Study the correlation of Snow Cover Area with large scale climatic signals in the South Western Iran

A great portion of precipitation in the South Western Iran falls in the snow formation. Surface runoff produced from melting snow has an important role in the recharging ground water, surface and subsurface flows. Rapid snowmelt can even cause flooding. Since the snowmelt cannot be easily detected from optical satellite images, Snow Cover Area (SCA) is derived as an important hydrological parameter. SCA has a good correlation with amount of snowmelt, and its spatial and temporal variations can be converted to the volume of water stored in the topographic basins. In addition, changes in SCA have a correlation with variations of large scale climatic signals such as Southern Oscillation Index (SOI), Northern Atlantic Oscillation (NAO), and ELÑino Southern Oscillation (ENSO). In this research, the correlation of large scale climatic signals and SCA variations was investigated. Monthly data of SOI, NAO, and ENSO in the regions of Nino3, Nino3.4, Nino4, and Nino1+2 for the period 1968 to 2007 over the South Western basins were collected from the National Center Environmental Prediction (NCEP). In order to estimate the SCA variations, satellite data of NOAA- AVHRR were used for the selected days in the cold period of 1986 to 2007. The areas covered by snow were detected by applying a threshold based method and using radiances in band 1 and 3, and land surface temperature derived from brightness temperature. In the end, the SCA was predicted three to six months in advance by applying an artificial neural network approach. The results showed that NAO, SOI,, Nino4, and Nino1+2 signals are the most effective signals on variations of SCA. These signals can be applied for prediction of SCA over region.