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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 | ||
| 2عضو هیئت علمی دانشگاه تربیت مدرس | ||
| 3عضو هیئت علمی موسسه 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 | ||
| روش‌های گوناگونی برای بدست آوردن رطوبت‌خاک در عمق ریشه توسعه یافته‌اند که از آن جمله می توان به استفاده از حسگرهای اندازه‌گیری رطوبت‌خاک و یا به مدل‌های شبیه‌سازی رطوبت خاک اشاره کرد. هر یک از روش‌ها مزایا و معایب خود را دارند. علم داده‌گواری به مجموعه روش‌هایی اطلاق می‌گردد که در آن به صورت توام از مدل‌های مبتنی بر فیزیک پدیده‌ی مورد مطالعه و مشاهدات اندازه‌گیری شده از آن، استفاده می‌گردد تا تخمین دقیق‌تری از پدیده مورد مطالعه بدست آید. در پژوهش حاضر، امکان کاهش تعداد عمق‌های اندازه‌گیری رطوبت توسط حسگرهای رطوبت سنج خاک در عمق و افزایش فاصله زمانی بین دو برداشت اطلاعات متوالی از حسگرهای رطوبت‌سنج آن‌ها، با استفاده از داده‌گواری به کمک فیلتر کالمن همادی بررسی شد. داده‌های مورد نیاز مدلسازی از دو مزرعه چغندرقند و دو مزرعه ذرت‌علوفه‌ای در منطقه جوین در استان خراسان رضوی در بازه زمانی اردیبهشت تا آبان 1399 به‌صورت میدانی برداشت شد. نتایج پژوهش مبین آن بود که سناریوهای داده‌گواری با استفاده از فیلتر کالمن همادی توانسته‌اند با استفاده از مشاهدات با فواصل زمانی طولانی‌تر به سطح مناسبی از دقت برسند و مجموع قطر ماتریس کوواریانس شبیه‌سازی را در مقایسه با شبیه‌سازی سیستم بدون داده‌گواری 61 تا 86 درصد کاهش دهند. nRMSD رطوبت‌خاک برای عمق لایه توسعه ریشه، در مقایسه با شبیه‌سازی سیستم بدون داده‌گواری، از0.04 تا 0.12 کاهش یافت. مقایسه نتایج سناریوهای داده‌گواری نشانگر آن بودند که با انتخاب مناسب تعداد عمق‌های مشاهدات، امکان دستیابی به تخمینی از رطوبت‌خاک با دقت مناسب، با استفاده از حداقل تعداد حسگرهای رطوبت‌خاک 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 | ||
| 2Faculty member | ||
| 3Faculty Member, Agricultural Engineering Research Institute, Agricultural Research Education and Extension Organization, Karaj, Iran | ||
| 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] | ||
| 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. 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 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 Ensemble Kalman filter, 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 Ensemble Kalman filter to effectively mitigate uncertainty. The normalized root mean square difference , was notably improved by the Data Assimilation scenarios. The normalized root mean square difference of scenarios ranged from 0.03 to 0.11, while the normalized root mean square difference for the Open Loop was 0.15, highlighting the capacity of Ensemble Kalman filter to minimize discrepancies between simulated and observed soil moisture profiles. Such reductions in normalized root mean square difference 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 Ensemble Kalman filter 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 | ||
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آمار تعداد مشاهده مقاله: 85 تعداد دریافت فایل اصل مقاله: 93 |
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