Snow Runoff Modeling Using Meteorological, Geological and Remotely Sensed Data

Geographic information and analysis provide a wide range of data and techniques to monitor and manage natural resources. as an important case, in arid and semiarid areas, water management is critical for both local governance and citizens. as a result, the estimation of potential water brought by snowmelt runoff and rainfalls has a significant importance for these dry areas. hydrological modeling needs vast knowledge about integrating all relating parameters. in this work, different data sources including remote sensing observations and meteorological and geological data are integrated to supply spatially detailed inputs for snowmelt runoff modeling in a watershed located in simin-dasht basin in the northeast of tehran, iran. because of high temporal frequency and suitable spatial coverage, modis products have been chosen to derive snow cover area. the modis 8-day snow map product with an spatial resolution of 500m (mod10a2.5) is used. in addition, during the snowmelt period in 2006-2007, archived meteorological and geological data are used to provide parameters and variables for empirical snow runoff modeling (srm). landsat etm+ images with a higher spatial resolution of 30m and less temporal coverage of 16 days are used in 2007 snowmelt period to compare the model accuracy using snow cover area estimations from different sources. evaluation of the runoff outputs in both models, using in situ measurements, reveals good agreements that prove srm capability in modeling basin’s daily and weekly runoff. model accuracy shows better satisfactory of snow runoff modeling results that employ snow cover area (sca) derived from landsat etm+ data compared to modis snow product modeling. although, using modis sca the model accuracy was less, still due to less further process and providing better temporal coverage during snowfall and snowmelt season modis is recommended. future works in this criterion could be concentrated on srm forecast improvement using data assimilation techniques or coupling the empirical model to physical models.