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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.)

The Eastern Himalaya region is highly susceptible to flood and other hydrological hazards due to frizzle geophysical setup, reshaping geomorphology, and heavy annual rainfall (1600-3200 mm). Despite that, anthropogenic activities have been enhancing this susceptibility which increases the intensity and impact of floods in terms of economic loss, human loss, and environmental degradation. Addressing this environmental problem, a geospatial technology-based case study of the Kohima district, Nagaland state (India), a part of the eastern Himalaya is presented here. Various experiential models are available for computing flood hazards; however, the geospatial technique-based analytic hierarchy process (AHP) method was applied in this study due to its robustness and high accuracy level. AHP integrates reclassified GIS layers of hazard-triggering factors and sub-factors by assigning relative weights 1-9 based on their corresponding impacts on flood occurrence. Overlay operation of reclassified GIS layer (causative factors and sub-factors) in ArcMap 10.8 software generated flood spatial variability map which shows four zones, namely low, moderate, high, and very high hazard zones, covers 23%, 35%, 28%, and 14% proportion of total area (978.96 km 2 ), respectively. The study poses a serious concern for the study area as most of the densely populated urban centers fall into moderate to very high flood hazard zones including the state capital city Kohima. So, to avert a worse flood disaster, a flood hazard zone study is the need of the hour. The present study can be used as a decision support system (DSS) for flood disaster risk reduction, infrastructural development, and land use planning in Kohima district., Competing Interests: Declarations. Ethical 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), 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.)

Environmental vulnerability is an important tool to understand the natural and anthropogenic impacts associated with the susceptibility to environmental damage. This study aims to assess the environmental vulnerability of the Doce River basin in Brazil through Multicriteria Decision Analysis based on Geographic Information Systems (GIS-MCDA). Natural factors (slope, elevation, relief dissection, rainfall, pedology, and geology) and anthropogenic factors (distance from urban centers, roads, mining dams, and land use) were used to determine the environmental vulnerability index (EVI). The EVI was classified into five classes, identifying associated land uses. Vulnerability was verified in water resource management units (UGRHs) and municipalities using hot spot analysis. The study employed the water quality index (WQI) to assess the EVI and global sensitivity analysis (GSA) to evaluate the model input parameters that most influence the basin's environmental vulnerability. The results showed that the regions near the middle Doce River were considered environmentally more vulnerable, especially the UGRHs Guandu, Manhuaçu, and Caratinga; and 35.9% of the basin has high and very high vulnerabilities. Hot spot analysis identified regions with low EVI values (cold spot) in the north and northwest, while areas with high values (hot spot) were concentrated mainly in the middle Doce region. Water monitoring stations with the worst WQI values were found in the most environmentally vulnerable areas. The GSA determined that land use and slope were the primary factors influencing the model's response. The results of this study provide valuable information for supporting environmental planning in the Doce River basin., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Nitrogen transport from terrestrial to aquatic environments could cause water quality deterioration and eutrophication. By sampling in the high- and low-flow periods in a highly disturbed coastal basin of Southeast China, hydrochemical characteristics, nitrate stable isotope composition, estimation of potential nitrogen source input fluxes, and the Bayesian mixing model were combined to determine the sources and transformation of nitrogen. Nitrate was the main form of nitrogen. Nitrification, nitrate assimilation, and NH 4 + volatilization were the main nitrogen transformation processes, whereas denitrification was limited due to the high flow rate and unsuitable physicochemical properties. For both sampling periods, non-point source pollution from the upper to the middle reaches was the main source of nitrogen, especially in the high-flow period. In addition to synthetic fertilizer, atmospheric deposition and sewage and manure input were also major nitrate sources in the low-flow period. Hydrological condition was the main factor determining nitrate transformation in this coastal basin, despite the high degree of urbanization and the high volume of sewage discharge in the middle to the lower reaches. The findings of this study highlight that the control of agricultural non-point contamination sources is essential to pollution and eutrophication alleviation, especially for watersheds that receive high amounts of annual precipitation., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

In this research, the effect of flooding caused by heavy precipitation, postulated to be one of the consequences of climate change, on toxic metal concentrations in two demersal fish species, whiting (Merlangius merlangus euxinus) and red mullet (Mullus barbatus), was investigated. For both demersal fish species, concentrations of Hg, Fe, Cd, Pb, Se, Al, Zn, Cu, Sr, B, Cr, Mn, Ni, Ba, and Li were compared between samples taken from Türkeli, Sinop, Black Sea, before and after the flood event in August 2021. Hg, Mn, Se, Li, B, and Sr metal concentrations increased in whiting and in red mullet in the post-flood samples. Estimated daily intake, target hazard quotient, cancer risk, the maximum allowable daily consumption rate and minimum daily requirements, and health risk analyses indicated that daily consumption of whiting and red mullet was risky due to the heavy metal Hg level after the flood. In addition, it was found that the samples had higher levels of Se than Hg, Se/Hg ratios were above 1, and Se-HBV were positive. Therefore, whiting and red mullet fishing should be restricted for a limited time period in the region., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The State of Kerala has frequently been facing a series of flooding phenomena that have adversely affected its multiple sectoral growths. The floods of 2018 have happened to be one of the most devastating floods that have occurred in the State of Kerala. It was seen that nearly thirteen out of fourteen districts in Kerala were tremendously affected during the 2018 August floods. The worst affected districts during the 2018 floods were Trivandrum, Pathanamthitta, Idukki, Thrissur, Ernakulam, and Kottayam. A sub-region near the Karamana basin located in the Trivandrum district is considered for the present study. The Karamana sub-region is a highly urbanized area that is also more or less prone to intense riverine flooding. The major rivers-Karamana and Killi-along with their respective tributaries, are the water bodies in the study region. Extensive urbanization, along with the overflowing of rivers during monsoon seasons, has paved the way for intense flooding in the region. This, in turn, necessitates developing a flood model for the sub-region. The development of an efficient flood model will aid in understanding the future challenges related to a flooding event in a region. In this study, the flood return probability water levels for the 5-year, 10-year, 25-year, 50-year, 100-year, 250-year, and 500-year were estimated for the Karamana sub-region. Besides, the flood risk zoning for the study area was conducted and elaborated as very high risk, high risk, moderate risk, and low risk for the different areas of the sub-region. Overall, the study can be helpful in identifying the most vulnerable areas to flooding in the Karamana region. By the proper identification of vulnerable areas in the region, proper planning and early warning measures can be devised and carried out by policymakers., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The impact of global warming presents an increased risk to the world's shorelines. The Intergovernmental Panel on Climate Change (IPCC) reported that the twenty-first century experienced a severe global mean sea-level rise due to human-induced climate change. Therefore, coastal planners require reasonably accurate estimates of the rate of sea-level rise and the potential impacts, including extreme sea-level changes, floods, and shoreline erosion. Also, land loss as a result of disturbance of shoreline is of interest as it damages properties and infrastructure. Using a nonlinear autoregressive network with an exogenous input (NARX) model, this study attempted to simulate (1991 to 2012) and predict (2013-2020) sea-level change along Merang kechil to Kuala Marang in Terengganu state shoreline areas. The simulation results show a rising trend with a maximum rate of 28.73 mm/year and an average of about 8.81 mm/year. In comparison, the prediction results show a rising sea level with a maximum rate of 79.26 mm/year and an average of about 25.34 mm/year. The database generated from this study can be used to inform shoreline defense strategies adapting to sea-level rise, flood, and erosion. Scientists can forecast sea-level increases beyond 2020 using simulated sea-level data up to 2020 and apply it for future research. The data also helps decision-makers choose measures for vulnerable shoreline settlements to adapt to sea-level rise. Notably, the data will provide essential information for policy development and implementation to facilitate operational decision-making processes for coastal cities., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Digital elevation models (DEMs) from different sources have been widely utilized in watershed modeling and environment management. Yet, little is known about how DEMs from different data sources affect modeling results and management decisions. This paper presents new insights into how the DEMs from three different sources affect model-simulated flow, nitrate (NO 3 ), phosphorus (P), and sediment by using the BASINS/HSPF watershed modeling system. It was found that DEM source-induced uncertainties in simulation results are higher than the DEM resolution-induced uncertainties regardless of watershed slope or delineation method. Moreover, DEM source introduces higher uncertainties in simulation results for automatically delineated low-gradient watersheds than high gradient watersheds. Sediment and NO 3 concentrations were the most and the least sensitive water quality parameters, respectively, to DEM sources. The uncertainties in simulation results may be reduced by using the manual method for watershed delineation but they cannot be completely eliminated. It is recommended that high precision (such as NED) DEMs be employed especially for flat watersheds. The findings provide guidelines for selection of DEM source based on available resources., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Soil erosion and sediment yield in watersheds are comprehensively affected by land use/cover changes and climatic factors. The current sediment yield index (SYI) model incorporates parameters of area (A), delivery ratio (DR), and curve number (CN), which reflect the character of underlying surface conditions, while the impact of rainfall intensity on sediment yield could not be properly considered. This study aims to improve the current SYI model by introducing rainfall-related factors such as rainfall erosivity (R) and applying it to estimate the sediment yield of river basin. Taking the Dongjiang River basin, South China, as a case study, the performances of the improved simplified SYI model (SYI-CN + R) were compared and demonstrated at multi-spatiotemporal scales. The results showed that (1) compared with the SYI model which only has the parameter CN (SYI-CN), the model SYI-CN + R achieves better simulation performances at yearly (the efficiency coefficient (CE) is 81% in the whole basin and 62% in the sub-basin) and half-month (CE is 69% in the whole basin and 57% in the sub-basin) time scales. (2) On the basin scale, the simulation performance in the whole basin is better overall compared to that in the sub-basin, and the model SYI-CN + R at the half-month time scale is more suitable for the sediment yield simulation in the Dongjiang River basin, with higher value of correlation coefficient (CC) of 87% and 83% for the whole basin and the sub-basin, respectively. And (3) the values of CN and R have an obvious spatial gradient in the whole basin, showing an increasing trend from northeast to southwest as a whole, with larger values concentrated in the lower reaches and smaller values in the middle and upper reaches. This study extends the application and improves the performance of the SYI model, and provides a basis for soil and water conservation in a river basin with fewer observation data., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Agriculture, meteorological, and hydrological drought is a natural hazard which affects ecosystems in the central India of Maharashtra state. Due to limited historical data for drought monitoring and forecasting available in the central India of Maharashtra state, implementing machine learning (ML) algorithms could allow for the prediction of future drought events. In this paper, we have focused on the prediction accuracy of meteorological drought in the semi-arid region based on the standardized precipitation index (SPI) using the random forest (RF), random tree (RT), and Gaussian process regression (GPR-PUK kernel) models. A different combination of machine learning models and variables has been performed for the forecasting of metrological drought based on the SPI-6 and 12 months. Models were developed using monthly rainfall data for the period of 2000-2019 at two meteorological stations, namely, Karanjali and Gangawdi, each representing a geographical region of Upper Godavari river basin area in the central India of Maharashtra state which frequently experiences droughts. Historical data from the SPI from 2000 to 2013 was processed to train the model into machine learning model, and the rest of the 2014 to 2019-year data were used for testing to forecast the SPI and metrological drought. The mean square error (MSE), root mean square error (RMSE), adjusted R 2 , Mallows' (Cp), Akaike's (AIC), Schwarz's (SBC), and Amemiya's PC were used to identify the best combination input model and best subregression analysis for both stations of SPI-6 and 12. The correlation coefficient ([Formula: see text]), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE) were used to perform evaluation for SPI-6 and 12 months of both stations with RF, RT, and GPR-PUK kernel models during the training and testing scenarios. The results during testing phase revealed that the RF was found as the best model in forecasting droughts with values of [Formula: see text], MAE, RMSE, RAE (%), and RRSE (%) being 0.856, 0.551, 0.718, 74.778, and 54.019, respectively, for SPI-6 while 0.961, 0.361, 0.538, 34.926, and 28.262, respectively, for SPI-12 scales at Gangawdi station. Further, the respective values of evaluators at Karanjali station were 0.913 and 0.966, 0.541 and 0.386, 0.604 and 0.589, 52.592 and 36.959, and 42.315 and 31.394 for PUK kernel and RT models, respectively, during SPI-6 and SPI-12. Machine learning models are potential drought warning techniques because they take less time, have fewer inputs, and are less sophisticated than dynamic or scientific models., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Coastal ecosystems are the most vulnerable to natural and anthropogenic pressures which should be assessed using various chemical and ecological indicators. Our study aims to provide practical monitoring of anthropogenic pressures related to metal discharges in coastal waters for identifying potential ecological deterioration. The spatial variability of various chemical elements concentrations and their main sources were determined in the surficial sediments of a Mediterranean coastal area submitted to high anthropogenic pressure, the semi-enclosed Boughrara Lagoon located in southeastern Tunisia, by conducting several geochemical and multi-elemental analyses. The grain size and the geochemical analyses both suggested a marine influence of the sediment inputs in the north of the area (near Ajim channel), whereas continental and aeolian features dominated the sedimentary inputs in the southwestern lagoon. This last area was also characterized by the highest concentrations of metals, in particular Pb (4.45-173.33 ppm), Mn (68.45-1469.27 ppm), Cu (7.64-134.26 ppm), Zn (28.74-244.79 ppm), Cd (0.11-2.23 ppm), Fe (0.5-4.9%), and Al (0.7-3.2%). By referring to background crustal values and the contamination factor calculations (CF), the lagoon is considered as highly polluted for Cd, Pb, and Fe (3 < CF < 6). Three possible sources of pollution were identified: phosphogypsum effluents (P, Al, Cu, and Cd), the ex-Pb mine (Pb and Zn), and the cliff weathering and streams input from the red clay quarry (Fe). Furthermore, pyrite precipitation was identified for the first time in the Boughrara lagoon, suggesting the occurrence of anoxic conditions in this lagoon., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Concern over the influences of constant addition of emerging anthropogenic chemicals to the environment has become a public issue during the rapid urbanization. Here, we investigated the occurrence of organophosphate esters (OPEs) in soil and corresponding tree bark in a megacity, Western China. Our results showed levels of OPEs in tree bark (1250 ± 573 ng/g dry weight (dw)) were 1-2 orders of magnitude higher than those in soil (40.4 ± 30.8 ng/g dw). Rooster Mountain is a background mountain area, exhibiting significantly lower concentrations of OPEs in soil and tree bark than those in other sites with relatively high population density. This result highlights the effect of human activities on the distribution of OPEs in environmental matrices. Alkyl-OPEs were predominant compounds in soil, whereas halogenated- (Cl-) OPEs were characterized in tree bark. Furthermore, tris(2-chloroethyl) phosphate (TCEP) positively correlated with tris(2-chloroisopropyl) phosphate (TCIPP) in soil (r 2  = 0.43, P < 0.05) while negatively correlated with TCIPP in tree bark (r 2  = 0.31, P < 0.05). The ratios of logarithm concentrations of OPEs in tree bark to those in soil correlated well with logK OA values of OPEs from 6 to 10, indicating the equilibrium status was achieved between OPE partitioning in soil and in tree bark. Nevertheless, tris (2-butoxyethyl) phosphate (TBEP) and tris(2-ethylhexyl) phosphate (TEHP) with high values of logK OA deviated from this linear tendency, which was possibly due to the fact that they were subjected to the particle-bound deposition process, leading to partition into the soil., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Environmental regulations are considered a prerequisite for environmental performance. However, very limited studies have explored the asymmetric relationship between clean energy consumption, environmental regulation, and CO 2 emissions. This study scrutinizes the asymmetry phenomenon in environmental regulation-clean energy consumption and environmental quality nexus in China by using the time series nonlinear ARDL approach by covering the period 1993-2019. The result reveals that the impact of environmental regulation on clean energy consumption and CO 2 emissions is asymmetric. A positive change in environmental regulation has a positive effect on clean energy consumption but a negative impact on CO 2 emissions in the long run. While a negative change in environmental regulation has insignificant effects on clean energy consumption and CO 2 emissions in the long run. The study suggests that China should need to revisit environmental regulation policies that could help in improving environmental quality., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The inundated area of dam and dike-break floods includes various types of land and factories that release considerable amounts of pollutants into floods, causing serious water pollution and further endangering human health. Many pollution sources and factors affect the water pollution risk in inundated areas. Accurate assessment of the water pollution risk for dam and dike-break floods enables people to take measures in advance to reduce public health problems. The existing evaluation methods cannot effectively analyze the water pollution risk for dam and dike-break floods because partial or all pollution sources and influencing factors are ignored. The main factors affecting flood water quality were summarized into point source (PS), non-point source (NPS), flood depth, velocity, duration, and temperature. The water pollution risk caused by NPSs and PSs were quantified, as well as the impact of all main factors on water pollution risk. The evaluation model proposed for water pollution risk in inundated areas of dam and dike-break floods considers all pollution sources and influencing factors. The WPR was proposed to represent the water pollution risk value. The dam-break flood of Luhun Reservoir was simulated to verify the feasibility of the evaluation model. We concluded that (1) WPR varied with space and time in the inundated area and was seriously affected by PS in local areas; (2) the annual average WPR of different land use types from high to low were construction land, cropland, urban, water, rural area, woodland, and grassland. The evaluation model can be used to evaluate the water pollution risk for dam and dike-break floods at macro and micro scales. People can use this method to evaluate the impact, range, and degree of specific pollution sources or pollutants in the inundated area, thus allowing for measures to be taken in advance to reduce associated damages., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The rapid development of coastal aquaculture in recent decades has led to excessive discharge of organic matter and nutrients into surrounding waters, which could result in eutrophication and potentially affect metal cycling. In our study, the influence of algal organic matter on metal accumulation was examined in three coastal sediment cores taken from a tropical region, Hainan Island, China. Overall, metal pollution adjacent to aquaculture ponds remained at low levels on the coast, except Zn, Cd, and Sn were moderately to highly enriched in the Dongjiao sediments. The δ 13 C values and the atomic C/N ratios indicated a major contribution of phytoplankton to sedimentary organic matter at the Dongjiao site. Moreover, both the algae-derived organic matter and effluent nitrogen were significantly associated with the enriched Zn, Cd, and Sn, suggesting that nutrient-induced phytoplankton growth and its organic matter may act as a "biological pump" to enhance the accumulation of metals. Wastewater treatment for aquaculture ponds should include the control of algal organic matter., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Sensitivity analysis (SA) is generally desirable for parameter optimization, mapping, and calibration in hydrological models, yet the implementation of SA in data-sparse regions is usually avoided due to a lack of continuous data. The present study proposes the novel concept of "minimum continuous data period" to overcome this constraint. It analyses the sensitivity profile of two data-suffice sub-catchments of a data-sparse watershed using data at various timescales to determine the minimum data period required for the SA. The results suggest that the SA employing a minimum data period (2 years in this study) replicated the actual sensitivity profile by an average of 77.5% while replicating the most sensitive and insensitive parameters by 100%. The study encourages the use of the data from sub-catchments to determine the sensitivity profile of the data-sparse catchment. It would benefit in improving the use of SA for rainfall-runoff modelling in data-scanty regions., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Quantifying the effect of hydraulic disturbances on sediment phosphate release is a key issue in the water quality assessment of lakes, especially for the shallow lakes which are susceptible to winds and waves. Here, we sampled the original sediment columns from 12 positions in the eastern, central, and western areas of the Chaohu Lake, a representative shallow lake in China, and observed phosphate release under three levels of hydraulic disturbances in the laboratory. When the disturbance was weak and sediment on the surface of bottom mud moved individually (the Individual Motion Mode), sediment phosphate release rate was insignificant (0.24 mg/m 2 /day). When the disturbance was medium and only a small percentage (< 16%) of surface sediment started to move (the Small Motion Mode), the phosphate release rate sharply increased to 4.81 mg/m 2 /day. When the disturbance was further strengthened and most (≥ 16%) of the surface sediment moved (the General Motion Mode), the phosphate release rate was more than doubled (10.23 mg/m 2 /day). With the increase in hydraulic disturbance intensity, the variation range of phosphate release also became wider. Spatial distribution showed that the release rate varies the most in the western area, followed by the eastern and the central areas. By extrapolating the experimental results to the real scale, it was found that the phosphate release fluxes would probably fall within a wide range between 203.43 to 7311.01 kg/day under different levels of hydrodynamic disturbances which considerably affects phosphate release from shallow lakes. This study also has implications for the pollutant management in other shallow lakes., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Urban road traffic network (URTN) plays an important role in city operation, while it is also suffered a lot from the urban flood disasters which caused negative impacts frequently, like traffic congestion, and road collapse. The function loss of URTN not only destroy normal urban life and work order, but also pose a serious threat to people's lives and properties. Therefore, it is urgent to quantitatively explore the flood resilience of URTN. The concept of resilience puts forward new ideas to help solve the problem of urban flooding disasters from a holistic view. Exploring the flood resilience of urban traffic network may help to mitigate urban flooding and improve the urban resilience. This paper developed a flood resilience evaluation model of URTN, which contains 26 indicators based on the 4R theory. A case study was conducted in southern China to validate the model with real data. It evaluated the urban flood resilience of road traffic network with a comparison of before and after reconstruction of the pipeline. The results demonstrated that the flood resilience of URTN is at a relatively low level in the study area, and the limitation of single traditional engineering measure to the flood resilience of URTN. Suggestions such as strengthening the citizen participation and enhancing the complementary capability of multiple engineering measures are proposed to further promote the flood resilience of the URTN., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)