Assessment of The Effect of Precipitation, Temperature and Potential Evapotranspiration of GLDAS Model on The Outputs of AquaCrop Model

Document Type : Original Research Paper

Author

PhD Student, Department of Water Engineering and Sciences, Imam Khomeini International University, Qazvin, Iran

Abstract

Remote sensing (RS) technology can be effective in many agricultural activities due to the associated valuable features such as the ability to take multi-time and multi-spectral images, the ability to distinguish between time and radiometry, and a wide and integrated view of the area. The RS technology also could help to estimate the actual evapotranspiration and to investigate the crop water productivity. In this study, the effect of precipitation, temperature and potential evapotranspiration of the GLDAS model on the outputs of Aqua Crop model in Qazvin synoptic station for two wheat and maize products from 1979 to 2013 have been investigated. Also, the parameters of GLDAS model, the precipitation during 1979-2015 and the evapotranspiration during 1979-2013 were examined. The Penman Monteith method was used to compute the potential evapotranspiration of Qazvin station. The results of the GLDAS model, the precipitation model data and station data, R2 = 0.97 and NRMSE = 0.38 show that there is a high determination coefficient between these two data sets. The statistical results show R2 = 0.99 and NRMSE = 0.10 between the evapotranspiration data obtained from the GLDAS model and station data. The results of the statistical evaluation of the outputs of Aqua Crop model, Qazvin station data and the GLDAS model for maize and wheat products showed that the model is more accurate in biomass and yield according to the RMSE and NRMSE indices.

Keywords

Main Subjects

References

Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P. and Nelkin, E. (2003). The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present). J. Hydrometeor, 4:1147-1167.
Alizadeh, A. (2010). The relationship between water, soil and plants, Astan Quds Razavi Publications, Tenth Edition, In Persian.
Alizadeh, H., Nazari, B., Parsinejad, M., Ramezani Etedali, H. and Veteran, H. R. (2010). Evaluation of Aqua Crop model in wheat irrigation management in Karaj region. Irrigation and Drainage of Iran, 2(4): 283-273.
Bi, H., Ma, J., Zheng, W. and Zeng, J. (2016). Comparingof soil moisture in GLDAS model simulation and in situ observations over the Tibetan Plateau. J. Geophysic. Atm. 121(6): 2658-2678.
Carroll, ML., Townshend, JR., DiMiceli, CM., Noojipady, P. and Sohlberg, R. A. (2009). A new global raster water mask at 250 m resolution. International Journal of Digital Earth, 2(4): 291-308.
Davitt, A. (2011). Climate variability and drought in the South Platte River basin. Thesis submitted in fulfillment of the requirement for the degree master of art (earth and atmospheric science). The city College of the City University of New York, USA.
Faraji, Z., Kaviani, A. and Shakiba, A. (2018). Evaluation of evapotranspiration, precipitation and air temperature from ground surface model (GLDAS) using observational data in Qazvin province. Soil and Water Conservation Research, 24(3): 297-283.
Faraji, Z. and Kaviani, A. (2019). Evaluation of water balance components obtained from GLDAS-2 and GLDAS2-1 land surface model in Qazvin province .Irrigation and Drainage of Iran, 2(13): 474-462.
Golabi, M. and Naseri, A. (2015). Evaluation of Aqua Crop model in predicting sugarcane yield of soil profile under salinity stress, Iranian Soil and Water Research, 46(4): 694-685.
Hsiao, T. C., Heng, L. K., Steduto, P., Rojas-Lara, B., Raes, D. and Fereres, E. (2009). AquaCrop—the FAO crop model to simulate yield response to water III. Parameterization and testing for maize. Agronomy Journal, 101: 448–459.
Kumar, S. V., Peters-Lidard, C. D., Tian, Y., Houser, J., Geiger, S., Olden, L., Lighty, J. L., Eastman, P. R., Doty, B., Dirmeyer, P., Adams, J., Mitchell, K., Wood, E. F. and Sheffield, J. (2006). Land Information System-An Interoperable Framework for High Resolution Land Surface Modeling. Environmental Modelling and Software, Vol. 21: 1402-1415.
Moiwo, J. P., Yang, Y., Li, H., Han, S. and Hu, Y. (2012). Comparison of GRACE with in situ hydrological measurement data shows storage depletion in Hai River basin, Northern China. Water SA, 35: 663-670.
Polroodi Moghaddam, M., Hamzeh, S. and Vazifehdoost, M. (2016). Assessment of changes in precipitation and runoff global land surface model (GLDAS) in the basin of Doosti Dam. Journal of Geographic Information, 98: 43-56.
Raes, D., Steduto P, Hsiao, T. C. and Fereres, E. (2012). Reference manual AquaCrop, FAO, Land and Water Division, Rome, Italy.
Rahimi Khoob, H., Sotoudehnia, A. and Masahubani, A. (2014). Calibration and evaluation of Aqua Crop model for forage maize in Qazvin regionIrrigation and Drainage of Iran, 1(8): 115-118.
Raziei, T. and Pereira, S. L. (2013). ‘Spatial variability analysis of reference evapotranspiration in Iranutilizing fine resolution gridded datasets’, Agri Water Manage, 126: 104-118.
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C. J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P. and Lohmann, D. (2004). The Global Land Data Assimilation System’, Bulletim of the American Meteorological Society, 85(3): 381-394.
Sheffield, J., Goteti, G. and Wood, E. (2006). Development of a 50-yr high-resolution global dataset of meteorological forcings for land  surface modeling. Journal of Climate, 19(13): 3088-3111.
Stackhouse, P. W., Gupta, S. K., Cox, S. J., Zhang, T., Mikovitz, JC. and Hinkelman, LM. (2011). 24.5-year SRB data set released. GEWEX News, 21:10–12 .
Seyedi, H., Anagnostou, E. N., Beighley, E. and McCollum, J. (2014). Satellite-driven downscaling of global reanalysis precipitation products for hydrological applications. Hydrology and Earth System Sciences. 18: 5077-5091.
Wang, F., Wang, L., Koike, T., Zhou, H., Yang, K., Wang, A. and Li, W. (2011). ‘Evolution and application of a fine-resolution global data set in a semiarid mesoscale river basin with a distributed biosphere hydrological model’, Journal of Geophysical Researches: Atmospheres, 10.1029/2011JD015990, 116, D21.
Water Productivity Journal
Volume 1, Issue 2
2021
Pages 21-32

Files

Share

How to cite

Statistics