Comparative Evaluation of Rain and Temperature Grid Data of Global Land Data Assimilation System (Case study: Helleh basin)

Document Type : Research Paper

Authors

1 Ph.D. Graduate, Department of Hydrology and Water Resources, Faculty of Water Science Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

2 Professor, Department of Hydrology and Water Resources, Faculty of Water Science Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

3 Associate Professor, Department of Hydrology and Water Resources, Faculty of Water Science Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

4 M.Sc. Graduate, Department of Water Engineering and Management, Faculty of Agriculture, Tarbiat Modares Universitys, Tehran, Iran.

Abstract

The present study, while introducing the Global Land Data Assimilation System (GLDAS) database, evaluates the performance of this database in estimating two meteorological variables of precipitation and air temperature in two daily and monthly time scales in the Helleh catchment area located in southern Iran. To achieve the objectives of the study, the data of eleven rainfall and temperature gauges in the catchment area was used for fourteen years. In this study, in order to compare the gridbased data and station points, laps rate downscale method of temperature and precipitation and statistical indicators were used. The results show that the performance of GLDAS database in estimating air temperature is much better than precipitation, so that on a daily time scale, the average value of the coefficient of determination in eleven stations for estimating precipitation is 0.329, respectively, while the performance in estimating air temperature with the coefficient of determination of 0.934 is Very suitable. On a monthly scale, the results of this study show that the performance of the GLDAS database is very good in estimating both temperature and precipitation variables, so that on a monthly basis, the coefficient of determination in air temperature and precipitation parameters are 0.984 and 0.857, respectively. Therefore, it can be said that the suitability of performance in a meteorological variable is not a reason for suitability in all parameters. According to the mean error index, the GLDAS database overestimates the temperature data, but underestimates the precipitation data. In Error zoning it can be seen that basin surface characteristics such as altitude can also be effective in evaluating the performance of the base.

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References

  1. Ahmadi, M., & Narangifard, M. (2012). Evaluation of precipitation zones using Joule satellite data in Fars province, Researches in Earth Sciences, 3(3),28-44.(In Persian).
  2. Alijanian, M., Rakhshandehroo, Gh. R., Mishra, A. K., & dehghani, M. (2017), Evaluation of satellite rainfall climatology using CMORPH, PERSIANN-CDR, Persiann, Trmm, Mswep Over Iran, International Journal of Climatology, Published online in Wiley Online Library. (wileyonlinelibrary.com) DOI: 10.1002/joc.5131.
  3. Ashrafzadeh Afshar, A., Joodaki, G. R., & Sharifi, M. A. (2016) Evaluation of Groundwater Resources in Iran Using GRACE Gravity Satellite Data. JGST, 5(4), 73-84. (In Persian).
  4. Chen, F., Mitchell, K., Schaake, J., Xue, Y., Pan, H., Koren, V., Duan, Y., EK, M., & Betts, A. (1996). Modeling of land-surface evaporation by four schemes and comparison with FIFE observions. Journal Geophys Res, 101 (D3), 7251-7268.
  5. Duan, Z., Liu, J., Tuo, Y., Chiongna, G., & Disse, M. (2016). Evaluation of eight high spatial resolution gridded precipitation products in Adige Basin (Italy) at multiple temporal and spatial scales. Science of the Total Environment, http://dx.doi.org/10.1016/j. scitotenv.2016.08.213.
  6. Emamifar, S., Rahimikhoob, A., & Noroozi, A. (2014). An Evaluation of M5 Model Tree vs. Artificial Neural Network for Estimating Mean Air Temperature as Based on Land Surface Temperature Data by MODIS-Terra Sensor. Iranian Journal of Soil and Water Research, 45(4), 423-433. doi: 10.22059/ijswr.2014.52595
  7. Fan, Y., & Van Den Dool, H. (2008). A global monthly land surface air temperature analysis for 1948- persent. Geophysical Research, 113, doi: 10.1029/2007/JD008470.
  8. Fars Regional Water (2011). Atlas Water Resources Report of Helleh Basin
  9. Faraji, Z., Kaviani, A., & Shakiba, A. (2017). Evaluation of Evapotranspiration, Precipitation and Air Temperature from Global Land Data Assimilation System (GLDAS) by Lysimeter Data in Qazvin. Journal of Water and Soil Conservation, 24(3), 283-297. doi: 10.22069/jwfst.2017.11535.2630. (In Persian).
  10. Farokhnia, A., & Morid, S. (2014). Assessment of GRACE and GLDAS Capabilities for Estimation of Water Balance in Large Scale Areas, a Case Study of Urmia Lake Watershed., Iran-Water Resources Research, 10(1), 51-62.
  11. Gao, L., Bernhardt, M., & Schulz, K. (2012). Elevation correction of ERA- Interim temperature data in complex terrain. Hydrology and Earth System Sciences, 16, 4661-4673, Doi: 10.5194/hess-16-4661-2012.
  12. Gao, l., Wei, J., Wang, L., Bernhardt, M., Schulz, K., & Chen, H. (2018). A High- resolution air temperature data set for the Chinese Tian Shan in 1979-2016. Earth System Science Data, 10, 2097-2114, Doi: 10.5194/essd-10-2097-2018.
  13. Hajihosseini, M., Hajihosseini, H., Najafi, A., & Morid, S. (2013). Evaluation of VIC and Noah global simulation models and SWAT simulation model in estimating water balance components of Helmand transboundary catchment, Fifth Iranian Water Resources Management Conference, Tehran, Iran (In Persian)
  14. Huld, T., & Pascua, I.P. (2015). Spatial Downscaling of 2- meter Air Temperature Using Operational Forecast Data, Energise, 8, 2381-2411, doi: 10.3390/en8042381.
  15. Ji, L., Senay, G. B., & Verdin, J, P. (2015). Evaluation of the Global Land Data Assimilation System (GLDAS) Air Temperature Data Products. Journal Of Hydrometeorology, 16, 2463-2480, DOI: 10.1175/JHM-D-14-0230.1.
  16. Knoche, ., Fischer, C., Pohl, E., Krause, P., & Merz, R. (2014). Combined uncertainty of hydrological model complexity and satellite-based forcing data evaluated in two data-scarce semi-arid catchments in Ethiopia. Journal of Hydrology, 519, 2049-2066. http://dx.doi.org/10.1016/j.jhydrol.2014.10.003
  17. Koren, V., Schaake, J., Mitchell., K., Duan, Q.Y., Chen, F., & Baker, J.M. (1999). A parameterzation of snowpack and frozen ground intended for NCEP weather and climate models. Journal Geophys Res, 104, 19569-19585.
  18. Liston, G.E. and Elder, K., 2006, A meteorological distribution system for high – resolution terrestrial modeling (micromet), J.Hydrometeorol,7,217-234.
  19. Madadi, G., Hamzeh, S., Noroozi, A. (2015). 'Evaluation of rainfall on a daily, monthly and annual basis using satellite imagery (Case study: west boundary basin of Iran)', Journal of RS and GIS for Natural Resources, 6(2), pp. 59-74.
  20. Ministry of Energy (2012). Update studies of the country's comprehensive water plan in the Helleh basin (Meteorological and climatological report section).
  21. Miri, M., Azizi, G., Mohammadi, H., & Pourhashemi, M. (2018). Introduction and Evaluation of Global Model of Land Data Assimilation. Scientific-Research Quarterly of Geographical Data (SEPEHR), 26(104), 5-17. doi: 10.22131/sepehr.2018.30514 (In Persian)
  22. Morovati, R., & Shokoohi, A. (2015). Evaluation of different interpolation methods for NCEP/NCAR temperature data over the 2nd order watersheds in Iran. Irrigation and Water Engineering, 5(2), 17-31. (In Persian)
  23. Pan, X. O., Li, X., Yang, K., & He, J. (2014). Comparison of downscaled percipitation data over mountainous watershed: a Case study in the Heihe river basin. Journal of Hydrometorology, 15, 1560-1574.
  24. Parviz, L., Kholghi, M., & Valizadeh, KH. (2011). Estimation of Air Temperature Using Temperature-Vegetation Index (TVX) Method. JWSS, 15(56), 21-34 (In Persian).
  25. Polroudimoghadam, M., Hamzeh, S., & Vazifehdoust, M. (2016). Investigating the Trend of Rainfall and Runoff Changes using The Global Land Data Assimilation System (GLDAS) in Doosti Dam Basin. Scientific - Research Quarterly of Geographical Data (SEPEHR), 25(98), 43-56. (In Persian).
  26. Pomeon, T., Jackisch, D., & Diekkruger, B. (2017). Evaluating the performance of remotely sensed and reanalyzed precipitation data over West Africa using HBV light. Journal of Hydrology, 547, 222-235, http://dx.doi.org/10.1016/j.jhydrol.2017.01.055
  27. Rasuli, A.A., Erfanian, M., Sari Sarraf, B., & Javan, Kh. (2016). Comparative assessment of TRMM estimated rainfall and recorded rainfall of ground stations in Lake Urmia basin. Quarterly Journal of Geographical Space, 16(54), 195-217 (In Persian)
  28. Rodell, M., Houser, P., Jambor, U.E.A., Gottschalck, J., Mitchell, K., Meng, C., & et al. (2004). The global land data assimilation system. Bulletin of the American Meteorological Society, 85(3), 381-394.
  29. Sanikhani, H., Mirabbasi Najaf Abadi, R., & Dinpashoh, Y. (2014). Modeling of Temperature and Rainfall of Tabriz Using Copulas, Irrigation and Water Engineering, 5(1), 123-133 (In Persian)
Water and Irrigation Management
Volume 12, Issue 2
July 2022
Pages 231-244

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