Quantification of geomorphodiversity and its spatial distribution with the flood inundation areas for Assam, India.

Assam, located in the Northeast of India, is highly flood-prone, and the erosional and depositional processes highly influence the landforms. The formation and development of landforms are directly related to the geology, geomorphology, drainage basin characteristics, and soil types of the region. In the present study, a remote sensing and GIS-based geomorphodiversity index (GMI) assessment of Assam is performed using three sub-indices: geodiversity, morphometric diversity, and drainage diversity index. Sixty-six potential geomorphosites are identified with their geological, geomorphological, and GMI classes. With the help of a flood inundation map, the inundated area of each GMI class is calculated. According to the result, 27.02%, 10.76%, and 3.7% of the total area of Assam fall under moderate, high, and very high GMI classes, respectively. Barak Valley and Central Assam region exhibit high to very high GMI values. Geology and geomorphology have a strong influence on GMI values. About 22.32%, 28.33%, 37.18%, 38.25%, and 35.37% of areas with low, moderate, high, and very high GMI are inundated, respectively. This study determined that areas having high GMI can increase the geomorphological heritage value of the region and can play a significant role in promoting geotourism with an increase in the scientific, educational, and aesthetic value of geomorphosites. This study can also help the local governing authorities to conduct and implement better management and conservation policies for vulnerable locations.

Projecting urban flood risk through hydrodynamic modeling under shared socioeconomic pathways.

Under future climate change, accurate risk assessment of urban flooding disasters is paramount for effective adaptation and mitigation strategies. However, conventional indicator-based assessment methods often fall short of accurately capturing the complexity of flooding dynamics. Current research predominantly focuses on predicting future hazard shifts while overlooking changes in other critical indicators. In this study, we establish a comprehensive index system for risk assessment, and quantified future changes in most indicators, utilizing the InfoWorks ICM model for hazard simulation and the CLUMondo model for land use predictions. Based on risk assessment results and regional characteristics, we further analyze the key factors driving future risk and discuss corresponding measures. The results indicate an exacerbation of future urban flood risk, with an 18% increase in high risk areas, primarily concentrated in the center of the study area. The dominant indicators are inundation depth and land use over the whole study area. However microtopography significantly affects risk in low-lying areas. Overall, under higher emission scenarios, the influence of GDP and population rises. These findings offer methodological insights for future urban flood risk assessment research and provide policymakers with valuable guidance to develop targeted adaptation measures in response to climate change.

Application of GRACE-based satellite estimates in the assessment of flood potential: A case study of Gangetic-Brahmaputra basin, India.

The high frequency of flood occurrences and the uneven distribution of hydrological stations make it difficult to monitor large-scale floods. Emergence of the Gravity Recovery and Climate Experiment (GRACE) satellite system sets up a new era of large-scale flood monitoring without much reliance on in situ hydrological observations. The GRACE-derived flood potential index (FPI) exhibits its ability to monitor major events of 2003, 2004, 2007, and 2008 over the Indo-Gangetic-Brahmaputra Basin (IGBB). Precipitation and soil moisture are the major influencing factors of flood. However, the response of potential flooding to such parameters is little known. Pearson's lag correlation analysis is used to examine the response of the GRACE-based FPI to precipitation and soil moisture over the study region comparing seasonal time series of the variables. Results exhibited a 2-month lagged response of FPI to precipitation in the Upper Gangetic Yamuna Chambal Basin (UGYCB) and the Lower Gangetic Basin (LGB) and 1-month lagged response in the Lower Brahmaputra Basin (LBB). With context to soil moisture, a 1-month lag is observed in the Gangetic basins, and no lag is observed in the LBB. Event wise analysis of the lags portrays slightly varying lags for different events; however, it provides a picture on the interaction between these variables. This study also assesses the agreement between FPI and satellite-based river discharge, i.e. Dartmouth Flood Observatory (DFO) discharge. A good correlation (> 0.60) between the two is observed. Threshold values of FPI are determined for the LBB due to its annual flood frequency. The nearly similar accuracy of threshold FPI, determined using DFO discharge, in monitoring floods and the predictive skill measure of FPI for LBB to the previous studies demonstrates the utility of satellite-based discharge in the quantification of threshold FPI values for different percentile floods.

Investigating the influence of urban morphology on pluvial flooding: Insights from urban catchments in England (UK).

As climate change intensifies, cities globally are experiencing more severe rainfall and frequent pluvial floods. Urban expansion is altering the permeability of the land, thus increasing the risk of flooding. This study investigates the impact of urban morphology on pluvial floodwater distribution in 15 urban catchments across England, UK, to provide an analysis of how urban morphology influences flood magnitude. Using a cellular automata-based model, pluvial flood simulations were conducted for catchments characterized by diverse urban morphologies. Then a series of machine learning models were adopted to reveal the relationships between the morphological characteristics of urban configurations (e.g., building footprints, impervious surfaces, street network, topography) and pluvial flooding. These models were used to identify and quantify the effects of key urban morphological indicators on pluvial flooding. The results indicate that, although the total area of impervious surfaces plays the most significant role in floodwater distribution, the edge density (ED) of building footprints and impervious surfaces also influences this process. Synthetic experiments with an exemplary urban fabric show that decreasing "ED of building footprint" and increasing "ED of impervious surface" can mitigate flood volume by up to 6.3 % at 100 % drainage efficiency and 7.8 % at 50 % efficiency. The results of this study are anticipated to aid urban planners and policymakers in developing strategies for implementing flood-resilient cities.

Clarifying urban flood response characteristics and improving interpretable flood prediction with sparse data considering the coupling effect of rainfall and drainage pipeline siltation.

With climate warming and accelerated urbanisation, severe urban flooding has become a common problem worldwide. Frequent extreme rainfall events and the siltation of drainage pipes further increase the burden on urban drainage networks. However, existing studies have not fully considered the effects of rainfall and pipeline siltation on the response characteristics of flooding when constructing numerical models of urban flooding simulations. To solve this problem, a surface-subsurface coupling model was constructed by combining the Saint-Venant equation, Manning equation, a one-dimensional pipeline model (SWMM), and a two-dimensional surface overflow model (LISFLOOD-FP). Then, the SWMM model considering pipeline siltation and the two-dimensional surface overflow model (LISFLOOD-FP) were coupled with the flow exchange governing equation, and the urban flooding response characteristics considering the coupling effect of "rainfall and drainage pipeline siltation" were analysed. To enhance the solvability of waterlogging prediction, an intelligent prediction model of urban flooding based on Bayes-CNN-BLSTM was established by combining a convolutional neural network (CNN), bidirectional long short-term memory neural network (BLSTM), Bayesian optimisation (Bayes), and an interpretable loss function error correction method. The actual rainfall events and flooding processes recorded by the monitoring equipment at Huizhou University were used to calibrate and verify the model. The results show that in the Rainfall 1 and Rainfall 2 scenarios, the overload rates of the pipelines in the current siltation scenario were 60.06 % and 68.37 %, respectively, and the proportions of overflow nodes were 24.87 % and 25.89 %, respectively. When the drainage network was initially put into operation, the overload rates of the pipeline were 36.67 % and 41.16 %, and the overflow nodes accounted for 3.05 % and 4.06 %, respectively. The inundated area and volume of urban flooding increased when the combined siltation coefficient (CSC) was 0.2; therefore, two desilting schemes were determined. Under Rainfall 1, Rainfall 2, and the four rainfall recurrence periods, the Bayes-CNN-BLSTM model had clear advantages in terms of accuracy, reliability, and robustness.

Impact of floods and landslides on rhizosphere bacterial communities: a high-throughput 16S rRNA gene sequencing study.

This study investigates the diversity and composition of soil bacterial communities in the rhizosphere of Attapadi and Nelliyampathy, prominent hill stations in Palakkad district, Kerala, India. The persistent flooding and landslides in 2018 and 2019 significantly impacted agricultural productivity in these regions. Utilizing high-throughput 16S rRNA gene sequencing (Illumina MiSeq), we conducted a comprehensive analysis of soil samples. Correlative assessments between soil parameters and microbial relative abundance at the phylum level revealed noteworthy positive associations. Notably, nitrogen (N) exhibited a positive relation with Crenarchaeota, Chloroflexi, Actinobacteriota, and Acidobacteriota; pH correlated with Firmicutes; organic carbon (OC) with WPS-2; and phosphorous with Proteobacteria. A total of 31,402 operational taxonomic units (OTUs) were identified, with the highest feature counts observed in undisturbed soils from Attapadi (AUD) and Nelliyampathy (NUD) (13,007 and 12,915, respectively). Disturbed soils in Nelliyampathy (ND) and Attapadi (AD) displayed a substantial decline in microbial diversity and composition, harbouring 1409 and 4071 OTUs, respectively. Alpha and beta diversity indices further underscored the more severe impairment of ND soils compared to AD soils. Interestingly, a majority of ND samples were landslide-affected (four out of five), while flood-affected soils accounted for four out of six AD samples. This indicates that landslides exert a more pronounced impact on microbial diversity and composition than floods. The observed decline in microbial count, composition, and diversity, even after 2 years of the disaster, raises concerns about potential threats to agricultural output. The findings emphasize the need for corrective measures, including the incorporation of microbial inoculum, to restore soil fertility in post-disaster landscapes.

Improving rapid flood impact assessment: An enhanced multi-sensor approach including a new flood mapping method based on Sentinel-2 data.

Rapid flood impact assessment methods need complete and accurate flood maps to provide reliable information for disaster risk management, in particular for emergency response and recovery and reconstruction plans. With the aim of improving the rapid assessment of flood impacts, this work presents a new impact assessment method characterized by an enhanced satellite multi-sensor approach for flood mapping, which improves the characterization of the hazard. This includes a novel flood mapping method based on the new multi-temporal Modified Normalized Difference Water Index (MNDWI) that uses multi-temporal statistics computed on time-series of Sentinel-2 multi-spectral satellite images. The multi-temporal aspect of the MNDWI improves characterization of land cover over time and enhances the temporary flooded areas, which can be extracted through a thresholding technique, allowing the delineation of more precise and complete flood maps. The methodology, if implemented in cloud-based environments such as Google Earth Engine (GEE), is computationally light and robust, allowing the derivation of flood maps in matters of minutes, also for large areas. The flood mapping and impact assessment method has been applied to the seasonal flood occurred in South Sudan in 2020, using Sentinel-1, Sentinel-2 and PlanetScope satellite imagery. Flood impacts were assessed considering damages to buildings, roads, and cropland. The multi-sensor approach estimated an impact of 57.4 million USD (considering a middle-bound scenario), higher than what estimated by using Sentinel-1 data only, and Sentinel-2 data only (respectively 24% and 78% of the estimation resulting from the multi-sensor approach). This work highlights the effectiveness and importance of considering multi-source satellite data for flood mapping in a context of disaster risk management, to better inform disaster response, recovery and reconstruction plans.

Enhancing flood risk mitigation by advanced data-driven approach.

Flood events in the Sefidrud River basin have historically caused significant damage to infrastructure, agriculture, and human settlements, highlighting the urgent need for improved flood prediction capabilities. Traditional hydrological models have shown limitations in capturing the complex, non-linear relationships inherent in flood dynamics. This study addresses these challenges by leveraging advanced machine learning techniques to develop more accurate and reliable flood estimation models for the region. The study applied Random Forest (RF), Bagging, SMOreg, Multilayer Perceptron (MLP), and Adaptive Neuro-Fuzzy Inference System (ANFIS) models using historical hydrological data spanning 50 years. The methods involved splitting the data into training (50-70 %) and validation sets, processed using WEKA 3.9 software. The evaluation revealed that the nonlinear ensemble RF model achieved the highest accuracy with a correlation of 0.868 and an root mean squared error (RMSE) of 0.104. Both RF and MLP significantly outperformed the linear SMOreg approach, demonstrating the suitability of modern machine learning techniques. Additionally, the ANFIS model achieved an exceptional R-squared accuracy of 0.99. The findings underscore the potential of data-driven models for accurate flood estimating, providing a valuable benchmark for algorithm selection in flood risk management.

Flood simulation using LISFLOOD and inundation effects: A case study of Typhoon In-Fa in Shanghai.

Urban flooding threatens residents and their property, necessitating timely and accurate flood simulations to enhance prevention measures. However, as a megacity, Shanghai presents a complex underlying surface that proves challenging to assess accurately in existing studies. To simulate the dynamic flooding caused by Typhoon In-Fa in Shanghai from July 23rd to 28th 2021, we employed the LISFLOOD hydrodynamic model with multi-source data and validated the flooded area using the S1FLOOD deep learning model with Sentinel-1 satellite imagery. Based on simulated flood results and a flood depth classification system, we quantified the impacts of flood inundation on population, land use, and buildings. Key findings include: (1) The most severe flooding period in Shanghai occurred on July 25th and 26th 2021. (2) The LISFLOOD model effectively captured the extent of inundation, with the very-high flood depth zone covering 98.07 % of the area identified as flooded by the S1FLOOD and Sentinel-1. (3) Peak-affected individuals were recorded on July 25th 2021. (4) Farmland experienced the most extensive flooding among land use types, while residential buildings were notably affected among building types. Our study reconstructed the spatiotemporal dynamics of Typhoon In-Fa-induced flooding in Shanghai. We mapped the spatial extent and water depths, revealing the dynamic impacts of inundation on population, land use, and buildings across urban areas. This comprehensive framework for flood simulation and inundation impact analysis offers a valuable approach to improve urban flood emergency response.

[Impact and Mechanism of Medium and Small Flood Regulation in the Three Gorges Reservoir on the Phytoplankton in Tributary Bays].

The regulation of small- and medium-sized floods （RSMF） has become the main mode of regulation in the flood season of the Three Gorges Reservoir （TGR）. To study the response of phytoplankton in the tributary bays of the TGR to the RSMF, a typical eutrophic tributary of the TGR, Xiangxi River, was investigated for the spatiotemporal distribution characteristics of phytoplankton and nutrients in the main and tributary streams from 2020 to 2021. The response characteristics of phytoplankton in the tributary bays to the RSMF were analyzed. The results indicated that during the RSMF, the chlorophyll a （Chl-a） in the water body of the Xiangxi River decreased with the increase in the water level in front of the dam, whereas during the reservoir impounding at the end of flood season, the concentration of Chl-a increased again. During the RSMF, the Chlorophyta and Diatoma were the main communities of planktonic algae in the Xiangxi River. The phytoplankton community changed with the RSMF. When the water level fluctuation increased, diatoms were the main species, whereas when the water level fluctuation was small, blue and green algae were the main species. The concentration of Chl-a was more sensitive to changes in TN concentration. When the flow velocity was >0.25 m·s-1 or the suspended sediment content was >10 mg·L-1, the concentration of Chl-a in the water was inhibited. After 2010, the typical outbreak time of algal blooms in the Xiangxi River Reservoir Bay shifted to the flood season, with only two non-flood season algal blooms. Further attention needs to be paid to the response of algal blooms in the reservoir to small- and medium-sized flood control during the flood season.

Changes in Healthcare Utilization After the 2022 Seoul Metropolitan Flood: Applying a Generalized Synthetic Control Approach.

Floods can have adverse health effects and impose a burden on healthcare systems. However, the potential consequences of floods on specific medical causes in densely populated metropolitan cities has not been characterized yet. Therefore, we evaluate the changes in healthcare utilization patterns after the 2022 Seoul flood using nationwide health insurance data. Based on the flood inundation map, districts within the flooded municipalities of Seoul were classified as severe-(n = 12), mild-(n = 22), or non-(n = 38) flood-affected districts. Capitalizing on the timing of the flood as a natural experiment, a generalized synthetic control method was applied to estimate changes in the number of disease-specific hospital visits in flood-affected districts during 2 weeks after the flood. We found excess hospital visits for external injuries (20.2 visits, 95% CI: -6.0, 45.2) and fewer visits for pregnancy and puerperium (-3.0 visits, 95% CI: -5.1, -0.5) in residents of flooded districts. When comparing severe- and non-flood districts, the increase in hospital visits for external injuries (56.2 visits, 95% CI: 17.2, 93.2) and a decrease in hospital visits related to pregnancy and puerperium (-5.3 visits, 95% CI: -8.4, -1.6) were prominent in residents living in severe-flood affected districts. Disease specific analysis showed an increase in hospital visits for injuries to the elbow and forearm, ankle and foot injuries, and chronic lower respiratory diseases in severe-flood-affected districts. However, these impacts were not observed when comparing the mild- and non-flood-affected districts. Our study suggests an immediate and substantial change in medical demand following flood exposure, highlighting the importance of public health responses after flood events.

Road mortality of water snakes in light of landscape structure and traffic intensity in north-eastern Hungary.

Road mortality can be a serious threat to different animals, including snakes. However, mortality patterns can vary between species, intraspecific groups, locations and time. We compared the number of road-killed individuals (carcasses) of two semiaquatic water snakes (Natrix natrix and N. tessellata) on 58 km of road sections bordered by an active floodplain and a flood-protected former floodplain on one side and mountainous areas on the other in NE Hungary based on surveys conducted once every two weeks in three non-consecutive years. The results showed high road mortality of snakes, with a spring and an autumn peak corresponding to the times when snakes emerge from and return to hibernating sites. The results show that small-scale spatial differences in road mortality were mediated by landscape structure along the road, while the effects of traffic volume, flood regime and the age and sex of the individuals were negligible. For conservation, the study suggests that establishing culvert passages under the road and/or artificial hibernating sites on the floodplain-side of the roads in critical sections can be promising in reducing road-related mortality.

Agricultural flood resistance enhanced after returning farmlands to lakes.

The "Returning Farmland to Lakes" (RFTL) project began in China following the catastrophic 1998 floods. It aims to recover flood storage capacity and mitigate flood risk to agriculture and people. This flood adaptation strategy divides the floodplain into three types of restoration polders with different flood control levels (double restoration polders, single restoration polders, and storage polders) and polders for intensive production and living (nonrestoration polders). During the substantial flooding in the Poyang Lake Basin in 2020, the double and single restoration polders were operated for flood diversion for the first time since 1999. This event provided an opportunity to assess the effectiveness of the RFTL project. Using satellite observations of rice planting and flooding areas, we found that 86% of paddy rice areas (3,400 km2) in the basin were successfully protected due to the timely flood diversion into different levels of polders. Compared to 1998, the flooded rice areas decreased overall by 58% (18 to 92% in different types of polders). Thus, the RFTL project has enhanced regional agricultural resistance to floods. A more comprehensive assessment of the RFTL project, including other ecosystem services and functions, is necessary in the future for regional sustainable development.

The impacts of climate change, early agriculture and internal fluvial dynamics on paleo-flooding episodes in Central China.

Floods clustered in episodes are the most prevalent natural disaster worldwide, causing substantial economic and human losses. Although these events are often linked to time-periods of extreme rainstorms and unique atmospheric circulation patterns, the river basin characteristics affected by anthropogenic land use changes could exert a strong influence. However, the way and extent of how land use changes across different time scales affect flooding periods are still unclear, especially considering the historical land use changes. This study uses the Landlab landscape evolution model, coupled with an evapotranspiration model, to investigate the forcing factors for the paleo-flooding trends in the Wei River catchment over the last 5000 years. The results indicate that the flooding period from 4400 to 4000 BP was caused by an increase of 28 % in antecedent moisture content as well as a decrease of 28 % in its spatial variability, which are primarily due to climate change, and that the contribution of land-use change is less than 5 %. The increases of about 14 % and 8 % in main channel sedimentation rate play a leading role in flood generation during the time periods from 3400 to 2800 BP and 2000-1400 BP, respectively. These two periods of increased flooding are primarily caused by the erosional effects of increasing anthropogenic land use, whose contributions range from 33 % to 64 %. Furthermore, based on our modelling results, we suggest that the downstream propagation of the main flooding locations, from the Wei River to the lower reaches of the Yellow River, can be explained by the downstream migrating sediment wave. In conclusion, our simulation results give new insights into the causes of Holocene flooding periods in the middle Yellow River from the perspective of dynamic changes in catchment characteristics, which is helpful to improve regional flood risk management under future climate change and anthropogenic activities.

Neglected pathways of heavy metal input into agricultural soil: Water-land migration of heavy metals due to flooding events.

Flooding, carrying sediments, inundates farmlands across the world due to extreme adverse weather conditions. The casualties and property damage associated with flooding are important direct impacts. However, there is currently insufficient understanding of the remobilization and distribution of heavy metals (HMs) caused by flooding. Few studies have specifically considered flooding as a pathway for HMs contamination of soil. Herein, a novel methodological framework for revealing the input pathways of HMs in agricultural soils in mining-intensive areas is proposed and applied. Flooding is considered one of the pathways for HMs inputs during source apportionment. The results demonstrated a high degree of overlap between the distribution characteristics of major HMs in agricultural soils and sediments. The degree of soil Cd pollution was significantly positively correlated with the inundation depth in the flooded area. It took 8.4-11.5 times of flood inundation or 98.5-119.9 years of accumulation of atmospheric deposition to reach HMs contamination levels in the soil of the study area. Flooding brought in most of the soil Cd, while atmospheric deposition was the primary input pathway for soil Pb and Zn. Our results identified the role of flood inundation on the input of HMs in mining-intensive areas. These results demonstrated the value of our framework for studying the impact of flooding on HMs in agricultural soils from the perspective of input pathways, providing new insights not only into identifying the sources of soil HMs but also into enhancing understanding of the impact of flooding on soil environments. With the potential increase in the frequency and intensity of flooding inundating farmlands in the future, it is essential to consider flooding as a pathway for HMs inputs in order to comprehensively assess their environmental impact.

Social dimensions of climate-induced flooding in Jakarta (Indonesia): The role of non-point source pollution.

Because of its low-lying location, urbanization, and inadequate infrastructure, Jakarta (Indonesia) has experienced an increase in annual flooding events, rising from an average of five significant floods per year in the 1990s to over 20 annually (2010-2020). With climate change exacerbating extreme weather events, Jakarta encounters escalating risks of flooding. Although the recurrent flooding is exacerbated by non-point source (NPS) of pollution such as urban runoff and agricultural discharge that contribute to 40% of total pollutants leading to flood-related issues in Jakarta, none has investigated this research gap. To reflect its novelty, this work explores the implications of climate change on the annual flooding in Jakarta by focusing on NPS and analyzes their impacts from social perspectives. This work also underscores the implications of flooding on livelihoods, health, and social cohesion in Jakarta. Focus group discussion with affected residents was used to shed light on the coping strategies employed in response to recurrent floods, ranging from community-based initiatives to reliance on informal networks. The empirical findings show that the implications of flooding extend beyond physical damages. Displacement of communities, loss of livelihoods, disruption of essential services, and increased health risks are among the social impacts experienced by local residents. Vulnerable populations, including low-income communities residing in informal settlements, bear their consequences. Economic losses from flooding amount to USD 500 million annually, impacting over 1 million residents. However, recent interventions have led to a 15% reduction in peak flood levels and a 20% reduction in flood duration in affected areas. Community resilience has also improved, with a 25% increase in flood insurance coverage and a 20% rise in community response initiatives. Overall, this study highlights that climate change exacerbates annual flooding in Jakarta, significantly impacting vulnerable communities through NPS pollution. Addressing the challenges requires integrated approaches combining effective pollution control, resilient infrastructure, and community engagement to mitigate social and long-term environmental impacts. PRACTITIONER POINTS: Climate-induced flooding disproportionately affects vulnerable communities in Jakarta. Non-point source pollution from urban runoff contributes to the severity of flooding in Jakarta. Waterborne diseases, disruption of livelihoods, and reduced access to clean water are major concerns identified in the study. The study highlights the importance of community-based adaptation strategies to mitigate the impact of flooding and pollution.

Discerning the dynamics of urbanization-climate change-flood risk nexus in densely populated urban mega cities: An appraisal of efficient flood management through spatiotemporal and geostatistical rainfall analysis and hydrodynamic modeling.

While the contribution of climate change towards intensifying urban flood risks is well acknowledged, the role of urbanization is less known. The present study, for the first time in flood management literature, explores whether and how unplanned-cum-urbanization may overshadow the contribution of extreme rainfall to flood impacts in densely populated urban regions. To establish this hypothesis and exemplify our proposed framework, the National Capital Territory (NCT) of Delhi in India, infamous for its concurrent flood episodes is selected. The study categorically explores whether the catastrophic 2023 urban flood could have resulted in a similar degree of urban exposure and damage, had it occurred anytime in the past. A comprehensive spatiotemporal and geo-statistical analysis of rainfall over 11 stations brought about through Innovative trend analysis, Omnidirectional and directional Semi-variogram analysis, and Gini Index indicates a rise in extreme rainfalls. High-resolution land-use maps indicate about 39.53 %, 52.66 %, 56.60 %, and 69.18 % of urban footprints during 1993, 2003, 2013, and 2023, while gradient direction maps indicate a prominent urban surge towards the North-West, West, and Southwest corridors. A closer inspection of the Greenness and Urbanity indices reveals a gradual decline in the green footprints and concurrent escalation in the urban footprints over the decades. A 3-way coupled MIKE+ model was set up to replicate the July 2023 flood event; indicating about 13 % of the area experience "high" and "very-high" flood hazards. By overlaying the flood inundation and hazard maps over land-use maps for 1993, 2003, and 2013, we further establish that a similar flood event would have resulted in lesser damage and building exposure. The study offers a set of flood management options for refurbishing resilience and limiting flood risks. The study delivers critical insights into the existing urban flood management strategies while delving into the urban growth-climate change-flood risk nexus.

Simulating block-scale flood inundation and streamflow using the WRF-Hydro model in the New York City metropolitan area.

This study assesses the performance of the Weather Research and Forecasting-Hydrological modeling system (WRF-Hydro) in the simulation of street-scale flood inundation. The case study is the Hackensack River Watershed in New Jersey, US, which is part of the operational Stevens Flood Advisory System (SFAS), a one-way coupled hydrodynamic-hydrologic system that currently uses the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) to simulate streamflow. The performance of the 50-m gridded WRF-Hydro model was assessed for potential integration into the operational SFAS system. The model was calibrated with the dynamically dimensioned search algorithm using streamflow observations. The model performance was assessed using (i) streamflow observations, (ii) USGS HWMs, and (iii) crowdsourced data on street inundation. Results show that WRF-Hydro outperformed the HEC-HMS model. WRF-Hydro over and underestimated flood inundation extent due to the inaccuracy of the synthetic rating curves and the modeling structure errors. An agreement was noticed between WRF-Hydro and crowdsourced data on flood extent.

Global population datasets overestimate flood exposure in Sweden.

Accurate population data is crucial for assessing exposure in disaster risk assessments. In recent years, there has been a significant increase in the development of spatially gridded population datasets. Despite these datasets often using similar input data to derive population figures, notable differences arise when comparing them with direct ground-level observations. This study evaluates the precision and accuracy of flood exposure assessments using both known and generated gridded population datasets in Sweden. Specifically focusing on WorldPop and GHSPop, we compare these datasets against official national statistics at a 100 m grid cell resolution to assess their reliability in flood exposure analyses. Our objectives include quantifying the reliability of these datasets and examining the impact of data aggregation on estimated flood exposure across different administrative levels. The analysis reveals significant discrepancies in flood exposure estimates, underscoring the challenges associated with relying on generated gridded population data for precise flood risk assessments. Our findings emphasize the importance of careful dataset selection and highlight the potential for overestimation in flood risk analysis. This emphasises the critical need for validations against ground population data to ensure accurate flood risk management strategies.

Integrated assessment for groundwater quality and flood vulnerability in coal mining regions.

Coal mining activities greatly damage water resources, explicitly concerning water quality. The adverse effects of coal mining and potential routes for contaminants to migrate, either through surface water or infiltration, into the groundwater table. Dealing with pollution from coal mining operations is a significant surface water contamination concern. Consequently, surface water resources get contaminated, harming nearby agricultural areas, drinking water sources, and aquatic habitats. Moreover, the percolation process connected with coal mining could alter groundwater quality. Subsurface water sources can get contaminated by toxins generated during mining activities that infiltrate the soil and reach the groundwater table. The aims of this study are the creation of models and the provision of proposals for corrective measures. Twenty-five scenarios were simulated using MODFLOW; according to the percolation percentage and contamination, 35% of the study area, i.e., the middle of the research area, was the most affected. About 38.08% of the area around the mining zones surrounding Margherita is prone to floods. Agricultural areas, known for applying chemical fertilizers, are particularly vulnerable, generating a risk of pollution to surrounding water bodies during flooding. The outputs of this research contribute to identifying and assessing flood-vulnerable regions, enabling focused measures for flood risk reduction, and strengthening water resource management.