Agricultural Meteorology
A. Gholami; H. Mir Mousavi,; M. Jalali; K. Raispour
Abstract
Introduction
Clouds can be considered as one of the most complex and influential variables of the atmosphere system in forming of the climate structure of the earth. When the condensation process takes place at a higher altitude than the earth's surface, it creates clouds. Cloudiness represents ...
Read More
Introduction
Clouds can be considered as one of the most complex and influential variables of the atmosphere system in forming of the climate structure of the earth. When the condensation process takes place at a higher altitude than the earth's surface, it creates clouds. Cloudiness represents the percentage of the atmosphere that is covered by clouds. Clouds, as one of the most complex variables of the climate system, besides changing the energy balance, are also effective in the spatial and temporal distribution of many climate variables. Clouds have a lot of temporal and spatial variability and can affect the climate through many complex relationships and affect the water cycle. The investigation of clouds holds great significance as they serve as the bridge between synoptic systems and the Earth's surface climatic conditions. Any alteration in cloud-related parameters can trigger a domino effect, influencing various other climatic variables. It's worth noting that Iran exhibits a lower average cloud cover of 26%, notably less than the global average of 50%. This places Iran in the category of countries with relatively minimal cloud cover.Hence, possessing insights into the atmospheric cloud cover conditions in Iran becomes imperative for early detection and management of hydroclimatic crises, particularly in the context of water scarcity and drought-related challenges.
Data and Methods
In the current research, the cloud data of 93 synoptic meteorological stations of Iran have been used in the daily time period during the statistical period of 1991-2021. The amount of cloudiness is an estimate of the nearest octa (eighth) and values 0 and 8 are completely clear and completely cloudy, respectively. In the present study, Kolmogorov-Smirnov, Anderson-Darling and Lilliefors test were used to determine the normality of the data at the 95% confidence level for annual, monthly and seasonal scales.
In the subsequent phase, we employed both parametric and nonparametric methods to discern trends within the cloudiness time series. The parametric approach involved a linear regression test based on the least squared error method, while the nonparametric method employed the Mann-Kendall test. These tests allowed us to identify data trends, accounting for both normal and non-normal distributions of cloudiness. Furthermore, we explored the interplay between cloud cover and spatial factors, namely latitude and longitude, employing Pearson's correlation coefficient. This analysis shed light on the relationships between these variables. Conclusively, we created a spatial distribution map depicting the extent of cloudiness across various stations. This mapping allowed us to dissect the temporal-spatial distribution of cloudiness, comprehend alterations in cloud cover, and investigate the contributing factors behind these changes.
Results and Discussion
The results of Normality Tests according to the Kolmogorov-Smirnov test showed that all the stations did not have a normal distribution however, during the other two tests, except Arak, Kashan, Sarakhs, Takab, Kahnuj, Ramhormoz and Ramsar, other stations had normal distribution. The tests to determine the trend based on the parametric linear regression test based on the least squares error method showed a decreasing trend in 44 stations and an increasing trend in 3 stations of Ardabil, Qom and Sarab. According to the non-parametric Mann-Kendall test, among the stations without normal distribution, Kahnuj, Ramhormoz and Sarakhs stations have a decreasing trend, and no special trend was observed in other stations. The relationship between the two factors of latitude and longitude with the cloudiness variable using the Pearson correlation coefficient indicates a negative relationship (-0.42) between the cloudiness variable and the longitude factor as the amount of cloudiness in Iran's atmosphere decreases with the increase of latitude. Hwoever, the relationship between cloudiness variable and latitude, a positive relationship (0.75) was obtained as the amount of cloudiness increases with the increase of latitude. The survey of the annual cloudiness map of the stations shows the highest amount of cloudiness is in the South, Southwest and East of Caspian Sea. The lowest amount of annual rainfall was in South and Southeast of Iran. The statistical analysis of annual cloudiness data in Iran showed that the amount of cloudiness in Iran is 27.5%. Examining the normal distribution of monthly and seasonal values indicates the non-normality of the data with the Kolmogorov-Smirnov test, but based on the Lilliefors and Anderson-Darling tests, the winter and spring seasons and the months of December, January, February, April and May had a normal distribution and the autumn and summer seasons and the months of June, July, August, September and October did not have normal distribution. Seasonal and monthly trend with linear regression method shows a decreasing trend in winter and spring seasons and cold months of the year. According to the Mann-Kendall method, there was a decreasing trend in the fall season and no significant trend was observed in the summer season.
Conclusion
The purpose of this research was to investigate the temporal and spatial changes of cloudiness in Iran. The results showed a decreasing trend in 47 stations and an increasing trend in only 3 stations and no significant trend was observed in other stations. Also, in monthly and seasonal scales results indicated a decreasing trend in all stations in the cold months of the year and winter, spring and autumn seasons. Examining the relationship between the spatial factors of longitude and latitude with the cloudiness variable using Pearson's correlation coefficient also indicates a negative correlation with longitude and a positive correlation with latitude, and this indicates a large spatial difference in the amount of cloudiness in the country. In general, it can be said that spatial factors (longitude and latitude) were internal factors in the spatial changes of clouds and climatic systems such as Siberian high pressure, sub-tropical high pressure, westerlies system and moisture from the seas of Oman, India and the Persian Gulf and sometimes the Red Sea as external factors were in the temporal changes of clouds. So, cloudiness was a variable that was directly related to other climate variables. Thus, cloud cover was a variable that was directly related to other climatic variables, and its decrease or increase causes the values of elements such as temperature, precipitation, and humidity to change. Therefore, studying this important climate variable and investigating its changes is very important and especially in the discussions of droughts and water crises, it has a special place.
Irrigation
K. Raispour; B. Salahe; B. Abad
Abstract
Introduction Precipitation is the most important element of water level that recognizing its temporal-spatial characteristics at different scales is an important step towards better understanding and modeling of the hydrological cycle and related phenomena such as floods. Drought, landslides, snow ...
Read More
Introduction Precipitation is the most important element of water level that recognizing its temporal-spatial characteristics at different scales is an important step towards better understanding and modeling of the hydrological cycle and related phenomena such as floods. Drought, landslides, snow and climate change are on a regional and global scale. Despite the large number of studies conducted in this field, there is still a lot of research need in many parts of the world for reasons such as lack of weather stations to access ground observation data and the non-uniform spatial distribution of these stations. Nowadays, with significant technological advances, including the advent of various satellites, access to a variety of precipitation data has been greatly facilitated. Among the latest precipitation products of various satellites, we can refer to the Global Precipitation Measurement (GPM) satellite data. Related to the subject of the present study, it is stated that most of the studies on rainfall in the Jazmourian catchment area have been based on station data, which due to the poor distribution of meteorological stations; it is not possible to estimate the temporal-spatial distribution of precipitation in the study basin. In this study, the temporal-spatial analysis of precipitation using GPM satellite precipitation products as one of the most important climatic parameters in the basin Due to the undeniable importance of rainfall in this basin, it seems that the analysis of variable rainfall can provide valuable climatic information to researchers and planners. To pave the way for new study platforms.Materials and Methods In this study, satellite data (GPM) with a spatial resolution of 0.1 × 0.1 degrees from January 2001 to December 2019 have been used for spatiotemporal analysis of precipitation in the Jazmourian catchment. The GPM satellite provides more accurate and realistic estimates than other TRMM satellites. In this study, a calibrated precipitation product of level 3 of 6 GPM satellite versions was used. Relevant data are in NCDF format and have UTM image system with WGS84 datum, which after quality control and preprocessing, by specialized software (ENVI, ArcGIS and EXCELL) is converted into network data and data tables and the necessary outputs based on the geographical boundary of the catchment was extracted. The average monthly rainfall was extracted from the average daily rainfall belonging to each month and the seasonal average was extracted from the average of three months related to each season. Spatially, the values of each pixel are the conditions of the average amount of precipitation related to each time series (monthly, seasonal and annual) during the statistical period.Results and Discussion Based on the results, the average rainfall in the Jazmourian catchment was estimated as 144 mm, the spatial distribution of which ranged from 83 to 232 mm. The maximum rainfall occurred in the northern and western parts and the minimum occurred in the central and eastern parts of the basin. Furthermore; based on the annual distribution of rainfall during the statistical period under study, the highest rainfall was in 2019 with 239 mm and the lowest with 53 mm in 2001. In terms of seasonal distribution, winter and spring with values of 118 and 88 mm, respectively, showed the highest and autumn and summer with values of 22 and 45 mm, showed the lowest values of precipitation. Also, during the statistical period under study, winter 2005 with 193 mm had the highest and autumn 2003 with 1 mm had the lowest seasonal rainfall in the basin. In addition, an interesting point is the spatial displacement of high-pressure nuclei in different seasons of the year; so that these nuclei are observed in the cold seasons of the year in the northern and western parts and in the warm seasons of the year in the southwestern and southeastern parts of the basin. The spatial distribution of monthly precipitation indicates the occurrence of the highest monthly precipitation in February and March and the lowest in May and September. Also, the monthly rainfall time series indicates the maximum incidence of precipitation in February 2001 (94 mm) and it’s minimum in January 2001 (no precipitation).Conclusion Precipitation as a source of fresh water on Earth is one of the most important hydrological parameters, the importance of which is undeniable in the survival of human communities and natural ecosystems. Due to the large temporal-spatial variations of precipitation, its study seems necessary. But one of the main challenges for studying this phenomenon is the lack of ground stations as well as their improper distribution. Today, with advancement of technology and remote sensing, a diverse range of satellite data has become available to environmental scientists. In this regard, in the present study, using GPM satellite data and in the statistical period 2001-2019, the temporal-spatial distribution of precipitation in the Jazmourian catchment area in southeastern Iran has been investigated. In general, the high variability of rainfall in Jazmourian catchment in different months and seasons of the year, shows the dominance of arid and low climate in this basin. Therefore, due to the rainfall situation and its high fluctuations under climate change conditions, in the near future, this basin will face serious challenges and crises in water resources management and the sustainability of natural ecosystems. The GPM satellite data used in this study showed appropriate and expected results from the spatial-temporal distribution of precipitation in the Jazmourian catchment and showed a good correlation with meteorological stations. In general, the use of GPM satellite data in the present study is appropriate, which due to its appropriate spatio-temporal separation, gives reliable and satisfactory results. On the other hand, inadequate spatial coverage of meteorological stations and their large statistical vacuum in such a relatively large basin justify the use of this valuable and useful satellite data.
