Yaser Amini; Abbas Alipour; Seyyed Mostafa Hashemi; Sajjad Bagheri SeyeedShokri
Abstract
Introduction
Snow cover represents the amount of stored water, and the water from melting snow plays an important role in the formation of surface water and groundwater in the country's watersheds. Detection and determination of snow and ice different characteristics by using remote sensing data, ...
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Introduction
Snow cover represents the amount of stored water, and the water from melting snow plays an important role in the formation of surface water and groundwater in the country's watersheds. Detection and determination of snow and ice different characteristics by using remote sensing data, which is widely used in hydrology, created new approaches in acquiring needed parameters in Hydrology.Results of the research show that the observations of the guesser have high potentials for detection of snowcover and the use of its data is suggested for calculating water of the equivalent snow in the areas such as Kerman Province which is facedwith the limitation of ground stations.
Materials & Methods
Since this area is able to have snow in winter, therefore the data about water equivalent to the snow in this area is necessary for many applications such as hydrology, meteorology, climatology and also producing hydroelectric and flood estimation. In this study, using brightness temperature from the Advanced Microwave Sounding Unit-A (AMSU-A), on board the NOAA satellites and the artificial neural networks as well as multiple regression techniques, the snow water equivalent forthe catchment basins of Tehran in the winter during a 10-year period (2015-2006) has been calculated and verified. In total, data from 5 monitoring stations of snow for 104 days during the study period was used for the estimation and verification.
Results & Discussion
Based on the results we obtained, the best estimate is related to the artificial neural networks with an RMSE=0/05, MSE=0/11, Bias= 0/0006 and r=0/14.The results indicate the superiority of the artificial neural networks over the regression method.
Conclusion
This results also show that, the observations of this sounding has the high potential for indicating the coverage of snow which are useful information and it is suggested to calculate snow water equivalent in the regions like Kerman where has a limited ground stations of snow measurement.
