پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 161 |
| تعداد شماره‌ها | 6,532 |
| تعداد مقالات | 70,504 |
| تعداد مشاهده مقاله | 124,122,157 |
| تعداد دریافت فایل اصل مقاله | 97,230,031 |
ارزیابی دقت محصولات بارش ماهواره ای در تخمین بارش های مربوط به ماه های سیلابی (مطالعۀ موردی: حوضۀ آبریز سد یامچی اردبیل) | ||
| اکوهیدرولوژی | ||
| دوره 9، شماره 2، تیر 1401، صفحه 317-331 اصل مقاله (1.14 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ije.2022.335393.1588 | ||
| نویسندگان | ||
| مهدی کنزی حق1؛ اتابک فیضی* 2؛ فرهاد هوشیاری پور3؛ سید سعید راثی نظامی4 | ||
| 1دانشجوی کارشناسی ارشد مهندسی عمران‌ـ مهندسی و مدیریت منابع آّب، دانشکدۀ فنی مهندسی، دانشگاه محقق اردبیلی | ||
| 2دانشیار گروه مهندسی عمران، دانشکدۀ فنی مهندسی، دانشگاه محقق اردبیلی، اردبیل، ایران | ||
| 3استادیار گروه مهندسی عمران، دانشکدۀ عمران، معماری و هنر، دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران | ||
| 4دانشیار گروه مهندسی عمران، دانشکدۀ فنی مهندسی، دانشگاه محقق اردبیلی | ||
| چکیده | ||
| تخمین دادههای بارش توسط محصولات ماهوارهای با دقت زیاد در مقیاس زمانی و مکانی که از اجزای اصلی مدلهای هیدرولوژیکی است، کمک زیادی به مدیریت منابع آب خواهد کرد. از اینرو، در این پژوهش اقدام به ارزیابی دقت دادههای بارش و همچنین، اصلاح اریبی آنها به روش چندک (RQUANT) برای ارتقای عملکرد دادههای ماهوارهای در حوضۀ آبریز سد یامچی واقع در استان اردبیل شد. دادههای ماهوارهای استفادهشده شامل دادههای بارش PERSIANN-CCS، PDIR-Now و GPM در مقیاس زمانی ساعتی، روزانه و ماهانه برای ماههای پربارش منطقه است که طی یازده سال توسط شاخصهای آماری متوسط بارش، انحراف معیار و ضریب تغییرات انتخاب شدند. اعمال روش اصلاح اریبی توانست تا حد امکان عملکرد دادههای ماهوارهای را در ماههای پربارش بهبود ببخشد. مقایسۀ نتایج بهدستآمده از ماهوارۀ GPM در مقیاس ماهانه با دادههای بارش زمینی نشان داد این محصول نسبت به مدلهای PERSIANN-CCS و PDIR-Now از عملکرد بهتری برخوردار بوده و دارای شاخص MAE و RMSE برابر 66/4 و 70/9 و ضریب همبستگی 74/0 است، در حالی که این مقادیر برای مدل PERSIANN-CCS و PDIR-Now به‌ترتیب برابر 24/45، 03/62 و 36/0 و 09/7، 52/13، 27/0 است. به همین ترتیب، در مقیاس روزانۀ محصول بارش ماهوارهای GPM عملکرد مطلوبتری را ارائه داد. مقادیر آماری برای ماهوارۀ GPM در مقیاس ساعتی نیز به‌ترتیب برابر 91/0، 66/2 و06/0 به دست آمد. به طور کلی، بارش بهدستآمده از GPM در مقایسه با سایر ماهوارهها نتایج بهتری ارائه میدهد، اگرچه در مقیاس روزانه و ساعتی نتایج مطلوبی نسبت به دادههای اندازهگیریشده حاصل نشد. | ||
| کلیدواژه‌ها | ||
| سد یامچی؛ ماههای سیلابی؛ اصلاح اریبی؛ سنجش از دور؛ بارش ماهوارهای | ||
| عنوان مقاله [English] | ||
| Evaluation of the Accuracy of Satellite Precipitation Products based on Measurement of Precipitation Related to Flood Months (Case study: Ardabil Yamchi Dam Basin) | ||
| نویسندگان [English] | ||
| Mahdi Kanzi Hagh1؛ Atabak Feizi2؛ Farhad Hooshyaripor3؛ Seyyed Saeed Rasi Nezami4 | ||
| 1M.Sc. student in Civil Engineering-Water Resource Management and Engineering, University of Mohaghegh Ardabili, Ardabil, Iran | ||
| 2Associate Professor, Department of Civil Engineering, Faculty of Engineering, University of Mohaghegh Ardabili, Ardabil, Iran | ||
| 33- Assistant Professor, Department of Civil Engineering, Architecture and Art, Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| 4Assistant Professor, Department of Civil Engineering, Faculty of Engineering, University of Mohaghegh Ardabili, Ardabil, Iran | ||
| چکیده [English] | ||
| High-precision precipitation data at the time and place scale, which is a main component of hydrological models, will greatly help to manage the water resources. Hence, in this research, evaluating precipitation data accuracy as well as their origin correction in Quantile method (RQUANT) was used to improve the performance of satellite data in the Yamchi Dam Basin in Ardabil province. The satellite data used included PERSIANN-CCS, PDIR-Now and GPM precipitation data on an hourly, daily and monthly time scale for the region's rainy months over 11 years that were selected by moderate precipitation, standard deviation and coefficient of variation. Comparison of the results obtained from GPM satellite with precipitation data showed that this product has better performance than PERSIANN-CCS and PDIR-Now models on a monthly scale. GPM data on a monthly scale had a MAE index and RMSE 4.66 and 9.70 mm per day, respectively and a correlation coefficient of 0.74; while these values for PERSIANN-CCS and PDIR-Now models were equal to 24.24, 62.03 and 0.36 and 07.09, 13.52, 0.27, respectively. Thus, the GPM satellite precipitation product was provided better performance on a daily scale. Since hourly data was required in flood analysis, then in this paper, precipitation data on an hourly scale was evaluated; the statistical values for GPM satellite were 0.91, 2.66 and 0.06, respectively. In general, among the study precipitation products, GPM precipitation gives better results, although on a daily and hourly scale, the desired results were not obtained compared to the measured data. | ||
| کلیدواژه‌ها [English] | ||
| Yamchi dam, Rainfall, Flood month, Bias correction, Remote sensing, PDIR-NOW, GMP, PERSIANN-CCS | ||
| مراجع | ||
|
[1]. Mianabadi A, Alizadeh M, Hosseini F. Statistical evaluation of CMORPH model output in precipitation estimation Northeast of Iran (Case Study: North Khorasan). Journal of Soil (Agricultural Sciences and Industries). 2013; 27(5): 919-927. ]Persian[
[2]. Sharifi E, Eitzinger J, Dorigo W. Performance of the State-Of-The-Art Gridded Precipitation Products over Mountainous Terrain: A Regional Study over Austria. Remote Sensing. 2019; 11: 1-20.
[3]. Xie P, Yatagai A, Chen M, Hayasaka T, Fukushima Y, Changming L, Yang S. “A Gauge-Based Analysis of Daily Precipitation Over East Asia. Journal of Hydrometeorology. 2007; (8): 607–626.
[4]. Hong YD, Gochis JT, Cheng KL, Sorooshian S. Evaluation of PERSIANN CCS rainfall measurement using the NAME event rain gauge network. Journal of Hydrometeor. 2007; 8(3): 469-482.
[5]. Javanmard S, Yatagai A, Nodzu MI, BodaghJamali J, Kawamoto H. Comparing high-resolution gridded precipitation data with satellite rainfall estimates of TRMM_3B42 over Iran. Adv. Geosci. 2010; (25): 119-125. ]Persian[
[6]. Beighley RE, Ray RL, Lee H, Schaller L, Andreadis K, Durand M, et al. Comparing satellite derived precipitation datasets using the Hillslope River Routing (HRR) model in the Congo River Basin. Hydrological Processes. 2011; 25(20): 3216-3229. ]Persian[
[7]. Cai X, Zou S, Wang W, Xu B. Evaluation of TRMM precipitation data over the Inland River Basins of Northwest China. Geomatics for Integrated Water Resources Management (GIWRM). International Symposium, Lanzhou Jiaotong University, Gansu, China. 2012.
[8]. HejazyZadeh A, Alijani B, Ziaeian P, Karimi M, Rafati S. Evaluation of satellite rainfall Mqadyrhasl 3B43 and comparison with Kriging interpolation technique. GIS remote sensing of Iran. 2012; 4(3): 64-49. ]Persian[
[9]. Kizza M, Westerberg I, Rodhe A, Ntale HK. Estimating areal rainfall over Lake Victoria and its basin using ground-based and satellite data. Journal of Hydrology. 2012; 464:401-411.
[10]. Chen Y, Ebert EE, Walsh KJ, Davidson NE. Evaluation of TRMM 3B42 precipitation estimates of tropical cyclone rainfall using PACRAIN data. J. Geophys. Res. Atmospheres. 2013; 118(5): 2184-2196.
[11]. MianaBad A, Alizadeh A, Banayanaval M, Faridhosseini A. Statistical evaluation of the model for estimating precipitation CMORPH North East of Iran (Case study: Northern Khorasan). Journal of Soil and Water. 2013; 27(5): 927-919. ]Persian[
[12]. Shirvani A, Fkharizadeh Shirazi A. Comparison of the observed precipitation and TRMM satellite estimates in Fars province. Journal of Agricultural Meteorology. 2014; 2(2): 15-1. ]Persian[
[13]. Moazami S, Golian S, Hong Y, Sheng C, Kavianpour M R. Comprehensive evaluation of four highresolution satellite precipitation products over diverse climate conditions in Iran. Hydrol. Sci. J. 2014; 61(2): 420-440. ]Persian[
[14]. Wang Z, Zhong R, Lai CH, Chen J. Evaluation of the GPM IMERG Satellite- Based Precipitation Products and the Hydrological Utility, Atmospheric Research. 2017; (196): 151-163.
[15]. Hosseini-Moghari S M, Araghinejad S, Ebrahimi K. Spatio-temporal evaluation of global gridded precipitation datasets across Iran. Hydrological Sciences Journal. 2018; 63(11): 1669–1688.]Persian [
[16]. Parisooj P, Goharnejad H, Moazami S. Rainfall-Runoff Hydrologic Simulation Using Adjusted Satellite Rainfall Algorithms, a Case Study: Voshmgir Dam Basin. Golestan. Iran-Water Resources Research. 2018; 14(3): 140-159. ]Persian[
[17]. Shayeghi A, Azizian A, Brocca L. The Reliability of Reanalysis and Remotely Sensed Precipitation Products for Hydrological Simulation over the SRB, Iran. Hydrological Sciences Journal. 2020; 65(2): 296-310.]Persian [
[18]. Koohi S, Azizian A, Brocca L. Calibration of VIC-3L Hydrological Model using Satellite Based Surface Soil Moisture Datasets. Iran-Water Resources Research. 2020; 15(4): 55-67. ]Persian[
[19]. Ahmadi M, Dadashi AA, Deyrmajai A. Runoff Estimation Using the IHACRES Model Based on CHIRPS Satellite Data and CMIP5 Models (Case Study: Gorganroud Basin- Aq Qala Area). Iranian Journal of Soil and Water Research. 2019; 51(3): 659-671. ]Persian[
[20]. Mahrooghy M, Anantharaj VG, Younan NH, Aanstoos J, Hsu KL. On an Enhanced FARSI-CCS Algorithm for Precipitation Estimation. Journal of Atmospheric and Oceanic Technology. 2012; 29(7): 922–932. ]Persian[
[21]. Nguyen P, Shearer EJ, Ombadi M, Gorooh VA, Hsu K, Sorooshian S, et al. PERSIANN Dynamic Infrared Rain Rate Model (PDIR) for High-Resolution, Real-Time Satellite Precipitation Estimation. Bulletin of the American Meteorological Society. 2020; 101(3): 286-302.
[22]. Kummerow C, Barnes W, Kozu T, Shiue J, Simpson J. The Tropical Rainfall Measuring Mission (TRMM) Sensor Package. Journal of Atmospheric and Oceanic Technology. 1998; 15(3): 809- 717.
[23]. Huffman G, Bolvin DT, Braithwaite D, Hsu K, Joyce R. Algorithm Theoretical Basis Document (ATBD) Version 4, 5: For the NASA Global Precipitation Measurement (GPM) Integrated Multi-satellite E Retrievals for GPM (IMERG), GPM Project. 2015.
[24]. Wu L, Seo DJ, Demargne J, Brown JD, Cong S, Schaake J. Generation of ensemble precipitation forecast from single-valued quantitative precipitation forecast for hydrologic ensemble prediction. Journal of Hydrology. 2011; 399 (3–4): 281–298.
[25]. Teutschbein C, Seibert J. Regional climate models for hydrological impact studies at the catchment scale: A review of recent modeling strategies. 2010; 4(7): 834-860.
[26]. Zollo A L, Rianna G, Mercogliano P, Tommasi P, Comegna L. Validation of a simulation chain to assess climate change impact on precipitation induced landslides. In Landslide Science for a Safer Geoenvironment. 2014; (1): 29-287.
[27]. Teutschbein C, Seibert J. Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. Journal of Hydrology. 2012; (457): 12–29.
[28]. Boé J, Terray L, Habets F, Martin E. Statistical and dynamical downscaling of the Seine basin climate for hydro-meteorological studies. International Journal of Climatology. 2007; 27(12): 1643–1655.
[29]. Piani C, Weedon GP, Best M, Gomes SM, Viterbo P, Hagemann S, Haerter JO. Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models. Journal of Hydrology. 2010; 395 (3–4): 199–215.
[30]. Bum-Kim K, Kwon HH, Han D. Precipitation ensembles conforming to natural variations derived from a regional climate model using a new bias correction scheme. Hydrology and Earth System Sciences. 2016; 20(5): 2019– 2034.
[31]. Gudmundsson L, Bremnes JB, Haugen JE, Engen-Skaugen T. Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations; a comparison of methods. Hydrology and Earth System Sciences. 2012; 16(9): 3383–3390.
[32]. Gudmundsson L. qmap: Statistical transformations for post-processing climate model output. R package version 1.0.3. 2014.
[33]. Liang S, Li X, Wang J. 2nd Edition. Advanced remote sensing: terrestrial information extraction and applications, Academic Press; 2020: 800. | ||
|
آمار تعداد مشاهده مقاله: 266 تعداد دریافت فایل اصل مقاله: 255 |
||