عنوان مقاله [English]
Background and Objectives: Water storage depletion is an increasing hydrological threat to agricultural production and socio-economic stability across the globe. It is fast approaching threshold levels especially in arid/semiarid regions like IRAN with low precipitation and excessive evapotranspiration (ET). The more accurate for the estimate evapotranspiration as one of the most important parameters that cause water loss can be a step towards enhancing human ability to control and manage the water crisis. Unfortunately, with outbreak Phenomenon of drought and excessive increase in water consumption and reducing groundwater resources, Qazvin province is facing a water shortage crisis. The purpose of this article is to introduce a method for increasing the accuracy of Evapotranspiration of Global Land Data Assimilation System (GLDAS) model and also introducing the modified GLDAS evapotranspiration as a suitable replacement for the lysimeter evapotranspiration data, especially in regions where have no data and Inaccessible places.
Materials and Methods: Qazvin province, with an area of 15821 square kilometers, respectively, between longitude and latitude 48 degrees 53 minutes and 36 degrees 50 minutes north west corner and 50 degrees and 35 minutes and 35 degrees 18 minutes south-east corner located at the central basin of Iran. In this study lysimeter evapotranspiration and evapotranspiration , rainfall and temperature of GLDAS and also rainfall of TRMM derived for the years 1379 to 1382 ,were studied. According to 50-years of rainfall data, years 80-79, 81-80 and 82-81, respectively, were selected as dry, normal and wet crop year. Quantitative indices that have been used to evaluate the results are such as root mean square error (RMSE), mean bias error, MBE and Mean Absolute Error MAE.
Results: The results of the evapotranspiration data of GLDAS and lysimeter R2=0.95,RMSE=0.68 shows that there is a high correlation between the two data series. In addition to the Evapotranspiration, temperature and precipitation as well as two parameters affecting evapotranspiration were evaluated. The statistical results indicate that R2 is more than 0.9 between air temperature of GLDAS and station and R2=0.82 between precipitation of GLDAS and station and also R2=0.76 between TRMM satellite precipitation data and station data.
Conclusion: According to results, using data from evapotranspiration, temperature and precipitation derived from GLDAS model as an alternative to the observational data in areas where have no data is suggested.
Key words: Evapotranspiration, Remote sensing, GLDAS, Precipitation
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