پیش‌آگاهی سرمازدگی بهاره باغات سیب با استفاده از مدل wrf

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.

Spring Frost Early Warning for Apple Orchards Using WRF Model

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 MartínezLüscher (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 48hour 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 72hour 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 MethodsIn order to achieve the aim of current study, 72hour 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 20162107. 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 3h 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 20072016 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 DiscussionExamining 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 72hour simulation. As a result WRF model can be applied in 72hour temperature simulation in the area of study. Another finding of this research indicated that in comparison to the fieldrecorded 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 24hour and then 48hour simulations than for 72hour simulation, but the 72hour simulation accuracy is not much different from 24hour and 48hour 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 72hour 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 72hour 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.