Evaluation of Daily, Decade and Monthly Data Satellite Images to Estimate of Precipitation Using Google Earth engine in Khuzestan Province

Subject Areas : Farm water management with the aim of improving irrigation management indicators

Arash Tafteh ¹ , Sina Mallah ² , Niazali Ebrahimipak ³

1 - Assistant professor of Department of Irrigation and soil physics, Soil and Water Research Institute, Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
2 - Resercher of Department of Irrigation and soil physics, Soil and Water Research Institute, Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
3 - Associate Professor of Irrigation and Soil Physics, Soil and Water Research Institute, Agricultural Research and Education Organization, Karaj, Iran

Received: 2019-12-24 Accepted : 2020-05-19 Published : 2020-05-21

Keywords: Rain, Khuzestan, EffectiveRain, Google Earth Engine,

Abstract :

Since synoptic metrological stations have non-uniformed scattering pattern in Iran and on the other hand precipitation determination and forecasting is essential for irrigation planning, a method precisely determine precipitation of agricultural lands in farm level has great importance. This study was carried out in Google Earth Engine Code programming environment using GPM, TRMM and CHIRPS satellite data which is daily, decade and monthly, respectively in Ahwaz and Izeh metrological stations for calibration and 9 meteorological stations for validation during 2015-2016 and 2017 - 2018 Growing season. Results showed that monthly interval could obtain better accuracy with R2 of 0.99 and NRMSE = 0.36, respectively. The validation results of the rest 9 meteoroidal station indicated that precipitation prediction had 51% and 3.1 mm error and under estimation on average, respectively. The efficiency was reasonable and F-Test showed no significant difference between observed and prediction samples. The standard error value was 14.2 mm which is a significant error and need to work on updated better functions. It can be concluded that this method can be a useful tool for monthly precipitation prediction of areas with no climatic data if integrated with Kriging, co-Kriging and Inverse Distance Weighted (IDW) geostatistical models for interpolation.

References:

بروجردی، پ. 1392. مقایسه داده‌های بارش ماهانه ماهواره‌ای و زمینی در شبکه‌های با تفکیک زیاد روی. مجله ژئوفیزیک ایران، جلد 7 شماره 4: صفحات 149 تا 160.

رسولی، ع.ا.، عرفانیان، م.، صراف، ب.، و جوان، خ. 1395. ارزیابی تطبیقی مقادیر بارندگی برآورد شده TRMMو بارش ثبت‌شده ایستگاه‌های زمینی. فصلنامه‌ی علمی-پژوهشی فضای جغرافیایی. جلد 16 شماره  : صفحات 195 تا 217.

قاجار نیا، ن.؛ لیاقت، ع.م.؛ آراسته، پ.؛ 1393.صحت سنجی داده‌های بارندگی ایستگاه‌های غیر ثبات سازمان هواشناسی و تماب در حوضه آبریز دریاچه ارومیه. نشریه حفاظت منابع آب‌وخاک، 4 (1)، 91-109.

متکان، ع؛شکیبا، ع؛ عاشور لو، د؛ بداق جمالی، ج؛محمدیان، و. 1388. قابلیت داده‌های ترکیبی مادون‌قرمز و ماکروویو غیرفعال سنجش‌ازدور و تخمین بارندگی و پایش سیلاب (مطالعه موردی: استان گلستان) ،سنجش‌ازدور و GIS ایران، شماره 1، ص 11-13.

مددی، غ.، حمزه، س.، و نوروزی، ع.ا. 1394. ارزیابی بارش در مقیاس‌های روزانه، ماهانه و سالانه با استفاده از تصاویر ماهواره‌ای (مطالعه موردی: حوزه مرزی غرب ایران. سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی (کاربرد سنجش از دور و  GISدر علوم منابع طبیعی):  دوره  6 , شماره  2: صفحات 59 تا 74.

Adler RF, Kidd C, Petty G, Morissey M, Goodm an HM. 2001. Intercomparison of global precipitation products: The third Precipitation Intercomparison Project (PIP-3). Bulletin of the American Meteorological Society, 82(7): 1377-1396.

Adler, R.F., G.J. Huffman, A. Chang, R. Ferraro, P. Xie, J. Janowiak, B. Rudolf, U. Schneider, S. Curtis, D. Bolvin, A. Gruber, J. Susskind, P. Arkin, E.J. Nelkin, 2003: The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present). J. Hydrometeor., 4(6), 1147-1167.

Collischonn B, Collischonn W, Tucci CEM. 2008. Daily hydrological modeling in the Amazon basin using TRMM rainfall estimates. Journal of Hydrology, 360(1): 207-216.

Fang J, Du J, Xu W, Shi P, Li M, Ming X. 2013. Spatial downscaling of TRMM precipitation data based on the aerographical effect and meteorological conditions in a mountainous area. Advances in Water Resources, 61: 42-50.

Feidas, H., Kokolatos, G., Negri, A., Manyin M., Chrysoulakis, N., Kmarianakis, y. (2009),"A validation of an infrared-based satellite algorithm to estimate accumulated rainfall over theMediterranean basin, Theroretical and Applied Climatology, 95:91-109.

Funk, Chris, Pete Peterson, Martin Landsfeld, Diego Pedreros, James Verdin, Shraddhanand Shukla, Gregory Husak, James Rowland, Laura Harrison, Andrew Hoell & Joel Michaelsen.2015. "The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes". Scientific Data 2, 150066. doi:10.1038/sdata.2015.662015.

Ghajarnia, N.; Daneshkar, A.P.; Liaghat, M.; Araghinejad, S.2019. Error Analysis on PERSIANN Precipitation Estimations: Case Study of Urmia Lake Basin, Iran. J. Hydrol. Eng. 2018, 23, DOI: 10.1061/(ASCE)HE.1943-5584.0001643.

Ghajarnia, N.; Liaghat, A.; Daneshkar, A.P. Comparison and evaluation of high resolution precipitation estimation products in Urmia Basin-Iran. Atmos. Res. 2015, 158, 50–65, Doi: 10.1016/j.atmosres.2015.02.010.

Haile, A.T., Rientjes, T., Gieske, A., Gebremichael, M.2010.Multispectral remote sensing for rainfall detection and estimation at the source of the Blue Nile River., Int. J. of Applied Earth Observation and Geoinformation, 12: 76–82.

Hosseini-Moghari, M., Araghinejad, Sh., Ebrahimi, K. (2018). “Spatio-temporal evaluation of global gridded precipitation datasets across Iran.” Hodrological Science, 63(11), 1669–1688, Doi: 10.1080/02626667.2018.1524986.

Huffman, G.J., Adler, R., Bolvin, D., Gu, G., Nelkin, E., Bowman, K., Stocker, E., Wolff, D., 2007.The TRMM multi-satellite precipitation analysis: quasiglobal, multi-year, combined-sensor precipitation estimates at fine scale. J. Hydrometeorol. 8, 38–55.

Huffman, G.J., 2012: Algorithm Theoretical Basis Document (ATBD) Version 3.0 for the NASA Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (I-MERG). GPM Project, Greenbelt, MD, 29 pp.

Hughes, D.A. Comparison of satellite rainfall data with observations from gauging station networks. J. Hydrol. 2006, 327, 399–410.

Javanmard, S., Yatagai, A., Nodzu, M.M., BodaghJamali, J., Kawamoto, H., 2010. Comparing high-resolution gridded precipitation data with satellite rainfall estimate of TRMM_3B42 over Iran. Adv. Geosci. 25, 119–125.

Khandu, J.L.A.; Forootana, E. An evaluation of high-resolution gridded precipitation products over Bhutan (1998–2012). Int. J. Climatol. 2016, 36, 1067–1087.

Katiraie, B.P.S., Nasrollahi, N., Hsu, L.K., Sorooshian, S., 2013. Evaluation of satellite-based precipitation estimation over Iran. J. Arid Environ. 97, 205–219.

Kidd C, Levizzani V. 2011. Status of satellite precipitation retrievals. Hydrology and Earth System Sciences, 15(4): 1109-1116.

Marchi, D.C., 2006, Probabilistic Estimation of Precipitation Combining Geostationary and TRMM Satellite Data, PhD. Thesis, Georgia Institute of Technology.

Mesinger, F.; Chou, S.C.; Gomes, J.L.; Jovic, D.; Bastos, P.; Bustamante, J.F.; Lazic, L.; Lyra, A.A.; Morelli, S.; Ristic, I. An upgraded version of the Eta model. Meteorol Atmos. Phys. 2012, 116, 63–79.

Meng J, Li L, Hao Z, Wang J, Shao Q. 2014.Suitability of TRMM satellite rainfall in driving a distributed hydrological model in the source region of Yellow River. Journal of Hydrology, 509: 320-332.

Moazami, S., Golian, S., Kavianpour, M.R., Yang, H., 2013. Comparison of PERSIANN and V7 TRMM multi-satellite precipitation analysis (TMPA) products with rain gauge data over Iran. Int. J. Remote Sens. 34 (22), 8156–8171.

Nogueira, S.M.C., Moreira, M.A. and Volpato, M.M.L. 2018. valuating Precipitation Estimates from Eta, TRMM and CHRIPS Data in the South-Southeast Region of Minas Gerais State—Brazil. Remote Sens. 10. 313. pp 16. NOAA National Centers for Environmental Information.

Porcù F, Milani L, Petracca M. 2014. On the uncertainties in validating satellite instantaneous rainfall estimates with rain gauge operational network. Atmospheric Research, 144: 73-81.

Schneider, U.; Ziese, M.; Becker, A.; Meyer-Christoffer, A.; Finger, P. Global Precipitation Analysis Products of the GPCC; Global Precipitation Climatology Centre (GPCC), Deutscher Wetterdienst: Offenbach, Germany, 2015.

Sorooshian, S.; Hsu, K.L.; Gao, X.; Gupta, H.V.; Imam, B.; Braithwaite, D. Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull. Am. Meteorol. Soc. 2000, 81, 2035–2046.

Funk, C.C.; Peterson, P.J.; Landsfeld, M.F.; Pedreros, D.H.; Verdin, J.P.; Rowland, J.D.; Romero, B.E.; Husak, G.J.; Michaelsen, J.C.; Verdin, A.P. A quasi-global precipitation time series for drought monitoring. U.S. Geol. Surv. Data Ser. 2014.

Sharma S. 2003. Study of precipitating systems by Doppler weather radar and TRMM rainfall radar, Department of Physics, Kohima Science College, Jotsoma, Kohima, Nagaland, India-797002.

Zhang WW, Yao L, Li H, Sun DF, Zhou LD. 2011. Research on land use change in Beijing Hanshiqiao wetland nature reserve using remote sensing and GIS. Procedia Environmental Sciences (3rd International Conference on Environmental Science and Information Application Technology ESIAT,10(A): 583– 588.

Li X, Zhang Q, Xu C-Y. 2014. Assessing the performance of satellite-based precipitation products and its dependence on topography over Poyang Lake basin. Theoretical and Applied Climatology, 115(3-4): 713-729.