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Flood incidence, especially in global south countries, is one of the most challenging natural disasters in the light of changing climates, especially in Africa. This is because African countries have a large sub-section of vulnerable people who either live within flood-prone areas or depend on flood-prone areas for their means of livelihood such as we have in Nigeria. Recent flood disasters in Nigeria have been of major concern to people, communities, and institutions. Several studies have been conducted on flood events and their impacts in Nigeria. However, most of these studies are on public perception, flood modeling (rainfall-runoff), and the provision of binary maps with few studies engaging in the use of satellite observations, especially the use of Synthetic Aperture Radar, SAR, to enhance flood early warning designs, especially in Sub-Saharan Africa. This study is aimed at assessing the 2018 flood event in Lokoja, Kogi State, Nigeria, using the Sentinel-1 imagery. The study confirmed that a total of 69 buildings out of 611 buildings were affected by the flood disaster with about 24,902 people displaced by this singular flood event. The study shows that backscattering from microwave sensors provides very useful information for highlighting inundated areas that could prove useful in forecasting, monitoring, and precision-based flood early warning designs before, during, and after flood events., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Diatoms are responsible for 75 % of the primary production in high-nutrient and coastal regions of the ocean, and their growth is sometimes limited by silicon availability. We investigated the genetic diversity of silicon transporter (Sit) genes and measured Sit gene expression in one dominant genus, Chaetoceros, as a molecular indicator of silicon deficiency in the East China Sea (ECS). A total of 34 Sit fragments were sequenced from species belonging to 13 genera. These sequences covered 12 genera of which no Sit sequences have been previously reported; thus, this information has greatly expanded the Sit gene database. These sequences were combined with the existing Sit sequences in GenBank, and the combined set was used to sort the homologous Sit identified in the ECS. Based on the Sit sequence alignments, specific primers were designed for the Chaetoceros genus to quantitatively estimate Sit expression levels using quantitative RT-PCR. In a cruise conducted in July 2010, the observed ChaetocerosSit transcript levels were comparable in range to those detected in the species, C. affinis, cultured under silicon-sufficient conditions. This finding may be explained by the devastating 2010 flood of the Changjiang River, which resulted in the influx of a large amount of silicic acid to the ECS., Author(s): Lee-Kuo Kang[sup.1] [sup.2] , Chao-Chang Feng[sup.3] , Jeng Chang[sup.1] [sup.2] [sup.3 ] , Gwo-Ching Gong[sup.1] Author Affiliations: (1) Institute of Marine Environmental Chemistry and Ecology, National Taiwan Ocean University, [...]

The phosphorus (P) fraction and its release characteristics from sediment in response to flood events across different land covers (i.e., reclaimed land with dominant vegetation of Phragmites australis and/or Typha orientalis, grassland with dominant vegetation of annual and perennial forbs, and bare land) in the lakeshore of Chaohu Lake were investigated. The results indicated that the re-flooding of a restored wetland led to P release. IP (inorganic P) was the major P fraction in the soils pre-flood and post-flood. For all the soil samples, the rank order of P fractions was Ca-P (P associated with calcium) > OP (organic P) > Fe/Al-P (P bound to Al, Fe, and Mn oxides and hydroxides). During flooding, Fe/Al-P contributed the most as the P release source in the soils and to the P sources for the overlying water. In reclaimed land, Fe/Al-P release correlated significantly with soil pH. In grassland, Fe/Al-P release correlated significantly with soil pH and Al content. In bare land, Fe/Al-P release correlated significantly with Al and clay content. The max TP release rates were also significantly influenced by land cover, and the values in bare land, grassland, and reclaimed land were 9.91 mg P m -2  day -1 , 8.10 mg P m -2  day -1 , and 5.05 mg P m -2  day -1 , respectively. The results showed that the P release processes might be regulated by different factors across different land covers, and that the re-introduction of vegetation during wetland restoration must be taken into account prior to flood events to avoid an undesirable degradation of water quality.

Monitoring surface water velocity during flood events is a challenging task. Techniques based on deploying instruments in the flow are often unfeasible due to high velocity and abundant sediment transport. A low-cost and versatile technology that provides continuous and automatic observations is still not available. Among remote methods, large-scale particle image velocimetry (LSPIV) is an optical method that computes surface water velocity maps from videos recorded with a camera. Here, we implement and critically analyze findings obtained from a recently introduced LSPIV experimental configuration during a flood event in the Tiber River at a cross section located in the center of Rome, Italy. We discuss the potential of LSPIV observations in challenging environmental conditions by presenting results from three tests performed during the hydrograph flood peak and recession limb of the event for different illumination and weather conditions. The obtained surface velocity maps are compared to the rating curve velocity and to benchmark velocity values. Experimental findings show that optical methods should be preferred in extreme conditions. However, their practical implementation may be associated with further hurdles and uncertainties.

An assessment of suspended sediment transport was carried out in a number of semiarid catchments during flood events in order to quantify the degradation rates. In order to quantify these, a systematic sampling procedure of the episodic flood events was proposed for representative catchments. The procedure allows for an integration over the whole run-off episode using both the rising and falling limbs of the run-off hydrograph to compute the sediment quantities for each individual flood event.Higher sediment concentrations occurred in the rising limb than those at the recession for any stage of flow. The maximum suspended sediment concentration was observed at the peak of the flood hydrograph. An integration of the sediment concentration over its duration gave the total sediment yield from the flood event. For the ephemeral channels, only a small number of flood events were observed over a three-year experimental period each with a duration of the order of 3-6 h. It is notable that high sediment loads were associated with high flow volumes which were effectively the result of the catchment characteristics and incident rainfall causing the flood events in the respective catchments. A large percentage of the annual sediment yield from a catchment is transported by the ephemeral streams during a small number of flood events. The correct determination of the total sediment yield from any of the flood events depends entirely on the accuracy of the measurements.The understanding of run-off and sediment loss for the representative catchments aims at assisting planning, management and control of water and land resources for sustainable development in the semi-arid parts of the tropics. The sediment rates reveal the degradation of catchments which have repercussions on the crop and pasture production and this has a bearing on the soil and water conservation programmes in the delicate ecological balance of the semi-arid areas. Further, these rates will determine the lifespan of the reservoirs planned for the dry river valleys (ephemeral streams) and existing ones which serve livestock and domestic needs. These occasionally will require costly rehabilitation and scooping to increase effective storage unless conservation measures are taken, and these measures are bound to vary from place to place as per the representative catchments output.

Abstract Flood is a widespread climate-related hazard with the potential to occur in almost any geographical location where fluvial processes are active and can occur due to the involvement of multiple factors. Flood frequency analysis (FFA) is considered a valuable hydrologic tool to know the magnitude and frequency of the recurrence of floods. In this research, we have examined the Lower Burhi Dehing River (LBDR) in Assam, India, which frequently experiences severe flooding due to its meandering course induced by heavy monsoon rainfall. Therefore, the present research aims to analyze past flood events and current trends and predict future flood frequencies using Gumbel’s extreme value distribution and Log Pearson Type III Method. In this particular investigation, 25 years of annual maximum peak discharge data from 1972 to 1997 was employed to examine the discharge records of LBDR. This research estimated the return periods of different flood magnitudes, quantifying the likelihood of future floods with varying degrees of severity. In this regard, the return periods are estimated at 2, 5, 10, 20, 50, 100, and 200 years. The study reveals that the largest flood event occurred in 1972, with a discharge of 1134.4 m3/s, and the smallest flood event occurred in 1997, with a discharge of 214.65 m3/s. The 2-year flood is a relatively common event with a 50% probability of occurring in any given year, characterized by a moderate discharge of 1384.90 m3/s and minor impacts of 0.3665. The 5-year flood, occurring with a 20% chance annually, brings a significantly higher water level of 2008.81 m3/s and moderate-to-severe impacts of 1.4999, potentially causing widespread flooding. In the 10-year and 50-year return period, which has a probability of returning 10% and 2% chance annually, the discharge will reach 2421.89 m3/s and 3331.01 m3/s, respectively. The outcome of this research will sustainably guide policymakers and environmental planners to mitigate flood hazards.

The Himalayas experiences several cloudburst events due to its varied physiographical, geomorphological, and geological conditions and high rainfall. Uttarakhand is one of the Indian states circumscribed by the Himalayan ranges and has experienced a rise in the number of cloudburst catastrophes in the last few decades. These events cause substantial loss of life and property; however, very few studies have characterized these unpredictable cloudburst-induced flash floods in different regions of Uttarakhand. This study examines the geological and hydrological factors associated with the Raipur-Kumalda cloudburst event that occurred from 20 to 21 August 2022 in the Dehradun district of Uttarakhand. The resulting flash flood caused significant damage to roads, bridges, and settlements across the valley. The study aims to understand the geological and geomorphological controls of the event by analyzing the peak discharge and various flood parameters. The basin geomorphometry and rainfall intensity of the region reveal poorly developed drainage networks with low drainage density, steep slopes, rapid peak flows, a sharp peak hydrograph, and intense, concentrated rainfall, all of which worsen the impact of the flood. Various flood indices, including the rising curve gradient (K), flood magnitude ratio (M), and flood response time (TP), indicate a discharge 50 to 100 times higher during the event compared to the average monsoonal discharge. This study also discusses the role of mountain topography, climate, regional geology, and irreversible land use-land cover (LULC) changes associated with urbanization in intensifying the destruction., Competing Interests: Declarations. Conflict of interest: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Abstract Floods are one of the most prevalent and costliest natural hazards globally. The safe transit of people and goods during a flood event requires fast and reliable access to flood depth information with spatial granularity comparable to the road network. In this research, we propose to use crowdsourced photos of submerged traffic signs for street-level flood depth estimation and mapping. To this end, a deep convolutional neural network (CNN) is utilized to detect traffic signs in user-contributed photos, followed by comparing the lengths of the visible part of detected sign poles before and after the flood event. A tilt correction approach is also designed and implemented to rectify potential inaccuracy in pole length estimation caused by tilted stop signs in floodwaters. The mean absolute error (MAE) achieved for pole length estimation in pre- and post-flood photos is 1.723 and 2.846 in., respectively, leading to an MAE of 4.710 in. for flood depth estimation. The presented approach provides people and first responders with a reliable and geographically scalable solution for estimating and communicating real-time flood depth data at their locations.

The high mountain ecosystem of the Indian Himalayas has frequently been experiencing primary hazards (like earthquakes, avalanches, and landslides). Often, these events are followed by the triggering of secondary hazards (like landslide dams, debris flows, and flooding), thereby posing massive risks to infrastructure and residents in the region. This study was taken up to understand the dynamics of an extraordinary debris flood disaster in the Rishiganga River valley, Chamoli district of Uttarakhand on 7th February 2021. Rapid mass movements (RAMMS)-debris flow software was employed to recreate the entire sequence of the hazard consisting of a rock-ice slide, mass deposition and erosion along the channel, and subsequent debris flood. Forty-nine scenarios were analyzed for accurate calibration of dry-Coulomb type friction coefficient (µ) and viscous-turbulent friction coefficient (ξ). Consequently, the geomorphologic characteristics of the debris flow were validated using high-resolution satellite image interpretation and field photographs. The volume of detached rock-ice mass was estimated to be 26.42 × 10 6 m 3 . At the same time, the RAMMS-derived model outputs for velocity, flow depth, and momentum were found in good agreement with the extent and height of actual debris on the ground. The study highlights an urgent need to identify the glaciers with a high risk of ice avalanches in the Indian Himalayas. The presented modeling approach may be applied in dynamic mountain ecosystems to simulate potential flash floods due to avalanches. Moreover, the information reported in this study can be vital input for improving the district-level disaster management plan., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Through the mobilization of movable objects due to the extreme hydraulic conditions during a flood event, blockages, damage to infrastructure, and endangerment of human lives can occur. To identify potential hazards from aerial imagery and take appropriate precautions, a change detection tool (CDT) was developed and tested using a study area along the Aisch River in Germany. The focus of the CDT development was on near real-time analysis of point cloud data generated by structure from motion from aerial images of temporally separated surveys, enabling rapid and targeted implementation of measures. The differences identified in the study area using distance comparison (M3C2) were segmented into individual components and categorized. Subsequently, the data was compared to existing two-dimensional hydrodynamic numerical calculation results (HQ 100 ). The implementation of the CDT is feasible for a variety of RGB camera-equipped aerial vehicles due to the point cloud-based analysis and postprocessing. By overlaying and visualizing the detected changes with numerical simulation results, a quick assessment of the hazard potential in the event of a possible flood can be made. In the case of the study area along the Aisch River, the localization of construction materials, a steel container with debris pile, and a motor vehicle in the flood hazard zone of a potential HQ 100 event could be confirmed, although no mobilization of the materials was to be expected due to the expected hydraulic conditions of a flood event., Competing Interests: Declarations. Ethics approval: All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

The threat of drought has been felt almost worldwide in recent years. It is critical to determine the causes of drought and how seasonal changes affect it. Additionally, it is necessary to determine the speed and impact area of drought, monitor drought areas, and attempt to find solutions against drought. With the developing satellite sensing systems, remote sensing methods are being used to investigate topics such as the increase and extent of drought, uncontrolled water consumption in agricultural activities, and the effects of unnatural pollutants on freshwater resources such as lakes and rivers. Using Synthetic Aperture Radar (SAR) satellite data to monitor changes in water bodies is a relatively new area of study in remote sensing. The spatial extent and seasonal change (spring and autumn) of droughts between 2017 and 2021 in Akşehir Lake were determined from Sentinel-1A SAR satellite data, and the Normalized Differential Water Index (NDWI) was calculated using Sentinel-2A optical satellite data and Standardized Precipitation Index (SPI) in this research. In addition, a different approach was applied to determine the change of wetland boundaries more accurately by converting the linear Sigma0 band to the decibel (dB) band and applying a non-linear 3 × 3 maximum filter to the dB band to Sentinel-1A data. Consequently, it has been established that Akşehir Lake, which used to have wetlands during the spring seasons but began to dry up in the autumn seasons, had completely dried up in both periods in 2021., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Floods in urban areas result in a detrimental impact on the natural environment and human health and pose major risks to assets and communication systems. In cities with high population density, the magnitude of flood damage largely depends upon flood inundation as well as floodwater depths. The present study compared recent flood inundation extent, damages caused, and possible floodwater depth in two highly developed metropolises of China and Japan, i.e., Wuhan and Kumamoto cities, for the year 2020. Sentinel-1 satellite data-driven change detection algorithm in Google Earth Engine (GEE) was applied to identify potentially flooded regions. Major land use land cover classes such as urban areas and croplands affected by the flood were mapped in conjunction with the exposed population. ALOS PALSAR digital elevation model (DEM) was used to study the inundation depth. The study revealed that 322 km 2 of the area has been inundated by floodwater in Wuhan city with 230 km 2 and 140 km 2 areas under damaged croplands and urban regions. Around 817,095 people were exposed to this natural catastrophe in Wuhan. The city Kumamoto has witnessed an inundation area of about 505 km 2 with damaged cropland of 350 km 2 and an urban area of 83 km 2 ., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Riverine sampling of pollutants is commonly used to understand pollutants' transport pathways, relationships with hydrology, and overall presence in a waterbody. However, temporal gaps between sample collection introduce errors to these efforts, and guidance prescribing sampling frequency remains sparse. The magnitude of error often depends on a contaminant's transport mechanisms and local hydrologic conditions, making the creation of comprehensive sampling guidance difficult. This study analyzed a unique dataset that measured 18 analytes, including pesticides, nutrients, and pathogens, in three Iowa rivers for 90 consecutive days (May 4-August 1, 2000). This dataset provided a novel opportunity to relate pollutants to local hydrology and quantify errors associated with recurring sampling. Pesticide concentrations followed the spring flush phenomenon, where values were greatest during high streamflow in May and June but often depleted by July. Fecal coliform and total phosphorus (TP) also coincided with high flow, but unlike pesticides, their concentrations never diminished. Nitrate exhibited more complex behavior; concentrations were diluted during high flows and then increased as streamflow receded. Autocorrelations were significant for nitrate and atrazine in larger rivers but negligible for fecal coliform and TP. Loads were calculated for four pollutants with minimal non-detects (atrazine, fecal coliform, nitrate, and TP). We simulated intermittent sampling by selecting evenly spaced subsets of measured values to estimate loads, which were compared to the loads calculated using every daily sample to quantify error. This method typically overestimated nitrate loads but underestimated other pollutants, and errors often decreased in larger watersheds. Nitrate generally had the lowest error, while fecal coliform had the highest. We used these results to approximate the sampling frequency needed to bind errors within a certain threshold., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Globally, the concentration of heavy metals and sediment toxicity analysis are significant liabilities to aquatic environments. This scrutiny outlines the sediment textures, heavy metals and toxicity status associated with environmental pollution indices in the core sediment of the Cauvery and Vettar estuaries, East coast of India. The impact of rapid industrialization, urbanization, harbour activities and agricultural activities influences on the twain estuary is a significant concern to designate the environment. The contamination status of the sediments affects the potential biodiversity, ecological risks and human health. A total of two core sediments were recovered from the Cauvery and Vettar estuaries in March 2023 to decipher the environmental pollution status. Meticulous observation of the textural studies underscores the prevalence of sand content in Cauvery, and Vettar sediments consist of predominate clay content and minor silt contents. Furthermore, the organic matter is augmented in the Vettar River due to the higher input of waste disposal, seaweeds and algae due to the surrounding landmass. Twain core sediments argue that heavy metal concentration is decreasing in order as Fe > Zn > Ni > Pb > Cu > Cr by using portable X-ray fluorescence (pXRF) spectrometry. Remarkable results of environmental pollution indices such as I geo , E f , C f , C d and m C d state very highly polluted, extreme enrichments, high contamination and very high degree of contamination. Furthermore, the potential ecological risk indices such as PLI, SQG s, and PERI argue polluted, medium to high toxicity and moderate adverse ecological risk to the estuarine regions. Statistical analysis of the heavy metal affirms the enrichment of Fe metals may derive from lithogenic and/or anthropogenic influences, and the other studied metals such as Cu, Ni, Zn, Pb and Cr may be influenced by the anthropogenic activities in the aspect of point and non-point pollution sources. This could result from both estuaries undergoing higher pollution, in which the Vettar estuary is a considerable environmental risk zone compared to the Cauvery river due to the impact of industrial effluents and rapid urbanization activities. This finding underscores the urgent need for enhanced estuarine sediment quality study and comprehensive assessment of sediment toxicity, regulating the beneficial acumen for the government to follow the suitable remediation on the embellish policy of river and marine environments., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The flash flood-induced erosion is the primary contributor to soil loss within the Indian Himalayan Region (IHR). This phenomenon is exacerbated by a confluence of factors, including extreme precipitation events, undulating topographical features, and suboptimal soil and water conservation practices. Over the past few decades, several flash flood events have led to the significant degradation of pedosphere strata, which in turn has caused landslides along with fluvial sedimentation in the IHR. Researchers have advocated morphometric, hydrologic, and semi-empirical methods for assessing flash flood-induced soil erosion in hilly watersheds. This study critically examines these methods and their applicability in the Alaknanda River basin of the Indian Himalayan Region. The entire basin is delineated into 12 sub-watersheds, and 13 morphometric parameters are analyzed for each sub-watershed. Thereafter, the ranking of sub-watersheds vulnerability is assigned using the Principal Component Analysis (PCA), compounding method (CM), Geomorphological Instantaneous Unit Hydrograph (GIUH), and Revised Universal Soil Loss Equations (RUSLE) approaches. While the CM method uses all 13 parameters, the PCA approach suggests that the first four principal components are the most important ones, accounting for approximately 89.7% of the total variance observed within the dataset. The GIUH approach highlights the hydrological response of the catchment, incorporating dynamic velocity and instantaneous peak magnifying the flash flood susceptibility, lag time, and the time to peak for each sub-watershed. The RUSLE approach incorporates mathematical equations for estimating annual soil loss utilizing rainfall-runoff erosivity, soil erodibility, topographic, cover management, and supporting practice factors. The variations in vulnerability rankings across various methods indicate that each method captures distinct aspects of the sub-watersheds. The decision-maker can use the weighted average to assign the overall vulnerability to each sub-watershed, aggregating the values from various methods. This study considers an equal weight to the morphometric, hydrological GIUH, and semi-empirical RUSLE techniques to assess the integrated ranking of various sub-watersheds. Vulnerability to flash flood-induced landslides in various sub-watersheds is categorized into three classes. Category I (high-priority) necessitates immediate erosion control measures and slope stabilization. Category II (moderate attention), where rainwater harvesting and sustainable agricultural practices are beneficial. Category III (regular monitoring) suggests periodic community-led soil assessments and afforestation. Sub-watersheds WS11, WS8, WS5, and WS12 are identified under category I, WS7, WS4, WS9, and WS6 under category II, and WS1, WS3, WS2, and WS10 under category III. The occurrence of landslides and flash-flood events and field observations validates the prioritization of sub-watersheds, indicating the need for targeted interventions and regular monitoring activities to mitigate environmental risks and safeguard surrounding ecosystems and communities., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Passive sampling is a crucial method for evaluating concentrations of hydrophilic organic compounds in the aquatic environment, but it is insufficiently understood to what extent passive samplers capture the intermittent emissions that frequently occur for this group of compounds. In the present study, silicone sheets and styrene-divinyl benzene-reversed phase sulfonated extraction disks with and without a polyethersulfone membrane were exposed under semi-field conditions in a 31 m 3 flume at three different flow velocities. Natural processes and spiking/dilution measures caused aqueous concentrations to vary strongly with time. The data were analyzed using two analytical models that account for these time-variable concentrations: a sampling rate model and a diffusion model. The diffusion model generally gave a better fit of the data than the sampling rate model, but the difference in residual errors was quite small (median errors of 19 vs. 25% for silicone and 22 vs. 25% for SDB-RPS samplers). The sampling rate model was therefore adequate enough to evaluate the time-integrative capabilities of the samplers. Sampler performance was best for SDB-RPS samplers with a polyethersulfone membrane, despite the occurrence of lag times for some compounds (0.1 to 0.4 days). Sampling rates for this design also spanned a narrower range (80 to 110 mL/day) than SDB-RPS samplers without a membrane (100 to 660 mL/day). The effect of biofouling was similar for all compounds and was consistent with a biofouling layer thickness of 150 µm., (© 2024. The Author(s).)

An increase in water temperature is one of the main factors that can potentially modify biogeochemical dynamics in lowland rivers, such as the removal and recycling of nitrogen (N). This effect of climate change on N processing deserves attention, as it may have unexpected impacts on eutrophication in the coastal zones. Intact sediment cores were collected seasonally at the closing section of the Po River, the largest Italian river and one of the main N inputs to the Mediterranean Sea. Benthic oxygen fluxes, denitrification, and dissimilatory nitrate reduction to ammonium (DNRA) rates were measured using laboratory dark incubations. Different temperature treatments were set up for each season based on historical data and future predictions. Higher water temperatures enhanced sediment oxygen demand and the extent of hypoxic conditions in the benthic compartment, favoring anaerobic metabolism. Indeed, warming water temperature stimulated nitrate (NO 3 - ) reduction processes, although NO 3 - and organic matter availability were found to be the main controlling factors shaping the rates between seasons. Denitrification was the main process responsible for NO 3 - removal, mainly supported by NO 3 - diffusion from the water column into the sediments, and much more important than N recycling via DNRA. The predicted increase in the water temperature of the Po River due to climate change may exert an unexpected negative feedback on eutrophication by strongly controlling denitrification and contributing to partial buffering of N export in the lagoons and coastal areas, especially in spring., (© 2024. The Author(s).)

A total of 21 surface sediment samples collected from Arasalar River are subjected to sediment texture (sand-silt-clay ratio), trace elements, organic matter, and CaCO 3 studies to understand the accumulation dynamics of sediments. To assess the impact of metal pollution in estuarine sediments, the essential parameters should be done by checking the sediment characteristics, spatial distribution of heavy metal sources, sediment dynamics, and geochemical analysis. To evaluate the contamination level of metal pollution, a basic standard reference is required to decipher the values ranging from natural to anthropogenic contribution. From this research analysis, it is observed that the study area predominantly comprises silty sand and sand silty nature. The calcium carbonate content in the sediment is found to be proportional to the presence of shell fragments, whereas the organic matter (OM) is derived from riverine and coastal input which acts as a primary source. The obtained results confessed the average contents (mg/g) for the following selected eight elements in the order of Fe (35249-49068) > Mn (286-519) > Ni (107-279) > Cu (78.30-155.70) > Cr (50-99) > Zn (38.70-91.90) > Pb (39.90-62.40), and Co (12.6-29.1) by using an atomic absorption spectrophotometer (AAS) are utilized for this analysis. Multivariate analysis of heavy metals affirms geogenic sources (weathering of parent rocks) for Fe, Mn, Cr, Co, and Zn, whereas Cu, Ni, and Pb originated from anthropogenic activities (urban and industrial activities). The pollution load index, sediment pollution index, I geo index, and PERI are categorized as under polluted, low to moderate polluted, and low potential environmental risk variety. Constant inspection and guidance are needed to prevent effluences from various agriculture and industrial activities in the nearby regions. The findings from this research may support and help the government to follow the suitable remediation on the better management of river and coastal areas., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Abstract Some areas of Uttarakhand faced heavy rainfall during October 17-19th, 2021. In October, rainfall hit its all-time high. A sudden heavy rainfall occurred due to this, which has affected the daily life of humans. More than 50 casualties were recorded during this study period. The main focus of this study was to monitor the Uttarakhand flood event using satellite observations. The Nainital and Almora districts of the state were mainly affected by this sudden heavy rainfall. On October 18th, Nainital received the maximum rainfall of about 21.51 mm at 11:00 am (UTC). The Nainital district has recorded a cumulative rainfall of more than 300 mm on October 18th, 2021. From the observation, we find out that this heavy rainfall occurred due to a sudden cloud burst at Ramgarh in Nainital district. This sudden extreme rainfall further caused a flash flood in the study area. The rainfall pattern has moved towards Assam after it caused a flash flood in the Nainital district of Uttarakhand, India.

Floods in Iran cause a lot of damage in different places every year. The 2019 floods of the Gorgan and Atrak rivers basins in the north of Iran were one of the most destructive events in this country. Therefore, investigating the flood hazard of these areas is very necessary to manage probable future floods. For this purpose, in the present study, the capability of Random Forest (RF) and Support Vector Machine (SVM) algorithms was investigated in combination with Sentinel series and Landsat-8 images to prepare the 2019 flood map. Then, the flood hazard map of these areas was prepared using the new hybrid Fuzzy Best Worse Model-Weighted Multi-Criteria Analysis (FBWM-WMCA) model. According to the results of the FBWM-WMCA model, 38.58%, 50.18%, 11.10%, and 0.14% of the Gorgan river basin and 45.11%, 49.96%, 4.17%, and 0.076% of the Atrak river basin are in high, medium, low, and no hazards, respectively. The highest flood hazard areas in Gorgan and Atrak rivers basins in the north, northwest, west, and east, and south and southwest are mostly at medium flood hazard. Also, the results of RF and SVM algorithms with an overall accuracy of more than 85% for Sentinel-1, Sentinel-2, and Landsat-8 images and 80% for Sentinel-3 images indicate that the flooding is related to the western, southwestern, and northern regions including agricultural, bare lands and built up. According to the obtained results and the efficiency of the FBWM-WMCA model, the Gorgan and Atrak rivers basins need proper planning for flood hazard management., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

A quantitative method based on quick, easy, cheap, effective, rugged, and safe technique (QuEChERS) sample extraction and ultra-performance liquid chromatography/tandem mass spectrometry (UHPLC-MS/MS) was evolved for the determination of 47 pesticide residues in fresh Mazafati date fruits from Bam City of Kerman Province, Iran. The recoveries for selected pesticides ranged from 88 to 110% with a relative standard deviation (RSD) of less than 20% at concentrations of 0.05 and 0.1 mg kg -1 . The proposed method had a linear range from the limit of quantification (LOQ) to 1.00 mg kg -1 , and the LOQ of the 47 pesticides was ≤ 0.005 mg kg -1 . The coefficients of determination (R 2 ) were more than 0.99. This technique was used on 12 fresh date fruits samples, three water samples, and three soil samples with three replications per sample. Forty-seven pesticide were detected collectively, but only diazinon was detected in the date fruit samples. The mean value of diazinon residues was 0.037 mg kg -1 , and the concentration of diazinon in most samples was below the national maximum residue limit (MRL) for date fruit (0.05 mg kg -1 ). Among the pesticides measured, diazinon residues were also detected in the water samples, but not in the soil samples. The dietary intake assessment showed no health risk to humans from the consumption of fresh date fruit concerning the pesticides investigated., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The mobilization of internal phosphorus (P) plays a crucial role in transitioning nutrient limitations within lake ecosystems. While previous research has extensively examined P release in littoral zones influenced by fluctuating water levels, there is a paucity of studies addressing the implications of sustained water level rise in this context, particularly as it pertains to nutrient limitations in benthic algae. To address this gap, we conducted an integrated study in Qinghai Lake. In the field sampling and microcosm experiment, we found that P concentrations are elevated in areas subjected to short-term inundation compared to those enduring prolonged inundation, primarily due to the dissolution of sedimentary P fractions. The results of nutrient diffusing substrata (NDS) bioassays indicated that benthic algae in Qinghai Lake displayed either P limitation or NP co-limitation. The transition from P limitation to NP co-limitation suggested that internal P release may serve to ameliorate nutrient limitations in benthic algae. This phenomenon could potentially contribute to the proliferation of Cladophora in the littoral zones of Qinghai Lake, thereby posing long-term implications for the lake's aquatic ecosystem, particularly under conditions of sustained water level rise., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The use of remote sensing and GIS methodology has accelerated the processing of data on pollution, but has also raised a question about the accuracy of the same. The research focuses on four main air pollutants (CO, NO, SO2, O 3 ), the data on which were obtained from satellite images of Landsat 8 and Landsat 9, for the period 2000-2020. The data on relative cloudiness were obtained from the database CHELSA (Climatologies at high resolution for the earth's land surface areas) for the period 1980-2010. All the data were further processed and analyzed through the procedures of numerical GIS analysis, multi-criteria analysis, supervised and unsupervised satellite classification, and pixel analysis. The results of the analysis of cloud cover in the Balkan region showed that the month with the highest cloud cover in this period was February, with the maximum of (93.18%), whereas the lowest cloud cover was in July (0.19%). The analyzed period (2000-2010) was in the middle range for the pollutants NO and SO 2 and in the lower range for CO; O 3 . In the period 2010-2020, there were high concentrations of NO, SO 2 , and CO and low concentrations of O 3 . The most polluted cities in the last twenty years are Ordu (Turkey), Sarajevo (Bosnia and Herzegovina), and Bor (Serbia). Finally, two most extreme air pollutants in the territory of Balkan countries were SO 2 and NO (2000-2020)., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Assessing and mapping flood risks are fundamental tools that significantly contribute to the enhancement of flood management strategies. Identifying areas that are susceptible to floods and devising strategies to reduce the risk of waterlogging is of utmost importance. In the present study, an integrated approach, combining advanced remote sensing technologies, Geographic Information Systems (GIS), and analytic hierarchy process (AHP), was adopted in the Patan district of Gujarat, India, with a coastline spanning over 1600 km, to evaluate the numerous variables that contribute to the risk of flooding and waterlogging. After evaluating the flood conditioning factors and their respective weights using the analytic hierarchy process (AHP), the results were processed in GIS to accurately delineate areas that are prone to flooding. The results highlighted exceptional precision in identifying vulnerable areas, allowing for a thorough evaluation of the impact severity. The integrated approach yields valuable insights for multi-criteria assessments. The findings indicate that a significant portion of the district's land, precisely 8.94%, was susceptible to very high- risk of flooding, while 27.76% were classified as high-risk areas. Notably, 35.17% of the region was identified as having a moderate level of risk. Additionally, 20.96% and 7.15% were categorized as low-risk and very low-risk areas, respectively. Overall, the study highlights the need for proactive measures to mitigate the impact of floods on vulnerable communities. The research findings were verified by conducting ground truth and visual assessments using microwave satellite imagery (Sentinel-1). The aim of this validation was to test the accuracy of the study in identifying waterlogged agricultural areas and their extent based on AHP analysis. The ground verification and analysis of satellite images confirmed that the model accurately identified approximately 74% of the area categorized under high and very high flood vulnerability to be waterlogged and flooded. This research can provide valuable assistance to policymakers and authorities responsible for flood management by gathering necessary information about floods, including their intensity and the regions that are most susceptible to their impact. Additionally, it is crucial to implement corrective measures to improve soil drainage in vulnerable areas during heavy rainfall events. Prioritizing the adoption of sustainable agricultural practices and improving land use are also crucial for environmental conservation., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

River nutrient enrichment is a significant issue, and researchers worldwide are concerned about phosphorus. The physicochemical characteristics and phosphorus (P) fractions of 36 sediment and water samples from the Ganga (Kanpur, Prayagraj, Varanasi) and Yamuna (Mathura, Agra, Prayagraj) rivers were examined. Among the physicochemical parameters, pH exceeded the permissible limit in Ganga and Yamuna River water and sediment samples. Electrical conductivity (EC) and alkalinity were within the permissible limits, whereas total nitrogen (TN) exceeded the limit in Yamuna water. The analysis of phosphorus fractions indicated the dominance of inorganic phosphorus (IP) (76% in Ganga and 96% in Yamuna) over organic phosphorus in both rivers, suggesting the mineralization and microbial degradation as major processes responsible for transforming OP to IP. The positive correlation of pH with IP, AP (apatite phosphorus), and NAIP (non-apatite inorganic phosphorus) explains the release of inorganic phosphorus under alkaline conditions. The correlation between total organic carbon (TOC), TN, and organic phosphorus (OP) indicated the organic load in the rivers from allochthonous and autochthonous sources. Phosphorus released from river sediments and the concentration of phosphate in overlying river water show a positive correlation, suggesting that river sediments may serve as phosphorus reservoirs. The average phosphorus pollution index (PPI) was above one in both rivers, with relatively higher PPI values observed in the Yamuna River, indicating the contamination of sediment with phosphorus, indicating the contamination of sediment with phosphorus. This study revealed variations in the P fractionation of the sediment in both rivers, primarily as a result of contributions from different P sources. This information will be useful for applying different mitigation techniques to lower the phosphorus load in both river systems., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Floods are arguably the most impactful of natural hazards. The increasing magnitude of their effects on the environment, human life, and economic activities calls for improved management of water resources. Flood susceptibility modeling has been used around the world to reduce the damage caused by flooding, although the extrapolation problem still presents a significant challenge. This study develops a machine learning (ML) model utilizing deep neural network (DNN) and optimization algorithms, namely earthworm optimization algorithm (EOA), wildebeest herd optimization (WHO), biogeography-based optimization (BBO), satin bowerbird optimizer (SBO), grasshopper optimization algorithm (GOA), and particle swarm optimization (PSO), to solve the extrapolation problem in the construction of flood susceptibility models. Quang Nam Province was chosen as a case study as it is subject to the significant impact of intense flooding, and Nghe An Province was selected as the region for extrapolation of the flood susceptibility model. Root mean square error (RMSE), receiver operating characteristic (ROC), the area under the ROC curve (AUC), and accuracy (ACC) were applied to assess and compare the fit of each of the models. The results indicated that the models in this study are a good fit in establishing flood susceptibility maps, all with AUC > 0.9. The deep neural network (DNN)-BBO model enjoyed the best results (AUC = 0.99), followed by DNN-WHO (AUC = 0.99), DNN-SBO (AUC = 0.98), DNN-EOA (AUC = 0.96), DNN-GOA (AUC = 0.95), and finally, DNN-PSO (AUC = 0.92). In addition, the models successfully solved the extrapolation problem. These new models can modify their behavior to evaluate flood susceptibility in different regions of the world. The models in this study distribute a first point of reference for debate on the solution to the extrapolation problem, which can support urban planners and other decision-makers in other coastal regions in Vietnam and other countries., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Addressing the critical need for precise streamflow measurements in hydro-environmental research, this study evaluates large-scale particle image velocimetry (LSPIV) using cost-effective closed-circuit television (CCTV) cameras, providing a detailed sensitivity analysis in both laboratory and real-world canal settings. In laboratory conditions, a 45° camera angle notably enhanced performance, exhibiting a 12% decrease in MAE and a remarkable 40% reduction in RMSE compared to the performance of orthographic form. Tracer particles further enhanced LSPIV accuracy, decreasing both mean absolute error (MAE) and root mean square error (RMSE) by around 0.05 m/s. Optimal velocity coefficients for the lab ranged between 0.85 and 0.90. Nighttime measurements, using projection-based illumination, showed a minor 3% MAE variation and 0.02 RMSE difference versus daytime. In field experiments, a 45° upstream CCTV camera configuration notably improved LSPIV accuracy, achieving a 3% MAE and 0.055 m/s RMSE. For best results across different turbidity levels, we recommend a velocity coefficient range of 0.84 to 0.88. This study highlights the robustness and cost-efficiency of LSPIV as a transformative method for streamflow gauging, demonstrating its wide applicability in diverse hydro-environmental scenarios., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Coastal pollution caused by the importation of agricultural herbicides is one of the main environmental problems that directly affect the coastal primary productivity and even the safety of human seafood. It is urgent to evaluate the ecological risk objectively and explore feasible removal strategies. However, existing studies focus on the runoff distribution and risk assessment of specific herbicides in specific areas, and compared with soil environment, there are few studies on remediation methods for water environment. Therefore, we systematically reviewed the current situation of herbicide pollution in global coastal waters and the dose-response relationships of various herbicides on phytoplankton and higher trophic organisms from the perspective of ecological risks. In addition, we believe that compared with the traditional single physical and chemical remediation methods, biological remediation and its combined technology are the most promising methods for herbicide pollution remediation currently. Therefore, we focus on the application prospects, challenges, and management strategies of new bioremediation systems related to biology, such as constructed wetlands, membrane bioreactor processes, and microbial co-metabolism, in order to provide more advanced methods for reducing herbicide pollution in the water environment., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Urban rainstorm and waterlogging occurred more frequently in recent years, causing huge economic losses and serious social harms. Accurate rainstorm and waterlogging simulation is of significant value for disaster prevention and mitigation. This paper proposed a numerical model for urban rainstorm and waterlogging based on the Storm Water Management Model (SWMM) and Geographic Information System (GIS), and the model was applied in Lianhu district of Xi'an city of China. Furthermore, the effects of rainfall characteristics, pipe network implementation level and urbanization level on waterlogging were explored from the perspectives of spatial distribution of waterlogging points, drainage capacity of pipe network and surface runoff generation and confluence. The results show that: (1) with the increase of rainfall recurrence period, the peak of total water accumulating volume, the average decline rate of water accumulating volume and the number of waterlogging nodes increase; (2) optimizing the pipe diameter can shorten the average overload time of the pipe network from the entire pipe network, but for a single pipe, optimizing the pipe diameter may lead to overloading of unoptimized downstream pipeline; (3) the lower the imperviousness, the less the number of waterlogging nodes and average time of water accumulating, and (4) the west, northwest and southwest areas are relatively affected by the imperviousness, only improving the underlying surface conditions has limited influence on waterlogging in the study area. This study can provide reference for urban waterlogging prevention and reduction and pipe network reconstruction., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This study analyses particulate organic carbon (POC) and particulate nitrogen (PN) export from Indian monsoonal rivers to the north Indian Ocean. Indian monsoonal rivers export approximately 1.2 Tg yr -1 (1Tg = 10 12  g) of POC and 0.14 Tg yr -1 of PN, with about two-thirds entering the Bay of Bengal (0.8 and 0.1 Tg yr -1 , respectively) and the remaining reaches to the Arabian Sea (0.4 and 0.04 Tg yr -1 , respectively). Remarkably, just four rivers from northwest India's black soil-dominated regions contribute about half of the total POC and PN exports (0.64 and 0.06 Tg yr -1 , respectively). This is due to substantial erosion in these catchments, resulting in suspended matter concentrations averaging 596 ± 252 mg L -1 , significantly higher than catchments dominated by red sandy, red loamy and alluvial soils (54 ± 56 mg l -1 ). In contrast, rivers originating from catchments with heavy precipitation, a tropical wet climate, red loamy soils (with peaty and marshy characteristics), rich tropical wet evergreen and moist deciduous forests, and higher soil organic carbon content yield more POC and PN (1704 ± 383 kgC km -2  yr -1 and 261 ± 56 kgN km -2  yr -1 , respectively) than the other rivers of India (951 ± 508 kgC km -2  yr -1 and 120 ± 57 kgN km -2  yr -1 , respectively). These findings stress that the export flux and yield of POC and PN from the Indian monsoonal rivers are primarily influenced by the interplay of hydrological, lithological, environmental, and climatic conditions within the catchment, rather than river size. Moreover, this study highlights the significant impact of incorporating POC data from medium-sized rivers worldwide, as it reveals that yield is independent of river size. This calls for a re-evaluation of global POC export fluxes, taking into account hydrological, lithological, environmental, and climatic factors., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Lake sediments can provide valuable insights into anthropogenic disturbances such as intensive aquaculture and land use changes. These disturbances often manifest as elevated levels of nutrients and elements within the sediments. This paper uses several analytical techniques, i.e., FTIR (Fourier-transform infrared spectroscopy), XRD (X-ray diffraction), EDS (energy-dispersive X-ray spectroscopy), and SEM (scanning electron microscopy), to examine the elemental constituents of lake sediments, along with their relative mineral abundances and surface morphology. The selected freshwater lakes are from the Central Gangetic Plain. The analysis provides a "fingerprint" of geogenic and biogenic mineral constituents of the sediments. Physicochemical, mineralogical, and elemental analysis shows that intensive aquaculture activities in lake alter the sediment chemistry as evidenced by the increase in pH, organic carbon, organic matter, and total phosphorus which is not observed in the lake where aquaculture is prohibited. Freshwater lake sediment is characterized by a high content of biogenic silica and carbonate minerals. The variations in sediment nutrients and mineral fluxes of the selected lakes are mainly attributed to diverse anthropogenic pressures, differences in lake productivity, and the overall ecological condition of the lakes. In the selected three lakes, major variation was reported in the autochthonous sediments in comparison to the allochthonous sediments. The study concludes that catchment and biotic deposit variations in the lakes cannot be evened out by in-lake mixing mechanisms due to variations in the terrigenous and pelagic deposits of the lake. The results highlight the importance of studying annual fluctuations and spatial variations in geogenic and biogenic mineral particle fluxes in lakes. Such investigations provide valuable insights into the annual dynamics of minerals within lakes, contributing to a more comprehensive understanding of their behavior and distribution., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

This study provides a comprehensive analysis of the hydrological effects and flood risks of the Hirakud Reservoir, considering different CMIP6 climate change scenarios. Using the HEC-HMS and HEC-RAS models, the study evaluates future flow patterns and the potential repercussions of dam breaches. The following summary of the work: firstly, the HEC-HMS model is calibrated and validated using daily stage-discharge observations from the Basantpur station. With coefficient of determination (R 2 ) values of 0.764 and 0.858 for calibration and validation, respectively, the model demonstrates satisfactory performance. Secondly, The HEC-HMS model predicts future flow for the Hirakud Reservoir under three climate change scenarios (SSP2-4.5, SSP3-7.0 and SSP5-8.5) and for three future periods (near future, mid future and far future). Thirdly, by analyzing time-series hydrographs, the study identifies peak flooding events. In addition, the HEC-RAS model is used to assess the effects of dam breaches. Downstream of the Hirakud Dam, the analysis highlights potential inundation areas and depth variations. The study determines the following inundation areas for the worst flood scenarios: 3651.52 km 2 , 2931.46 km 2 and 4207.6 km 2 for the near-future, mid-future and far-future periods, respectively. In addition, the utmost flood depths for these scenarios are determined to be 31 m, 29 m and 39 m for the respective future periods. The study area identifies 105 vulnerable villages and several towns. This study emphasizes the importance of contemplating climate change scenarios and implementing proactive measures to mitigate the peak flooding events in the Hirakud reservoir region., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Although the improvement of sanitation facilities has been a major contributor to improving public health, it is not guaranteed to prevent negative health outcomes. This is especially true in areas affected by severe natural disasters, such as flooding or extreme rainfall. Previous studies have examined the association between catastrophic natural disasters and negative health outcomes. However, studies on disaster-prone areas are limited. This study focused on the impact of flood risks and examined whether the improvement of sanitation facilities would be sufficient to suppress the prevalence of diarrhea in flood-prone areas. Two secondary datasets including geodata on flood-prone areas were used for the analysis: one each was obtained from the Bangladesh Demographic and Health Survey and Bangladesh Agricultural Research Council. Two models with categorizations of sanitation facilities based on containment type and excreta flow were applied for analysis. Results showed that the severe flood-prone areas and "diffused" type of sanitation, where the feces are diffused without any containment, had significant positive associations with diarrhea prevalence; however, the interaction between them was negative. Moderate flood-prone areas had a significant positive association with diarrhea prevalence; however, the interaction with unimproved sanitation, which includes containment without clear partition from feces, was significantly negative. These findings indicate that improved sanitation or containment type of sanitation may not positively contribute to the prevention of diarrhea in these severe- and moderate-flood prone areas. The urgent need for alternative sanitation technologies should be addressed in flood-prone regions., (© 2023. The Author(s).)

Flooding events are determining a significant amount of damages, in terms of economic loss and also casualties in Asia and Pacific areas. Due to complexity and ferocity of severe flooding, predicting flood-prone areas is a difficult task. Thus, creating flood susceptibility maps at local level is though challenging but an inevitable task. In order to implement a flood management plan for the Balrampur district, an agricultural dominant landscape of India, and strengthen its resilience, flood susceptibility modeling and mapping are carried out. In the present study, three hybrid machine learning (ML) models, namely, fuzzy-ANN (artificial neural network), fuzzy-RBF (radial basis function), and fuzzy-SVM (support vector machine) with 12 topographic, hydrological, and other flood influencing factors were used to determine flood-susceptible zones. To ascertain the relationship between the occurrences and flood influencing factors, correlation attribute evaluation (CAE) and multicollinearity diagnostic tests were used. The predictive power of these models was validated and compared using a variety of statistical techniques, including Wilcoxon signed-rank, t-paired tests and receiver operating characteristic (ROC) curves. Results show that fuzzy-RBF model outperformed other hybrid ML models for modeling flood susceptibility, followed by fuzzy-ANN and fuzzy-SVM. Overall, these models have shown promise in identifying flood-prone areas in the basin and other basins around the world. The outcomes of the work would benefit policymakers and government bodies to capture the flood-affected areas for necessary planning, action, and implementation., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)