Estimation of evaporation in Andalusian reservoirs: Proposal of an index for the assessment and classification of dams by Santiago García-López, Marcia Salazar-Rojas, Mercedes Vélez-Nicolás, Jorge. M.G.P. Isidoro, Verónica Ruiz-Ortiz

“…The monthly average flooded area was calculated from the Area-Volume-Elevation (AVE) curve, while monthly average evaporation rate was calculated through the FAO Penman-Monteith equation using meteorological data. The combination of both variables has allowed to estimate the mean monthly volume of water evaporated in each reservoir. …”
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Analyses of the Spatial Pattern of Drought Risk in Tigray Region, Northern Ethiopia by A.S. Tefera, J.O. Ayoade, N.J. Bello

“… Drought risk index uses the meteorological drought hazard index and the socioeconomic drought vulnerability aspects to assess the level of drought risk in an area. …”
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Toward a Next Generation Particle Precipitation Model: Mesoscale Prediction Through Machine Learning (a Case Study and Framework for Progress) by Ryan M. McGranaghan, Jack Ziegler, Téo Bloch, Spencer Hatch, Enrico Camporeale, Kristina Lynch, Mathew Owens, Jesper Gjerloev, Binzheng Zhang, Susan Skone

“…We have compiled, curated, analyzed, and made available a new and more capable database of particle precipitation data that includes 51 satellite years of Defense Meteorological Satellite Program (DMSP) observations temporally aligned with solar wind and geomagnetic activity data. …”
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Evaluation of the Possible Impacts of New Residential Areas on Air Pollution in the Development Plan by Merve Arslan, Doğan Dursun

“…The analysis was conducted based on four fundamental data groups: topographic data, which included elevation analysis: meteorological data, which included temperature analysis: air pollution data including PM10-SO2 analyses: and planning decision data, which included green areas, building height, population density, industrial areas and artificial surface analyses. …”
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Optimizing water resource management in tropical drought-prone regions through hybrid MCDM techniques: A water-stress mapping approach by Suman Mukherjee, Suman Paul, Subhasis Bhattacharya, Aznarul Islam, Sadik Mahammad, Edris Alam

“…New hydrological insights: Thirty-three indicators-based thematic layers under topographical, hydrological, environmental, hydro-meteorological, and socio-demographic dimensions with integrated CRITIC (quantitative), DEMATEL (qualitative), VIKOR and TOPSIS produced four relative water-stress maps of the study region. …”
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Future climate change impact on hydrological regime of river basin using SWAT model by V. Anand, B. Oinam

“…This study highlights that change in meteorological parameters will lead to significant change in the hydrological regime of the basin. …”
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Climate Regionalization of Asphalt Pavement Based on the K-Means Clustering Algorithm by Yanhai Yang, Baitong Qian, Qicheng Xu, Ye Yang

“…In order to better adapt to the climate characteristics of a region, this study developed a multi-index method of climate regionalization of asphalt pavement. First, meteorological data from the research region were statistically analyzed and the major climate variables were identified. …”
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Calculation and Analysis of Aircraft Pollutant Emission Based on Time Wake Separation Mode under Coastal Airport and Headwind Conditions by Pan Wei-Jun, Zhang Heng-Heng, Zhang Xiao-Lei, Wu Tian-Yi

“…According to the aircraft landing schedule of each airport, the ICAO (International Civil Aviation Organization) aircraft engine emission database, Boeing Fuel Flow Method 2 (BFFM2), and meteorological data of Pu-dong airport, this study uses the modified P3-T3 aviation pollutant emission model to calculate, respectively, the fuel consumption and pollutant emissions based on distance separation mode and time separation mode. …”
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Hydrologic responses of watershed assessment to land cover and climate change using soil and water assessment tool model by R.C.C. Puno, G.R. Puno, B.A.M. Talisay

“…Model inputs used include interferometric synthetic aperture radar-digital elevation model, 2016 land cover map, soil map, meteorological and hydrologic data. The model was calibrated using appropriate statistical parameters (R²=0.80, NS=0.80 and RSR=0.45). …”
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Impact of Wave COVID-19 Responses on Black Carbon Air Pollution in Moscow Megacity Background by Olga B. Popovicheva, Marina A. Chichaeva, Roman G. Kovach, Ekaterina Yu. Zhdanova, Victor M. Stepanenko, Alexander Varentsov, Nikolay S. Kasimov

“…Economic and population activities in conjunction with meteorological parameters and air mass transportation are evaluated by studying the variability and concentration levels of black carbon. …”
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A comprehensive environmental index for monitoring ecological quality of typical alpine wetlands in Central Asia by Jiudan Zhang, Junli Li, Changming Zhu, Anming Bao, Amaury Frankl, Philippe De Maeyer, Tim Van de Voorde

“…Furthermore, the driving factors behind these changes were analyzed by combining meteorological precipitation data with human activities. …”
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Accuracy comparison of Elamn and Jordan artificial neural networks for air particular matter concentration (PM 10) prediction using MODIS satellite images, a case study of Ahvaz. by javad sadidi, hani rezayan, Mohammad reza barshan

“…The value is because of MODIS spatial resolution, inadequacy in numbers as well as spatial distribution of meteorological station inside the study area. According to the results of the current research, it seems that air pollution monitoring stations have to increase in terms of numbers and suitable spatial distribution. …”
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Neural network quantification for solar radiation prediction: An approach for low power devices by Brenda Alejandra Villamizar-Medina, Angelo Joseph Soto Vergel, Byron Medina-Delgado, Darwin Orlando Cardozo-Sarmiento, Dinael Guevara-Ibarra, Oriana Alexandra Lopez-Bustamante

“… Accurate solar radiation prediction leverages various machine learning techniques, with artificial neural networks (ANN) being the most common and precise due to their ability to detect and learn relationships between meteorological variables and solar radiation. Traditionally, training and deploying these models require high-capacity computers. …”
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Assessments of the Emission Contributions from an Ultra-Supercritical Coal-Fired Power Plant to Ambient PM2.5 in Taiwan by Yi-Cheng Lin, Fang-Yi Cheng, Yi-Ju Lee, Thi-Thuy-Nghiem Nguyen, Chuen-Jinn Tsai, Huan-Cheng Wen, Cheng-Hung Wu, Wei-Chieh Chang, Chung-Chi Huang

“…Enhancing thermal efficiency can significantly reduce air pollutant emissions; however, its impact on ambient air pollutant concentrations under various meteorological conditions is rarely studied. To clarify the issue, we utilized the Community Multiscale Air Quality (CMAQ) model to estimate the contributions of the emissions from old and renovated LPP on the ambient PM2.5 concentrations in Taiwan. …”
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Frequent Glacial Hazard Deformation Detection Based on POT-SBAS InSAR in the Sedongpu Basin in the Himalayan Region by Haoliang Li, Yinghui Yang, Xiujun Dong, Qiang Xu, Pengfei Li, Jingjing Zhao, Qiang Chen, Jyr-Ching Hu

“…Furthermore, historical meteorological and seismic data were collected to analyze the mechanisms of multiple ice avalanche chain hazards. …”
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A systematic review and meta-analysis of the farmers\' attitudes towards the use of climate information by leyla sharifi, saeed bazgeer, hosain mohmmadi, alireza darbaneh astaneh, mostafa karimi

“…For this purpose, the following keywords, including climate, climate change, climate changes, meteorological information, global warming, drought, flood, chilling, frost, climate hazard, precipitation, temperature with the aid of the following keywords namely, farmer, farmerchr('39')s attitude, farmerchr('39')s perception, farmerchr('39')s knowledge, and indigenous knowledge were used in both languages, Persian and English, among the articles published between the year of 2000 and 2019. …”
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Analysis of Sea Water Temperature Change on The Coast of Turkey with GIS and Evaluation of Its Ecological Effects by Erkan Kalıpcı, Volkan Başer, Mustafa Türkmen, Nihal Genç, Hüseyin Cüce

“…The data used in the study were obtained from the Turkish State Meteorological Service of the Ministry of Agriculture and Forestry. …”
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Spatial-Temporal Variation of Vegetation NDVI and Its Response to Climate in Guangdong-Hong Kong-Macao Greater Bay Area from 2012 to 2021 by FU Xiang, LIU Hao

“…Exploring the normalized difference vegetation index (NDVI) variation of vegetation and its response to climate change in the Guangdong-Hong Kong-Macao Greater Bay Area is of great significance for regional ecological environment restoration and vegetation improvement.According to MODIS NDVI data and meteorological data,Sen trend analysis,coefficient of variation (CV),and correlation analysis were adopted to analyze the spatio-temporal variation characteristics of vegetation and its response to climate change in the Greater Bay Area in recent 10 years.The results show that:①The NDVI of the vegetation in the Greater Bay Area generally shows a slow upward trend,with a rising slope of 0.003 3 /a,and the effect of continuous improvement of vegetation is great;②the NDVI stability of vegetation in the Greater Bay Area is relatively strong,with an average CV of 0.054 8.The areas with low and lower fluctuation changes of vegetation account for 89.94%,while those with high and higher fluctuation changes are mainly distributed in Foshan,Zhongshan,Dongguan,Shenzhen,Zhuhai,south of Guangzhou,and other regions with rapid economic development;③the correlation between NDVI of vegetation and average temperature in the Greater Bay Area is higher than that between NDVI of vegetation and precipitation.Temperature is the dominant factor affecting vegetation growth,and precipitation is mainly negatively correlated with the NDVI of vegetation.The research results can provide a theoretical basis for the ecological environment governance and green sustainable development of the Guangdong-Hong Kong-Macao Greater Bay Area.…”
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Forecasting power generation of wind turbine with real-time data using machine learning algorithms by Asiye Bilgili, Kerem Gül

“…We differentiated this research by evaluating not only wind conditions but also meteorological factors and physical measurements of turbine components, thus considering their combined influence on overall wind power production. …”
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A global gross primary productivity of sunlit and shaded canopies dataset from 2002 to 2020 via embedding random forest into two-leaf light use efficiency modelZenodo by Zhilong Li, Ziti Jiao, Ge Gao, Jing Guo, Chenxia Wang, Sizhe Chen, Zheyou Tan

“…Then, we used the RF technique to integrate various environmental stress factors, including meteorological factors, hydrological variables, soil properties, and elevation, which originate from the NASA MERRA-2 dataset, ISRIC soil Grids, and USGS data center. …”
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