Atmospheric and Oceanic Sciences

Abstract Sea fog is a common hazardous marine weather phenomenon driven by combined meteorological factors. Traditional monitoring and modeling methods face significant challenges in accurately representing their complex relationships. Hence, this study introduces a geographically weighted regression (GWR) model that investigates the spatially non‐stationary relationships between sea fog occurrence and marine meteorological factors (mean sea level pressure [MSL], relative humidity [RH], air–sea temperature difference [TD], and wind speed [WSD]) in the Yellow and Bohai Seas. Two key features distinguish the methodology: (1) the integration of multisource data, including Himawari‐9 satellite‐based sea fog detection and the fifth‐generation atmospheric reanalysis, achieving a more accurate and comprehensive representation of the spatially non‐stationary relationship; and (2) the standardization of these datasets into monthly averages and hexagonal grid cells, improving spatial consistency and the reliability of local parameter estimation within the GWR framework. Results show that sea fog occurrence and marine meteorological factors exhibit significant spatial clustering, based on separate spatial autocorrelation analyses. The GWR model performed significantly better and had an R 2 value of 0.536 higher than the ordinary least squares (OLS) model. Additionally, for peak sea fog (March–June), optimal formation ranges are: MSL 1013.73–1021.74 hPa, RH 76.48%–86.38%, TD 0.18–0.84°C, WSD 5.44–6.35 m·s −1 . The GWR's local regression coefficients revealed that MSL and RH primarily regulate sea fog occurrence. Notably, the dominant factors and their influence magnitudes vary substantially across sea areas, regions, and months. Thus, monitoring these high‐impact factors dynamically across regions enhances the effectiveness of sea fog early warning systems.

Climate change and anthropogenic activities have substantially altered runoff generation and soil erosion processes. This study investigated the spatiotemporal patterns and influencing factors of the Potential Soil Erosion–Runoff Ratio (PERR), defined as the ratio of RUSLE-derived annual potential soil erosion amount to annual runoff volume, in the mountain–hill–plain transitional of Sanmenxia, China, from 1990 to 2015. Spatial statistical methods were integrated with comparative machine learning and SHAP-based interpretation. Among six candidate models, XGBoost achieved the best predictive performance, with R2 values of 0.914 and 0.839 for the temporal and spatial holdout sets, respectively. PERR exhibited marked interannual fluctuations without a statistically significant monotonic trend, while the sequential Mann–Kendall test identified candidate temporal shifts around 1994–1995 and 2014. Spatially, persistent hot spots were concentrated in the southern mountainous and hilly regions, whereas persistent cold spots occurred mainly in the northern plains, revealing a clear geomorphic gradient. Slope, cropland cover, and elevation had the highest mean absolute SHAP values within the fitted model. A pronounced nonlinear transition in the modelled PERR response occurred near a slope of 17°, representing a model-derived and scale-dependent transition range rather than a universal physical threshold. These findings demonstrate the utility of explainable machine learning for identifying spatial heterogeneity and nonlinear controls in the runoff–potential erosion relationship and provide quantitative support for spatially targeted soil and water conservation.

Soil salinization severely restricts ecosystem stability and the sustainable development of agricultural productivity. However, current understanding of the spectral–salinity quantitative relationships under the influence of surface cracking still remains limited. To address this gap, this study collected hyperspectral reflectance data (350–2500 nm) from salt-affected soil in both cracked and uncracked surface conditions across the Songnen Plain, and applied fractional-order differentiation (FOD) processing with orders ranging from 0 to 2 and a step size of 0.1. Based on this, 14 types of FOD spectral indices were constructed, incorporating one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) structures. For each spectral index, the optimal fractional order and corresponding band combinations were first selected through Pearson correlation analysis for pH and EC under both surface conditions; subsequently, feature selection was performed using XGBoost-SHAP explainable analysis among the 14 optimal indices across different dimensions. Furthermore, the predictive performance of four modeling methods, including partial least squares regression (PLSR), Gaussian process regression (GPR), support vector regression (SVR), and random forest regression (RFR), was evaluated. The results showed that FOD transformations significantly enhanced correlations with EC and pH compared to raw reflectance. All prediction models demonstrated higher prediction accuracy under cracked surface conditions than uncracked surface conditions, indicating that desiccation cracks positively modulate spectral signals to enhance salinity information expression. Across different surface states, model performance generally followed the ranking: PLSR > GPR > SVR > RFR, with PLSR achieving the best predictions for EC and pH under cracked surfaces (R2 of 0.88 and 0.76, RMSE of 0.29 dS/m and 0.35). This study not only deepens the understanding of fractional-order spectral response mechanisms in saline–alkali soils but also provides methodological support for regional monitoring of soil salinization.

Abstract Concentration‐discharge (C‐Q) relations are used to infer water and solute storage and transport but are often informed by coarse‐temporal data. Here, we use high‐frequency and long‐term C‐Q datasets from two adjacent, but geologically and geomorphically distinct, watersheds in Colorado to evaluate how vertical and lateral flowpaths collectively control outlet water quality. We calculate C‐Q slopes at monthly, annual, and long‐term time scales and evaluate hysteresis behavior for three major solute classes—geogenics, metals, and biogenics. Geogenic C‐Q behavior is the most consistent across temporal scales and reflects vertical flowpath shifts from snowmelt to groundwater. In contrast, metals and biogenics have greater variability, particularly during high‐flow periods, as driven by laterally distributed landscape features, such as mine adits, an iron fen, floodplains, wetlands, and beaver ponds. Lateral heterogeneity in solute stores exerts major control on downstream chemistry and should be considered when predicting water quality changes.

Seagrass restoration is increasingly guided by habitat suitability models, yet restoration outcomes depend on more than biophysical suitability alone. In coastal social-ecological systems, fishers and anglers hold fine-scale, time-integrated knowledge of habitat condition, human use, and local constraints that are rarely incorporated at the outset of restoration planning. Here, we tested whether fisher and angler knowledge could generate a spatially explicit basis for seagrass restoration site selection along the southern Welsh coastline, United Kingdom. Using participatory mapping and a Wisdom of Crowds approach, 33 coastal resource users identified locations perceived as suitable for seagrass restoration and areas where restoration should be avoided. Weighting responses by regional participation and converting them into kernel density surfaces, we tested whether participatory-derived suitability predicted independently observed seagrass occurrence and assessed stakeholder perceptions of seagrass benefits. Participatory mapping revealed coherent hotspots of perceived restoration opportunity in sheltered bays and estuarine environments, whereas avoidance areas clustered around ports, sediment-influenced estuaries, and high-use tourism locations, indicating that participants integrated both ecological opportunity and human-use constraints. Net suitability was a strong predictor of seagrass occurrence, demonstrating close correspondence between aggregated local knowledge and observed seagrass distribution. Perceptions of seagrass restoration were broadly positive, with strong agreement that seagrass benefits marine life and the wider environment. Our findings show that fisher and angler knowledge can generate spatially coherent and ecologically meaningful information for restoration planning. Rather than acting as a simple substitute for habitat suitability modelling, participatory mapping functions as a social–ecological diagnostic layer, identifying areas of opportunity, constraint, and potential conflict. Integrating local knowledge early in restoration planning can improve site selection, guide choices between protection, passive recovery and active restoration, and strengthen legitimacy, stewardship, and long-term support for seagrass recovery.

The spread of the invasive species Spartina alterniflora poses a serious threat to coastal salt marsh ecosystems, requiring accurate, scalable monitoring solutions. We introduce the Normalized Difference Spartina Phenology Index (NDSPI), a phenology-based metric derived from the Normalized Difference Phenology Index, to improve discrimination of S. alterniflora using Sentinel-2 time-series imagery. Phenological curves for dominant salt marsh vegetation—S. alterniflora, Phragmites australis, Suaeda salsa, and mixed weeds—were reconstructed via Harmonic Analysis of Time Series. The Jeffries–Matusita distance was employed to identify key phenological stages and spectral indices, guiding the development of NDSPI to exploit species-specific temporal patterns. Using NDSPI combined with K-means++, the spatial distribution of multiple salt marsh vegetation types in the Linhong Estuary of Haizhou Bay from 2019 to 2023 was mapped, with S. alterniflora achieving a precision of 0.82, a recall of 0.88, and an F1-score of 0.85. Comparative experiments confirmed the innovation of NDSPI, demonstrating its superior capability to amplify target separability over traditional NDVI-based and supervised machine learning methods. Validation experiments in the Dongtan wetland of Chongming Island demonstrated strong generalization. By overcoming the reliance on extensive training data and mitigating background noise, NDSPI offers an effective, highly automated tool for large-scale monitoring of S. alterniflora invasions in complex coastal wetlands.

Thermal shifts have accelerated ice-wedge degradation and reorganized polygonal trough networks along the Utqiaġvik, Alaska coastline. Thus, quantifying their structural variability and hydrologic connectivity across spatial scales remains challenging. This study applies high-resolution remote sensing and terrain analysis to evaluate hydrologic controls on ice-wedge polygon morphology. Using a 0.5 m lidar-derived DEM, polygon boundaries were manually digitized and compared with Thiessen (Voronoi) tessellations to quantify structural divergence, boundary misalignment, and differences in area. Hydrologic influences on polygon development were assessed through compound terrain analysis, drainage network extraction, and surface flow modeling. Spatial intersection analyses reveal geometric discrepancies underscoring the limitations of automated polygon proxies in permafrost terrain. Modeled flow paths exhibit spatial congruence with mapped trough networks, indicating that surface hydrology plays a role in trough evolution. Hydrologic and morphometric parameters demonstrate high runoff potential associated with low-relief topography. Elevated Topographic Wetness Index (TWI > 12 ) and Stream Power Index (SPI > 60) values delineate zones of saturation and flow accumulation that frequently coincide with trough depressions. Despite high predicted runoff, minimal gradients produce ponded flow regimes that promote wetland formation. This integrative framework enhances detection and interpretation of permafrost terrain features, providing a scalable methodology for monitoring Arctic landscape dynamics.

Abstract Inter-basin water transfers (IBWTs) are increasingly implemented to alleviate water scarcity; however, their environmental impacts remain heterogeneous and difficult to compare across projects. This study combined a bibliometric analysis of 643 unique articles and reviews indexed in Scopus and Web of Science (1971–2025) with a PRISMA-guided systematic review to examine the evolution of environmentally oriented IBWT research and to identify the environmental impacts quantified through remote sensing (RS) within the eligible evidence base. A total of 1,018 records were initially retrieved; 13 studies met the final inclusion criteria and were incorporated into the comparative synthesis. Results indicate substantial growth in the field over the last two decades, accompanied by increasing international collaboration, predominantly led by China and the United States, and growing thematic convergence around water management, sustainability, governance, water quality, and biodiversity. However, within the systematic review subset, the eligible RS evidence base was narrow and geographically concentrated, with 11 of the 13 included studies conducted in China and 2 in Iran. In this 13-study subset, RS applications focused mainly on terrestrial and landscape-scale impacts, particularly vegetation, land-use/land-cover change, riparian condition, and integrated eco-environmental assessment. In contrast, aquatic, hydraulic, and process-level impacts were less consistently captured within the eligible RS evidence base. Landsat-based optical workflows, typically operating at a 30 m spatial resolution and relying on discrete multi-year observations, were the most commonly reported configuration among the included RS studies. By linking bibliometric breadth from the full indexed corpus with systematically extracted evidence from a small and geographically concentrated RS subset, this study provides a more reproducible picture of where RS-based IBWT environmental monitoring is strongest within the available eligible evidence, where major gaps persist, and which methodological limitations still constrain cross-case comparability and long-term environmental decision making. These findings suggest that future research should prioritize standardized, explicitly reported, and methodologically comparable remote-sensing approaches to strengthen environmental monitoring and improve cross-case assessment in inter-basin water transfer systems.

Introduction Oil spills in the marginal seas surrounding the Korean Peninsula pose major ecological and socioeconomic threats, yet proactive spatial risk assessment remains limited because spill occurrence, transport-mediated exposure, and receptor sensitivity are often conflated. Methods We developed a high-resolution probabilistic Oil Spill Risk Assessment (OSRA) framework that explicitly separates spill likelihood from modeled oil exposure probability and integrates exposure with environmental and socioeconomic receptor sensitivity. Spill likelihood was mapped on a 300 m grid using 20 years of spill records, AIS-derived vessel traffic and collision likelihood, oil transport volume, and coastal oil storage capacity. A total of 310 representative spill locations were identified and 31,000 oil spill fate and trajectory simulations were conducted using 100 seasonally stratified release times. Results The resulting simulations were used to estimate oil exposure probability, which was subsequently combined with receptor sensitivity to generate an integrated spatial risk map. The Yellow Sea exhibited the highest oil exposure probabilities and overall risk, particularly in shallow semi-enclosed coastal areas where tidal flats, wetlands, and fisheries coincide with persistent oil retention. The South Sea showed moderate risk with localized hotspots near major industrial and port areas, whereas the East Sea exhibited generally low risk because receptor sensitivity was lower and open-coast circulation promoted rapid dilution and offshore advection. Discussion By distinguishing spill likelihood, transport-mediated exposure, and receptor sensitivity, the framework explains why areas with frequent spill sources do not necessarily correspond to the highest shoreline risk. The proposed OSRA framework provides an operationally relevant basis for prioritizing prevention, preparedness, and response planning and is adaptable to other heavily trafficked marginal seas under comparable data conditions.

) between sediment and bottom water in macroalgae-amended and unamended sediment cores showed a clear impact of macroalgae additions, with elevated carbon (C) turnover in these cores. The differences in total DIC release between unamended cores and macroalgae-amended cores were more than 100% and up to 298% of the added macroalgae C. The macroalgae addition, therefore accelerated the degradation of pre-existing organic matter in the sediment. This observation, known as a "priming effect," is well described in soils and freshwater systems but has only recently been considered in marine systems. Results from this experiment suggest that short term priming (<three months) did not depend on the macroalgae species added to the sediment, nor nutrient enrichment of the added tissue. The modification of organic matter in marine sediments, driven by secondary production from degrading macroalgal detritus, as well as the conditions that promote this priming, are poorly understood. These uncertain features of the C cycle are critical for understanding overall C turnover and assessing the C mitigation potential of both cultivated and natural macroalgae populations.

Researchers combined observations and modeling to track the movement of the Congo’s freshwater plume, noting that eddies play a significant role in the water’s transport.

Sediment input is one of the most significant threats to coral reef ecosystems globally, yet its ecological impacts on biodiversity and community structure remain underappreciated. In this study, we utilized a multimarker environmental DNA (eDNA) metabarcoding technique to investigate how sediment load affects diverse taxa within algal turfs in coral reefs of the South China Sea. Using three assays (16S rRNA, nuclear 18S rRNA, and mitochondrial COI), we identified 1,143 genera, dominated by Proteobacteria, Cyanobacteria, Rhodophyta, Dinoflagellata, Mollusca, and Arthropoda. The diversity and species compositions of prokaryotes and eukaryotes varied across algal turf types subjected to different sediment load levels. Prokaryotic and eukaryotic diversity were higher in short algal turfs, whereas cyanobacteria dominated long, sediment-laden algal turfs (up to 62.30% of 16S sequences), revealing a clear shift in the dominance of primary producers from diverse eukaryotic algae to unique photosynthetic cyanobacteria. Sediment-related variables (algal canopy height, sediment mass, and grain size) were critical, explaining 25.42% and 28.00% of the variation in prokaryotes and eukaryotes, respectively─far more than other eco-environmental variables. These findings indicate that sediment load is strongly associated with the community structure of algal turfs and may be linked to reduced diversity of primary producers.

Poly(vinyl chloride) (PVC) poses a persistent environmental challenge due to its high chlorine content and additive-mediated recalcitrance. Herein, we report an N,O-dual-coordinated iron single-atom catalyst (Fe–N 2 O 2 -hCN) integrated with palladium nanoparticles (Pd NPs) for efficient hydrothermal Fenton-like upcycling of PVC into fuel-range hydrocarbons. The asymmetric Fe–N 2 O 2 configuration modulates the electronic structure of Fe centers, promoting H 2 O 2 activation and hydroxyl radical generation for efficient C–Cl and C–C bond cleavage under mild conditions, outperforming the conventional Fe–N 4 catalyst. The bifunctional Pd NPs/Fe–N 2 O 2 -hCN system achieves high PVC degradation efficiency (97.38%) and near-complete dechlorination, while selectively hydrogenating depolymerized intermediates into fuel-range alkanes (C 3 –C 20 ) with a high selectivity (86.01%). Mechanistic studies reveal enhanced electron transfer and a lowered energy barrier for H 2 O 2 dissociation, with Pd NPs generating reactive hydrogen species for olefin saturation. Life cycle assessment (LCA) demonstrates a 77% reduction in carbon emissions and significantly lower eco-costs than incineration. This work provides a coordination-engineered platform for converting hazardous plastic waste into valuable fuels, advancing a circular plastic economy.

Abstract Ongoing climate warming has intensified the influence of rising temperatures on terrestrial ecosystems. However, it remains unclear how the optimal land surface temperature (LST) for vegetation photosynthesis (LSTopt) has changed, and the frequency and magnitude of LST exceeding this optimum remains poorly understood. Here, we investigated the spatial distribution and temporal evolution of LSTopt across China using 8-day satellite-derived solar-induced chlorophyll fluorescence and LST from 2001 to 2024. Results show that LSTopt exhibits an overall increasing trend, whereas LST shows a decreasing tendency, indicating a divergence between optimal and observed temperature conditions. To further characterize vegetation thermal conditions, we introduced temperature exceedance metrics, including exceedance frequency (EF) and exceedance intensity (EI), to quantify the frequency and magnitude of LST exceeding LSTopt. Both EF and EI decrease over time, suggesting reduced exposure to supra-optimal temperatures. However, this does not imply improved thermal suitability, but rather indicates a structural shift in vegetation thermal regimes toward sub-optimal conditions. These findings highlight the importance of considering temperature relative to optimal conditions and provide a new perspective for understanding vegetation-climate interactions under ongoing climate change.

Water resources are essential for human well-being. However, water pollution is a major global problem with significant implications for the environment and public health. To address these challenges, this study presents an integrated perspective on water pollution by correlating pollution sources, transport pathways, exposure routes, and associated risks to human health. The methodology combined a systematic review conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines with a bibliometric analysis performed by using VOSviewer version 1.6.19, a software tool for constructing and visualizing bibliometric networks. A total of 332 publications published between 2015 and 2025 were retrieved from the Scopus and Google Scholar databases and met the PRISMA eligibility criteria. The findings indicate that both natural and anthropogenic sources contribute to water contamination, introducing pollutants such as heavy metals, pesticides, pharmaceutical residues, microplastics, and pathogenic microorganisms with potential human health impacts. Bibliometric analysis revealed a transition from conventional water quality assessments toward integrated approaches emphasizing health risks and environmental interactions. The study further identified important knowledge gaps regarding contaminant mixture effects and synergistic toxicity, which remain insufficiently addressed in current scientific and regulatory frameworks. These findings highlight the need for strengthened regulatory strategies, advanced treatment technologies, and evidence-based water governance to support environmental sustainability and public health protection.

Achieving scalable monitoring of Alternate Wetting and Drying (AWD) for methane mitigation in rice cultivation depends on establishing field benchmarks for drainage behavior and demonstrating that satellite observations can reliably detect corresponding changes in water status. We analyzed about two million high-frequency in situ water-level observations from hundreds of sensors deployed in rice fields across the Philippines and Japan to quantify drainage duration from near-surface conditions to 15 cm below the soil surface and to test the sensitivity of open-access PALSAR-2 dual-polarization L-band SAR to vertical water-level variations. Across 564 drainage events, the median drainage duration was 19.0 h, and only 0.9% of events exceeded 240 h, indicating that drainage happens generally within a day. Seasonal differences were evident in Pangasinan, while small Chiba and Cagayan samples suggested exploratory longer-duration patterns; multiple drainage events occurred in 48.0% of Philippine dry-season fields but only 21.6% of wet-season fields. PALSAR-2 data showed a statistical significance in detecting inundation at Mid crop growth stage with cross-polarization band, but the significant overlap induces challenges in operational applications. These results provide empirical benchmarks for AWD-related drainage dynamics while showing that dual-polarization PALSAR-2 alone is unlikely to support robust field-scale monitoring of rice-field water status.

In increasingly complex navigation environments, maritime traffic supervision needs to look beyond the instantaneous collision risk of individual-ship pairs. A multi-ship scene may become difficult to monitor because of vessel aggregation, spatial compression, encounter urgency, and inconsistent motion states. To support proactive Vessel Traffic Services (VTS), this study proposes a prediction-driven framework for assessing multi-ship traffic pressure by combining AIS-based short-term motion prediction with a Spatio-Temporal Encounter Traffic Pressure Index (ST-TPI). In the proposed framework, cleaned and resampled AIS trajectories are used to train an LSTM model for short-term vessel motion prediction. The predicted vessel states are then synchronized into future multi-ship traffic snapshots over a 30 min horizon, and ST-TPI is used to evaluate traffic pressure at the ship-pair, individual-ship, regional, and scene levels. Different from conventional collision-risk or traffic-complexity methods, the proposed framework focuses on how future traffic pressure forms, changes, and is transferred among vessels and vessel pairs. The method was tested using five typical multi-ship scenarios and a real-waterway case in the western precautionary area of the Laotieshan Channel. The prediction results showed stable short-term forecasting performance with low meter-level position errors under the observation-updated rolling evaluation, providing a basis for future multi-ship snapshot generation. The typical scenarios revealed different pressure-evolution patterns, including low-pressure persistence, temporary compression and release, delayed crossing pressure, complex interaction release, and High-level pressure formation. The real-waterway case further showed low and Low-medium pressure fluctuations, local pressure peaks, pressure release, and pressure-source transfer under practical AIS conditions. Prediction-error perturbation analysis indicated that the main high-pressure vessel pairs and pressure-level interpretations remained stable under tested position perturbations. Consistency analysis further showed that ST-TPI scene pressure was significantly correlated with conventional CRI-based encounter-risk indicators. These results indicate that the proposed framework can provide interpretable information on future pressure-evolution and dominant pressure sources, supporting proactive monitoring, early warning, and traffic organization in complex waterways, and contributing to a safer maritime traffic environment.

In recent years, there has been growing interest in applying vision–language models (VLMs) to quantitative remote sensing. This study evaluates whether three commercial VLMs (GPT-4o, GPT-5.5, and Claude Sonnet 4.6) can detect and classify the severity of harmful algal blooms (HABs) from Sentinel-2 satellite imagery of western Lake Erie and compares them against classical machine learning classifiers (Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost)) trained on both a three-band red, green, blue (RGB) composite representation of the imagery and a 10-band multi-spectral reflectance representation. Forty bloom events identified from the National Oceanic and Atmospheric Administration (NOAA) Harmful Algal Bloom Operational Forecast System (HAB-OFS) severity assessments were assembled into the evaluation dataset, spanning seven bloom seasons (2019–2025). For binary bloom detection, the VLMs did not match the classical RGB classifiers; their F1 scores (0.69–0.75) fell below the best RGB classifier (Random Forest, 0.76) and below a trivial always-present baseline (F1 = 0.77), and they carried false positive rates of 73–93% on bloom-absent images, against 27–40% for the RGB classifiers. The VLMs reached high recall by labeling most scenes as bloom-positive, which makes them operationally unreliable in this configuration. For severity classification, the VLMs assigned 60–70% of their predictions to the “moderate” category regardless of actual conditions and identified at most one of the two severe blooms, whereas the classical classifiers tracked the ground-truth distribution and delivered two to nearly three times the exact-match accuracy (0.44–0.59 vs. 0.20–0.225). The strongest method across all metrics was the multi-spectral SVM (F1 = 0.833, false positive rate 27%, accuracy 0.795). Switching the same SVM from RGB to multi-spectral features raised accuracy from 0.675 to 0.795, a 12-percentage-point gain that measures the spectral information carried by red-edge and shortwave infrared bands that are accessible through multi-spectral sensors but unavailable to standard VLM vision encoders. Feature-importance analysis showed that the multi-spectral classifiers ranked chlorophyll-specific indices, the Normalized Difference Chlorophyll Index (NDCI) and the Floating Algae Index (FAI), among their top predictors, the same signatures used in established operational algorithms, while the RGB classifiers relied on red-channel variability and green-dominant pixel fractions because RGB inputs cannot compute those indices. Two compounded limitations therefore constrain off-the-shelf VLMs for aquatic remote sensing: the limited spectral information available through standard RGB channels and a mismatch between the land-dominated training distributions of these models and aquatic optical conditions. Domain-specific classifiers operating on multi-spectral data remain the more suitable tools for continued development of HAB monitoring and water-quality retrieval.

Flash flood events are among the most critical hydrological hazards in arid and semi-arid regions, posing extreme threats to critical infrastructure, human safety, and sustainable development plans. This paper evaluates the flash flood susceptibility of the Al’Ataya catchment, a key watershed on the southern Red Sea coast, using an integrated geospatial analysis approach. To assess and quantify the flood hazard, we investigated 15 morphometric parameters for 24 particular sub-catchments within a sixth-order drainage system. Two complementary methods, the Morphometric Ranking Method and El-Shamy’s approach, were utilized to classify the catchment into different flood susceptibility levels. Results from the Ranking Method identified seven sub-catchments (SC-2, SC-3, SC-6, SC-7, SC-8, SC-9, and SC-19) as having high flood hazard levels, mainly driven by large watershed areas, steep slopes, and high relief ratios. In contrast, El-Shamy’s approach resulted in a different evaluation, identifying sub-catchments in Zone B (SC-23, SC-16, SC-17, SC-15, SC-6, SC-20) as high hazard sub-catchments due to the particular relationship between the bifurcation ratio parameter and the drainage density and stream frequency parameters. The integration of the two methods suggests that the susceptibility factor is controlled by the combined influence of a low drainage density and steep mountainous terrain draining toward the coastal zone. These results provide a spatial model for flood mitigation and early warning systems, supporting Saudi Vision 2030 through improvement to the development of southern urban centers such as Al’Ataya and Sabya.

Abstract Urban water scarcity is one of the most pressing governance challenges of our time, driven by climate change and rapid urbanization. Despite a growing body of research, scholarship remains fragmented across disciplines and regions, with the social and institutional dimensions of scarcity governance receiving far less attention than physical and economic drivers. This systematic literature review synthesizes social science research on acute urban water shortages involving rationing or restrictions published between 1995 and 2025, a period that captures the rise of urban water scarcity as a global research concern in connection with increased frequency and severity of major city-level water crises. Searches across three databases yielded 1,295 records, of which 64 articles met inclusion criteria for full-text review. Nearly half of the studies frame water crises as a supply-demand imbalance, reflecting a predominantly technocratic problem framing. Geographic attention clusters around several well-documented cases, including Cape Town’s “Day Zero”, Australia’s Millennium Drought, and the Brazilian Drought. Dimensions such as social equity and trust in leadership are well represented, whereas gender, informal water access, and water quality are thematically underrepresented. By reconceptualizing the diversity of urban water scarcity governance strategies in relation to research fields, water governance paradigms, temporality, and institutional pluralism, we highlight the need for increased reflexivity about underlying assumptions embedded in research. This review identifies theoretical, methodological, and empirical gaps and calls for greater engagement with sociological and human geographical perspectives on water-related risk, more comparative and longitudinal research, attention to underrepresented issues such as water quality, and future research avenues like flood-drought coupling and increased cooling demand. Reflexive and explicit engagement with underlying paradigmatic assumptions in research and practice, as well as a diverse and institutionally plural governance approach, will allow the field of urban water scarcity governance to be more future-proof.

Infrared Fourier transform spectrometers using interferometric spectroscopy are widely used in space remote sensing owing to their high spectral resolution and sensitivity. We investigated the distorted spectral characteristics introduced by nonlinear errors of different orders through simulation for infrared detectors with strong nonlinear effects. A high-order nonlinear correction scheme was proposed based on two iterative correction methods for in-band and out-of-band spectra. Further, the effects of second-order, third-order, in-band, and out-of-band correction methods were compared using prelaunch radiometric calibration experimental data from the DQ-2 satellite infrared hyperspectral atmospheric composition sounder. The results showed that the third-order in-band correction scheme performed the best, while various other correction schemes also effectively reduced nonlinear errors. The maximum average deviation was 0.18–0.25 K for the long-wave band and 0.11–0.19 K for the mid-wave band in the temperature range of 230–300 K. According to the correction evaluation and methods comparison, the proposed method is appropriate for nonlinearity detectors to improve radiometric calibration accuracy.

Vegetation net primary productivity (NPP) is a key indicator of terrestrial carbon sequestration and ecological restoration effectiveness. The karst mountainous region of Southwest China is characterized by fragmented terrain and high ecological vulnerability, making quantification of NPP dynamics and drivers essential for regional management. Using MOD17A3 NPP data (2000–2020), this study applied trend analysis, Hurst exponent analysis, partial correlation analysis, residual trend analysis, and Geodetector to investigate NPP spatiotemporal patterns and driving mechanisms in Guizhou Province. Results show a significant increasing trend in NPP (3.653 gC·m−2·a−1, p &lt; 0.01), with 78.61% of the area exhibiting growth and a spatial pattern of higher values in the south and lower values in the north. NPP shows persistence, indicating a continued increasing tendency. Along elevation gradients, NPP exhibits a unimodal pattern, peaking at 1000–1200 m, while growth rates increase with elevation and slope, with greater variability at higher altitudes. Temperature exerts a stronger and more extensive influence on NPP than precipitation, with significant correlations over 34.35% and 10.16% of the study area, respectively (p &lt; 0.05). Residual trend analysis indicates that non-climatic factors accounted for a larger share of NPP variation (64.49%) than climatic factors (35.51%), with ecological restoration likely the leading non-climatic driver. Geomorphological type is the primary driver of spatial heterogeneity (q = 0.220), followed by precipitation, temperature, and land use, with interaction effects mainly showing nonlinear enhancement. These findings provide insights for ecological restoration and vegetation management in karst regions.

Study region Nansi Lake Basin, a water-scarce region in northern China. Study focus This study conducted a data-driven vulnerability assessment of the Nansi Lake Basin water resource system. The Driver–Pressure–State–Impact–Response conceptual model was applied to define a three-tier vulnerability evaluation system comprising a target layer, 5 first-level indicators, and 26 second‐level indicators. Particle swarm optimization (PSO) was employed to determine the optimal hyperparameters of a long short-term memory (LSTM) network trained on data comprising randomly generated evaluation indicator values and their corresponding vulnerability grading standards. This hybrid PSO–LSTM model was used to assess the spatiotemporal vulnerability of the Nansi Lake Basin water resource system from 2010 to 2021, and its rationality was verified by comparison with the variable fuzzy assessment method. New hydrological insights for the region The hybrid PSO–LSTM model exhibited high accuracy ( R 2 > 0.999 for both training and validation samples) and stability, indicating its suitability for vulnerability assessments. Temporally, the vulnerabilities in the Nansi Lake Basin and its six subregions fluctuated downward over the study period, evolving from highly vulnerable in 2010 to slightly vulnerable by 2021. Spatially, vulnerability decreased faster in the western than eastern subregions. Finally, the obstacle degree model identified the causes of vulnerability to provide a scientific foundation for the utilization and management of water resources in the Nansi Lake Basin.