Evaluation of evapotranspiration, precipitation and air temperature from global land data assimilation system (GLDAS) by lysimeter data in Qazvin province

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 estimating 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. This paper introduces a new method to increase the accuracy of Evapotranspiration and calibration of rainfall data of Global Land Data Assimilation System (GLDAS) model and also introducing GLDAS Evapotranspiration, rainfall and temperature as a suitable replacement for the records data, especially in regions where have no data and Inaccessible places.
In this study which was proceed in Qazvin, evapotranspiration and temperature for the years 1379 to 1382 and rainfall for the years 1995 to 2010 has been 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 correlation coefficient (R), root mean square error (RMSE), mean bias error (MBE) and Mean Absolute Error (MAE).
The results of the GLDAS evapotranspiration data and lysimeter R2=0.95, RMSE=0.68 (mm/month) 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=0.67 between precipitation of GLDAS and station data.
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.