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- Enhancing urban temperature monitoring through high-resolution remote
sensing and advanced data processing techniques-
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Abstract: This study leverages high-resolution remote sensing and data processing advanced techniques to improve ambient urban temperature monitoring. Using Landsat 8 and Sentinel-2A satellite imagery, the methodology combines spectral harmonization and correction techniques with Convolutional Neural Network (CNN)-based super-resolution models to achieve a high spatial accuracy in Land Surface Temperature (LST) and Air Temperature (Ta) estimations. The analysis integrates key environmental indices such as the Normalized difference vegetation, water, and built-area indices, and corrects for atmospheric and surface effects to refine LST data. Results demonstrate that CNN models improve temperature spatial detail significantly to a resolution of 1 m with an R2 above 0.85, and with estimations of aerial temperature optimized using Météo-France and validated against Météociel data, showing errors within 2°C. Regression models further estimate Ta from LST with R2 values above 0.75, effectively mapping temperature distributions at fine resolutions for urban settings. This study bridges critical gaps in remote-sensing based temperature monitoring, hence offering a framework for high-resolution urban thermal analysis in regions with limited meteorological data.
PubDate: 2025-07-02
- Remote sensing-based impact analysis of artificial lighting on land
surface temperature using google earth engine-
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Abstract: UHIs are exacerbated by urbanization, which is on the rise in both developed and developing countries and is seen as a marker of progress. Changes to metropolitan landscapes have led to varied climates, even among urban areas situated within the same climatic zone. Utilizing remote sensing data from Thailand between 2015 to 2023, this study examines the spatiotemporal patterns of night-time light (NTL) and night land surface temperature (LST), employing the Google Earth Engine (GEE). The geographical response relationship between these factors was also investigated using hotspot analysis, local Moran's I spatial autocorrelation, and standard deviation techniques. According to research findings, NTL increased while night LST decreased in Bangkok, Pattaya City, Phuket, Hat Yai, and Chiang Mai, falling from 28.13 °C in 2015 to 27.54 °C in 2023. From 2015 to 2023, the highest Bidirectional Reflectance Distribution Function's (BRDF) NTL rose from 232.36 nW/sr/cm2 to 265.91 nW/sr/cm2. Reports indicate that 99% of hotspots in Bangkok, Chiang Mai, Hat Yai, and Phuket often form high clusters. The midnight LST and NTL for the years studied exhibit a strong correlation as indicated by R2 values. A year-by-year analysis of national data suggests that innovative adaptation strategies should be implemented to protect Thailand's tourism assets.
PubDate: 2025-07-02
- Climate challenges in castor bean production: agroclimatic zoning and
future prospects for sustainable biofuel-
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PubDate: 2025-07-02
- A dual-source energy balance model-assisted ensemble learning approach for
estimating latent heat flux-
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Abstract: Latent heat flux (LE) plays a crucial role in the global water cycle and surface energy exchange. The accuracy of machine learning models depends on the quality of input data. Therefore, this study proposes a physical model-assisted ensemble learning (EML) framework to improve data quality during the training process. The Heihe River Basin (HRB) in Northwest China was selected as the study area. The joint models were compared with the physical model (TSEB) and three EML models (XGBoost, LightGBM, CatBoost). The SHAP method was used to interpret the results of the joint models and quantify the contributions of input features to LE estimation. The modeling errors of different sites in the joint model were evaluated and retrieved in the region. The results show that EML models exhibited good fitting and generalization capabilities, with good accuracy in terms of MAE and RMSE. The XGBoost model showed an improvement in estimation accuracy by 4.20–8.92% and 7.11–13.88% compared to LightGBM and CatBoost models, respectively. The joint models improved accuracy by 0.2–4.74% over the individual EML models, with the TSEB-assisted XGBoost model demonstrating the best overall performance. The joint models effectively captured the impacts of energy, temperature, moisture, and vegetation on LE in the HRB. The joint models were within an acceptable range when modeling at all sites, and the regional spatial patterns of LE were consistent.
PubDate: 2025-07-02
- Micro-level assessment of agricultural vulnerability to climate
variability in Mirzapur District, Uttar Pradesh-
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Abstract: The variation in climatic parameters like temperature, precipitation and humidity significantly influences agricultural ecosystem and human societies by affecting crop yield, cropping pattern and overall agricultural practices. Understanding agricultural vulnerability to these variations is crucial for preventing long-term consequences such as food security, changing human settlement patterns and economic instability. Thus, this study attempted to study micro level vulnerability in agriculture in Mirzapur district of Uttar Pradesh. The study aimed to introduce Agriculture Vulnerability Index (AVI) and evaluate the farmer perception about climate change and significant impact on agriculture utilising both primary and secondary data. The primary data was collected from 240 respondent using multistage random stratified sampling. The AVI was computed utilising Shannon Information Entropy method based on four indicators such as exposure to climatic variability, exposure, sensitivity, and adaptive capacity divided into 23 indicators. Ground Water Extraction (17.03%), Agricultural Wasteland (9.92%), Rural population (7.30%), and Barren land (6.21%) disproportionately influenced the agricultural vulnerability. The findings of the study revealed significant variation in agricultural vulnerability in which Hallia, Kon, Nagar, and Pahari block were found to be severely vulnerable to climatic variation. The farmers opinion revealed that the study area is experiencing erratic climatic variation like unseasonal rains and droughts causing serious distress to them. The agricultural landscape has undergone a notable transformation, characterized by a transition to a rice–wheat-gram cropping system, primarily facilitated by enhanced irrigation infrastructure as observed from primary survey. The farmers are compelled to revert to their old system of millet-gram-wheat cropping system due to increased ground water exploitation and present climatic variability. The research underscores the critical imperative of implementing agricultural diversification strategies, drought-resistant crop varieties, micro-irrigation, or policy incentives to mitigate climate vulnerability and improve food sustainability. The study reinforces the farmers opinion-based research to integrate indigenous knowledge system with conventional science-based knowledge to enhance resilience and ensure agricultural development at the micro level.
PubDate: 2025-07-02
- Evaluating the performance of ANN and ANFIS models for spatial
precipitation prediction in complex terrain: a case study in central
Anatolia-
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PubDate: 2025-07-02
- The role of Tropical Easterly Jet on improving the predictability of
summer rainfall variability over the Upper Blue Nile Basin in Ethiopia-
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Abstract: Effective management of rain-fed agriculture in drought-prone countries like Ethiopia requires a comprehensive understanding of rainfall variability and improved predictive capabilities. There is a significant research gap remains in understanding the combined interaction and dynamic influence on rainfall patterns in the Upper Blue Nile Basin (UBNb). Moreover, vertically integrated water content and surface temperature have not been adequately explored for their potential to improve rainfall prediction. This study addresses these gaps by investigating the role of the TEJ in shaping summer rainfall variability over the UBNb. We utilize the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset from 1981 to 2022, specific humidity and wind data from the ECMWF Fifth Generation reanalysis (ERA5) for the period 1979–2022, 200 hPa wind data and SSTs from the NCEP reanalysis (1981–2022) are used both individually and in combination with other climatic variables at Bahir Dar, Debre Markos, Nekemte, and Gore. Ground-based station data from 1990 to 2020 serve to validate two neural network models: NARX-SST and NARX-SST-200 hPa ECMWF. We employed Artificial Neural Network (ANN) models to forecast rainfall by integrating CHIRPS rainfall data, total column water vapor (TCWV), and surface temperature for spatial prediction, while using sea surface temperature (SST), 200 hPa zonal wind, and ECMWF precipitation data for temporal forecasting. The ANN outputs demonstrate a strong association (correlation = 0.99) with the CHIRPS dataset, and low error values are identified. The spatio-temporal analysis significantly enhances the quality and precision of summer rainfall forecasts. This study contributes to improved rainfall prediction critical for sustainable water resource management and agricultural planning in Ethiopia and the broader Nile Basin. The study improves rainfall prediction using ANN models but is limited by the number of stations and variables, suggesting future work should include more data and broader climate drivers for greater accuracy.
PubDate: 2025-07-02
- Improved summer monsoon rainfall extreme indices over India from CMIP6
simulations and projections-
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Abstract: In the present study, the Expert Team on Climate Change Detection and Indices (ETCCDI) for extreme rainfall over India in the Coupled Model Intercomparison Project Phase-6 (CMIP6) historical simulations and future projections are assessed before and after downscaling and bias correction (DBC). The relative performance of the Multi-model mean (MMM) and individual models in representing the frequency and intensity of extreme rainfall events over India and their significant improvement after DBC, is reported. The representation of RX1Day, RX5Day, R99p, R95p, R20mm, and R10mm over India has improved by 83.18%, 80.74%, 94.36%, 83.85%, 64.37%, and 33.25%, respectively in AD-MMM (MMM after DBC) compared to BD-MMM (MMM before DBC). Higher extreme indices, such as RX1Day and R99p, are well captured in the DBC historical product, an important information for choosing the right index for estimating the CMIP6 model projected extremes. However, milder extreme indices, including RX5Day and R95p, and lower extreme indices, like R20mm and R10mm, exhibit limited skill in representing the characteristics of extreme rainfall. The projections indicate that under SSP245 (SSP585), RX1day is expected to increase by 38% (41.65%) in the near future and by 48.53% (62.07%) in the far future. Similarly, extremes based on R99p are projected to increase by 33.12% (37.57%) in the near future and 44.32% (59.33%) in the far future over the Indian region. Both indices indicate a 1.61-fold increase in extreme rainfall over the Indian subcontinent in the far future under the SSP585 scenario compared to the historical period. Particularly, in the near future, the projected increase is expected to be more pronounced over northwest India, while in the far future, the central, northwest and northeast region is projected for most significant rise. In short, this study highlights the potential of CMIP6 models in capturing extreme rainfall indices and their future projections.
PubDate: 2025-07-02
- Evolution of glacier mass balance and surface velocity in response to
climate change in Eastern Himalaya using landsat imageries-
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Abstract: The present study evaluates various glacier parameters (glacier area, mass budget, surface ice velocity) to understand climate change responses to the eastern Himalayan glacier. The different Landsat and ASTER DEM images were retrieved to estimate these parameters during 1990 – 2020. This study used the Accumulation Area Ratio (AAR) approach to estimate the mass balance of the studied region, and the sub-pixel correlation method was utilized for surface ice velocity (SIV) mapping. The glacier region has experienced significant aerial shrinkage (24.5%) and mass loss (-0.41 ± 0.047), associated with increased temperature due to ongoing climate change. The mean SIV of the three selected glaciers ranges from 15 – 21 m/year, along with the central flow line ranging from 45 – 85 m/year during 1990 – 2020. This study contributes to hydrological modelling, which will play an essential role in hydropower generation and social stability response to the global Climate.
PubDate: 2025-07-02
- Assessment of the cooling effect of urban green spaces (UGS) and urban
parks in Varanasi City, India-
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Abstract: Cities around the globe are facing the urban heat island (UHI) effect, driven by increased impervious surfaces, a reduction in green and blue spaces such as trees, parks, and water bodies, as well as anthropogenic heat, and others. Urban green spaces (UGS) and urban parks can effectively mitigate localized UHI and enhance urban thermal comfort by creating a cooling island effect. This study investigated: a) land surface temperature (LST) dynamics in Varanasi, India, over a span of 31 years (1993–2024) using Landsat imageries; and b) the contribution of urban green spaces (UGS) and urban parks to the cooling effect in the city. High-resolution Google Earth imagery was utilized to locate the UGS and urban parks and to extract their internal land surface characteristics. Forty-three (43) UGS and urban parks were selected from various parts of the city for the study. The Cool Island Intensity (CII) index was employed to assess the cooling effect created by UGS and urban parks. Statistical techniques like correlation and regression analysis were also used to explore the relationships between LST, CII, and land surface characteristics. The findings of this study revealed that 1) the city experienced an upward trend in LST over the 31 years. 2) UGS/urban parks had a cooling effect in the city (the 43 UGS/urban parks have CII of 0.66 °C and 0.81 °C with respect to two buffers that were equidistant and 200 m, respectively). 3) In general, smaller parks are not effective in exhibiting cooling effects (CII of 0.06 °C and 0.24 °C with respect to equidistant and 200 m, respectively). 4) The area, perimeter, and land surface features of the UGS/urban parks, including the percentage of water bodies and tree cover, showed a significant and positive correlation with CII, indicating their impact on urban cooling. The study concludes that urban local bodies should adopt a scientific approach to park management. They should also incorporate more green spaces into urban planning as a crucial strategy to maintain a healthy thermal environment and enhance the city's overall environmental sustainability.
PubDate: 2025-07-02
- Enhancing multi-model ensemble simulations of climate extremes over the
Tibetan Plateau using machine learning-
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Abstract: Multi-model ensembles are commonly employed to improve the performance of General circulation models (GCMs) projections. The design and practical implementation of ensemble techniques over the Tibetan Plateau (TP) are not yet well understood. In this study, we assess the capability of various ensemble methods to simulate climate extremes over the TP, using 20 GCMs from the Coupled Model Intercomparison Project Phase 6, including the arithmetic mean (AM), Bayesian model averaging (BMA), support vector machine (SVM), random forest (RF), artificial neural networks (ANN), and long short-term memory (LSTM) models. Subsequently, climate extremes over the TP under global warming scenarios of 1.5 °C, 2 °C, and 3 °C above pre-industrial levels are projected using the most effective ensemble method. The results show that SVM, RF, ANN, and LSTM outperform the AM and BMA approaches. Among these, RF performs best, demonstrating superior performance in capturing the spatial distribution of temperature and precipitation indices. Most climate extreme indices, except for CDD, TN10p, TX10p, FD, ID, and CSDI, exhibit an increasing trend at global warming levels of 1.5 °C, 2 °C, and 3 °C, with more pronounced changes occurring at higher warming levels. In terms of geographic distribution, precipitation and temperature extreme indices exhibit substantial spatial variability. This study suggests that machine learning techniques offer a novel perspective for extracting deeper insights from large datasets and can enhance the accuracy and reliability of climate projections.
PubDate: 2025-06-27
- Performance of a 179-year high-resolution climate simulation of Southern
Alaska-
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Abstract: Alaska has one of the world's largest glaciated areas and is highly sensitive to climate change. Alaskan glaciers currently contribute about a third of the global sea level rise, with tidewater glaciers playing a significant role through rapid retreat. Meteorological observations in this region are insufficient to assess climatic influences on the tidewater glacier cycle, and existing model datasets are either too coarse or cover too short a period. This study reconstructs the regional climate of southern Alaska by downscaling the NOAA-CIRES-DOE 20th Century Reanalysis (20CRv3) from 1836–2015 using the Weather Research and Forecasting model (WRF) to produce a high-resolution 4-km dataset. The new downscaled dataset (20CRv3-WRF) was validated for 1981–2015 against observational records (GSOD) and the Parameter-elevation Regression on Independent Slopes Model (PRISM) datasets and compared to European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5). Compared to the observational records, 20CRV3-WRF performed well for annual mean temperature (0.61 ≤ r ≤ 0.96) and moderately for annual precipitation (0.16 ≤ r ≤ 0.76). For temperature, 20CRv3 downscaling output was more consistent with PRISM than with the coarser resolution ERA5, suggesting a more accurate representation of temperature than the reanalysis. Precipitation was mostly overestimated in comparison to observations. The spatial variability of precipitation was better represented in 20CRv3-WRF than ERA5. The results demonstrate that 20CRv3-WRF provides a reliable high-resolution dataset to assess the influence of climate on southern Alaskan tidewater glaciers, enabling future studies requiring long-term atmospheric data.
PubDate: 2025-06-27
- Streamlined meteorological drought monitoring through fuzzy clustering and
deep learning-
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Abstract: This work presents a rigorous mathematical framework for monitoring meteorological drought by integrating advanced machine learning, feature selection, and clustering strategies. The central aim is to predict drought events based on a combination of meteorological indicators, using the Standardized Precipitation Index (SPI) as the target variable. A new Composite Drought Index (CDI) is introduced to encapsulate multivariate drought information, constructed from the Precipitation Concentration Index, Temperature Condition Index, Wind Speed Condition Index, and Soil Moisture Condition Index. The CDI demonstrates strong empirical consistency with the established drought index, SPI, based on four decades of data from thirty-two meteorological stations. To capture spatial heterogeneity, the study employs fuzzy clustering to group stations into meteorologically homogeneous classes. Within each cluster, the Boruta algorithm is used to isolate the most relevant features by assessing their relative importance, ensuring that only statistically informative variables contribute to model construction. Drought prediction is then performed using a suite of machine learning models, including Random Forest, Support Vector Regression, Extreme Gradient Boosting, and Deep Feedforward Neural Networks. A hybrid model combining deep neural networks with random forests achieves the best overall performance by extracting latent features through deep architectures and refining predictions via ensemble methods. This hybrid yields the lowest prediction errors, with Mean Absolute Error ranging from 0.1570 to 0.2664, Mean Squared Error between 0.0409 and 0.1093, and Root Mean Squared Error between 0.2022 and 0.3306. It also attains the highest Nash-Sutcliffe Efficiency, from 0.8973 to 0.9547, and Kling-Gupta Efficiency, from 0.7253 to 0.8807. The study’s main contributions include the formal definition of CDI as a multivariate index, the incorporation of fuzzy clustering to enhance spatial generalization, and the deployment of a deep-ensemble model to capture complex nonlinear and temporal dependencies in meteorological data. Empirical results demonstrate that CDI significantly outperforms univariate indices, and that the hybrid model provides better predictive performance than conventional deep learning approaches such as CNN and LSTM. The framework is adaptable for real-time drought monitoring and early warning systems, offering practical value for climate resilience in drought-prone regions.
PubDate: 2025-06-26
- Farmers’ perception and choices of adaptation strategies to climate
change in the upper Gelana watershed, northeastern highlands of Ethiopia-
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Abstract: Climate change and its impacts are increasing, which requires the timely implementation of adaptation measures. This study aimed to assess smallholder farmers’ perceptions of climate change and determinants in the adoption or choice of adaptation strategies in the upper Gelana watershed, located in the northeastern highlands of Ethiopia. This research employed mixed research methods, involving both qualitative and quantitative approaches. Empirical data were gathered from 165 randomly selected households using structured questionnaires, complemented by 12 key informant interviews to provide in-depth qualitative insights. We used descriptive statistics, chi-square test and multivariate probit (MVP) models to analyze the data. The findings revealed that a significant proportion of farmers in all agroecological zones (AEZs) perceive increasing temperature and declining precipitation. However, key informants noted a decreasing trend in rainfall, mainly observed in the belg season. The majority of the respondents also perceived an increase in the occurrence of drought, disease incidence, erosion hazards, pests, and a decline in crop yields due to climate change and variability. There is a significant variation in the adaptation strategies to climate change implemented across the AEZs particularly in soil and water conservation (SWC), agroforestry, and irrigation. The MVP models indicated that farming in the lower and upper Weina Dega AEZs, education status, extension support, and access to climate information significantly affect the choices of most climate adaptation strategies in the study area. The findings will help improve climate adaptation measures by leveraging the positive contributions of agroecology, demographic, socioeconomic, and institutional factors and addressing negative influences, ultimately benefiting stakeholders in the upper Gelana watershed.
PubDate: 2025-06-25
- Evaluation of optimal normalization techniques in multi-criteria
decision-making to rank CMIP6 climate models-
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Abstract: Global climate models (GCMs) have become essential tools for water resources engineers, enabling accurate identification of climatic factors and supporting effective planning and strategic decision-making. The capability to simulate and predict climate scenarios highlights the importance of evaluating GCM performance. Therefore, selecting an appropriate normalization technique in Multi-Criteria Decision-Making (MCDM) is crucial for identifying the ideal GCMs. Ensuring that the normalization technique accurately reflects the variations and significance of different criteria can significantly influence the reliability of the GCM selection process. Using the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset and India Meteorological Department (IMD) dataset, this investigation analyzes four normalization techniques and five MCDM methods such as Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR), Multi-Objective Optimization based on Ratio Analysis (MOORA), Simple Additive Weighting (SAW), Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and Compromise Programming (CP). The results identify that vector normalization is the optimal technique for all MCDM methods, except for VIKOR. According to the Consistency Index Ranking (CIR), max-min normalization is the recommended technique for VIKOR. The Spearman’s rank Correlation Coefficient (SCC) test reveals that the consistency of VIKOR differs from that of other MCDM methods, suggesting that VIKOR is not recommended for ranking GCMs. Among the evaluated models, CanESM5 (M9), along with GFDL-ESM4 (M3) and MIROC-ES2L (M18), consistently performed well across multiple normalization techniques and MCDM methods, reflecting their robustness in different decision-making contexts. Overall results lead to an improvement in research quality in selecting GCMs using MCDMs that include the best set of normalization techniques in each MCDM method.
PubDate: 2025-06-25
- ‘Greening the edges’ – role of peri-urban green spaces in mitigating
local temperature in English Bazar Urban Agglomeration, India-
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Abstract: Urban areas are increasingly vulnerable to heat waves and the urban heat island (UHI) effect, exacerbated by rapid land use changes and diminishing green cover. Enhancing urban resilience to climate change requires effective nature-based solutions, particularly the strategic integration of peri-urban green spaces. This study investigates the role of peri-urban green spaces (PUGS) in mitigating local land surface temperature (LST) in the English Bazar Urban Agglomeration (EBUA), Eastern India. The objective is to assess the spatial cooling potential of green edges and their contribution to thermal comfort and climate resilience. Field-based temperature observations were carried out along a designated transect across the PUGS during peak summer under clear sky conditions. Spatial analysis of temperature patterns revealed a significant cooling effect within green areas, with a maximum temperature difference of 6.89 °C at 16:00, emphasizing the role of vegetation in diurnal heat moderation. Central green sites consistently recorded the lowest temperatures, while outer, impervious zones exhibited higher heat intensities. Vegetation cover showed a strong negative correlation with temperature at 12:00 (R2 = 0.486), while outside impervious surfaces showed a positive correlation at 14:00 (R2 = 0.393). Public perception further validated these findings, with 73% of respondents acknowledging the cooling benefits of green spaces. The study advocates for “greening the edges” as a viable planning strategy to lower urban temperatures and enhance thermal comfort. Integrating peri-urban green spaces into city planning can support climate-resilient development and sustainable urban environmental management.
PubDate: 2025-06-25
- An assessment of the Statistical Bias Correction Techniques for CSIRO-Mk
3–6-0 model Rainfall and Temperature in Punjab, India-
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Abstract: Compared to observed data, this study assesses the accuracy of CSIRO-Mk-3–6-0 model climate data in predicting climatic events in Punjab, India. It aims to minimize bias in rainfall and temperature data by computing correction factors using 2010–2017 datasets and their validation using 2018–2020 datasets by statistical analysis. The study evaluated six rainfall corrections (Basic Quantile Mapping (BAQMBC), Modified Quantile Mapping (MOQMBC), Normal Mapping (NOMBC), Gamma Mapping (GAMBC), Quantile Mapping linear correction (QMLCBC) and QM second-order polynomial correction (QMPOLBC)) and three temperature correction (Simple Seasonal Bias Correction (SSBCMBC) monthly basis, Change Factor (CFDBC) daily basis and Nudging (BCDBC) methods. The results were analyzed with descriptive statistics, cumulative distribution function (CDF) plots and the Kolmogorov–Smirnov (KS) non-parametric test. The CDF plots and KS tests show both significant distribution differences (QMLINBC and QMPOLBC) and non-significant distribution differences (BAQMBC, MOQMBC, NOMBC, GAMBC) for rainfall parameters compared to raw model data and bias-corrected data compared to observed data. The bias-corrected rainfall data did not achieve a closer distribution alignment with the observed as indicated by the evaluation metrics (RMSE, NRMSE, SD and mean) across five study locations. In temperature datasets, the CFDBC technique outperformed SSBCMBC and BCDBC as indicated by the excellent statistical evaluation metrics.
PubDate: 2025-06-25
- Unveiling the landscape fragmentation and its impact on land surface
temperature using machine learning approach in Kolkata Metropolitan Area
(India)-
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Abstract: Understanding historical and anticipated changes in land use and land cover (LULC) is essential for assessing the effects of urban thermal dynamics, especially in swiftly urbanizing cities in developing countries. This study aims to examine LULC changes from 1990 to 2020, with future forecasts for 2030, 2040, and 2050 in Kolkata Metropolitan Area (KMA), India using Cellular Automata–Markov Chain (CA–MC) model. This study evaluated seasonal fluctuations in land surface temperature (LST) and fragmentation in landscape metrics such as core area (CA), edge density (ED), patch density (PD), and perforated landscapes. The findings indicated a substantial 70% rise in built-up areas from 1990 to 2020, alongside a 35.92% and 13.17% decrease in agricultural land and water bodies. As per as the LULC projection, agricultural areas anticipated to decrease from 24.02%5 in 2020 to 13.83% by 2050, and vegetation from 7.09% in 2020% to 5.27 in 2050%. The mean LST increased by 7.05%, with winter exhibiting the most significant seasonal increase at 9.03%. The areas with extremely high LST areas increased from 11.57% in 1990 to an anticipated 28.77% by 2050. Landscape fragmentation escalated significantly due to considerable increases in ED, PD, CA, and perforated landscapes from 1990 to 2050. The alterations were accompanied by a significant increase in LST values across all landscape metrics. The results suggested the necessity for sustainable urban planning policies that protect natural ecosystems (such as green and blue spaces), limit uncontrolled urban sprawl, and incorporate green infrastructure to improve thermal regulation and landscape connectedness.
PubDate: 2025-06-23
- Future spatiotemporal inequality of precipitation extreme in
monsoon-driven country using Gini coefficient and random forest model-
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PubDate: 2025-06-23
- Urban Park Waterfront Microclimate Optimization Using the Orthogonal
Experimental Method-
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Abstract: With the gradual increase in global temperatures, mitigating the negative impacts of the urban heat island effect has become increasingly important. Water features are distinctive elements of many urban parks. This study explored the effects of the layout of water features and vegetations on thermal comfort. The orthogonal experimental method was used to facilitate the study of a large number of factors. ENVI-met were used to simulate the 34 models based on 9 factors. The results show that increasing the water body area ratio is beneficial for improving thermal comfort. The optimal water landscape shape index (WLSI) varies from 1.4 to 1.8 at different times of the day. The 'W'-shaped water body maintains better thermal comfort over longer durations compared to the more compact 'A'-shaped and 'O'-shaped designs. Placing the water body in the upwind position relative to the dominant wind direction significantly enhances thermal comfort. The use of trees to fully enclose the water body can provide some benefits for thermal comfort. Shrubs can play an active role in improving waterfront thermal comfort. Elliptical tree crowns play a more positive role in microclimate improvements than umbrella-shaped and hemispherical crowns. Maintaining a certain distance between vegetation and water bodies is beneficial for making full use of their cooling effects.
PubDate: 2025-06-23
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