| تعداد نشریات | 282 |
| تعداد نشریات سازمان | 80 |
| تعداد شمارهها | 3,208 |
| تعداد مقالات | 35,560 |
| تعداد مشاهده مقاله | 53,008,187 |
| تعداد دریافت فایل اصل مقاله | 92,291,300 |
ارزیابی حساسیت دادهگواری رطوبت خاک به تعداد عمقهای نمونهبرداری و فواصل زمانی بین اندازهگیریها با استفاده از حل عددی معادله ریچاردز | ||
| تحقیقات مهندسی سازه های آبیاری و زهکشی | ||
| دوره 24، شماره 91، آبان 1402، صفحه 87-108 اصل مقاله (1.31 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/idser.2024.363852.1562 | ||
| نویسندگان | ||
| سید حسن طباطبایی1؛ سید مجید میرلطیفی* 2؛ حسین دهقانی سانیج3؛ سید محمدرضا ناقدی فر4؛ اشکان شکری5 | ||
| 1دانشجوی دکتری تخصصی مهندسی آب-آبیاری و زهکشی، گروه مهندسی و مدیریت آب دانشکده کشاورزی دانشگاه تربیت مدرس | ||
| 2گروه مهندسی و مدیریت آب دانشگاه تربیت مدرس | ||
| 3موسسه تحقیقات فنی مهندسی کشاورزی کرج | ||
| 4گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه فردوسی، خراسان رضوی، ایران. | ||
| 5سازمان پژوهشهای علمی و صنعتی مشترک المنافع، کانبرا، استرالیا. | ||
| چکیده | ||
| روش های گوناگونی برای بهدست آوردن رطوبت خاک در عمق ریشه توسعه یافته اند که از آن جمله می توان به استفاده از حسگرهای اندازه گیری رطوبت خاک یا به مدل های شبیه سازی رطوبت خاک اشاره کرد. هر یک از روش ها مزایا و معایب خود را دارد. علم داده گواری به مجموعه روش هایی اطلاق میشودکه در آن به صورت توام از مدلهای مبتنی بر فیزیک پدیدۀ مورد مطالعه و مشاهدات اندازه گیری شده از آن استفاده می گردد تا تخمین دقیقتری از پدیدۀ مورد مطالعه به دست آید. در پژوهش حاضر، امکان کاهش تعداد عمق های اندازه گیری رطوبت توسط حسگرهای رطوبت سنج خاک در عمق و افزایش فاصلۀ زمانی بین دو برداشت اطلاعات متوالی از حسگرهای رطوبت سنج آن ها، با استفاده از دادهگواری به کمک فیلتر کالمن همادی بررسی شد. دادههای مورد نیاز مدل سازی از دو مزرعۀ چغندرقند و دو مزرعۀ ذرت علوفهای در منطقۀ جوین در استان خراسان رضوی در بازۀ زمانی اردیبهشت تا آبان 1399 بهصورت میدانی برداشت شد. نتایج پژوهش مبین آن بود که سناریوهای داده گواری با استفاده از فیلتر کالمن همادی توانسته اند با استفاده از مشاهدات با فاصله های زمانی طولانیتر به سطح مناسبی از دقت برسند و مجموع قطر ماتریس کوواریانس شبیه سازی را در مقایسه با شبیهسازی سیستم بدون دادهگواری 61 تا 86 درصد کاهش دهند. nRMSD رطوبت خاک برای عمق لایۀ توسعۀ ریشه، در مقایسه با شبیهسازی سیستم بدون دادهگواری، از 0/04 تا 0/12 کاهش یافت. مقایسۀ نتایج سناریوهای داده گواری نشانگر آن بودند که با انتخاب مناسب تعداد عمق های مشاهدات، امکان دستیابی به تخمینی از رطوبت خاک با دقت مناسب، با استفاده از حداقل تعداد حسگرهای رطوبت خاک فراهم است. | ||
| کلیدواژهها | ||
| تخمین بهینه حالت؛ تبخیر-تعرق؛ جزء برداشت؛ فیلترکالمن همادی | ||
| عنوان مقاله [English] | ||
| Sensitivity Assessment of Soil Moisture Data Assimilation to the Number of the Sampling Depths and the Time Intervals Between Measurements Using Numerical Solution of the Richards Equation | ||
| نویسندگان [English] | ||
| Seyed Hasan Tabatabaii1؛ Seyed Majid Mirlatifi2؛ Hosein Dehghanisanij3؛ Seyed Mohammad Reza Naghedifar4؛ Ashkan Shokri5 | ||
| 1PhD Candidate in Water Engineering-Irrigation and Drainage , Department of Water Engineering and Management, Faculty of Agriculture, Tarbiat Modares University | ||
| 2Department of Engineering and Water Management, Tarbiat Modares University | ||
| 3Agricultural Engineering Research Institute, Karaj, | ||
| 4Department of Water Engineering and Science, Faculty of Agriculture, University of Ferdowsi, Khorasan Razavi, Iran. | ||
| 5Commonwealth Scientific and Industrial Research Organisation(CSIRO), Canberra, Australia. | ||
| چکیده [English] | ||
| Extended Abstract Introduction The fundamental principles of smart irrigation hinges upon precise assessments of soil moisture content within the root zone layer. Various techniques have been developed to ascertain root zone soil moisture content, such as using soil moisture measurement sensors or simulation models. Each one of these methods has its own distinct advantages and disadvantages. Data assimilation encompasses an array of approaches that combine model estimates with the corresponding observed data to derive a more precise estimations of the required data. The purpose of this research is to ascertain the feasibility of reducing the number of depths at which soil moisture measurements were taken and increasing the time interval between two consecutive soil moisture measurements using the Ensemble Kalman filter (EnKF). Methodology This study was conducted synthetically based on information collected from four farms in Jovein, Khorasan Razavi Province, cultivating sugar beets and corn. Data was collected from four farms during the period of April to November 2020. The numerical solution of the Richards equation with the inclusion of the sink term was used to simulate the soil moisture changes in the root zone layer. To mitigate data assimilation's vulnerability to potential result divergence among members, an identification and correction mechanism, along with handling divergent members, were integrated into the system. This mechanism was found on the sudden model result shift throughout the entire root profile between two consecutive days. Two indicators were used to evaluate the scenarios: a) the sum of covariance matrix diameters at the last simulation time step, and b) the normalized root mean square difference (nRMSD) of the soil moisture content within the soil profile, comparing the scenarios with the scenario having the largest number of soil moisture measurement depths and the shortest time interval between two consecutive measurements. Results and Discussion The results indicated that with the application of EnKF, it is possible to improve the accuracy of the results using a longer time interval between measurements. The Data Assimilation scenarios exhibited a remarkable capability in reducing the diameter of the covariance matrix. This reduction, ranging from 61% to 86%, compared to the open-loop scenario, emphasizes the ability of EnKF to effectively mitigate uncertainty. The normalized root mean square difference (nRMSD), was notably improved by the Data Assimilation scenarios. The nRMSD of scenarios ranged from 0.03 to 0.11, while the nRMSD for the open Loop was 0.15, highlighting the capacity of EnKF to minimize discrepancies between simulated and observed soil moisture profiles. Such reductions in nRMSD values signify the model's improved ability to capture actual soil moisture variations, thus contributing to more reliable predictions and better decision-making in agricultural water management. The application of EnKF helped to select the proper measurement depths and ultimately to reduce the number of required soil measurement points. Conclusions Data Assimilation successfully diminished the uncertainty of the soil moisture content results, even when utilizing the minimum number of soil moisture measurement depths and maximum time intervals between observations. Both of these findings—increasing the time interval between consecutive measurements and reducing the required number of measurement depths—indicate that with the application of data assimilation, it is possible to decrease the cost of the implementation of the smart irrigation. Acknowledgement This research was carried out with the financial support of the Water, Climate, and Environment Knowledge-Based Economy Development Headquarters, under the Vice President for Science, Technology, and Knowledge-Based Economy. We are also grateful to the Jovin Agriculture and Industry Group, particularly the CEO, the research unit, and the new technologies unit, for their support in conducting this research | ||
| کلیدواژهها [English] | ||
| Ensemble Kalman Filter, Evapotranspiration, Optimal State Estimation, Sink Term | ||
| مراجع | ||
|
Abolafia‐Rosenzweig, R., Livneh, B., Small, E. E., & Kumar, S. V. (2019). Soil moisture data assimilation to estimate irrigation water use. Journal of Advances in Modeling Earth Systems, 11(11), 3670-3690. 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, Italy, 301 pp. Celia, M. A., Bouloutas, E. T., & Zarba, R. L. (1990). A general mass‐conservative numerical solution for the unsaturated flow equation. Water resources research, 26(7), 1483-1496. Deb, P., Abbaszadeh, P., & Moradkhani, H. (2022). An ensemble data assimilation approach to improve farm-scale actual evapotranspiration estimation. Agricultural and Forest Meteorology, 321, 108982. Evensen, G. (1994). Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research: Oceans, 99(C5), 10143-10162. Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., & Thépaut, J. N. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999-2049. Farrokhi, G., Gapeleh, M. (2019). Smart farming based on internet of things. Iranian Journal of Development and Technology,59(15), 29-36 (In Persian) Hirschi, M., Michel, D., Lehner, I., & Seneviratne, S. I. (2017). A site-level comparison of lysimeter and eddy covariance flux measurements of evapotranspiration. Hydrology and Earth System Sciences, 21(3), 1809–1825. https://doi.org/10.5194/hess-21-1809-2017 Holton, J.R. and Hakim, G.J., An Introduction to Dynamic Meteorology 5th edition (2012). Jensen, M.E. and Allen, R.G. eds., 2016, April. Evaporation, evapotranspiration, and irrigation water requirements. American Society of Civil Engineers. Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. ASME. J. Basic Eng, 35-45 Kurtz, W., Hendricks Franssen, H. J., & Vereecken, H. (2012). Identification of time‐variant river bed properties with the ensemble Kalman filter. Water Resources Research, 48(10). Kutz, J. N., & Kutz, J. N. (2013). Data-driven modeling & scientific computation: methods for complex systems & big data. OUP Oxford. Li, D., Franssen, H. J. H., Han, X., Jiménez-Bello, M. A., Alzamora, F. M., & Vereecken, H. (2018). Evaluation of an operational real-time irrigation scheduling scheme for drip irrigated citrus fields in Picassent, Spain. Agricultural water management, 208, 465-477. Li, Z., Li, Y., Wang, Z., & Zheng, J. (2023). An Ensemble Kalman Filter Data Assimilation Method for the Sea Surface Temperature in the China Seas: Implementation and Simulation Experiments. In Journal of Physics: Conference Series (Vol. 2486, No. 1, p. 012028). IOP Publishing. Linker, R., & Kisekka, I. (2022). Concurrent data assimilation and model-based optimization of irrigation scheduling. Agricultural Water Management, 274, 107924. Liu, K., Huang, G., Šimůnek, J., Xu, X., Xiong, Y., & Huang, Q. (2021). Comparison of ensemble data assimilation methods for the estimation of time-varying soil hydraulic parameters. Journal of Hydrology, 594, 125729. Lu, M., Lu, S., Liao, W., Lei, X., Yin, Z., & Wang, H. (2022). Research and application of parameter estimation method in hydrological model based on dual ensemble Kalman filter. Hydrology Research, 53(1), 65-84. Pauwels, V. R., & De Lannoy, G. J. (2006). Improvement of modeled soil wetness conditions and turbulent fluxes through the assimilation of observed discharge. Journal of hydrometeorology, 7(3), 458-477. Moradkhani, H., Hsu, K. L., Gupta, H., & Sorooshian, S. (2005). Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter. Water resources research, 41(5). W05012, 1-17. Montzka, C., Pauwels, V. R., Franssen, H. J. H., Han, X., & Vereecken, H. (2012). Multivariate and multiscale data assimilation in terrestrial systems: A review. Sensors, 12(12), 16291-16333. Mualem, Y. (1976). A new model for predicting the hydraulic conductivity of unsaturated porous media. Water resources research, 12(3), 513-522. Naghedifar, S. M., Ziaei, A. N., Playán, E., Zapata, N., Ansari, H., & Hasheminia, S. M. (2019). A 2D curvilinear coupled surface–subsurface flow model for simulation of basin/border irrigation: theory, validation and application. Irrigation Science, 37, 151-168. Naghedifar, S. M., Ziaei, A. N., Naghedifar, S. A., & Ansari, H. (2020). A new model for simulation of collection and conveyance sections of Qanat. Journal of Hydrology, 590, 125218. Narváez-Ortiz, I., Ibánez-Castillo, L., Arteaga-Ramírez, R., & Vázquez-Pena, M. (2022). Ensemble Kalman Filter for Hourly Streamflow Forecasting in Huaynamota River, Nayarit, México. Ingeniería e Investigación, 42(3). Rakovec, O., Weerts, A. H., Hazenberg, P., Torfs, P. J. J. F., & Uijlenhoet, R. (2012). State updating of a distributed hydrological model with Ensemble Kalman Filtering: effects of updating frequency and observation network density on forecast accuracy. Hydrology and Earth System Sciences, 16(9), 3435-3449. Reichle, R. H. (2008). Data assimilation methods in the Earth sciences. Advances in water resources, 31(11), 1411-1418. Saxton, K. E., Rawls, W., Romberger, J. S., & Papendick, R. I. (1986). Estimating generalized soil‐water characteristics from texture. Soil science society of America Journal, 50(4), 1031-1036. Tabatabaii, S.H., Mirlatifi, S.M., Dehghanisanij, H., Shokri, A. (2021). Comparison of computed sugar beet evapotranspiration by the Penman-Monteith equation using measured climatological parameters and predicted products of GFS, ECMWF and MeteoBlue meteorological forecasting models in the Jovein region. Irrigation and Drainage Structures Engineering, 83(22), 1-20. (In Persian) Van Genuchten, M. T. (1980). A closed‐form equation for predicting the hydraulic conductivity of unsaturated soils. Soil science society of America journal, 44(5), 892-898. Van Genuchten, M. V., Leij, F. J., & Yates, S. R. (1991). The RETC code for quantifying the hydraulic functions of unsaturated soils. Rep. EPA-600/2-91/065. 92 pp., U.S. Environ, Prot. Agency, Ada, Okla. Yin, J., Zhan, X., Barlage, M., Kumar, S., Fox, A., Albergel, C.,& Liu, J. (2023). Assimilation of Blended Satellite Soil Moisture Data Products to Further Improve Noah-MP Model Skills. Journal of Hydrology, 621, 129596. Zhang, C., Cai, S., Tong, J., Liao, W., & Zhang, P. (2022). Flood Forecasting via the Ensemble Kalman Filter Method Using Merged Satellite and Measured Soil Moisture Data. Water, 14(10), 1555. | ||
|
آمار تعداد مشاهده مقاله: 667 تعداد دریافت فایل اصل مقاله: 336 |
||