Atmospheric and Oceanic Sciences

Abstract Colder supercell outflow—generally linked to lower boundary-layer relative humidity—is often detrimental to tornado genesis and maintenance. However, the rear flank is anything but homogeneous; surface temperatures within a single rear-flank downdraft may vary by as much as 10–20 K over just a few kilometers. In this study, we analyze how local variations in supercell outflow and the near-inflow environment might influence tornado genesis and evolution. This is accomplished with a 25-member ensemble of high-resolution idealized simulations, with each member made unique by the addition of a small region of cooler air prior to tornadogenesis. In each simulation, the resulting “blob” of cooler near-surface air advects toward the developing vortex and, in some cases, meaningfully alters the resulting vortex-scale evolution. Regardless of the initial blob location, all of the ensemble members featured a vortex with a weaker peak intensity than the blob-less control run. Blobs inserted in the near inflow exhibited the least impact on tornado genesis and evolution; the blob encountered the rear-flank gust front and was advected away from the low-level updraft. Vortices were meaningfully weaker in simulations with a blob inserted in the forward flank. Although vortex-bound Lagrangian vorticity diagnostics were similar between this run and the control run, the blob resulted in a sub-optimal horizontal separation of the developing vortex from the low-level updraft core. These findings highlight the sensitivity of vortex development to local cooling, perhaps reminiscent of rear-flank internal surges or cold pools from cell mergers in the real atmosphere.

Abstract Land use and land cover change (LULCC) affects two climate-relevant landscape dimensions: land cover composition and its spatial configuration. While the climate impacts of changes in land cover composition are well documented, the role of changes in land cover configuration remains poorly understood. Here, we examine how configuration changes in cropland-forest landscapes affect the ecosystem´s climate-regulating functions by conducting a multi-scenario experiment. We use the Terrestrial System Modeling Platform (TSMP), a modeling framework to run coupled high-resolution regional climate models (RCMs) with a focus on terrestrial hydrology. We employ stylized landscapes as input for TSMP and combine machine learning with SHAP (SHapley Additive exPlanations) values, an explanatory AI technique, to disentangle direct and mediating contributions. Our results indicate that changes in land cover configuration influence topsoil and 2-meter air temperatures when composition is kept constant. Greater forest fragmentation is associated with overall warming, particularly over cropland during the growing season. This can mainly be attributed to reduced wind speed, lower latent heat flux, and higher sensible heat flux. These findings support the hypothesis that increasing forest fragmentation reduces the landscape’s capacity to mitigate local climate. However, we also discuss potential biases towards warming effects in contemporary RCMs. Our study demonstrates the potential of RCMs to address key land system science questions and promote interdisciplinary research at the intersection of climate change and landscape management. The type of experiments conducted may help to leverage both modeling and observational studies to advance our knowledge about the climate-relevance of landscape characteristics and arrive at scalable adaptation strategies.

Abstract Reforestation is a widely considered nature-based solution for climate change mitigation because of the large carbon sequestration capacity of forests. Yet, how the climate effects of reforestation depend on different future climate scenarios is not well understood. In this study, we use an Earth system model forced with contrasting land-use and a range of emissions scenarios to investigate the scenario dependence of biogeochemical and biogeophysical effects of reforestation. We find that the combined biogeochemical and biogeophysical effect of reforestation is a global cooling which decreases in high emission scenarios. This scenario dependence of total effects of reforestation is driven by the scenario dependence of biogeochemical effects. The biogeochemical effects on global mean surface air temperature increase from low to intermediate emission scenarios and decrease in high emission scenarios because of the non-monotonic nature of the radiative forcing due to reforestation with background emissions in future climate scenarios. The biogeophysical effects of reforestation cause a warming effect that counteracts the biogeochemical cooling effect globally. Further, the biogeophysical warming effects show weaker scenario dependence than biogeochemical cooling effects. We also show that estimating biogeochemical cooling effects of reforestation using model TCRE and diagnosed land carbon uptake leads to overestimation in high emission scenarios. Our results highlight the importance of accounting for the scenario dependence of effects of reforestation when developing climate mitigation strategies.

Abstract Tropical Cyclones (TCs) are intense storms that pose a persistent and considerable risk to coastal communities and infrastructure in the global tropics and subtropics, including the United States (US). With known limitations associated with observations and high-resolution earth system models, synthetic TC models that capture a wide spectrum of storm possibilities have been developed to robustly quantify TC risk. Here we examine the simulation of various TC features in the North Atlantic relevant for US coastal risk in three synthetic TC models forced with ERA5 reanalysis: MIT, CHAZ and RAFT. While there is a broad agreement among these models in terms of their representation of salient TC characteristics, certain differences do exist. To connect these modeling uncertainties with energy infrastructure resilience, we apply fragility curves that link simulated TC intensities to damage probabilities, demonstrating how uncertainty in storm states may translate into that in coastal impacts. Our study indicates that acknowledging and accounting for inter-model uncertainty leads to more reliable risk assessments, strengthening science-to-action pathways for managing risks associated with TCs.

• Evaluation of 18 bias correction methods across climatic, hydrological, and radiative variables. • Comparison of statistical, distribution-based, and machine learning approaches under diverse climatic conditions. • Multi-scale evaluation using complementary performance metrics and consistency with extremes and temporal cycles. • Analysis of calibration length, temporal sequencing, and iterative corrections. • Practical recommendations for context-specific bias correction applications.

Abstract. FRIDA is a new contribution to the portfolio of integrated assessment models (IAMs) that address the climate – energy – economy – and society nexus. The FRIDA acronym stands for Feedback-based knowledge Repository for IntegrateD Assessments. Naming it a “knowledge repository” signals that the FRIDA model is never finished; it represents the current state of knowledge of the development team at any given time. FRIDA was developed through the European Horizon project WorldTrans – Transparent Assessments for Real People (2022–2026). The journal Geoscientific Model Development has given us space to document FRIDA, including its submodules and spin-offs, in a special GMD collection (https://gmd.copernicus.org/articles/collection12.html, last access: 18 May 2026). This brief paper is the introduction to the GMD FRIDA collection of papers. The purpose of the introductory paper, written by the project lead on behalf of the consortium, is to provide the conceptual and institutional context for the original model and to make explicit the initial design requirements that guide FRIDA's ongoing development as a living knowledge repository. FRIDA is implemented as a computationally efficient system-dynamics model and is accompanied by an Interactive Learning Environment. This combination makes it suitable not only for research, but also for education and broader outreach. In particular, FRIDA can be used in interdisciplinary climate science courses to show how individual disciplines (e.g. climatology, economics, demography) are tightly interwoven within the coupled climate–human system, thereby lowering the barrier to entry for users beyond the IAM community. What sets FRIDA apart from traditional IAMs is its shift from exogenous, narrative-based scenarios to a fully feedback-driven framework, in which human activities and climate change co-evolve within a single system of equations. By explicitly representing these two-way feedbacks, FRIDA accounts for the impacts that climate change has already begun to exert on human systems; without them, projections of the human activities that drive climate change will become increasingly unreliable. Preliminary results suggest that including these feedbacks lead to systematically less optimistic projections than conventional IAM baselines.

Abstract This study uses a data-driven modelling approach to integrate drought indicators and weather data to enhance crop yield simulations. By employing a sequential hybridization method, the research combines drought indices such as SPI, SPEI, or SMI with weather data to refine wheat and sugar beet yield predictions by the statistical model ABSOLUT at the voivodship level in Poland. The modelling approach systematically evaluates possible combinations of 15 input features to identify the most effective configurations for multiple linear regressions. The findings reveal that regression models incorporating both weather data and drought indicators (particularly SPEI and SMI) deliver superior performance compared to those relying solely on weather variables. This improvement is especially pronounced under conditions of variable moisture availability. For example, a model that includes SPEI and weather data more precisely estimates wheat yield, especially during extreme events like the 2018 drought. Additionally, using SMI as the only feature demonstrated that ABSOLUT performed better than when combined with weather data in the case of sugar beet. These results underscore the critical role of incorporating drought indicators to bolster the reliability of crop yield predictions, offering significant insights for agricultural planning in regions susceptible to climatic variability. The research also highlights the potential of hybrid modelling approaches, which combine the strengths of process-based and data-driven models to enhance predictive accuracy. Moreover, the study suggests that these models could be further refined by incorporating additional environmental factors for more robust agro-hydrological simulations.

Air quality in the San Joaquin Valley (SJV) ranks among the worst in the US. Exposures to traffic-related air pollutants have been associated with pediatric health complications, and few studies have investigated respiratory complications in relation to short-term exposures to PM less than 2.5 microns in diameter (PM2.5) in the SJV. We used Bayesian Poisson spatiotemporal conditional autoregressive models to analyze the association between PM2.5 and pediatric respiratory emergency department (ED) visits in Fresno County, California. Additional analyses stratified respiratory outcomes by sex and age group. Weekly ambient PM2.5 levels were estimated for each zip code using community science and regulatory air monitors. Weekly residential zip code counts of respiratory ED visits were provided by Fresno County Department of Public Health and Valley Children’s Hospital from 2 April 2022 to 31 December 2024. A ten-fold increase in PM2.5 was associated with increased asthma ED visits among females (Relative Risk (RR):1.15; 95% Credible Interval (CrI):1.01, 1.32) and children aged 0 to 4 (RR:1.18; 95% CrI:1.03, 1.34) and other chronic respiratory conditions among males (RR:1.93; 95% CrI:1.19, 3.16) and ages 10 to 14 (RR:2.90; 95% CrI:1.32, 6.30). Findings suggest that efforts to better assess and reduce pollution exposures will improve public health in the SJV.

Abstract. Climate change is expected to increase the frequency and severity of Multi-Year Droughts (MYDs), but their impacts on vegetation remain poorly understood. While satellite records offer valuable insights, they only cover recent decades, limiting the number of MYDs available for analysis. The dynamic global vegetation model LPJmL-5 can simulate vegetation dynamics under varying climate conditions and over longer temporal scales than are typically available from satellite observations. However, its ability to capture vegetation responses to drought, including MYDs, has not yet been systematically evaluated against observation-based datasets. In this study, we benchmark LPJmL-5 against MODIS-derived gross primary production (GPP) to assess how well the model reproduces vegetation responses to drought. We find that LPJmL-5 captures the key temporal and spatial dynamics of drought-related GPP observed by MODIS, although notable differences remain. In particular, LPJmL-5 tends to overestimate vegetation response at the onset of MYDs and shows some rapid recovery behaviour, resulting in muted overall drought impacts. Vegetation responses also vary by type: drought dynamics in croplands are captured relatively well, whereas responses in boreal and temperate vegetation are underestimated in magnitude. These discrepancies appear to be linked to simplified model representations of vegetation stress and mortality, which limit long-term vegetation loss. Beyond drought conditions, LPJmL-5 reproduces absolute GPP values reasonably well in some regions, but performance declines in parts of the Southern Hemisphere and in cropland-dominated areas. This suggests that general GPP simulation performance is not necessarily linked to performance during drought conditions. Overall, this benchmarking highlights strengths and limitations in how LPJmL-5 represents vegetation responses to drought and provides a foundation for future studies of vegetation responses to multi-year droughts.

Abstract Early warnings for climate tipping points should clearly convey their significant uncertainties, to maintain transparency and avoid perceptions of overstatement. This includes doubts about the existence of climate tipping points, which must often be assumed a priori in order to assess their early-warning signals, as well as doubts about their proximity. When such uncertainties are not clearly communicated, both updated assessments and continued absence of detected tipping can erode public trust and scientific credibility concerning future warnings for climate tipping. Moreover, this could even be misinterpreted by the public as more general ignorance about impacts of climatic change. So, perceived overstatement in this context may weaken support for climate policy in general. We stress that regardless of uncertain climate tipping points, climate action is already imperative because of the undisputed impacts of climate change. Also, an overemphasis on uncertain tipping points may sometimes obscure well-established evidence for the urgent need for climate policy. Despite strong mathematical foundations, Earth system complexity and unresolved uncertainties constrain the application and communication of tipping-point theory, highlighting the need for further elaboration. The uncertainty of climate tipping points furthermore underscores the need for adaptive planning, considering both tipping-point and non-tipping point scenarios, while safeguarding that climate policy remains based on scientific evidence.

Predicting high-resolution Normalized Difference Vegetation Index (NDVI) in mountainous ecosystems is challenging due to topographic complexity and climate heterogeneity. Existing methods often struggle to balance fine-grained spatial patterns with multi-scale meteorological drivers. This paper introduces the Hierarchical Multi-Modal Fusion Network (HMMFN), which employs a conditioned reconstruction strategy to decouple spatial learning from environmental forcing. The architecture utilizes a dual-stream encoder to process NDVI imagery and meteorological data in parallel. A Transformer module captures long-term temporal dependencies, while a multi-level fusion decoder integrates climate semantics with local vegetation details. The model is optimized using a hybrid loss function that combines Mean Squared Error and Structural Similarity Index Measure to ensure both numerical precision and spatial fidelity. Evaluated in the Liupan Mountains, HMMFN consistently outperforms baseline models across multiple lead times. For prediction horizons ranging from one to five months, the model maintains high accuracy with R2 values between 0.9123 (1-month horizon) and 0.8195 (5-month horizon), achieving a 10.1% and 3.6% reduction in RMSE compared to the optimal baseline model, respectively. These results demonstrate that HMMFN effectively preserves fine-scale spatial structures while maintaining accurate temporal trends across various time steps.

Underground coal fires are persistent subsurface hazards threatening energy resources. UAV thermal infrared remote sensing provides high-resolution observations of surface thermal anomalies, but these signals may be spatially offset from underlying fire sources. An integrated framework was developed for subsurface temperature-field reconstruction and surface–subsurface correspondence and offset analysis. Surface thermal anomaly centers were extracted using statistical thresholding, adaptive kernel density estimation, and intensity-weighted centroids. Subsurface temperature fields were reconstructed using an MGSM-RBF model that combines multi-Gaussian fire-source representation with residual correction. The framework was applied to the Sandaoba coal fire area using UAV thermal infrared data and 370 borehole temperature measurements from 39 boreholes, covering depths of approximately 0–85 m. Reconstruction accuracy was evaluated using spatially buffered cross-validation and compared with eight baseline methods. MGSM–RBF achieved the best performance, with RMSE = 92.49 °C, MAE = 61.26 °C, and R2 = 0.81. Two surface thermal anomaly centers and three subsurface fire sources were identified, with primary combustion concentrated at 30–55 m depths. Surface anomalies were not vertical projections of subsurface sources. The horizontal offsets were approximately one-fifth to one-third of burial depth, reflecting depth-dependent and multi-source-controlled surface thermal responses. These findings support UAV-based coal fire interpretation and fire-control planning.

Abstract Organic farming systems have expanded across North America, yet the drivers and magnitude of soil N 2 O fluxes remain understudied. To quantify organic fertilizer-induced N 2 O and identify key management and environmental drivers, we conducted a meta-analysis with 315 observations from 43 North American field studies. We also extracted 68 covariates representing crop type and properties, nitrogen inputs and management practices, hydrology, soil properties, topography, climate, and remote sensing indices to model N 2 O fluxes. Categorical comparisons showed that swine manure and liquid amendments produced significantly (P < 0.05) higher emissions and emission factors (EFs) than other amendment types and forms. Corn had the highest monthly area-scaled emissions (0.36 kg N ha -1 month -1 ) among all crops. Significant differences also occurred across photosynthetic pathway (C4 > C3), life cycle (annual > perennial), and plant type (monocot > dicot) for emissions and EF. Tile-drainage did not significantly affect any N 2 O metrics, but differences were observed between irrigated and non-irrigated sites, likely reflecting variation in the completeness of the denitrification process. Random Forest (RF) models identified remote sensing indices as the strongest predictors (up to 25% importance) of emissions and EFs, followed by soil chemical properties (19%), climate (17%), and soil physical properties (16%). The RF results also showed that organic fertilizer-induced N 2 O emissions can be estimated (R 2 = 0.54, 0.72, and 0.69 for area and yield-scaled monthly emissions and EFs) with key environmental and management factors. EF comparisons with IPCC Tier-1 benchmarks indicated that the 2006 benchmark (1%) might overestimate EFs, while the 2019 dry region (0.5%) benchmark overestimates EFs and the 2019 wet region benchmark (0.6%) underestimates EFs. The meta-analysis and modeling results provide a basis for researchers, policymakers, and farmers to refine N 2 O estimates, target high-emission drivers, and improve nitrogen management in organic systems.

Study region National Groundwater Recharge Observing System (NGROS) sites, Australia Study focus Identifying controls on groundwater recharge occurrences remains a central challenge in hydrology. Although broad consensus exists on key variables influencing recharge, there is less agreement on their relative importance and whether observed relationships are causal or merely correlative. Existing studies rely on approaches limited in capturing non-linear behavior or only capture single measures of association without addressing causal linkages. This study quantifies both correlative and causal relationships between key variables and potential groundwater recharge occurrences across nine NGROS sites. NGROS is the first dedicated sensor network to observe groundwater recharge at event scale in underground spaces across Australia. Using high-resolution drip monitoring data as direct indicators of recharge events, relationships between recharge occurrences and potential drivers, including precipitation, soil moisture, evapotranspiration (ET), and vegetation dynamics (NDVI), were quantified using both correlation-based and causality-oriented analyses, including first application of Convergent Cross Mapping (CCM) to identify causal linkages influencing recharge occurrences. New hydrological insights Results show that soil moisture and vegetation are the strongest predictors of recharge occurrences. ET shows moderate seasonal importance, particularly during austral autumn, but remains secondary overall. Although daily rainfall shows weak association, cumulative precipitation over 6–7 days shows stronger predictive association. Soil moisture also has longer response lags (> 3 weeks) compared to rainfall (7–10 days).

Urban blue-green spaces play an important role in mitigating thermal environmental stress, yet their long-term configuration and relative thermal environmental benefits remain insufficiently understood at the metropolitan scale. This study examined Beijing from 2000 to 2020 by integrating Landsat time-series imagery, land-cover data, landscape metrics, land surface temperature retrieval, Geodetector analysis, and a configuration-oriented Blue-Green Environmental Benefit Index (BGEBI). The results showed that Beijing’s blue-green spaces experienced three stages: rapid decline during 2000–2003, gradual recovery during 2004–2012, and rapid expansion during 2013–2020. Spatially, low-temperature zones were mainly concentrated in the northwestern ecological conservation areas, whereas high-temperature zones were mainly distributed in the southeastern core and plain areas. Green-space landscape indicators, especially forest-related metrics, showed stronger explanatory associations with LST spatial heterogeneity than most wetland-related indicators at the metropolitan scale. The BGEBI results indicated an overall increase in relative thermal environmental benefits from 2000 to 2020, with high-value areas mainly located in the northwestern and central-western mountainous regions and low-value areas mainly distributed in southeastern urbanized areas. These findings suggest that blue-green space planning in high-density megacities should pay greater attention to landscape configuration, spatial connectivity, and scale-sensitive management. The proposed BGEBI framework provides a relative spatial-prioritization tool for identifying areas where blue-green configuration optimization may support thermal-environment improvement.

Food security is a growing global concern, and accurate crop mapping in major grain-producing regions like China’s Sanjiang Plain—which contributes approximately 7% of national grain output—is essential for agricultural resource management. However, crop classification in this area is hindered by frequent cloud cover, complex phenological rhythms, and spatial heterogeneity. To address these challenges, this study proposes Spatial-Temporal Attention U-Net (STA-UNet), a crop classification model based on time-series Sentinel-2 imagery, incorporating four key modules: Convolutional Block Attention for enhanced sensitivity to parcel boundaries, Temporal Attention Encoder for adaptive capture of temporal dependencies under cloud interference, Dynamic Upsampling for improved boundary recovery of small parcels, and Adaptive Feature Fusion for bridging semantic gaps between heterogeneous features. Extensive experiments on rice, maize, and soybean classification demonstrate that STA-UNet achieves an overall accuracy of 93.61% and an F1-score of 0.925, outperforming state-of-the-art methods. In spatial generalization tests, STA-UNet maintains overall accuracy above 85.02% in the left-subregion transfer setting and achieves the best three-year average OA of 81.34% in the rice-dominated right-subregion stress test, while temporal generalization tests confirm limited inter-annual performance degradation. These results indicate that STA-UNet provides a robust and effective framework for crop mapping in cloud-prone, phenologically complex agricultural regions.

Abstract The comma heads of winter cyclones have a variety of precipitation structures ranging from cells to bands. Much of the previous research has explored the environmental conditions for larger (primary) snowbands in the cyclone comma head, with less work comparing the environments of the broader spectrum of snowband structures. This study looks at these environments for a full range of object sizes and shapes for cool season cyclones over the northeast U.S. (NEUS) from 1996–2023. The ERA5 reanalysis is used to obtain the environmental parameters and cyclone tracks. Only a weak relationship exists between different object characteristics and parameters such as frontogenesis, stability, and vertical shear. A self-organizing map (SOM) approach was applied to specific regions of the cyclone comma head, and the analysis was separated into different cyclone track orientations over the NEUS. The environmental relationships are somewhat more robust using the SOM technique, such as stronger mid-level frontogenesis in regions with more prevalent large bands and greater low-level vertical shear in regions with more frequent amorphous objects; however, the environments are still not statistically different for each precipitation object type. Given this result and the large spread in environmental ingredients for each object type it is hypothesized that the objects may have environmental differences that evolve from the development to mature stages.

Abstract Blue-green infrastructure (BGI)—engineered, nature-based stormwater controls such as bioretention cells and green roofs—can reduce combined sewer overflows (CSOs). However, hydrological performance can decline over time through sediment deposition and clogging. Despite this, catchment-scale studies commonly assume stationary BGI performance, potentially underestimating CSO volumes and associated risks. This study integrates spatial sensitivity analysis with dynamic deterioration modelling to examine where and when BGI performance loss most affects CSO volumes.

A semi-Markov chain model simulated condition-state transitions for individual BGI assets, coupled with EPA SWMM to assess CSO mitigation over time in a realistic case study. Under the modelled parameters, the difference in system-level CSO volume between fully functioning and fully failing BGI reached 18% by the end of the study period. Critically, while most assets exert minimal influence, a subset of high-leverage BGI assets disproportionately governs CSO volumes—primarily determined by asset size and capture ratio. Furthermore, early performance losses are masked by annual rainfall variability, introducing significant delays before failures become detectable through CSO measurements alone.

These findings indicate that effective monitoring requires asset-level rather than system-level observation. Targeting a high-leverage subset of BGI can substantially reduce monitoring effort while preserving CSO performance. This study demonstrates how such critical assets can be identified and contributes a methodology for explicitly incorporating BGI deterioration dynamics into catchment-scale models. These findings enable risk-informed monitoring and maintenance prioritisation, treating BGI as a dynamic rather than static component of urban drainage management.

Abstract The Yangtze River Basin (YRB) is historically prone to frequent summer droughts caused by local land-atmosphere feedbacks and large-scale circulation. This study focuses on the decadal variation of the intraseasonal decline of soil moisture in the YRB, which has demonstrated a significant decadal shift in summer soil moisture occurred around 2009, characterized by an intensified decline of soil moisture with a prolonged duration, from about 30 days before 2009 to nearly 50 days after 2009. Diagnostic analysis shows that sensible heat flux (SH) is the more dominant factor driving the intraseasonal decline of soil moisture in the YRB compared with latent heat flux. Since 2009. the mechanism of positive feedback of SH has shifted from a dual-factor mode by wind speed and temperature to a one-factor mode solely by temperature. The transition rooted in large-scale circulation shifts forced by sea surface temperature anomalies (SSTAs). Beyond the internal variability of the western pacific subtropical high, the superposition of Arctic-amplification-triggered wave train and decadal warming of mid-latitude Pacific plays a decisive role. Before 2009, these two types of oceanic forcings jointly induced an anticyclonic circulation anomaly on the northern or southern side of the YRB. But after 2009, this circulation anomaly tended to completely cover the YRB, triggering relatively stronger subsidence currents and continuous high temperatures in the YRB, providing a powerful circulation background condition for the temperature single-factor driven SH positive feedback process.

Study region Jiuzhaigou Valley is a UNESCO World Heritage Site renowned for its outstanding scenic and ecological values, preserving important forest ecosystems and high biodiversity, and it is also recognized as a UNESCO Biosphere Reserve. Study focus To provide a mechanistic resistance parameter applicable to obstacle-rich channel reaches (tree stems and pier-like elements), we conducted long-duration quasi-steady flume experiments of debris flows passing a single circular cylinder using a recirculating system. Drag forces were measured under controlled mixture properties and flow regimes and converted to drag coefficient C d . In addition, we provide an application framework that links the experimentally derived mean drag coefficient to an equivalent reach-scale Manning roughness for obstacle- or forest-belt-rich reaches, enabling implementation in depth-averaged routing models, pending field-scale calibration and validation. New hydrological insights for the region The results show that Froude-number-only descriptions are insufficient for debris-flow interactions with cylindrical obstacles when geometric effects become important. The drag coefficient depends systematically on the depth-to-diameter ratio, reflecting a transition from front-dominated loading to flow diversion and shear-influenced interaction along the cylinder. Incorporating depth–diameter scaling and viscous control yields a practical, physically based drag closure for a single cylinder under quasi-steady motion. This closure provides an element-scale basis for a preliminary upscaling framework for obstacle-induced resistance in channel-routing calculations in Jiuzhaigou-type catchments.

Lake Vrana on Cres Island (northern Adriatic Sea) is a rare hydrogeological system consisting of a large freshwater body located within a karst cryptodepression with its bottom below sea level and surface above it. This study investigates long-term hydroclimatological changes using daily records of lake water level (1978–2024), water temperature (1979–2024), precipitation, and air temperature (1981–2024). Linear regression, the Mann–Kendall trend test, Sen’s slope estimator, and day-to-day variability metrics were applied to quantify long-term trends and system responses. A multi-index approach was used to enable a robust assessment of drought dynamics in this unique karst system: the Standardized Precipitation Index (SPI), representing meteorological conditions based on precipitation; the Standardized Hydrological Index (SHI), reflecting hydrological response derived from lake levels; and the New Drought Index (NDI), integrating precipitation and temperature to account for evapotranspiration effects. Results indicate a statistically significant decline in lake water levels (−4.5 to −5.2 cm yr−1), while precipitation shows no significant trend. In contrast, both air and water temperatures exhibit a significant increase (~0.5 °C per decade) and are strongly correlated (R2 = 0.767). The lake demonstrates pronounced thermal inertia and delayed response to atmospheric forcing. Day-to-day analysis reveals increasing variability in water temperature and decreasing variability in air temperature, suggesting changes in system energy dynamics. Drought indices (SHI and NDI) show significant negative trends, whereas SPI does not, indicating that drought intensification is primarily driven by rising temperatures and enhanced evapotranspiration rather than precipitation deficits. These findings demonstrate that Lake Vrana acts as a sensitive integrator of climatic forcing.

The hyporheic zone (HZ) mediates biogeochemical exchanges between rivers and aquifers, yet its spatial and temporal dynamics in large, regulated rivers remain poorly characterized due to limitations of point-based measurements. Here, we combined three time-lapse electrical resistivity tomography (T-ERT) surveys with continuous hydrological and hydrochemical monitoring along a meandering reach of the lower Yellow River, generating a two-dimensional, profile-integrated view of HZ geometry under three hydrodynamic states: low flow (1 December 2020), natural rising stage (1 March 2021), and peak stage during the Xiaolangdi (XLD) water-and-sediment regulation (1 July 2021). Absolute tomograms identified two hydrostratigraphic units: an upper sandy-silt cap (35–170 Ω·m) and an underlying sand aquifer (12–35 Ω·m). Percent-difference tomograms, relative to the low-flow baseline, revealed lateral HZ expansion from ~15 m and vertical growth of 2.5 m at the rising stage to ~36 m and 4.5 m at peak stage, with local resistivity decreases exceeding 38%. In contrast, the deeper mixing zone varied by <10% across surveys. Temperature, rainfall infiltration, and groundwater freshening could not explain the observed patterns. These results were corroborated by three independent lines of evidence: lateral conductivity excursions and in-well temperature records at floodplain well W2, and analytical Darcy–Archie calculations, all consistent with the predicted lateral extent and mixing fraction. River stage, amplified by the XLD release, emerged as the dominant control on two-dimensional HZ geometry. This study provides direct empirical evidence of hyporheic dynamics in a large regulated river and demonstrates that T-ERT, supported by sparse hydrological data, offers a minimally invasive and effective tool for characterizing hyporheic zones.

The relevance of this study is determined by the need for a scientifically grounded assessment of environmental risks associated with rocket launches and by the necessity of ensuring environmental safety in areas potentially affected by space activities. Comprehensive monitoring of rocket-stage impact zones and adjacent populated areas is especially important because pollutant distribution depends on natural, climatic, and spatial factors. This study assesses the environmental impact of the “Soyuz-2.1a” launch with the “Progress MS-29” cargo spacecraft in Kazakhstan using integrated field monitoring, laboratory analysis, and geoinformation methods. The work should be interpreted as a single-event environmental monitoring assessment, while historical monitoring data from 2020–2023 were used only as a retrospective comparative background for the U-25 impact area and were not included in the main BACI statistical analysis. The study covered the launch site, adjacent populated areas, and the U-25 stage impact zone. A before–after control-impact (BACI) design with distance stratification and consideration of wind direction was applied to identify post-launch changes. Measurements below the limit of detection and limit of quantification were processed using censored-data methods, including Regression on Order Statistics (ROS) and the Kaplan–Meier estimator. Spatial analysis was used to generate concentration fields, contour maps, and risk zones, revealing an anisotropic distribution of environmental stress in the downwind sector. An integrated hazard quotient (HQ) metric was applied to compare air, water, and soil conditions on a unified scale. The results indicate that the post-launch impact was localized and time-limited, with the greatest sensitivity observed in the soil component of the U-25 zone during the early post-launch period. Atmospheric air and water indicators remained within regulatory limits in populated areas. The proposed approach combines BACI monitoring, censored-data analysis, spatial modeling, and GIS-based visualization, providing a reproducible framework for the environmental assessment of rocket-stage impact areas. The practical recommendations include staged post-launch monitoring, temporary restriction of access to high-stress zones, primary reclamation of contaminated soil, and the use of WebGIS tools to support environmental decision-making.

Abstract Log jams enhance hydraulic and geomorphic diversity in river corridors. Channel‐spanning log jams induce backwatering, increase local flow heterogeneity, promote sediment deposition, and improve aquatic habitat diversity. Despite their increasing popularity in river restoration, predicting their hydraulic effects remains a challenge. We developed a model to predict dimensionless head loss through log jams for sub‐bankfull flows as traditional backwater methods are limited in variable natural channels. We developed the model from historical flume studies and tested the model application on field data from natural jams. As solid volume fraction increased, we found that dimensionless head loss also increased. Field application of our model successfully predicted head loss in naturally occurring log jams. Roughness values (Manning's and Darcy‐Weisbach ) varied but generally decreased with increased unit discharge. Our approach for determining head loss and roughness allows for better prediction and design of the localized hydraulic impacts of log jams.