ارزیابی مکانی-زمانی محصولات بارش ماهواره‌ای در مناطق شمال غرب ایران

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی آب، دانشکده کشاورزی و منابع طبیعی و عضو پژوهشکده مدیریت آب،دانشگاه محقق اردبیلی، اردبیل، ایران

2 گروه مهندسی آب، دانشکدگان ابوریحان، دانشگاه تهران، تهران، ایران.

3 گروه مهندسی آبیاری و آبادانی، دانشکده مهندسی فناوری و کشاورزی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، تهران، ایران

چکیده

در تحلیل رویدادهای اقلیمی و هیدرولوژیکی، بارش به عنوان یک پارامتر اصلی مطرح است و لذا، اندازه‌گیری داده‌های بارش با تفکیک مکانی و زمانی بالا در پیش‌بینی الگوهای آب و هوایی بسیار مهم است. اندازه‌گیری دقیق میزان بارش در سطح زمین به دلیل پراکندگی شبکه‌های باران‌سنجی، تنوع مکانی و زمانی رخدادها، اثرات باد و توپوگرافی، بسیار چالش‌برانگیز است. ازاین‌رو در چند دهه اخیر، استفاده و توسعه از محصولات ماهواره‌ای و تکنیک‌های سنجش از دور بسیار رایج شده است که در تخمین بارش‌ها استفاده می‌گردد. این پژوهش، با هدف ارزیابی داده‌های بارش ماهواره‌ای TRMM، CHIRPS، Persiann-CDR و GPM-IMERG و مقایسه آن‌ها با داده‌های زمینی در منطقه شمال و شمال غرب کشور (شامل استان‌های گیلان، اردبیل، آذربایجان شرقی و آذربایجان غربی) انجام شد. برای این منظور، ارزیابی بین داده‌های ماهواره‌ای در مقیاس زمانی روزانه، ماهانه و فصلی با داده‌های مشاهده‌ای ایستگاه‌های زمینی با استفاده از شاخص‌های قطعی شامل POD، CSI، FAR، Bias و معیارهای آماری شامل همبستگی (Corr) و نرمال مجذور میانگین مربعات خطا (nRMSE) انجام گرفت. دوره مطالعاتی از تاریخ 12 دی 1395 (1 ژانویه 2017) تا 10 دی 1400 (31 دسامبر 2021)، بر روی 56 ایستگاه سینوپتیک انتخاب شد. نتایج اکثر شاخص‌ها و معیارهای آماری (Corr، nRMSE، POD و CSI) نشان داد که در همه محصولات کمترین خطا مربوط به جنوب غربی منطقه مورد مطالعه (جنوب استان آذربایجان غربی) است و با حرکت به سمت شرق منطقه و نوار ساحلی دریای خزر، خطا افزایش می‌یابد. در ارزیابی میانگین منطقه‌ای بارش، نتایج IMERG، CHIRPS و Persiann-CDR نزدیک به یکدیگر بود و با اختلافی جزئی (به‌جز در معیار nRMSE) محصول IMERG برتری دارد. همچنین در بررسی برآوردهای فصلی، نتایج دو محصول CHIRPS و Persiann-CDR قابل‌اطمینان‌تر بودند، اما برای استفاده از IMERG و TRMM پیشنهاد می‌شود که با استفاده از روش‌های مختلف تصحیح خطا، برآوردها تدقیق گردد. در نهایت بر اساس نتایج این پژوهش، هر محصول بر اساس نوع توپوگرافی و اقلیم منطقه، نتیجه‌ای متفاوت در تخمین بارش ارائه می‌دهد و نیاز به مطالعات بیشتر با توجه به نوع رخدادها در هر منطقه و بررسی جزئی‌تر هر محصول می‌باشد.

کلیدواژه‌ها

عنوان مقاله [English]

Spatio-temporal Evaluation of Satellite Precipitation Products in Northwestern Iran

نویسندگان [English]

  • Ali Rasoulzadeh 1
  • Sajad Mahmoudi Babolan 2
  • Saeed Nastarani Amoghin 3
1 Water Engineering Dept., Faculty of Agriculture and Natural Resources, member of Water Management Research Center, University of Mohaghegh Ardabili, Ardabil, Iran
2 Department of Water Engineering, College of Aburaihan, University of Tehran, Tehran, Iran
3 Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Tehran, Iran
چکیده [English]

In the analysis of climatic and hydrological events, precipitation is considered as a main parameter and therefore, measuring precipitation data with high Spatio-temporal resolution is very important in predicting weather patterns. Accurate measurement of precipitation on the land surface is very challenging due to the scattering of rain gauge networks, temporal and spatial diversity, wind effects, and topography. In recent decades, the use and development of satellite products and remote sensing techniques have become widespread which is used in precipitation estimation. The aim of this study was to evaluate the satellite precipitation data of TRMM, CHIRPS, Persiann-CDR and GPM-IMERG and compare them with rain gauge data in the north and northwestern region of the country (including Gilan, Ardabil, East Azerbaijan, and West Azerbaijan provinces). For this purpose, after receiving the satellite data series and pre-processing them, an evaluation was performed between the satellite data on a daily, monthly and seasonal time scale with the observational data. Evaluation of the results is performed using definite indicators including POD, CSI, FAR, Bias and statistical criteria including correlation coefficient (Corr) and Normalized Root Mean Square Error (nRMSE). The study period was selected from January 1, 2017 to December 31, 2021 on 56 synoptic stations. The results of most indicators and statistical criteria (Corr, nRMSE, POD, and CSI) showed that in all products the lowest error is related to the southwest of the study area which increases (south of West Azerbaijan province) by moving toward the east of the region and the Caspian coast.  In assessing the regional average precipitation, the results of IMERG, CHIRPS and Persiann-CDR were close to each other and with a slight difference (except in the nRMSE criterion) the IMERG product is superior. Also, in the study of seasonal estimates, the results of CHIRPS and Persiann-CDR were more reliable, but in order to use IMERG and TRMM, it is suggested that the estimates be accurized using different error correction methods. Finally, according to the results of this study, each product based on the type of topography and climate of the region provides a different result in estimating rainfall and there is a need for further studies according to the type of events in each region and a more detailed study of each product.

کلیدواژه‌ها [English]

  • precipitation estimate
  • GPM-IMERG
  • CHIRPS
  • Persiann-CDR
  • TRMM

مراجع

Akbari Yangehghaleh, M., sanaeinejad, S., Faridhosseini, A. and Akbari, M. (2017). The Study of Spatial -Temporal Distribution of Rainfall, using TRMM data (Case study: Khorasan Razavi province). Journal of Climate Research, 1396(29), 1-18. (In Farsi)
Ashouri, H., Hsu, K. L., Sorooshian, S., Braithwaite, D. K., Knapp, K. R., Cecil, L. D., Nelson, B. R., and Prat, O. P. (2015). PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies. Bulletin of the American Meteorological Society, 96(1), 69–83.
Atger, F. (2001). Verification of intense precipitation forecasts from single models and ensemble prediction systems. Nonlinear Processes in Geophysics, 8(6), 401–417.
Azizi mobaser, J., Rasoulzadeh, A., rahmati, A., shayeghi, A., Bakhtar, A. (2021). Evaluating the Performance of Era-5 Re-Analysis Data in Estimating Daily and Monthly Precipitation, Case Study; Ardabil Province. Iranian Journal of Soil and Water Research, 51(11), 2937-2951. (In Farsi)
Chen, C., Chen, Q., Duan, Z., Zhang, J., Mo, K., Li, Z. and Tang, G. (2018). Multiscale Comparative Evaluation of the GPM IMERG v5 and TRMM 3B42 v7 Precipitation Products from 2015 to 2017 over a Climate Transition Area of China. Remote Sensing, 10(6).
Dinku, T., Funk, C., Peterson, P., Maidment, R., Tadesse, T., Gadain, H. and Ceccato, P. (2018). Validation of the CHIRPS satellite rainfall estimates over eastern Africa. Quarterly Journal of the Royal Meteorological Society, 144, 292–312.
Duan, Z., Liu, J., Tuo, Y., Chiogna, G. and 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, 573, 1536–1553.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A. and Michaelsen, J. (2015). The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific Data, 2(1), 1–21.
Gao, F., Zhang, Y., Chen, Q., Wang, P., Yang, H., Yao, Y. and Cai, W. (2018). Comparison of two long-term and high-resolution satellite precipitation datasets in Xinjiang, China. Atmospheric Research, 212, 150–157.
Gorjizade, A., AkhondAli, A., Shahbazi, A. and Moridi, A. (2019). Comparison and Evaluation of precipitation estimated by ERA-Interim, PERSIANN-CDR and CHIRPS models at the upstream of Maroon dam. Iran-Water Resources Research, 15(1), 267-279. (In Farsi)
Goshime, D. W., Absi, R., Haile, A. T., Ledésert, B. and Rientjes, T. (2020). Bias-Corrected CHIRP Satellite Rainfall for Water Level. Journal of Hydrologic Engineering, 25(9), 05020024.
Hsu, K., Gao, X., Sorooshian, S. and Gupta, H. V. (1997). Precipitation estimation from remotely sensed information using artificial neural networks. Journal of Applied Meteorology, 36(9), 1176–1190.
Huffman, G. ., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., Xie, P. and Yoo, S.-H. (2015). NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG). Algorithm Theoretical Basis Document (ATBD) Version, 4(26).
Huffman, G. J., Adler, R. F., Bolvin, D. T. and Nelkin, E. J. (2010). The TRMM multi-satellite precipitation analysis (TMPA). In Satellite rainfall applications for surface hydrology (pp. 3–22).
Javanmard, S., Yatagai, A., Nodzu, M. I., BodaghJamali, J. and Kawamoto, H. (2010). Comparing high-resolution gridded precipitation data with satellite rainfall estimates of TRMM_3B42 over Iran. Advances in Geosciences, 25, 119-125.
Kidd, C., Becker, A., Huffman, G. J., Muller, C. L., Joe, P., Skofronick-Jackson, G. and Kirschbaum, D. B. (2017). So, How Much of the Earth’s Surface Is Covered by Rain Gauges? Bulletin of the American Meteorological Society, 98(1), 69–78.
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), 59-74. (In Farsi)
Mahmoudi Babolan, S., Nastarani Amoghin, S., Rasoulzadeh, A. (2022). Evaluation of satellite precipitation products for estimating heavy precipitation on the Caspian coast. Water and Soil Management and Modelling, 2(4), 103-118. (In Farsi)
Moazami, S., Golian, S., Hong, Y., Sheng, C. and Kavianpour, M. R. (2016). Comprehensive evaluation of four high-resolution satellite precipitation products under diverse climate conditions in Iran. Hydrological Sciences Journal, 61(2), 420-440.
Mosaffa, H., Sadeghi, M., Hayatbini, N., Afzali Gorooh, V., Akbari Asanjan, A., Nguyen, P., and Sorooshian, S. (2020). Spatiotemporal variations of precipitation over Iran using the high-resolution and nearly four decades satellite-based PERSIANN-CDR dataset. Remote Sensing, 12(10), 1584.
New, M., Todd, M., Hulme, M. and Jones, P. (2001). Precipitation measurements and trends in the twentieth century. International Journal of Climatology: A Journal of the Royal Meteorological Society, 21(15), 1889–1992.
Paredes-Trejo, F., Barbosa, H. and dos Santos, C. A. C. (2019). Evaluation of the performance of SM2RAIN-derived rainfall products over Brazil. Remote Sensing, 11(9), 1113.
Ramadhan, R., Yusnaini, H., Marzuki, M., Muharsyah, R., Suryanto, W., Sholihun, S., Vonnisa, M., Harmadi, H., Ningsih, A. P., Battaglia, A., Hashiguchi, H. and Tokay, A. (2022). Evaluation of GPM IMERG Performance Using Gauge Data over Indonesian Maritime Continent at Different Time Scales. Remote Sensing, 14(5), 1172.
Sadeghi, H., Masoompour, J., Miri, M. (2019). The Evaluation of GPM Precipitation Remote Sensing Data with Observed Data (Case Study: Mid-West of Iran). Iranian Journal of Remote Sensing & GIS, 11(2), 115-124. (In Farsi)
Sharifi, E., Steinacker, R. and Saghafian, B. (2016). Assessment of GPM-IMERG and other precipitation products against gauge data under different topographic and climatic conditions in Iran: Preliminary results. Remote Sensing, 8(2), 135.
Taghizadeh, E. and Ahmadi-Givi, F. (2018). Evaluation of GPM precipitation products and mapping soil moisture using SMAP data in the northwest of Iran. Iranian Journal of Geophysics, 12(3), 70-86. (In Farsi)
Tan, M. L. and Santo, H. (2018). Comparison of GPM IMERG, TMPA 3B42 and PERSIANN-CDR satellite precipitation products over Malaysia. Atmospheric Research, 202, 63–76.
Tartaglione, N. (2010). Relationship between precipitation forecast errors and skill scores of dichotomous forecasts. Weather and Forecasting, 25(1), 355–365.
Tian, Y., Peters-Lidard, C. D., Choudhury, B. J. and Garcia, M. (2007). Multitemporal analysis of TRMM-based satellite precipitation products for land data assimilation applications. Journal of Hydrometeorology, 8(6), 1165-1183.
Vaghefi, S. A., Keykhai, M., Jahanbakhshi, F., Sheikholeslami, J., Ahmadi, A., Yang, H., & Abbaspour, K. C. (2019). The future of extreme climate in Iran. Scientific reports, 9(1), 1-11.
West, T. K., Steenburgh, W. J. and Mace, G. G. (2019). Characteristics of sea‐effect clouds and precipitation over the sea of Japan region as observed by A‐Train satellites. Journal of Geophysical Research: Atmospheres, 124(3), 1322-1335.
Xiao, S., Xia, J., & Zou, L. (2020). Evaluation of multi-satellite precipitation products and their ability in capturing the characteristics of extreme climate events over the Yangtze River Basin, China. Water, 12(4), 1179.
Yang, X., Lu, Y., Tan, M. L., Li, X., Guoqing Wang and He, R. (2020). Nine-Year Systematic Evaluation of the GPM and TRMM Precipitation Products in the Shuaishui River Basin in East-Central China. Remote Sensing, 12(6).
Zambranoa, F., Wardlow, B., TsegayeTadesse, Lillo-Saavedra, M. and Lagos, O. (2017). Evaluating satellite-derived long-term historical precipitation datasets for drought monitoring in Chile. Atmospheric Research, 186, 26–42.
تحقیقات آب و خاک ایران
دوره 53، شماره 9
آذر 1401
صفحه 2141-2160

فایل ها

هم رسانی

ارجاع به این مقاله

آمار