Climatic Variations Research Articles

Crop Recommendation and Monitoring using AI

Abstract: Agriculture is crucial for India's economy, with over 50% relying on it for survival. Climate and weather variations pose risks to agriculture's health. AI can monitor crops by using machine learning methods. Crop monitoring includes Crop Recommendation, Weed Detection, Plant Disease Detection, Yield Prediction. The models are trained with Image and numerical datasets. A website will be developed to monitor crops and provide solutions. The optimal crop can be suggested based on the surrounding conditions by analysing important variables like composition of Nitrogen, Phosphorous and Potassium in the soil, its pH value, humidity, and rainfall using various models namely Gaussian Naive Bayes, Logistic Regression, Gradient boosting, Ensemble which fall under the domain of Machine Learning. ANN can be used for crop yield prediction. Weed and Plant disease can be detected using ResNet which can be utilized for deep neural networks. The intent of this project is to help farmers choose suitable crops, differentiate crops from weeds, detect diseases, provide remedies to protect crop. It enables to improve yield and productivity, Enhanced sustainability, Increased Profitability

Revealing Decadal Glacial Changes and Lake Evolution in the Cordillera Real, Bolivia: A Semi-Automated Landsat Imagery Analysis

The impact of global climate change on glaciers has drawn significant attention; however, limited research has been conducted to comprehend the consequences of glacier melting on the associated formation and evolution of glacial lakes. This study presents a semi-automated methodology developed on the cloud platforms Google Earth Engine and Google Colab to effectively detect dynamic changes in the glaciers as well as glacial and non-glacial lakes of the Cordillera Real, Bolivia, using over 200 Landsat images from 1984 to 2021. We found that the study area experienced a rise in temperature and precipitation, resulting in a substantial decline in glacier coverage and a simultaneous increase in both the total number and total area of lakes. A strong correlation between glacier area and the extent of natural glacier-fed lakes highlights the significant downstream impact of glacier recession on water bodies. Over the study period, glaciers reduced their total area by 42%, with recent years showing a deceleration in glacier recession, aligning with the recent stabilization observed in the area of natural glacier-fed lakes. Despite these overall trends, many smaller lakes, especially non-glacier-fed ones, decreased in size, attributed to seasonal and inter-annual variations in lake inflow caused by climate variability. These findings suggest the potential decline of natural lakes amid ongoing climate changes, prompting alterations in natural landscapes and local water resources. The study reveals the response of glaciers and lakes to climate variations, including the contribution of human-constructed water reservoirs, providing valuable insights into crucial aspects of future water resources in the Cordillera Real.

Bridging the gap between crop breeding and GeoAI: Soybean yield prediction from multispectral UAV images with transfer learning

Despite significant progress has been made towards crop yield prediction with remote sensing, there exist knowledge gaps on (1) the impacts of temporal resolution of imaging frequencies on yield prediction, (2) transferability of the models among different genotypes and test sites, and (3) translation of these research developments to crop breeding that benefit farmers. Existing research predominantly provides an on-site perspective, frequently missing the complexities of real-world applications. The objectives of this paper are to investigate the transferability and generalization capabilities of yield prediction models for crop breeding across test sites located in North and South Americas. Toward that goal, we tested different machine learning techniques including Random Forest Regressor (RF), Gradient Boosting Regression (GB), and Deep Neural Networks (DNN) for soybean yield prediction with experiments conducted in different climate and growth conditions. A novel transfer learning approach was proposed for genotype selection and categorizing soybean yield for screening high-yield varieties. Furthermore, we studied the effect of temporal resolution on yield prediction, focusing on the critical development stages and optimal aerial survey frequencies for precise yield prediction using large 31,404 sample data. Results demonstrated that the combined dataset of Argentina and United States representing different climate regimes provided the highest performance with an R2 of 0.76 using RF and GB algorithms. The classification approach was proven to be most useful for crop breeding as demonstrated by accurately identifying the high-yielding genotypes. Increasing temporal sampling of key phenological stages significantly improved yield prediction. Although transfer learning yielded promising outcomes across trials within Argentina the efficacy of transferring models from Argentina to the United States was limited, attributed to significant seasonal and climate variations. This study pioneered the use of transfer learning for model adaptability in real-world breeding scenarios, training and transferring models within the South and North Americas, providing actionable insights and strategies for the breeding community, aiming to facilitate improved decision-making for agricultural productivity.

Influence of Climatic Variables on the Stem Growth Rate in Trees of a Tropical Wet Forest

The growth of tropical wet forests has a significant relationship with the climate; aspects such as temperature and precipitation affect the species; however, few studies have characterized the stem growth rate of tropical tree species. This study’s objective was to characterize the effects of climatic variation on the interannual stem growth rate of eight species in tropical wet forest. Six trees per species were selected (n = 48 trees), and a dendrometer was installed to measure diametric growth bi-monthly between 2015 and 2018 (3 years), complemented with environmental measurements, to determine their growth equations from environmental variables and, finally, to define the relationship between the wood density and the stem growth rate. The results showed an average stem growth from 0.45 to 4.35 mm year−1, and 40 to 70% growth occurred in the months with the highest rainfall. Also, species with higher wood densities were found to have lower stem growth rates. Finally, the analysis of stem growth rate showed a significant relationship in all species between the variables of temperature and precipitation (R2 adj 0.88 to 0.96). Our results suggest that species with greater stem growth rates in wet tropical forests are more susceptible to climate changes, which may affect their dynamics in the face of potential drought scenarios and heat waves associated with climate change.

Topography influences diurnal and seasonal microclimate fluctuations in hilly terrain environments of coastal California.

Understanding the topographic basis for microclimatic variation remains fundamental to predicting the site level effects of warming air temperatures. Quantifying diurnal fluctuation and seasonal extremes in relation to topography offers insight into the potential relationship between site level conditions and changes in regional climate. The present study investigated an annual understory temperature regime for 50 sites distributed across a topographically diverse area (>12 km2) comprised of mixed evergreen-deciduous woodland vegetation typical of California coastal ranges. We investigated the effect of topography and tree cover on site-to-site variation in near-surface temperatures using a combination of multiple linear regression and multivariate techniques. Sites in topographically depressed areas (e.g., valley bottoms) exhibited larger seasonal and diurnal variation. Elevation (at 10 m resolution) was found to be the primary driver of daily and seasonal variations, in addition to hillslope position, canopy cover and northness. The elevation effect on seasonal mean temperatures was inverted, reflecting large-scale cold-air pooling in the study region, with elevated minimum and mean temperature at higher elevations. Additionally, several of our sites showed considerable buffering (dampened diurnal and seasonal temperature fluctuations) compared to average regional conditions measured at an on-site weather station. Results from this study help inform efforts to extrapolate temperature records across large landscapes and have the potential to improve our ecological understanding of fine-scale seasonal climate variation in coastal range environments.

Late Holocene climate dynamics in the Azores archipelago

The location of the Azores Archipelago makes this group of islands an excellent setting for investigating past long-term temperature and precipitation changes in the central North Atlantic region. Here, we present a chironomid-based quantitative temperature reconstruction and a record of oxygen isotope composition of chironomid head capsules for the last ca. 1200 years, based on the Lake Prata core (São Miguel Island, Azores). Chironomids from Lake Prata show that the temperature trend is aligned with the prevailing past global temperature trends, indicating warming during the Medieval Climate Anomaly (MCA; ca. 950–1300 CE) and subsequent cooling during the Little Ice Age (LIA; ca. 1300–1800 CE). Furthermore, changes in lake water oxygen isotope composition from chironomid head capsules were recorded with more positive δ18O values (18.2 ± 0.2‰) during the MCA and more negative values (varying between 14.8‰ and 17.6‰) during the LIA, indicating a positive shift in the precipitation-evaporation balance and/or heavier precipitation in winter relative to summer. Our study revealed that, in subtropical regions with a temperate oceanic climate and low thermal variation, there is a limited correlation between the isotopic composition of precipitation and temperature. Additionally, the temporal sequence of oxygen isotope values obtained from chironomid head capsules closely corresponds to variations in precipitation seasonality inferred from distinct climatic proxies. This consistency underscores the robustness of quantitative reconstructions and indicates that δ18Ochironomid records are promising for future investigations, especially in the context of lakes located on oceanic islands characterised by limited thermal variability.

Multi-Dimensional Impacts of Climate Change on China’s Food Security during 2002–2021

Climate change poses a significant threat to food security, necessitating a thorough examination across multiple dimensions. Establishing appropriate food security evaluation indicators that align with the evolving concept of food security is imperative. This study enhances food security evaluation by designing a multi-dimensional framework and analyzing the impact of climate variations across various regions from 2002 to 2021. By constructing a food security evaluation system that encompasses the production quantity and quality, sustainability, affordability, and resources, and utilizing the entropy method for accurate weighting, the impacts of climate variations on food security are accessed using a climate–economic model. The food security structure in China largely mirrors the regional division of grain, with the production quantity being the primary contributor. Overall, China’s food security has generally demonstrated improvements across various dimensions, with the exception of production quality. Regarding climate change, which encompasses variations in mean states and climate extremes, the panel regression analysis uncovers a negative linear relationship between food security and temperature. Conversely, the impact of precipitation on food security is non-linear, manifesting as inverse U-shaped patterns. In regions with balanced production and consumption, both accumulated temperatures and extreme high temperatures have a negative linear effect on food security. On the other hand, both accumulated and extreme precipitation exhibit inverse U-shaped non-linear impacts on food security in the main production and main consumption regions. These findings highlight the intricate interplay between climate change, regional disparities, and food security in China, emphasizing the need to consider multi-dimensional factors and regional variations in addressing food security challenges. These insights are invaluable for policymaking and planning aimed at enhancing food security in China.

Resistance of Gastrointestinal Nematodes to Anthelmintics in Sheep Production in Zimbabwe

Introduction: Sheep play a crucial role in enhancing the socio-economic welfare of individuals in numerous nations. Within sub-Saharan Africa, productivity levels are hindered by various factors, such as prevalent diseases, inadequate nutrition, climate variations, predators, severe weather, as well as infestations of both external and internal parasites. The purpose of the present study was to assess the extent of anthelmintic resistance (AR) observed on a sheep farm operating for commercial purposes in Zimbabwe. Materials and methods: Forty female weaner Dorper sheep, averaging 43 kg in body weight, were selected from Ballineety commercial sheep farm in Mashonaland Central, Zimbabwe. These sheep were then divided into four groups of ten each, receiving different treatments including ivermectin, albendazole, levamisole, and no treatment as a control. Each treatment was administered according to the manufacturer’s recommendations. Pre-treatment faecal samples (day zero) and post-treatment (day 14) were collected and subjected to the Faecal Egg Count Reduction Test (FECRT). Anthelminthic resistance was suggested (AR) by the presence of faecal eggs after treatment. Results: All the drugs indicated a decrease in egg counts by less than 90%. The effectiveness of ivermectin, albendazole, and levamisole was measured at 58.5%, 70.1%, and 85.8% respectively. These findings indicate that nematodes have developed resistance to all treatments. The larval cultures of the samples before and after treatment indicated a significant presence of Haemonchus spp. in the pooled samples. In contrast, the quantities of Trichostrongylus spp. and Oesophagostomum spp. were low but present in all treatments. Cooperia spp. was recovered in post-treatment larval cultures of groups that received anthelmintic. The present study reports lower-than-expected efficacy for ivermectin, albendazole, and levamisole based on the reduction of egg excreted post-treatment. Conclusion: A common worms population in sheep are AR in the current study area. Therefore, comprehensive nematode management programs along with complementary eradication strategies are essential for sustainable sheep production.

Integrating historical and recent information to understand chondrichthyan dynamics in the central Mediterranean

Chondrichthyans (sharks, rays, and chimaeras) are highly susceptible to the impacts of fisheries due to their vulnerable life-history traits. Over the last 100 years, several cases of local extinction have been documented in heavily fished areas across the Mediterranean Sea. In the Strait of Sicily (SoS), one of the main demersal fishing grounds of the Mediterranean, chondrichthyans constitute a significant component of both commercial and discarded bycatch. In this area, the lack of long-term data series on these species hinders our ability to fully comprehend the extent of changes due to both overfishing and climate variations. Here we aim to use historical data from the end of the 19th century, provided by Döderlein, to uncover evidence of long-term changes in the occurrence and diversity of these fishes. We employ a semi-quantitative approach to compare past data with recent frequency of occurrence estimates, to improve our ability to propose management advice. We report a decline in both the number of species and the frequency of occurrence of sharks and ray species in the study region over the past 150 years. Our findings shed light on the current status of sharks and rays compared to the historical data from the 19th century and highlight the urgent need to develop management strategies to mitigate the impact of harvesting on these vulnerable species.

Impact of climatic variations in horticulture sector, Kinnaur, Himachal Pradesh, India

Climate change has an impact on the horticulture crops of Himachal Pradesh in relation to the varying climatic conditions in the Himalayan Region. The purpose of the study was to determine how frequently these differences in horticultural crops occur in the Kinnaur district of Himachal Pradesh since the horticulture industry in Himachal Pradesh greatly benefits from this district. Long-term temperature (maximum, minimum, and diurnal) and rainfall data from 1990 to 2020 were analyzed for different phenological stages (pre-flowering, flowering, and fruit setting). The findings revealed that the average maximum and diurnal temperatures were found to have increased significantly, at the rate by 0.027 °C and 0.042 °C, respectively, during the flowering stages. However, there were no appreciable changes in the variations for other phenological stages. Under trend analysis of crop yield, the productivity of Pear and Almond notably fell over the past 20 years, at -0.029 t/ha/year and -0.016 t/ha/year, respectively, whereas Walnut productivity climbed at 0.008 t/ha/year. The relationship or impact between the climate and the crop was also examined in addition to the trend analysis for crop and climatic variables. The results showed that among the three phenological stages, Apple and Apricot were highly impacted (54.4%) during pre-flowering stage, followed by Apricot, i.e., 72.6% during flowering, and Grapes (53.2%) during the fruit setting stage. In the current situation, climate fluctuations had a favorable effect on Apples, Walnuts and Grapes while having a negative effect on Apricots, Plums, Pears and Almonds.

Climatic variation of the last 29.000 years BP in the northern Santos basin (Rio de Janeiro Shelf) inferred by an alternation of cysts Tuberculodinium vancampoae and Operculodiniumcentrocarpum

During the transition from the Pleistocene to the Holocene, significant changes occurred on the Brazilian continental shelf. In this study, we investigated the environmental changes that occurred during this period through the quantification and classification of cosmopolitan dinoflagellate cysts. We used three cores collected in the northern part of the Santos Basin and analyzed a total of 42 samples. The results showed a predominance of cysts of O. centrocarpum, L. machaerophorum and T. vancampoae, whose occurrence was influenced by sea-level fluctuations during the Pleistocene-Holocene transition and by changes in ocean currents due to temperature variations. Oligotrophic species were established during the Late Pleistocene, followed by mixotrophic species from the Holocene. Analysis of the dinocysts showed the presence of colder waters due to upwelling and the entry of waters with lower salinity due to the formation of transitional estuarine systems. These environmental changes have contributed to the configuration of the current Brazilian coastal scenario.

Global relevance of Argentinean rainfed crops in a climatic variability context: A water footprint assessment in Buenos Aires province

Argentina has a relevant international role as a producer of agricultural commodities. Buenos Aires is the province with the largest cultivated area of cereals and oilseeds of the country. Rainfed crops depend exclusively on green water, meaning a comparative advantage for Buenos Aires province. The green virtual water content in the crops produced in Buenos Aires has implications for water allocation at international level. A great amount of countries depends on the Argentinean rainfed agriculture. Therefore, it is important to understand the effects of climate variations on Argentinean crop production at local level and the role of rainfed crops in regional and international trade. We analysed the temporal and territorial variations of crops green water demand in a climatic variability context and their influence on the water footprint. The green water footprint of the main crops of Buenos Aires was assessed, including soybeans, maize, sunflower, wheat and barley, in different climatic conditions: for the period 2008–2018, which include a dry year, a humid year and an ordinary year. A dataset about the green water footprint at municipality level was provided, and the results were presented on maps for each crop and for the different climatic conditions. The relevance of green water of main crops in the world water-dependent supply chains was shown. This comprehensive green water footprint assessment provides a useful database for researchers, companies and policy makers in Argentina and beyond.

Unleashing the power of machine learning and remote sensing for robust seasonal drought monitoring: A stacking ensemble approach

Droughts cause significant economic losses in many regions around the world, highlighting a need to more accurately quantify implications of drought on production and water management. Remote sensing technologies and machine learning-based models offer promising solutions for timely and accurate regional drought monitoring, but the accuracy of such approaches is constrained by both the conceptual design and algorithms underpinning such approaches. We developed a machine learning stacking ensemble approach to overcome such limitations, drawing upon Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record (PERSIANN-CDR), Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing products, and climate zoning data to estimate 3-month scale SPEI (Standardized Precipitation Evapotranspiration Index, SPEI-3) in 9 sub-regions of China. We compared 19 individual machine learning models and used the stacking approach to select the most robust meta-model for SPEI-3 prediction. We found that CatBoost Regressor (CBR), Extra Trees Regressor (ETR), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), and Random Forest (RF) were the top individual models for predicting drought, while using CBR as the stacked meta-model achieved the best performance. The R2 values for the stacking model with CBR as the meta-model were 0.9065 and 0.8218 in the eastern and western regions, respectively. We then employed the stacking model with CBR as the meta-model to generate seasonal drought maps based on SPEI-3 across different years and seasons. Our predicted SPEI-3 was compared with drought maps generated by the GPCC and ERA5 reanalysis datasets. We found a strong correlation between them with R2 exceeding 0.8 across multiple years and seasons, indicating our machine learning stacking ensemble approach had a good performance in monitoring seasonal drought conditions. The findings of our study establish a standardized protocol for predicting droughts in the agricultural sector, enabling its widespread application irrespective of variations in terrain and climate.

Variations in weathering conditions related to sea level changes during the Albian-Santonian interval in the Western Australian margin as evidenced by clay minerals and Nd/Sr isotopes

The Albian-Santonian interval (∼113–83 Ma) is a key interval for Cretaceous climatic and environmental changes. This interval is associated to a rise in sea level until the Late Cenomanian (∼ 94 Ma) and to a progressive increase in oceanic temperatures, leading to consider the Late Cenomanian-Santonian interval (∼ 94–83 Ma) as the warmest period of the last 200 Myr. While the Albian-Santonian interval has been well studied in the Northern Hemisphere, the climatic and environmental variations and their consequences on weathering conditions are less documented of the Southern Hemisphere, especially in mid and high latitudes. To better understanding the evolution of weathering conditions, associated to continental climate and sea level changes during this key period, an integrated study, based on a coupled mineralogical and geochemical approach, was carried out on the clay-sized (< 2 μm) fraction from sediments of International Ocean Discovery Program Site U1513 (Mentelle Basin, South-western Australia) and Oceanic Drilling Program Site 763 (Carnarvon Basin, North-western Australia). To determinate variations in weathering conditions, the mineral assemblages of the clay fraction was determined by X-ray diffraction and observed for selected samples by electron microscopy. To identify sources of clay minerals, the concentrations of major and selected trace elements, including rare earth elements, together with strontium and neodymium isotopic measurements were performed on the clay-sized fraction. The X-ray diffraction analyses reveal that clay fraction is dominated by R0-type illite/smectite mixed-layers (smectites), followed by variable proportions of illites, kaolinites for both sites and only on Site 763 of palygorskites. These clay minerals are associated with opal and clinoptilolites. Electron microscopy observations have highlighted the dominance of detrital fleecy smectites but also the occurrence of recrystallized lathed smectites and volcanogenic folded smectites for Site U1513, and authigenesis of clay minerals, associated to palygorskites especially on Turonian to Santonian deposits for Site 763. This authigenesis, confirmed by negative cerium anomaly, is also carried by other minerals in the clay-sized fraction (e.g., clinoptilolites, barite). These different authigenic minerals record a seawater-derived isotopic signature. Therefore, isotopic compositions of the clay-sized fraction reflect a mix between a continental contribution, carried by detrital clay minerals and marine one, supported by authigenic minerals, which complicates the identification of sediment sources. However, despite this, our study demonstrates the major influence of sea level variations on mineralogical variations from Albian to Cenomanian in Western margin of Australia. The decrease in kaolinite proportions at Site U1513 from the Albian to the end of the Cenomanian, is probably related to the increase of sea level, which cause a decrease in detrital inputs in the Mentelle Basin associated with a decrease of weathering and drainage conditions. The progressive sea level rise has also enhanced the effect of the differential settling process of clay minerals by the deposition of coarse clays (e.g., kaolinites and illites) on the continental shelf. Kaolinite-bearing clay deposits, derived from the weathering of the Western Australian Craton during the Albian-Cenomanian times, have been progressively substituted from the Turonian by a carbonate sedimentation, for which the terrigenous component consists only of rare detrital and volcanogenic smectites. In Site 763, the occurrence in kaolinite content during the Coniacian would indicate a decrease of differential settling process related to the weathering of the north part of the Western Australia Craton.

Effects of Climatic Variations and Changing Land Use/Land Cover on Flooding in Southern Nigeria

The prevalence of flooding in Nigeria in the past few years (with its devastating consequences) has become a source of concern for policymakers and researchers alike. Scholars have attributed the high frequency of flooding, particularly in the southern region of Nigeria, to several factors; climatic variation and land use/land cover changes are themes that fall under the domain of natural and anthropogenic causes of flooding, respectively. This study examined climatic variations and changing land use/land cover in southern Nigeria with the view of determining their synergistic effects on flooding. We adopted a mixed approach for data collection with the use of Geographic Information System (GIS), retrieval of primary data from meteorological stations in the region, and field surveys. Findings show a significant decrease in rural land uses like arable lands, wetlands, and undisturbed forests; while there was a corresponding increase in urban/peri-urban land uses like settlements and tree-crop plantations over the period from 1999 to 2019. The land use/land cover changes have manifested in the destabilization of the microclimate, distortion of the carbon circle, loss of biodiversity, reduction of ecosystem services of the natural areas, and flooding. The decadal rainfall trends over the past three decades show consistent extremities with mean rainfall duration decreasing while the mean intensity increases; aggravating soil erosion, urban runoff challenges, and flooding. Climatic variations (mainly extreme rainfall patterns) synergize with inefficient land use management to exacerbate flooding in southern Nigeria. While policymakers in this region may not have the standalone capacity to significantly control climate change and the consequential variations in the local microclimate, they can re engineer land use policies to eliminate or substantially degrade anthropogenic factors that exacerbate flooding.

TEMPORAL PATTERN OF AEDES SOLLICITANS AND AEDES TAENIORHYNCHUS IN AN INTERTIDAL WETLAND SYSTEM, NORTHEASTERN FLORIDA: A LITERATURE REVIEW

Temporal and spatial patterns of mosquito species distributions are influenced by weather conditions and climate, changes in water quality, and many other factors. Aedes taeniorhynchus and Aedes sollicitans are common salt marsh mosquitoes that cause many nuisance problems for human populations. St. Johns County (north-eastern Florida) has, over the past decades, been one of the fastest growing counties in the U.S.A. This is related to changes in land use and land cover, and local climate variations. It has been recently noticed that two species, Ae. taeniorhynchus and Ae. sollicitans, switch their temporal pattern in St. Johns County. To explore factors influencing potential changes between the temporal distribution of the two species of salt marsh mosquitoes, a literature search was conducted. Based on the literature, there are many factors influencing the potential species switch or temporal patterns of the two species. These include meteorological and tidal patterns as well as land use and land cover changes. The results of the literature analysis indicate that the switch and change in temporal pattern between the two species needs to be further explored, especially with respect to temperature, tidal flooding, water quality (especially salinity), sea level rise and land use/cover.

Internet of Things (IoT) for Smart Precision Agriculture and Farming in Rural Areas

Precision Agriculture (PA) is a management strategy that utilizes communication and information technology for farm management. It is a key to improve productivity by using the best agricultural practices and optimal usage of resources. Agriculture faces diverse challenges due to soil degradation, climate variation, and increasing costs. To unfold these challenges, PA uses Wireless Sensor Networks (WSNs) and exploits acquisition, communication, and processing of the data as basic enabling technologies to amplify the crop yield. Also, many other multidisciplinary technologies are supporting PA in finding the most novel use cases for PA. The use of Machine Learning (ML) and Artificial Intelligence (AI) has transformed PA at almost every level. The fog/edge paradigm is mitigating many challenges such as network bandwidth and security by bringing computation closer to the deployed network Key Words: Precision agriculture, Machine learning, WSN

A Study of the Application of AI &amp; ML to Climate Variation, with Particular Attention to Legal &amp; Ethical Concerns

INTRODUCTION: This research investigates the utilization of artificial intelligence and machine learning in comprehending various climatic variations, emphasizing the associated use of legal and ethical considerations. This escalating impact of climatic change necessitates innovative approaches and the potential of AI/ML to offer tools for analysis and prediction. OBJECTIVES: The primary objective here, was to assess the effectiveness of AI/ML in the deciphering of varying climatic patterns and projecting the future trends. Concurrently, this study aims for the identification and analysis of legal and ethical challenges that may arise from the integration of these technologies in climatic research and policy. METHODS: Here, the literature review forms the basis for understanding various AI/ML applications related to climate science. This study employs various case analyses to examine the existing models to gauge the accuracy and efficiency of predictions. Legal frameworks and ethical principles need to be scrutinized through the qualitative analysis of relevant policies and guidelines. RESULTS: This extensive research reveals the various significant contributions of AI/ML in the enhancement of climatic modeling precision and the prediction of extreme events. However legal and ethical considerations such as data privacy, accountability, and transparency also emerged as crucial challenges which required careful attention. CONCLUSION: While AI/ML exhibited great potential in the advancement of climate research, a balanced approach is imperative to navigate the associated legal and ethical concerns. Striking this equilibrium will be pivotal for ensuring responsible and effective deployment of these technologies in the pursuit of best understanding and mitigating varying climatic variations.

Experimental assessment of integral-type terminal sliding mode control designed for a single-phase grid-interlinked PV system

Due to the increasing global adoption of grid-connected photovoltaic (PV) systems and their unpredictable power production behavior, as well as their interactions with the electrical network, PV system controllability is a major aspect that should be taken into consideration. The paper under discussion implements an integral-type terminal sliding mode control (ITTSMC) technique for a single-stage, single-phase grid-interlinked PV system. The ITTSMC approach is dedicated to feeding the electrical network with a sinusoidal current with low total harmonic distortion (THD), providing accurate reference tracking with a finite time convergence, and increasing the system robustness against parameter mismatch and climatic conditions variation. In pursuit of this goal, the system's mathematical model is established under the proposed method, and its stability is proved using Lyapunov stability criterion. Reliability and performance assessment of the suggested control scheme have been carried out through a numerical simulation using Matlab/Simulink software. In addition, laboratory experiments employing a PV panel and Dspace 1104 controller board-based test bench are executed. The results obtained from various scenarios have unequivocally demonstrated the superior effectiveness of the adopted approach concerning power quality and dynamic performances compared to other existing controllers.

Interacting effects of fire and hydroclimate on oak and beech community prevalence in the southern Great Lakes region

Abstract Rising temperatures, increasing hydroclimate variability and intensifying disturbance regimes increase the risk of rapid ecosystem conversions. We can leverage multi‐proxy records of past ecosystem transformations to understand their causes and ecosystem vulnerability to rapid change. Prior to Euro‐American settlement, northern Indiana was a mosaic of prairie, oak‐dominated forests/woodlands and beech‐dominated hardwood forests. This heterogeneity, combined with well‐documented but poorly understood past beech population declines, make this region ideal for studying the drivers of ecosystem transformations. Here, we present a new record from Story Lake, IN, with proxies for vegetation composition (pollen), fire (charcoal) and beech intrinsic water use efficiency (δ13C of beech pollen; δ13Cbeech). Multiple proxies from the same core enable clear establishment of lead–lag relationships. Additionally, δ13Cbeech enables direct comparisons between beech population abundance and physiological responses to changing environments. We compare Story Lake to a nearby lake‐level reconstruction and to pollen records from nearby Pretty and Appleman Lakes and the distal Spicer Lake, to test hypotheses about synchrony and the spatial scale of governing processes. The 11.7 ka sediment record from Story Lake indicates multiple conversions between beech‐hardwood forest and oak forest/woodland. Beech pollen abundances rapidly increased between 7.5 and 7.1 ka, while oak declined. Oak abundances increased after 4.6 ka and remained high until 2.8 ka, indicating replacement of mesic forests by oak forest/woodland. At 2.8 ka, beech abundances rapidly increased, indicating mesic forest reestablishment. Beech and oak abundances correlate with charcoal accumulation rates but beech abundance is not correlated with δ13Cbeech. Fluctuations in beech abundances are synchronous among Story, Appleman and Pretty Lakes, but asynchronous between Story and Spicer Lakes, suggesting regulation by local‐scale vegetation‐fire‐climate feedbacks and secondarily by regional‐scale drivers. Holocene forest composition and fire dynamics appear to be closely co‐regulated and may be affected by local to regional climate variations. The importance of extrinsic drivers and positive/negative feedbacks changes over time, with higher ecoclimate sensitivity before 2.8 ka and greater resilience afterwards. Synthesis: Overall, oak‐ and beech‐dominated ecosystems were highly dynamic over the Holocene, with multiple ecosystem conversions driven by shifting interactions among vegetation, hydroclimate and fire regime.