Precipitation in Iran is less than one third of the average precipitation in the world ) Kazemi & Ghorbani, 2004). According to water resources experts, one of the most serious crises in the coming decades will be water supply for all the people worldwide. The severe and persistent droughts that have afflicted Iran in recent decades have caused raised concerns in diverse sectors including agriculture, natural resources and especially water resources.
Many studies have explored cloud seeding, determinants of cloud seeding, project implementation, the right time and place of the project, the effect of cloud seeding on different basins, etc. In this research, we used fuzzy logic for proper location of cloud seeding projects in GIS software. The idea of artificial rainfall by cloud seeding was first proposed by Russian scientists in 1932 with the establishment of the Artificial Rain Institute to study the possibility of climate change (NajaFI & Hosseinzadeh, 2013)
The first cloud seeding test was performed in 1937 in the Netherlands on dry ice. In 1942, a German scientist, Findesen, experimented with cloud seeding. He used sands for cloud seeding, but the result was not successful (Javanmard, 2007). In 1946, Schaefer in his tests in General Electric's laboratory discovered that dry ice could transform cold cloud water droplets (liquid water droplets below zero degrees Celsius) into ice crystals. In an experiment, he injected about 1.5 kg of dry ice into the stratocumulus clouds by a plane only to observe snowfall after about 5 min under the tested cloud (sin kevich & et al, 2013).
As reported by the World Meteorological Organization, more than 51 countries are currently undertaking research on cloud seeding technology. Russia and the United States as two leading countries conducting thorough and extensive research in this field, have a long history of implementation projects about cloud seeding. In Europe, especially countries such as Spain and Italy, projects are underway to overcome the quantitative and qualitative limitations of water resources. France and Austria, among other counties, are attempting to deal with unexpected events such as hail suppression and fog dispersal. Some Middle Eastern countries such as Syria, Jordan, Libya, Morocco, Israel and even countries like China have employed this technology in various ways. In China, the technology was recently used to extinguish large-scale fire in Tibetan forest (Javanmard, 2007).
In Iran, cloud seeding research has been carried out for over 20 years by the National Cloud Seeding Center, which is based in Yazd, along with several other projects nationwide. So far, 16 provinces, including Gilan, Mazandaran, Zanjan, West and East Azerbaijan, Ardabil, Kermanshah, Isfahan, Yazd, Kerman, Fars, South Khorasan, Chaharmahal and Bakhtiari, Kohgiluyeh and Boyer Ahmad, Qom and Markazi have joined these projects. Rainmaking projects in Yazd province were conducted not only in the heights of this province, which have huge potentials for seeding, but also in Zagros Mountains, especially the Koohrang heights, which has a crucial role in water supply to Yazd province. The results of these projects have been promising (Portabari, 20117).
Griffith and Solak (2002), in their study on economic feasibility of cloud seeding in the Idaho Basin, concluded that the average cost of feasibility studies relative to the total cost of winter cloud seeding projects was 12%] (Khalili, 2014). Hunter (2006) examined the rise in water storage through cloud seeding in the Colorado River Basin. According to his research, on average, about one acre of harvesting water per year (or even higher in wet years, and up to 500,000 acres in dry years) can be produced using cloud seeding (Cloud Fertility branch, 2018). Hunter (2007), in a research project funded by the California Energy Commission, reported that cloud seeding was less costly and had a higher cost-benefit ratio than other water-enhancing technologies. Therefore, cloud seeding is an excellent option to help reduce water problems (Hunter, 2007). Javanmard et al. (2007), presented the preliminary results of cloud seeding location studies for rainmaking in Iran. The process was in compliance with report No. 3 of the World Meteorological Organization's project for rainmaking. The results of their study suggested that the possibility of cloud seeding is higher in the northwest, north and northeast of Iran with a decreasing trend from north to center and south and east (Javanmard, 2007). Dorfa et al. (2013) demonstrated how salt powder distribution works on cloud models. The experimental results showed the positive effects of salt powder on climate adjustment operations and precipitation. The dispersion of salt powders in clouds leads to the formation of large cloud droplets and the expansion of the droplet spectrum. This is a positive factor for stimulating coagulation processes and precipitation (Drofa, 2013). According to a 2013 study by Najafi and Hosseinzadeh, the injection of liquid carbon dioxide in the cumulonimbus cloud in the point position increased the quantity and intensity of rainfall, which was greater than the linear and temporal modes. If the cloud spread was about half a kilometer and an altitude of 10.5 km, this operation would be more effective (Najafi,& Hosseinzadeh, 2013). Sin Kevich et al. (2013) analyzed three cases of cumulonimbus cloud seeding in Saudi Arabia. The results revealed huge potentials of cumulonimbus clouds for seeding to stimulate precipitation (Sin’kevich, 2013). Poormohammadi and Golkar (2011) conducted a feasibility study of cloud seeding in Golestan province. First, using climatic parameters of temperature, precipitation, wind speed, cloudiness, number of frost days and fog in synoptic stations of Golestan province, they determined the appropriate times for cloud seeding in the basin. The results of this study illustrated that October, November, December, February, March and April are suitable for cloud seeding. Also, to identify appropriate places for cloud seeding, they drew a map of waterways, dams, land use, etc. Using the model data, they prepared isothermal maps of the upper atmosphere in the months designated for cloud seeding to investigate the possibility of aerial and ground seeding in the region (Pourmohammadi & Golkar, 2011). Pourmohammadi and Khalili (2013) investigated the possibility of cloud seeding in Hamadan province by addressing the climatic parameters of temperature, precipitation, wind speed, number of frost days and fog in six stations of Hamadan province. In this way, appropriate cloud seeding times in the basin were determined. In the next step, isothermal, wind speed and direction maps were designed for the upper atmosphere during the months designated for cloud seeding in order to analyze the feasibility of this project in the province. The results of this study revealed that November, December, January, February, March and April are appropriate for cloud seeding (Pourmohammadi & Khalili, 2013). Poor Mohammadi and Golkar (2016) evaluated the effect of cloud seeding on rainmaking and increasing surface and groundwater resources in the catchments of East Azerbaijan province in the water year of 2011–2012. The findings of their study suggested that cloud seeding in this basin prompted 19% surge in rainfall, with a subsequent rise of 97.6 and 22.04 million cubic meters in the production of surface and groundwater resources, respectively ((Pourmohammadi & Golkar, 2016). Kazemi and Ghorbani (2015) utilized different interpolation methods including inverse distance weighting, normal kriging, radial base function, etc. for estimation and zoning of rainfall variables in Aqqala agricultural lands. The results of this study showed that the local polynomial method, compared to other methods, can offer a more accurate estimate of climatic variables and that the spherical method was the best fitted model is (Kazemi & Ghorbani, 2004). Naum and Tsanis (2004) introduced the best model for estimating frequency maps of exponential and general models in Switzerland . Coulibaly and Baker (2007) recommended the conventional kriging method, which involved comparing different statistical methods for internalizing annual, monthly, and daily rainfall (Coulibaly & Becker, 2007). Zhang and Srinivasan (2009), by examining kriging family methods Inverse Distance Weighted (IDW), showed that the kriging method with an external trend yielded the lowest errors among other methods (Zhang & Srinivasan, 2009).
The goal of this study is to investigate the feasibility of implementing a cloud seeding project in Sistan and Baluchestan province. For this purpose, the appropriate time and place of the project to increase water extraction in the province are determined.