تحلیل تطبیقی دقت محصولات بارش ماهواره‌ای در استان مازندران: ارزیابی کمّی‌وکیفی با تأکید بر داده‌های ایستگاهی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی آب، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری، ایران

2 گروه مهندسی آب، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران

3 گروه علوم و مهندسی آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری، ایران

چکیده

سابقه و هدف: برآورد الگوهای مکانی‌ـ زمانی بارش در استان مازندران، به‌دلیل عوامل متعددی ازجمله فقدان مشاهدات زمینی، مناطق متنوع آب‌وهوایی و شیب‌های شدید کوه‌نگاری، مشکل است. محصولات بارش ماهواره‌ای می‌توانند راه‌حلی مناسب برای اندازه‌گیری میزان بارش، به‌ویژه در نواحی دارای ایستگاه‌های زمینی پراکنده، فراهم کنند و رویکردی جدید برای مشاهدة بارش در سطح جهانی، با سنجش از دور، به دست دهند. بااین‌حال، به‌رغم استفادة گستردة این محصولات درزَمینه‌های مطالعاتی گوناگون، ارزیابی کمّی این محصولات به‌دلیل خطای ذاتی و عدم‌قطعیت‌ آن‌ها، چالشی اساسی است که قبل‌از استفادة مستقیم از آن‌ها باید در مقیاس‌های زمانی و مکانی متفاوت مورد توجه قرار گیرد.
مواد و روش‌ها: در گام نخست، محصولات بارش شبکه‌ای CHIRPS، CMORPH، SM2RAIN، PERSIANN-CDR و IMERG از پایگاه دادة هریک از محصولات، با فرمت NetCDF در سطح جهانی استخراج شد. سپس داده‌های بارش هر محصول برای شبکه‌های واقع در پهنة استان مازندران، با کدنویسی در محیط برنامه‌نویسی R و سامانة اطلاعات جغرافیایی (GIS)، انتخاب شد. در گام بعد، این محصولات در مقابل پانزده ایستگاه هم‌دیدی در سطح استان مازندران، در مقیاس‌های مکانی ایستگاه‌ـ شبکه و منطقه‌ای و مقیاس‌های زمانی ماهیانه و سالیانه ارزیابی و مقایسه شدند. این کار با استفاده از معیارهای ارزیابی آماری ضریب همبستگی اسپیرمن (CC)، ریشة میانگین مربعات خطا (RMSE)، میانگین خطای مطلق (MAD)، کارآیی کلینگ‌ـ گوپتا (KGE) و کارآیی نش‌ساتکلیف (NSE) و همچنین رسم نمودارهای آماری (تیلور و ...) انجام شد. به‌منظور ارزیابی شبکه به ایستگاه، داده‌های نزدیک‌ترین شبکه به هر ایستگاه هم‌دیدی برای هر محصول بارش، استخراج و با داده‌های بارش آن ایستگاه در مقیاس‌های زمانی متفاوت، مقایسه شد.
نتایج و بحث: نتایج بررسی منطقه‌ای محصولات بارش ماهواره‌ای، در مقیاس ماهیانه و سالیانه، بیانگر انطباق بیشتر محصولات IMERG و CMORPH بوده است؛ درحالی‌که محصول CHIRPS فقط در ماه‌های خشکِ سال عملکرد مناسبی داشت. بااین‌حال دقت محصولات در مقیاس ماهیانه بیشتر از سالیانه بود. نتایج دیاگرام تیلور گویای دقت نسبتاً بالای محصولات IMERG، CHIRPS و CMORPH (همبستگی 7/0-8/0) در ایستگاه‌های واقع در نواحی ساحلی و دقت نسبتاً پایین در ارتفاعات (همبستگی 35/0) بوده است. درعین‌حال برای محصول بارش PERSIAN، دقت داده‌ها درمورد تمامی مناطق، اندک بود و در نواحی ساحلی، مقدار همبستگی منفی داشت. باوجوداین‌، نتایج ارزیابی محصولات بارش در سطح ایستگاه‌ها بیانگر عملکرد بهتر محصول IMERG و سپس CMORPH (CC=0.7-0.8 و RMSE=2-4 mm) در برآورد بارش ماهیانه، عمدتاً در نیمۀ شرقی استان بود. کمترین دقت و ضعیف‌ترین عملکرد را نیز محصول PERSIANN-CDR داشته است. بیشترین مقدار CC محصولات بارش برابر 8/0 و اغلب در نواحی شرقی و ساحلی استان بود اما کمترین آن به محصول PERSIANN-CDR (CC=0.05) تعلق داشت. مقادیر RMSE معمولاً بین 2 تا 15 میلی‌متر، به‌ترتیب، در نواحی کوهستانی و نیمه‌شرقی استان متغیر بود که کمترین آن به محصول IMERG و CMORPH تعلق داشت. مقادیر KGE و NSE نیز بیشتر در نواحی ساحلی و شرقی به مقدار بهینه نزدیک‌تر بوده است (NSE=0.5 و KGE=0-1) که درمورد محصول CMORPH بهتر بود. بااین‌حال مقادیر بایاس (BIAS)، درمورد همة محصولات، بین 4/0- تا 1 میلی‌متر متغیر و در نواحی ساحلی و کم‌ارتفاع، دارای کم‌برآوردی بود اما در نواحی مرتفع، بیش‌برآوردی داشت. بررسی تأثیر فاصلة ایستگاه هم‌دیدی و بارش شبکه‌ای در دقت محصول بارش نیز نشان داده است که برای محصولات CMORPH، IMERG (در نواحی مرتفع) و SM2RAIN، فاصلة کم یا زیاد بین ایستگاه و شبکه باعث کاهش یا افزایش عدم‌قطعیت شده است. بااین‌حال، درمورد اغلب محصولات بارش، افزایش شایان ملاحظة ارتفاع موجب افزایش عدم‌قطعیت در برآورد بارش ماهواره‌ای شده است.
نتیجه‌گیری: دقیق‌ترین محصول بارش در استان مازندران شامل محصول IMERG بوده است. یکی از مشکلات اصلی در دقت محصولات بارش، در این منطقه، توپوگرافی پیچیده (اختلاف ارتفاع زیاد) و هم‌جواری با دریای خزر است. بااین‌حال استفاده از روش‌های نوین، مانند سامانه‌های سنجش از دور به‌منزلة راهکاری برای برآورد بارش، نشان‌دهندۀ پیشرفت‌های علمی در این حوزه است و می‌تواند راهنمای تصمیم‌گیرندگان در مطالعات آب‌وهوایی و هیدرولوژیکی باشد.

کلیدواژه‌ها

عنوان مقاله [English]

Comparative Analysis of the Accuracy of Satellite Precipitation Products in Mazandaran Province: Quantitative and Qualitative Evaluation with Emphasis on Station Data

نویسندگان [English]

  • Sedigheh Bararkhanpour Ahmadi 1
  • Reza Norooz-Valashedi 1
  • Mehdi Nadi 1
  • Khalil Ghorbani 2
  • Karim Solaimani 3
1 Dep. of Water Engineering, Sari University of Agricultural Sciences and Natural Resources, Sari, Iran
2 Dep. of Water Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
3 Dep. of Watershed Science and Engineering, Sari University of Agricultural Sciences and Natural Resources, Sari, Iran
چکیده [English]

Introduction: Estimation of spatiotemporal patterns of precipitation is difficult in Mazandaran province due to several factors, including the lack of ground observations, diverse climate zones, and extreme mountain slopes. Satellite precipitation products can provide a suitable solution for measuring the amount of precipitation, especially in areas with scattered ground stations, and provide a new approach to observe precipitation globally with remote sensing. However, despite the wide use of these products in various fields of study, the quantitative evaluation of these products is a fundamental challenge due to their inherent error and uncertainty, which should be considered in different temporal and spatial scales before their direct use.
Materials and Methods: In the first step, CHIRPS, CMORPH, SM2RAIN, PERSIANN-CDR and IMERG gridded precipitation products were extracted from the database of each product in NetCDF format at the global level. Then, precipitation data for each product was selected for grids located in Mazandaran province by coding in the R programming environment and Geographic Information System (GIS). In the next step, the evaluation and comparison of these products against 15 synoptic stations in the Mazandaran province at the station-grid and regional spatial scales and monthly and annual time scales using the statistical evaluation criteria of Spearman's correlation coefficient (CC), Root Mean Square Error (RMSE), Mean Absolute Error (MAD), Kling-Gupta efficiency (KGE) and Nash-Sutcliffe efficiency (NSE), as well as drawing statistical diagrams (Taylor, etc.) were performed. In order to evaluate the grid to the station, the data of the closest grid to each synoptic station was extracted for each precipitation product and compared with the precipitation data of station in different time scales.
Results and Discussion: The results of the regional evaluation of satellite precipitation products on a monthly and annual scale have shown that the IMERG and CMORPH products are more compatible, while the CHIRPS product performed well only in the dry months of the year. However, the accuracy of the products was higher on a monthly scale than on an annual scale. The Taylor diagram results indicated relatively high accuracy of IMERG, CHIRPS and CMORPH products (correlation 0.8-0.7) at stations located in coastal areas and relatively low accuracy at high altitudes (correlation 0.35). While for the PERSIAN product, the data accuracy was low for all regions, with a negative correlation in coastal areas. However, the results of the evaluation of precipitation products at the station level showed the better performance of the IMERG product and then CMORPH (CC=0.7-0.8 and RMSE=2-4 mm) in estimating monthly precipitation mainly in the eastern half of the province. While the lowest accuracy and the weakest performance was for the PERSIANN-CDR product. The highest CC value of precipitation products was equal to 0.8, which was mainly in the eastern and coastal areas of the province, but the lowest value was related to the PERSIANN-CDR product (CC=0.05). The RMSE values are mainly between 2 and 15 mm in the mountainous and eastern half of the province, respectively, and the lowest values are for the IMERG and CMORPH products. The values of KGE and NSE were also closer to the optimal value mainly in the coastal and eastern areas (NSE=0.5 and KGE=0-1), which was better for the CMORPH product. However, the BIAS values for all products varied between (-0.4)-1 mm, which was underestimated in coastal and low-altitude areas, but overestimated in high-altitude areas. Investigating the effect of the distance between the synoptic station and grid precipitation on the accuracy of the precipitation product has also shown that for the CMORPH, IMERG (in high areas) and SM2RAIN products, a low or higher distance between the station and the grid has reduced or increased the uncertainty. However, for most of the precipitation products, the significant increase in elevation has increased the uncertainty in satellite precipitation estimation.
Conclusion: The most accurate precipitation product in Mazandaran province includes the IMERG product. One of the main problems in the accuracy of precipitation products in this region is the complex topography (large height difference) and the proximity to the Caspian Sea. However, the use of modern methods such as remote sensing systems as a solution for estimating precipitation represents scientific advances in this field, and this can be used as a guide for decision-makers in climate and hydrological studies.

کلیدواژه‌ها [English]

  • Precipitation estimation
  • CHIRPS
  • IMERG
  • SM2RAIN
  • PERSIANN-CDR
  • CMORPH

مراجع

Aksu, H. & Akgül, M.A., 2020, Performance Evaluation of CHIRPS Satellite Precipitation Estimates over Turkey, Theoretical and Applied Climatology, 142, PP. 71-84, https://doi.org/ 10.1007/s00704-020-03301-5.
Ashouri, H., Hsu, K., Sorooshian, S., Braithwaite, D.K., Knapp, K.R., Cecil, L.D., Nelson, B.R. & 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, PP. 69-83, https://doi.org/ 10.1175/BAMS-D-13-00068.1.
Bai, X., Shen, W., Wu, X. & Wang, P., 2020, Applicability of Long-Term Satellite-Based Precipitation Products for Drought Indices Considering Global Warming, Journal of Environmental Management, 255, P. 109846, https://doi.org/10.1016/j.jenvman.2019.109846.
Barari Siavashkolaei, M., Sharifi, A.R. & Karimaei Tbarestani, M., 2021, Analysis of the Efficiency of Remote Sensing Data for Estimation of Water Balance Components (Case Study: Tajan Catchment, Mazandaran Province), JGST, 11(1), PP. 161-175, http://jgst.issgeac.ir/ article-1-1027-fa.html.
Biswal, T., 2022, Climate Change and Its Impact on Soil Fertility and Life Forms, In Research Anthology on Environmental and Societal Impacts of Climate Change, PP. 1229-1255.
Boluwade, A., Stadnyk, T., Fortin, V. & Roy, G., 2017, Assimilation of Precipitation Estimates from the Integrated Multisatellite Retrievals for GPM (IMERG, Early Run) in the Canadian Precipitation Analysis (CaPA), Journal of Hydrology: Regional Studies, 14, PP. 10-22, https://doi.org/10.1016/j.ejrh.2017.10.005.
Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B. & Wagner, W., 2019, SM2RAIN–ASCAT (2007–2018): Global daily Satellite Rainfall Data from ASCAT Soil Moisture Observations, Earth System Science Data, 11, PP. 1583-1601, https://doi.org/10.5194/essd-11-1583-2019.
Chen, H. & Wen, D., 2023, Dependency of Errors for Four Global Reanalysis and Satellite Precipitation Estimates on Four Crucial Factors, Atmospheric Research, 296, PP. 107076,‏ https://doi.org/10.1016/j.atmosres. 2023.107076.
Dehaghani, A.M., Gohari, A., Zareian, M.J. & Haghighi, A.T., 2023, A Comprehensive Evaluation of the Satellite Precipitation Products Across Iran, Journal of Hydrology: Regional Studies, 46, P. 101360,‏ https://doi.org/ 10.1016/j.ejrh.2023.101360.
Dhungana, S., Shrestha, S., Van, T.P., Kc, S., Das Gupta, A. & Nguyen, T.P.L., 2023, Evaluation of Gridded Precipitation Products in the Selected Sub-Basins of Lower Mekong River Basin, Theoretical and Applied Climatology, 151(1), PP. 293-310,‏ https://doi.org/10.1007/s00704-022-04268-1.
Dollan, I.J., Maina, F.Z., Kumar, S.V., Nikolopoulos, E.I. & Maggioni, V., 2024, An Assessment of Gridded Precipitation Products over High Mountain Asia, Journal of Hydrology: Regional Studies, 52, P. 101675,‏ https://doi.org/10.1016/ j.ejrh.2024.101675.
 
Du, Y., Wang, D., Zhu, J., Lin, Z. & Zhong, Y., 2022, Intercomparison of Multiple High-Resolution Precipitation Products over China: Climatology and Extremes, Atmospheric Research, 278, P. 106342,‏ https://doi.org/10.1016/j.atmosres.2022.106342.
Et-Takaouy, C., Aqnouy, M., Boukholla, A. & Stitou El Messari, J.E., 2024, Exploring the Spatiotemporal Variability of Four Satellite-Based Precipitation Products (SPPs) in Northern Morocco: A Comparative Study of Complex Climatic and Topographic Conditions, Mediterranean Geoscience Reviews, PP. 1-22,‏ https://doi.org/ 10.1007/s42990-024-00119-5.
Farsadnia, F., Rostami Kamrod, M. & Moghadam Nia, A., 2012, Rainfall Trend Analysis of Mazandaran Province Using Regional Mann-Kendall Test, Iran-Water Resources Research, 8(2), PP. 60-70.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., … et al., 2015, The Climate Hazards Infrared Precipitation with Stations-A New Environmental Record for Monitoring Extremes, Scientific Data, 2, P. 150066,. https://doi.org/10.1038/sdata.2015.66.
Ghorbanian, A., Mohammadzadeh, A., Jamali, S. & Duan, Z., 2022, Performance Evaluation of Six Gridded Precipitation Products throughout Iran Using Ground Observations over the Last Two Decades (2000-2020), Remote Sensing, 14(15), P. 3783,‏ https://doi.org/10.3390/rs14153783.
Gu, L., Yin, J., Wang, S., Chen, J., Qin, H., Yan, X., Zhang, Y. & Zhao, T., 2023, How Well Do the Multi-Satellite and Atmospheric Reanalysis Products Perform in Hydrological Modelling, Journal of Hydrology, 617, P. 128920, ‏https://doi.org/ 10.1016/j.jhydrol.2022.128920.
Guo, B., Xu, T., Yang, Q., Zhang, J., Dai, Z., Deng, Y. & Zou, J., 2023, Multiple Spatial and Temporal Scales Evaluation of Eight Satellite Precipitation Products in a Mountainous Catchment of South China, Remote Sensing, 15(5), P. 1373,‏ https://doi.org/10.3390/rs15051373.
Gyasi-Agyei, Y., 2020, Identification of the Optimum Rain Gauge Network Density for Hydrological Modelling Based on Radar Rainfall Analysis, Water, 12(7), P. 1906,‏ https://doi.org/10.3390/w12071906.
Huffman, G.J., Bolvin, D.T., Braithwaite, D., Hsu, K., Joyce, R., Xie, P. & Yoo, S., 2015, NASA Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for GPM (IMERG), In Algorithm Theoretical Basis Document (ATBD), Version 4.5; 2015, Available online: https://gpm.nasa.gov/sites/ default/files/document_files/IMERG_ATBD_V4.5.pdf (accessed on 1 December 2021).
Hussein, A.A. & Baylar, A., 2023, Hydrological Model Evaluation of Ground, GPM IMERG, and CHIRPS Precipitation Data for Shabelle Basin in Ethiopia, Journal of Electronics, Computer Networking and Applied Mathematics (JECNAM), 3(01), PP. 41-60, https://doi.org/10.55529/jecnam.31. 41.60.
Joyce, R.J. & Xie, P., 2011, Kalman Filter–Based CMORPH, Journal of Hydrometeorology, 12, PP. 1547-1563, https://doi.org/10.1175/JHM-D-11-022.1.
Joyce, R.J., Janowiak, J.E., Arkin, P.A. & Xie, P., 2004, CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution, Journal of Hydrometeorology, 2004(5), PP. 487-503, https://doi.org/10.1175/1525-7541 (2004)005%3C0487:CAMTPG%3E2.0.CO;2.
Keikhosravi-Kiany, M.S., Masoodian, S.A. & Balling Jr., R.C., 2023, Reliability of Satellite-Based Precipitation Products in Capturing Extreme Precipitation Indices over Iran, Advances in Space Research, 71(3), PP. 1451-1472,‏ https://doi.org/ 10.1016/j.asr.2022.10.003.
Khettouch, A., Hssaisoune, M., Hermans, T., … et al., 2023, Ground Validation of Satellite-Based Precipitation Estimates over Poorly Gauged Catchment: The Case of the Dr^aa Basin in Central-East Morocco, Mediterranean Geoscience Reviews, 5(3), PP. 159-175, https://doi.org/10.1007/ s42990-023-00108-0.
Khorrami, B., Sahin, O.G. & Gunduz, O., 2024, Comprehensive Comparison of Different Gridded Precipitation Products over Geographic Regions of Türkiye, Journal of Applied Remote Sensing, 18(3), PP. 034503-034503, https://doi.org/10.1117/1.JRS.18. 034503.
Kunkel, K.E., Robinson, D.A., Champion, S., Yin, X., Estilow, T. & Frankson, R.M., 2016, Trends and Extremes in Northern Hemisphere Snow Characteristics, Current Climate Change Reports, 2, PP. 65-73,‏ https://doi.org/10.1007/s40641-016-0036-8.
Lai, Y., Tian, J., Kang, W., Gao, C., Hong, W. & He, C., 2022, Rainfall Estimation from Surface Soil Moisture Using SM2RAIN in Cold Mountainous Areas, Journal of Hydrology, 606, P. 127430, https://doi.org/10.1016/ j.jhydrol.2022.127430.
Lamontagne, J.R., Barber, C.A. & Vogel, R.M., 2020, Improved Estimators of Model Performance Efficiency for Skewed Hydrologic Data, Water Resources Research, 56, P. e2020WR027101, https://doi.org/ 10.1029/2020WR027101.
Lei, H.J., Li, Y., Gao, Y., Chen, H., Zhang, X. & Wang, G., 2022, Ground Validation and Error Decomposition for Six State-of-the-Art Satellite Precipitation Products over Mainland China, Atmospheric Research, 269, P. 106017, https://doi.org/10.1016/ j.atmosres.2022.106017.
Li, Y., Pang, B., Zheng, Z., Chen, H., Peng, D., Zhu, Z. & Zuo, D., 2023, Evaluation of Four Satellite Precipitation Products over Mainland China Using Spatial Correlation Analysis, Remote Sensing, 15(7), P. 1823,‏ https://doi.org/10.3390/ rs15071823.
Maggioni, V., Sapiano, M.R. & Adler, R.F., 2016, Estimating Uncertainties in High-Resolution Satellite Precipitation Products: Systematic or Random Error?, Journal of Hydrometeorology, 17(4), PP. 1119-1129,‏ https://doi.org/10.1175/JHM-D-15-0094.1.
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), PP. 107-122, https://doi.org/10.22098/ mmws.2022.11147.1102.
Meng, C., Mo, X., Liu, S. & Hu, S., 2023, Improving Near-Real-Time Satellite Precipitation Products through Multistage Modified Schemes, Atmospheric Research, 292, P. 106875, https://doi.org/10.1016/ j.atmosres.2023.106875.
Mishra, R.K., 2023, Fresh Water Availability and Its Global Challenge, British Journal of Multidisciplinary and Advanced Studies, 4(3), PP. 1-78,‏ https://doi.org/10.37745/ bjmas.2022.0208.
‏Moazami, S., Abdollahipour, A., Zakeri Niri, M. & Ashrafi, S.M., 2016, Hydrological Assessment of Daily Satellite Precipitation Products over a Basin in Iran, Journal of Hydraulic Structures, 2(2), PP. 35-45, https://doi.org/10.22055/jhs.2016.12850.
Nadi, M. & Yousefi Kebriya, A., 2024, A Method for Correction of Tropical Rainfall Measuring Mission Satellite Temperature Network in Mazandaran Province, Iranica Journal of Energy & Environment, 15(1), PP. 100-110, https://doi.org/10.5829/ijee.2024. 15.01.10.
Nasseri, M., Schoups, G. & Taheri, M., 2022, A Spatiotemporal Framework to Calibrate High‐Resolution Global Monthly Precipitation Products: An Application to the Urmia Lake Watershed in Iran, International Journal of Climatology, (4), PP. 2169-2194,‏ https://doi.org/10.1002/joc.7358.
Nguyen, P., Ombadi, M., Sorooshian, S., Hsu, K., AghaKouchak, A., Braithwaite, D., Ashouri, H. & Thorstensen, A.R., 2018, The PERSIANN family of Global Satellite Precipitation Data: A Review and Evaluation of Products, Hydrology and Earth System Sciences, 22, PP. 5801-5816, https://doi.org/10.5194/hess-22-5801-2018.
Nicholson, S.E. & Klotter, D.A., 2021, Assessing the Reliability of Satellite and Reanalysis Estimates of Rainfall in Equatorial Africa, Remote Sensing, 13, P. 3609, https://doi.org/10.3390/rs13183609.
Nozarpour, N., Mahjoobi, E. & Golian, S., 2024, Assessment of Satellite-Based Precipitation Products in Monthly, Seasonal, and Annual Time-Scale over Iran, International Journal of Environmental Research, 18(5), P. 76,‏ https://doi.org/10.1007/s41742-024-00619-0.
Rasoulzadeh, A., Mahmoudi Babolan, S. & Nastarani Amoghin, S., 2022, Spatio-Temporal Evaluation of Satellite Precipitation Products in Northwestern Iran, Iranian Journal of Soil and Water Research, 53(9), PP. 2141-2160, https://doi.org/10.22059/ijswr.2022.345392.669311.
Rostamzadeh, H., Rasouli, A.A., Vazifeh Doost, M. & Maleki, N., 2020, Comparative Comparisons of Precipitation Obtained from TRMM, GPM and Doppler Radars with Ground Station Data (Case Study of Surface Wave from October 26 to 28, 2015 in Western Iran), Journal of Climate Research, 1398(38), PP. 49-61.
Savina, M., Schäppi, B., Molnar, P., Burlando, P. & Sevruk, B., 2012, Comparison of a Tipping-Bucket and Electronic Weighing Precipitation Gage for Snowfall, Atmospheric Research, 103, PP. 45-51, https:// doi.org/10.1016/j.atmosres.2011.06.010.
Shabankareh, R.N.T., Ziaee, P. & Abedini, M.J., 2024, Evaluation of IMERG Precipitation Product over Various Temporal Scales in a Semi-Arid Region of Southern Iran, Journal of Arid Environments, 220, P. 105102,‏ https://doi.org/10.1016/j.jaridenv. 2023.105102.
Skofronick-Jackson, G., Kirschbaum, D., Petersen, W., Huffman, G., Kidd, C., Stocker, E. & Kakar, R., 2018, The Global Precipitation Measurement (GPM) Mission’s Scientific Achievements and Societal Contributions: Reviewing Four Years of Advanced Rain and Snow Observations, Quarterly Journal of the Royal Meteorological Society, 144, PP. 27-48, https://doi.org/10.1002/qj.3313.
Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S. & Hsu, K.L., 2018, A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons, Reviews of Geophysics, 56, PP. 79-107, https://doi.org/10.1002/ 2017RG000574.
Vijay, P. & Singh, P.D.D.S., 2017, Handbook of Applied Hydrology, 2nd ed.; McGraw-Hill Education: New York, NY, USA, https://doi.org/10.1080/02626666509493376.
Xie, P., Joyce, R., Wu, S., Yoo, S.H., Yarosh, Y., Sun, F. & Lin, R., 2017, Reprocessed, Bias-Corrected CMORPH Global High-Resolution Precipitation Estimates from 1998, Journal of Hydrometeorology, 18(6), PP. 1617-1641, https://doi.org/10.1175/ JHM-D-16-0168.1.
Xie, Y., Sun, W., Ren, M., Chen, S., Huang, Z. & Pan, X., 2022, Stacked Ensemble Learning Models for Daily Runoff Prediction Using 1d and 2d Cnn, https://dx.doi.org/10.2139/ ssrn.421135.
Yang, Y., Ji, W., Niu, L., Zheng, Z., Huang, W., Zhang, C., Xu, X., Li, J. & Li, H., 2024a, Assessing Satellite and Reanalysis-Based Precipitation Products in Cold and Arid Mountainous Regions, Journal of Hydrology: Regional Studies, 51, P. 101612,‏ https://doi.org/10.1016/j.ejrh.2023.101612.
Yang, L., Shi, Z., Liu, R. & Xing, M., 2024b, Evaluating the Performance of Global Precipitation Products for Precipitation and Extreme Precipitation in Arid and Semiarid China, International Journal of Applied Earth Observation and Geoinformation, 130, P. 103888, https://doi.org/ 10.1016/j.jag.2024. 103888.

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