Search Results (1,502)

Temperature variability and weather-related fluctuations significantly affect the energy, agricultural, and industrial sectors that are highly sensitive to meteorological changes. These conditions may lead to financial losses caused by demand fluctuations and operational disruptions. This study aims to develop a fractional weather-derivative pricing model based on temperature dynamics by integrating the Ornstein–Uhlenbeck (OU) process, the classical Black–Scholes model (BSM), and the fractional Black–Scholes model (fBSM). Daily temperature data from 2016 to 2025 obtained from the Bandung Geophysical Station, West Java, Indonesia, were used as the basis of analysis. Temperature dynamics were modeled using an OU process, and parameter estimation was conducted using Ordinary Least Squares (OLS). The strike price was determined using Historical Burn Analysis (HBA), whereas weather-derivative pricing was performed using call and put option approaches under both the BSM and fBSM frameworks, incorporating the Hurst parameter to capture long-term memory effects. The results indicate that the fractional Black–Scholes model analytical solution is obtained using the Daftardar–Gejji Aboodh method. Furthermore, the OU process successfully captured daily temperature dynamics, yielding a Mean Absolute Percentage Error (MAPE) of 4.344% and a Root Mean Square Error (RMSE) of 1.396 C, indicating high predictive accuracy across both relative and absolute error measures. In addition, the fBSM consistently generated higher option values than the classical BSM, particularly under higher observed temperatures during the study period and at higher strike prices. These findings demonstrate that long-term memory significantly influences effective volatility and option valuation. This study is expected to contribute to the development of weather derivative models that more realistically represent temperature dynamics and to serve as a reference for weather derivative pricing, hedging, and decision-making, as well as for more measurable, systematic, and sustainable climate-related financial analysis using derivative pricing frameworks. Full article

Urban heat stress is intensifying under climate change, particularly in outdoor public spaces where conventional mechanical cooling is impractical. This study develops a climate-driven, system-level numerical framework to evaluate the pre-deployment feasibility of modular, solar-powered spray cooling canopies across 110 cities in Türkiye. Hourly Typical Meteorological Year (TMYx) weather files, representing a single typical year constructed from 2009 to 2023 source data, are used to estimate photovoltaic (PV) energy yield, electrical load, feasible misting duration, water demand, and PV-to-load autonomy under summer daytime conditions. The misting operation is governed by a rule-based adaptive control strategy based on air temperature, relative humidity, and plane-of-array irradiance. To support transferable comparison, the cities are classified into six summer climate-archetype zones using k-means clustering of standardized climate variables, including temperature, humidity, irradiance, wind speed, and summer precipitation. Results show that evaporative cooling feasibility is governed primarily by humidity rather than temperature alone. Hot–Dry Inland cities exhibit the longest mean misting duration (501.90 h) and highest water demand (30,152 L per module), but the lowest PV-to-load autonomy ratio (1.55) because of high pump-driven electrical demand. In contrast, Humid Black Sea cities show minimal misting duration (11.43 h) and water use (465 L per module), but the highest autonomy ratio (39.68) due to very limited system activation. Thus, high autonomy does not necessarily indicate high cooling usefulness. The proposed framework provides a reproducible screening tool for identifying where PV-powered spray cooling canopies are climatically suitable, where water and PV sizing become limiting, and where alternative outdoor heat-mitigation strategies may be more appropriate. Full article

This study investigates recent changes in the surface area of Lake Marmara, a shallow lake located in western Türkiye under Mediterranean climate conditions, and their relationship with hydrometeorological variability. Lake surface area dynamics were quantified using the Modified Normalized Difference Water Index (MNDWI) derived from Landsat satellite imagery processed on the Google Earth Engine (GEE) platform. Climatic conditions were characterized by using precipitation, air temperature, and potential evapotranspiration data obtained from the ERA5-Land reanalysis dataset, from which drought indices including the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) were derived. Temporal analyses covering the period 2000–2025 were conducted to identify long-term tendencies and seasonal variability in lake area and climatic indicators. The results indicate that the rapid post-2015 lake desiccation cannot be explained by a statistically significant monotonic meteorological drought trend alone, highlighting the likely contribution of basin-scale hydrological pressures. Full article

Accurate urban building energy modeling (UBEM) is constrained by mismatches between standard climate inputs and actual urban microclimate conditions. This study introduces a scalable, bottom-up, framework that integrates EnergyPlus building energy modeling simulation outputs with Earth observational and geographical-based urban morphology data, which are enhanced through machine learning techniques to improve energy demand predictions in urban settings. Applied to Los Angeles (LA), California, we evaluate the representativeness of typical meteorological year (TMYx) sampling sites against actual urban environmental conditions. We find that while satellite-derived surface temperatures show reasonable alignment with average city conditions, significant discrepancies are observed in urban form metrics such as tree cover, street cover, and building density, suggesting that TMYx stations should be placed in denser urban areas. We augment EnergyPlus simulations for 19 single-family buildings, with remote sensing data using machine learning models, to generate city-wide residential energy consumption heatmaps corrected for microclimate conditions. Models capture substantial intra-urban variation, with predicted energy use differing by approximately 10% between neighborhoods. Feature importance analysis highlights land surface temperature as a key predictor, underscoring its relevance to building energy research. We also find the majority of TMY3 sampling sites to be in low-vulnerability areas, underscoring the structural mismatch that is embedded in urban form and climate. This framework offers a scalable path for integrating urban microclimate effects into energy modeling to enable more precise and equitable energy policy and planning. Full article

Climate projections indicate significant changes in temperature patterns and other meteorological parameters under different climate change scenarios, with temperature receiving special attention due to its influence on thermal conditions and human discomfort. This study examines the relationship between diurnal temperature range (DTR) and thermal discomfort in the five largest cities of Greece during summer. Thermal discomfort is assessed using Thom’s discomfort index (DI), where values ≥ 21 indicate the onset of thermal discomfort, focusing on thermal conditions at the upper (DI_h) and lower (DI_c) boundaries of daily variability. The analysis uses multiple CMIP6 projections for the reference period (1981–2010) and the near future (2031–2060) under the SSP2-4.5 and SSP5-8.5, representing intermediate and high greenhouse gas forcing pathways, respectively. The study aims to investigate associations between DTR and DI-based thermal discomfort. DTR is projected to increase in most cities in the near future relative to the reference period. This reflects a regional specific response that differs from the global tendency reported in the literature for minimum air temperatures (T_min) to increase faster than maximum air temperatures (T_max). Effect size analysis of DTR indicates generally small effects in Thessaloniki, medium to large effects in Larissa depending on the scenario, and large effects in Heraklion, Athens and Patra. Projected differences in DTR are consistent with the asymmetrical response of air temperature, specifically to the higher increase rate in T_max than in T_min in most cities. DI-based thermal discomfort shows a clear contrast between upper (DI_h) and lower (DI_c) boundaries of daily variability, reflected in higher discomfort classes for DI_h and lower classes for DI_c. Higher DTR values are associated with higher DI_h-based thermal discomfort, while the corresponding association between DTR and DI_c is weak or absent. The positive association observed for the DI_h-based conditions is largely governed by the shared contribution of T_max to both DTR and the discomfort index, whereas the absent or weak association for DI_c-based conditions may reflect the weaker association between DTR and T_min as well as the relatively smaller variability of T_min. Full article

Climate, and especially cities with hot climatic conditions, directly impact human life. In this study, hourly datasets from the central meteorological station in Diyarbakır city center for the years 1990–2022 were utilized. These data were analyzed using RayMan Pro-2.1 software, and Physiological Equivalent Temperature values were derived. The obtained Physiological Equivalent Temperature values were analyzed using the SARIMAX model implemented on a machine learning infrastructure to uncover the changes between 2022 and 2050. According to the results obtained, the Physiological Equivalent Temperature value, which was 15.42 °C in 1990 in real terms, increased by 21.3% to 18.66 °C in 2022. According to the SARIMAX model predictions, Physiological Equivalent Temperature values in 2022 are estimated to rise to 21.42 °C by 2050, reflecting an increase of 14.79%. The aim of this study is to examine the temporal variations in human bioclimatic comfort values and provide a foundation for future predictions. This will contribute to the development of urban master plans by local and administrative authorities. Full article

Sustainable urban air quality in tropical cities is threatened by interactions between climate change, extreme drought, and long-range wildfire smoke transport. This study investigated the causes of PM_2.5 pollution in Manaus, Brazil, under El Niño conditions during the 2023 drought, focusing on long-range wildfire smoke transport. The links among hydroclimatic drying, wildfire activity, and urban air quality were examined using hourly PM_2.5 observations, meteorological data, long-term climate records, MODIS hotspot and fire radiative power (FRP) data, and air-mass trajectory analyses. Significant long-term warming, decreasing precipitation, and a declining standardized precipitation evapotranspiration index were observed around Manaus during 1981–2024, indicating persistent drying. In 2023, severe drought and increased wildfire activity caused an annual mean PM_2.5 concentration of 15.09 µg m⁻³. Directional analyses, upwind FRP, potential source contribution function, and backward trajectories consistently highlighted the eastern and southeastern source regions approximately 500–2200 km from Manaus. These results indicated that PM_2.5 levels were more sensitive to spatial alignment between upwind fires and prevailing winds than to total fire activity alone. In conclusion, the 2023 PM_2.5 surge was driven by long-range wildfire smoke transport under intensified drying and drought, with implications for urban sustainability, public health, and climate-resilient early warning systems. Full article

In the context of subtropical cities, the slow-moving environment of HOD (Hospital-Oriented Development) faces the dual challenges of spatial fragmentation and an extreme hot and humid climate, which also restricts the outdoor space’s thermal environment performance. Taking the Macau Light Rapid Transit (LRT) Union Hospital Station as an example, this study constructs a “topology-climate” dual quantitative assessment framework that integrates space syntax and parametric universal thermal climate index (UTCI) simulation. In response to the current problems of mixed pedestrian and vehicular traffic and high-intensity heat radiation, a comprehensive intervention strategy combining three-dimensional stitching and spatial optimization is proposed. The results show that: (1) The implantation of three-dimensional corridors improved the spatial integration of the core area of the site by 67.0%, significantly optimizing network connectivity. (2) During the extreme high-temperature period of daytime (9:00–18:00) in summer and autumn, the intervention strategy precisely opened up a continuous low-heat-stress linear shade zone through the synergistic mechanism of building projection shadows, physical shading of connecting corridors, (landscape shading effect, original evaporation removed). (3) The study confirms that landscape-coupled shading layout is the most effective method, reducing potential pedestrian heat exposure across the entire area, while the three-dimensional connecting corridors precisely control the thermal environment of core walkways. Together, these two elements construct a “topology-climate” optimization framework, achieving a synergistic improvement in spatial accessibility and simulated thermal comfort performance under standard meteorological input and quantitatively verifying the optimization effectiveness of the tiered intervention scheme. This study provides a data-driven decision-making basis for optimizing potential walking thermal conditions for vulnerable groups and reshaping the space’s potential to improve microclimate via shading design of medical hub areas and also provides a scientific paradigm for TOD microclimate planning focused on shading-based thermal environment optimization. Full article

This study investigates the hydroclimatic impacts of climate change on the Göynük Stream Basin, a snow-fed tributary within the Euphrates River Basin, utilizing flow, precipitation, and temperature data from 1975 to 2022. The Göynük Stream Basin is characterized by high-altitude, harsh continental conditions, with its flow regime heavily influenced by snowmelt, rendering it particularly sensitive to climate change. Employing a suite of trend analysis methods, including Mann–Kendall, Spearman Rho, Theil–Sen, Şen-Innovative Trend Analysis (ITA), and Innovative Polygon Trend Analysis (IPTA), the research evaluated annual and seasonal data from one stream and four meteorological stations across multiple significance levels (90%, 95%, 99%). Unlike conventional hydroclimatic studies based solely on monotonic trend detection, this study integrates classical trend tests, innovative trend approaches, temporal regime-based analysis (RAPS), and machine learning techniques within a unified assessment framework to evaluate both hydroclimatic variability and runoff predictability under climate change conditions. Key findings indicate a significant decline in annual flow rates by approximately 9.37%, with a notable decrease in maximum flow rates evidenced by a negative trend slope of −0.2726 m³/s/year. While precipitation trends were generally decreasing, temperature data exhibited significant increases, especially during winter and spring. Seasonal analysis revealed substantial flow reductions in summer and autumn, coupled with an earlier timing of the annual maximum flow, shifting from mid-May to late March/early April, suggesting earlier snowmelt. The study concludes that the Göynük Stream Basin is experiencing increasing hydroclimatic pressures attributable to climate change. These insights are crucial for water resource management and serve as a guideline for similar snow-fed sub-basins within the broader Euphrates River Basin. Furthermore, the integration of a machine learning approach, utilizing meteorological and seasonal data, demonstrated strong monthly runoff prediction capabilities with NRMSE of 4.11% and R² equal to 0.951. Feature importance analysis highlighted seasonality and temperature as primary predictive factors. However, a marked decline in model accuracy after 2011 was observed, indicating a non-stationarity in the hydroclimatic system, likely driven by climate change impacts and underscoring the need for adaptive management strategies. Full article

Weather–yield relationships in arid agricultural regions are shaped jointly by temperature and precipitation exposure, irrigation conditions, crop choice, and management under water constraints. This study combines county-level cotton yield and maize grain-yield data for Xinjiang, China, from 2000 to 2020 with daily meteorological station records assigned to county-level weather exposures. We estimate two-way fixed-effects models that include temperature degree-day indicators and a quadratic precipitation term to examine crop-specific weather–yield associations in irrigation-intensive oasis agriculture. The baseline two-way fixed-effects estimates indicate that a 100 °C d increase in growing degree days is associated with a 2.85% increase in cotton yield and a 1.88% decrease in maize yield. For cotton, the baseline and common-trend specifications indicate a convex precipitation–yield association, with an estimated turning point of 141.07 mm (95% CI: 27.75–225.63 mm), while the pattern is less stable under prefecture-by-year fixed effects. Maize yield is more consistently negatively associated with growing-season heat accumulation. Post-2010 interaction terms indicate crop-differentiated changes in heat sensitivity, consistent with different temporal evolution of weather–yield associations across cotton and maize. Overall, the results show that climate-risk assessment in irrigation-intensive arid agriculture should distinguish between crop types, precipitation regimes, and the management conditions under which weather exposure is translated into yield outcomes. Full article

Investigating the spatiotemporal dynamics of urban heat islands and their responses to urban forest (UF) landscape patterns is crucial for mitigating urban thermal stress. However, the nonlinear influence and conditional constraints of UF landscape composition and configuration on the warming effects across varying urbanization gradients remain inadequately understood. By integrating land use/cover data, MODIS-derived land surface temperature (LST), and meteorological datasets, this study employed the XGBoost-SHAP model to quantify the nonlinear and interaction effects of UF landscape patterns on developed and developing urban regions of the Pearl River Delta. The results indicate that (1) spatial clustering patterns of warming varied significantly between the two regions, with substantial seasonal heterogeneities (p < 0.05). Summer exhibited the most intense warming, characterized by more rapid temperature increase in developed areas than in developing regions. (2) Relative to UF landscape metrics, the proportion of impervious surfaces, precipitation, and temperature exerted greater influence on regional warming. Coverage area, fragmentation, and connectivity of UFs emerged as the primary landscape drivers modulating warming. In developed areas, spatial configuration metrics exerted greater influence on LST than compositional metrics. (3) The responses of LST to diverse UF landscape patterns are characterized by nonlinearity and pronounced threshold effects. These landscape thresholds vary by season, revealing critical tipping points for warming suppression; however, this regulatory effect is highly context-dependent. Specifically, under high percentages of impervious surface, the warming-suppression capacity of UFs intensifies with increasing percentage of UF area or core. Our findings highlight the necessity of strategic UF planning and forest fragmentation mitigation for developing effective climate resilience strategies. These results provide a foundation for adaptive planning tailored to specific urbanization stages and the implementation of targeted UF cooling strategies. Full article

Mining activities elevate environmental noise and represent a major disturbance in terrestrial ecosystems. Vegetation belts are often used as mitigation measures. This study evaluates the role of Robinia pseudoacacia L. forest plantations in reducing noise at the lignite complex of western Macedonia, in Greece. Field measurements of noise level (LAeq) were conducted inside and outside the plantations from spring to autumn during 2020 and 2021. Measurements were taken at five points across four sites differing in their distance from the noise source. Leaf Area Index (LAI) was recorded, and meteorological variables were measured concurrently. Linear mixed-effect models were used to assess the effects of forest presence, distance from source, climatic conditions, and LAI, while accounting for repeated measurements across sampling days and sites. Noise levels were significantly lower within plantations than outside, indicating that restored forest stands can act as buffers to mining noise. The distance of trees from the noise source and atmospheric conditions are also significant drivers of noise levels. These findings highlight the potential of post-mining plantations to provide an additional acoustic regulation service in restored industrial landscapes. Full article

This study presents a dataset of meteorological and soil-hydrological instrumental observations collected at three agrometeorological stations in the East Kazakhstan Region during the growing seasons of 2022–2025. The dataset includes time series from automatic weather stations: WS “OCES-1” (Solnechnoe village) provides hourly data over four years (2022–2025; 14,614 records; 65 variables), while WS “OCES-2” (Lugovoe village; 203,279 records) and WS “Altyn Kazan” (Sulusary village; 207,115 records) provide minute-resolution data for 2025 (49 variables each). Measured parameters at 200 cm height include air temperature and humidity, atmospheric pressure, precipitation, wind speed and direction; soil measurements down to 100 cm depth include temperature and moisture. Also, field-based express measurements of volumetric soil moisture within a 1 m profile (every 10 cm) were collected during three campaigns (May–August 2025), resulting in a total of 253 measurements. The stations are located across steppe and forest-steppe landscapes of the transboundary Altai–Sayan mountain region on active agricultural lands under diverse soil–climatic conditions. Climate types correspond to Dfb and Dfa per the Köppen–Geiger classification. Soils are classified under WRB as Chernozems and Calcic Chernozems. The dataset is published in CSV format on Zenodo under a CC-BY 4.0 license. Full article

The risk of wildfires is increasing due to high temperatures and dry weather conditions caused by climate change. Outbreaks and spread of wildfires are usually conditioned by weather, topography, and fuel characteristics. In the Republic of Korea (hereafter, the ROK), most wildfires are caused by anthropogenic factors rather than natural ones. However, the current forest fire forecasting system being operated in the ROK does not account for anthropogenic factors. To analyze the impact of human and physical factors on wildfire occurrence, a binary logistic regression model was constructed using data from the Gangwon and Gyeongbuk provinces from January 2022 to August 2025. The dependent variable was defined as the occurrence of a wildfire, while the independent variables comprised meteorological, seasonal, stand, and anthropogenic factors. To address multicollinearity, variables with high correlation coefficients were excluded from the independent variables, which were selected by three estimating approaches, including logistic regression and two machine learning techniques (namely, Random Forest and XGBoost). With machine learning, the variables with high feature importance were identified. The explanatory power of the logistic regression analysis with independent variables selected by the machine learning models was about 1.3 times higher than that of the model using variables adjusted solely for multicollinearity. The results of logistic regression analysis revealed that weather and coniferous forests are the most important factors fostering wildfires, while the mean stand age was the most significant factor in hindering wildfires. Among the anthropogenic factors, forest road density acted as a suppressor of wildfire spread rather than a promoter of occurrence. Conversely, trail density tends to increase the risk of wildfire occurrence. Among forest management activities, plantation forests may increase the risk of forest fires, although this remains uncertain. These findings suggest that preventing wildfires requires a paradigm shift in forest resource management policies, including extending forest rotation ages and converting coniferous forests to broadleaf forests. Meanwhile, it also indicates the need to restrict the expansion of hiking trails and improve regulations regarding hiker access and behavior to prevent wildfires. Full article

Photovoltaic (PV) systems integrated with green roofs have attracted increasing research interest due to their potential influence on rooftop microclimatic conditions and photovoltaic operating performance. This study experimentally investigated the thermal and electrical behavior of two identical PV modules installed above green and asphalt roof surfaces under real operating conditions in a Central European climate. Rear-side module temperatures and meteorological parameters were monitored, while electrical performance was evaluated using on-site I–V curve measurements. The observed rear-side temperature differences ranged from 0.01 °C to 0.86 °C during the monitored short-term summer periods. A representative I–V measurement indicated approximately 13% higher instantaneous maximum power output for the PV module installed above the green roof configuration under comparable operating conditions. However, the electrical results should be interpreted cautiously due to short-term environmental variability and irradiance-related uncertainty during consecutive field measurements. The presented results correspond to a short-term summer field-monitoring study and should not be generalized to annual photovoltaic performance without extended long-term multi-season experimental validation. The scientific contribution of this study lies in the synchronized side-by-side evaluation of identical PV modules using combined rear-side thermal monitoring and in-situ electrical characterization under real operating conditions. Full article