تعداد نشریات | 161 |
تعداد شمارهها | 6,532 |
تعداد مقالات | 70,501 |
تعداد مشاهده مقاله | 124,098,973 |
تعداد دریافت فایل اصل مقاله | 97,206,549 |
بررسی تأثیر متغیرهای هواشناسی بر دمای اعماق مختلف خاک و برآورد آن بر مبنای روش رگرسیونی در استان گیلان | ||
تحقیقات آب و خاک ایران | ||
دوره 53، شماره 11، بهمن 1401، صفحه 2613-2624 اصل مقاله (1.88 M) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/ijswr.2022.342814.669263 | ||
نویسندگان | ||
سید محمد تقی سدیدی شال1؛ زهرا امین دلدار1؛ ابراهیم اسعدی اسکویی* 2؛ جلیل هلالی3 | ||
1گروه مطالعات و تحقیقات، اداره کل هواشناسی استان گیلان، رشت، ایران | ||
2پژوهشکده اقلیم شناسی و تغییر اقلیم، پژوهشگاه هواشناسی و علوم جو، تهران، ایران | ||
3گروه مهندسی آبیاری و آبادانی، پردیس کشاورزی و منابع طبیعی دانشگاه تهران، کرج، ایران | ||
چکیده | ||
خاک به عنوان بستر رشدونمو گیاهان تأثیر مهمی بر تولید محصولات کشاورزی دارد. از سوی دیگر، این بخش از بومسامانه تحت تأثیر عوامل اقلیمی قرار دارد. هدف این مطالعه در وهله اول بررسی ارتباط بین متغیرهای هواشناسی با دمای اعماق مختلف خاک و سپس استفاده از مهمترین عامل مؤثر در آن با استفاده از روش رگرسیونی بدون نیاز به مدلهای پیچیدهتر در ایستگاههای استان گیلان بود. بنابراین، ارتباط بین دادههای هواشناسی شامل دمای هوا در ارتفاع دو متری، ابرناکی، ساعات آفتابی، بارندگی، رطوبت نسبی، تبخیر و سرعت باد با دمای خاک اعماق 5، 10، 20، 30، 50 و 100 سانتیمتری در ایستگاههای مختلف استان گیلان در دوره 10 ساله از 2009 تا 2018 میلادی در مقیاس روزانه به روش تحلیل همبستگی بررسی شد. در نهایت مدلسازی دمای اعماق مختلف با روش رگرسیونی انجام شد که 70 و 30 درصد دادهها به ترتیب بهمنظور واسنجی و صحتسنجی مورد استفاده قرار گرفت. بررسیها نشان داد که از بین کلیه متغیرهای مستقل مورد استفاده عامل میانگین روزانه دمای هوا در ارتفاع 2 متری بیشترین همبستگی را با دمای خاک در اعماق مختلف ایستگاههای مورد مطالعه داشته است که این همبستگی در ایستگاههای مختلف بین 71/0 تا 97/0 متغیر است. نتیجه نهایی مشخص نمود روابط رگرسیونی به دست آمده در سطح معنیداری 5 درصد میتوانند در تخمین دمای اعماق مختلف خاک به خصوص اعماق سطحیتر دقت قابل قبولی ارائه نمایند به طوری مقادیر RMSE بین 7/1 تا 9/4 درجه سلسیوس و مقادیر ضریب تعیین بین 62/0 تا 96/0 متغیر است. | ||
کلیدواژهها | ||
دمای خاک؛ متغیرهای اقلیمی؛ رگرسیون تک متغیره | ||
عنوان مقاله [English] | ||
Investigation of effect metrological variables on different depth of temperature and its estimation base on regression method in Guilan province | ||
نویسندگان [English] | ||
Seyed Mohammad Taghi Sadidi Shal1؛ Zahra Amin Deldar1؛ Ebrahim Asadi Oskouei2؛ Jalil Helali3 | ||
1Studies and Research Group, Guilan Meteorological Organization, Rasht, Iran | ||
2Climatological Research and Climate Change Institute, Atmospheric Science and Meteorological Research Center (ASMERC), Tehran, Iran | ||
3Department of Irrigation and Reclamation Engineering Department, Faculty of College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran. | ||
چکیده [English] | ||
Soil is the base of plant growth and has a significant effect on agricultural production. On the other hand, this section of the ecosystem is strongly affected by climate factors. The aim of this study was to investigate the relationship between meteorological variables with soil temperature at different depthes and to use the most effective factor for estimation of it using the regression method without the need for more complex models in stations of Guilan province. Therefore, the relationship between meteorological data including air temperature at 2m-elevation, cloudiness, sunshine hours, rainfall, relative humidity, evaporation, and wind speed with soil temperature at depths of 5, 10, 20, 30, 50, and 100 cm at stations of Guilan province in a 10-year period from 2009 to 2018 was studied by correlation analysis. Finally, a regression equation was developed based on 70 percent of the data and it was validated by another 30 percent of the data to estimate soil temperature at different depths. The results illustrated that among the various independent variables, the average daily temperature at 2m-elevation had the highest correlation with the soil temperature at different depths. The correlation coefficient for different station was 0.70 - 0.97. Finally, it can be concluded that the regression method is an acceptable method for estimation of soil temperature at different depths, especially at shallower depths. So that the RMSE values range from 1.7 to 4.9 ° C and the determination coefficient values range from 0.62 to 0.96. | ||
کلیدواژهها [English] | ||
climatic variables, soil temperature, univariable regeression | ||
مراجع | ||
Alipour, H. and Kazemzadeh, M. (2019). Pattern Changes Analysis of Soil Temperature in different depths under the influence of humidity and air temperature (Case Study: Taleghan Watershed). Iran Wathershed Management Science and Engineering. 13 (46), 1-10. Amir Morady, K. and Bahmani, O. (2014). Estimation of Daily Temperature of Soil Using Neural and Artificial Neural Networks. Soil Research Journal, 28 (3), 544-55. Asadi Oskouei, E., Mousavi Baygi, M., Yazdany, M., Alizadeh, A. and Zohd Ghodsi, M. (2017). The effect of submergence depth on water and soil temperature in paddy field (Case study: Rasht). Journal of Agricultural Meteorology, 5(1), 48-56. Baaghideh, M., Entezari, A. and Kordi, A. (2018). Investigation of the Relationship between Soil Temperature and Climate Parameters in the Northwest of Iran (1992-2015). Journal of Geography and Regional Development, 16(1), 279-307. Bahmani, F., Pirisahragard, H., and Piri, J. (2019). Application of artificial intelligence methaods to estimate soil daily temperature in arid and semi-arid climates. Iranian Journal of Range and Desert Research, 26(1), 201-213. Behaegel, M. Sailhac, P. and Marquis, G. (2007). On the use of surface and ground temperature data to recover soil water content information. J. Appl. Geophys., 62, 234–243. Behar, O., Khellaf, A. and Mohammedi, K. (2015). Comparison of solar radiation models and their validation under Algerian climate–the case of direct irradiance. Energy Convers. Manage. 98, 236-251. Bhargava, A. (1989). Missing Observations and the Use of the Durbin-Watson Statistic, Biometrik, 76, 828-831. Chow, T. T., Long, H., Mok, H. Y., and Li, K. W. (2011). Estimation of soil temperature profile in Hong Kong from climatic variables. Energy and Buildings, 43(12), 3568-3575. Ghaeminia, A. M. and Azimzadeh, H. R. (2013). Evaluation of Linear and Quadratic Models for Estimating Soil Surface Temperature Using Air Temperature in Four Climate Zones of Iran. Iranian Journal of Soil Research, 27(2), 253-262. Ghaeminia, A.M., Azimzadeh, H.R. & Mobin, M.H. (2011). Simulating temperature variations of soil different depths and study of some effective atmospheric parameters (case study-Yazd synoptic station). Iranian journal of Range and Desert Reseach, 18 (1), 42-57. Ghahreman, N., Irannejad, P & Norooz Valashedi, R. (2014). Comparison of Performance of Two Simulation and Regression Models for an Estimation of Soil Temperature under Grass Cover in Karaj Climatic Conditions. Iranian Journal of Soil and Water Research, 45 (3), 243-253. Helali, J., & Rasouli, M. (2016). Protection of crop against frost and chilling. Press organization Jahade Daneshgahi. Tehran, Iran, 291p. Helali, J., Momenzadeh, H., Oskouei, E. A., Lotfi, M., & Hosseini, S. A. (2021). Trend and ENSO-based analysis of last spring frost and chilling in Iran. Meteorology and Atmospheric Physics, 133(4), 1203-1221. Hemati, S., Nasiri, B., and Karampoor, M. (2020). Determination of Soil Temperature Change Trend in Different Climates of Kermanshah Province. Iranian Journal of Soil and Water Research, 51(10), 2641-2650. Jafari Golestan, M., Raeini sarjaz, M. and Zia tabar ahmadi, M. (2008). Estimation of soil depth temperature using analysis method and curves and regression correlations for Sari city. Journal of Agricultural Sciences and Natural Resources, 5, 112-123. Karampoor, M. & Yarmoradi, Z. (2015). Investigation of the trend of soil temperature changes in Khoramabad station. Journal of Environmental Science and Engineering, 2, 13-23. Keryn, I. P. Polglase, P.J. and Smethurst. P. J. (2004). Soil temperature under forests: a simple model for predicting soil temperature under a range of forest types. Agricultural and Forest Meteorology, 121, 167–182. Khatar, B. and Bahmani, O. (2015). Predicted Temperature of Deep Soil Layers Using Time Series Models. Iranian Journal of Soil Research, 29(2), 199-210. Khoshhal Jahromi, F., Sabziparvar, A. A., & Mahmoudvand, R. (2021). Spectral analysis of soil temperature and their coincidence with air temperature in Iran. Environmental Monitoring and Assessment, 193(2), 1-14. Khoshkhoo, Y. (2019). Simulating temperature of different depths of soil in hourly and daily scales using a SVAT model. Journal of Water and Soil Conservation, 25(6), 223-237. Ma, C. and Iqbal, M. (1984). Statistical comparison of solar radiation correlations monthly average global and diffuse radiation on horizontal surfaces. Solar Energy, 33, 143-148. Maclean Jr S. F. and Ayres M. P. (1985). Estimation of soil temperature from climatic variables at Barrow, Alaska, USA. Arctic and Alpine Research, 17, 425-432. Mesbahzadeh, T., azareh, A., Rafiiei sardooi, E. and FarzanePei, F. (2018). Modeling of daily soil temperature using synoptic data and neural network. Journal of Range and Watershed Managment, 71(1), 285-295. Mojarrad, F. and Sadeghi, H. (2013). Assessing the Relationship between Ground and Soil Temperature at Different Depths: A Case Study of Kermanshah Province. Physical Geography Research Quarterly, 45(1), 101-118. Mousavi Baygi, M., AsadiOskouei, E., Yazdany, M. & Alizadeh, A. (2017). The comparison of temperature elements measured in station and in paddy filed. Journal of Water and Soil Conservation, 24(5), 129-145. Najafimod, M. Alizadeh, A. and Mosavi, J. (2008). Investigation of the relationship between air temperature and temperature of different soil depths and estimation of ice depth. Journal of Water and Soil, 22 (2), 456-465. Parsafar, N and Marofi, S. (2011). Estimation of different soil depths from air temperature using regression relations, neural network and neural network. Journal of Soil and Water Science, 21, 139-152. Plauborg, F. (2002). Simple model for 10 cm soil temperature in different soils with short grass. Eur. J. Agron., 17, 173–179. Popiel, C. O., Wojtkowiak, J. and Biernacka, B. (2001). Measurements of temperature distribution in ground. Experimental thermal and fluid science, 25(5), 301-309. Sabziparvar, A. Tabari, H and Aeeni, A. (2010). Estimation of daily average soil temperature in some climatic samples of Iran using meteorological data. Journal of Agricultural Science and Technology and Natural Resources, 52, 125-137. Sabziparvar, A.A., Siroos, N. and Bayat, H. (2014). Effect of using time-lag between maximum screen temperature and soil temperature in improving annual soil regression equation. J. of Water and Soil Conservation, 21(3), 31-53. Sabziparvar, A., & Khoshhal Jahromi, F. (2022). Evaluating the most effective climatic parameters affecting the monthly mean soil temperature estimates using the PLS method. Arabian Journal of Geosciences, 15(11), 1-10. Seefeldt, S. S., Kidwell, K. K. and Waller, J. E. (2002). Base growth temperature, germination rate and growth response of contemporary spring wheat cultivars from the USA Pacific North West. Field Crop Research, 75, 47-52. Sommers, L.E., Gilmour, C.M., Wildung, R.E., and Beck, S.M. (1981). The effect of water potential on decomposition processes in soils, in Water Potential Relations in Soil Microbiology. Edited by J.E. Parr, W.R. Gardner and W.R. Elliot, SSSA Spec. Publ., 9, 97-117. Willmott, C.J. and Matsuura, K. (2006). On the use of dimensioned measures of error to evaluate the performance of spatial interpolators. Int. J. Geogr. Inf. Sci., 20, 89-102. Yazdani, V., Ghahreman, B., Farahi, G. and Nori, H. (2011). Modeling soil depth temperature by using meteorological parameters. J. of Water and Soil Conservation, 19(4), 1-23. Zheng, D., Hunt Jr, E. R. and Running, S. W. (1993). A daily soil temperature model based on air temperature and precipitation for continental applications. Climate Research, 2(3), 183-191. | ||
آمار تعداد مشاهده مقاله: 247 تعداد دریافت فایل اصل مقاله: 195 |