Weather Research Articles

Youth suicidality risk relative to ambient temperature and heatwaves across climate zones: A time series analysis of emergency department presentations in New South Wales, Australia.

Youth suicidality prevalence continues to rise alongside hot weather severity. Links between these two variables are underexplored. We examined associations between daily temperature and emergency department suicidality presentations by young people. We assessed these associations for five regions covering New South Wales as determined by 'climate zone' and analysed for heatwave effects as well as based on demographic subgroups. Daily emergency department presentations for suicidality by people aged 12-24 years across New South Wales, Australia, during warmer months (November to March) from 2012 to 2019 were examined in relation to daily mean temperature and heatwaves (⩾3 consecutive days ⩾ 95th percentile of long-term daily mean temperature) and by climate zone, using a generalised additive model with negative binomial distribution. Risks for age- and sex-based subgroups were also calculated. New South Wales youth suicidality presentation rates were significantly higher on hotter days. For every 1°C rise above average daily mean temperature, youth suicidality presentations to New South Wales emergency departments increased by 1.3%. Heatwaves did not increase presentation rates beyond single-day daily mean temperature effects. These findings were predominantly replicated across climate zones and demographic subgroups, though the association between suicidality and ambient temperature was weaker in coastal regions including Eastern Sydney. There is a positive linear association between ambient temperature and youth suicidality presentations to emergency departments. Risks are increased on single hot days, not only during heatwaves. Public health, broader societal approaches to heat and health system planning should consider impacts on youth suicidality of predicted increases in hot weather severity and frequency.

Precision Agriculture using ML for Soil and Weather Prediction

Agriculture is the backbone of many economies and plays a critical role in global food security. However, with growing challenges posed by climate change, erratic weather patterns, and soil degradation, the need for precise and predictive techniques in agriculture has never been more urgent. This paper focuses on the application of machine learning (ML) techniques in soil fertility prediction and weather forecasting, two critical components that can significantly impact agricultural productivity. By analysing soil properties, such as moisture, pH, and nutrient levels, and combining them with accurate weather predictions, our system aims to help farmers make informed decisions regarding crop selection, irrigation scheduling, and pest control. Leveraging algorithms like Naive Bayes for soil classification and Long Short-Term Memory (LSTM) networks for weather prediction, we provide a comprehensive solution for precision agriculture. This system not only enhances productivity but also promotes sustainable agricultural practices by optimizing resource use and reducing wastage

A comprehensive review of climate information systems in the arid and semi-arid lands (ASALs) of Kenya for enhanced decision-making process

The reality of climate change has had far-reaching effects on climate and weather patterns globally. High carbon emissions have resulted in wildfires causing migration and death from natural disasters such as droughts, floods, and strong winds. Climate change has also led to reduced crop and livestock farming yields, and worsening incidences of hunger and famine. The use of climate information systems (CIS) in the Arid and Semi-Arid regions of Kenya has not been extensively reviewed. This study reviews the use of CIS in Kenya's Arid and Semi-Arid Lands (ASALs) to improve decision-making and adapt to climate change. We reviewed past and present research on Kenya's ASAL counties’ smallholder farmers’ CIS use. National and international attempts to develop early warning systems through CIS to help stakeholders and end users make farm-level decisions were reviewed. Kenya leads Eastern African countries in emphasizing CIS to reduce climate change's effects on sustainable agriculture. In ASAL countries, the gap between CIS information and its applicability for smallholder farmers’ farm-level decisions, low finance, weak infrastructure, and limited capability are major issues. Limited indigenous knowledge integration and the absence of regular updates and end-user co-development were also noted. Despite these obstacles, CIS can improve decision-making, catastrophe risk management, and socioeconomic growth in these places. Tailored CIS solutions are essential to address the specific needs of smallholder farmers in ASAL regions. Improved coordination and leveraging newer technologies are crucial for effective CIS implementation.

Nested Sentiment Analysis for ESG Impact: Leveraging FinBERT to Predict Market Dynamics Based on Eco-Friendly and Non-Eco-Friendly Product Perceptions with Explainable AI

In the current era, the environmental component of ESG is recognized as a major driver due to the pressing challenges posed by climate change, population growth, global warming, and shifting weather patterns. The environment must be considered a critical factor, and as evidenced by existing research, it is regarded as the dominant component within ESG. In this study, the ESG score is derived primarily from the environmental score. The increasing importance of the environmental, social, and governance (ESG) factors in financial markets, along with the growing need for sentiment analysis in sustainability, has necessitated the development of advanced sentiment analysis techniques. A predictive model has been introduced utilizing a nested sentiment analysis framework, which classifies sentiments towards eco-friendly and non-eco-friendly products, as well as positive and negative sentiments, using FinBERT. The model has been optimized with the AdamW optimizer, L2 regularization, and dropout to assess how sentiments related to these product types influence ESG metrics. The “black-box” nature of the model has been addressed through the application of explainable AI (XAI) to enhance its interpretability. The model demonstrated an accuracy of 91.76% in predicting ESG scores and 99% in sentiment classification. The integration of XAI improves the transparency of the model’s predictions, making it a valuable tool for decision-making in making sustainable investments. This research is aligned with the United Nations’ Sustainable Development Goals (SDG 12 and SDG 13), contributing to the promotion of sustainable practices and fostering improved market dynamics.

The impact of weather patterns on inter-annual crop yield variability

Inter-annual variations in crop production have significant implications for global food security, economic stability, and environmental sustainability. Existing crop yield prediction models primarily using meteorological variables may not adequately encapsulate the full breadth of weather influences on crop development processes, such as compound or extreme events. Incorporating weather patterns into crop models could provide a more comprehensive understanding of the environmental conditions affecting growth, enabling more accurate and earlier yield predictions.Our study examines 30 distinct UK Met Office weather patterns (MO30) based on mean sea level pressure. We investigate their association with weather conditions that limit winter wheat yield in the UK (1990–2020). Blocked, negative North Atlantic Oscillation (NAO) patterns create the highest risk of temperatures that are below optimal for crop yield. However, the connection between weather patterns and yield is complex, with differing effects at a regional scale and even at which point in the growth cycle they appear. It was found that anticyclonic weather patterns during sowing, emergence, vernalisation, anthesis, and grain filling exhibit a relationship with good crop yields with a Spearman correlation coefficient of up to 0.55 for a single weather pattern (WP3 during vernalisation in South East England), whilst cyclonic patterns can help during the terminal spikelet phenological phase. The strongest positive correlations were during sowing, emergence, and vernalisation, whilst the largest negatives were observed in anthesis and grain filling.The potential of combining weather patterns with existing crop simulation models to produce earlier and more accurate yield predictions is shown. This would enable effective crop management and climate mitigation strategies, critical to strengthening food security. Projected changes in weather pattern occurrences in the late 21st century will likely reduce crop yields. This is due to increased cyclonic weather patterns, which bring warmer, wetter conditions during the wheat's vernalisation stage, followed by warmer, drier conditions during the anthesis and grain-filling phases.

Comparative study on slaughter performance, meat quality, and rumen microbiota of Hainan Dong goat and its hybrid with Nubian goat

IntroductionThe Hainan Dong goat (DG) is a local meat breed widely raised in Hainan, China because of its good adaptability to local hot and humid weather. However, the growth rate of these DG is much slower than that of commercialised breeds improved in European countries, resulting in poor carcase characteristics, including smaller slaughter weight and carcase weight, which have become increasingly prominent. In recent decades, Nubian goats have been continuously imported into China to improve the production performance of local breeds.MethodsIn this study, the effects of breed on growth performance, carcase and meat quality, and ruminal microbiota were analysed in 40 goats, including 20 DGs and 20 hybrid F3 offspring generated by crossing the DG and Nubian hybrids (NH). All the goats were averagely aged 90 days and weighed at 11 ± 1.34 kg. They were assigned to two treatments, with three replicates per treatment. The two groups were fed the same diet for 90 days before slaughter.ResultsThe results showed that the average daily gain, F/G ratio, slaughter weight, and carcase weight of the NH group were higher than those of the DG group (p < 0.05). However, tube circumference, meat-to-bone ratio, and eye muscle pH were lower in the NH group than in the DG group (p < 0.05). The NH group showed a smaller fibre crosssectional area and fibre diameter, but a larger fibre density than the DG group (p < 0.05). Bacteroidetes and Firmicutes were the most dominant phyla in the two groups; however, the two breeds had different ruminal microbial communities.DiscussionIn the present study, the differences in growth performance between two groups of goats under the same feeding environment and feed conditions were compared. The correlation between feed sources and rumen flora has been demonstrated, and the results of this study show that the same diet has similar effects on rumen microorganisms, which in turn have related effects on growth and production performance.ConclusionIn summary, hybrids can improve the growth and slaughter performance of local breeds, which may be related to changes in the rumen microorganisms. This study revealed that crossbreeding of Nubian goats with Dong goats has the potential to be used in a wide range of applications owing to its effectiveness in increasing production efficiency.

Enhanced composite controller for PV/PMSG/PEMFC and BESS‐based DC microgrids voltage regulation: Integrating integral terminal sliding mode controller and recursive backstepping controller

AbstractThe variability of renewable energy sources due to weather patterns often leads to a mismatch between power generation and consumption within microgrids (MGs). This challenge is exacerbated when integrating bio‐renewable units, complicating stability maintenance in MGs. This research work introduces a novel solution to address this issue: a composite controller merging an integral terminal sliding mode controller with a recursive backstepping controller for direct current MGs (DCMGs). The proposed DCMG incorporates solar photovoltaic units, wind farms based on permanent magnet synchronous generators, proton exchange membrane fuel cells fuelled by hydrogen gas, an electrolyser, battery energy storage systems, and DC loads. First, a comprehensive mathematical model for the components within the DCMG is developed to design the proposed composite controller. This controller not only overcomes the inherent limitations and convergence issues of conventional SMCs but also ensures stable DC‐bus voltage and maintains power balance across various operational conditions. Moreover, a fuzzy logic‐based energy management system is introduced to regulate power flow, considering factors like battery state of charge and renewable energy sources' total power output. The control Lyapunov function confirms the DCMG system's asymptotic stability. Finally, the proposed controller's effectiveness is validated through simulations on both MATLAB/Simulink and Arduino Mega 2650 processor‐in‐the‐loop platforms under various operational conditions. In both platforms, the proposed controller surpasses an existing controller in terms of settling time, overshoot, and tracking error of the DC‐bus voltage.

Improving seedling recruitment and dryland restoration using a targeted fungicide seed coating

The success of seeding projects in dryland systems is sporadic, with failure primarily driven by low seedling emergence and survival. This sporadic nature is influenced by a litany of biotic and abiotic factors that act in synergy to reduce recruitment success. These factors include erratic seasonal weather patterns, competition, predation, plant material selection, and restoration strategies. While some events are unpredictable and uncontrollable, others may be mitigated through thoughtful deliberation. Pathogenesis from soil‐ and seed‐borne fungi is one form of predation that can decrease restoration success. Fungicide seed coatings are technologies used in the agriculture industry to mitigate fungal pathogens, yet these technologies are novel to wildland seeding. We evaluated the ability of a fungicide seed coating to improve bluebunch wheatgrass (Pseudoroegneria spicata) recruitment across a broad range of sites and determined how soil and climate affected seed treatments across six sites over 650 linear kilometers of sagebrush‐steppe habitat in the Great Basin, United States. Treatment effects varied by site; however, on average, the fungicide treatment increased germination by 16%, emergence by 42%, and juvenile plant establishment by 60%. Fungicide seed treatments had a greater effect on seedling emergence in soils with lower pH, reduced wet‐thermal accumulation during the winter, lower bulk density, and higher organic matter content. Most notably, the fungicide seed treatment resulted in higher plant establishment when emerged seedlings were exposed to longer periods of available soil moisture. Fungicide seed treatments can improve the establishment of native bunchgrasses, when soil and climate regimes are considered.

Analysis of Wheat Export Performance in India

Wheat is a plant which is valued highly for its kernel as an edible part and an important source of carbohydrates. India ranks second position in wheat production in the world with a contribution of roughly 13.53 per cent to global wheat output (Naga Latha et. al., 2022). The study analyses the export performance of wheat and examines the trends in the growth performance of wheat in terms of area, production and productivity, export quantity and export value during 1973-2022 which is a period of 50 years. The analysis reveals that the highest compound growth rate in terms of area was 1.16 per cent, production was 4.49 per cent and productivity was 3.29 per cent respectively. The results of the study on the compound growth rates during the study periods have shown positive and increasing value which indicates high potential for the export of wheat from India. In the analysis of instability, there exists a high positive correlation between the export quantity and value of export in the overall period. Wheat area, production and productivity exhibit higher variability and instability in the overall period. Period II shows lower variability and instability, indicating increased stability. Period I exhibits moderate variability and instability. The overall period has the highest instability, suggesting unpredictable production. Instability indices show high instability at 126.97 per cent in Period I and 138.18 per cent overall, while Period II exhibits moderate instability at 101.94 per cent. Market volatility, trade policies, economic fluctuations, weather patterns and production variability contribute to these fluctuations. As of trend analysis, there is a positive and significant increase. The NPC value of wheat during the overall period was 0.051, it indicates that the commodity is not protected. NPC<1 indicates that the commodity is exportable and possesses export competitiveness and hence the hypothesis i.e. Indian wheat has better competitiveness in the International market.

The Impact of Climate Change on the Transmission of Vector-borne Diseases and Drug Administration: A Review

Climate change continues to be a big threat to humanity, particularly due to its significant impacts on human health. Although there have been some studies on the impacts of climate change on human health, there is a huge gap on its prospective effects on the transmission of vector-borne diseases and drug administration. Therefore this review provides a comprehensive impacts of climate change on the transmission of vector-borne diseases and drug administration It is estimated that the global temperature on average will potentially rise by 1.0 – 3.0 oC, thus increasing the chance of the emergent and increased rate of transmission of vector-borne diseases. Climate change continues to have a significant impact on overly poor and vulnerable populations across tropical and sub-tropical regions across the world. Some studies have established the influence of climate change on the transmission of diseases due to increasing changes in weather patterns such as rainfall and increase in temperature, as a result of global warming. It is believed that climate change will have a long-term effect on the propagation of diseases, and subsequently affect treatment from drug administration. Rising temperatures and global warming due to climate change can alter disease patterns, drug stability and change drug properties, thus potentially increasing drug toxicity and reducing drug efficacy.

Anthropology of and from the Ocean

The ocean has a key, though often unremarked, role in shaping everyday life, from impacting weather patterns and food supplies to facilitating, and contesting, systems of capitalism, including contemporary logistics, empires, mobility, and migration. Beginning with early debates on maritime anthropology, this review traces the shift from maritime anthropology to an anthropology of and from the ocean. It notes the ways that the ocean appears and disappears as metaphor or material space of encounter and engagement within the past, present, and possible futures of anthropology. It shows how absence and presence as well as metaphor and materiality are the modes through which oceans are imagined and inhabited. While there is no distinct oceanic turn in anthropology in contrast with a number of other disciplines, the anthropology of and from the ocean holds the possibility to reenergize anthropology's interdisciplinary encounters, including with history and geography, as well as modes of engaging scale and specificity.

The impact of technological advancements on global warming

Global warming, or climate change, refers to long term shifts in temperature and weather patterns. These shifts may be natural, however, from 1870, which is the start of the technical or second industrial revolution, there has been abnormally large amount of carbon dioxide (CO2) and other greenhouse gases such as methane and nitrous oxide getting trapped in the atmosphere, resulting in an enhanced greenhouse effect causing global temperatures to increase. This study seeks to assess and analyse the impact technology, its advancements and how it has impacted the global temperatures. By examining the correlation between technological development and CO2 emissions from machinery, this study will provide a comprehensive analysis of the relationship between the two.

Estimating dynamics of dengue disease in Colombo district of Sri Lanka with environmental impact by quantifying the per-capita vector density

Dengue is a vector-borne disease transmitted to humans by vectors of genus Aedes causing a global threat to health, social, and economic sectors in many of the tropical countries including Sri Lanka. In Sri Lanka, the tropical climate, marked by seasonal weather primarily influenced by monsoons, fosters optimal conditions for the virus to spread efficiently. This heightened transmission results in increased per-capita vector density. In this work, we investigate the dynamic influence of environmental conditions on dengue emergence in Colombo district − the geographical region with the highest recorded dengue threat in Sri Lanka. An iterative approach is employed to dynamically estimate dengue cases leveraging the Markov chain Monte Carlo simulations, utilizing the dynamics of four seasons per year influenced by monsoon weather patterns governing in the region. The developed algorithm allows to estimate the risk of dengue outbreaks in 2017 and 2019 with high precision, facilitating accurate forecasts of upcoming disease emergence patterns for better preparedness. The uncertainty quantification not only validated the accuracy of outbreak estimates but also showcased the model’s capacity to capture extreme cases and revealed undisclosed external factors such as human mobility and environmental pollution that might affect dengue transmission in the Colombo district of Sri Lanka.

Analysis of the Response Relationship Between PWV and Meteorological Parameters Using Combined GNSS and ERA5 Data: A Case Study of Typhoon Lekima

Precipitable water vapor (PWV) is a crucial parameter of Earth’s atmosphere, with its spatial and temporal variations significantly impacting Earth’s energy balance and weather patterns. Particularly during meteorological disasters such as typhoons, PWV and other meteorological parameters exhibit dramatic changes. Studying the response relationship between PWV and typhoon events, alongside other meteorological parameters, is essential for meteorological and climate analysis and research. To this end, this paper proposes a method for analyzing the response relationship between PWV and meteorological parameters based on Wavelet Coherence (WTC). Specifically, PWV and relevant meteorological parameters were obtained using GNSS and ERA5 data, and the response relationships between PWV and different meteorological parameters before and after typhoon events were studied in time–frequency domain. Considering that many GNSS stations are not equipped with meteorological monitoring equipment, this study interpolated meteorological parameters based on ERA5 data for PWV retrieval. In the experimental section, the accuracy of ERA5 meteorological parameters and the accuracy of PWV retrieval based on ERA5 were first analyzed, verifying the feasibility and effectiveness of this approach. Subsequently, using typhoon Lekima as a case study, data from six GNSS stations affected by the typhoon were selected, and the corresponding PWV was retrieved using ERA5. The WTC method was then employed to analyze the response relationship between PWV and meteorological parameters before and after the typhoon’s arrival. The results show that the correlation characteristics between PWV and pressure can reveal different stages before and after the typhoon passes, while the local characteristics between PWV and temperature better reflect regional precipitation trends.

Seasonal and monthly stroke incidence in relation to weather variables: a preliminary single-centre epidemiologic study

This single-centre epidemiologic study investigated seasonal/monthly stroke incidence and stroke-related mortality in relation to weather variables, gender, age, as well as stroke subtype and stroke aetiology. The study involved 638 patients hospitalised due to stroke at the Neurology Ward with a stroke unit at the Regional Hospital in Poland, during one complete calendar year. The data collected included the date of admission to the ward, year of birth, sex, type and subtype of stroke relative to the aetiology and location within different arteries as well as mortality. The data related to wind speed, atmospheric pressure, and temperature were acquired from the archive at the weather station in Tarnów, Poland. Majority of the patients in the study group were aged 66–85 years (57.2%), and there were more women than men. Ischaemic stroke was significantly more common than haemorrhagic stroke (87 vs 13%). Large artery atherosclerosis was the predominant cause of stroke, particularly in June and October, and total anterior circulation stroke (TACS) was the most common type, particularly in January and December. The analysis showed that stroke type and aetiology, as well as weather variables such as temperature and pressure, were associated with stroke incidence and mortality rates. The findings suggest that both high and low temperatures may contribute to an increased stroke incidence, as significant differences in the number of stroke cases are observed during periods of extreme temperatures. Therefore, increased vigilance is necessary during periods of both hot and cold weather. Further research is needed to better understand these relationships and develop effective prevention strategies.

Climate-Smart Horticultural Practices: Building Resilience in a Changing Environment: A Scoping Review

Climate change disrupts weather patterns, intensifies water scarcity and exacerbates pest and disease pressures, posing a significant threat to horticultural production systems. This scoping review explores a range of climate-smart practices to enhance adaptation and resilience within the sector. Core practices like water-efficient irrigation, heat stress mitigation strategies, and adjustments to cropping patterns for altered rainfall are investigated. Integrated pest management is presented as a cornerstone for sustainable pest control. The review further explores the potential of precision agriculture, controlled-environment agriculture, and vertical farming to optimize resource use and mitigate climate risks. Beyond technical solutions, the review emphasizes continuous research and development for breeding climate-resistant varieties, refining existing practices, and exploring novel technologies. It advocates for an integrated approach, tailoring climate smart practices to specific contexts and socioeconomic considerations. Knowledge-sharing initiatives, training programs, economically viable technologies, and supportive government policies are identified as crucial for widespread adoption, particularly among smallholder farmers. The paper concludes with a call for collaboration among researchers, extension services, policymakers, and producers. By fostering knowledge dissemination, technology transfer, and financial incentives, stakeholders can empower farmers to adapt and thrive in a changing climate. Through collective action and unwavering commitment to innovation, the horticultural sector can ensure a secure and sustainable future for food production.

Brain-derived neurotrophic factor in older adults exposed to simulated indoor overheating.

Brain-derived neurotrophic factor (BDNF) is a neuroprotective growth factor that increases in young adults during short, intense bouts of passive heat stress. However, this may not reflect the response in heat-vulnerable populations exposed to air temperatures more consistent with indoor overheating during hot weather and heatwaves, especially as the BDNF response to acute stressors may diminish with increasing age.We therefore evaluated the ambient and body temperature-dependent responses of BDNF inolder adults during daylong passive heating. Sixteenolder adults (6 females; aged 66-78years) completed 8-h exposure to four randomized ambient conditions simulating those experienced indoors during hot weather and heatwaves in continental climates: 22°C (air-conditioning; control), 26°C (health-agency-recommended indoor temperature limit), 31°C, and 36°C (non-airconditioned home); all 45% relative humidity. To further investigate upstream mechanisms of BDNF regulation during thermal strain, we also explored associations between BDNF and circulating heat shock protein 70 (HSP70; taken as an indicator of the heat shock response). Circulating BDNF was elevated by ~ 28% (1139 [95%CI: 166, 2112] pg/mL) at end-exposure in the 36°C compared to the 22°C control condition (P = 0.026; 26°C-and 31°C-22°C differences: P ≥ 0.090), increasing 90 [22, 158] pg/mL per 1°C rise in ambient temperature (linear trend: P = 0.011). BDNF was also positively correlated with mean body temperatures (P = 0.013), which increased 0.12 [0.10, 0.13]°C per 1°C rise in ambient temperature (P < 0.001). By contrast, serum HSP70 did not change across conditions (P ≥ 0.156), nor was it associated with BDNF (P = 0.376). Our findings demonstrate a progressive increase in circulating BDNF during indoor overheating in older adults.

Water, salt, and heat in raised field agriculture: Using hydrologic modeling and thermal imagery to investigate soil drainage and temperature dynamics

On the north coast of Peru in the Casma Valley exist relict raised agricultural field systems dating to the Late Intermediate Period Chimu Empire (ca. 1300 – 1470 CE). While similar in many ways to other inland and coastal raised fields in South America, these fields are relatively unique in climate, weather patterns, and layout. The topography and hydrology of the Casma Valley provide clues on how these fields operated and why they differ from the regional norm. This paper reports satellite and drone-based aerial reconnaissance results used to collect multiscalar data for flow modeling and thermal photogrammetry. Flow modeling methods are compared to determine the best way to gain insight into surface hydrology using only elevation data, and thermal photogrammetry is used to analyze temperature dynamics in the raised fields. These data results provide insight into the function of the field system and its unique field morphology.

Partial migration pays off in black-faced cormorants: insights from post-breeding GPS tracking

Seasonal variation in food availability or adverse weather patterns can create challenging conditions for year-round survival. Birds experiencing such fluctuations may migrate to locations with better food availability in the non-breeding period. While some species are obligate migrants, others are facultative migrants where various post-breeding strategies exist within populations. Several cormorant and shag species are facultative migrants with a range of post-breeding movement strategies within populations. Currently, most of the information available on cormorant post-breeding strategies originates from banding studies and little is known about their movements, time budget or habitat use during the non-breeding period. In the present study, Australian endemic black-faced cormorants (Phalacrocorax fuscescens), breeding on Notch Island, Victoria, were tracked with GPS data loggers during the non-breeding period. Different post-breeding strategies were observed. Individuals were either resident (N = 36, 69%), remaining within a short distance of the colony (16.7 ± 2.2 km) or migratory (N = 16, 31%), travelling greater distances (250.7 ± 24.4 km) to establish a new central place foraging location. Data from individuals which covered the entire non-breeding period indicated that, across strategies, individual birds had one area of residency during the non-breeding period. Migratory individuals were found to have lower daily activity levels, spending less time flying and foraging, than resident individuals during the early non-breeding period, possibly indicating that they had access to greater prey resources. Regardless of the post-breeding strategy, individuals utilised foraging locations that were of similar seabed depths year-round suggesting individual specialisation in prey resources or foraging habitats.

Climate Change Effects on Spatiotemporal Distribution of Precipitation over West Central India: A Statistical Downscaling Approach

The long-term shifts in temperatures and weather patterns are referred to as climate change. Climate change not only leads to long-term shifts in average temperatures but also changes in the spatial and temporal distribution of rainfall over continents. This study aims to predict likely changes in the rainfall pattern induced by climate change over the West Central (WC) region of India. The approach uses a statistical downscaling technique that converts coarse-scale outputs of the global climate model (GCM) to high-resolution future precipitation projections giving refined distribution. The decision support tool that uses a robust statistical downscaling technique viz. statistical downscaling model (SDSM) is used for assessing local climate change impacts. The research includes the study of likely regional climate variability, by integrating historical observational data and empirical relationships between large-scale climate variables and local weather patterns. Historical data and the SDSM, version 4.2, are employed to forecast future rainfall trends. Rainfall data from the India Meteorological Department and the National Centre for Environmental Prediction (NCEP) from 1961 to 2001 are used along with outputs from general circulation models (GCMs), viz. Hadley Centre coupled model, version 3 (HadCM3), and coupled global climate model, version 3 (CGCM3), for the period 1961–2099. Rainfall scenarios are presented for three future time periods (2011–2040, 2041–2070, and 2071–2099). The study indicates a significant increase in the mean annual precipitation across the West Central India region, particularly in the 2050s and 2080s. Mean annual rainfall is projected to rise by 10–19.4% under HadCM3 A2 and B2 scenarios. The HadCM3 indicates the month of September as the month of the highest precipitation in later time periods, whereas it is the month of August, according to CGCM3 simulations. When comparing the results of the two models, HadCM3 gives better results, as indicated by better R2 value in validation. Thus, the analysis gives climate change-induced likely changes in the spatiotemporal distribution of precipitation over the West Central India region. The insight given by the work will be useful for decision making in many sectors like agriculture, water management, disaster risk reduction, and infrastructure planning.