پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 161 |
| تعداد شماره‌ها | 6,532 |
| تعداد مقالات | 70,501 |
| تعداد مشاهده مقاله | 124,098,469 |
| تعداد دریافت فایل اصل مقاله | 97,206,110 |
مقایسه دو مجموعه داده بارش شبکه‌بندی شده با وضوح بالا در بالادست سد مارون در ایران | ||
| تحقیقات آب و خاک ایران | ||
| مقاله 2، دوره 50، شماره 3، مرداد 1398، صفحه 527-541 اصل مقاله (1.65 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2018.269435.668058 | ||
| نویسندگان | ||
| علی گرجی زاده* 1؛ علی محمد آخوندعلی2؛ علی شهبازی3؛ علی مریدی4 | ||
| 1دانشجوی دکترا، گروه هیدرولوژی و منابع آب، دانشکده مهندسی علوم آب، دانشگاه شهید چمران اهواز ، اهواز، ایران | ||
| 2استاد گروه هیدرولوژی و منابع آب دانشگاه شهید چمران اهواز | ||
| 3سازمان آب و برق خوزستان، اهواز، ایران | ||
| 4استادیار دانشکده عمران، آب و محیط زیست دانشگاه شهید بهشتی تهران، تهران، ایران | ||
| چکیده | ||
| برآورد ماهواره‌ای بارش مهم و ضروری است چرا که برای جبران اندازه‌گیری‌های محدود بارش باران در مناطقی که نظارت مستمر و پیوسته بارشها با توجه به پراکندگی شبکه‌های باران‌سنجی وجود ندارد، کاربرد دارند. سیستم‌های برآورد بارش ماهواره‌ای می‌توانند اطلاعات را در مناطقی که اطلاعات باران‌سنجی در دسترس نیست ارائه دهند. لذا بررسی دقت این نوع داده‌ها از اهمیت بالایی برخوردار است. در این مطالعه از داده‌های باران دو مجموعه داده بارش ماهواره‌ای PERSIANN-CDR و PERSIANN-CCS در بالا دست سد مارون (ایستگاه‌های باران‌سنجی دهنو، قلعهرییسی، ایدنک، مارگون) در سال‌های 2003 تا 2014 استفاده گردید و ارزیابی در مقیاس‌های روزانه، ماهانه، فصلی و سالانه انجام گرفت. نتایج نشان میدهد که بارش سالانه در هر دو مجموعه داده در تمامی ایستگاه‌ها کمبرآورد میشوند ولی مدل PERSIANN-CCS نسبت به PERSIANN-CDR تناسب نزدیک‌تری با داده‌های مشاهداتی دارد. در برآورد بارش فصلی، نتایج نشان دهنده مناسب‌تر بودن مدل PERSIANN-CCS در تخمین بارش و تشخیص وقایع بارش نسبت به مدل دیگر می‌باشد. در برآورد بارش ماهانه و روزانه نتایج نشان‌دهنده مناسب‌تر بودن داده‌های PERSIANN-CDR نسبت به مجموعه داده دیگر می‌باشد. همچنین با توجه به مقادیر POD (احتمال آشکارسازی) و FAR (شاخص هشدار اشتباه) برآورد شده مشخص گردید که از لحاظ شاخص POD، داده‌های روزانه بارش مدل PERSIANN-CCS و طبق شاخص FAR داده‌های بارش روزانه مدل PERSIANN-CDR عملکرد بهتری در آشکارسازی روزهای بارانی و غیر بارانی دارند. | ||
| کلیدواژه‌ها | ||
| تخمین بارندگی؛ داده های شبکه بندی شده؛ شاخص‌های ارزیابی؛ منطقه‌ایدنک | ||
| عنوان مقاله [English] | ||
| Comparison of two high-resolution gridded precipitation data sets at the upstream of the Maroun dam in Iran | ||
| نویسندگان [English] | ||
| Ali Gorjizade1؛ Alimohammad Akhoond-Ali2؛ Ali Shahbazi3؛ Ali Moridi4 | ||
| 1PhD Candidate, Department of Hydrology and Water Resources, Faculty of Water Engineering, Shahid Chamran University of Ahvaz, Ahwaz, Iran | ||
| 2Professor, Department of Hydrology and Water Resources, Faculty of Water Engineering, Shahid Chamran University of Ahvaz, Ahwaz, Iran | ||
| 3Khuzestan Water and Power Organization, Ahwaz, Iran | ||
| 4Assistant professor, Faculty of Civil, Water and Environmental Sciences, Shahid Beheshti University of Tehran, Tehran, Iran | ||
| چکیده [English] | ||
| Satellite-based precipitation estimations are important and necessary because they are used to compensate the limited rain measurements in areas where there is no continuous monitoring of rainfall due to the dispersion of rain ague networks. Satellite-based precipitation estimation systems can provide information in areas where rainfall data are not available. Therefore, the accuracy of this type of data is very important. In this study, rainfall data of two long-term satellite data sets (FARSI-CDR and PERSIANN-CCS) at the upstream of Maroun Dam (Dehno, Ghale-Raeesi, Idenak, Margoon stations) during 2003-2014 were used and evaluated on daily, monthly, seasonally and annually basis. The results show that the annual precipitation of each dataset is underestimated in all stations, but the PERSIANN-CCS model compare to the PERSIANN-CDR has better estimations for annual observations. For estimation of seasonal precipitation, the results indicate that the PERSIANN-CCS model is better than the other one for rainfall estimation and rainfall detection. For estimation of monthly and daily precipitation, the results indicate that PERSIANN-CDR data are more appropriate than the other data set. Also, regarding to POD (probability of detection) and FAR (False alarm rate) estimated data, It was found that according to POD index, PERSIANN-CCS precipitation daily data and according to FAR, daily precipitation data of PERSIANN-CDR model have better performance in detecting rainy and non-rainy days. | ||
| کلیدواژه‌ها [English] | ||
| Rainfall estimation, gridded dataset, Evaluation indicators, Idenak region | ||
| مراجع | ||
|
Abdollahi, B., Hosseini moghari, M., & Ebrahimi, K. (2017). Evaluation of CMORPH and TRMM 3B42RT V7 satellite data in order to estimate rainfall in the Gorganroud Basin. Iran Watershed Engineering and Scinces, 36, 55–68. (In Farsi) Adjei, K. A., Ren, L., & Appiah-adjei, E. K. (2012). Validation of TRMM Data in the Black Volta Basin of Ghana, (May), 647–654. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000487. Ashouri, H., Hsu, K. L., Sorooshian, S., Braithwaite, D. K., Knapp, K. R., Cecil, L. D., et al. (2015). FARSI-CDR: Daily precipitation climate data record from multisatellite observations for hydrological and climate studies. Bulletin of the American Meteorological Society, 96(1), 69–83. https://doi.org/10.1175/BAMS-D-13-00068.1 Bitew, M. M., & Gebremichael, M. (2011). Assessment of satellite rainfall products for streamflow simulation in medium watersheds of the Ethiopian highlands. Hydrology and Earth System Sciences, 15(4), 1147–1155. https://doi.org/10.5194/hess-15-1147-2011 Dezfuli, D., Hosseini moghari, M., & Ebrahimi, K. (2016). Comparison of TRMM-3B42 V7 and FARSI satellite data with observations of ground stations (Case study: Gorganroud Basin). Journal of Soil and Water Sciences, 76, 85–98. (In Farsi) Duan, Z., Liu, J., Tuo, Y., Chiogna, G., & 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. https://doi.org/10.1016/j.scitotenv.2016.08.213 Eghtedari, M., Iran Nejad, P., Vazife doost, M., Bazrafshan, J., & Ghahraman, N. (2018). Comparison of spring rainfall from four products networked and simulated by RegCM and their evaluation with observations in Qazvin Plain. Iran-Water Resources Research, 14(4), 32–44. Retrieved from http://www.iwrr.ir/article_61893.html (In Farsi) Einfalt, T., Arnbjerg-Nielsen, K., Golz, C., Jensen, N.-E., Quirmbach, M., Vaes, G., & Vieux, B. (2004). Towards a roadmap for use of radar rainfall data in urban drainage. Journal of Hydrology, 299(3–4), 186–202. https://doi.org/10.1016/j.jhydrol.2004.08.004 Fujihara, Y., Yamamoto, Y., Tsujimoto, Y., & Sakagami, J.-I. (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, 06(6), 1316–1325. https://doi.org/10.4236/jwarp.2014.614121 Gao, F., Zhang, Y., Chen, Q., Wang, P., Yang, H., Yao, Y., & Cai, W. (2018). Comparison of two long-term and high-resolution satellite precipitation datasets in Xinjiang, China. Atmospheric Research, 212, 150–157. https://doi.org/10.1016/j.atmosres.2018.05.016 Hong, Y., Hsu, K.-L., Sorooshian, S., & Gao, X. (2004). Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System. Journal of Applied Meteorology, 43(12), 1834–1853. https://doi.org/10.1175/JAM2173.1 Javanmard, S., Yatagai, A., Nodzu, M. I., Bodaghjamali, J., & Kawamoto, H. (2010). Comparing high-resolution gridded precipitation data with satellite rainfall estimates of TRMM-3B42 over Iran. Advances in Geosciences, 25, 119–125. https://doi.org/10.5194/adgeo-25-119-2010 Jia, S., Zhu, W., Lu, A., & 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. https://doi.org/10.1016/j.rse.2011.06.009 Katiraie-Boroujerdy, P. S., Akbari Asanjan, A., Hsu, K. lin, & Sorooshian, S. (2017). Intercomparison of FARSI-CDR and TRMM-3B42V7 precipitation estimates at monthly and daily time scales. Atmospheric Research. https://doi.org/10.1016/j.atmosres.2017.04.005 Lashkari, A., Banayan, M., Koochaki, A., & Alizade, A. (2016). Investigation of the feasibility of using the AgMERRA database for the production of incomplete and missing data in synoptic station data (Case study: Mashhad Plain). Water and Soil Journal, 1749–1758. (In Farsi) Li, M., & Shao, Q. (2010). An improved statistical approach to merge satellite rainfall estimates and raingauge data. Journal of Hydrology, 385(1–4), 51–64. https://doi.org/10.1016/j.jhydrol.2010.01.023 Maggioni, V., Meyers, P. C., & Robinson, M. D. (2016). A Review of Merged High-Resolution Satellite Precipitation Product Accuracy during the Tropical Rainfall Measuring Mission (TRMM) Era. Journal of Hydrometeorology, 17(4), 1101–1117. https://doi.org/10.1175/JHM-D-15-0190.1 Mahrooghy, M., Anantharaj, V. G., Younan, N. H., Aanstoos, J., & Hsu, K.-L. (2012). On an Enhanced FARSI-CCS Algorithm for Precipitation Estimation. Journal of Atmospheric and Oceanic Technology, 29(7), 922–932. https://doi.org/10.1175/JTECH-D-11-00146.1 Sharifi, E., Steinacker, R., & 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. https://doi.org/10.3390/rs8020135 Tan, M. L., & Duan, Z. (2017). Assessment of GPM and TRMM precipitation products over Singapore. Remote Sensing, 9(7), 720. https://doi.org/10.3390/rs9070720 Tan, M. L., & Santo, H. (2018). Comparison of GPM IMERG, TMPA 3B42 and FARSI-CDR satellite precipitation products over Malaysia. Atmospheric Research, 202, 63–76. https://doi.org/10.1016/j.atmosres.2017.11.006 Tan, M. L., Ibrahim, A. L., Duan, Z., Cracknell, A. P., & Chaplot, V. (2015). Evaluation of six high-resolution satellite and ground-based precipitation products over Malaysia. Remote Sensing, 7(2), 1504–1528. https://doi.org/10.3390/rs70201504 Tao, H., Fischer, T., Zeng, Y., & Fraedrich, K. (2016). Evaluation of TRMM 3B43 precipitation data for drought monitoring in Jiangsu Province, China. Water (Switzerland), 8(6), 221. https://doi.org/10.3390/w8060221 Thiemig, V., Rojas, R., Zambrano-Bigiarini, M., & De Roo, A. (2013). Hydrological evaluation of satellite-based rainfall estimates over the Volta and Baro-Akobo Basin. Journal of Hydrology, 499, 324–338. https://doi.org/10.1016/j.jhydrol.2013.07.012 Xie, P., & Xiong, A. Y. (2011). A conceptual model for constructing high-resolution gauge-satellite merged precipitation analyses. Journal of Geophysical Research Atmospheres, 116(21). https://doi.org/10.1029/2011JD016118 Xu, R., Tian, F., Yang, L., Hu, H., Lu, H., & Hou, A. (2017). Ground validation of GPM IMERG and trmm 3B42V7 rainfall products over Southern Tibetan plateau based on a high-density rain gauge network. Journal of Geophysical Research, 122(2), 910–924. https://doi.org/10.1002/2016JD025418 Yuan, F., Zhang, L., Wah Win, K. W., Ren, L., Zhao, C., Zhu, Y., et al. (2017). Assessment of GPM and TRMM multi-satellite precipitation products in streamflow simulations in a data sparse mountainous watershed in Myanmar. Remote Sensing, 9(3), 302. https://doi.org/10.3390/rs9030302 Zhong, R., Chen, X., Lai, C., Wang, Z., Lian, Y., Yu, H., & Wu, X. (2018). Drought monitoring utility of satellite-based precipitation products across mainland China. Journal of Hydrology. https://doi.org/10.1016/J.JHYDROL.2018.10.072 | ||
|
آمار تعداد مشاهده مقاله: 572 تعداد دریافت فایل اصل مقاله: 559 |
||