Author/Authors :
Rahmati، Mehdi نويسنده Electrical Engineering Department , , Neyshabouri، Mohammad Reza نويسنده Department of Civil Engineering, Faculty of Engineering, Monash University, Melbourne, Australia , , Fakherifard، Ahmad نويسنده Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran , , Mohammadi Oskouei، Majid نويسنده Department of Mining Engineering, Mining Engineering Faculty, Sahand University of Technology, Tabriz, Iran , , Ahmadi، Abass نويسنده Department of Civil Engineering, Faculty of Engineering, Monash University, Melbourne, Australia , , Vahedberdi Sheikh، Javad نويسنده Department of Watershed Management, Faculty of Range Land and Watershed Management, Gorgan University of Agricultural Sciences & Natural Resources, Go ,
Abstract :
Several rainfall-runoff models have been developed for rapid prediction of runoff using remotely sensed data rather than field measurements. The Limburg soil erosion model (LISEM) is capable to use both 1) laboratory/field measured data and 2) remotely sensed data beside a few field experiments for fast prediction of runoff and soil erosion. This research aimed to first order evaluation of LISEM model for runoff prediction at Lighvan watershed, North West of Iran using laboratory/field measurements beside a few remotely sensed data. Three different datasets used in the model are consisting of: 1) Hydrological, climatological, and physiographical sets supplied by East Azerbaijan Regional Water Company, 2) Soil physical parameters determined at field as well as laboratory, 3) The Moderate Resolution Imaging Spectro-radiometer (MODIS) images supplied by NASAʹs website. According to the results, the accuracy of the predicted runoff hydrograph (d, index of agreement = 0.83, dimensionless) and its parameters i.e. peak flow (EQp=1.86 %,) and time to peak (ETp=-4.78 %) by LISEM model are reasonably well. The LISEM model, therefore, showed a reliable functionality to predict runoff (peak discharge) in crucial times that will be helpful in intelligent alarming.
