Seyed Mahdi Sadat Rasoul; Ebrahim Omidvar; Reza Ghazavi
Abstract
In the recent years, science and technology in urban green space have largely focused on technologies that facilitate infiltration and reduce runoff (such as rain gardens and permeable sidewalks). Trees in urban green space reduce the net rainfall by interception, and on the other hand, their extensive ...
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In the recent years, science and technology in urban green space have largely focused on technologies that facilitate infiltration and reduce runoff (such as rain gardens and permeable sidewalks). Trees in urban green space reduce the net rainfall by interception, and on the other hand, their extensive root systems allow them to store and direct significant amounts of water into the soil. The present study investigates the effect of rainfall amount and tree species on rainfall interception in Hashtgerd city of Alborz province during two seasons of winter 2017 and spring 2018. For this purpose, during seven rainfall events, the amount of throughfall was measured by the number of five rain gauges installed under each tree. In order to record rainfall events, a rain gage container was installed in a location that was sufficiently distant from buildings and trees, and rainfall events ranging from 2.1 to 6.8 mm were recorded. The results showed that the percentages of rainfall interception for spruce, apricot, fig, willow, walnut, and oak species were 44.6, 42.6, 36.4, 35.1, 33.6 and 30.4 percent, respectively. The results of statistical analysis showed that there is a significant difference among the values of rainfall interception in different tree species (P <0.01). Also, there is a significant difference among the rainfall interception in the rainfall classes (low (lower than 4 mm), medium (4-6 mm), and high (higher than 6 mm)) (P <0.01). Among the studied species, sparrow and apricot species have the highest rainfall interception, which it is possible to make more use of these two types in the control of runoff with urban planning.
Abdolhosain Mohammadi; Reza Ghazavi; rohollah Mirzaei; Hamidreza Naseri
Abstract
Rainfall is one of the most important factors affecting vegetation cover. Fluctuation and year-to-year variation of rainfall always affect vegetation cover patterns. The main aim of this study was to investigate and modeling the effects of rainfall changes on the variation of the vegetation cover in ...
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Rainfall is one of the most important factors affecting vegetation cover. Fluctuation and year-to-year variation of rainfall always affect vegetation cover patterns. The main aim of this study was to investigate and modeling the effects of rainfall changes on the variation of the vegetation cover in the Meyghan basin in Arak province using MODIS satellite images. NDVI, DVI, RVI and EVI indices were used to manifest vegetation covert variations linear and nonlinear relationship between vegetation cover changes and rainfall investigated simultaneously (May), with one and two month delay (April and March) ) during the statistical period of 2000-2017. The rainfall trend analysis was done using non-parametric Mann-Kendall test. According to the results, the minimum rainfall during the 18-year period was increased. Between vegetation indices, NDVI index showed the maximum and the average incremental trend. Between precipitation and vegetation, third-order non-linear relationship was stronger than linear, quadratic, power, logarithmic and exponential. The maximum correlation between DVI index and rainfall was obtained for synchronous times, while, the maximum correlation was observed between NDVI index and precipitation. The study with two month delay showed that the maximum correlation (0.52) was between the RVI index and precipitation. Vegetation modeling using simultaneous rainfall and delay of up to two months showed that the indices of DVI, RVI and EVI provided the best regression relationship at the same time, while the NDVI index had the best regression relationship with rainfall of two months ago.