بررسی روش‌های مختلف بازسازی بارش روزانه

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه مهندسی آب، فاضلاب و محیط زیست، دانشکده مهندسی عمران، آب و محیط زیست، دانشگاه شهید بهشتی، تهران، ایران.

10.22059/jwim.2023.349023.1021

چکیده

یکی از مشکلات متخصصان و طراحان، سری‌های زمانی ناقص در مطالعات هیدرولوژی است که باعث ایجاد خطا در نتایج شده و اجرای پروژه‌ها را دچار مشکل می‌کند. این مسئله در مناطقی که تعداد ایستگاه‌های باران‌سنجی محدود است، حادتر است. در حال حاضر استفاده از روش‌های آماری به‌منظور رفع خلأهای آماری داده‌ها متداول است. پژوهش حاضر با هدف ارزیابی عملکرد روش بازسازی مقادیر گم‌شده بارندگی روزانه با استفاده از بسته waterData در نرم‌افزار R و روش شکننده زمانی مقادیر بازسازی‌شده سالانه به مقادیر روزانه در بازه زمانی 1990 تا 2020 با استفاده از 43 ایستگاه دارای آمار کامل در بین 87 ایستگاه سینوپتیک منتخب واقع در ایران انجام شد. براساس مقادیر میانگین شاخص‌های ارزیابی برای دو روش شکننده زمانی و بازسازی با استفاده از بسته waterData در نرم‌افزار R، برای شاخص CC به‌ترتیب 1 و 95/0، برای شاخص MBE به‌ترتیب صفر و 01/0-، برای شاخص RMSE به‌ترتیب 3/0 و 1/1، برای شاخص NSE به‌ترتیب 99/0 و 89/0 و برای شاخص CSI و POD به‌ترتیب 94/0 و 63/0 است که عملکرد بهتر روش شکننده زمانی را نشان داده‌ است. مقادیر میانگین شاخص Bias و FAR برای دو روش به‌ترتیب برابر 01/0- و صفر بوده و نشان‌دهنده عملکرد مشابه دو روش است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Examining Different Methods of Daily Rainfall Reconstruction

نویسندگان [English]

  • Hanie Sadat Karbasi
  • Ali Moridi
  • seyed saied mousavi nadoushani
Department of Water, Waste Water and Environmental Engineering, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran.
چکیده [English]

One of the problems of specialists and designers is the incomplete time series in hydrology studies, which causes errors in the results and complicates the implementation of projects. This issue is more acute in areas where the number of rain gauge stations is limited. Currently, it is common to use statistical methods in order to solve statistical data gaps. The current research aims to evaluate the performance of the method of reconstructing missing values ​​of daily rainfall using the waterData package in R software and the time disaggregation method of reconstructing annual values ​​to daily values ​​in the period from 1990 to 2020 using 43 stations with complete statistics among 87 selected synoptic stations. It was done in Iran. Based on the average values ​​of the evaluation indices for two times disaggregation and reconstruction using the waterData package in R software methods, for the CC index 1 and 0.95 respectively, for the MBE index 0 and -0.01 respectively, for the RMSE index 0.3 and 1.1 respectively, for The NSE index is 0.99 and 0.89, respectively, and the CSI and POD index are 0.94 and 0.63, respectively, which shows the better performance of the time disaggregation method. The average values ​​of Bias and FAR index for two methods are equal to -0.01 and 0, respectively, and indicate the similar performance of the two methods.

کلیدواژه‌ها [English]

  • Daily precipitation
  • double mass curve
  • missing data
  • R software
  • time disaggregation
  • WaterData package

مراجع

  1. Anandhi, A., Frei, A., Pierson, D. C., Schneiderman, E. M., Zion, M. S., Lounsbury, D., & Matonse, A. H. (2011). Examination of change factor methodologies for climate change impact assessment. Water Resources Research, 47(3).
  2. Bahrami, M., Amiri, M., Maharloiee, , Rezaie, & Ghafari, K. (2017). Determining the effect of data ‎preprocessing on the performance of artificial neural network in order to predict monthly rainfall in ‎Abadeh city. Ecohydrology, 1, 29-37. (In Persian).‎
  3. Bitew, M. M., Gebremichael, M., Ghebremichael, L. T., & Bayissa, Y. A. (2012). Evaluation of high-resolution satellite rainfall products through streamflow simulation in a hydrological modeling of a small mountainous watershed in Ethiopia. Journal of Hydrometeorology, 13(1), 338-350.
  4. Breinl, K., & Di Baldassarre, G. (2019). Space-time disaggregation of precipitation and temperature across different climates and spatial scales. Journal of Hydrology: Regional Studies, 21, 126-146. Available at: https://doi.org/10.1016/j.ejrh.2018.12.002
  5. Chivers, B. D., Wallbank, J., Cole, S. J., Sebek, O., Stanley, S., Fry, M., & Leontidis, G. (2020). Imputation of missing sub-hourly precipitation data in a large sensor network: A machine learning approach. Journal of Hydrology, Elsevier 588, 125126. Available at: https://doi.org/10.1016/j.jhydrol.2020.125126
  6. 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.
  7. Villazón, M. F., & Willems, P. (2010, May). Filling gaps and daily disaccumulation of precipitation data for rainfall-runoff model. International Scientific Conference on Water Observation and Information Systems for Decision Support. (pp. 25-29).
  8. Faghih, H., Bahmanesh, J., & Khalili, K. (2018). Spatio-temporal simulation of annual rainfall using stochastic models. Journal of water and soil sciences (Agricultural sciences and natural resources). (In Persian).
  9. Farzandi, M., Sanaeinejad, H., Ghahraman, B., & Sarmad, M. (2019). Imputation of missing meteorological data with evolutionary and machine learning methods, case study: long-term monthly precipitation and temperature of Mashhad. Journal of Water and Soil, 33(2), 361-377.
  10. Gao, P., Mu, X. M., Wang, F., & Li, R. (2011). Changes in streamflow and sediment ‎discharge and the response to human activities in the middle reaches of the Yellow River. ‎Hydrology and Earth System Sciences, 15(1), 1-10.‎
  11. Gyau-Boakye, P., & Schultz, G. A. (1994). Filling gaps in runoff time series in West Africa. ‎Hydrological Sciences Journal, 39(6), 621-636.‎
  12. Eslami Jamal Abad, S., Sharafati, A., Mohammadi Golafshani, E., & Farsadania, F. (2019). ‎Estimation of missing daily rainfall and runoff data using self-consistent mapping, Case study: ‎Mazandaran province. Journal of Water and Soil Sciences, JWSS, 23(4), 1-17 (In Persian).‎
  13. John, A., Fowler, K., Nathan, R., Horne, A., & Stewardson, M. (2021). Disaggregated monthly hydrological models can outperform daily models in providing daily flow statistics and extrapolate well to a drying climate. Journal of Hydrology. Elsevier B.V. 598(February): 126471. Available at: https://doi.org/10.1016/j.jhydrol.2021.126471
  14. Kassomenos, P. A., Paschalidou, A. K., & Vlachogianni, A. (2013). One-day-ahead prediction of maximum carbon monoxide concentration in urban environments. Stochastic Environmental Research and Risk Assessment, 27, 561-572.
  15. Khalili, A., & Rahimi, J. (2014). High-resolution spatiotemporal distribution of precipitation in Iran: a comparative study with three global-precipitation datasets. Theoretical and applied climatology, 118, 211-221.
  16. Kosari, M. R., Hosieni, M., Esmaielzade, S., & Miri, M. (2021). Investigating the efficiency of reconstruction methods of statistical defects in relation to precipitation parameters in dry areas of Iran. Earth and space physics. (In Persian).
  17. Lookzadeh, S. (2005). Evaluation of several methods in reconstruction of missing precipitation data ‎in ‎different periods at central Alborz region, MSc Thesis. Tehran University.
  18. Mengistu, S., Gessesse, B., Bedada, T. B., & Tibebe, D. (2019a). Evaluation of long-term satellite-based retrieved precipitation estimates and spatiotemporal rainfall variability: The case study of Awash basin, Ethiopia. Extreme Hydrology and Climate Variability: Monitoring, Modelling, Adaptation and Mitigation. Elsevier Inc. Available at: http://dx.doi.org/10.1016/B978-0-12-815998-9.00003-8
  19. Mengistu, S., Gessesse, B., Bedada, T. B., & Tibebe, D. (2019b). Evaluation of long-term satellite-based retrieved precipitation estimates and spatiotemporal rainfall variability: The case study of Awash basin, Ethiopia. Extreme Hydrology and Climate Variability: Monitoring, Modelling, Adaptation and Mitigation, 23-35.
  20. Mianabadi, A., Alizadeh, A., Sanaeinejad, H., Awal, M. B., & Faridhosseini, A. (2013). The Statistic Assessment of CMORPH Model Output For Precipitation Estimation Over The Northeast of Iran (Case Study: North Khurasan Province). Journal of Water and Soil, 27(5), 919-927. (In Persian).
  21. Mirzaiee, N., & Saraf, A. (2021). Application of data integration models in simulating river flow using large-scale climate signals, case study: Jiroft Dam watershed. Journal of Watershed Engineering and Management, 13(4), 672-689. (In Persian).
  22. Matinzahe, M. M., Fatahi, R., Shayannejad, M., & Abdulahi, K. (2013). Estimation and reconstruction ‎of 24-hour annual maximum rainfall data using the integrated model of genetic algorithm and neural ‎networks (Case study: Chahar Mahal Bakhtiari province). Iranian Journal of Watershed Management Science, jwmseir 2013, 7(22), 53-62 (In Persian).‎
  23. Mwale, F. D., Adeloye, A. J., & Rustum, R. (2012). Infilling of missing rainfall and streamflow data in the Shire River basin, Malawi–A self organizing map approach. Physics and Chemistry of the Earth, Parts A/B/C, 50, 34-43.
  24. Mwale, F. D., Adeloye, A. J., & Rustum, R. (2012b). Infilling of missing rainfall and streamflow data in the Shire River basin, Malawi-A self organizing map approach. Physics and Chemistry of the Earth. Elsevier Ltd 50-52, 34-43. Available at: http://dx.doi.org/10.1016/j.pce.2012.09.006
  25. Tayefeh Neskini,, Zahraie B and Saghafian B (2016) Evaluation of different simulations of ‎artificial neural network and geostatistical methods in supplementing missing data of daily precipitation. ‎Journal of water resources engineering, 8(26), 69-88. (In Persian).‎
  26. Hamed, K., & Rao, A. R. (Eds.). (2019). Flood frequency analysis. CRC press.
  27. Ryberg, K. R., & Vecchia, A. V. (2017). Vignette for waterData-An R Package for Retrieval, Analysis, and Anomaly Calculation of Daily Hydrologic Time Series Data.
  28. Sachindra, D. A., & Perera, B. J. C. (2016). Annual statistical downscaling of precipitation and evaporation and monthly disaggregation. Theoretical and Applied Climatology. Theoretical and Applied Climatology, 131(1-2), 181-200. Available at: http://dx.doi.org/10.1007/s00704-016-1968-6
  29. Sadatinejad, S. J., Shayannejad, M., & Honarbakhsh, A. (2010). Investigation of the Efficiency of the Fuzzy Regression Method in Reconstructing Monthly Discharge Data of Hydrometric Stations in Great Karoon River Basin. Journal of Agricultural Science and Technology, JAST; 12 (1), 111-119.
  30. Searcy, J. K., & Hardison, C. H. (1960). Double-Mass Curves. WaterSupply Paper 1541B. Available at: http://dspace.udel.edu:8080/dspace/handle/19716/1592
  31. Serrano-Notivoli, R., de Luis, M., & Beguería, S. (2017). An R package for daily precipitation climate series reconstruction. Environmental Modelling and Software. Elsevier Ltd 89. Available at: http://dx.doi.org/10.1016/j.envsoft.2016.11.005
  32. Shirvani, A., & Shirazi, E. F. Z. (2014). Comparison of ground based observation of precipitation with TRMM satellite estimations in Fars Province. Journal of Agricultural Meteorology, 2, 1-15. (In Persian).
  33. Tang, G., Clark, M. P., Papalexiou, S. M., Ma, Z., & Hong, Y. (2020). Have satellite precipitation products improved over last two decades? A comprehensive comparison of GPM IMERG with nine satellite and reanalysis datasets. Remote sensing of environment, 240, 111697.
  34. Tardivo, G., & Berti, A. (2012). A dynamic method for gap filling in daily temperature datasets. Journal of Applied Meteorology and Climatology, 51(6), 1079-1086.
  35. Teegavarapu, R. S. (2014). Missing precipitation data estimation using optimal proximity metric-based imputation, nearest-neighbour classification and cluster-based interpolation methods. Hydrological Sciences Journal, 59(11), 2009-2026.
  36. Teetor, P. (2011). Recipes for State Space Models in R. (July):20
  37. Vakili, S. (2017). Monthly precipitation prediction with M5 tree model and its comparison with classical ‎statistical methods (Case study: Urmia synoptic station). Iran-Water resources research, 13(4), 179-183, (In Persian).‎
  38. Zahmatkesh, Z., Karamouz, M., Goharian, E., & Burian, S. J. (2015). Analysis of the effects of climate change on urban storm water runoff using statistically downscaled precipitation data and a change factor approach. Journal of Hydrologic Engineering, 20(7), 05014022. (In Persian).
  39. Zhang, T., Yang, Y., Dong, Z., & Gui, S. (2021). A multiscale assessment of three satellite precipitation products (TRMM, CMORPH, and PERSIANN) in the three Gorges Reservoir Area in China. Advances in Meteorology, 2021, 1-27.
مدیریت آب و آبیاری
دوره 13، شماره 2
تیر 1402
صفحه 323-340

فایل ها

هم رسانی

ارجاع به این مقاله

آمار