| تعداد نشریات | 286 |
| تعداد نشریات سازمان | 84 |
| تعداد شماره‌ها | 2,996 |
| تعداد مقالات | 33,553 |
| تعداد مشاهده مقاله | 44,160,366 |
| تعداد دریافت فایل اصل مقاله | 20,533,871 |
مقایسه تبخیر-تعرق برآوردی چغندرقند با معادله پنمن‌مانتیث بر اساس داده های واقعی و محصولات GFS، ECMWF و MeteoBlue در منطقه جوین | ||
| تحقیقات مهندسی سازه های آبیاری و زهکشی | ||
| دوره 22، شماره 83، مرداد 1400، صفحه 1-20 اصل مقاله (996.27 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/idser.2021.353726.1459 | ||
| نویسندگان | ||
| سید حسن طباطبایی1؛ سید مجید میرلطیفی* 2؛ حسین دهقانی سانیج3؛ اشکان شکری4 | ||
| 1دانشجوی دکتری تخصصی، گروه مهندسی و مدیریت آب، دانشکده کشاورزی، دانشگاه تربیت مدرس، تهران، ایران | ||
| 2دانشیار گروه مهندسی و مدیریت آب، دانشکده کشاورزی، دانشگاه تربیت مدرس، تهران، ایران | ||
| 3دانشیار موسسه تحقیقات فنی و مهندسی کشاورزی، سازمان تحقیقات آموزش و ترویج کشاورزی کرج ایران | ||
| 4محقق وزارت هواشناسی استرالیا، ملبورن، استرالیا | ||
| چکیده | ||
| در این پژوهش نیاز آبی گیاه چغندرقند بهاره در منطقه جوین با استفاده از پیش‌بینی‌های 5روزه مدل‌های هواشناسی ECMWF، GFS و MeteoBlue برآورد و با نیاز آبی محاسبه شده براساس اطلاعات ایستگاه هواشناسی جوین مقایسه شدند. بررسی‌ها در چهار سطح ارزیابی دقت مدل‌ها در پیش‌بینی 1.متغیرهای هواشناسی دخیل در رابطه پنمن-مانتیث، 2.دو جزء تابشی (ET_0^Rad)و همرفتی (ET_0^Adv) تبخیر-تعرق گیاه‌مرجع (ET_0)، 3. ET_0و 4. نیازآبی چغندرقند بهاره انجام شد. مدل‌ها ET_0^Adv را کمتر و ET_0^Rad را بیشتر از مقدار واقعی برآورد می‌نمایند. به‌طوری‌که خطای اریب مدل‌های GFS، ECMWF و MeteoBlue در برآورد ET_0^Rad به ترتیب برابر 4/0- ،5/0-و2/0- میلی‌متر در روز و برای ET_0^Adv به ترتیب برابر 4/0، 5/0 و 7/0 میلی‌متر در روز به دست آمد. میانگین خطای مربعات (RMSE) این سه مدل برای ET_0^Rad به ترتیب 6/0، 7/0و7/0 میلی‌متر در روز و برای ET_0^Adv به ترتیب برابر 7/0، 7/0 و 9/0 میلی‌متر در روز محاسبه شد. رفتار متضاد خطاهای ET_0^Rad و ET_0^Adv سبب شد تا خطای اریب برآورد ET_0 توسط مدل‌ها به ترتیب برابر 03/0، 03/0- و 47/0 میلی‌متر در روز شود. نیازآبی چغندرقند بهاره در منطقه جوین 841 میلی‌متر در طول دوره رشد بر اساس روش فائو-پنمن-مانتیث محاسبه شد. مجموع خطای برآورد نیازآبی روزانه مدل‌های GFS، ECMWF و Meteoblue در طول دوره رشد به ترتیب برابر 65، 64 و 100 میلی‌متر بدست آمد. در نتیجه توصیه می‌شود برای پیش‌بینی نیاز آبی گیاه چغندرقند در منطقه جوین از بروندادهای مدل ECMWF استفاده شود. | ||
| کلیدواژه‌ها | ||
| برنامه ریزی آبیاری؛ تبخیر-تعرق گیاه مرجع؛ ضریب گیاهی؛ نیاز آبیاری | ||
| عنوان مقاله [English] | ||
| Comparison of computed sugar beet evapotranspiration by the Penman-Monteith equation using measured climatological parameters and predicted products of GFS, ECMWF and GFS meteorological forecasting models in the Jovein region | ||
| نویسندگان [English] | ||
| Seyed Hasan Tabatabaii1؛ Seyed Majid Mirlatifi2؛ Hosein Dehghanisanij3؛ Ashkan Shokri4 | ||
| 1PhD Candidate in Water Engineering-Irrigation and Drainage , Department of Water Engineering and Management, Faculty of Agriculture, Tarbiat Modares University | ||
| 2Associate professor, Department of Water Engineering and Management, Faculty of Agriculture, Tarbiat Modares University,Tehran, Iran. | ||
| 3Faculty Member, Agricultural Engineering Research Institute, Agricultural Research Education and Extension Organization, Karaj, Iran | ||
| 4Scientific Researcher, Bureau of Meteorology | ||
| چکیده [English] | ||
| Introduction Irrigation should be applied in accordance with an accurate estimate of the crop water requirement and the crop growth stages (Jensen and Allen, 2014). In recent years, several meteorological forecasting models (MFM) have been developed which are capable of forecasting weather data. Such data could be used to calculate and predict crop water requirements during the next few days. The performance of irrigation canals and water delivery systems can be significantly improved if future short-term demands based on the predicted crop water requirement are available. The appropriate performance of these models in the agricultural sector depends on the quality of their predictions of various weather variables. The aim of this study is to evaluate the accuracy of the predicted spring sugar beet water requirement in the Jovien region when 5-day forecasted meteorological variables by ECMWF, GFS, and MeteoBlue MFM were used as the climatological parameters in the Penman-Monteith equation. The prediction accuracy of these models was evaluated under four categories, 1. Inclusion of the three main meteorological variables involved in calculating reference evapotranspiration (ET_0), including maximum air temperature (T_max) and minimum air temperature (T_min), and maximum wind speed, 2. Inclusion of radiation term (ET_0^Rad) and advection term (ET_0^Adv) of the Penman-Monteith equation, 3. Inclusion of ET_0 and water requirement of spring sugar beet. Methodology Meteorological forecasts for ECMWF, GFS, and MeteoBlue databases were obtained from https://www.windy.com. The meteorological variables presented by these models and used in this research were temperature, wind speed, and the degree of cloudiness. The study period was selected from May to December of 2020. ET0 was calculated using the FAO-Penman-Monteith method presented in the FAO 56(Allen et al., 1998). The first part of the FAO-Penman-Monteith (0.408∆(R_n-G)/(∆+γ(1+0.34 u_2 ) )) represents the contribution of the energy terms to the process of evapotranspiration and the second term ((γ 900/(T+273) u_2 (e_s-e_a ))/(∆+γ(1+0.34 u_2 ) )) signifies the importance of advective forces. Meteorological data obtained from the Jovein weather station was checked to take into consideration the possibility of non-standard surface cover surrounding the weather station in accordance with the method recommended in Annex 6 of FAO 56. Crop coefficients in early, developing, middle, and ending growth periods were also calculated based on the method recommended by the FAO 56. Results and Discussion The GFS model had the best performance in estimating T_maxwith the lowest Bias and RMSE errors of 1.4 and 2.3 degrees Celsius, respectively. Also, MeteoBlue with Bias and RMSE values of -2.2 and 1/3, respectively, had the best performance in estimating T_min. All three models underestimated the air temperature. The bias error of GFS, ECMWF, and MeteoBlue models in predicting the maximum daily wind speed in the Jovein region were 0.1, 1.3, and 1.8 m/s, respectively, and their RMSE Respectively 3.3, 3.3, and 3.4 m/s. Since the GFS model estimated Tmean and consequently vapor pressure deficit (VPD) with a higher degree of accuracy as compared with the other model, the advective term (ET_0^Adv) computed using data estimated by the GFS model was more accurate than that of the other models. The bias and RMSE errors of this model in estimating the mentioned variable were 0.4 and 0.7 mm per day, respectively. All the other three models underestimated ET_0^Adv. The bias error of GFS, ECMWF, and MeteoBlue models in estimating ET0Rad were -0.4, -0.5, and -0.2 mm/day, respectively, and their RMSE were 0.61, 0.67, and 0.65 mm per day, respectively. When the overestimated ET0Rad and underestimated ET_0^Adv terms were summed up to calculate ET_0 according to the FAO-Penman-Monteith method, the bias error of the outcome (ET_0) was significantly reduced for all the models. The bias error of the ET_0 for the GFS, ECMWF, and Meteoblue models were 0.03, -0.03, and 0.47 mm/day, respectively. Their RMSE values were 0.62, 0.58 and 0.94 mm/day, respectively. K_(c_ini ) of sugar beet for Jovein region with 4-day wetting intervals and light soil texture with an average ET_0 of 5.9 mm/day was 0.56, and K_(c_mid ) and K_(c_end ) were also calculated as 1.176 and 0.676, respectively. The seasonal water requirement of spring sugar beet for the Jovein region was 866 mm. The seasonal water requirements calculated using the outputs of GFS, ECMWF, and Meteoblue were 860, 866, and 788, respectively. However, the cumulative error of these models were 66, 65, and 101 mm, respectively, during the growing season. Conclusion A notable result in this study is the interaction of the ET_0^Adv and ET_0^Rad terms, which canceled out each other over and under estimations—all the three models underestimated and overestimated ET_0^Adv and ET_0^Rad terms, respectively. Thus the absolute value of the oblique error of the computed ET_0 was less than that of the ET_0^Adv and ET_0^Rad terms. However, the RMSE of ET_0, which indicates the noise and random behavior of the error sources, was not reduced. | ||
| کلیدواژه‌ها [English] | ||
| Irrigation Management, Reference Crop Evapotranspiration, Crop Coefficient, Irrigation Requirement | ||
| مراجع | ||
|
Ahmadi, M., DadashiRoodbari., & Ahmadi, H., (2019). Temporal-spatial analysis of the height of the Iranian border layer based on the output of the European Medium Term Forecast Center (ECMWF) database. Quarterly Journal of Geography and Development, 54, 163-184(in Persian) Alidoost, F., Stein, A., Su, Z., & Sharifi, A. (2021). Multivariate copula quantile mapping for bias correction of reanalysis air temperature data. Journal of Spatial Science, 66(2), 299-315. Allen, R.G., Pereira, L.S., Raes, D. & Smith, M., (1998). Crop Evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome, 300(9), p. D05109. Azizian, A., & Ramezani Etedali., H (2019). Spatiotemporal Assessment of Reanalysis and Remotely-Sensed Precipitation Datasets. Iran-Water Resources Research, 15(1), 163-177(in Persian) Allen, R. G., Pereira, L. S., Smith, M., Raes, D., & Wright, J. L. (2005). FAO-56 dual crop coefficient method for estimating evaporation from soil and application extensions. Journal of irrigation and drainage engineering, 131(1), 2-13. Deputy of Planning and Economy of the Ministry of Agriculture-Jahad. (2017) Agricultural Statistics Volume One: Crop Products (2016-2011). Deputy of Planning and Economics of Information and Communication Technology Center.(in Persian) Droogers, P., (2004). Adaptation to climate change to enhance food security and preserve environmental quality: example for southern Sri Lanka. Agricultural water management, 66(1), 15-33. Dumont, A., Mayor, B. & López-Gunn, E., (2013). Is the rebound effect or Jevons paradox a useful concept for better management of water resources? Insights from the irrigation modernisation process in Spain. Aquatic procedia, 1, 64-76. FAO., (2007). Climate Change Adaptation Water Sector NENA, Gharedaghi, M.H ., Homaee, M., Mirlatifi, S.M., Noroozi, A.A. (2020). Using Forecasts of WRF Regional Model to Improve the Accuracy of Reference Evapotranspiration Estimation, Iranian Soil and Water Research, 51(1), 165-177. (in Persian) Gharedaghi, M.H (2020).Improving water productivity in field scale using real-time weather and satellite data(PhD thesis), Tarbiat Modares University, Iran.(in Persian) Hargreaves, G.H., & Samani, Z.A. (1985). Reference Crop Evapotranspiration from Temperature. Applied Engineering in Agriculture, 1(2), 96–99. Hagedorn, R., Hamill, T. M., & Whitaker, J. S. (2008). Probabilistic forecast calibration using ECMWF and GFS ensemble reforecasts. Part I: Two-meter temperatures. Monthly Weather Review, 136(7), 2608-2619. Jensen, M.E. & Allen, R.G. eds., (2016). Evaporation, evapotranspiration, and irrigation water requirements. American Society of Civil Engineers. Mirltifi, M. (2003). Chapter One: Water Required by Plants. Farshi, A., Kheirabi, J., Siadat, H., Mirlatifi, M., Darbandi, S., Salamat, A., Entesari, M., Sadat Miraii, M. arm water management, Iran, National Irrigation and Drainage Committee of Iran.(in Persian) Molinos-Senante, M., Hernández-Sancho, F., Mocholí-Arce, M. & Sala-Garrido, R., (2014). A management and optimisation model for water supply planning in water deficit areas. Journal of hydrology, 515, 139-146. Pereira, L. S., Allen, R. G., Smith, M., & Raes, D. (2015). Crop evapotranspiration estimation with FAO56: Past and future. Agricultural Water Management, 147, 4-20. Perry, C., (2011). Accounting for water use: Terminology and implications for saving water and increasing production. Agricultural Water Management, 98(12), 1840-1846 Ponce, V. M., Pandey, R. P., & Ercan, S. (2000). Characterization of drought across climatic spectrum. Journal of Hydrologic Engineering, 5(2), 222-224. Raziei, T., & Sotoudeh, F., (2017). Investigation of the accuracy of the European Center for Medium Range Weather Forecast (ECMWF) in forecasting observed precipitation in different climates of Iran. Earth and Space Physics, 43(1), 133-147(in Persian) Shahi, A., Atan, R. B., & SULAIMAN, N. (2009). An effective Fuzzy C-Mean and type-2 fuzzy logic for weather forcasting. Journal of Theoretical & Applied Information Technology, 5(5), 550-561. Paparrizos, S., Smolenaars, W., Gbangou, T., Slobbe, E. V., & Ludwig, F. (2020). Verification of weather and seasonal forecast information concerning the peri-urban farmers’ needs in the lower ganges delta in bangladesh. Atmosphere, 11(10), 1041. | ||
|
آمار تعداد مشاهده مقاله: 513 تعداد دریافت فایل اصل مقاله: 389 |
||