Seasonal Wind Research Articles

Investigation of the role of southwestern Asia dust events on urban air pollution: a case study of Ahvaz, a highly polluted city

Investigating aerosol composition and particle dynamics in densely populated and polluted urban centers is crucial for understanding and managing urban air quality. Ahvaz, in southwestern Iran, consistently ranks among the most polluted cities globally, primarily due to high PM10 concentrations. This study analyzes trends in suspended particle concentrations in Ahvaz over a 12-year period (2008–2019) to identify the contributions of natural and anthropogenic sources to air pollution. Diurnal, monthly, and annual variations in PM10 and PM2.5 levels were examined, revealing key insights into the city’s pollution dynamics. Diurnal PM10 peaks around noon (232 µg/m3), mainly driven by natural dust sources, with minimal anthropogenic impact indicated by similar weekend and weekday concentrations (only 1.5% difference). Monthly analysis reveals significant dust activity in June and July (maximum PM10 concentration of 388.18 µg/m3), while higher PM2.5 levels in winter (average 54.8 µg/m3) are attributed to fossil fuel combustion. The PM2.5/PM10 ratio (mean = 0.24) highlights the dominance of coarse particles from dust events, especially in summer. The Hoffmann classification identifies 3425 dusty days in the study period, with PM10 levels notably higher due to dust sources in southern Iraq and southwestern Iran. Seasonal wind patterns, particularly Shamal winds, facilitate dust transport, corroborated by Windrose and PM10 rose data. The study underscores the need for regional dust suppression strategies in southern Iraq and southwestern Iran to mitigate air pollution in Ahvaz, highlighting the importance of regional cooperation.

Environmental aspects and their effects on blackish water lagoon management and ecosystem sustainability: A case study of Chilika Lagoon, India

The geomorphology, hydrodynamics, and ecological balance of blackish water lagoons are significantly shaped by environmental factors like wave action, tidal currents, longshore drift, and relative sea level rise. These factors also have a direct impact on the management and long-term sustainability of these lagoons. This study looks at shoreline dynamics and related environmental effects in the Chilika Lagoon in India as a representative case. Natural episodic events like waves, tides, currents, and global sea level rise are all responsible for the Chilika Lagoon's dynamic coastal environment's constant reshaping. This study specifically examines shoreline lateral displacement, recognising that volumetric sediment changes are not captured by this method. Because of the high-energy interactions between wave action, tidal currents, and longshore drift along the coastal front, lateral movement of the shoreline can result in either accretion or erosion. This study evaluates the shoreline configuration changes of Chilika Lagoon and surrounding coastal areas over a 42-year period using the Digital Shoreline Analysis System (DSAS) of the U.S. Geological Survey. The analysis shows that, with some notable exceptions in certain depositional zones like Palibandha, the western flank of the new mouth inlet, the section from the old mouth to Harchandi Temple, and the stretch from Pentukota to Konark beach, the majority of the region shows net erosional trends. Shoreline retreat can reach 40.29 meters in the northeastern lagoon sector, which is closest to the present new mouth and shows the highest rates of erosion. A secondary line of barrier bars is now visible due to the frontal barrier spit breaching, which is the cause of this erosion. At the Rushikulya River's mouth, a comparable erosional pattern is visible. However, with a maximum advancement of 11.69 meters, the western side of the new mouth inlet exhibits notable accretion. The dynamic behaviors of the lagoonal inlets affects these morphological changes, with longshore drift and wave energy concentration becoming more dominant due to the Rushikulya River's reduced sediment supply. The foreshores of the Chilika Sand Spits and the Puri township coast exhibit moderate shoreline changes. These are mostly caused by high water levels during the monsoon season and wave breakers created by seasonal winds. Coastal retreat and sediment loss have been made worse by the lack of notable post-event recovery after the effects of cyclones Phailin and Hudhud. Gornitz et al. (1994) developed the Coastal Vulnerability Index (CVI) method to measure coastal vulnerability. This method incorporates a number of factors, such as shoreline change rate, mean tidal range, mean wave height, slope, relative sea level rise, and coastal geomorphology. Relative sea level rise stands out as the most important factor influencing vulnerability among these. A relative sea level rise of roughly 0.77 mm/year is indicated by data from the Paradip station (NOAA-PSMSL, 2015). The study area's CVI values, which are based on these parameters, show notable spatial variability in coastal risk throughout the Chilika region, ranging from 2.64 (very low vulnerability) to 21.45 (high vulnerability).

Hydro-Morphological Conditions of the Coastline Post-Construction of Sea Dikes in Pekalongan, Central Java

Pekalongan is one of the cities in Central Java affected by tidal flooding. Tidal floods occur due to sea level rise and land subsidence in Pekalongan, so far the tidal floods have had an impact on the community. Infrastructure damage and disruption of community activities are examples of the impact of tidal floods in Pekalongan. One of the government policies in overcoming tidal floods is to build sea walls along the coast of Pekalongan. This policy will certainly have an impact on the morphological pattern of the coastal area. This study was conducted to determine the pattern of currents, waves, and sediment characteristics on the coast of Pekalongan after the sea wall. The analysis was conducted using current and wave modeling of delft-3d application. Sediment analysis was conducted by analyzing sediment grain size (Granulometry). The results show that currents and waves are influenced by seasonal winds that occur in Pekalongan. The dominant season in coastal Pekalongan occurs between December - January (West Season). Current conditions in the west season move with a speed of 0.2 -0.3 m/set moving eastward. West season wave conditions move with a height of 7.2 meters with a peak period of 7.2 seconds. While the sediment characteristics in Pekalongan are dominated by mud - sandy. From these sediment characteristics, it can be concluded that the influence of currents and waves of the west season can cause the distribution of sediments towards the east.

Comparison of Surface Tidal and Mean Currents in the Gulf of the Farallones between HF Radar Observations and Operational Model Simulations

Abstract The Gulf of the Farallones, an ecologically significant region off northern California, is characterized by complex ocean currents influenced by seasonal winds and tidal forces. However, the performance of the operational models in the bay remains unclear. This study compares surface tidal and mean currents derived from high-frequency (HF) radar observations (2012–18) with outputs from three operational ocean models: the Oregon State University (OSU) tidal model, the Copernicus Marine Environment Monitoring Service (CMEMS), and the Hybrid Coordinate Ocean Model (HYCOM). The spatial patterns of tidal currents generally show good overall agreement between the tidal model and the observations, with notable differences near the mouth of San Francisco Bay and over the continental slope. The observations reveal three distinct seasonal flow patterns: strong equatorward currents during upwelling, weak currents during relaxation, and poleward flows during storm periods. The CMEMS agrees well with the observations, particularly over the shelf regions during upwelling and relaxation periods, while HYCOM tends to overestimate flow velocities with larger misfits. These findings highlight the potential of integrating radar data and operational ocean models to enhance our understanding of coastal oceanography. Significance Statement This study presents a comprehensive comparison between surface tidal and mean currents observed through high-frequency (HF) radar and operational model simulations in the Gulf of the Farallones. The findings reveal the agreements and discrepancies between observed and simulated currents. This research not only enhances our understanding of coastal circulation patterns but also offers valuable insights into the potential improvements needed for operational models, ultimately contributing to more accurate predictions of oceanographic conditions essential for navigation, environmental monitoring, and resource management.

Chapter 6. Albertine Rift marginal lacustrine environments

Marginal lacustrine depositional environments in the Albertine Rift are highly sensitive to short-term, climatically controlled lake level fluctuations. As such, they act as one of the prime recorders of palaeoshoreline transgression or regression in rift-fill stratigraphy. In both Lakes Edward and Albert, modern-day seasonal winds blow parallel with the long axis of the rift basin, developing wave-dominated shorelines at the end of a lake, but sheltered rift flanks display coastal features associated with longshore drift. The different types of shorelines around Albertine Rift lakes today produce characteristic geomorphological features and associated lithofacies, with key sedimentary structures and fauna and flora which can include hippopotamus, elephant, crocodile and fish remains. These can be identified in Pleistocene–Holocene onshore sedimentary successions around the Lake Edward and Lake Albert rift basins, providing a record of glacial–interglacial climatic cyclicity in the Equatorial Tropics of East Africa. Northern Hemisphere interglacials result in a wet phase and lake level rise in the Albertine Rift, with marginal lacustrine shorelines back-stepping landward. Whereas high-latitude glacials cause corresponding periods of aridification and rift lake level fall, with palaeoshorelines prograding out towards the centre of the basin.

Determining the trend behavior of the wind turbine powertrain using mechanical vibration and seasonal wind data

Determining the trend behavior of the wind turbine powertrain using mechanical vibration and seasonal wind data

Large-scale forcing of principal near-surface wind speed variability patterns in the Arctic since 1979

Abstract The ERA5 reanalysis dataset is used to characterize the principal patterns of annual and seasonal near-surface wind speeds in the Arctic from 1979 to 2023 by the empirical orthogonal function (EOF) method, together with their relationship with large-scale atmospheric circulation. The leading EOF (EOF1) accounts for about 14%–22% of annual and seasonal wind speed variability, and is composed of widely positive anomalies across most of the Arctic Ocean largely influenced by the positive Arctic Oscillation. Seasonally, the positive anomalies shift from the central Arctic Ocean in summer toward the Greenland and Norwegian Seas in winter, driven by the North Atlantic Oscillation (NAO). The second EOF (EOF2) explains 9.5% of annual wind speed changes and consists of a meridional dipole structure, i.e. positive anomalies over the Russian Arctic seas and negative anomalies over the Greenland and Norway Seas, mainly driven by the Arctic dipole and NAO. The time series of EOF2 shows a decadal switch from negative to positive anomalies at the beginning of the 21st century, related to the phase transition of the Pacific Decadal Oscillation and the Atlantic Multidecadal Oscillation.

Climate and en route thermals affect long‐term population dynamics of soaring birds

Abstract Migration allows animals to capitalize on the availability of seasonal food resources or to avoid inclement weather that may jeopardize survival. However, migratory birds are challenged physiologically and ecologically by varying climatic conditions within a migratory annual cycle. Studies of the effects of seasonal climate on age‐specific survival of avian migrants are indispensable for understanding the fitness benefits and costs of migration. We investigated the effects of seasonal climatic and wind conditions on stage‐specific annual survival of soaring American white pelican (Pelecanus erythrorhynchos) (hereafter, pelicans) using Bayesian integrated population models and 55 years of band recovery‐resighting data and nest counts. We tested two hypotheses: that increases in winter temperature and precipitation would enhance annual survival of pelicans (Hypothesis 1) and that favorable winds and thermal updraft en route would increase survival of immature pelicans (Hypothesis 2). We calculated wind speed and thermal updraft along migration corridors, which were estimated with GPS tracking data during seasonal migration. Increased winter precipitation on the non‐breeding grounds enhanced annual survival of yearling and adult pelicans, supporting our Hypothesis 1. Increased summer breeding‐ground precipitation improved annual survival of adult pelicans but reduced hatch‐year survival with increasing Pacific Decadal Oscillation (PDO) index. Furthermore, increases in vertical velocity during autumn migration enhanced hatch‐year pelican survival, supporting Hypothesis 2. Uplift winds and thermals are the primary energy sources for soaring bird flight, particularly on the first migration trip of immature birds. Our findings demonstrated the joint signals of high‐frequency local climate and low‐frequency PDO on pelican population dynamics.

ENSO‐Driven Seasonal Variability in Near‐Surface Wind Speed and Wind Power Potential Across China

Abstract Seasonal variations in near‐surface wind speed (NSWS) significantly impact wind energy production, yet the role of the El Niño–Southern Oscillation (ENSO) in shaping these variations remains insufficiently explored. This study investigates ENSO‐driven seasonal NSWS variability across China and their implications for wind power density (WPD). We demonstrate that ENSO exerts strongly season‐dependent impacts on WPD in subregions, spanning the ENSO‐developing summer to the decaying summer. During these active ENSO phases, 15%–50% of stations within that regions exhibit WPD anomalies exceeding ±10% relative to their seasonal climatology in response to a 1°C sea surface temperature change in the central‐eastern Pacific during the ENSO peak winter. Furthermore, 850 hPa wind speeds show coherent variations with NSWS, indicating a strong dynamic connection between the lower troposphere and surface. These findings deepen our understanding of ENSO's influence in driving seasonal wind resources, providing actionable insights for regional wind energy management and strategic resource planning.

Marine emissions and trade winds control the atmospheric nitrous oxide in the Galapagos Islands

Abstract. Nitrous oxide (N2O) is a potent greenhouse gas emitted by oceanic and terrestrial sources, and its biogeochemical cycle is influenced by both natural processes and anthropogenic activities. Current atmospheric N2O monitoring networks, including tall-tower and flask measurements, often overlook major marine hotspots, such as the eastern tropical Pacific Ocean. We present the first 15 months of high-frequency continuous measurements of N2O and carbon monoxide from the newly established Galapagos Emissions Monitoring Station in the region. Over this period, N2O mole fractions vary by approximately 5 ppb, influenced by seasonal trade winds, local anthropogenic emissions, and air masses transported from marine N2O emission hotspots. Notably, between February and April 2024, we observe high variability linked to the southward shift of the intertropical convergence zone and weakened trade winds over the Galapagos Islands. Increased variability during this period is driven by stagnant local winds, which accumulate emissions, and the mixing of air masses with different N2O contents from the Northern Hemisphere and the Southern Hemisphere. The remaining variability is primarily due to differences in air mass transport and heterogeneity in surface fluxes from the eastern tropical Pacific. Air masses passing over the Peruvian and Chilean upwelling systems – key sources of oceanic N2O efflux – show markedly higher N2O mole fractions at the station.

Interpretable machine learning for coastal wind prediction: Integrating SHAP analysis and seasonal trends

Accurate wind speed prediction plays an important role in developing effective coastal management strategies and risk assessments, especially in coastal region managements to reduce erosion damage. In offshore wind energy, precise forecasts optimize wind farm layout and operations, maximizing energy yield and minimizing downtime. Additionally, accurate wind speed forecasts significantly improve maritime transportation safety by predicting hazardous conditions. Understanding wind patterns is also important for coastal ecosystem management and safer navigation activities. However, accurate wind speed prediction in dynamic coastal environments remains challenging due to (1) limited applications of robust machine learning (ML) models tailored to coastal meteorological complexity, (2) insufficient integration of interpretable feature analysis with predictive modeling for actionable insights, and (3) gaps in understanding how seasonal and diurnal wind patterns influence model performance in understudied regions like tropical Queensland. This study focuses on Abbot Point, Queensland, Australia, using meteorological data collected hourly from January 1 to December 31, 2023 (Latitude: -19.9496; Longitude: 148.0482). It evaluates three machine ML models—Linear Regression (LR), Decision Tree Regressor (DT), and Random Forest (RF)—to identify the most reliable approach for wind speed forecasting. The dataset includes wind direction, air temperature, relative humidity, precipitation, and barometric pressure as feature variables, with wind speed as the target variable. Novel integration of SHapley Additive exPlanations (SHAP) analysis and seasonal decomposition addresses interpretability gaps, while rigorous validation across training (70%), testing (15%), and validation (15%) datasets ensures model robustness. The RF model consistently outperformed others across training, validation, and test datasets, achieving the lowest mean square error (MSE: Train 0.183, Validation 0.875, Test 0.803), highest R2 (Train 0.966, Validation 0.831, Test 0.844), and superior Nash–Sutcliffe Efficiency (NSE: Train 0.96, Validation 0.83, Test 0.84). These results reflect the model's robust ability to capture complex relationships in the data. In contrast, LR and DT exhibited moderate accuracy, with higher MSE and lower NSE values, struggling particularly with consistency and extreme values. Complementary analyses, including wind rose plots and time series of wind speed, relative humidity, and barometric pressure, revealed high-risk periods characterized by strong winds (> 10 m/s), high humidity (> 90%), and low barometric pressure (< 1000 hPa). Seasonal analysis revealed spring/summer peaks in hazardous winds (> 10 m/s), with diurnal cycles (24-h periodicity) significantly influencing prediction accuracy—a pattern underemphasized in prior coastal ML studies. This study bridges critical gaps by demonstrating how interpretable ML enhances coastal wind prediction through: a) quantitative validation of RFR's superiority over traditional models in handling coastal meteorological variability, b) SHAP-driven identification of dominant predictors (wind direction, pressure) for targeted monitoring, c) Seasonal-temporal analysis framework for site-specific risk mitigation strategies. These findings confirm the interactions between meteorological variables that intensify storm risks and coastal hazards. Key insights include the dominant influence of southeast and south-southwest winds (100°–200°) and the critical role of barometric pressure in driving extreme wind events. Also, findings enable improved storm surge modeling and early warning systems by providing 6-h wind forecasts with 84% accuracy, directly informing coastal defense alignment with dominant wind-driven erosion patterns. This approach addresses the critical need for ML applications that combine predictive power with operational interpretability in coastal management contexts. The integration of ML models with detailed meteorological patterns supports the identification of high-risk periods, enabling targeted interventions such as strengthening coastal defenses and issuing early warnings. This study underscores the value of ML techniques, particularly RF, in enhancing predictive frameworks for coastal risk management and promoting sustainable, resilient coastal environments.

Data-Driven Selection of the Most Cost-Effective Turbine for Basrah Governorate Using Advanced Software Tools

Determining the optimal location for wind turbine installation is crucial in developing wind energy projects. This study employs a data-driven methodology to examine wind energy potential, patterns, and distribution accurately. Advanced analytical software, such as WAsP and Windographer, was employed to illustrate wind distributions and calculate Weibull parameters, in addition to seasonal and monthly wind patterns. A simulation was performed to assess various wind turbines and identify the most suitable turbine for the research region. The results indicated that the average wind speed was 5.74 m/s, and the power density was 190 watts/m² at a height of 50 m. The summer months of June, July, and August demonstrated the highest wind velocities compared to the other months of the year. The Vestas V100 wind turbine demonstrates the highest capacity factor of 27.3%, owing to its large rotor diameter, which facilitates superior wind capture, thereby augmenting power output and energy generation capability. This study provides a conclusive viewpoint for those seeking to promote wind energy projects in Basrah Governorate, together with decision-makers and stakeholders.

ĐÁNH GIÁ SỰ PHÁT TÁN CỦA NGUỒN GIỐNG THEO MÙA TỪ CÁC VÙNG BIỂN SINH SẢN PHÍA ĐÔNG NAM VIỆT NAM

SEASONAL PATTERNS OF SEED DISPERSAL IN SOUTHEAST VIETNAM SEA A comparative analysis of flow velocity and seed dispersal patterns in the Southeast Vietnam Sea revealed significant seasonal variations. During the southwest monsoon, flow velocities were substantially higher than those observed during the northeast monsoon. Notably, areas with flow velocities ranging from 0.6 to 1.0 m/s constituted approximately one-third of the simulated region. To gain deeper insights into seed source dispersal dynamics, we employed Delft3D modeling software to simulate continuous seed reproduction over seven-day periods during both the northeast (November-December) and southwest (July-August) monsoons. Our findings underscore the profound influence of wind season and reproductive cycles on seed dispersal patterns. In both seasons, seeds were predominantly dispersed southward and southwestward. However, the density and dispersal range of seeds in the surface layer were generally lower than those in the middle and bottom layers. Over time, the dispersal trend stabilized after approximately 24 days. Importantly, the dispersal range of seeds during both monsoons could extend several hundred kilometers from their original location.

Pacific Meridional Mode Implicated as a Prime Driver of Decadal Summer Temperature Variation over Taiwan

Abstract The Pacific meridional mode (PMM) is a climate phenomenon in the eastern Pacific, characterized by strong wind–sea surface temperature (SST) interactions. During its positive phase, an atypical north–south SST gradient develops in the northeastern subtropical Pacific, leading to significant global teleconnections, which refer to the mutual influence or correlation between atmospheric or oceanic changes in two distant regions of Earth. This study examines the relationship between historical summer temperature data for Taiwan (June–August) and PMM indices over a 59-yr period (1960–2018), revealing a significant correlation between the two. Regressed large-scale circulations indicate the eastward propagation of a wave train across the northern Pacific into East Asia, inducing anticyclonic circulations near southeastern China and Taiwan, accompanied by subsidence, which refers to the vertical downward movement of air masses in the atmosphere, typically associated with high pressure systems and dry weather conditions, that stabilize the atmosphere. This stabilization reduces the influence of seasonal southwesterly winds while increasing shortwave radiation at the surface, thereby raising surface temperatures in Taiwan. Our findings are further supported by linear baroclinic model (LBM) experiments featuring PMM-like heating forcing and by large-scale responses to PMM-related SST patterns simulated in CMIP6 historical runs. While these results suggest that the PMM could be a valuable tool for predicting Taiwan’s summer climate on decadal time scales, we also observe model uncertainties in simulating the teleconnection patterns of PMM-related SSTs in East Asia, including Taiwan’s summer temperature response. These findings underscore the need for further investigation into the complex interplay between PMM circulation responses, seasonal monsoons, and other components of climate systems simulated in climate models.

Gathering Cities, Speculating Wind: Listening in Dry Season Gulu

ABSTRACTWhat is the place of the wind in analyses of the city? In late 2016, and into 2017, forceful dry season winds in Gulu, Uganda, provoked frequent commentary. This short ethnographic essay endeavors to think with these winds (of weather and breath) and to speculate their implications for “the city” vis‐à‐vis its lived, sensory geographies. Using listening methodologies drawn from ethnography, sonic praxis, and Black studies, I consider windy encounters as gatherings of the city in audible movements of air. From these windy attunements emerges a city unsettled by changing seasonal winds, a city that endures, and a city living with wind as relational presence. For each, performed displacements of air enact wind as a situating force amidst unsteady linkages of geography, climate, and urban development. Can winds attune us to ways of unsettling human geographies of enclosure?

Drivers of anomalous wintertime temperature around Japan revealed by neural networks and dynamical mechanisms of the Indian Ocean Dipole influence

A neural network that predicts the sign of wintertime temperature anomalies around Japan from global sea surface temperature and 500 hPa geopotential height anomalies in the preceding season is first constructed to obtain its optimal inputs. It is revealed that prior to early winter warm events, a negative Western Pacific (WP)-like pattern, a positive Indian Ocean Dipole (IOD)-like pattern, and a weak positive El Niño/Southern Oscillation-like pattern are observed. On the other hand, a negative WP-like pattern along with a positive Arctic Oscillation-like pattern and a positive Pacific-North American-like pattern are seen prior to late winter warm events. Since the influence of the IOD-like pattern on wintertime temperature around Japan, which is statistically identified by the neural network, has not been systematically examined, its physical mechanisms are examined in detail. Dynamical analyses reveal that during the positive (negative) IOD in early winter, Rossby wave sources are generated over the Arabian Peninsula (the Middle East) and East Asia, and Rossby waves are emitted towards the North Pacific. An anomalous anticyclone (cyclone) centered over the northwestern Pacific in the upper troposphere strengthens (weakens) westerly winds over the Sea of Okhotsk, and that in the lower troposphere weakens (strengthens) seasonal northwesterly winds, resulting in anomalously warm (cold) winters around Japan.

Seasonal Source Water Changes and Winds Contribute to the Development of Hypoxia in St Helena Bay Within the Southern Benguela Upwelling System

Abstract St Helena Bay (SHB), a retentive zone in the productive southern Benguela Upwelling System off western South Africa, experiences seasonal hypoxia and episodic anoxic events that threaten local fisheries. To understand the drivers of oxygen variability in SHB, we queried 25 years of dissolved oxygen (DO) observations alongside high‐resolution wind and hydrographic data, and dynamical data from a high‐resolution model. At 70 m in SHB (mid‐bay), upwelling‐favorable winds in spring drove replenishment of cold, oxygenated water. Hypoxia developed in summer, becoming most severe in autumn. Bottom waters in autumn were replenished with warmer, less oxygenated water than in spring—suggesting a seasonal change in source waters upwelled into the bay. Downwelling and deep mixing in winter ventilated mid‐bay bottom waters, which reverted to hypoxic conditions during wind relaxations and reversals. In the nearshore (20 m), hypoxia occurred specifically during periods of upwelling‐favorable wind stress and was most severe in autumn. Using a statistical model, we extended basic hydrographic observations to nitrate and DO concentrations and developed metrics to identify the accumulation of excess nutrients on the shelf and nitrogen‐loss to denitrification, both of which were most prominent in autumn. A correspondence of the biogeochemical properties of hypoxic waters at 20 m to those at 70 m implicates the latter as the source waters upwelled inshore in autumn. We conclude that wind‐driven upwelling drives the replenishment of respired bottom waters in SHB with oxygenated waters, noting that less‐oxygenated water is imported later in the upwelling season, which exacerbates hypoxia.

Isotopic fractionation of gaseous mercury in a large industrial city: Spatio-temporal variations and source apportionment.

Isotopic fractionation of gaseous mercury in a large industrial city: Spatio-temporal variations and source apportionment.

Land management and conservation of the habitat of Bruguiera hainesii C.G. Rogers in Vietnam

Comprehending the bidirectional interactions between ecosystems and plant communities is essential for ensuring the conservation and sustainable development of Bruguiera hainesii. The study identified nine accompanying plant species within a range of 1.2-5.5 m, including five frequently encountered species and four commonly found ones. The research site is influenced by two distinct wind seasons: the Northeast monsoon (November to April) and the Southwest monsoon during the rainy season, with an average wind speed of 1.77 m/s. Correspondingly, the wave regime alternates with these seasons, featuring opposing directions. During the Northeast monsoon, wave heights average 2-2.5 m, while during the Southwest monsoon, they decrease to 1.5-1.7 m. The tidal regime in Con Dao is mixed semi-diurnal, with tidal ranges of 3-4 m at high tide and 1.5-2 m at low tide. Tidal currents primarily drive the flow regime, with dominant Northeast flow during early summer and seasonal shifts between winter and summer. Flow velocities range from 0.15 to 0.92 m/s, influenced by the Manning roughness coefficient. Sediment dynamics reveal that increased settling velocities correlate with higher suspended sediment concentrations, with total suspended sediment at point P1 approximately 70% greater than at point P2. These findings elucidate the seasonal and hourly dynamics of tidal waves and sediment transport, offering critical insights to support the effective conservation of B. hainesii.

Skilful seasonal forecasts of wind energy generation in India during the western summer monsoon

Abstract India’s ambitious climate goals include a significant role for wind energy, with plans for a nearly threefold expansion of the existing wind fleet within the next decade. At greater levels of wind deployment, the increased likelihood of extended periods of generation surplus and deficit presents a challenge for managing power supply. It is essential to characterise and predict how this energy source performs within India’s monsoon climate to ensure the reliable operation of the electricity system. This study demonstrates, for the first time, how large-scale atmospheric variables are related to seasonal wind energy generation anomalies in India during boreal summer. Furthermore, an operational seasonal forecasting system is shown to skilfully predict the atmospheric predictor variables at a lead time of 1–4 months, indicating an ability to forecast summer wind energy generation at the country and regional level in India. The explanatory power of the chosen atmospheric predictor variables remains high under the near-term planned expansion of the Indian wind fleet. These findings demonstrate the potential utility of seasonal forecast information for electricity system management in India.