Atmospheric and Oceanic Sciences

ABSTRACT Meridional positions of the Asian Subtropical westerly jet (AWJ) in the upper troposphere and the South Asian high (SAH) in the lower stratosphere are closely linked to extreme weather and climate events in East Asia. Given the regional characteristics of these two circulation systems, this study separates each system into eastern and western centers—namely the East Asian jet (EAJ), the Central Asian jet (CAJ), the Tibetan High (TH) and the Iranian High (IH). We find that the north–south shifts of the EAJ and TH exert a more pronounced impact on precipitation and near‐surface temperature over East Asia than those of the CAJ and IH. Furthermore, the in‐phase configuration of meridional displacements of the EAJ and TH significantly modulates large‐scale three‐dimensional (3D) circulation over East Asia. In particular, when the EAJ and TH simultaneously shift northward, the Yangtze River Basin (YRB) and Tibetan Plateau (TP) region are controlled by an intense horizontal anticyclonic circulation and a common descending branch of two reversed meridional circulations, while the eastern TP is covered by the descending branch of a local zonal circulation. Consequently, there are widespread precipitation deficits and heat anomalies from the TP to the YRB. Diagnostic analysis identifies the central‐eastern equatorial Pacific SST cooling in the pre‐winter and the tropical Indian Ocean cooling in May–June as the main forcing factors of the simultaneous northward movement of the EAJ and TH during midsummer. These findings suggest that greater attention to the eastern centers of the AWJ and SAH (EAJ and TH) may benefit East Asian climate prediction.

ABSTRACT Tropical cyclones (TCs) are among the most damaging natural hazards affecting Australia, and climate change may alter their frequency, tracks, intensity, and associated exposure. Historical records provide a baseline for assessing changes. Here, we develop a climatology‐based statistical model of TC weather extent using Australian Bureau of Meteorology estimates of the radius of the outermost closed isobar, wind radii, and positional uncertainty. Explicitly incorporating positional uncertainty allows long and heterogeneous historical records to be analysed while accounting for changes in record confidence. The model is applied to International Best Track Archive for Climate Stewardship records of 1960/61–2023/24 to examine: (1) the variability in TC formation across the Southeast Indian–South Pacific basin, (2) Australia's differential exposure to TCs from different formation areas, and (3) decadal variability in TC activity. Results show that 95% of TCs producing TC weather events in Australia had their early lifecycle between 107.5°E and 166°E. TC weather events occur annually along northern Western Australia and every 2 years along north‐east Queensland. Event likelihood depends strongly on formation longitude and early lifecycle direction, reaching ~95% for formation longitudes between 135°E and 143°E and decreasing eastward and westward. TC‐frequency trends are spatially heterogeneous, with no robust basin‐wide change since 1960. Periodic reassessment as historical records lengthen and improve may refine estimates of TC exposure, while formation longitude and early lifecycle direction provide climatological indicators of the potential for developing TCs to affect Australia. Overall, the findings provide a long‐term climatological basis for Australian TC exposure assessment and risk management.

Abstract Observations from the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign are used to examine the processes controlling where cumulus clouds form over the Sierras de Córdoba (SDC) in Argentina. Although orographic lifting favors cloud initiation, the factors determining whether clouds develop over the ridge crest, east or west of the crest, or in multiple bands remain uncertain. Coordinated measurements from scanning radars, stereo cloud cameras, radiosondes, and GOES-16 imagery reveal how variations in thermally driven upslope flow, cross-barrier winds, and boundary-layer thermodynamics regulate cloud location. Crest-anchored clouds occur when deep thermally forced easterlies reach the ridge under weak opposing westerlies. East-of-crest and double-banded clouds develop when stronger westerlies aloft suppress upslope penetration, shifting convergence and cloud initiation to the lee slopes; small decreases in lifting-condensation-level height or boundary-layer stability can toggle these regimes between single and double bands. West-of-crest clouds arise when easterlies extend above crest height and mechanically lift air over the ridge. Across all cases, the superposition of mechanical (cross-barrier) and thermal (upslope) forcing—quantified using principal components derived from radar velocity profiles—systematically explains the observed cloud displacements. Positive or neutral superposition yields crest clouds, negative (i.e., destructive) superposition produces east-of-crest or double-banded convection, and reversed mechanical forcing favors west-of-crest clouds. These findings extend conceptual models of mountain convection by showing how small variations in forcing balance and boundary-layer thermodynamics control orographic cloud initiation.

This study analyzes the performance of several forecasting models applied to Standardized Precipitation Index (SPI) time series for the 1967–2021 period, using data from six weather stations in southeastern Romania. Four temporal aggregation scales (SPI3, SPI6, SPI12, and SPI24) were considered for one-step-ahead (one-month) forecasting. The evaluated models included Random Forest, XGBoost, Support Vector Regression (SVR), N-BEATS (generic variant), and SARIMA (Seasonal Autoregressive Integrated Moving Average), covering machine learning, deep learning, and statistical approaches. To ensure a rigorous out-of-sample evaluation, SPI distribution parameters were estimated solely based on the pre-test calibration period and held constant when calculating SPI values for the independent test period, thereby preventing information leakage from the preprocessing stage. Forecast performance was assessed using RMSE, MAE, NSE, and a skill score relative to a persistence benchmark, while differences in forecast accuracy were statistically evaluated using the Diebold–Mariano test. The results revealed a clear scale-dependent pattern: absolute forecast accuracy increased with the SPI aggregation scale—alongside more pronounced temporal persistence—though this did not necessarily translate into superior skill compared to the persistence model. Among the data-driven models, SVR demonstrated the most robust performance, particularly at shorter timescales, while N-BEATS also provided competitive forecasts without consistently outperforming SVR. SARIMA showed the clearest gains over the persistence model at the SPI12 and SPI24 scales. The maximum NSE increased from 0.61 for SPI3 and 0.81 for SPI6, both obtained with SVR, to 0.94 for SPI12 (obtained with SARIMA) and 0.97 for SPI24 (obtained with SARIMA, N-BEATS and SVR). In conclusion, the results demonstrate that the SPI accumulation scale, temporal structure, and performance relative to persistence must be considered simultaneously when selecting forecasting models for drought prediction.

Abstract Phrases like ‘a month’s worth of rain in a day’ make for dramatic news headlines. But how often do such events occur? Are they more likely in certain locations or seasons? And can we expect more in the future? Here we examine these questions for the UK using observations and high‐resolution model simulations. We find that almost all such events occur in summer in regions with the smallest monthly rainfall climatology and that future projections exhibit substantial increases in frequency due to a simultaneous intensification of extreme rainfall events coupled with a marked drying of UK summers.

Space and time play a crucial role in multi-hazard impact assessment. When two or more natural hazards occur simultaneously or in sequence at the same location, the physical integrity of assets and infrastructures can be compromised, and the resulting damage can be greater than that generated by individual hazards occurring in isolation. Despite widespread conceptual recognition of these interactions, the literature lacks quantitative, standardised methods for the systematic analysis of multi-hazard impacts. This study presents a quantitative methodology for evaluating multi-hazard physical damage to the built environment, translating qualitative impact dynamics into a transparent and reproducible analytical framework, implemented as modular Python code. The approach covers both concurrent and consecutive hazards by modelling: (i) the increased damage resulting from the combined impact of two or more concurrent hazards that overlap in space and time, and (ii) the effects of cumulative damage on asset vulnerability and the recovery dynamics in the case of consecutive hazards that overlap in space. Using the Python implementation, a model behaviour analysis is conducted to systematically explore how variations in inter-event time intervals, vulnerability interactions, and recovery trajectories affect cumulative physical damage. The methodology is applied to a real multi-hazard sequence in Puerto Rico, including the concurrent wind and flood impacts of Hurricane Maria and the consecutive seismic impacts of the 2019–2020 earthquake sequence. The reconstruction of past damage dynamics highlights that ignoring residual hurricane damage would significantly underestimate the subsequent earthquake losses, and that damage accumulation is path-dependent, strongly influenced by event timing and recovery processes. By providing a generalised, transparent, and reproducible quantitative structure, this study offers a tool for forensic analysis of past multi-hazard events, systematic exploration of damage drivers, and scenario-based assessment of alternative hazard and recovery conditions, supporting both post-disaster learning and planning-oriented applications.

Fine-scale ocean dynamics strongly influence coastal mixing, sediment transport, and vertical exchange, yet remain difficult to observe at the spatial scales at which they occur. Here, we use total surface current vectors measured by the airborne Ocean Surface Current Airborne Radar (OSCAR) to investigate sub-kilometer divergence, vorticity, shear rate, and vertical velocities in the tidally dominated Iroise Sea around Ushant Island. Observations acquired during ebb and flood tides in May 2022 reveal two distinct mechanisms generating intense fine-scale dynamics. North and west of Ushant Island, interactions between strong tidal currents and complex bathymetry produce alternating zones of divergence and convergence associated with flow acceleration and deceleration over bathymetric gradients. South of the island, a tidal jet flowing alongside comparatively calm waters generates strong lateral shear, producing divergence of up to O(20 f ) and vertical velocities approaching 0.2 m.s −1 . Comparison with the high-resolution MARS2D model shows that currents and bathymetry-driven divergence are reproduced reasonably well, whereas the shear-driven divergence associated with the southern tidal jet is underestimated by approximately an order of magnitude. Coarsening the OSCAR observations from 200 m to 1 km reduces divergence, vorticity, and shear rate from O(10 f ) to O(1 f ), demonstrating the strong sensitivity of derivative quantities to spatial resolution. These results highlight the importance of sub-kilometer observations for resolving fine-scale coastal dynamics, provide a unique observational benchmark for evaluating high-resolution numerical models, and illustrate the complementary roles of airborne, in situ and satellite measurements in characterizing coastal and submesoscale processes across a wide range of spatial and temporal scales.

Marine heatwaves (MHWs) and ocean acidity extremes (OAXs) are intensifying under anthropogenic climate change, and their compound co-occurrence can amplify ecological impacts. Yet the Agulhas Leakage, a key gateway transferring warm, saline Indian Ocean water into the Atlantic, remains understudied. Here we characterise MHWs over 1982–2024 and OAXs, and their compound events in this region over 1982–2021, comparing analyses of original and linearly detrended data. Under a fixed baseline, MHW cumulative intensity rose from approximately 29.95 to 109.51 °C-year in the original data and 49.4 to 66.7 °C-year after detrending between the first and fourth decades. OAX intensity increased from approximately 0.24 to 4.10 nmol kg - ¹-year in the original data and from 0.35 to 0.46 nmol kg - ¹-year after detrending. Compound events emerge only after the 2000s. Detrending reveals that most of this increase reflects the long-term warming and acidification trends, while residual variability remains in the dynamically active Agulhas Retroflexion, where anticyclonic eddies trap warm, high [H + ] Indian Ocean waters. A likelihood multiplication factor confirms statistically significant positive MHW–OAX dependence that persists after detrending, indicating statistically significant positive co-occurrence between MHW and OAX on interannual timescales, consistent with physical coupling through carbonate chemistry. Baseline choice critically shapes compound-extreme risk assessment.

Abstract This study examines the global relationship between monthly mean near-surface temperature and monthly total precipitation using gridded climate data. Significant correlations (p < 0.01) are found across all regions, with positive correlations in approximately 74.5% of locations and negative correlations in the remainder. To move beyond correlation and assess directional predictability, we apply Granger causality analysis (GCA), impulse-response analysis (IRA), and cross-correlation (CC) to investigate causal and lead-lag relationships between the two variables. The results show significant Granger causality from temperature to precipitation across most regions, except for a few locations in the northern temperate zone. Conversely, precipitation does not Granger-cause temperature in many tropical and subtropical regions, while two-way Granger causality is more common in temperate zones. IRA indicates that a 1°C shock to temperature (1°C innovation in the temperature equation ) produces precipitation responses ranging from −8 to 10 mm, whereas a 1mm shock to precipitation ( 1mm innovation in the precipitation equation ) results in temperature changes of −0.3°C to 0.1°C. Precipitation responds more strongly and rapidly to temperature shocks than temperature responds to precipitation shocks. Overall, GCA, IRA, and CC consistently indicate that temperature generally leads precipitation in their Granger-causal relationship, referring to temporal precedence and incremental predictability within the bivariate system, except in a limited number of northern regions.

This study investigates a new methodology to estimate plant area index ( PAI ) and its spatial distribution from 3D point clouds acquired by a system of 3 LiDARs (2 nadir-viewing and one at 45°) mounted on the Phenomobile Unmanned Ground Vehicle designed for high-throughput phenotyping of herbaceous crops. It relies on the computation of the gap fraction at the voxel level using the trajectories of the laser beams to numerically invert the Beer-Lambert law (BL), enabling the simultaneous estimation of PAI and AIA (Average Inclination Angle, in°). The method is evaluated on wheat crops both in silico (i.e. simulations based on the AdelWheat functional structural crop model coupled with a point cloud simulator) and in actual phenotyping trials of a panel of 10 bread wheat genotypes grown in two study sites under different treatments of plant density, water stress and sowing dates. The in silico validation indicates that the numeric inversion of BL with the LiDAR system of the Phenomobile leads to a relative error of 9.8% (RMSE = 0.32, R 2 = 0.99) and 8.4% (RMSE =5.4°, R 2 = 0.95) in the estimation of PAI and AIA , respectively. The validation in actual phenotyping experiments against destructive measurements produced satisfactory results for canopy PAI (RMSE = 1.44, R 2 = 0.82), while requiring prior knowledge on AIA , e.g. either by (i) introducing a regularization term in the cost function used to invert BL, to avoid unexpected high AIA estimations, (ii) using a fixed, reference value of 60°. The analysis of the results indicates that the inclusion of a regularization term mitigates the impact of actual differences in leaf inclination among cultivars on PAI accuracy, while providing a relative description of the actual variability in the vertical profile of leaf inclination across the different cultivars.

Water utilities face challenges from aging infrastructure, operational complexity, and limited resources. Most of their investment is in distribution systems, and the scale of the challenge they pose is large in terms of water main breaks, costs of repairs, and damages to property with many mains past their useful lives. To address the problems with investment constraints, intelligent management tools are becoming available across two primary domains, system operations and asset management. Water utilities face important decisions when implementing these tools, but they cannot review or implement all simultaneously. To address the selection process, this paper applies a prioritization framework to identify the most promising tools by using bibliometric-based publication volume derived from the Web of Science Core Collection (January 2015 to May 2026), with emphasis on uses of machine learning as objective prioritization criteria. Using this approach, pipe break prediction models emerged as the primary tool for focus, supported by the highest publication volume among asset management tools and machine learning as the focus in 20.7% of the articles. The paper includes classification and bibliometric prioritization frameworks and a literature review of pipe break prediction methods, as well as an analysis of models and a discussion of implementation possibilities and development needs.

Abstract This study integrates satellite and surface NO₂ measurements through the ensemble square root filter (EnSRF) and the GEOS‑Chem model to estimate anthropogenic NOₓ emissions for China, the contiguous U.S., and Europe over 2019–2020, and further examines how the COVID‑19 pandemic altered emission patterns and sectoral contributions. Our results show that in 2020, anthropogenic NOₓ emissions were 19.20 ± 5.87 Tg in China, 8.96 ± 2.90 Tg in the contiguous U.S., and 10.56 ± 2.95 Tg in Europe. Compared with 2019, emissions decreased by about 5.38% in China, 8.94% in the contiguous U.S., and 15.38% in Europe. In China, emission reductions came from transportation, power plants, and industry. In contrast, reductions in the contiguous U.S. and Europe were almost entirely from transportation. These differences reflect the fundamental gap in energy structure and industry models between developed and developing economies. During the first wave of the pandemic, emissions fell sharply compared to the same period in 2019. The decreases were 18.04% in China, 10.56% in the contiguous U.S., and 21.74% in Europe. Large spatial variations were observed within each region. This work provides observation-based evidence of NOₓ emission changes across major world regions during COVID 19. It highlights how emissions can behave differently under sudden public health crises. The findings are useful for checking bottom-up emission inventories, assessing extreme events’ impacts on air quality, and designing better emission reduction policies.

Multi-year wildfire risk projection requires environmental drivers to be estimated beyond the observational period. This study develops a two-stage framework for Guangxi, China. FactorLSTM recursively estimates nine time-varying environmental factors, and an LSTM–Transformer combines these estimates with four projection-static factors to generate monthly wildfire probability estimates on a 1 km grid for 2019–2028. Observed-driver classification and recursive, predicted-driver evaluation are distinguished. An additional retrospective experiment using a separately trained classifier with a positive-class weight of 10 and Platt calibration yielded a pooled ROC-AUC of 0.7835 and average precision of 0.001475 for available valid observations in 2019–2025. However, its mean predicted probability was approximately 4.50 times the observed positive fraction, indicating residual overestimation. Seasonal maps summarise monthly outputs for January–April, May–September, and October–December. The ten-year outputs are conditional projections under extrapolated environmental drivers and fixed projection-stage land cover. The results identify limitations in probability calibration, rare-event detection, and observation coverage; they do not establish reliable monthly forecasting across the complete ten-year horizon.

Transboundary river basins combine spatially continuous ecological processes with nationally differentiated land use, development intensity, and environmental governance, yet it remains unclear how these asymmetries shape landscape ecological risk (LER) and whether alternative near-term land-use pathways can substantially modify the shared risk pattern. Taking the Sino–DPRK transboundary area of the Tumen River Basin as a case study, we assessed land-use and LER changes in 2000, 2010, and 2020 using a landscape-pattern-based LER index, global and local spatial autocorrelation, the geodetector, and geographically weighted regression (GWR), and simulated 2030 land use and LER under natural development (NDS), cropland protection scenario (CPS), and ecological conservation (EPS) scenarios using the Patch-generating Land Use Simulation (PLUS) model. Basin-wide mean LER increased from 0.0604 in 2000 to 0.0662 in 2010 and to 0.0666 in 2020, with most of the increase occurring during 2000–2010. In 2020, mean LER was higher on the DPRK side than on the Chinese side (0.0701 vs. 0.0646), and the combined proportion of relatively high- and high-risk units reached 40.22% and 27.03%, respectively. LER showed persistent positive spatial autocorrelation, with Moran’s I values of 0.6393, 0.6237, and 0.6347, and 55, 38, and 44 cross-border H-H adjacent pairs identified in the three periods. Slope consistently showed the highest explanatory power (q = 0.799–0.921), while the explanatory power of normalized difference vegetation index (NDVI) increased from 0.097 to 0.318; distance to roads was the strongest socioeconomic proxy (q = 0.131–0.202). GWR further indicated significant spatial non-stationarity in several major environmental relationships. For 2030, mean LER changed by +0.69%, −0.13%, and +0.60% under the NDS, CPS, and EPS, respectively, indicating only limited near-term divergence among scenarios. These results reveal persistent cross-border asymmetry together with recurrent H-H adjacency in boundary valley sections and suggest that near-term land-use adjustment alone may have limited capacity to substantially restructure basin-wide LER. The findings provide a spatial basis for prioritizing cross-border monitoring and coordinated land-use management in recurrent high-risk boundary valley segments.

This study presents ShyBFM, a high-resolution coupled physical–biogeochemical modelling system based on an unstructured grid. The physical component is the parallel computing finite element, ocean circulation model SHYFEM-MPI, while the marine ecosystem is described through the Biogeochemical Flux Model (BFM) which resolves the coupled pelagic and benthic lower trophic level interactions. The unstructured grid framework enables an accurate representation of complex coastal geometries while maintaining the influence of larger-scale dynamics through open boundary conditions. The numerical implementation of the model coupling is described, including the treatment of lateral and surface boundary conditions, and its application is illustrated through a reference case study. Model validation is performed for a coastal region of the northern Adriatic Sea (Mediterranean Sea), nested within an existing large-scale coupled physical–biogeochemical model that provides initial and lateral boundary conditions and serves as a calibration and validation benchmark. Simulated biogeochemical tracers from both the large-scale model and ShyBFM are compared against observational climatology. Results indicate that ShyBFM successfully reproduces the seasonal variability of key biogeochemical variables, exhibiting enhanced temporal variability and improved skill scores relative to the coarser-resolution model, although some limitations remain to be addressed. ShyBFM constitutes a robust and flexible tool for investigating interactions between physical dynamics and biogeochemical processes in coastal environments, which are strongly constrained by geomorphology, bathymetry, and riverine inputs. As such, ShyBFM is particularly well suited for applications supporting coastal management and environmental assessment.

Study region The Aggtelek Karst of northeastern Hungary, part of the transboundary Gömör–Torna Karst system, is one of the most important carbonate aquifers in Central Europe. Study focus This study investigates the hydrochemical and stable water isotope characteristics of 27 springs to identify hydrogeochemically coherent groundwater groups and evaluate the factors controlling groundwater variability. Combined Cluster and Discriminant Analysis was applied to major-ion and stable water isotope data and interpreted within the geological framework of the aquifer. New hydrological insights for the region Seven statistically robust hydrogeochemical groups were identified, demonstrating that the Aggtelek Karst is not a hydrochemically uniform carbonate aquifer despite the predominance of Ca–HCO₃ waters. The differences among the groups are associated with variations in carbonate lithology, particularly the relative influence of limestone and dolomitic units, variable contributions from non-carbonate and evaporite-bearing lithologies, and heterogeneity in hydrogeological setting and groundwater-flow conditions. Importantly, hydrochemical similarity is not determined primarily by geographical proximity: springs from different parts of the karst may belong to the same group, whereas geographically close springs may show distinct hydrogeochemical characteristics. The results demonstrate that conventional facies-based classifications may underestimate the internal hydrogeochemical complexity of carbonate aquifers. These findings improve the conceptual understanding of groundwater circulation in the Aggtelek Karst and demonstrate the value of integrating hydrochemical, stable water isotope, and multivariate statistical approaches in regional karst investigations.

Extreme rainfall in mountain catchments can trigger shallow landslides, debris flows, and sediment-laden floods as interconnected geohazard chains. This study proposes a coupling-oriented modeling framework that links rainfall-induced landslide-source mobilization with downstream flow-state transition. Rather than introducing separate submodels, the main methodological contribution is to convert hydrologically activated slope instability into time-, geometry-, and volume-explicit sediment supply and to route this supply as a single evolving solid–water mixture for identifying slide-like, debris-flow-like, and flood-dominated pathways. The method was applied to the 2023 extreme rainfall event in Beijing, China. Hydrological benchmarking showed that the dynamic front–perched layer formulation better captured localized pressure-head development near landslide-prone locations than bucket-type storage and Green–Ampt infiltration. The cellular-automaton (CA)-based source module generated rainfall-aligned shallow landslide release areas with explicit timing, depth, and object-scale morphology. Downstream simulation showed that released material mainly formed short-lived, high-concentration pulses rather than a sustained debris-flow-dominated process. During the main rainfall stage, released landslide volume was approximately 3.29 × 10 4 m 3 , whereas additional scour contributed only approximately 7%. The event therefore followed a rapid-dilution pathway toward flood-dominated, sediment-laden transport.

Soil-biodegradable plastic (BDP) mulches are increasingly used as alternatives to conventional plastic films because they can be incorporated into soil after use. However, their degradation introduces an exogenous carbon source that may affect microbial activity, soil aggregation, and carbon (C) and nitrogen (N) stabilization. This study investigated the effects of increasing BDP doses on microbial biomass carbon (MBC), bacterial 16S rRNA gene-copy abundance, and fungal target-gene abundance, aggregate-size distribution, and aggregate-associated C, N, δ¹³C, and δ¹ 5 N in a loamy and a sandy soil. The soils were incubated for one year with BDP fragments at 100, 1, 000, and 10, 000 mg kg - ¹ soil, corresponding to 0.01, 0.1, and 1% w/w, respectively. Microbial biomass C and bacterial and fungal target-gene abundance were assessed during incubation, while macroaggregates, microaggregates, and the silt–clay fraction were separated at the end of the experiment. The highest BDP dose induced responses in both soils, increasing microbial biomass C by 69% in the loamy soil and 25% in the sandy soil relative to their respective controls after one year. This treatment also increased macroaggregate abundance by 32% and 11% in the loamy and sandy soils, respectively, while decreasing the free silt–clay fraction. These changes were accompanied by higher C and N contents in macroaggregates, and also in sandy-soil microaggregates. Lower BDP doses produced weaker and more soil-specific effects, including increased microaggregate recovery in the loamy soil. δ¹ 5 N progressively increased from macroaggregates to finer fractions in both soils, but was not affected by the presence of BDP. In contrast, δ¹³C enrichment toward finer fractions occurred only in the loamy soil, demonstrating that soil characteristics, such as texture, may regulate C transformation and potential stabilization. Overall, BDP effects were dose-dependent and potentially modulated by soil properties. Although the strongest responses occurred at a concentration exceeding realistic annual inputs, detectable effects at lower doses highlight the need for long-term field studies evaluating repeated BDP incorporation.

Worldwide, urban rainwater flooding is increasingly affecting small-scale public infrastructure in tropical cities. Consequently, drainage decisions are often made with limited hydrometric data. Therefore, this study aims to evaluate flood control alternatives using the Nature-based Solutions (NBS) approach through participatory assessment and modeling with the Stormwater Management Model (SWMM) at a vulnerable educational infrastructure (VEI) in Durán, Ecuador. The methodological design of this research involves case study analysis, rainfall data and the SWMM model. Then, a community workshop was developed with the VEI's stakeholders to identify areas of recurrent waterlogging, reported flood depths and drainage constraints. A reference condition and three Low-Impact Development (LID) scenarios were then simulated under 15 synthetic design storms, combining return periods of 5, 10 and 20 years with durations of 30 min, 1, 2, 3 and 4 hours. Finally, the model's performance was evaluated using variables such as runoff, outflow volume, peak flow, flood volume, maximum depth, the multi-criteria LID performance index, bootstrap confidence intervals and an exploratory robustness analysis. The findings of this study highlight the participation of 28 stakeholders in the workshop, including parents and teachers, which enabled the identification of flood-prone areas and the location of LID scenarios. This research demonstrated that individual LID scenarios reduced certain hydrological indicators but, in some cases, increased the flood volume or maximum depth. The combined LID scenario provided the most balanced response, reducing average runoff by 37.90%, flood volume by 50.90%, maximum water depth by 46.44%, and outflow volume by 17.37% compared with the baseline. Of particular note is the integration of hydrological modeling and the effectiveness of LIDs in reducing runoff and redistributing peak flows. The SUDS/LID alternative most widely accepted by the school community and demonstrating the best hydrological performance was the sustainable urban drainage system, which should be strategically located near the main runoff-generating surfaces or upstream of recurrent flooding areas. This study provides a flood risk management tool that can be adapted to other VEI environments in urban-coastal regions where hydrological data is scarce.

The Santa Cruz Mid-County Basin Groundwater Sustainability Agency conducted a Regional Water Optimization Study to support the selection of water supply projects and management actions within the critically overdrafted Santa Cruz Mid-County Groundwater Basin, for long-term operations and shared regional benefits including sustainable groundwater management and regional water supply needs. To support this objective, the Study utilized an integrated groundwater surface-water flow (GSFLOW) model of the Basin and surrounding areas to simulate water supply projects and management actions into the future (water year 2023 to 2075), covering the sustainability planning horizon under California’s Sustainable Groundwater Management Act. Water supply projects and management actions considered under this study include aquifer storage and recovery, indirect potable reuse, and interagency transfers. Many different implementations are possible for each type of project, such that the number of potential project configurations is practically endless. To facilitate optimization, we developed a novel workflow utilizing machine learning algorithms to conduct optimization by autonomously designing, preprocessing, post-processing, and evaluating physical model scenarios, which we call Machine Learning Guided Optimization (MLGO). MLGO improved project configurations within a complex system by generating a diverse set of management options and identifying effective strategies. These results demonstrate the potential for MLGO to support strategic decision-making and regional water resources planning.

The flash flooding across Central Texas on 4 July 2025, caused more than 130 fatalities and property losses exceeding USD 20 billion. The objective of this study is to evaluate the performance of the NOAA operational flood forecasting pipeline, including Quantitative Precipitation Forecasts, National Water Model short-range streamflow forecasts, and Office of Water Prediction flood inundation mapping during this catastrophic event, and to characterize how forecast skill and impact-based predictions varied with forecast lead time at gauged and ungauged locations. Using the Operational National Water Model short-range streamflow forecast product, we generated 306 forecasted flood inundation maps between 3 and 4 July 2025. For evaluation, we constructed an inundation extent benchmark derived from USGS high water marks. Both impact based and skill-based assessments are presented.

The Direct air capture (DAC) of CO 2 has seen widespread inclusion in net-zero modeling which, in turn, has heightened concerns that it could trigger moral hazard. Although recent studies suggest awareness of DAC development lowers individuals' emissions reduction ambition, their reliance on first-person measures leaves them susceptible to social desirability bias. To address this limitation, the present study uses indirect questioning to determine whether perceptions of moral hazard are ascribed to others but not to oneself and provides an explanation for this asymmetry based on Attribution theory. A representative U.S. sample ( N = 900) was randomly assigned to either a control or DAC development condition. We found that exposure to the DAC development condition increased participants' reported climate ambition relative to the control group. Participants in the DAC condition rated their own emissions-reduction intentions as more ambitious than those attributed to others. Consistent with Attribution theory, we also found that emission-reduction expectations were judged more ambitious when motivations were construed as internal (dispositional) rather than external (situational). Importantly, the significant differences observed in the control group between attributing others' emission reduction expectations to internal factors and external factors converged in the DAC scenario. Demonstrating that dispositional motivations lead to decreased mitigation ambition provides evidence of moral hazard. These results have implications for climate governance because behavioral climate policy research suggests that expectations about moral hazard may undermine mitigation efforts.

Using selected summer Landsat 8 scenes for Taiyuan, Chengdu, and Yinchuan, this study compared functional zone land surface temperature (LST) patterns, green-space–LST associations, and the relationships of 2D landscape configuration, blue-green composition, 3D building form, and terrain with LST. Separate city-specific Light Gradient Boosting Machine (LightGBM) models with SHapley Additive exPlanations (SHAP) interpretation characterized within-model predictor contributions, SHAP dependence patterns, and selected representative pairwise interactions. Polygon-level LST distributions differed among functional zones within every city (Kruskal–Wallis, all p < 0.001), and Commercial zones had higher LST than Park/green-space zones in all three cases with large pairwise effect sizes. Other pairwise contrasts were city-specific. Most city-zone regressions showed negative green-space–LST associations, whereas Chengdu Industrial showed no clear association. Building volume ranked among the Top-3 predictors in every city model, but the co-dominant structures differed: building volume–green-space proportion–elevation in Taiyuan, mean building height–building volume–green-space proportion in Chengdu, and building volume–aggregation index–elevation in Yinchuan. Five-fold fixed-specification spatial block cross-validation provided a more conservative spatially held-out sensitivity assessment than ordinary random splitting, while reference and repeated spatial-block out-of-fold SHAP analyses showed stable within-model predictor rankings and contribution shares. An 85-scene multi-date Landsat comparison with selected-scene leave-one-out sensitivity supported broad summer spatial consistency in Taiyuan and Yinchuan, with partial, context-dependent support in Chengdu. After excluding each city’s selected 2019 scene from its own reference median, the relative–LST Spearman correlations remained 0.895, 0.861, and 0.909 for Taiyuan, Chengdu, and Yinchuan, respectively. These findings distinguish recurring comparative anchors from context-dependent predictor structures, supporting context-sensitive planning hypotheses rather than universal predictor rankings.

A new method for handling satellite aerosol observations boosts simulation accuracy, with potential benefits for weather and climate forecasting.

Vertical momentum entrainment is driven by vertical shear and turbulent mixing and plays a key role in the recovery of wind turbine and wind-farm wakes, but remains poorly documented by field measurements. The LOLland offshore Lidar EXperiment (LOLLEX) campaign introduced a novel measurement approach to address this knowledge gap. The primary objective of this campaign was to develop a new atmospheric measurement strategy using Doppler wind lidar technology to better understand atmospheric conditions favourable to enhanced momentum entrainment inside and outside an offshore wind farm. LOLLEX was conducted from September 2022 to August 2023 in Denmark, in and around the Rødsand II wind farm, just south of the island of Lolland. During this campaign, two pulsed Doppler wind lidars, a scanning lidar (WindCube100S) and a lidar wind profiler (WindCubeV2), were deployed onboard a crew transfer vessel (CTV) commuting daily between the harbour and the Rødsand II offshore wind farm. Additionally, a scanning pulsed Doppler wind lidar (Halo Photonics) was mounted on a transformer platform north of the wind farm to perform range height indicator scans across the farm. Horizontal wind speed data were collected up to 300 m above the sea surface by the lidar wind profiler. The scanning lidar on the CTV collected data up to 2.5 km, alternating between the wind profiling mode and vertical stare mode. The latter scan operated at a sampling frequency of 1 Hz and along-beam spatial resolution of 10 m, allowing for the study of the turbulent vertical wind velocity component. The dataset includes several thousand hours of vertical scans. As a result of the moving vessel, many of the observations occurred inside or near the wind farm, providing insight into the near and far wakes of individual and multiple turbines. The potential and limitations of the new measurement strategy are illustrated using four case studies: (1) the observation of a Kelvin-Helmholtz instability above the wind farm, examined further in a companion paper; (2) turbulent mixing propagating downward from the top of the boundary layer; (3) internal atmospheric waves; and (4) wake characterisation inside the wind farm using the range-height indicator scans from the lidar deployed on the platform. This work demonstrates a novel methodology integrating remote sensing with a mobile offshore platform to measure turbulence at unprecedented altitudes. The dataset offers valuable data for wind energy research, boundary-layer meteorology, and further development of atmospheric measurement techniques.