Seyyed Ghasem Rostami; Hassan Emami
Abstract
Extended AbstractIntroductionVarious religions, including Islam, Judaism, Hinduism, and Chinese, have utilized lunar calendars for chronology. Methods for forecasting the first sighting of the new lunar crescent existed as early as the Babylonians, and maybe earlier. The Babylonians reasoned that the ...
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Extended AbstractIntroductionVarious religions, including Islam, Judaism, Hinduism, and Chinese, have utilized lunar calendars for chronology. Methods for forecasting the first sighting of the new lunar crescent existed as early as the Babylonians, and maybe earlier. The Babylonians reasoned that the lunar crescent can be seen with the naked eye under two conditions at sunset. First, the moon is older than 24 hours, and the moon's lag time is greater than 48 minutes. Fotheringham and Maunder developed standards for the seeing of the crescent moon at the beginning of the nineteenth century, and Bruin used his own criteria in 1977. Schaefer recently addressed crescent visibility extensively and integrated weather conditions into his work. Yallop then utilized the same database that Shaffer developed in 1997, but he overhauled some of the observation records extensively. Furthermore, many Muslim astronomers had developed their own criteria and published them in their literature. Despite the fact that different study organizations have created different criteria, there are still mistakes in the best time to forecast the crescent moon sighting. The use of old and conventional observations in modeling is one of these limitations, as is the use of non-uniform and heterogeneous observations. The Yallop criterion, for example, forecasts the visibility of the crescent moon for older crescents pessimistically. The Odeh criterion, on the other hand, forecasts young crescents with optimism. New Iranian criteria, such as the phase and altitude criteria (Mirsaeed criterion) and the triangular model (Iran criterion), have been presented in Iran. The goal of these criteria is to find the best timing between sunset and the first sighting of the crescent moon. Bruin, Schaefer, and Yallop have spent the last four decades developing the notion of the best moment. Because, after sunset, the sky darkens and the conditions for seeing the narrow crescent improve, while the moon approaches the horizon and the conditions for viewing the crescent moon worsen. Because the thickness of the atmosphere along the horizon is 3.7 times more than that of the zenith, the moonlight travels a greater distance than it did just a few minutes before. As a result, the sky towards the horizon is red or orange, and the crescent is not visible in this part of the sky. Material and Methods The objective of this study is to verify the rate of sky darkening in various regions and its influence on modeling the crescent visibility parameters of the moon, as well as to identify the best time to find out. To that end, 268 observational reports gathered from different divisions of Iran during the previous 20 years (2000-2021) were used to model the lunar crescent sighting. The proposed models are based not only on an examination of 20-year data to provide all effective tidal frequencies of the moon (the minimum period of moon’s notation motion is 18.61 years), but also on the use of sky-changing parameters such as local darkening rate and local sun occultation epoch time, the effect of the moon's distance from Earth, and the altitude of the moon from the horizon. The darkening rate of the sky factor was confirmed using various parameters and variables such as each point's geodetic latitude. Furthermore, unlike prior studies, the proposed models are developed using categorized observational reports with the least amount of error and can forecast the crescent sighting time in the presence of the sun (daylight time). The statistical correlation between the waiting time of each observation and the effective parameters in the lunar crescent visibility was studied in the first step. Following that, the parameters with the highest correlation values were chosen as the key quantities for modeling. After that, 17 alternative mathematical models with 2, 3, 4, and 5 parameters were implemented and tested, and the coefficients of the final two models (two and five parameter models) were determined using the least squares method as the suggested models. Results As a simple model, the two-parameter model can forecast crescent visibility with an average root-mean-square error (RMSE) of 4.7 minutes. The five-parameter model, on the other hand, was a more full and accurate model than the prior model, which was tested in two separate situations. They were evaluated over data for perigee distances of moon orbit (less than 375 thousand km) and observations for apogee distances of moon orbit (distance more than 390 thousand km) in the first and second cases, respectively. The findings of the 5-parameter model revealed that the first and second forms of the model had an average RMSE of 3.6 and 4.0 minutes to forecast the best time to see the crescent moon with the naked eye, respectively. Conclusion The results revealed that the best period to observe the crescent moon is from 32 minutes after sunset to 12 minutes earlier than sunset owing to the angular separation of the moon from the sun (10 to 20 degrees) and the difference in the altitude of the moon from the sun (5 to 20 degrees). When a result, as the local darkening epoch time increases, so does the waiting epoch time. In other words, the lunar crescent appears earlier in the northern part of Iran than in the southern half.
Hassan Emami; Hassan Shahriyari
Abstract
Extended Abstract Introduction Forests play numerous critical roles in nature. They stabilize and fertilize soil, purify water and air, store carbon, and nurture environments abundant in biodiversity. Moreover, forests offer numerous job opportunities and hidden wealth toany economy. Unfortunately, wildfires ...
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Extended Abstract Introduction Forests play numerous critical roles in nature. They stabilize and fertilize soil, purify water and air, store carbon, and nurture environments abundant in biodiversity. Moreover, forests offer numerous job opportunities and hidden wealth toany economy. Unfortunately, wildfires have turned into a serious natural risk nowadays. Wildfires are a natural disaster threatening forests and ecosystem, from local to global level. Evaluating the risk of wildfires is an important factor in fire management. This can be performed at different spatial and temporal scales: global and local; short term, and long-term. At global scales, it can contribute to the establishment of general guidelines for fire management at continental level, while at local scales,it is more suitable for resources focusing on preventing specific fires in small regions. Long-term estimation addresses general, more permanent planning of firefighting resources, which is related to the more structural factors affectingwildfires or their spread, such as topography or terrain characteristics, vegetation structure, human activities or weather patterns. Materials & Methods Wildfire risk has become a major concern in recent years, particularly in areas where human settlements are in close proximity to forests. Wildfire origin canbe determined largely by environmental factors. However, fire related data is either unavailable, or mostly incomplete. Thus, reaching an overall annual estimate of wildfires is difficult. Some common methods are used toestimate the risk ofwildfires, including qualitative methods, quantitative methods based on specialized knowledge (multi-criteria evaluation techniques), regression techniques (linear regression and logical regression), and artificial neural networks. Wildfire initiation and spread depend on several important factors, including precipitation, presence of ignition elements, factors like topography, temperature, thunder, spreading of fuel, relative humidity, wind speed, and etc. The present study integrates data produced by remote sensing with data received from geographic information system. It also takes advantage of LDCM satellite imagery, and digital elevation model, along with natural/human factors such as wind speed and direction, vegetation, land surface temperature, slope, proximity to roads and residential areas. The present study seeks to quantify environmental and human elements effective in occurrence and spread of wildfires in the protected jungles of Arasbaran. To this end, a risk zone map was produced for the area, along with a map for areas with 50% risk. In the present study, the final map of risk zone was produced using the Fire Risk Index (FRI) and spatial statistics method. Results & Discussion In the present study, factors such as land cover type, slope, distance from residential area, distance from the road, and elevation were taken into account. During the process, different indices were assigned to each class of these factorsbased on their sensitivity to fire or their flammability. Land cover was one of the most important factors affecting the occurrence of wildfires. Slope was another important factor with a significant influence on the spread of fire. This natural factor affects fire spread and fire intensity. Proximity of human settlements to jungles is another important factor which sometimes threatsjungles. Therefore, forests in proximity of human settlements face a higher risk of wildfires. Elevation is another important topographical factorclosely related to wind behaviour, with a significant role in fire spreading. In Arasbaran forest, northern, eastern, and north-easternareas are more elevatedand thus, more prone to wildfires. In this study, a combination of environmental and human factors was applied to produce fire hazard maps along with a map for areas with 50% risk of wildfire. Conclusion Occurrence and spread of wildfires depends on many factors, some of which are more important and play a more significant role in these fires. A risk zone map was produced for wildfiresusing an integrated method consisting ofremote sensing and GIS methods. Risk zone was divided into 5 areas, i.e. very low, low, average, high, very high.Results indicate that the methodology presented based on a combination of RS and GIS techniquesin this study, is a reliable approach and tool for the prevention and mitigation of forest fires. They are also useful for all active institutes working in crisis management and emergency services, while helping jungle protectingorganizations to prevent fires or manage them. In addition, quantitative results indicate that vegetation index with a correlation of 58.36%, and slope with a correlation of 38.38 are the most affective factors, and other parameters are in the next ranks.Moreover, land cover, land surface temperature, direction, and slope with 29.20%, 29.11%, 21.93% and 19.75% normalized correlation coefficient respectively, have the highest correlation with the map of fire risk zone. In addition, results of evaluating 50% risk zone map indicate that around 17% of the study area have a high fire risk and more than 50% of the area is located in a high fire risk zone. In addition to environmental elements, results indicate that proximity to the road was the most affective factor in the occurrence of fire. Quantitative results showed that roads and residential areas were at least 32% and at most 68% correlated with fire risk in the study area.
