شبیه‌سازی رطوبت خاک برای دوره آتی به‌کمک مدل swapبا استفاده از مدل‌ها و سناریوهای گزارش پنجم تغییر اقلیم (مطالعه موردی: مزرعه گندم (triticum aestivum l.) فاروب نیشابور)

افزایش گازهای گلخانه‌ای در دوره‌های آتی و در نتیجه، تشدید تغییرات پارامترهای اقلیمی، می‌تواند تاثیرات منفی زیادی را بر سیستم‌های مختلف از جمله منابع آب، محیط زیست، صنعت، بهداشت، کشاورزی و کلیه بخش‌های مرتبط با سیستم اقلیم بگذارد. لذا در این تحقیق برای شبیه‌سازی رطوبت ظرفیت زراعی خاک در دوره آتی (2039-2020) نسبت به دوره پایه (2011-1992) از مزرعه گندم (triticum aestivum l.) فاروب واقع در دشت نیشابور استفاده شد. داده‌های اقلیمی به کمک شش مدل gcm و دو سناریو انتشار 4.5 و 8.5 برآورد گردید و به کمک مدل larswg ریزمقیاس شد و وارد مدل swap گردید. همچنین به‌منظور ارزیابی و دقت مدل swap از معیارهای آماری rmse، mae و r2 استفاده شد. نتایج تغییرات پارامترهای اقلیمی نشان داد، دمای حداقل، حداکثر و بارش در دوره آتی نسبت به دوره پایه افزایش یافته است و سناریو 8.5 نسبت به سناریو 4.5، دمای بیشتر و بارش کمتری را برآورد کرده است. همچنین رطوبت خاک در دوره آتی نسبت به دوره پایه برای هر دو سناریو کاهش یافته است. بررسی دامنه تغییرات مدل‌های gcm نشان داد، برای رطوبت هفتگی تحت هر دو سناریو، مدل  canesm2و برای رطوبت سالیانه تحت سناریوهای 4.5 و 8.5، به‌ترتیب مدل‌های‌ miroc و‌ ipsl دارای بیشترین قطعیت نسبت به سایر مدل‌ها می‌باشند. از طرفی، بررسی دامنه تغییرات سناریوهای انتشار نشان داد، رطوبت هفتگی و سالیانه، به‌ترتیب از کمترین و بیشترین قطعیت نسبت به دوره پایه برخوردار هستند.

Simulation of Soil Moisture for the Upcoming Period by SWAP Model using Climate Change Models and Fifth Report Scenarios (Case Study: Wheat (Triticum aestivum L.) Field in Faroub of Neyshabour)

IntroductionClimate change is one of the major challenges which society will face during current century. Temperatures are projected to increase up to 2 ºC by 2100, which is expected to result in major changes in the atmosphere’s energy balance and the hydrological cycle. The term soil moisture generally refers to temporary storage of precipitation at a depth of 1 to 2 meters from the soil profile). Soil moisture in the root zone well known as a very important factors that severely effects on crop productivity. Hence, the study of soil moisture changes is crucial for the planning and managing water resources in the coming periods. Accordingly, this study was conducted in order to predict and stimulate soil moisture in upcoming period (20202039) according to baseline (19922011).Materials and MethodsThe study area in this research was Neyshabour plain located in Khorasan Razavi province. At first, SWAP model implemented using meteorological and agronomic data from study area, and soil moisture of field capacity is simulated for 30 cm soil depth. Then, to ensure the results of moisture simulation, the moisture content of the model is calibrated and validated using the measured soil moisture content of the 20082009. After ensuring the accuracy of the model results, soil moisture is simulated for the baseline (19922011). Then, to estimate moisture content in the upcoming period (20202039) six models (CanESM2, GFDL, MIROC, IPSL, CSIROMK3.6, and GISSESR) and two emotion scenarios (RCP4.5 and 8.5) were used, as well as the ratio of the weekly and yearly values of the meteorological parameters of the upcoming period, which estimated using the baseline. In addition, to evaluate SWAP model accuracy, Root MeanSquared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2) were used. Data obtained by Neyshabur Synoptic Station (19922011) were utilized in order to determine daily weather patterns and future weather parameters. Temperature and precipitation parameters determined by the LARSWG model.Results and DiscussionIn order to use the SWAP simulated moisture, initially the model calibrated and validated with measured soil moisture data from 20082009 growth season. So, after soil moisture simulation for 20082009, the measured data by TDR used for calibration and validation model. The coefficient of determination factor (R2) between simulated and measured results was 79.5%. The climate parameters used in this study include minimum and maximum temperatures and precipitation, which are known as the most important factors affecting the soil moisture. According to table 6, the minimum and maximum temperatures and precipitation for the upcoming period will increase compared to the baseline. On the other hand, According to table 9 and table 10, under two scenarios, the means, maximum and minimum soil moisture decrease compared to baseline. The weekly and yearly uncertainty in soil moisture under two scenarios showed in figures 4 and 5, respectively. Under the 4.5 scenario, the MIROC model due to the lower band thickness and the IPSL model, due to the higher bandwidth thickness, have the highest and lowest accuracy, respectively. Whereas, under the 8.5 scenario, the IPSL model has the highest accuracy and the CanESM2 and GISSESR models have the lowest accuracy compared to other models. Results of weekly and yearly uncertainty showed less and more uncertainty for weekly and yearly soil moisture, respectively.ConclusionsIn general, results of this study revealed that minimum, maximum temperature and precipitation will increase in upcoming period compared to baseline. Soil moisture decrease in upcoming period compared to baseline under two scenarios (4.5 and 8.5). Moreover, temperature was higher and precipitation was lower under 8.5 scenario rather than 4.5 scenario. Therefore, 8.5 scenario showed worse condition compared to 4.5 scenario. Results of this study showed lower soil moisture in 1, 3, 6, 13, 18, and 19 years in upcoming period under two scenarios. Therefore, these years known as warning condition compared to baseline.