Analysis Of Climate Data Research Articles

Impact of Drought in Northeastern Algeria: Comparative Study of the SPI and SPEI Índices

Purpose: The study aimed to evaluate drought trends in Annaba, Algeria, considering global warming and extreme weather, using quantitative indices for improved drought comprehension and prediction. Theoretical Framework:The study assesses changes in drought frequency and severity in the Annaba region of Algeria due to global warming and extreme weather conditions. It employs quantitative indices to enhance drought understanding and prediction. Methodology: The study analyzes temperature and precipitation data (1981-2021) from an Annaba weather station to assess drought characteristics.Standardized indices (SPI, SPEI) and extreme value analysis (Generalized Pareto Distribution) are used to quantify drought intensity, duration, and temperature impacts. Findings: Our analyses reveal a critical divergence in drought assessment tools. While stable monthly temperatures suggest a constant influence on evapotranspiration, the SPEI index (including temperature) indicates increasing drought compared to the precipitation-only SPI. This highlights the importance of considering temperature alongside precipitation for accurate drought assessment. Research Practical and Social Implication: This study emphasizes the significance of considering the interplay among temperature, precipitation, and evapotranspiration for better understanding and predicting drought changes in the Annaba region, Algeria. These insights are crucial for sustainable water resource management and climate change adaptation in the region. Originality/Value:This study distinguishes itself by its comprehensive analysis of long-term climate data to examine drought trends in a specific region (Annaba, Algeria) severely impacted by climate change. By comparing the effectiveness of two drought indices (SPEI and SPI), it offers valuable insights into how to improve drought assessments by considering the role of temperature in evapotranspiration. This research enriches the existing literature on drought management and climate change adaptation, providing a valuable case study for similar arid and semi-arid regions worldwide.

GNSS signal ray-tracing algorithm for the simulation of satellite-to-satellite excess phase in the neutral atmosphere

Traditionally, GNSS space-based and ground-based estimates of tropospheric conditions are performed separately. It leads to limitations in the horizontal (e.g., a single space-based radio occultation profile covers a 300 km slice of the troposphere) and vertical resolution (e.g., ground-based estimates of troposphere conditions have spacing equal to stations’ distribution) of the tropospheric products. The first stage to achieve an integrated model is to create an effective 3D ray-tracing algorithm for the satellite-to-satellite (radio occultation) path reconstruction. We verify the consistency of the simulated data with the RO observations from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC-1) Data Analysis and Archive Center (CDAAC) in terms of excess phase and bending angle. The results show that our solution provides an effective RO excess phase, with a relative error varying from 35% at the height of 25–30 km (1.0–1.5 m) to 0.5% at heights 5–10 km (0.1–1 m) and 14 to 2% at heights below 5 km (2–14 m). The bending angle retrieval on simulated data attained for high-resolution ray-tracing, bias lower than 2% with respect to the observed bending angle. The optimal solution takes about 1 s for one transmitter–receiver pair with a tangent point below 5 km altitude. The high-resolution processing solution takes 3 times longer.

Cfr (v2024.1.26): a Python package for climate field reconstruction

Abstract. Climate field reconstruction (CFR) refers to the estimation of spatiotemporal climate fields (such as surface temperature) from a collection of pointwise paleoclimate proxy datasets. Such reconstructions can provide rich information on climate dynamics and provide an out-of-sample validation of climate models. However, most CFR workflows are complex and time-consuming, as they involve (i) preprocessing of the proxy records, climate model simulations, and instrumental observations; (ii) application of one or more statistical methods; and (iii) analysis and visualization of the reconstruction results. Historically, this process has lacked transparency and accessibility, limiting reproducibility and experimentation by non-specialists. This article presents an open-source and object-oriented Python package called cfr that aims to make CFR workflows easy to understand and conduct, saving climatologists from technical details and facilitating efficient and reproducible research. cfr provides user-friendly utilities for common CFR tasks such as proxy and climate data analysis and visualization, proxy system modeling, and modularized workflows for multiple reconstruction methods, enabling methodological intercomparisons within the same framework. The package is supported with extensive documentation of the application programming interface (API) and a growing number of tutorial notebooks illustrating its usage. As an example, we present two cfr-driven reconstruction experiments using the PAGES 2k temperature database applying the last millennium reanalysis (LMR) paleoclimate data assimilation (PDA) framework and the graphical expectation–maximization (GraphEM) algorithm, respectively.

Multivariate analysis of long-term climate data in connection with yield, earliness and the problem of global warming.

Climate change is the key challenge to agriculture in the XXI century. Future agricultural techniques in the Russian Federation should involve the optimization of land utilization. This optimization should apply algorithms for smart farming and take into consideration possible climate variations. Due to timely risk assessment, this approach would increase profitability and production sustainability of agricultural products without extra expenditures. Also, we should ground farming optimization not on available empirical data encompassing limited time intervals (month, year) or human personal evaluations but on the integral analysis of long-term information bodies using artificial intelligence. This article presents the results of a multivariate analysis of meteorological extremes which caused crop failures in Eastern and Western Europe in last 2600 years according to chronicle data and paleoreconstructions as well as reconstructions of heliophysical data for the last 9000 years. This information leads us to the conclusion that the current global warming will last for some time. However, subsequent climate changes may go in any direction. And cooling is more likely than warming; thus, we should be prepared to any scenario. Plant breeding can play a key role in solving food security problems connected with climate changes. Possible measures to adapt plant industry to the ongoing and expected climate changes are discussed. It is concluded that future breeding should be based on the use of highly adapted crops that have already been produced in pre-breeding programs, ready to meet future challenges caused by potential climate change.

Present day and future urban cooling enabled by integrated water management

The process of urbanisation has increased public health risks due to urban heat, risks that will be further exacerbated in future decades by climate change. However, the growing adoption of integrated water management (IWM) practices (coordinated stormwater management of water, land, and resources) provides an opportunity to support urban heat amelioration through water supply provision and irrigated and vegetated infrastructure that can provide cooling benefits. This study examines the thermal impacts of future implementations of IWM for nine Australian cities based on a review of Government policy documents in the present and over two future time frames (2030 and 2050) under different greenhouse gas emission scenarios (SSPs 1.2-6, 3.7-0 and 5.8-5). Statistical analysis of the future climate data using historical data shows that future warming is nuanced, with changes variable in both time and place, and with extremes becoming more pronounced in future. We have developed a unique approach to morph the future climate projections onto historical data (derived from the ERA5 Reanalysis product) for the 2010-2020 period. Additionally, we use locally appropriate Local Climate Zones (LCZs) for Australian cities, resulting from a holistic and global approach that is widely adopted by the urban climate modelling community. We developed scenarios for business-as-usual as well as implementation of moderate and high levels of IWM across each of the Australian LCZs and modelled them using TARGET (The Air temperature Response to Green infrastructure Evaluation Tool). Results generated at the LCZ level are aggregated to Australian statistical areas (SA4, the largest sub-city area) and city-wide levels. The thermal impacts associated with the various degrees of IWM were marked and geographically differentiated, depending on the climatic characteristics of the various cities. For the current climate, high IWM intervention provided reductions in annual mean daily maximum temperature ranging from -0.77°C in Darwin, up to -1.86°C in Perth. Generally, the drier southern cities of Sydney, Canberra, Albury, Melbourne, Adelaide, and Perth produced the greatest thermal response to implementation of IWM and the more tropical cities with higher rainfalls the least response. For some southern cities cooling was > -3.0°C at the time of maximum summer temperatures. Interestingly high levels of IWM in winter produced modest warming of minimum overnight temperatures, especially for the cooler southern cities. The cooling benefits of IWM were seen across all future climate scenarios and are a real opportunity to offset-projected temperature increases resulting from climate change.

EXPLORING THE MICRO AND MACRO TERROIR OF FETEASCĂ NEAGRĂ WINE FROM MOLDOVA

This study delves into the intricate realm of Moldovan winemaking, focusing on the iconic Fetească Neagră grape variety. The research highlights the importance of indigenous grape varieties in shaping a unique wine identity for the region. Drawing on the concept of terroir, the study investigates the microbial terroir specific to three designated geographical regions in Moldova during the 2022 vintage. The research employs a multifaceted approach, combining climate data analysis, mini vinification experiments, microbial DNA extraction, and sensory evaluation techniques. Results reveal significant variations in climatic conditions and microbial diversity across the three regions, influencing the sensory characteristics of the resulting wines. On vintage 2022 the Codru is temperature， and the Stefan-Voda and Vadul lui Traian are warm climates. All the 3 regions are moderately dry. The Stefan Voda is 14.0%， and manifests the highest ethanol content among the regions, alongside the lowest pH level. The sensory evaluations show Stefan Voda evincing heightened creaminess, Codru showcasing prominent herbal nuances, and Vadul lui Traian displaying elevated scores in berry, cherry, violet, and black pepper descriptors. Through meticulous analysis, the study underscores the dynamic interplay between environmental factors, microbial communities, and wine attributes, shedding light on the complex tapestry of Moldovan winemaking. Ultimately, this research contributes to a deeper understanding of terroir-driven wine production and emphasizes the importance of preserving indigenous grape varieties to uphold the distinctiveness of Moldovan wines on the global stage.

AGROCONNECT - CONNECTING FARMERS TO SMART AGRICULTURE

Crop yield prediction focuses mostly on agricultural research, which have an enormous impact on taking decisions for example import-export, price, along with crop management. Soil is the main component and plays a significant role in agriculture. Based on the nutrients and pH value of the soil, crop yielding is determined. Farmers are still using traditional approach to analysis the soil quality. The techniques like Data Mining, Artificial Intelligence, Machine Learning, Deep learning and Predictive Analytics are the emerging technologies in research to improve the agricultural field. Predictive analysis is a technique of machine learning that predicts the future outcomes and analysis is based on the historical or past data. In agriculture, predictive analytics helps to predict or identify the soil nutrients level required for the crops like Paddy, Raagi, etc., Predicting the crop yield well ahead of its harvest would help farmers and market contractors strategize befitting actions to market and store their produce. These kinds of predictions will also help farmers minimize losses due to crop failure and can also help businesses that depend on agricultural products to plan their business logistics and resources. In this project, a method is proposed which would help predict the estimate of the crop yield for a specific land based on the analysis of geographical and climatic data using Machine Learning using LSTM. Firstly, it is able to capture the time dependency on temperature and rainfall. Secondly, it is able to work on a large and diverse dataset, unlike most models which only perform well in small regions. Lastly, it is able to use several diverse features - geographical, social, and economic to make a prediction. In addition to crop prediction, the system helps farmers to monitor the soil nutrients evolution so that action can be done on real time. The main chemical elements which are taken into the proposed model are nitrogen, phosphorus, potassium, hydrogen along with rainfall and temperature. Keywords—Crop, Machine Learning, LSTM, deep learning (key words)

Using agro-ecological zones to improve the representation of a multi-environment trial of soybean varieties.

This research introduces a novel framework for enhancing soybean cultivation in North America by categorizing growing environments into distinct ecological and maturity-based zones. Using an integrated analysis of long-term climatic data and records of soybean varietal trials, this research generates a zonal environmental characterization which captures major components of the growing environment which affect the range of adaptation of soybean varieties. These findings have immediate applications for optimizing multi-environment soybean trials. This characterization allows breeders to assess the environmental representation of a multi-environmental trial of soybean varieties, and to strategize the distribution of testing and the placement of test sites accordingly. This application is demonstrated with a historical scenario of a soybean multi-environment trial, using two resource allocation models: one targeted towards improving the general adaptation of soybean varieties, which focuses on widely cultivated areas, and one targeted towards specific adaptation, which captures diverse environmental conditions. Ultimately, the study aims to improve the efficiency and impact of soybean breeding programs, leading to the development of cultivars resilient to variable and changing climates.

The use of information technology for analysis and prediction of climate changes

The article provides a brief overview of the issue of global warming and the factors that influencing climate change. The relevance of the research lies in the need for a comprehensive analysis of climate data and prediction of further trends, both in the short term and the long term, as climate affects evolutionary processes, biosphere balance, the availability of resources essential for the functioning of the technosphere, the amount of primary production produced by producers, all of which collectively ensure the existence of human civilization. Significant fluctuations in the climate, both towards warming and cooling, can disrupt stability and lead to the destruction of the technosphere, economic and social upheavals. The research is based on previous studies conducted using data from 1 100 meteorological stations in Australia, which showed the need to revise the approach to grouping monitoring stations: trends in average temperature changes across the continent, and monitoring stations grouped according to their distance from the coast, showed a general synchronicity of warming and cooling periods, but different dynamic patterns in recent decades. It was decided to study the climate data of one state (Victoria) and compare the results with previous studies. Python programming language packages were used for data processing, analysis, visualization, and forecasting. Data from two monitoring stations in cities located in the south and north of the state were separately analyzed, showing a general trend of slight warming. The trend in temperature changes in the state showed a pattern different from the continental dynamics obtained in previous studies, which confirmed the validity of the chosen approach to studying the climate on the continent. Based on the information obtained, the average temperature was predicted for both the entire state and selected cities.

A Mixed-Methods Approach for Evaluating the Influence of Residential Practices for Thermal Comfort on Electricity Consumption in Auroville, India

With rapid global economic growth and a rise in disposable household income, particularly within a progressively warming planet, the escalating demand for energy to achieve thermal comfort has become a salient concern in the Global South, notably in emerging economies like India. This burgeoning need for cooling solutions has not only underscored the vital role of energy consumption but has also accentuated the imperative of comprehending the ensuing implications for electricity policy and strategic planning, particularly within the ambit of the Global South. This study explored the nuanced landscape of active cooling within an intentional community, Auroville, in southern India, aiming to discern the factors underpinning household preferences and practices in the pursuit of thermal comfort. Employing a mixed-methods approach, this study contributed empirically and methodologically to the interdisciplinary discourse by analysing residential electricity consumption patterns and cooling practices within selected households in the specified community. The study unfolded in three methodological stages: firstly, an analysis of climatic data coupled with an environmental stress index (ESI) assessment; secondly, the monitoring of end-user electricity consumption followed by rigorous data analysis; and lastly, the utilisation of qualitative in-depth interviews and observational techniques. This study’s outcome yielded empirical insights into the unprecedented shifts in the ESI for Auroville since 2014. Furthermore, the study unravelled the intricate complexities inherent in occupant behaviour within residential structures, thereby offering valuable insights into the practices that shape householders’ cooling preferences. This research enriched the understanding of the dynamics of energy consumption in the pursuit of thermal comfort and contributes to the broader discourse on sustainable development and energy policy in the context of climate change.

Impacts of Climate Change on Groundwater Resources in Afghanistan: A Case Study of Kandahar Province

This paper presents a case study examining the impacts of climate change on groundwater resources in the Kandahar province of Afghanistan. The study assesses the vulnerability of groundwater sources to changing climatic patterns and explores the implications for water availability, quality, and sustainability in the region. Through a combination of field surveys, hydrogeological investigations, and analysis of climatic data, the research aims to provide insights into the specific challenges faced by Kandahar province in managing its groundwater resources in the context of a changing climate. The findings of the study reveal the significant influence of climate change on groundwater availability, with altered precipitation patterns and increasing temperatures leading to shifts in recharge rates and aquifer dynamics. Moreover, the study examines the potential exacerbation of water stress, groundwater depletion, and saltwater intrusion in coastal areas due to rising sea levels and changing rainfall patterns. The insights derived from this case study contribute to a deeper understanding of the complex interactions between climate change and groundwater resources, providing valuable knowledge for policymakers, water resource managers, and stakeholders involved in sustainable water management and climate adaptation initiatives in Afghanistan.

Climatic Data Trend Analysis by Mann Kendal Test and Innovative Trend Analysis: A Case Study of Taloqan River at Takhar, Afghanistan.

Climate change is taking place due to increased population and anthropogenic activities. The impacts of climate change have increased the demand for sustainable management of water resources. The semi-arid climatic zone is widely affected by climate change. Afghanistan is vulnerable to climate change because of its climate and variable precipitation. This research has studied the trend analysis of climatic data in the Taloqan River, which is being utilized for drinking and irrigation. The recorded data of the Tangi-Farkhar hydro meteorological station from 2008 to 2021 is used in this work. The non-parametric Mann-Kendall (MK) trend test and the Innovative Trend Analysis (ITA) method are utilized in this investigation. This study has found an increasing trend in the mean value of temperature in May across the period of study but no trend in annual temperature data according to the MK test with a 95 percent confidence level. The ITA shows an increasing trend in the annual temperature data, a decreasing trend in precipitation, and a non-monotonic negative increasing trend in discharge. The MK test has presented no trend in the annual data of precipitation and discharge and showed an increasing trend in the mean discharge value in October and November across the study period.

The Impact of Climate Change on Sultan Dam

The Sultan Dam, located in the Jaghatu District of wardak province of Afghanistan, is a crucial water resource supporting irrigation, hydropower generation, and domestic and industrial water supply. However, the dam is increasingly vulnerable to the impact of climate change. This study assesses the specific impact of climate change on the Sultan Dam during the period of 2015 to 2023. By employing a multidisciplinary approach, including historical climate data analysis, hydrological modeling, and GIS, the study examines changes in water resources, sedimentation patterns, and reservoir dynamics. The analysis focuses on climate change-related effects such as shifts in precipitation patterns, temperature variations, and changes in the frequency and intensity of extreme weather events like droughts and floods. The findings highlight the significant challenges faced by the dam and its water resources due to climate change such as a lack of water capacity, the collapse of the wall of sultan dam, lack of agricultural products, heavy rains, increase of floods and soil erosion and so on. Recommendations include enhancing water management practices, upgrading dam infrastructure, and integrating climate-informed operations. Incorporating an integrated water resource management approach, promoting public awareness, and investing in ecological restoration efforts are crucial for improving the dam's resilience and ensuring sustainable water availability. The study's outcomes provide valuable insights for policymakers, decision-makers, and researchers working towards effective climate change adaptation strategies for the Sultan Dam and similar water resources in the region.

Changes in water surface area of the Middle Atlas-Morocco lakes: A response to climate and human effects

The lakes represent crucial surface water resources and an integral part of wetlands. The most concerning aspect of the degradation of these areas is the complete drying up of the lakes. In the Mediterranean region, successive changes in land use practices in the context of climate change have strongly influenced wetland areas. In this study, we used Landsat TM, OLI, and OLI-2 satellite images to monitor the water surface area in two representative lakes (Aoua and Ifrah) of the Tabular Middle Atlas and to map land use across the entire study area. To extract information related to lakes and land use, we employed the Support Vector Machine machine learning algorithm, widely used in remote sensing studies. However, we identified drought periods from precipitation data using the Standardized Precipitation Index (SPI) recommended by the World Meteorological Organization (WMO). The results obtained from the processing of Landsat satellite images indicate a significant reduction in the surface area of the lakes, with periods of drying for Aoua lake, endangering their fragile ecosystems and biodiversity. The critical situation of the two lakes is attributed to a combination of natural and anthropogenic factors. The analysis of climatic data shows a significant climate change from the 1980s, with long periods of drought. In parallel, the study area has undergone remarkable modifications in land use patterns, mainly characterized by a significant extension of irrigated agricultural surfaces to the detriment of grazing and rainfed lands. In three decades, the area of irrigated crops has increased from approximately 1300 hectares in 1985 to 7070 hectares in 2022, representing an increase of 542%. The findings presented in this study reveal the extent of lake degradation in the TMA and reflect the alarming decline in groundwater levels. This situation indicates the necessity of formulating a strategy to protect water resources and wetlands in the Middle Atlas.

A Bayesian model for predicting monthly fire frequency in Kenya.

This study presents a comprehensive analysis of historical fire and climatic data to estimate the monthly frequency of vegetation fires in Kenya. This work introduces a statistical model that captures the behavior of fire count data, incorporating temporal explanatory factors and emphasizing the predictive significance of maximum temperature and rainfall. By employing Bayesian approaches, the paper integrates literature information, simulation studies, and real-world data to enhance model performance and generate more precise prediction intervals that encompass actual fire counts. To forecast monthly fire occurrences aggregated from the Moderate Resolution Imaging Spectroradiometer (MODIS) data in Kenya (2000-2018), the study utilizes maximum temperature and rainfall values derived from global GeoTiff (.tif) files sourced from the WorldClim database. The evaluation of the widely used Negative Binomial (NB) model and the proposed Bayesian Negative Binomial (BNB) model reveals the superiority of the latter in accounting for seasonal patterns and long-term trends. The simulation results demonstrate that the BNB model outperforms the NB model in terms of Root Mean Square Error (RMSE), and Mean Absolute Scaled Error (MASE) on both training and testing datasets. Furthermore, when applied to real data, the Bayesian Negative Binomial model exhibits better performance on the test dataset, showcasing lower RMSE (163.22 vs. 166.67), lower MASE (1.12 vs. 1.15), and reduced bias (-2.52% vs. -2.62%) compared to the NB model. The Bayesian model also offers prediction intervals that closely align with actual predictions, indicating its flexibility in forecasting the frequency of monthly fires. These findings underscore the importance of leveraging past data to forecast the future behavior of the fire regime, thus providing valuable insights for fire control strategies in Kenya. By integrating climatic factors and employing Bayesian modeling techniques, the study contributes to the understanding and prediction of vegetation fires, ultimately supporting proactive measures in mitigating their impact.

Climate Change, Pressures, and Adaptation Capacities of Farmers: Empirical Evidence from Hungary

This paper aims to analyze comprehensively the climate exposure, sensitivity, perception, adaptive capacity, vulnerability, and resilience of the Hungarian agricultural sector, particularly focusing on fruit, vegetable, and grape producers. Four distinct Hungarian case studies were examined, representing different regions with diverse environmental and socioeconomic conditions. The research combined quantitative and qualitative methods, including statistical and GIS analysis of climate, agricultural, and socioeconomic data, as well as field research and semi-structured interviews. The study investigated exposure, sensitivity, perception, and adaptation, leading to the identification of key components and influencing factors. Qualitative research revealed that farms operating in geographically close proximity, in the same regulatory and support environment, can have different adaptive capacities. In the current state of the adaptation process, the extent to which farmers can rely on supportive professional networks and seek out and adopt new practices is crucial. Field experience suggests that without a strong and supportive producer organization (extension network), farmers may prefer to resort to extensification (afforestation) to mitigate production risks. From a development policy perspective, it is worthwhile to present good practices and provide information on possible adaptation techniques through existing local sectoral organizations.

Enhancing Building Energy and Thermal Performance in Metro Manila, Philippines through Computational Fluid Dynamics-Assisted Analysis and Optimization

This study presents a detailed investigation of the energy and thermal performance of buildings in the Philippines’ metropolitan cities, targeting sustainability and indoor comfort against the backdrop of the country’s tropical maritime climate. Utilizing a blend of occupant surveys, energy audits, climate data analysis, and building envelope assessments, the research employs Computational Fluid Dynamics (CFD) simulations to accurately evaluate energy use and thermal behaviors in residential and commercial structures. Key findings include distinct nighttime energy consumption patterns in residential buildings and the identification of climatic challenges such as high temperatures and humidity. The study proposes effective design interventions like enhanced ventilation, shading solutions, and the integration of Phase Change Materials (PCMs) for improved thermal efficiency. Significantly, the research contributes to policy development by suggesting updates in building energy codes and advocating for energy-efficient practices. Overall, the study provides actionable insights for enhancing building sustainability and occupant comfort in urban Philippine settings.

Assessing the Risk of Climate-induced Natural Disasters using Risk Assessment Models

The escalation of climate-induced natural disasters—such as hurricanes, floods, droughts, and wildfires—constitutes an increasing hazard to populations and infrastructure globally. This research examines the incorporation of risk assessment models in forecasting, assessing, and alleviating the effects of severe calamities. We evaluate the susceptibility of different locations to climate-related risks by conducting a thorough analysis of historical climate data and use sophisticated risk modeling methodologies. Catastrophe modeling (CAT), geographic information systems (GIS), and statistical hazard analysis are used to delineate high-risk areas and assess possible losses. The results highlight the significance of early warning systems and sustainable adaption measures, particularly for areas increasingly impacted by severe climatic events. The research underscores the constraints of existing models and stresses the need for cohesive, data-informed strategies to enhance the prediction and management of catastrophe risks.

Influence of urban greenery on thermal comfort: a case study in a tropical city

This case study focused on the city of São Luís (MA), aiming to analyze the role of urban greenery in the thermal comfort of São Luís, identifying urban and landscape planning strategies that promote the integration of urban vegetation to create a more livable and thermally comfortable environment in the city. To do so, a research methodology with an exploratory and quantitative-qualitative character was employed, involving data collection on temperature and precipitation provided by INMET, as well as a floristic survey in the central region of São Luís, considering the significant impact of urban vegetation on the city's temperature and precipitation. The results showed a strong association between the urbanization process and the increase in urban temperatures, a phenomenon known as the "urban heat island," while urban greenery has been proven as an effective strategy to mitigate this effect, even under conditions aggravated by the El Niño phenomenon. In this context, the research also revealed that an increase in urban vegetation can have positive impacts on reducing temperature and improving the thermal comfort of residents, with the analysis of climate data from 2018 to 2023 being crucial in understanding the relationship between urban vegetation and thermal conditions in the city. The results emphasize the importance of the quantitative-qualitative approach, which allows for a comprehensive and in-depth analysis of the studied phenomenon. Furthermore, the use of climate data provides a solid foundation for analysis and encourages the exploration of specific urban and landscape planning strategies, taking into consideration the effectiveness of these strategies in enhancing thermal comfort within urban centers.

Trend Analysis of Seasonal Drought in Northern Karnataka, India

Trend analysis of climatic data is often carried out in water resources management studies to understand its distribution over a given region. This paper presents an analysis of trends in meteorological drought in 12 districts in the northern part of Karnataka state, India over the period 1991–2020. Trends in the 3-month timescale standardized precipitation index (SPI-3) were analyzed using the Mann-Kendall (MK) trend test and Sen’s slope estimator for the Kharif season. The results of analysis showed an increasing trend of droughts in four districts however, the trend in most of the districts was not statistically significant. The findings were validated with the trend of the declared area under drought (AUD) by the state government for the study area. The findings of this work might be useful for a better understanding of regional drought trends and also for establishing effective water resources management policies.