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نوع مقاله : مقالات پژوهشی

نویسندگان

1 دانشجوی دکتری آب و هواشناسی کشاورزی، گروه جغرافیای طبیعی، دانشکده علوم جغرافیایی و برنامه ریزی، دانشگاه اصفهان، اصفهان، ایران

2 دانشیار اقلیم شناسی گروه جغرافیای طبیعی، دانشکده علوم جغرافیایی و برنامه ریزی، دانشگاه اصفهان، اصفهان، ایران

3 دانشیار اقلیم شناسی، گروه جغرافیای طبیعی، دانشکده علوم جغرافیایی و برنامه ریزی، دانشگاه اصفهان، اصفهان، ایران

4 استادیار هواشناسی، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه شهرکرد، شهرکرد، ایران

5 استادیار سنجش از دور، گروه مهندسی نقشه برداری، دانشکده مهندسی عمران و حمل و نقل، دانشگاه اصفهان، اصفهان، ایران

چکیده

سرمازدگی محصولات کشاورزی در فصل بهار، همه‌ساله زیان‌های سنگین مالی را به بخش کشاورزی به‌ویژه در باغات شمال غرب ایران وارد می‌کند. هدف این مقاله، ارزیابی سیستمی برای پیش‌آگاهی سرمازدگی با استفاده از شبیه‌سازی دمای حداقل 72 ساعته به‌وسیله مدل WRF و تشخیص مراحل فنولوژی سیب از تصاویر لندست است تا با شناخت مراحل فنولوژی محصول و دمای بحرانی در آن مرحله چنانچه دمای حداقل در 72 ساعت آینده به دمای بحرانی برسد پیش‌آگاهی سرمازدگی صورت گیرد. داده‌های دمای 2 متری خروجی مدل WRF برای شبکه محاسباتی داخلی، در 51 ایستگاه سینوپتیک با دمای حداقل مشاهداتی در ایستگاه‌ها مقایسه شد. مقادیر شاخص NDVI نیز با استفاده از تصاویر لندست 7 و 8 سنجنده‌های ETM+ و OLI در سال‌های 2016-2007 برای باغ سیب واقع در ایستگاه تحقیقات هواشناسی کشاورزی کهریز ارومیه محاسبه و با زمان مراحل فنولوژی ثبت‌شده در محل مقایسه شد. نتایج نشان داد که معنی‌داری همبستگی و مدل رگرسیونی بین متغیر دمای 2 متری خروجی مدل WRF و متغیر دمای حداقل مشاهداتی در مجموع کل ایستگاه‌ها برای شبیه‌سازی 72 ساعته وجود دارد. درنتیجه می‌توان از مدل WRF در شبیه‌سازی 72 ساعته دما در منطقه موردمطالعه بهره برد. یافته دیگر این تحقیق نشان داد که در مقایسه با داده‌های زمینی ثبت‌شده در منطقه، مقادیر NDVI به‌دست‌آمده از تصاویر لندست به‌خوبی گویای تغییرات مراحل فنولوژی در باغ سیب موردمطالعه است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Spring Frost Early Warning for Apple Orchards Using WRF Model

نویسندگان [English]

  • S.M. Ebnehejazi 1
  • H. Yazdanpanah 2
  • S. Movahedi 3
  • M.A. Nasr-Esfahani 4
  • M. Moradizadeh 5

1 PhD Student of Agricultural Climatology at Department of Physical Geography, Faculty of Geographical Sciences and Planning, University of Isfahan, Isfahan, Iran

2 Associate Professor of Climatology at Department of Physical Geography, Faculty of Geographical Sciences and Planning, University of Isfahan, Isfahan, Iran

3 Associate Professor of Climatology at Department of Physical Geography, Faculty of Geographical Sciences and Planning, University of Isfahan, Isfahan, Iran

4 Assistant Professor of Meteorology at Department of Irrigation, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran

5 Assistant Professor of Remote Sensing at Department of Geomatics, Faculty of Civil and Transportation Engineering, University of Isfahan, Isfahan, Isfahan, Iran

چکیده [English]

Introduction
 Agricultural products frost in spring imposes heavy financial losses to agriculture particularly in northwest of Iran’s orchards. Not only temperature is one of the most important climate parameters but also it is a very crucial element in the agricultural sector. Untimely temperature fluctuations and rise and fall which are usually unexpected will cause shock and heavy damages. Therefore taking into consideration the agricultural products frost and offering an approach would be of great importance for reducing relevant damages. In studies carried out by Omidvar and Dehghan Banadoki (2012) and Hesari et al. (2015) characteristics and different types of frosts have been considered in relation to the agricultural products. Different models were introduced to predict flowering date in different investigated regions. In more studies, in addition to determining the best model for predicting the date of occurance of flowering stage, probable date of last frost has been estimated as well. Investigating long term temperature changes is a method which applied by Martínez-Lüscher (2017) and Vitasse et al. (2018) to find out about established changes in flowering date and also changes in the last frost date. Nasr Esfahani and Yazdanpanah (2019) realized that 48-hour early warning for frost occurrence can be performed with adequate precision. Despite all studies in the field of products frost particularly during flowering date, it seems a rapid frost warning system must be established and provided to make early warning for each orchard. In this essay, since our goal is to make such early warning three days before frosting, so we have to investigate accuracy and validity of 72-hour minimum temperature simulation using WRF model. On the other hand, we must know phonological stage of each product in each orchard to inform the farmer about frost hazards based on critical temperature, therefore the second goal of this research is to detect phonological stages through Landsat 7 and Landsat 8 images.
Materials and Methods
In order to achieve the aim of current study, 72-hour minimum temperature simulation through the Weather Research and Forecasting (WRF) model was investigated and values of vegetation index were derived for a 30 meters pixel at an experimental orchard in Kahriz, West Azerbaijan Province, in 2016-2107. Computational grid for 2 meters temperature simulation using WRF model contains of three nested grid with horizontal resolution of 27, 9 and 3 kilometers. Horizontal resolution of terrain height and land use data is equal to 30 second (about 1 km). The initial and 3-h boundary conditions with 0.5º horizontal resolution from the Global Forecast System (GFS) were obtained from National Centers for Environmental Information (NCEI). Based on the previous research KFMYJ physical scheme configuration for WRF model were used in this research. Model's hindcasts at 03:00 UTC hour for each of 51 synoptic weather stations of northwest of Iran in internal computational grid were interpolated by MATLAB software with interp 3 function using linear method, then the obtained values were compared to minimum temperature observed in the stations by using MAE, MSE, RMSE and MSSS indicators. Phenological statistics, the time of beginning and end of growth stages were obtained from Iran Meteorological Organization. Besides, 77 Landsat 7 satellite images of ETM+ sensor, and 41 Landsat 8 images of OLI sensor were downloaded from United States Geological Survey website from March to September 2007-2016 with a spatial resolution of 30 meters. In this research, atmospheric and radiometric correction were performed with the FLAASH method on the metadata file in the ENVI software environment and then vegetation index was calculated using NDVI index.
Results and Discussion
Examining the evaluation indicators of the WRF model, results revealed a significant correlation and regression model between 2 meters temperature variable from WRF model output and minimum temperature variable observed in the entire stations for 72-hour simulation. As a result WRF model can be applied in 72-hour temperature simulation in the area of study. Another finding of this research indicated that in comparison to the field-recorded data, NDVI values gained from Landsat images properly indicates changes of phenology stages in the relevant apple orchard. In this study, the indicators used to evaluate the model error showed model hindcasts are more accurate for 24-hour and then 48-hour simulations than for 72-hour simulation, but the 72-hour simulation accuracy is not much different from 24-hour and 48-hour simulations. In northwestern Iran, which is a mountainous region, it is very difficult to simulate airflow in areas with complex topography, therefore the total correlation coefficient of all stations in all three simulations is in the range of 0.5, and the error rates of MAE and RMSE, respectively reaches about 2.8 and 3.8 Celsius. According to the second finding of this research, the NDVI indicator obtained from Landsat 7 and Landsat 8 satellite images can show the progress and changes in the phenological stages of apple trees.
Conclusion
 This study showed the efficiency of the WRF model for 72-hour simulation of the minimum temperature as well as the potential of Landsat 7 and Landsat 8 images in detecting apple phenological stages in the study area. Therefore, by using the WRF model for 72-hour minimum temperature simulation and recognizing the phenological stages from Landsat images, if the temperature in any orchard reaches a critical level in the next 72 hours due to the phenological stage, frost warning can be announced and then frost mitigation should be done by the farmer.

کلیدواژه‌ها [English]

  • Early warning system
  • Identification of phenological stages
  • NDVI
  • Spring frost
  • WRF model
  1. Agricultural Mechanization Development Center. 2019. Frost and hail damage reduction plan using new mechanization technologies. Ministry of Agriculture-Jahad, Agricultural Mechanization Development Center: 10 p. Available at http://www.agmdc.ir (visited 22 December 2020).
  2. Ahmadi K., ebadzadeh HR., Hatami F., Hoseinpour R., and Abdshah H. 2020. Amarnameh Keshavarzi 2019- 3: Horticultural products. Ministry of Agriculture-Jahad, Deputy of Planning and Economy, Information and Communication Technology Center, Tehran: 156 p. Available at https://www.maj.ir (visited 10 November 2020).
  3. Azadi M., Taghizadeh E., and Memarian MH. 2012. Verification of WRF precipitation forecast over IRAN country during NOV.2008-JUN.2009. Iran-Water Resources Research, 8(2): 48-59. (In Persian with English abstract)
  4. Chmielewski F., Blumel K., Henniges Y., Blanke M., Weber R., and Zoth M. 2011. Phenological models for the beginning of apple blossom in Germany. Meteorologische Zeitschrift 20(5): 487-496. https://doi.org.10.1127.0941-2948.2011.0258.
  5. Duan H., Li Y., Zhang T., PU Z., ZHAO C., and LIU Y. 2018. Evaluation of the forecast accuracy of near-surface temperature and wind in northwest China based on the WRF model. Journal of Meteorological Research 32: 469–490. http://dx.doi.org.10.1007.s13351-018-7115-9.
  6. Fallah Ghalhari GA., and Ahmadi H. 2017. Trend analysis of phenological stages length and chilling requirements of apple tree (Case study: Karaj station). Journal of Agricultural Meteorology 5(1):57-70. (In Persian with English abstract)
  7. Farajzadeh M., Rahimi M., Kamali GA., and Mavrommatis T. 2010. Modelling apple tree bud burst time and frost risk in Iran. Meteorological Applications 17(1): 45-52. https://doi.org.10.1002.met.159.
  8. Fazel Dehkordi L., Azarnivand H., Zare Chahouki M., Mahmoudi Kohan F., and Khalighi Sigaroudi S. 2016. Drought Monitoring Using Vegetation Index (NDVI) (Case study: Rangelands of Ilam Province). Journal of Range and Watershed Management, 69(1) :141-154. (In Persian with English abstract). http://dx.doi.org.10.22059.jrwm.2016.61739.
  9. Ghafarian P., and Barekati SM. 2013. Verification of the weather research and forecasting model (WRF) for the heavy precipitation forecasting in the Karun basin. A case study (8-9 February 2006). Journal of Climate Research 4(15): 129-149. (In Persian with English abstract)
  10. Gholami S., Ghader S., Khaleghi Zavareh H., and Ghafarian P. 2018. Verification of WRF wind field hindcast forced by different initial and boundary conditions over the Persian Gulf: Comparison with synoptic data and QuikSCAT and ASCAT satellites data. Journal of the Earth and Space Physics, 44(1): 227-243. (In Persian with English abstract)
  11. Graczyk D., and Szwed M. 2020. Changes in the occurrence of late spring frost in Poland. Agronomy 10(11): 2-14. https://doi.org.10.3390.agronomy10111835.
  12. Hesari B., Rezaee R., Nikanfar R., and Tayefe Neskili N. 2015. Study and preparation of frost maps for field and orchard crops in West Azerbaijan. Journal of Geography and Environmental Hazards 4(14): 117-135. (In Persian with English abstract)
  13. Hiratsuka Y., and Zalasky H. 1993. Frost and other climate-related damage of forest trees in the prairie provinces. Forestry Canada, Northwest Region, Northern Forestry Centre, Edmonton, Alberta. Information Report NOR-X-331: 25 p. Available at https://d1ied5g1xfgpx8.cloudfront.net.pdfs.11782.pdf (visited 16 June 2019).
  14. Huete AR. 2012. Vegetation indices, remote sensing and forest monitoring. Geography Compass 6(9): 513–532.  https://doi.org.10.1111.j.1749-8198.2012.00507.x.
  15. Hur J., and Ahn JB. 2015. Seasonal prediction of regional surface air temperature and first-flowering date over South Korea. International Journal of Climatology 35: 4791–4801. https://doi.org.10.1002.joc.4323.
  16. Kamali Gh., Rahimi M., Mohammadian N., and Mahdavian A. 2007. Prediction of flowering time of Golden apple cultivar based on cumulative chilling requirments for preventing frost damage in Golmakan area of Khorasan. Journal of Humanities the University of Isfahan 1(22): 171–182. (In Persian with English abstract)
  17. Khalili A. 2014. Quantitative evaluation of spring frost risk to agricultural and horticultural crops in Iran and modeling. Journal of Agricultural Meteorology 2(1): 17-31. (In Persian with English abstract)
  18. Khare S., Drolet G., Sylvain JD., Paré MC., and Rossi S. 2019. Assessment of Spatio-Temporal Patterns of Black Spruce Bud Phenology across Quebec Based on MODIS-NDVI Time Series and Field Observations. Remote Sensing 11(23): 2745. https://doi.org.10.3390.rs11232745.
  19. Khosravi M., Habibi No Khandan M., and Esmaeli R. 2008. Zonation of late chilblain risk impacts on orchards case study: Mahvalat region. Geography and Development Iranian Journal 6(12): 145-162. (In Persian with English abstract)
  20. Liu L., Ma Y., Menenti M., Zhang X., and Ma W. 2019. Evaluation of WRF Modeling in Relation to Different Land Surface Schemes and Initial and Boundary Conditions: A Snow Event Simulation Over the Tibetan Journal of Geophysical Research: Atmospheres 124: 209-226. https://doi.org.10.1029.2018JD029208.
  21. Martínez-Lüscher J., Hadley P., Ordidge M., Xu X., and Luedeling E. 2017. Delayed chilling appears to counteract flowering advances of apricot in southern UK. Agricultural and Forest Meteorology 237–238: 209-218. https://doi.org.10.1016.j.agrformet.2017.02.017.
  22. McFeeters SK. 1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing 17(7): 1425-143. https://doi.org.10.1080.01431169608948714.
  23. Müller OV., Lovino MA., and Berbery EH. 2016. Evaluation of WRF model forecasts and their use for hydroclimate monitoring over southern south America. Weather and Forecasting 31(3): 1001-1017. https://doi.org.10.1175.WAF-D-15-0130.1.
  24. Myneni RB., Hall FG., Sellers PJ., and Marshak AL. 1995. The interpretation of spectral vegetation indexes. IEEE Transactions on Geoscience and Remote Sensing 33(2): 481-486.
  25. Nasr Esfahani M., and Yazdanpanah 2019. Prognosis of frosting occurrence almond orchards in Najafabad region. Physical Geography Research Quarterly 51(3): 497-512. (In Persian with English abstract). http://dx.doi.org.10.22059.JPHGR.2019.281346.1007380.
  26. Nasr Esfahani M., Yazdanpanah H., and Nasr Esfahani MA. 2018. Evaluation of WRF model for temperature forecast and frosting occurrence in Zayandeh Rud Basin. Physical Geography Research Quarterly 51(1): 163-182. (In Persian with English abstract). https://dx .doi.org.10.22059.JPHGR.2019.262062.1007258.
  27. Omidvar K., and Dehghan Banadoki Z. 2013. Studying and Analyzing Strong Spring Frostbite Phenomenon of Pistachio Orchards in Yazd Province. Journal Of Geography and Regional Development Reseach 10(19): 237-253. (In Persian with English abstract)
  28. Plan and Budjet Organization, Management and Planning Organization of Azerbaijan-e-Gharbi Province, Statistical Year book of West Azerbaijan Province-2018. Available at https://azgharbi.mporg.ir (visited 11 October 2020).
  29. Raper TB., Varco JJ., and Hubbard KJ. 2013. Canopy-based normalized difference vegetation index sensors for monitoring cotton nitrogen status. Agronomy Journal 105(5): 1345-1354. https://doi.org.10.2134.agronj2013.0080.
  30. Rouse JW., Haas RH., Schell JA., and Deering DW. 1974. Monitoring vegetation systems in the Great Plains with ERTS. NASA Specefic Publication 351: 309-317.
  31. Safa , Khalili A., Teshnehlab M., and Liaghat A. 2014. Artificial Neural Networks (ANNs) application to predict occurrence of phenological stages in wheat using climatic data. International Journal of Agricultural Policy and Research 2(10): 352-361. http://dx.doi.org.10.15739.IJAPR.007.
  32. Sahebjalal , and Dashtekian K. 2013. Analysis of land use-land covers changes using normalized difference vegetation index (NDVI) differencing and classification methods. African Journal of Agricultural Research 8(37): 4614-4622. https://doi.org.10.5897.AJAR11.1825.
  33. Shuai , Schaaf C., Zhang X., Strahler A., Roy D., Morisette J., Wang Z., Nightingale J., Nickeson J., Richardson AD., Xie D., Wang J, Li X., Strabala K., and Davies JE. 2013. Daily MODIS - 500 m reflectance anisotropy direct broadcast (DB) products for monitoring vegetation phenology dynamics. International Journal of Remote Sensing 34(16): 5997–6016. https://doi.org.10.1080.01431161.2013.803169.
  34. Valashedi RN., and Sabziparvar AA. 2016. Evaluation of winter chill requirement models using the observed apple tree phenology data in Kahriz (Urmia, Iran). Iranian Horticultural Science 47(3): 561-570.. (In Persian with English abstract). https://doi.org.10.22059.IJHS.2016.59818.
  35. Vitasse Y., Schneider L., Rixen C., Christen D., and Rebetez M. 2018. Increase in the risk of exposure of forest and fruit trees to spring frosts at higher elevations in Switzerland over the last four decades. Agricultural and Forest Meteorology 248: 60–69. https://doi.org.10.1016.j.agrformet.2017.09.005.
  36. Von Bennewitz Alvarez E., Cazanga-Solar R., and Carrasco-Benavides M. 2018. Studying phenological stages of cherry (Prunus avium) using field observations and satellite-derived vegetation indexes. IDESIA 36(1): 65-71. http://dx.doi.org.10.4067.S0718-34292018000100065.
  37. Yáñez-Morroni G., Gironás J., Caneo Marta., Delgado R., and Garreaud R. 2018. Using the Weather Research and Forecasting (WRF) Model for Precipitation Forecasting in an Andean Region with Complex Topography. Atmosphere 9(8): 304. https://doi.org.10.3390.atmos9080304.
  38. Yazdanpanah , Ohadi D., and Soleimanitabar M. 2010. Forecasting different phenological phases of apple using artificial neural network. Journal of Research in Agricultural Science 6(2): 97-106. (In Persian with English abstract)
  39. Yoder BJ., and Waring RH. 1994. The normalized difference vegetation index of small Douglas-Fir canopies with varying chlorophyll concentrations. Remote Sensing of Environment 49: 81-91. https://doi.org.10.1016.0034-4257(94)90061-2.
  40. Zakeri Z., Azadi M., and Sahraeiyan F. 2014. Verification of WRF forecasts for precipitation over Iran in the period Feb-May 2009. Nivar, 38(87-86): 3-10. (In Persian with English abstract)
  41. Zheng Y., Wu B., Zhang M., and Zeng H. 2016. Crop Phenology Detection Using High Spatio-Temporal Resolution Data Fused from SPOT5 and MODIS Products. Sensors, 16(12), 2099. https://doi.org.10.3390.s16122099
  42. Zoljoodi M., Ghazi Mirsaeed M., and Seifari Z. 2013. Verification of WRF model on accuracy and precision of various schemes and evaluation of precipitation forecast in Iran. Geographical Research 28(2): 187-194. (In Persian with English abstract)
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