|نویسندگان||Saghafian, B - Davtalab, R|
|نشریه||International Journal of Climatology|
|ارائه به نام دانشگاه||Soil Conservation & Watershed Management Institute, Tehran, Iran|
|ضریب تاثیر (IF)||3.1|
|نوع مقاله||Full Paper|
|کشور محل چاپ||ایالات متحدهٔ امریکا|
Measurement/estimation of snow water equivalent (SWE) is a difficult task in water resources studies of snowy regions. SWE point data is measured at snow courses that are normally operated with low density owing to high costs and great difficulty in reaching the stations in cold seasons. Moreover, snow is known to exhibit high spatial variability, which makes SWE studies based solely on sparse station data more uncertain. Ever‐increasing availability of satellite images is a promising tool to overcome some of the difficulties associated with analyzing spatial variability of snow. Although National Oceanic and Atmospheric Administration (NOAA) satellite images have low spatial resolution with approximately 1.1‐km pixel size, they are adequate for mapping snow cover at regional scales and enjoy a moderate length of record period. In this paper, rain and snow records of synoptic stations and the time series of NOAA‐based snow cover maps were used to map average SWE of a vast area in southwestern Iran. First, monthly and annual snow coefficient (SC) at synoptic stations were determined on the basis of analysis of hourly observation of type and amount of precipitation. Then, two new spatially distributed snow characteristics were introduced, namely, average frequency of snow observation (FSO) and monthly frequency of maximum snow observation (FMSO), on the basis of existing satellite snow observations. FSO and monthly FMSO maps were prepared by a geographic information system on the basis of snow map time series. Correlation of these two parameters with SC was studied and spatial distribution of SC was estimated on the basis of the best correlation. Moreover, the distribution of mean annual precipitation was derived by comparing a number of interpolation methods. SWE map was generated by multiplying SC and precipitation maps and its spatial variability in the region was analyzed. Copyright © 2007 Royal Meteorological Society.