Atmospheric and Oceanic Sciences

Abstract. Marine Cloud Brightening (MCB) is a suggested solar radiation management approach to mitigate global warming by increasing the reflectance of clouds through the emission of additional aerosols. While stratocumulus are considered the preferred target for MCB, the present study investigates trade-wind cumulus clouds, which may be the dominant cloud type for certain regional MCB deployments. In this study, high-resolution large-eddy simulations with detailed Lagrangian cloud microphysics are used to assess the role of different aerosol sprayer heights on the efficacy of MCB. The study indicates that surface sprayers are the optimal placement, as they facilitate the most efficient dispersion of aerosol within the boundary layer, which increases the fraction of clouds affected by the sprayed aerosols, as well as the transport of the sprayed aerosols into the developing clouds, which increases the number of cloud droplets developing from the sprayed aerosols.

To make better use of solar energy, many crops and orchards are planted in rows around the world. Accurate monitoring of the heat radiation status of row-planted crops and orchards is important for water management and yield forecasting. However, the radiation observed by a sensor is generally angle-dependent due to the complex architecture of the canopy, which must be normalized with respect to a reference direction (e.g., the nadir direction). Compared to physical models with numerous input parameters about the canopy structure and thermal property (e.g. canopy height, leaf area index, leaf emissivity and leaf temperature), semi-empirical kernel-driven models are much easier to be calibrated using only multi-angle sensor observations. However, all existing kernel-driven models have been developed for discrete and continuous canopies, without considering the specific architecture of row structure. To extend the fitting ability to row-planted canopies, a unified kernel-driven modeling framework in the thermal infrared domain has been developed in this study. It includes an isotropic kernel, a base shape kernel, a hotspot kernel and a hot belt kernel. Both base shape and hot belt kernels can represent the angular effect of row structure. Two instantiated models (i.e., Kimes-RL-Ganis and Kimes-Chen-Ganis) are proposed within this unified framework. Then, three DART (Discrete Anisotropic Radiative Transfer)-simulated datasets and three airborne-measured multi-angle datasets have been adopted to evaluate our two new models against six existing models (i.e., Vinnikov, RL, Vinnikov-RL, Vinnikov-Chen, LSF-RL and LSF-Chen). Results show RMSE reduction percentages as large as 71.0–84.9% for the three simulated scenes (1.322–1.445 K to 0.300–0.349 K, 1.105–1.263 K to 0.265–0.321 K, and 1.926–2.047 K to 0.269–0.291 K) and 42.5–61.2% for the three measured scenes (2.043–2.227 K to 0.78–0.792 K, 1.316–1.426 K to 0.614–0.649 K, and 1.580–1.654 K to 0.882–0.909 K). Given the demonstrated adaptability of these new models to both continuous and discrete canopies, they offer significant potential for advancing angular normalization of land surface temperature across diverse vegetation types in applications. This framework is well suited for the bi-directional reflectance distribution function simulation over row-planted scenes in the optical domain.

Abstract. The teleconnections of the Quasi-Biennial Oscillation are revisited using ∼65 000 years of model output contributed by four modeling centers to the Large Ensemble Single Forcing Model Intercomparison Project (LESFMIP). The large ensemble size (at least 10, and in many cases 50) allows isolation of weak signals that are usually hidden by internal variability, as well as better quantification of the role of internal variability in possible model–observation discrepancies in the magnitude of the signals. All four models simulate a Holton–Tan effect, and two of the models also simulate a subtropical downward arching wind horseshoe teleconnection that is most prominent in the Pacific sector. The magnitudes of these teleconnections are statistically indistinguishable from those observed in two of the models but not in the other two; this is a notable improvement from previous work that analyzed small ensembles. These large-scale teleconnections lead to surface temperature and precipitation anomalies over the mid-latitude continents, including an impact on western North America surface temperature which appears to have not been noted before. Furthermore, all models show impacts of the QBO on tropical surface temperature and precipitation, however the nature of these responses differs across the models due, in part, to qualitatively different interactions with El Niño. Remarkably, one of the models simulates a connection between the QBO and the Madden Julian Oscillation that mimics observations, although it remains too weak. Finally, the LESFMIP simulations allow an exploration of external forcings impacting the magnitude of teleconnections. Among these experiments, greenhouse gas forcing is seen to significantly strengthen the subtropical wind horseshoe of the QBO.

Abstract This study introduces a new generalisable methodology for evaluating the representation of boundary‐layer turbulence in sub‐kilometre numerical weather simulations through direct comparison with high‐resolution Doppler lidar and sonic anemometer measurements. We derive key boundary‐layer parameters, such as aerosol height, cloud‐base height, turbulent mixing height, sensible heat flux, and boundary‐layer classification, using an established observational algorithm and similar diagnostics from newly available high temporal resolution outputs from UKV (1.5 km) and WMV (300 m) simulations of the Unified Model. For the case study considered here, there is agreement in the temporal evolution and magnitude of bulk boundary‐layer characteristics for both simulations, including the timing of the morning transition from stable to unstable surface conditions. However, significant differences are found in both the shape and magnitude of vertical velocity variance profiles. In the UKV simulations, peak vertical velocity variance is underestimated by up to a factor of 5 compared with observations, while differences in the WMV simulations are smaller (up to a factor of 3). In the UKV simulations, turbulence is predominately unresolved (parametrized). By contrast, in the WMV over 90% of the vertical velocity variance is resolved explicitly in the middle of the boundary layer once the convective regime is established. These results demonstrate that, for this case study, parametrized turbulence is underestimated in both numerical weather prediction simulations. More broadly, this work highlights the value of high‐resolution observations in diagnosing model performance and provides a transferable evaluation framework applicable at any site with high‐frequency simulation data and Doppler lidar observations.

Abstract. This study presents the first systematic field evaluation of dock-based UAV (Uncrewed Aerial Vehicle) systems for geohazard monitoring in mountainous terrain. We assess their potential to provide reliable, high-frequency, and automated monitoring of surface changes across three different hazard scenarios: (1) a fast-moving glacier icefall (Supphellebreen, Norway), (2) an unstable rock slope (Skjøld, Norway), and (3) a post-failure landscape resulting from a catastrophic rock-ice avalanche (Blatten, Switzerland). Effective hazard management requires timely detection of displacement patterns and terrain change. To address these issues, we introduce an automated workflow integrating multitemporal UAV dock data acquisition with an end-to-end processing pipeline for displacement field generation and change detection. The results show that this workflow has the potential to provide data at centimetre-level accuracy before, during, and after hazard events, supporting both precautionary risk assessments and timely decision-making in critical phases of potential hazard evolution. Wider adoption will depend on supportive regulatory frameworks, reliable power and communication infrastructure, and sufficient expertise to ensure effective operation, maintenance, data interpretation and risk management. Overall, dock-based UAV systems represent a significant technological advancement in efficient geohazard monitoring, facilitating rapid response in critical situations, thereby contributing to increased resilience of communities living in vulnerable mountain environments.

Missing data in periodic time series can bias inference when temporal dependence is not preserved during imputation. We propose a framework that integrates the Variable Bandpass Periodic Block Bootstrap (VBPBB) with multiple imputation using Amelia II by incorporating statistically significant periodic components as auxiliary covariates. Performance was evaluated using simulated missingness in temperature time series data with seasonal structure under a Missing at Random (MAR) mechanism. Imputation accuracy was assessed using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), comparing Amelia II models with and without VBPBB-derived periodic covariates. Incorporating periodic components reduced RMSE and MAE by approximately 55%, indicating improved reconstruction of seasonal patterns. These results suggest that preserving periodic dependence can enhance imputation performance in time series with strong seasonal structure.

Abstract Sundarbans, the world’s largest contiguous mangrove ecosystem and representing shallow coastal Bay of Bengal of the Northern Ocean, faces dynamic climate variations, including ocean acidification. To delineate ocean acidification from natural pH variations, it is crucial to perform long-term measurements of multiple carbonate chemistry parameters such as pH, total alkalinity (TA), and dissolved nutrients, among others. In the present study, surface water carbonate chemistry parameters, including TA, pH, and dissolved nutrients (o-phosphate and silicate), were analysed monthly between 2014 and 2022 in three pre-defined stations, namely Stn1, Stn2, and Stn3, part of Sundarbans Biological Observatory Time Series (SBOTS) located in Sagar Island, the largest island of the Indian Sundarbans. The observed deviation from the linear TA-Salinity curve in the studied sites of SBOTS showed the influence of freshwater in modulating TA. Generalized Additive Model (GAM) revealed substantial seasonal variability in the controls on TA. During monsoon, salinity was a dominant driver of carbonate chemistry, consistent with enhanced freshwater discharge. In contrast, during the post-monsoon season, primary productivity as indicated by the relationship with Chla, dissolved silicate, was found to exert a stronger influence on TA variability. Multilinear regression (MLR) analysis of calculated pCO2 further supported these seasonal trends. Overall, the findings highlight the importance of season-specific assessments, highlighting the critical role of freshwater discharge in shaping estuarine carbonate dynamics. These insights are vital for predicting the vulnerability and response of mangrove estuaries under future climate change scenarios.

Sustainable water resource management requires actionable information on water availability, water quality, water-related hazards, and water use [...]

The Piacenzian Age (3.60–2.58 Ma) of the Pliocene Epoch was characterized by globally warmer climates, higher sea levels, and atmospheric CO 2 similar to present. Utilizing a robust dataset of 2,101 samples and over 637,000 foraminifer specimens from 77 deep-sea core sites worldwide, we document planktic foraminifer biogeography and biodiversity during the mid-Piacenzian Warm Period (mPWP). Cluster analysis and multidimensional scaling reveal five major bioregions: tropical, warm subtropical, transitional, polar, and a distinct North Atlantic polar bioregion. Each bioregion was dominated by characteristic species with well-established temperature preferences. Analyses demonstrate higher species richness and evenness in low and mid-latitudes, with increased diversity associated with periods of climatic warming and poleward expansion of warm water assemblages. The long-term stability of biogeographic patterns underscores ecological conservatism but also highlights potential vulnerability to rapid anthropogenic climate change. Our findings emphasize the critical role of planktic foraminifers in reconstructing past ocean conditions and offer valuable insights into links between planktic biogeography, climate, and socioeconomic impacts on marine ecosystems. This work advances our understanding of marine ecosystem responses to climate extremes and provides a foundation for future regional and temporal analyses of planktic foraminifer biogeography under global change scenarios.

The implementation of an ecosystem-based Maritime Spatial Planning (MSP) requires a holistic understanding of the interactions between maritime activities and marine ecosystems. Cumulative Effects Assessments (CEA) are essential for providing planners with such knowledge. However, despite multiple national and international initiatives, their application in MSP still needs to be further developed. Significant challenges remain: methodological limitations such as the integration of the scale of pressures caused by each activity or the relative nature of the risk of cumulative effects calculated by most tools. In this context, we developed enhancements to the global framework for Cumulative Effects Assessments. To better integrate the scales at which different activities affects the environment, for each group of activities and category of pressures, we attributed a magnitude value approximating the proportion of the environment affected by the activity. We then integrated these magnitudes in an existing CEA tool to improve the binary relationship between activities and pressures. We also used these weighted relationships in combination with the sensitivities of ecosystem components to define an unsuitability value for activities: the unsuitability of an activity is higher wherever ecosystem components are highly sensitive to whichever pressures the activity emits at high magnitude. We completed a CEA applying these improvements in two North Sea case studies, respectively at local and sea-basin scales. Drawing on insights from these studies, we present conclusions regarding the application of CEA for MSP at local and sea-basin scales: weighting pressures by their magnitude refines the calculation without altering the overall distribution of results. The unsuitability of activities provides relevant information to organize activities sustainably. Finally, regarding scales, the use of a finer resolution, where possible, provides accuracy and reliability of results for the CEA that is most useful in the case of geographically restricted areas.

Abstract. As critical inputs for global climate studies, watershed hydrologic modeling, and satellite soil moisture product validation, in situ soil moisture measurements are frequently compromised by sensor-derived data gaps that disrupt hydrological continuity. To overcome this challenge, we develop ST-GapFill, a novel spatiotemporal reconstruction framework integrating multi-source contextual information through two key innovations: (1) Spatial correlation-guided neighbor selection that identifies optimal auxiliary stations; (2) A long short-term memory (LSTM) network is employed to capture the complex temporal dependencies within the soil moisture time series. Validation on in-situ networks demonstrates that ST-GapFill successfully reconstructs soil moisture dynamics with preserved diurnal-phase fluctuations, achieving 0.91 correlation coefficients with ground truth under low missing-rate conditions (<50 %). Comparative analysis reveals the ST-GapFill 's statistically superior performance (RMSE reduction: 27.0 % vs IDW, 67.8 % vs ARIMA). This method establishes a robust spatiotemporal imputation paradigm for environmental sensor networks, effectively bridging observation gaps to support precision agriculture and climate change impact assessments.

Introduction Cetaceans are key indicators of ecosystem health, yet their spatial ecology in the Mediterranean Sea remains incompletely resolved, limiting the effectiveness of conservation measures and marine spatial planning. Here, we provide a basin-scale assessment of habitat suitability for eight cetacean species and identify the environmental determinants shaping their distribution using survey-based occurrence data collected from 2006 to 2016 across the Mediterranean Sea. Methods We conducted a principal component analysis and a K-means cluster analysis on oceanographic variables to identify homogeneous areas that could explain cetacean distribution. A species distribution modelling tool, MaxEnt, was used to predict and assess habitat suitability for each species using environmental and topographic variables, with a bias file to account for unequal sampling effort. Results Principal component analysis identified the gradients along which the oceanographic variables change. The K-means clustering identified nineteen clusters which were associated with oceanographic features. MaxEnt modelling indicated that chlorophyll-a, sea surface temperature (SST), and distance to coast were the main environmental predictors shaping the potential distribution of Risso’s dolphin, whereas fin whale distribution was primarily influenced by SST. Discussion Overall, the models indicated that SST, productivity gradients, and seabed topography are the main drivers of cetacean habitat preferences. Habitat suitability maps were generated for each species to illustrate potential distribution. By integrating oceanographic zoning with species distribution modeling, this study provides reproducible, policy-relevant spatial products to support the design of conservation areas, reduce risks from human activities (e.g., shipping and fisheries), and safeguard ecosystem services that underpin human well-being in the Mediterranean.

Abstract The Heat Index (HI) developed by NOAA combines air temperature ( T ) and relative humidity to characterize human heat stress for real-time public warnings. While widely used, the HI framework exhibits two limitations: (1) under dry conditions, HI values can fall below T , which may reduce the diagnosed severity of hazardous heat in operational classification; and (2) the threshold for the Extreme Danger category is so high that it is rarely reached in surface observations or ERA5 reanalysis, limiting its practical usefulness. This study revisits the HI framework for sub-daily heat assessment by: (1) evaluating an HI ≥ T convention as a pragmatic classification choice for sensitivity testing in very hot, dry environments; (2) showing that percentile-based HI thresholds yield spatially uniform exceedance frequencies that obscure regional contrasts and underrepresent intense conditions in hotter climates; and (3) subdividing the upper HI range beginning at 90 °F into 5 °F increments as a simple descriptive framework to improve resolution at the upper end of heat severity. Using hourly ERA5 data from 1961–2025, we find that applying HI ≥ T avoids reductions in assigned hazard category relative to temperature-based classification in dry environments. The proposed 5 °F subcategories preserve geographic differences, enhance resolution at higher severity, and provide a compact descriptive summary of the frequency and temporal evolution of extreme heat. These features are illustrated through three examples: the 1995 Chicago heat wave, the 2024 Death Valley extreme heat episodes, and recent heat-related emergency department visits in New York City.

Abstract. Sediment resuspension, driven by wind-wave-induced shear stress, is a key process influencing coastal water quality, biogeochemical cycles, and the transport of pollutants and organisms. The critical shear stress, τcr, is a central parameter in sediment transport models, since initiation of motion can occur when wave-induced shear stress exceeds the critical value. In this study, we implemented a high-resolution (20 m) spectral wave model to simulate near-bottom orbital velocities across the complex archipelago of southwestern Finland. We then used laboratory measurements from in situ sediment samples to determine a model for the critical shear stress that accounts for physical properties using the median grain size and the dry bulk density, and the time-varying biological variation using chlorophyll a. Our proposed model, τcrd50,ρB,Chla(t), explained 66 % of the variation of the measured critical shear stress for our data collected from three different sediment types (Mud, Sand and Mixed sediments). The modelled mean critical shear stress differed between sediment classes, with values of 0.49 N m−2 for Mud, 1.56 N m−2 for Sand, and 1.02 N m−2 for Mixed sediments. The variability in the critical shear stress around the mean values driven by a non-constant biological contribution was approximately 30 % for Mud and Sand, and approximately 50 % for Mixed sediments. Finally, we used a class-level map of the sea floor and the in situ grain size data to translate the wave model orbital velocities to near-bottom shear stresses. Based on the numerical model data, the critical shear stresses from the newly proposed model, τcr(d50,ρB,Chla(t)), were rarely exceeded based on only wave-induced motions in most of the model grid, but could, nonetheless, be exceeded to up around 10 % of the times in smaller areas. This study highlights the importance of incorporating both physical and biological factors – and their temporal dynamics – into sediment transport models to achieve reliable predictions of critical shear stresses and resuspension potential.

Generating satellite-based deforestation alerts with actionable latency requires frequent imaging, creating an imperative to use different sensors together. We introduce a simple and open-source framework called the Disturbance Index Alert System (DIAS), which is based upon transformation of imagery from different sources into an interoperable stream of Disturbance Index (DI) values. Whereas most alert systems target divergence of forested pixels from historical states, DIAS targets movement of a pixel’s Z-score position relative to the image-wide population of forest pixels along a forest-sensitive axis. This strategy provides the following practical benefits: (1) it reduces the need to process the historical archive; (2) it reduces dependence upon stable sensor calibration; (3) it allows Z-score-based DI values to be combined across sensors; and (4) it accommodates changes to the group of sensors providing measurements. We demonstrated in Madagascar that sensor integration through DIAS can provide more timely alerts than both conventional individual-sensor systems and additive combination of such systems. Across our study sites, using a commercial source of daily imaging (PlanetScope) in conjunction with imagery from public sources (Landsat, Sentinels-1 and -2) allowed high-confidence detection (false alert rate of approximately 20%) of two-thirds of deforestation occurring at 10 m reference pixels within one month; 40% were detected in that timeframe with public data alone. As commercial options for Earth observation proliferate, flexible and computationally lightweight approaches such as DIAS are needed to accommodate diverse and sometimes only loosely calibrated instruments in support of timely forest monitoring.

Accurate daily runoff prediction is essential for flood control, reservoir operation, and scientific water resources management. However, runoff processes are increasingly affected by climate change and human activities, leading to pronounced nonlinearity and nonstationarity that limit the performance of single data-driven models. This study aims to improve the reliability and hydrological credibility of daily runoff prediction by systematically evaluating recurrent neural network (RNN) structures and explicitly modeling prediction residuals. Three commonly used RNN architectures—long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional long short-term memory (BiLSTM)—are systematically compared for daily runoff prediction in the Andi Reservoir watershed under identical hydrometeorological conditions. Based on the comparative results, BiLSTM is selected as the base model to capture dominant temporal dependencies. To further address systematic prediction errors under complex hydrological conditions, a residual-learning framework is constructed by integrating BiLSTM with extreme gradient boosting (XGBoost), in which XGBoost is employed to model and correct the nonlinear residuals of BiLSTM predictions. In addition, the Shapley Additive Explanations (SHAP) method is applied to interpret the contributions of input variables and to examine the learning mechanisms of both the base model and the residual-correction stage. Results indicate that BiLSTM performs better than LSTM and GRU for daily runoff prediction and that residual correction using XGBoost further enhances prediction accuracy and robustness, particularly under nonstationary conditions and peak-flow scenarios. The contribution of this study lies in providing a systematic modeling framework that combines model comparison, residual learning, and interpretability analysis to support more reliable daily runoff prediction in complex watersheds.

Understanding how groundwater storage responds to extreme precipitation is essential for assessing aquifer resilience under climate variability. In this study, we developed a 1 km groundwater storage anomaly (GWSA) dataset for the Baiyangdian Watershed from 2002 to 2024 by downscaling GRACE observations with a Light Gradient Boosting Machine (LightGBM) model. The downscaled GWSA showed good consistency with independent hydrological datasets, including GLDAS and groundwater-level anomalies. Based on the downscaled product, we characterized long-term groundwater changes and quantified GWSA responses to extreme precipitation events (EPEs). Groundwater storage exhibited three distinct phases: rapid depletion before 2014 (−1.35 cm/yr), a slower decline during 2014–2019 (−1.04 cm/yr), and marked recovery after 2020 (+3.45 cm/yr). Spatially, GWSA generally increased from the southwest to the northeast of the watershed. Composite analysis of 11 EPEs revealed a delayed groundwater response, with the strongest signal occurring approximately two months after precipitation. Monthly effective precipitation was more closely associated with GWSA recovery than short-duration daily precipitation extremes, and the response was stronger in plains than in mountainous areas. These findings indicate that EPEs provide episodic recharge pulses, while sustained groundwater recovery depends on cumulative climatic inputs and human water-management influences.

Hydrological conditions in the Mackenzie River Basin (MRB) are becoming increasingly variable due to climate change, permafrost degradation, and cumulative industrial impacts. While scientific assessments have documented many of these trends, far less is known about how changing water levels and flow patterns affect the daily lives, livelihoods, and cultural responsibilities of Indigenous Peoples across the Basin. This paper synthesizes basin wide Indigenous Knowledge related to water level and flow variability, drawing on 31 Indigenous-led research projects. The analysis highlights shared concerns across regions, including reduced travel safety, restricted access to harvesting areas, shifting river and lake behaviour, and emotional and spiritual impacts associated with hydrological extremes. These observations align with scientific evidence of earlier breakup, prolonged low-water periods, and increased hydrological unpredictability, while also revealing social and cultural dimensions not captured through conventional monitoring. By synthesizing basin wide Indigenous Knowledge of water level and flow variability, this study provides new insight into the cumulative social ecological consequences of freshwater change in the MRB and underscores the importance of Indigenous-led research and governance in responding to accelerating hydrological variability.

Abstract The ASCE/SEI/AMS Standards Committee on Estimating Wind Speeds in Tornadoes and Other Windstorms has undertaken a long-term effort to develop a multi-method standard for improving estimations of tornado windspeeds. While these methods show promise at discriminating general tornado intensity trends among themselves, at least two significant challenges remain: (1) discriminating finer details of intensity estimates among upper-echelon-intensity tornadoes and (2) comparing across different estimation techniques. Most techniques approach effective limits at high tornado intensities that represent a lower-bound intensity estimate. Furthermore, different intensity estimation techniques are often inherently representing different windspeed variables or characteristics—e.g., the EF-Scale estimates a peak 3-s gust speed at essentially single points, treefall pattern estimation methods estimate peak quasi-instantaneous wind gusts over an area, and gust periods associated with radar measurements can vary based on the radar gate spacing and tornado wind intensity. In this study, we use a simple modified Rankine vortex model to illustrate the sensitivity of the relationship between the peak instantaneous wind gust and peak 3-s-average wind gusts associated with idealized tornadic vortices as functions of vortex intensity, size, translation speed, and ratio of inflow to rotational flow. We illustrate that all of these factors impact the instantaneous-to-3-s gust speed relationship non-linearly, even under the most idealized condition of a modified Rankine vortex. These results highlight the importance of understanding what each tornado-intensity estimation method is actually estimating and the need for improved understanding of which gust periods are most relevant for causing tornado damage to various damage indicators.

Study region The Yellow River Basin(YRB), a region in China characterized by significant water scarcity and intense human activity. Study focus Water resource value(WRV) serves as a critical signal and instrument for supporting governance decision-making. This study developed an integrated framework bridging historical insights, future trends, and practical applications, employed the fuzzy comprehensive evaluation method to quantify WRV at the city scale in the YRB and reveal its spatiotemporal characteristics, applied Geographically and Temporally Weighted Regression (GTWR) to examine the influencing factors of WRV, and further used time-series forecasting and machine learning models to predict both WRV and its determinants, ultimately constructed a zonal governance framework based on the Water Supply Index (WSI), Water Demand Index (WDI), and WRV to provide prospective governance recommendations for cities in different zones. New hydrological insights for the region The results indicated pronounced spatial heterogeneity in WRV across the YRB along both east-west and north-south directions. In the early study period, cities in the western YRB exhibited higher WRV than those in the east, whereas this pattern reversed in the later period. Meanwhile, cities in the northern YRB consistently showed higher WRV than those in the south. GTWR results revealed that supply-side factors exerted a stronger overall influence on WRV than demand-side factors, with annual precipitation, technological innovation capacity, and mean elevation ranking as the top three supply-side determinants, and industrial upgrading level, industrialization level, and social consumption level ranking as the top three demand-side determinants. Predictions indicated that WRV in approximately 60% of cities is likely to decline slightly in the future, suggesting that supply-demand tensions in some regions may be alleviated under strengthened water-saving constraints and improved utilization efficiency. Finally, a four-quadrant zonal governance framework was constructed based on the predicted WSI, WDI, and WRV. By treating WRV as both a governance signal and a governance instrument, zone-specific governance recommendations were proposed to support differentiated water resource management and high-quality development in the YRB.

Abstract Low‐level jets (LLJs)—wind speed maxima typically occurring a few hundred meters above the surface—are common off the U.S. East Coast and influence many atmospheric processes with societal importance, including cloud formation, aviation safety, and search‐and‐rescue. However, their vertical structure and frequency remain poorly quantified due to limited offshore observations. This study presents new scanning Doppler LiDAR and infrared spectroradiometer data from the 2024 summer deployment of an offshore barge during the Wind Forecast Improvement Project 3. These coupled wind and temperature profiles provide unprecedented resolution to assess LLJ behavior and model performance. LLJs occurred in over 21% of observed profiles, with a weak diurnal preference for nighttime and early morning hours and maximum winds typically near 300 m. Both ERA5 and High‐Resolution Rapid Refresh analysis underestimate jet wind speeds and misrepresent the boundary layer thermal structure. These results highlight persistent model biases and the critical need for high‐resolution offshore observations.

ABSTRACT Understanding how terrestrial primary productivity responds to climate change is essential for regional carbon budgeting and climate adaptation planning. This study investigated historical (1985–2014) and future (2015–2100) changes in Gross Primary Productivity (GPP) over India using biogeochemically coupled Earth system models (ESMs) from CMIP6, together with a comparative assessment of CMIP5 and CMIP6 simulations under high‐emission scenarios. Model projections are evaluated in the context of climatic variability, land‐cover changes and biosphere–climate interactions. Results indicate a robust increase in annual GPP over India during the historical period, with continued enhancement under the SSP5–8.5 scenario. CMIP6 projects substantially stronger future GPP increases than CMIP5, with future GPP trends up to ~2.5 times the historical trend magnitude over the country. Spatially, the largest increases occur over the Indo‐Gangetic Plain, Northeastern India and the Western Ghats region. These modelled trends are broadly consistent with observed increases in forest and crop cover. However, CMIP models generally underestimate the magnitude of GPP relative to flux tower and satellite‐based estimates. A comparison between CMIP5 and CMIP6 indicates that CMIP6 multimodel average (MMA) projections show stronger GPP trends than CMIP5, particularly after mid‐century. However, improvements in the strength of the climate–GPP correlation are not statistically significant. The enhanced GPP trends projected by CMIP6 appear to be linked to stronger precipitation increases together with coupled CO 2 fertilisation effects. Spatial attribution analyses suggest a moderate coupling between increasing rainfall and increasing GPP, particularly across semi‐arid and water‐limited ecosystems. Detrended analyses further suggest that higher rainfall years generally enhance productivity, whereas higher temperature years suppress GPP through heat and moisture stress. However, partial correlation and regression analyses reveal substantially weaker climate–GPP relationships after detrending, indicating a strong influence of shared long‐term forcing. Overall, the study demonstrates that long‐term greening over India appears to be associated with combined CO 2 fertilisation and enhanced precipitation trends. In contrast, regional rainfall anomalies and temperature‐related stress responses modulate interannual variability in productivity. These findings highlight the importance of accurately representing hydroclimatic processes and biosphere–atmosphere interactions in projecting future terrestrial productivity over monsoon‐dominated regions.

Data-driven aeration optimization is an effective approach for reducing energy consumption in wastewater treatment plants (WWTPs). However, in information-limited scenarios, newly established or emerging-market WWTPs often lack historical labels for aeration actions, making it difficult to construct high-precision surrogate models. Conventional cross-plant model deployments face severe data distribution shifts, and standard multi-objective optimization algorithms are prone to generating non-physical extrapolation errors, such as achieving compliance with “zero aeration” under low-concentration conditions. To break through inter-plant data barriers, this study proposes an intelligent aeration decision-making framework that integrates cross-domain transfer learning with physics-informed constraints. First, this study designs an adversarial network incorporating a state-action decoupling bypass. By employing a gradient reversal layer (GRL) to extract domain-invariant representations while the decoupling bypass preserves the physical sensitivity of control commands, this network achieves robust cross-plant knowledge transfer. Second, this study proposes a physics-informed multi-objective particle swarm optimization (PI-MOPSO) algorithm, which embeds the theoretical oxygen demand as a physical penalty into the fitness function, ensuring the physical reliability of the optimization decisions. Experiments demonstrate that the surrogate model restricts the prediction errors for effluent chemical oxygen demand (COD) and effluent ammonium nitrogen removal rates to within 1%. Validated by statistical tests, the improved algorithm effectively circumvents non-physical prediction biases. Its Pareto front achieves a spacing metric of 0.0027, outperforming baseline algorithms in hypervolume stability. This framework provides reliable aeration scheduling references conforming to biochemical dynamics for target WWTPs lacking historical action labels, offering a promising theoretical foundation for future practical engineering applications.

Abstract Machine learning models for microseismicity detection are often limited by the scarcity of large and high‐quality labeled data sets in many regions. To address this need, we introduce the Oklahoma Labeled AI Dataset (OKLAD), a manually curated data set compiled by the Oklahoma Geological Survey (OGS). OKLAD is designed to support studies of induced seismicity and serves as a benchmark for evaluating deep‐learning detection models in local and regional monitoring contexts. Using OKLAD, we fine‐tuned several established phase‐picking models and observed substantial improvement in local and regional detection. The best performing model achieved recalls of 91.1% for first arrival P‐ detection and 89.8% for first arrival S‐wave detection. Validating this model on continuous data in Oklahoma, we recovered 96.8% of the OGS‐cataloged events and identified 146.8% more events after associative comparison with the events reported by routine network operations. Comparable improvements were also observed when applying the best performing models to other induced seismicity settings, such as west Texas. These results establish OKLAD as a benchmark data set for induced seismicity and demonstrate the effectiveness of transfer learning for improving regional seismic monitoring. This approach provides a replicable framework for enhancing microseismicity detection in challenging environments and can be extended to other regions where generalized deep‐learning pickers may underperform.