مقایسه و ارزیابی بارش برآورد شده توسط مدل‌های ERA-Interim، PERSIANN-CDR و CHIRPS در بالادست سد مارون

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشگاه شهید چمران اهواز

2 گروه هیدرولوژی منابع آب، دانشکده مهندسی علوم آب، دانشگاه شهید چمران اهواز

3 مدیر دفتر مدل‌های آب و محیط زیست، سازمان آب و برق خوزستان، اهواز، ایران.

4 دانشکده عمران، آب و محیط زیست دانشگاه شهید بهشتی تهران

چکیده

بارش یک جزء اصلی چرخه هیدرولوژیک است که دارای تغییرات قابل‌توجهی در مکان و زمان می‌باشد و نبود داده‌های مناسب این پارامتر سبب ایجاد مشکل در پیش‌بینی‌های هیدرولوژیک می‌گردد. ازآنجایی‌که داده‌های ماهواره‌ای-باران‌سنجی و داده‌های بازتحلیل راه‌حل جدیدی از برآورد میزان بارش با تنوع مکانی و زمانی ارائه می‌دهند و مشکلات ناشی از کمبود داده‌ها و کیفیت نامناسب آن‌ها را برطرف می‌کند، این مطالعه به بررسی دقت برخی از این نوع داده‌ها شامل داده‌های با وضوح مکانی بالا ERA-Interim، CHIRPS و PERSIANN-CDR در بالادست سد مارون پرداخته و جهت ارزیابی از داده‌های بارش روزانه، ماهانه و سالانه سال‌های 2003 تا 2014 داده‌های شبکه‌بندی بارش و داده‌های باران-سنجی بهره گرفته شده است. نتایج نشان می‌دهد در برآورد بارش سالانه داده‌های مدل‌های شبکه‌بندی شده فرو برآورد عمل نموده و میانگین بارش سالانه را کمتر از میانگین بارش سالانه مشاهداتی برآورد نموده است. در برآورد بارش ماهانه با توجه به ضریب نش-ساتکلیف در ایستگاه‌های دهنو، ایدنک و مارگون مدل ERA-Interim و در ایستگاه قلعه رییسی مدل CHIRPS بهترین عملکرد را نسبت به مدل‌های دیگر نشان می‌دهد. در تخمین بارش روزانه، همچون بارش ماهانه بهترین برآورد در ایستگاه ایدنک مربوط به مدل ERA-Interim بوده که دارای 63/0=NSE می‌باشد و بهترین تخمین میزان بارش در تمام ایستگاه‌ها توسط ERA-Interim صورت گرفته است. همچنین در آشکارسازی صحیح روزهای بارانی مدل ERA-Interim بهترین عملکرد را از بین 3 مدل ماهواره‌ای داشته و بهترین عملکرد این مدل در تشخیص صحیح روزهای بارانی با 53/0POD= در ایستگاه ایدنک صورت پذیرفته است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Comparison and Evaluation of precipitation estimated by ERA-Interim, PERSIANN-CDR and CHIRPS models at the upstream of Maroon dam

نویسندگان [English]

  • Ali Gorjizade 1
  • Alimohammad AkhondAli 2
  • Ali Shahbazi 3
  • Ali Moridi 4
1 Chahid chamran University of Ahwaz
2 Department of Water Resources, College of Water Sciences Engineering, Shahid Chamran University of Ahwaz, Ahwaz, Iran
3 Head of Water and Environment Modelling Center, Khuzestan Water and Power Authority, Ahvaz, Iran.
4 Faculty of Civil, Water and Environmental Sciences, Shahid Beheshti University of Tehran, Tehran, Iran
چکیده [English]

Precipitation is a major component of the hydrological cycle, which has significant changes in location and time. The lack of suitable data for this parameter causes a problem in hydrological forecasts. Since satellite data provides a new solution for estimating rainfall with spatial and temporal variation, this study evaluate the accuracy of some of these data types, including high-resolution spatial data consist of ERA-Interim, CHIRPS and PERSIANN-CDR at the upstream of the Maroon Dam on daily, monthly and annual timescales. In order to evaluate gridded precipitation data and observational data from 2003 to 2014, it was considered. The results show that estimation of the annual rainfall data of the gridded precipitation models is underestimated and estimates the average annual precipitation less than the mean annual observational precipitation. In the estimation of monthly precipitation with regard to the Nash-Sutcliff coefficient at Dehno, Idenak and Margoon stations, the ERA-Interim model and at the Ghale-Raeesi station CHIRPS model indicate the best performance compared to other models. In the daily rainfall estimation, like the monthly rainfall, the best estimate at the Idenak station is the ERA-Interim model, which has a NSE of 0.63 and the best estimate of precipitation in all stations is by ERA-Interim. ERA-Interim has the best performance from the 3 gridded models in the correct detection of rainy days. The best performance of this model is in determining the correct rain days with POD = 0.53 at Idenak station.

کلیدواژه‌ها [English]

  • Rainfall estimation
  • Evaluation indexes
  • Satellite-gauge data
  • Reanalysis Data

 

Ashouri H, Hsu KL, Sorooshian S, Braithwaite DK, Knapp KR, Cecil LD, Nelson BR and Prat OP (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, Available at: http://journals.ametsoc.org/doi/ 10.1175/BAMS-D-13-00068.1

Balsamo G, Albergel C, Beljaars A, Boussetta S, Brun E, Cloke H, Dee D, Dutra E, Muñoz-Sabater J, Pappenberger F, … Vitart F (2015) ERA-Interim/Land: a global land surface reanalysis data set. Hydrology and Earth System Sciences 19(1):389–407, Available at: https://www.hydrol-earth-syst-sci.net/19/389/2015/

Collischonn B, Collischonn W and Tucci CEM (2008) Daily hydrological modeling in the Amazon basin using TRMM rainfall estimates. Journal of Hydrology 360(1-4):207-216, Available at: https://www.sciencedirect.com/science/article/pii/S0022169408003806

Darand M and Zand Karimi S (2016) Evaluation of the accuracy of rainfall data of the global precipitation climatology center on Iran. Iranian Geophysical Journal 95-113 (In Persian)

Dezfuli D, Hosseini Moghari S and Ebrahimi K (2016) Comparison of PERSIANN and TRMM 3B42 satellite data with observation of ground stations. Journal of Soil and Water Sciences 85-98 (In Persian)

Duan Z and Bastiaanssen WGM (2013) First results from Version 7 TRMM 3B43 precipitation product in combination with a new downscaling-calibration procedure. Remote Sensing of Environment 131:1-13

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, Available at: https://www.sciencedirect.com/science/article/pii/S0048969716319143

Fujihara Y, Yamamoto Y, Tsujimoto Y and Sakagami JI (2014) Discharge simulation in a data-scarce basin using reanalysis and global precipitation data: A case study of the White Volta Basin. Journal of Water Resource and Protection, Scientific Research Publishing 06(6):1316-1325, Available at: http://www.scirp.org/journal/doi.aspx?DOI=10.4236/jwarp.2014.614121

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, Nature Publishing Group 2:150066, Available at: http://www.nature.com/articles/ sdata201566

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, Available at:https://www.sciencedirect.com/ science/article/pii/S0169809517311079

Ghahraman B, Zangane Inalo M and Alireza F (2018) Comparison of precipitation and precipitation data of PERSIANN and CMORPH satellite rainfall and daily methods. Iran Water Resources Research 1-12 (In Persian)

Ghajarnia N, Liaghat A and Daneshkar Arasteh P (2015) Comparison and evaluation of high resolution precipitation estimation products in Urmia Basin-Iran. Atmospheric Research 158: 50-65

Hejazizade Z, Alijani B, Ziaeiyan P, Karimi M and Rafati S (2012) Satellite evaluation of 3B43 and its comparison with the amounts of the Kriging interpolation technique. Remote Sensing and Iranian GIS 49-64 (In Persian)

Hosseini Moghari S M, Araghi nejad S, and Ebrahimi k (2017) Investigation of the accuracy of global networked rainfall data in the Urmia Lake Basin. Iran Water and Soil Research 587-598 (In Persian)

Javanmard S, Yatagai A, Nodzu MI, 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, Available at: https://www.adv-geosci.net/25/119/2010/

Jia S, Zhu W, Lu A and Yan T (2011) A statistical spatial downscaling algorithm of TRMM precipitation based on NDVI and DEM in the Qaidam Basin of China. Remote Sensing of Environment 115(12):3069-3079, Available at: https://www.sciencedirect.com/science/article/pii/S0034425711002331

Madadi G, Hamzeh S, and Norouzi A (2015) Detection of precipitation on daily, monthly and annual scales using satellite imagery. Remote Sensing and Geographic Information Systems in Natural Resources 59-74 (In Persian)

Mianabadi A, Amin A, Sanaei Nejad H, Banayan Aval M and Farid Hosseini A (2013) Statistical evaluation of the output of the CMORPH model in the estimation of north-east precipitation in Iran. Water and Soil Journal 919-927 (In Persian)

Miri M, Azizi G, Khosh akhlagh F and  Rahimi M (2016) Statistical evaluation of rainfall and temperature gridded data with rain observation data. Iranian Journal of Watershed Management Sciences and Engineering 39-50 (In Persian)

Miri M, Raziei T and Rahimi M (2016) Evaluation and comparison of TRMM and GPCC precipitation data with observational data in Iran. Earth and Space Physics 672-657 (In Persian)

Poméon T, Jackisch D and Diekkrüger B (2017) Evaluating the performance of remotely sensed and reanalysed precipitation data over West Africa using HBV light. Journal of Hydrology 547:222-235, Available at: https://linkinghub.elsevier.com/ retrieve/pii/S0022169417300653

Tan ML and Santo H (2018) Comparison of GPM IMERG, TMPA 3B42 and PERSIANN-CDR satellite precipitation products over Malaysia. Atmospheric Research 202:63-76, Available at: https://www.sciencedirect.com/science/article/pii/S0169809517307287

Tapiador FJ, Turk FJ, Petersen W, Hou AY, García-Ortega E, Machado LAT, Angelis CF, Salio P, Kidd C, Huffman GJ and de Castro M (2012) Global precipitation measurement: Methods, datasets and applications. Atmospheric Research 70-97, Available at: https://www.sciencedirect.com/ science/article/pii/S0169809511003607

Thiemig V, Rojas R, Zambrano-Bigiarini M and De Roo A (2013) Hydrological evaluation of satellite-based rainfall estimates over the Volta and Baro-Akobo Basin. Journal of Hydrology 499:324-338, Available at: https://www.sciencedirect.com/science/article/ pii/S0022169413005295

Worqlul AW, Maathuis B, Adem AA, Demissie SS, Langan S and Steenhuis TS (2014) Comparison of rainfall estimations by TRMM 3B42, MPEG and CFSR with ground-observed data for the Lake Tana basin in Ethiopia. Hydrology and Earth System Sciences 18(12):4871-4881, Available at: https://www.hydrol-earth-syst-ci.net/18/4871/2014/

Worqlul AW, Yen H, Collick AS, Tilahun SA, Langan S and Steenhuis TS (2017) Evaluation of CFSR, TMPA 3B42 and ground-based rainfall data as input for hydrological models, in data-scarce regions: The upper Blue Nile Basin, Ethiopia. Catena 152:242-251, Available at: https://www.sciencedirect.com/ science/article/pii/S0341816217300267

Xie P and Xiong AY (2011) A conceptual model for constructing high-resolution gauge-satellite merged precipitation analyses. Journal of Geophysical Research Atmospheres 16(21), Available at: http://doi.wiley.com/10.1029/2011JD0161