B6, BU B7, BU B8, BU B9, BU S1, BU S2, BU S3, BU S4, BU S5, BU S6, BU S7, BU K1 and BU K2 were identified according to the colony color and shape. All the isolated bacteria showed positive response to produce IAA. Isolates BU S4, BU B7 and BU S1 showed highest IAA production ability. Among the 18 isolates, 12 were Gram positive and showed negative reaction on KOH test and the rest 6 isolates were Gram negative and showed positive reaction on KOH test. The isolates BU B1, BU B4, BU B6, BU S6, BU K1 and BU K2 were slow growing bacteria and the rest were fast grower. Biochemical tests indicate that 13 isolates were positive for catalase and P solubilization test. Whereas, 11 isolates could degrade the cellulose. For screening of bacterial isolates against NaCl tolerance, the isolates were cultured on NA medium having different salt concentrations. Experimental results reveal that all the isolates could tolerate 4.0% NaCl concentration except BU B6. Ten isolates showed the ability to tolerate NaCl up to 8.0%. The isolates BU B7 and BU S4 showed highest salinity tolerance along with better response to different biochemical characteristics. Therefore, these isolates may become promising for the bioremediation of soil salinity in the saline affected areas of Bangladesh. [ABSTRACT FROM AUTHOR]

Soil carbon (C) mineralization was studied in an incubation experiment comprised of two factors having six organic materials and three nitrogen (N) rates. Cow dung (CD), rice straw (RS), wood ash (WA), cow dung biochar (CB), rice straw biochar (RB) and wood biochar (WB) considering 2.5 g C kg−1 soil along with three levels of N, i.e., 0, 0.05 and 0.10 g N kg−1 soil were mixed with 400 g of soil used in each pot. The pots were placed for 0, 30, 60, 90, 120, 150 and 180 days of incubation, and soils were collected after each incubation and analyzed for C and N. Irrespective of treatment factors, C decreased in an irregular fashion until 180 days of incubation. From the initial level of 1.91%, C contents decreased to 1.08, 1.10, 1.06, 1.23, 1.17 and 1.12% in soil mixed with CD, RS, WA, CB, RB and WB, respectively, and to 1.28, 1.11 and 0.99% in 0, 0.05 and 0.10 g N kg−1 soil, respectively, at 180 days of incubation. The mineralization followed the order of WA > CD > RS > WB > RB > CB. Biochars could supply stable C in soil, while N enhances mineralization; optimization of N is therefore essential to ensure soil C accretion. [ABSTRACT FROM AUTHOR]

The changing climate and anthropogenic activities raise the likelihood of damage due to compound flood hazards, triggered by the combined occurrence of extreme precipitation and storm surge during high tides and exacerbated by sea-level rise (SLR). Risk estimates associated with these extreme event scenarios are expected to be significantly higher than estimates derived from a standard evaluation of individual hazards. In this study, we present case studies of compound flood hazards affecting critical infrastructure (CI) in coastal Connecticut (USA). We based the analysis on actual and synthetic (considering future climate conditions for atmospheric forcing, sea-level rise, and forecasted hurricane tracks) hurricane events, represented by heavy precipitation and surge combined with tides and SLR conditions. We used the Hydrologic Engineering Center's River Analysis System (HEC-RAS), a two-dimensional hydrodynamic model, to simulate the combined coastal and riverine flooding of selected CI sites. We forced a distributed hydrological model (CREST-SVAS) with weather analysis data from the Weather Research and Forecasting (WRF) model for the synthetic events and from the National Land Data Assimilation System (NLDAS) for the actual events, to derive the upstream boundary condition (flood wave) of HEC-RAS. We extracted coastal tide and surge time series for each event from the National Oceanic and Atmospheric Administration (NOAA) to use as the downstream boundary condition of HEC-RAS. The significant outcome of this study represents the evaluation of changes in flood risk for the CI sites for the various compound scenarios (under current and future climate conditions). This approach offers an estimate of the potential impact of compound hazards relative to the 100-year flood maps produced by the Federal Emergency Management Agency (FEMA), which is vital to developing mitigation strategies. In a broader sense, this study provides a framework for assessing the risk factors of our modern infrastructure located in vulnerable coastal areas throughout the world. [ABSTRACT FROM AUTHOR]

The changing climate and adverse anthropogenic activities raise the likelihood of damages due to compound flood hazards, triggered by the combined occurrence of extreme precipitation and storm surge during high tides, and exacerbated by sea-level rise (SLR). Risk estimates associated with these extreme event scenarios are expected to be significantly higher than estimates derived from a standard evaluation of individual hazards. In this study, we present case studies of compound flood hazards affecting critical infrastructure (CI) in coastal Connecticut (USA) based on actual and synthetic (that is, under future climate conditions) hurricane events, represented by heavy precipitation and surge combined with high tides and SLR conditions. We used the Hydrologic Engineering Center's River Analysis System (HEC-RAS), a two-dimensional hydrodynamic model to simulate the combined coastal and riverine flooding on selected CI sites. We forced a distributed hydrological model (CREST-SVAS) with weather analysis data from the Weather Research and Forecasting (WRF) model for the synthetic events and from the National Land Data Assimilation System (NLDAS) for the actual events, to derive the upstream boundary condition (flood wave) of HEC-RAS. We extracted coastal tide and surge time series for each event from the National Oceanic and Atmospheric Administration (NOAA) to use as the downstream boundary condition of HEC-RAS. The significant outcome of this study represents the evaluation of changes in flood risk for the CI sites for the various compound scenarios (under current and future climate conditions). This approach offers an estimate of the potential impact of compound hazards relative to the 100-year flood maps produced by the Federal Emergency Management Agency (FEMA), which is vital to developing mitigation strategies. In a broader sense, this study provides a framework for assessing risk factors of our modern infrastructure located in vulnerable coastal areas throughout the world. [ABSTRACT FROM AUTHOR]

Compound flood hazards triggered by combined occurrence of extreme precipitation and storm surge during high tides, exacerbated by future sea level rise, are likely given the changing climate and adverse anthropogenic activities. Risk estimates associated with these extreme scenario events are likely to be significantly higher than standard evaluation of individual hazard scenarios. However, there have been relatively few efforts to quantify the vulnerability assessment and mitigation of compound hazards. This study focuses on assessing coastal power-grid substation flood vulnerability in Connecticut (USA) caused by a hypothetical compound hazard induced by hurricanes causing heavy precipitation, coastal winds and surges. Local electricity supply depends on the uninterrupted functioning of the substations and so does the day to day activities of the local communities. A cascade of events of different magnitudes has been evaluated to measure the power outcomes and responses for both coastal and riverine flooding by using the HECRAS 2D hydrodynamic model. A distributed hydrologic model CREST-SVAS was forced with weather analysis data from the WRF model for hurricane scenarios to facilitate the upstream boundary conditions of the HECRAS model with stream flow predictions. The downstream boundary condition for coastal sea level is based on coastal tide, storm surge, and sea level from NOAA and the WRF winds during each scenario. The significant outcome of this study represents the dimensions of flood risks due to compound extreme events, in particular, the vulnerability of the substations during these events. This approach offers an estimate of the potential compound hazards impact, which is vital to developing mitigation strategies. This will allow researchers and stakeholders to analyze the risk factor of combined hazards and prepare for taking necessary mitigation measures to protect the vulnerable infrastructure within the flood zone. This study has broader impacts by providing new insights on the impacts of inevitable future hazards and by developing a framework for assessing risk factors of our modern infrastructures and facilities in vulnerable coastal areas throughout the world. [ABSTRACT FROM AUTHOR]