Research on the Anthropogenic Impact on Climate Change

The article is a presentation of the results of studies and calculations of climate parameters associated with anthropogenic thermal pollution of the environment. They indicate that the increase of atmospheric concentrations of carbon dioxide, methane, and nitrous oxide cannot be a cause of climate warming. The article provides information about the working principle of the natural mechanism of automatic maintenance of temperature climate parameters. The authors show that all of the gases molar mass of differs from the molar mass of nitrogen are the working bodies of the procedures for moving heat from the stratosphere to space. Water vapor is the only greenhouse gas, and to deal effectively with water vapor in the atmosphere is impossible. However, the work contains an assessment of the feasibility of some ways to assist the natural mechanism of removing heat to space. Some methods and tools of world energy development are suggested in the article. These methods could lead to a substantial reduction of anthropogenic thermal pollution of the planet.

Analysis of Climate Parameters as Indicators of Climate Changes in Central and Eastern Iraq: Khanaqin Climate Conditions as A Case Study

The climate parameters of rainfall, and average of maximum and minimum temperature were investigated for the period of 2000-2019 in three stations in central and eastern Iraq (Baghdad, Diyala, and Kut provinces). Evidence of climate change was noticed from the results, reflected by decreased mean values of annual rainfall with increased mean of maximum and minimum temperature. In addition, the water balance and the climatic conditions were determined for Khanaqin station for the years 1981-2019. The analysis of Khanaqin climate parameters demonstrated total annual mean values of rainfall of 260.86mm and evaporation of 3119.59mm. The mean monthly relative humidity was 46.65%, sunshine was 8.39h/day, temperature was 23.36Co and wind speed was 1.86m/sec. Water surplus was recorded in Khanaqin area, with values of (24.00mm) (34.18mm) (40.31mm) (28.83mm) and (19.92mm) in November, December, January, February and March respectively. The climate classification of Khanaqin area indicated semi dry and dry climates. Moreover, the region has water deficit that reached up to 43.56 % of the total rainfall values.

Kemajuan Penelitian Pemodelan Prediksi Demam Berdarah Dengue menggunakan Faktor Iklim di Indonesia : A Systematic Literature Review

Since discovered firstly in 1968, number of cases and areas affected by DHF in Indonesia has been increased. In 2019, dengue cases have found in all provinces within 481 districts/cities (94%). Our research is conducted to analyze the current status and gaps of climate relationship and its modeling to DHF in Indonesia. A systematic searching of literature was carried out through the search engine PubMed and Google Scholar. The method includes determining questions, publication period, keywords, and criteria of literature. Thirty-two literatures have been selected according to the criteria. The study area has covered all provinces in Java, Bali and West Nusa Tenggara, several locations in Sumatra, Kalimantan, Sulawesi, while the eastern region has still limited study. Spatial and temporal variations were used predominantly at the city with monthly data scale. Relationship analysis between DHF cases and climate/non-climate has been used the Spearman’s and Pearson’s correlation. DHF prediction modeling involves dominant climate parameters such as rainfall, temperature, humidity and non climate parameters using linear/non-linear relationships and static/dynamic models. Climate model development needs to be improved with a narrower spatial resolution and shorter time scale, elevation, mobilization, regional climate, and climate change scenarios to get appropriate model on a specific location.

Global Sensitivity Analysis for CERES-Rice Model under Different Cultivars and Specific-Stage Variations of Climate Parameters

Global sensitivity analysis (SA) has become an efficient way to identify the most influential parameters on model results. However, the effects of cultivar variation and specific-stage variations of climate conditions on model outputs still remain unclear. In this study, 30 indica hybrid rice cultivars were simulated in the CERES-Rice model; then the Sobol’ method was used to perform a global SA on 16 investigated parameters for three model outputs (anthesis day, maturity day, and yield). In addition, we also compared the differences in the sensitivity results under four specific-stage variations (vegetative phase, panicle-formation phase, ripening phase, and the whole growth season) of climate conditions. The results indicated that (1) parameter Tavg, G4, and P2O are the most influential parameters for all model outputs across cultivars during the whole growth season; (2) under the vegetative-phase variation of climate parameters; the variability of model outputs is mainly controlled by parameter P2O and Tavg; (3) under the panicle-formation-phase or ripening-phase variation of climate parameters, parameter P2O was the dominant variable for all model outputs; (4) parameter PORM had a considerable effect (the total sensitivity index, STi; STi>0.05) on yield regardless of the various specific-stage variations of the climate parameters. Findings obtained from this study will contribute to understanding the comprehensive effects of crop parameters on model outputs under different cultivars and specific-stage variations of climate conditions.

A Study of The Relationship Between Bioclimatic Comfort Zones And Land Use: The Case of Sivas Province (Turkey)

The aim of this study is to reveal the relationship between bioclimatic comfort zones and land use in Sivas province. In this context, the relationship between the climatic data of 1990 and 2018 and the land use data of Sivas province belonging to the same years was evaluated as seasonal and annual periods. The bioclimatic comfort zones in the study area were determined depending on environmental climatic parameters (ECP) [temperature (T), relative humidity (RH) and wind speed (WS)] and bioclimatic indices [Physiological Equivalent Temperature (PET), Thermo Hygrometric Index (THI), and Universal Thermal Climate Index (UTCI)]. The values of the environmental climate parameters of Sivas province for the relevant years were obtained from 9 meteorological stations, the height of which varies between 1121 m and 1528 m. With the help of the Geographical Information System (GIS), the spatial distribution of the bioclimatic comfort zones determined depending on the environmental climate parameters and bioclimatic indices were created. Land use maps of the study area for reference years were obtained by using CORINE land cover data. The relationship between bioclimatic comfort zones and land use was also determined with the help of GIS. According to the results of this study; It was determined that the land use type in which the bioclimatically comfortable areas overlap in Sivas province differs according to the used parameter/indexes, years and annual periods.

Critical analysis of temporal climate change and ensuing frequency of crop yield constraining temperatures

Critical analysis of magnitudes andtrends of temporal changes in maximum (Tmax) and minimum (Tmin) temperatures as well as rainfall during three time series i.e. past (observed data, 1970-1990), present (observed data, 1989-2018) and future (bias corrected modelled data, 2021-2050) reveals that on the whole, inter–series mean of all the three climate parameters increased, and variation decreased in future time series. The magnitude of these trends varied with the model as well as scenario; was highest in RCP 8.5 scenario. Intra-series trends at annual and seasonal scales are dictated by inconsistent monthly trends. This study also adds that decline incrop yields of rice-wheat system with warming in future can be ascribed to increased frequency of days having yield constraining temperatures (above ceiling and below critically low during crop growth and reproduction stages) rather than their elevated magnitudes only as anticipated previously.

Quantitative accuracy assessment of the revised sparse Gash model using distinct time-step climatic parameters

Rainfall interception (I) can considerably influence the transport process of water. The revised sparse Gash model (RSGM) is a tool for determining the I, which assumes that the two climate parameters in the model are equal for all storms. However, few studies have provided additional cases to reexamine the correctness of this assumption and investigated the response of I of single storms to the time-step variability in climatic parameters. Hence, running the RSGM separately on an event basis during the growing season in 2017 and using three time-step climatic parameters (storm-based, monthly, and fixed) to estimate I for the forest stands of Pinus tabuliformis, Platycladus orientalis, and Acer truncatum in Northern China. In summary, the modeling accuracy of both cumulative I and individual I was enhanced by increasing the time step of the climatic parameters in this study. These positively support the assumption in the RSGM. These results suggest that it is more appropriate to run the RSGM using fixed climate parameters to estimate I for these tree species during the growing season in northern China. Additionally, the assumption in the RSGM should be appealed to be further confirmed across the widest possible range of species, regions, and time scales.

Identification of Spatial Clusters of COVID-19 in Yogyakarta using Moran’s Index

Various studies on the spreading pattern of COVID-19 were carried out because, every day, there are always new progresses from scientists about this virus, including its unique characteristics in each region with different geographical conditions. The objective of this research is to find the spatial distribution pattern of COVID-19 and to identify the climate parameters influencing it in the Yogyakarta. This study used Moran's Index in analyzing COVID-19 distribution pattern. The results of the analysis show that out of 5 regencies/cities in Yogyakarta, Sleman Regency has the highest average spread of COVID-19 or an average infection rate of 1.09, followed by Bantul with 0.75. Kulonprogo is the regency with the lowest number of cases. The result of Moran's index of 0.32 means that there are correlation between cases.The correlation between temperature and infection rate is R2 = 0.05, while the correlation between humidity and infection rate is R2 = 0.003. The spread of COVID-19 in Yogyakarta is more influenced by mobility or interactions between infected and uninfected individuals.