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BackgroundAquatic opportunistic pathogen Aeromonas hydrophila, known to persist in low-nutrient chlorinated waters, can cause life-threatening infections. Two intensive care units experienced a cluster of Aeromonas infections following outdoor temperature spikes coinciding with recurrent plumbing issues, with fatalities due to severe underlying comorbidities co-occurring with extensively-drug resistant (XDR) Aeromonas.MethodsWe investigated this cluster using whole genome sequencing to assess genetic relatedness of isolates and identify antimicrobial resistance determinants. Three A. hydrophila were isolated from patients staying in or adjacent to rooms with plumbing issues during or immediately after periods of elevated outdoor temperatures. Sinks and faucets were swabbed for culture.ResultsAll A. hydrophila clinical isolates exhibited carbapenem resistance but were not genetically related. Diverse resistance determinants corresponding to extensively-drug resistant were found, including co-occurring KPC-3 and VIM-2, OXA-232, and chromosomal CphA-like carbapenemase genes, contributing to major treatment challenges. All 3 patients were treated with multiple antibiotic regimens to overcome various carbapenemase classes and expired due to underlying comorbidities. Environmental culture yielded no Aeromonas.ConclusionsWhile the investigation revealed no singular source of contamination, it supports a possible link between plumbing issues, elevated outdoor temperatures and incidence of nosocomial Aeromonas infections. The diversity of carbapenemase genes detected in these wastewater-derived Aeromonas warrants heightened infection prevention precautions during periods of plumbing problems especially with heat waves.

The article delves into the propagation of underwater wave groups in a compressible ocean with an elastic seafloor, focusing on compression waves and seafloor waves as groups. It examines energy balance relations for wavenumber spectra and the interaction of waves in mildly non-uniform water-column-seafloor systems. The research aims to understand energy conversion, tropical cyclone tracking, ocean ambient noise, and microseism activity, highlighting the importance of wave interactions in various oceanic phenomena and applications. [Extracted from the article]

The architecture, engineering, construction, and operation (AECO) industry has brought transformations within the field. Despite the changes, traditional teaching methods such as analyzing two-dimensional plans and models are still being used in training processes, which do not effectively promote the development of spatial skills. In this context, augmented reality (AR) can be used as a tool to translate two-dimensional information into three-dimensional space. Thus, the present study explores the use of the VT-Platform AR tool to develop and reinforce spatial skills among undergraduate civil engineering students. The study follows a quantitative approach with a descriptive scope and an experimental design. Students in the sanitary engineering subject were divided into two groups: an experimental group and a control group. A short-term teaching-learning methodology was proposed for the experimental group using the VT-Platform AR tool. Both groups took the differential aptitude test (DAT) to measure their spatial skills. The experimental group showed a higher average score compared to the control group, with a difference of 29.8%. Furthermore, the use of the VT-Platform AR tool helped standardize the level of spatial skills within the experimental group. It was concluded that using the VT-Platform AR tool can effectively develop and reinforce spatial skills among students. [ABSTRACT FROM AUTHOR]

For users of hydrological models, the suitability of models can depend on how well their simulated outputs align with observed discharge. This study emphasizes the crucial role of factoring in discharge observation uncertainty when assessing the performance of hydrological models. We introduce an ad hoc approach, implemented through the eWaterCycle platform, to evaluate the significance of differences in model performance while considering the uncertainty associated with discharge observations. The analysis of the results encompasses 299 catchments from the Catchment Attributes and MEteorology for Large-sample Studies Great Britain (CAMELS-GB) large-sample catchment dataset, addressing three practical use cases for model users. These use cases involve assessing the impact of additional calibration on model performance using discharge observations, conducting conventional model comparisons, and examining how the variations in discharge simulations resulting from model structural differences compare with the uncertainties inherent in discharge observations. Based on the 5th to 95th percentile range of observed flow, our results highlight the substantial influence of discharge observation uncertainty on interpreting model performance differences. Specifically, when comparing model performance before and after additional calibration, we find that, in 98 out of 299 instances, the simulation differences fall within the bounds of discharge observation uncertainty. This underscores the inadequacy of neglecting discharge observation uncertainty during calibration and subsequent evaluation processes. Furthermore, in the model comparison use case, we identify numerous instances where observation uncertainty masks discernible differences in model performance, underscoring the necessity of accounting for this uncertainty in model selection procedures. While our assessment of model structural uncertainty generally indicates that structural differences often exceed observation uncertainty estimates, a few exceptions exist. The comparison of individual conceptual hydrological models suggests no clear trends between model complexity and subsequent model simulations falling within the uncertainty bounds of discharge observations. Based on these findings, we advocate integrating discharge observation uncertainty into the calibration process and the reporting of hydrological model performance, as has been done in this study. This integration ensures more accurate, robust, and insightful assessments of model performance, thereby improving the reliability and applicability of hydrological modelling outcomes for model users.

The value of historical data for flood frequency analysis has been acknowledged and studied for a long time. A specific statistical framework must be used to comply with the censored nature of historical data, for which only floods large enough to induce written records or to trigger flood marks are usually recorded. It is assumed that all floods which exceeded a given perception threshold were recorded as written testimonies or flood marks. Conversely, all years without a flood record in the historical period are assumed to have had a maximum discharge below the perception threshold. This paper proposes a binomial model that explicitly recognizes the uncertain nature of both the perception threshold and the starting date of the historical period. This model is applied to a case study for the Rhône River at Beaucaire, France, where a long (1816–2020) systematic series of annual maximum discharges is available along with a collection of 13 historical floods from documentary evidence over 3 centuries (1500–1815). Results indicate that the inclusion of historical floods reduces the uncertainty of 100- or 1000-year flood quantiles, even when only the number of perception threshold exceedances is known. However, ignoring the uncertainty around the perception threshold leads to a noticeable underestimation of flood quantile uncertainty. A qualitatively similar conclusion is found when ignoring the uncertainty around the historical period length. However, its impact on flood quantile uncertainty appears to be much smaller than that of the perception threshold.

Global statistical irrigation modeling relies on geospatial data and traditionally adopts a discrete global grid based on longitude–latitude reference. However, this system introduces area distortion, which may lead to biased results. We propose using the ISEA3H geodesic grid based on hexagonal cells, enabling efficient and distortion-free representation of spherical data. To understand the impact of discrete global grid choice, we employ a non-parametric statistical framework, utilizing random forest methods, to identify the main drivers of historical global irrigation expansion using, among other data, outputs from the global dynamic vegetation model Lund-Potsdam-Jena managed Land (LPJml). Irrigation is critical for food security amidst growing populations, changing consumption patterns, and climate change. It significantly boosts crop yields but also alters the water cycle and global water resources. Understanding past irrigation expansion and its drivers is vital for global change research, resource assessment, and the prediction of future trends. We compare predictive accuracy, simulated irrigation patterns, and identification of irrigation drivers between the two grid systems. Using the ISEA3H geodesic grid system increases the predictive accuracy by up to 28 % compared to the longitude–latitude grid. The model identifies population density, potential productivity increase, evaporation, precipitation, and water discharge as key drivers of historical global irrigation expansion. Gross domestic product (GDP) per capita also shows some influence. We conclude that the geodesic discrete global grid system significantly affects predicted irrigation patterns and identification of drivers and thus has the potential to enhance statistical modeling, which warrants further exploration in future research across related fields. This analysis lays the foundation for comprehending historical global irrigation expansion.

Flood hazard and risk assessments (FHRAs) and their underlying models form the basis of decisions regarding flood mitigation and climate adaptation measures and are thus imperative for safeguarding communities against the devastating consequences of flood events. In this perspective paper, we discuss how FHRAs should be validated to be fit for purpose in order to optimally support decision-making. We argue that current validation approaches focus on technical issues, with insufficient consideration of the context in which decisions are made. To address this issue, we propose a novel validation framework for FHRAs, structured in a three-level hierarchy: process based, outcome based, and impact based. Our framework adds crucial dimensions to current validation approaches, such as the need to understand the possible impacts on society when the assessment has large errors. It further emphasizes the essential role of stakeholder participation, objectivity, and verifiability in assessing flood hazard and risk. Using the example of flood emergency management, we discuss how the proposed framework can be implemented. Although we have developed the framework for flooding, our ideas are also applicable to assessing risk caused by other types of natural hazards.

Taking into account the spatial dependence of floods is essential for an accurate assessment of fluvial flood risk. We propose novel extensions to the Fisher copula to statistically model the spatial structure of observed historical flood record data across North America. These include the machine-learning-based XGBoost model, exploiting the information contained in 130 catchment-specific covariates to predict discharge Kendall's τ coefficients between pairs of gauged–ungauged catchments. A novel conditional simulation strategy is utilized to simulate coherent flooding at all catchments efficiently. After subdividing North America into 14 hydrological regions and 1.8 million catchments, applying our methodology allows us to obtain synthetic flood event sets with spatial dependence, magnitudes, and frequency resembling those of the historical events. The different components of the model are validated using several measures of dependence and extremal dependence to compare the observed and simulated events. The obtained event set is further analyzed and supports the conclusions from a reference paper in flood spatial modeling. We find a nontrivial relationship between the spatial extent of a flood event and its peak magnitude.

Extreme sea levels (ESLs) are a major threat for low-lying coastal zones. Climate-change-induced sea level rise (SLR) will increase the frequency of ESLs. In this study, ocean and wind-wave regional simulations are used to produce dynamic projections of ESLs along the western European coastlines. Through a consistent modelling approach, the different contributions to ESLs, such as tides, storm surges, waves, and regionalized mean SLR, as well as most of their non-linear interactions, are included. This study aims at assessing the impact of dynamically simulating future changes in ESL drivers compared to a static approach that does not consider the impact of climate change on ESL distribution. Projected changes in ESLs are analysed using non-stationary extreme value analyses over the whole 1970–2100 period under the SSP5-8.5 and SSP1-2.6 scenarios. The impact of simulating dynamic changes in extremes is found to be statistically significant in the Mediterranean Sea, with differences in the decennial return level of up to +20 % compared to the static approach. This is attributed to the refined mean SLR simulated by the regional ocean general circulation model. In other parts of our region, we observed compensating projected changes between coastal ESL drivers, along with differences in timing among these drivers. This results in future changes in ESLs being primarily driven by mean SLR from the global climate model used as boundary conditions, with coastal contributions having a second-order effect, in line with previous research.

Rip currents are the single largest cause of beach safety incidents globally, but where an estuary mouth intersects a beach, additional flows are created that can exceed the speed of a typical rip current, significantly increasing the hazard level for bathers. However, there is a paucity of observations of surfzone currents at estuary mouth beaches, and our understanding and ability to predict how the bathing hazard varies under different wave and tide conditions are therefore limited. Using field observations and process-based XBeach modelling at an embayed, estuary mouth beach, we demonstrate how surfzone currents can be driven by combinations of estuary discharge and wave-driven bathymetric and boundary rip currents under various combinations of wave and tide forcing. While previous studies have demonstrated the high hazard that rip currents pose, typically during lower stages of the tide, here we demonstrate that an estuary mouth beach can exhibit flows reaching 1.5 m s−1 – up to 50 % stronger than typical rip current flows – with a high proportion (> 60 %) of simulated bathers exiting the surfzone during the upper half of the tidal cycle. The three-dimensional ebb shoal delta was found to strongly control surfzone currents by (1) providing a conduit for estuary flows that connect to headland boundary rips and (2) acting as a nearshore bar system to generate wave-driven “river channel bathymetric rips”. Despite significant spatio-temporal variability in the position of the river channels on the beach face, it was possible to hindcast the timing and severity of past bathing incidents from model simulations, providing a means to forewarn bathers of hazardous flows.

Ecosystem water use efficiency (WUE) is pivotal for understanding carbon–water cycle interplay. Current research seldom addresses how WUE might change under future elevated CO2 concentrations, limiting our understanding of regional ecohydrological effects. We present a land–atmosphere attribution framework for WUE in the Yellow River basin (YRB), integrating the Budyko model with global climate models (GCMs) to quantify the impacts of climate and underlying surface changes induced by CO2. Additionally, we further quantitatively decoupled the direct and secondary impacts of CO2 radiative and biogeochemical effects. Attribution results indicate that WUE in the YRB is projected to increase by 0.36–0.84 gC kg−1H2O in the future, with climate change being the predominant factor (relative contribution rate of 77.9 %–101.4 %). However, as carbon emissions intensify, the relative importance of land surface changes becomes increasingly important (respective contribution rates of −1.4 %, 14.9 %, 16.9 %, and 22.1 % in SSP126, SSP245, SSP370, and SSP585). Typically, WUE is considered a reflection of an ecosystem's adaptability to water stress. Thus, we analyzed the response of WUE under different scenarios and periods and various drought conditions. The results show a distinct “two-stage” response pattern of WUE to drought in the YRB, where WUE increases under moderate–severe drought conditions but decreases as drought intensifies across most areas. Furthermore, GCM projections suggest that plant adaptability to water stress may improve under higher-carbon-emission scenarios. Our findings enhance the understanding of regional ecohydrological processes and provide insights for future predictions of drought impacts on terrestrial ecosystems.

Rainfall is intrinsically linked to the occurrence of landslide catastrophes. Identifying the most suitable rainfall threshold model for an area is crucial for establishing effective daily landslide hazard warnings, which are essential for the precise prevention and management of local landslides. This study introduces a novel approach that utilizes multilayer perceptron (MLP) regression to calculate rainfall thresholds for 453 rainfall-induced landslides. This research represents the first attempt to integrate MLP and ordinary least squares methods for determining the optimal rainfall threshold model tailored to distinct subregions, categorized by topographical and climatic conditions. Additionally, an innovative application of a three-dimensional convolutional neural network (CNN-3D) model is introduced to enhance the accuracy of landslide susceptibility predictions. Finally, a comprehensive methodology is developed to integrate daily rainfall warning levels with landslide susceptibility predictions using a superposition matrix, thus offering daily landslide hazard warning results for the study area. The key findings of this study are as follows. (1) The optimal rainfall threshold models and calculation methods vary across different subregions, underscoring the necessity for tailored approaches. (2) The CNN-3D model substantially improves the accuracy of landslide susceptibility predictions. (3) The daily landslide hazard warnings were validated using anticipated rainfall data from 19 July 2020, thereby demonstrating the reliability of both the landslide hazard warning results and the rainfall threshold model. This study presents a substantial advancement in the precise prediction and management of landslide hazards by employing innovative modeling techniques.

Abstract Algogenic organic matter (AOM) generated from cyanobacteria-impacted reservoirs poses a significant risk to drinking water. This study aimed to investigate the molecular degradation signature of Microcystis aeruginosa (MA)-derived AOM by electrochemical oxidation and the corresponding disinfection by-product formation potential (DBPFP). Boron-doped diamond (BDD)-based electro-oxidation (EO) and electro-Fenton (EF) were implemented at pH 3 and 10 mA cm−2 within 1 h. The fluorophore of extracellular organic matter (EOM), the mixture of EOM and intracellular organic matter (IOM), were characterized, and their corresponding molecular weight (MW) were fractionated. The results showed that dissolved organic carbon (DOC) degradation efficiency for BDD-EF treatment is superior and maintains DOC attenuation up to 84% for the EOM suspensions alone, while a low degradation efficiency occurs for IOM-EOM mixture. In contrast, BDD-EO exhibits a maximum DOC degradation around 66% for EOM suspensions alone, but DOC reduction is as low as 20% for IOM-EOM mixture. The H2O2 generated by BDD-EO preferentially degrades humic acid-like substances in EOM suspensions, whereas BDD-EF effectively degrades multiple fluorescent AOM by •OH. For IOM-EOM mixture, BDD-EO efficiently decomposes humics, but BDD-EF preferentially minimizes soluble microbial product-like and aromatic protein-like substances. Meanwhile, BDD-EF favors degrading biopolymers, humics, and low-MW substances, while BDD-EO merely degrades partial biopolymers and humic substances. After either EF or EO, specific DBPFP decreases as EOM presents alone where the toxicity of corresponding DBPs is mitigated effectively, instead the increased specific DBPFP appears for IOM-EOM mixture where the toxic potency ([DBP]/LC50) of corresponding DBPs increases. In summary, EO and EF are powerful in attenuating MA-derived DBP precursors of EOM in the absence of IOM, depending on the molecular signature.

Abstract Microplastic (MPL) abundance is a well-elucidated problem in the marine environment but not so much in the terrestrial environment. In order to contribute to this research gap, a field study was performed in the Yadkin-Pee Dee River Basin. Due to their heterogenous nature and difficulty in characterization, a diverse set of pictorial training data from µ-Raman was used to perform transfer learning on 2 CNNs of interest: GoogLeNet (GN) and ResNet-18 (RN). In the first trial, using 10% of the initial training dataset, the CNNs exhibited high levels of accuracy rates, generally above 90%. Irrespective of spectroscopic mode, marginal improvements in accuracy rates were seen, with the best improvements occurring in the Raman-based models (U[GN(FTIR), RN(Raman), GN(FTIR), and RN(Raman)]: 39, 42, 38.5, and 34.5; p-value: 1, .6753, .9719, and .4978). However, for the external trial, pictorial data from Primpke (FTIR) and DongMiller (Raman) was predicted less accurately, with the largest loss occurring across the following sets: U[GN(Raman) and RN(FTIR)], 45.5 and 35; p-value:, .3268 and .5476. However, set RN fared marginally better, and due to the usage of µ-Raman, and its performance in the 10% trial, RN18_ADAM_.0011 was selected as the champion model for the field study data. In the unknown microparticle (MP) trial, generally, the most ID’d polymer type was CA, PET, and PE representing a relative concentration range for a given water source and area (MPL/MP) of 4.17–37.5%, 4.17–8.33%, and 4.17–8.33% for CNN and OpenSpecy (OS). A FEDS algorithm, equipped with natural and synthetic polymer standards and biological material, used to compare the strength of each model determined similar frequency in ascertaining positive MPL results across both models with corroboration between the CNN and OS around 1/3 of the time. Results indicate the models detect MPLs with similar frequency elucidating comparable strength of the CNN as well as a focus on particle type distribution rather than individual identification. Moreover, the largest influential factor in this study appears to be either laundry wastewater effluent or atmospheric deposition, which is stressed as a primary focus of remediating MPLs in similar freshwater environments. Lastly, it appears that these MPL are of primary origin as opposed to secondary in the oceanic and coastal environments.

Abstract At the global level, landslides are a dreadful hazard that restricts socio-economic and ecological balances. Recent human activities in hilly areas, coupled with geological predispositions, have potentially exacerbated landslide frequency and magnitude. However, the impacts of these factors on landslide occurrences in the upper Blue Nile basin of Ethiopia remain largely unexplored. This study aims to identify landslide triggers, quantify landslide-susceptible zones, and validate the landslide models. Topographic parameters, geology, hydrology, and land use-land cover inventories were utilized to generate a landslide susceptibility map. The factors were analyzed using a combination of logistic regression (LR) and frequency ratio (FR) models. The area under the curve (AUC) under the receiver operating characteristic (ROC) was used to compare the performance of the models. The result indicates that about 185 sq. km (40.2%) of the total falls under high to very-high susceptible landslide zones, and 92 sq. km (20%) falls under moderate susceptibility. Yet, 183.1 sq. km (40.2%) of the total is classified in the low-to-no landslide hazard zones. The LR and FR model validation demonstrated an average predictive performance of 75 and 81.45%, indicating good precision. The landslide evaluation can help policymakers identify LSH zones for early warning systems and mitigation purposes.

Recent successes with machine learning (ML) models in catchment hydrology have highlighted their ability to extract crucial information from catchment properties pertinent to the rainfall–runoff relationship. In this study, we aim to identify a minimal set of catchment signatures in streamflow that, when combined with meteorological drivers, enable an accurate reconstruction of the entire streamflow time series. To achieve this, we utilize an explicit noise-conditional autoencoder (ENCA), which, assuming an optimal architecture, separates the influences of meteorological drivers and catchment properties on streamflow. The ENCA architecture feeds meteorological forcing and climate attributes into the decoder in order to incentivize the encoder to only learn features that are related to landscape properties minimally related to climate. By isolating the effect of meteorology, these hydrological features can thus be interpreted as landscape fingerprints. The optimal number of features is found by means of an intrinsic dimension estimator. We train our model on the hydro-meteorological time series data of 568 catchments of the continental United States from the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) dataset. We compare the reconstruction accuracy with models that take as input a subset of static catchment attributes (both climate and landscape attributes) along with meteorological forcing variables. Our results suggest that available landscape attributes can be summarized by only two relevant learnt features (or signatures), while at least a third one is needed for about a dozen difficult-to-predict catchments in the central United States, which is mainly characterized by a high aridity index. The principal components of the learnt features strongly correlate with the baseflow index and aridity indicators, which is consistent with the idea that these indicators capture the variability of catchment hydrological responses. The correlation analysis further indicates that soil-related and vegetation attributes are of importance.

Mounting evidence points to elevated regional flood hazards in a changing climate, but existing knowledge about their processes and controls is limited. This is partially attributed to inadequate characterizations of the spatial extent and potential drivers of these floods. Here we develop a machine-learning-based framework (mainly including the Density Based Spatial Clustering Applications with Noise (DBSCAN) clustering algorithm and a conditional random forest model) to examine the processes and controls of regional floods over eastern China. Our empirical analyses are based on a dense network of stream gauging stations with continuous observations of annual maximum flood peaks (i.e. magnitude and timing) during the period 1980–2017. A comprehensive catalogue of 318 regional floods is developed. We reveal a pronounced clustering of regional floods in both space and time over eastern China. This is dictated by cyclonic precipitating systems and/or their interactions with topography. We highlight contrasting behaviours of regional floods in terms of their spatial extents and intensities. These contrasts are determined by fine-scale structures of flood-producing storms and anomalous soil moisture. While land surface properties might play a role in basin-scale flood processes, it is more critical to capture spatial–temporal rainfall variabilities and soil moisture anomalies for reliable large-scale flood hazard modelling and impact assessments. Our analyses contribute to flood science by better characterizing the spatial dimension of flood hazards and can serve as a basis for collaborative flood risk management in a changing climate.

Rapid urbanization and high population density in three major cities in Indonesia, Bandung, Jakarta, and Tangerang have led to significant air quality issues. Fine inhalable particles (PM2.5) with distinct morphologies and elemental compositions pose considerable health risks. This study evaluates the morphology and chemical composition of PM2.5 in these urban areas on Java Island. PM2.5 samples were collected for 24-hour periods using a Teflon filter with the Super Speciation-Air Sampling System (SuperSASS) following the EPA sampling schedule, from May to September 2022. The Teflon sample with the highest PM2.5 concentration, representative of both weekdays and weekends, was selected for morphological analysis using Scanning Electron Microscopy-Energy Dispersive X-ray (SEM-EDX) and elemental characterization using Energy-Dispersive X-ray Fluorescence (ED-XRF) Epsilon. SEM images revealed distinct morphological characteristics at each site. In Bandung, particles were irregularly shaped, agglomerated, and flaky, with sizes ranging from 1.1-1.6 µm on weekdays and 0.9-1.3 µm on weekends. Jakarta has particles with semi-crystalline, tabular, elongated, and puff-like morphology, with sizes predominantly from 0.5-0.8 µm on weekdays and 0.9-1.3 µm weekends. In Tangerang, particles were irregularly faceted and agglomerated, with sizes between 0.5-1.3 µm on weekdays and 0.9-1.4 µm on weekends. Teflon-derived minerals (C,F) were present across all sites. EDX spectra revealed Ca-rich particles in Bandung, while S-rich particles were observed in Jakarta and Tangerang. XRF analysis further proved the major and minor elements, reflecting local pollution sources. The combined use of SEM-EDX and XRF offers a comprehensive profile of PM2.5, highlighting specific pollution sources in each city.

In recent decades, air pollution has negatively affected human health and the environment. One of the important features contributing to air pollution is called PM2.5. However, daily prediction of PM2.5 is still lacking, especially using feature selection infused into the model. Hence, the main objective of this research is to utilize the feature selection procedures by proposing two stages feature selection methods namely adjusted correlation sharing t-test (adjcorT) and radial basis function neural network (RBFNN) in identifying the important features. This consequently also helps enhance the prediction of daily PM2.5 concentrations. Secondary data were obtained from the Department of Environment Malaysia (DOE) from 2018 until 2022 that consists of 5 years of air pollutant daily data. The results found that adjcorT-RBFNN identified the NO2, PM2.5, PM10, CO, O3, wind speed and SO2 as important features. The finding revealed that the accuracy, sensitivity, specificity, precision, F1 score and AUROC value, for a day-ahead prediction in Shah Alam are 0.756, 0.801, 0.717, 0.717, 0.757, and 0.758 respectively. Additionally, the predicted model may serve as an instrument for an early warning system, providing local authorities with information on air quality for formulation of strategies of air quality improvement.

Actinomycetes, predominantly found in soil, represent a remarkable source of natural products. The natural antioxidants obtained from them have been employed for human infectious diseases. In particular, Streptomyces strains serve as significant sources of natural antioxidants with demonstrated health benefits. The in vitro antioxidant activity and the inhibition of intracellular radical generation by crude extracts from various Streptomyces strains were evaluated and the genome sequence of selected strains was analyzed. Strains CT2-10, NE1-12, and ET3-23 showed 16S rRNA gene sequence similarity to Streptomyces capoamus JCM 4734T (98.94%), Streptomyces nigra 452T (99.78%), and Streptomyces morookaense LMG 20074T (99.49%), respectively. The genome analysis of strain ET3-23 had 106 contigs, with a total length of 8121874 bp and an average G+C content of 71.46%. Ethyl acetate extracts of strains CT2-10, ET3-23, and NE1-12 exhibited, 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging activity with IC50 555.9 µg/mL, 66.5 µg/mL, and 463.3 µg/mL and showed NO scavenging activity, except for CT2-10. Strain NE1-12 significantly inhibited ROS production induced by hydrogen peroxide on macrophage cells, while strains NE1-12 and CT2-10 inhibited NO production induced by lipopolysaccharides with IC50 82.4 µg/mL and 2.3 µg/mL, respectively. These findings suggest their ability to modulate NO production which is crucial in inflammatory responses and tissue injury. The antioxidant activities of Streptomyces strains indicate their potential as valuable sources of bioactive natural products and effective antioxidant agents, warranting further investigation for therapeutic applications.

As the development of renewable energy has become imperative, biodiesel fuel (BDF) has been used as renewable biofuel with the advantage of having lower levels of greenhouse gas emissions. However, the transesterification reaction that produces BDF generates a high-salinity crude glycerol (CG) byproduct, which is difficult to recycle. Halomonas elongata is a moderately halophilic bacterium being used as a cell factory due to its ability to assimilate varieties of substrates for growth in high-salinity conditions. Previously, we engineered a recombinant H. elongata GOP-Gad to biosynthesize and accumulate γ-aminobutyric acid (GABA) as a high-value product. Here, we tested the ability of H. elongata GOP-Gad to use CG as a substrate for cell growth and GABA production. The CG byproduct was obtained from a BDF facility in Unzen City, Nagasaki, Japan, where geothermal energy catalyzed BDF production from waste cooking oil and biomethanol. Prior to use as the sole carbon (C) source in culture media, the CG byproduct was partially purified to remove soap substances and other impurities. Finally, we showed that H. elongata GOP-Gad could grow and accumulate GABA up to 28 μmol/g cell fresh weight in a minimal M63 medium containing 7% w/v NaCl with 4% w/v glycerol from the partially purified CG as a C source and 15 mM (NH4)2SO4 as a nitrogen (N) source. This result demonstrates a new circulatory bioprocess of C and N, in which H. elongata GOP-Gad can use partially purified CG and (NH4)2SO4 as the sole C and N sources for growth and GABA production.

Earthquake hazard analyses rely on seismogenic source models. These are designed in various fashions, such as point sources or area sources, but the most effective is the three-dimensional representation of geological faults. We here refer to such models as fault sources. This study presents the European Fault-Source Model 2020 (EFSM20), which was one of the primary input datasets of the recently released European Seismic Hazard Model 2020. The EFSM20 compilation was entirely based on reusable data from existing active fault regional compilations that were first blended and harmonized and then augmented by a set of derived parameters. These additional parameters were devised to enable users to formulate earthquake rate forecasts based on a seismic-moment balancing approach. EFSM20 considers two main categories of seismogenic faults: crustal faults and subduction systems, which include the subduction interface and intraslab faults. The compiled dataset covers an area from the Mid-Atlantic Ridge to the Caucasus and from northern Africa to Iceland. It includes 1248 crustal faults spanning a total length of ∼95 100 km and four subduction systems, namely the Gibraltar, Calabrian, Hellenic, and Cyprus arcs, for a total length of ∼2120 km. The model focuses on an area encompassing a buffer of 300 km around all European countries (except for Overseas Countries and Territories) and a maximum of 300 km depth for the subducting slabs. All the parameters required to develop a seismic source model for earthquake hazard analysis were determined for crustal faults and subduction systems. A statistical distribution of relevant seismotectonic parameters, such as faulting mechanisms, slip rates, moment rates, and prospective maximum magnitudes, is presented and discussed to address unsettled points in view of future updates and improvements. The dataset, identified by the DOI https://doi.org/10.13127/efsm20 (Basili et al., 2022), is distributed as machine-readable files using open standards (Open Geospatial Consortium).

The southwest region of Bangladesh is particularly vulnerable to a range of climatic threats, including cyclones, prolonged flooding, sea level rise, saltwater intrusion, drought, and riverbank erosion. The study investigates how these different drivers affect aquaculture systems and aims to provide critical insights for sustainable management. The survey work focused on the problems, vulnerabilities, migration, and adaptive strategies of communities of the southwest region of Bangladesh, that rely heavily on shrimp, fish, and crab production for their livelihoods. Data were collected from the 80 respondents across four unions (Atulia, Burigoalini, Gabura, and Bhurulia) in Shyannagar Upazila, Satkhira District, using a structured questionnaire survey and focus group discussions. Findings reveal that climate change events have significantly changed shrimp farming in enclosures, pond aquaculture, and crab point management, and negatively impacted livelihoods. Pond aquaculture appeared to be the most vulnerable to climate change conditions, followed by shrimp farming in enclosures and crab points. Furthermore, the adverse effects of climate change compelled human migration within the study area, primarily driven by the search for employment. This study provides evidence of the effects of various climate change stressors on shrimp, fish, and crab production systems and the adaptive difficulties of the communities dependent on aquatic ecosystems. As the natural calamity like cyclone cannot be prevented, understanding the impact of previous events may help people of the affected area as well as the policy makers to plan for better survival.

This paper presents the conduct of Waste Analysis and Characterization Study (WACS) in the Industrial Technology Development Institute of the Department of Science and Technology (DOST-ITDI) in Taguig City, Philippines. The study was produced following the introduction of 2020 guidelines developed by the Japan International Cooperation Agency for the Philippines to determine waste generation and generation rate per source, as well as waste composition, per capita generation, and bulk densities of the generated wastes. This then provided fundamental data in the formulation of initial plans and programs for the proper management of solid wastes in the institution. From the gathered results, DOST-ITDI generates 91.56 kg/day of solid waste during the dry season, and 77.54 kg/day during the wet season. The generated waste is composed of residuals for disposal (35.93% and 37.75%, dry and wet season), biodegradables (27.80% and 21.46%), and recyclables (22.54% and 28.95%). Per capita generation rates of 0.21 kg/capita/day (dry season) and 0.18 kg/capita/day (wet season) were also recorded. The research and development cluster of divisions was determined as a major contributor of waste at 42.37 to 57.62 kg/day. Bulk density values varied between waste fractions, with the main residuals for disposal components being the bulkiest at 29.27 to 32.90 kg/m3. The data collected from the study provided significant information useful in efforts to initiate programs for diversion of biodegradables, recyclables, and residuals-with further potential for recycling wastes, reducing residuals for disposal through proper segregation and recovery, and determining gaps and opportunities in plans, policies, and programs to improve solid waste management of the institution.

Economic progress, urban expansion, and enhanced quality of life in India have led to the formation of densely populated megacities and a significant increase in municipal solid waste generation. Such waste consists of a substantial volume of residues from food waste, kitchen waste originating from residential complexes, restaurants, and remnants of agricultural activities. Consequently, handling municipal solid waste has become a critical issue. Though composting is being practiced as an eco-friendly means of recycling organic waste, it is laborious and time-consuming. In the recent past, researchers have suggested the use of black soldier fly larvae (BSFL) as an effective solution for solid waste management. This study aims to assess the degradation potential of BSFL, which is gaining attention in recycling technology due to its composting capabilities, and its ability to produce soil amendments suitable for agricultural purposes. To evaluate the ability of BSFL to degrade different types of organic wastes, with a focus on cooked food waste, the compost formed after 14 days of degradation was analyzed based on elemental composition and other parameters. Comparative examinations were made with different vermicompost samples and hermicompost produced using BSFL to check the effect on plant growth. The analysis showed a higher percentage of nitrogen (4.21%), and phosphorus (0.5%) in hermicompost. The C:N ratio was 12:1 which is suitable for agronomical purposes. This study concludes that, BSFL are useful as versatile bioconversion agents of cooked food waste and provide a promising organic recycling strategy for sustainable waste management.

This study conducted a project to assess carbon sequestration in the forest area of Chiang Mai Rajabhat University, Mae Rim Campus, covering a total area of approximately 5,600 rai, with about 75% consisting of dry dipterocarp forest. The Sentinel-2 satellite data from 2019 to 2023 were used to analyze and classify forest density using the Normalized Difference Vegetation Index (NDVI). It was classified into four NDVI levels: highest, high, moderate, and low. Then, eight sample plots were distributed across all density levels to collect field data on tree species, number of trees, height, and diameter. The biomass and carbon sequestration in the sample plots showed a strong correlation with vegetation density, with the highest average correlation in February, particularly on February 13, 2023, showing the highest correlation coefficient of 0.817. This relationship is described by the equation y=78.601x‒25.726, indicating that this model is effective for estimating carbon sequestration. The analysis revealed that the area with the highest NDVI level of dry dipterocarp forest had the highest above-ground carbon sequestration rate of 16.25 tons per rai, whereas the forest with the lowest NDVI level had an above-ground carbon sequestration rate of 0.21 tons per rai. In total, the above-ground carbon sequestration for the trees amounted to 50,907.35 tons. This preliminary assessment serves as a promising foundation for future efforts the conservation and restoration of the university’s forest area, contributing to sustainable strategies for mitigating global warming.

Urban flooding poses a significant threat to vulnerable underground infrastructure systems, such as metro stations. Building collapse induced by earthquakes alters urban building layout and coverage, consequently influencing flood inundation and propagation patterns. This study employs GPU-accelerated hydrodynamic simulation to investigate the mechanisms by which building collapse affects subsequent pluvial or fluvial flooding in the Huangpu District of Shanghai. Massive building collapse layouts are randomly generated, on which hydrodynamic simulations are performed and the inundation process of the metro stations is analyzed. The results reveal that pluvial floods are strongly influenced by localized topography distributed across the city. Consequently, building collapse has a more substantial impact on pluvial flooding when more buildings have collapsed. In contrast, fluvial floods are sensitive to the source location (e.g., location of levee breach) and the long travel route. Building collapse can either positively or negatively influence fluvial flooding by constricting or blocking the flow path. This work highlights the complex mechanism of earthquake–flood multi-hazard processes, emphasizing the importance of performing local-to-local analysis when both the hazard (e.g., individual building collapse, fluvial flood) and the hazard-bearing body (e.g., metro station) are localized. To better serve urban disaster prevention and mitigation, more efforts should be directed to developing physics-based high-resolution urban earthquake–flood simulation methods, as well as to acquiring data to drive such simulations.

The rivers of High-mountain Asia provide freshwater to around 1.9 billion people. However, precipitation, the main driver of river flow, is still poorly understood due to limited in situ measurements in this area. Existing tools to interpolate these measurements or downscale and bias-correct precipitation models have several limitations. To overcome these challenges, this paper uses a probabilistic machine learning approach called multi-fidelity Gaussian processes (MFGPs) to downscale the fifth ECMWF climate reanalysis (ERA5). The method is first validated by downscaling ERA5 precipitation data over data-rich Europe and then data-sparse upper Beas and Sutlej river basins in the Himalayas. We find that MFGPs are simpler to implement and more applicable to smaller datasets than other state-of-the-art machine learning methods. MFGPs are also able to quantify and narrow the uncertainty associated with the precipitation estimates, which is especially needed over ungauged areas and can be used to estimate the likelihood of extreme events that lead to floods or droughts. Over the upper Beas and Sutlej river basins, the precipitation estimates from the MFGP model are similar to or more accurate than available gridded precipitation products (APHRODITE, TRMM, CRU TS, and bias-corrected WRF). The MFGP model and APHRODITE annual mean precipitation estimates generally agree with each other for this region, with the MFGP model predicting slightly higher average precipitation and variance. However, more significant spatial deviations between the MFGP model and APHRODITE over this region appear during the summer monsoon. The MFGP model also presents a more effective resolution, generating more structure at finer spatial scales than ERA5 and APHRODITE. MFGP precipitation estimates for the upper Beas and Sutlej basins between 1980 and 2012 at a 0.0625° resolution (approx. 7 km) are jointly published with this paper.

The World Wide Lightning Location Network (WWLLN) operates a distributed network of stations which detect lightning signals at a planetary scale. Very high currents from lightning strokes radiate strong very low frequency signals in the 6–22 kHz band, which are detected up to 10 000 km away by the WWLLN stations and which are used to determine the time and position of the lightning stroke detected by triangulation, similarly to global positioning systems. Studies of the performance of the WWLLN in different areas around the world have already been reported in the literature, but similar studies for west European regions are still unavailable. This work presents a study to determine the detection efficiency and location accuracy of the WWLLN over Spain by comparing its data with those of the Spanish State Meteorological Agency, AEMET, during 2012 taken as the ground truth. The study provides a detection efficiency for the WWLLN of around 29 % and a location accuracy of between 2 and 3 km. The efficiency for high-energy strokes is considerably higher. A study of four subregions with different geographical features is also considered. The peak current distribution of lightning events in these regions is obtained, and a possible link to the WWLLN performance is discussed. Finally, an application of the WWLLN data for three major storms in 2020, 2021, and 2022 in the Mediterranean area of Spain demonstrates that the WWLLN is well suited for tracking the time evolution of adverse meteorological phenomena.

Groundwater is an important source of freshwater; drinking water; and service water for irrigation, industrial and geothermal uses. It is also the largest terrestrial freshwater biome in the world. In many areas, this habitat is naturally or anthropogenically threatened. This study uses long-term groundwater data from southwestern Germany to identify shifts in groundwater fauna due to natural or anthropogenic impacts. Comprehensive analysis of metazoan groundwater fauna and abiotic parameters from 16 monitoring wells over 2 decades revealed no overall temporal trends in faunal abundance or biodiversity (in terms of number of species) and no significant large-scale trends in abiotic parameters. While 9 wells out of 16 show stable ecological and hydrochemical conditions at a local level, the remaining wells exhibit shifting or fluctuating faunal parameters. At some locations, these temporal changes are linked to natural causes, such as decreasing dissolved oxygen contents or fluctuating temperatures. A multivariate PHATE (Potential of Heat-diffusion for Affinity-based Trajectory Embedding) analysis suggests that, beside the hydrogeological setting, varying contents of sediment and detritus impact faunal abundance. By examining aerial images of the surroundings of individual wells, we found that anthropogenic impacts, such as construction sites and surface sealing, can cause significant shifts in groundwater fauna and changes in the ecological status in positive as well as negative direction. However, variable faunal compositions and abundances were also observed for sites with very stable abiotic conditions in anthropogenically less affected areas such as the Black Forest. These findings indicate that hydro(geo)logical changes and surface conditions, such as land use, should be assessed in line with hydrochemical parameters to better understand changes in groundwater fauna. Accordingly, reference sites for natural conditions in ecological assessment and biomonitoring schemes for groundwater protection should be selected carefully.

The gravity-driven flow in an unsaturated porous medium remains one of the most important unsolved problems in multiphase flow. Sometimes a diffusion-like flow with a uniform wetting front, known as stable flow, is observed, but, at other times, the flow is unstable with distinct preferential pathways. The formation of an unstable wetting front in a porous medium depends on many factors, including the type of porous medium, the initial saturation, and the applied infiltration rate. As the infiltration rate increases, the wetting front first transitions from stable to unstable at low infiltration rates and then from unstable to stable at high infiltration rates. We propose a governing equation and its discretized form, the semi-continuum model, to describe this significant non-monotonic transition. We show that the semi-continuum model is able to capture the influx dependence together with the correct finger width and spacing. Moreover, we demonstrate that the instability of the wetting front is closely related to the saturation overshoot in one dimension. Finally, we show that the flow can still be preferential even when the porous medium is completely wetted.

Flow regimes are increasingly impacted by more extreme natural hazards of droughts and floods as a result of climate change, compounded by anthropogenic influences in both urban and intensively managed rural catchments. However, the characteristics of sustainable flow regimes that are needed to maintain or restore hydrologic, biogeochemical and ecological functions under rapid global change remain unclear and contested. We conducted an intercomparison of two streams in the Berlin–Brandenburg region of northeast Germany, which are both mesoscale subcatchments of the Spree river: an intermittent rural agricultural stream (the Demnitzer Millcreek) and a heavily anthropogenically impacted urban stream (the Panke). Through tracer-based analyses using stable water isotopes, we identified the dominant physical processes (runoff sources, flowpaths and age characteristics) sustaining streamflow over multiple years (2018–2023), including three major drought years (2018–2020, 2021–2022). In the urban stream, low flows are regulated through artificially increased baseflow from treated wastewater effluents (by up to 80 %), whilst storm drainage drives rapid, transient high-flow and runoff responses (up to 80 %) to intense convective summer rainfall. The intermittent groundwater-dominated rural stream experienced extended no-flow periods during drought years (∼ 60 % of the year) and only moderate storm runoff coefficients (< 10 %) in winter along near-surface flow paths after heavy rainfall. In both streams, groundwater dominance with young water influence prevails, with low water ages in the urban stream (< 10 %) despite significant urban runoff and higher ones in the rural stream (∼ 15 %). Urban cover resulted in a mean transit time of ∼ 4 years compared to arable land at ∼ 3 years, highlighting the interlinkages of land use and catchment properties on catchment transit times. Understanding seasonal and interannual variability in streamflow generation through a tracer-based hydrological template has the potential to assess the impacts of natural hazards on the sustainability of future baseflow management, including wider water quality and ecological implications across anthropogenically impacted environments.

We have conducted an entropy analysis in Alaska, a seismic-rich region in a subduction zone that exhibits a nontrivial behavior: the subduction arc alters the seismic activity from the eastern zone to the western zone, demonstrating a decrease in activity along the subduction. We analyze this zone through the Tsallis entropy and the mutability (or dynamic entropy) for the first time. Considering 13 870 seismic events after appropriate filtering, we analyzed a data set for the selected Alaska zone between 2000 and 2023. We have found agreement between the results for the two entropies. We have followed the value of the q parameter of the Tsallis entropy (Sq) finding values between 1.70 and 1.85, in concordance with values found in other seismic regions of the planet. The values of Sq decrease slightly over time but show a broad increase before the major earthquakes. Just opposite to Tsallis entropy, mutability shows a tendency to decrease prior to the major earthquakes. We used the simpler mutability method to further analyze this zone upon dividing the region into four subzones. The results show how mutability can identify the seismic activity in each zone. This study shows how an entropy approach can shed light on understanding the seismicity in subduction zones.

We demonstrate how high spatial and temporal resolution spaceborne synthetic aperture radar (SAR) imagery can improve slope deformation monitoring. We process ICEYE data over the Brienz/Brinzauls slope instability in the Swiss Alps, where a catastrophic failure occurred on 15 June 2023. Image correlation applied to retrieve surface velocities shows results comparable to in situ measurements. Pre- and post-failure acquisitions used to map areas invaded by debris and to compute associated volumetric changes are in good agreement with photogrammetric data. This study sets the baseline for new-generation satellite SAR data aimed at providing timely information in landslide early warning scenarios.

In October 2023, the Baltic Sea coasts of Germany and Denmark experienced a severe storm surge, predominantly impacting the German state of Schleswig-Holstein and parts of southern Denmark. The surge led to extensive flooding in cities like Flensburg and Schleswig, causing the breaching of at least six (regional) dikes and causing over EUR 200 million in damages in Schleswig-Holstein. By chance, the peak water levels of this storm surge aligned well with those of recent hydrodynamic flood modelling studies of the region. This rare coincidence offers crucial insights for our understanding of flooding impacts, flood management, and modelling. By comparing those studies to the real-world example using extensive media reports, we aim to extract key insights and identify gaps to be tackled in order to improve flood risk modelling in the Baltic Sea region and beyond.

A fully probabilistic earthquake risk model was developed for five countries in Central Asia, providing updated earthquake loss estimates with a higher level of detail on all components with respect to previous studies in the region. Additionally, a regionally consistent approach that, on the one hand, allows for direct comparisons at different disaggregation levels (e.g., country and oblast) was used. On the other hand, this updated earthquake risk assessment aims to facilitate initiating a policy dialogue regarding national and regional disaster risk financing and insurance applications. This earthquake risk model made use of a regional probabilistic seismic hazard analysis and a comprehensive exposure database on which different types of assets and sectors were included and for which two scenarios (the years 2020 and 2080) were modeled. For each type of asset, a unique vulnerability function was derived and later used for convolution with the hazard data, which allowed for the estimation of the loss exceedance curve, at different disaggregation levels, from where other risk metrics such as the average annual loss (AAL) and specific return period losses were obtained. The regional earthquake AAL for the five countries and for the 2020 exposure scenario has been estimated to be around USD 2 billion, with the Kyrgyz Republic and Tajikistan being the countries with the highest earthquake risk levels in the region. Besides the probabilistic earthquake risk results, as-if scenarios were modeled using a pseudo-deterministic approach to assess the human and economic losses for realistic and representative earthquakes for the main cities within earthquake-prone regions in the five countries of the study area.

Hail is one of the costliest natural hazards in Switzerland and causes extensive damage to agriculture, cars, and infrastructure each year. In a warming climate, hail frequency and its patterns of occurrence are expected to change, which is why understanding the long-term variability and its drivers is essential. This study presents new multidecadal daily hail time series for northern and southern Switzerland from 1959 to 2022. Daily radar hail proxies and environmental predictor variables from ERA5 reanalysis are used to build an ensemble statistical model for predicting past hail occurrence. Hail days are identified from operational radar-derived probability of hail (POH) data for two study domains, the north and south of the Swiss Alps. We use data from 2002 to 2022 during the convective season from April to September. A day is defined as a hail day when POH surpasses 80 % for a minimum footprint area of the two domains. Separate logistic regression and logistic generalized additive models (GAMs) are built for each domain and combined in an ensemble prediction to reconstruct the final time series. Overall, the models are able to describe the observed time series well. Historical hail reports are used for comparing years with the most and least hail days. For the northern and southern domains, the time series both show a significant positive trend in yearly aggregated hail days from 1959 to 2022. The trend is still positive and significant when considering only the period of 1979–2022. In all models, the trends are driven by moisture and instability predictors. The last 2 decades show a considerable increase in hail days, which is the strongest in May and June. The seasonal cycle has not shifted systematically across decades. This time series allows us to study the local and remote drivers of the interannual variability and seasonality of Swiss hail occurrence.

Abstract This study investigates the impact of the long-term use of inlet-heated tubes on the performance of mid-cost optical particle counters (OPCs) for ambient air monitoring of fine particles (PM2.5). Two OPCs, equipped with inlet-heated tubes, were deployed over a 6-month period, with a beta attenuation monitor (BAM) serving as the reference device. The performance of the OPCs using the same inlet tubes for the first 3 months was compared to their performance after the frequent replacement of clean tubes during the final 3 months. The correlation coefficients (r 2) for the 1 h and 24 h average PM2.5 concentrations between the OPCs and the BAM were lower with long-term contaminated tubes (0.82 0.93). The relative mean errors and biases significantly increased over time with contaminated tubes. Temperature, humidity, precipitation, and wind speed were found to have an insignificant effect (r 2

Abstract Seosan is a concentrated industrial complex in the midwestern region of Korea. A study was conducted from December 2020 to January 2021, measuring PM2.5 and chemical components in Seosan using online instruments every hour. The concentration of PM2.5 during the winter season was 31.4±17.8 μg/m3, exceeding the national ambient air quality standard of Korea. The mass fraction of organic matter, elemental carbon, three major ions, five minor ions, crustal elements, and trace elements in PM2.5 accounted for 24.5%, 4.36%, 32.0%, 2.82%, 4.11%, and 5.17% of the total PM2.5 mass concentration, respectively. Source identification was conducted using positive matrix factorization modeling, revealing eight sources of PM2.5: Secondary inorganic aerosol (SIA), vehicle exhaust, industry, coal combustion, biomass burning/incinerator, oil combustion, soil, and aged sea salt. Source contributions varied during high pollution episodes (HPE), with SIA dominating in HPE1 and soil and aged sea salt in HPE2. The potential source contribution function and conditional probability function were utilized to estimate the potential local and regional emission areas for the identified sources. In Seosan, vehicle exhaust and biomass burning/incinerator were primarily influenced by local sources. SIA, industry, and oil combustion sources were significantly affected by short-range transport from eastern China. Soil and aged sea salt, which exhibited high contributions during HPE2, were associated with long-range transport from Inner Mongolia. Coal combustion was attributed to both local sources, particularly large industrial complexes near Seosan, and long-range transport from Northeast China and Inner Mongolia. Graphical Abstract

Abstract In the south-eastern region of Nigeria, the application of rainfall (P) and runoff (Q) data directly into the Natural Resources Conservation Service Curve Number (NRCS-CN) method hasn’t been thoroughly studied. This research aimed to determine representative values of the initial abstraction ratio (λ) and corresponding curve number (CN), fit the P and Q data using theoretical probability distributions, and establish confidence intervals for CN. The least squares minimization method and Kolmogorov-Smirnov test were employed on data from 129 sub-basins across 4 major basins. Findings revealed optimal initial abstraction ratio (λopt) = 0.24 and optimal CN (CNopt) = 80, with rainfall best fitted by Gamma, runoff by Weibull, and CN by Normal distributions. However, the study was limited to the available 8-year record period with 96 storm events. The 96 storm events over an 8-year period may seem limited for a humid tropical region, the available data were the most comprehensive and reliable dataset for this study area. Additional data collection over a longer time frame could enhance future studies. The localized CN value and associated confidence intervals can enhance runoff prediction accuracy for flood mitigation and water resources management in this humid tropical region, though further validation is recommended.

Landslide inventories have become cornerstones for estimating the relationship between the frequency and size of slope failures, thus informing appraisals of hillslope stability, erosion, and commensurate hazard. Numerous studies have reported how larger landslides are systematically rarer than smaller ones, drawing on probability distributions fitted to mapped landslide areas or volumes. In these models, much uncertainty concerns the larger landslides (defined here as affecting areas ≥ 0.1 km2) that are rarely sampled and often projected by extrapolating beyond the observed size range in a given study area. Relying instead on size-scaling estimates from other inventories is problematic because landslide detection and mapping, data quality, resolution, sample size, model choice, and fitting method can vary. To overcome these constraints, we use a Bayesian multi-level model with a generalised Pareto likelihood to provide a single, objective, and consistent comparison grounded in extreme value theory. We explore whether and how scaling parameters vary between 37 inventories that, although incomplete, bring together 8627 large landslides. Despite the broad range of mapping protocols and lengths of record, as well as differing topographic, geological, and climatic settings, the posterior power-law exponents remain indistinguishable between most inventories. Likewise, the size statistics fail to separate known earthquakes from rainfall triggers and event-based triggers from multi-temporal catalogues. Instead, our model identifies several inventories with outlier scaling statistics that reflect intentional censoring during mapping. Our results thus caution against a universal or solely mechanistic interpretation of the scaling parameters, at least in the context of large landslides.

The hydrological cycle of sub-arctic areas is dominated by the snowmelt event. An understanding of the mechanisms that control water fluxes during high-volume infiltration events in sub-arctic till soils is needed to assess how future changes in the timing and magnitude of snowmelt can affect soil water storage dynamics. We conducted a tracer experiment in which deuterated water was used to irrigate a plot on a forested hilltop in Lapland, tracked water fluxes of different mobility and monitored how the later snowmelt modifies the labelled soil water storage. We used lysimeters and destructive soil coring for soil water sampling and monitored and sampled the groundwater. Large spatiotemporal variability between the waters of different mobility was observed in the subsurface, while surface water flow during the tracer experiment was largely controlled by a fill-and-spill mechanism. Extensive soil saturation induced the flow of labelled water into the roots of nearby trees. We found that labelled water remained in deeper soil layers over the winter, but the snowmelt event gradually displaced all deuterated water and fully homogenized all water fluxes at the soil–vegetation interface. The conditions required for the full displacement of the old soil water occur only during a snowmelt with a persistently high groundwater table. We propose a conceptual model where infiltration into the soil and eventual soil water replenishment occur in three stages. First, unsaturated macropore flow is initiated via the surface microtopography and is directed towards the groundwater storage. The second stage is characterized by groundwater rise through the macropore network, subsequent pore water saturation and increased horizontal connectivity of macropores. Shallow subsurface lateral fluxes develop in more permeable shallow soil layers. In the third stage, which materializes during a long period with a high groundwater table and high hydrological connectivity within the soil, the soil water is replenished via enhanced matrix flow and pore water exchange with the macropore network.

The calibration of hydrological models through the use of automatic algorithms aims at identifying parameter sets that minimize the deviation of simulations from observations (often streamflows). Further, the choice of objective function (i.e. the criterion or combination of criteria for optimization) can significantly impact the parameter set values identified as optimal by the algorithm. This article discusses how mathematical transformations, which are sometimes applied to the target variable before calculating the objective function, impact model simulations. Such transformations, for example square root or logarithmic, aim at increasing the weight of errors made in specific ranges of a hydrograph. We show in a catchment set that the impact of these transformations on the obtained time series can sometimes be different from their expected behaviour. Extreme transformations, such as squared or inverse squared transformations, lead to models that are specialized for extreme streamflows but show poor performance outside the range of the targeted streamflows and are less robust. Other transformations, such as the power 0.2 and the Box–Cox and logarithmic transformations, can be categorized as more generalist and show good performance for the medium range of streamflows, along with acceptable performance for extreme streamflows.

The volcanic island of Stromboli (southern Tyrrhenian Sea, Italy) is renowned for its persistent, periodic, low-intensity explosive activity, whose spectacular manifestations attract tens of thousands of tourists every year. However, sporadic more intense major explosive and effusive eruptions and paroxysms pose serious threats to the island. In addition to direct hazards, granular slides of volcanic debris and pyroclastic avalanches, which can rapidly reach the sea and potentially generate tsunamis, are often associated with such unpredictable eruptive activity. Due to the very fast propagation of the tsunami around the island and the consequent short tsunami warning time (ranging from less than a minute to only a few minutes), mitigation efforts and evacuation from the Strombolian coast must be carefully planned. In this paper, we describe a new GIS-assisted procedure that allows us to combine the outputs of an ensemble of 156 pre-computed landslide-generated tsunami hazard scenarios (with variable landslide volume, position, and density), statistical exposure data (i.e. the number of inhabitants and tourists), and digital geographic information to obtain a quantitative (scenario-based) risk analysis. By means of the analysis of the road network and coastal morphology, we develop a model with routes and times to reach a safe area from every pixel in the inundated area and an appraisal of the time needed to escape versus the wave arrival time. This allows us to evaluate and quantify the effectiveness of potential risk mitigation by means of evacuation. The creation of an impact score linking the predicted inundation extent and the tsunami warning signals is intended, in the long term, to be used to predict the intensity of future tsunamis and to adapt evacuation plans accordingly. The model, here applied to Stromboli, is general and can be applied to other volcanic islands. Evacuating an island hosting several thousand tourists every summer with very little warning time underlines the absolute necessity for such mitigation efforts, aimed at informing hazard planners and managers and all other stakeholders.

Criteria and indicators are frequently used for assessing the resilience of critical infrastructure (CI). Moreover, to generate precise information on conditions, the assessment designed for CI resilience could rely on indicator systems. However, few practical tools exist for guiding CI managers to build specific indicator systems for considering real cases. Therefore, the main objective of this study is to develop a step-by-step guide that contains guidance on operational steps and required resources for criteria and indicator setting, reference definition, and data collection. This guide enables CI managers to build systems of indicators tailored to different real cases. This guide could assist CI managers in their decision-making process, as it is structured based on a multi-criteria framework that takes into account the cost–benefit analysis and side effects of implementable actions. This guide could furthermore advance the application of indicator-based CI resilience assessment in practical management. In addition, this study provides an example to demonstrate how to use this guide. This example is based on specific circumstances for the Nantes Ring Road (NRR) network: when the ring road is flooded and closed, the road network manager suggests alternative roads to the public. An indicator system consisting of 4 criteria, 7 sub-criteria, and 11 indicators was built for these circumstances using the guide developed in this paper. This example relates to criteria and indicators in the technical, social, and environmental dimensions and involves 62 676 data points.

Soil moisture is important in many hydrological and ecological processes. However, data sets which are currently available have issues with accuracy and resolution. To translate remotely sensed data to an absolute measure of soil moisture requires mapped estimates of soil hydrological properties and estimates of vegetation properties, and this introduces considerable uncertainty. We present an alternative methodology for producing daily maps of soil moisture over the UK at 2 km resolution (“SMUK”). The method is based on a simple linear statistical model, calibrated with 5 years of daily data from cosmic-ray neutron sensors at ∼ 40 sites across the country. The model is driven by precipitation, humidity, a remotely sensed “soil water index” satellite product and soil porosity. The spatial variation in the parameter describing the soil water retention (and thereby the response to precipitation) was estimated using daily water balance data from ∼ 1200 catchments with good coverage across the country. The model parameters were estimated by Bayesian calibration using a Markov chain–Monte Carlo method, so as to characterise the posterior uncertainty in the parameters and predictions. The approach reduces uncertainty by integrating multiple data sources, all of which have weaknesses but together act as a better constraint on the true soil moisture. The model explains around 70 % of the variance in the daily observations with a root-mean-square error of 0.05 m3 m−3, better than results from more complex process-based models. Given the high resolution of the inputs in time and space, the model can predict the very detailed variation in soil moisture which arises from the sporadic nature of precipitation events, including the small-scale and short-term variations associated with orographic and convective rainfall. Predictions over the period 2016 to 2023 demonstrated realistic patterns following the passage of weather fronts and prolonged droughts. The model has negligible computation time, and inputs and predictions are updated daily, lagging approximately 1 week behind real time.

The latest generation of national and regional probabilistic seismic hazard assessments (PSHAs) in Europe presents stakeholders with multiple representations of the hazard in many regions. This raises the question of why and by how much seismic hazard estimates between two or more models differ, not only where models overlap geographically but also where new models update existing ones. As modern PSHA incorporates increasingly complex analysis of epistemic uncertainty, the resulting hazard is represented not as a single value or spectrum but rather as probability distribution. Focusing on recent PSHA models for France and Germany, alongside the 2020 European Seismic Hazard Model, we explore the differences in model components and highlight the challenges and strategy for harmonising the different models into a common PSHA calculation software. We then quantify the differences in the source model and seismic hazard probability distributions using metrics based on information theory, illustrating their application to the Upper Rhine Graben region. Our analyses reveal the spatial variation in and complexity of model differences when viewed as probability distributions and highlight the need for more detailed transparency and replicability of the models when used as a basis for decision-making and engineering design.

Climate change increases the risk of wildfires and floods in the Mediterranean region. Yet, wildfire hazards are often overlooked in flood risk assessments and treated in isolation, despite their potential to amplify floods. Indeed, by altering the hydrological response of burnt areas, wildfires can lead to increased runoff and amplifying effects. This study aims to comprehensively assess flood risk using a multi-hazard approach, considering the effect of wildfires on flood risk, and integrating diverse socio-economic indicators with hydrological properties. More specifically, this study investigates current and future flood risks in the Ebro River basin in Spain for the year 2100 under the Shared Socioeconomic Pathway 1-2.6 (SSP1-2.6) and SSP5-8.5 scenarios, taking into account projected socio-economic conditions and the effect of wildfires. An analytical hierarchy process (AHP) approach is employed to assign weights to various indicators and components of flood risk based on insights gathered from interviews with seven experts specializing in natural hazards. Results show that the influence of wildfires on the baseline flood risk is not apparent. Under the SSP1-2.6 scenario, regions with high flood risk are expected to experience a slight risk reduction, regardless of the presence of wildfires, due to expected substantial development in adaptive capacity. The highest flood risk, almost double compared to the baseline, is projected to occur in the SSP5-8.5 scenario, especially when considering the effect of wildfires. Therefore, this study highlights the importance of adopting a multi-hazard risk management approach, as reliance solely on single-risk analyses may lead to underestimating the compound and cascading effects of multi-hazards.

O estudo avalia as diferenças entre as áreas de preservação permanentes (APPs) do reservatório Orós, calculadas segundo os critérios do Código Florestal vigente (Lei nº 12.652/2012) e do revogado (Lei nº 4.771/65). As APPs foram estimadas para dois conjuntos de parâmetros operacionais do reservatório: 1) a partir da cota máxima maximorum (CMM) do projeto do Orós de 1960 e, 2) da CMM atualizada do Orós. No Código de 1965, a APP é definida a partir do conceito de Cota Máxima Operativa Normal (CMON). No Código de 2012, a área é estimada a partir da CMM. Utilizou-se o software ArcGis 10.2 para avaliar as APPs nas quatro situações: dois quadros de critérios legais e dois conjuntos de parâmetros operacionais do Orós. Os resultados mostraram que a atual legislação é menos restritiva, e estabelece uma área 26% menor do que a delimitada pela legislação anterior. A alteração da lei também promoveu modificações quanto à classificação territorial dos municípios nos quais o Açude Orós está inserido

O uso de lâmpadas UV de média e baixa pressão de vapor de mercúrio tem sido adotado para inativação de microrganismos na água como uma alternativa ao cloro. No entanto, as lâmpadas UV de vapor de mercúrio apresentam desvantagens, como alto gasto de energia elétrica, alta fragilidade e uso de mercúrio, que é um agente tóxico. Nesse sentido, o uso de tecnologias menos impactantes, mais econômicas e de menor demanda de energia elétrica, como os LEDs, tem ganhado espaço nas pesquisas de tratamento de água e esgoto. Os LEDs apresentam vantagens em relação às lâmpadas UV, como baixo consumo de energia, resistência, facilidade de modulação, possibilidade de ser alimentado por energia solar, emissão de pico de onda específico, entre outras. Os resultados de inativação com LEDs têm demonstrado que a tecnologia é promissora para aplicação nos setores de saneamento, com eficiência comparável às lâmpadas de vapor de mercúrio para eliminação de microrganismos com potencial patogênico, como bactérias e vírus.