نوع مقاله : مقاله پژوهشی

نویسندگان

گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه بوعلی سینا، همدان، ایران

چکیده

سابقه و هدف:
تبخیر – تعرق، یک فرایند کلیدی تعادل آب و همچنین یک عنصر مهم از تعادل انرژی است. بنابراین پیش بینی و تخمین تبخیر-تعرق در مدیریت آب زراعی، پیش بینی و نظارت بر خشکسالی و توسعه و بهره برداری از منبع­ های آبیِ موثر، می ­تواند بسیار با ارزش و کاربردی باشد. هدف از این مطالعه مدل سازی سری زمانی تبخیر-تعرق مرجع در ایستگاه سینوپتیک رشت توسط دو مدل SARIMA و GRNN در دوره 1956-2017، و پیش ­بینی آن برای سال ­های 2018-2027 می ­باشد.
مواد و روش­ ها:
شهر رشت در منطقه معتدل و مرطوب شمال ایران و در نوار جنوبی دریای خزر واقع است. در این مطالعه از روش تورنت‌وایت اصلاح شده برای برآورد ET0 استفاده شده است که پیشتر به نقل از محققان، در برآورد نرخ تبخیر-تعرق مرجعِ منطقه‌ی رشت عملکرد مطلوبی بیان کرده ست. میزان تبخیر-تعرق در سال­ های 1956-2017 برآورد شد. دو مدل برای مدل سازی و اعتبارسنجی سری‌‌زمانی ET0 انتخاب گردید. مدل SARIMA از مدل­ های استوکستیک فصلی و مدل GRNN بر پایه‌ی هوش مصنوعی استوار است. ورودی‌های مدل‌ها تا 3 گام زمانی قبل ماهانه و سالانه‌ انتخاب شدند. ماتریس­ های ورودی-هدف، به دو بخش واسنجی (75%) و اعنبارسنجی (25%) تقسیم شدند. تابع ACF نشان دهنده وجود روند فصلی در سری ماهانه ET0، با دوره‌بازگشت 12 بود. با چهار مرتبه تفاضل گیری مشخص شد که بهترین درجه تفاضل گیری مدل SARIMA در مرتبه اول می­ باشد. سایر عملگرهای SARIMA نیز، اعم از اتورگرسیو و میانگین متحرک فصلی و غیر فصلی، توسط سعی و خطا انتخاب شدند. بهینه‌سازی مدل GRNN نیز توسط سعی و خطای پارامتر گستره انجام شد.در این مطالعه معیارهایی همچون RMSE، NS و R برای بررسی خطا و همبستگی خروجی­های مدل استفاده شد.
نتایج و بحث:
بهترین مدل از الگوی SARIMA، مدل SARIMA(0,0,1)(0,1,1)12 معرفی شد. میزان RMSE و NS برای این مدل به‌ترتیب برابر با 8.89 میلی متر و 0.97 بود. مدل GRNN با اعمال کل ورودی ­ها بهترین نتیجه را نشان داد. مقادیر RMSE و NS  در بهترین خروجی GRNN برابر با 9.22 میلی متر و 0.96 محاسبه شد. تفاوت دو مدل در برآورد کمینه‌ها (ماه‌های ژانویه و فوریه) گزارش شد که بنا برآن SARIMA عملکرد بهتری داشت. برای مقایسه‌ این دو مدل از دیاگرام تیلور نیز استفاده شد. دیاگرام تیلور نشان داد دقت SARIMA نه‌تنها در میزان خطا، بلکه در همبستگی و برآورد انحراف معیار مقادیر واقعی، کمی دقیق تر از GRNN عمل نموده است. پس از صحت سنجیِ مدل­ ها و ارزیابیِ عملکرد مطلوبِ آن­ها، بهترین مدل‌های مستخرج از SARIMA و GRNN، بجهت پیش‌بینی نرخ تبخیر-تعرق مرجعِ 10سال آتی برای (سال ­های 2018-2027) استفاده شدند.
نتیجه‌گیری:
نتایج پیش‌بینی‌های بیان شده برای سال­ های آینده‌ی رشت، وجود روند صعودیِ شدید ET0 در سال­های 2018- 2027 را )نسبت به دوره‌ی 1956-2017( نشان داده‌است. این موضوع افزایش سریع‌ترِ نرخ تبخیر-تعرق مرجع را در سال‌های آتی، برای منطقه مرطوب رشت هشدار می‌دهد. به جهت برنامه‌ریزی منابع آب سطحی و زیرزمینی برای استفاده­ های کشاورزی و زراعی، این مساله بسیار دارای اهمیت بوده و هشداری بسیار جدی برای کشاورزان و مدیران آب در این منطقه خواهد بود.

کلیدواژه‌ها

عنوان مقاله [English]

Long-term forecast of monthly reference evapotranspiration of the period 2018-2027 using SARIMA and GRNN models (case study: Rasht synoptic station)

نویسندگان [English]

  • Pouya Aghelpoor
  • Vahid Varshavian
  • Mehraneh Khodamoradpoor

Department of Water Science and Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran

چکیده [English]

Introduction:
Evapotranspiration is a key principle of water balance and an important element of energy balance. Therefore, forecasting and estimation of evapotranspiration in agricultural water management, forecasting and monitoring drought, and the development and exploitation of effective water resources might be valuable and practical. The purpose of this study was to model the Reference Evapotranspiration time series (ET0) at Rasht Synoptic Station with two SARIMA and GRNN models during 1956-2017 and its forecast for 2018-2027.
Material and methods:
Rasht is located in the temperate and humid parts of northern Iran and in the southern strip of the Caspian Sea. In this study, the Adjusted ThornthWaite method (ATW) was used to estimate ET0, the credibility of which was previously confirmed by researchers for estimation of reference evapotranspiration rate in Rasht. Evapotranspiration values were estimated for the time period of 1956-2017. Two models were selected for modeling and validation of the ET0 series. The SARIMA model is based on seasonal stochastic models, and the GRNN model is based on artificial intelligence. The models’ inputs were selected on the basis of three previous monthly and yearly. The target-input matrices were divided into calibration (75%) and validation (25%) sections. Autocorrelation Function (ACF) indicated a seasonal trend in the ET0 monthly series, with a return period of 12. Four times seasonal differentiation, revealed that the best degree of SARIMA’s seasonal integrated degree was the first-order. Other SARIMA operators, including seasonal and non-seasonal autoregressive, and average seasonal and non-seasonal moving, were selected by trial and error. Optimization of the GRNN model was accomplished by trying and error of the spread parameter. In this study, criteria such as RMSE, NS, and R were used to check the error and correlate the outputs of the model.
Results and discussion:
The best model of SARIMA pattern was SARIMA (0, 0, 1) (0, 1, 1)12 which has RMSE and NS values of 8.89 mm and 0.97, respectively. The GRNN model had its best performance by applying the total inputs. The RMSE and NS values were 9.22 and 0.96, respectively, for GRNN’s best output. The difference between the two models was reported in predicting the year’s minima (January-February), which showed SARIMA’s better performance. To compare these two models, the Taylor diagram was also used, which showed that the accuracy of SARIMA not only in error but also in the correlation and estimation of the true deviation of the real values was slightly more accurate than GRNN. After evaluating the models and assessing their acceptable performances, best extracted models from both SARIMA & GRNN were used for ET0’s long-term forecasting up to the next ten years (for the period of 2018-2027).
Conclusion:
The results of the forecasts for Rasht’s future showed a sharp ascending trend in the rate of evapotranspiration in the years 2018-2027 (compared to the period of 1956-2017). This is a warning of a rapid increase in the evapotranspiration rate in the years ahead, in the wet area of Rasht. This issue is very important for the surface water and groundwater resources planning, agricultural uses, and will be a serious warning to farmers and water managers in this area.

کلیدواژه‌ها [English]

  • Adjusted ThornthWaite
  • GRNN
  • Long-term forecast
  • SARIMA
  • Taylor diagram

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