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پیش‌بینی تغییرات کوتاه مدت و میان مدت شاخص فرسایندگی باران با روش میانگین متحرک خود همبسته‌ی فصلی (SARIMA)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار گروه خاکشناسی دانشکده کشاورزی دانشگاه شهید چمران اهواز، اهواز، ایران

2 مربی گروه خاکشناسی، دانشکده کشاورزی، دانشگاه شهید چمران اهواز، اهواز، ایران

چکیده

اقلیم یکی از مهمترین عوامل تاثیرگذار بر فرایندهای تشکیل، تکامل و تخریب خاک است که با توجه به تاثیر آن بر رخدادهای فرسایشی، پایش دائمی آن اهمیت دارد. در این پژوهش به منظور ارزیابی تغییرات اقلیمی در قالب سری‌های زمانی چهار ایستگاه هواشناسی شامل اردل، سامان، ایذه و دهدز به‌عنوان ایستگاه‌های مطالعاتی منتخب مورد بررسی قرار گرفت. با بررسی آماری داده‌های بارش در ایستگاه‌های منتخب، محاسبه‌ی شاخص فرسایندگی باران از 1990 تا سال 2017 انجام شد. پس از تحلیل روند داده‌ها و بررسی ایستایی داده‌های بارش، گراف‌های Auto-correlation function (ACF) و Partial auto-correlation function (PACF) ترسیم شد، سپس آزمون دیکی‌فولر (ADF) در سطوح اطمینان 1، 5 و 10 درصد صورت پذیرفت. در مرحله‌ی بعد، عملیات ایستا نمودن داده‌های ناایستا و یافتن پارامترهای مناسب p، r و q انجام شده و مدل Seasonal auto-regressive integrated moving average (SARIMA) تهیه و ایجاد شد. ارزیابی‌های آماری توسط نرم‌افزارهای StataSE، Minitab 18 و SPSS 19 صورت گرفت. نتایج نشان داد که جهت آشکارسازی روند تغییرات بارش، مدل ARIMA (0,0,1)×(1,1,1)12 دارای بیشترین نیکویی برازش است. همچنین روش میانگین متحرک خودهمبسته‌ی فصلی به‌خوبی تغییرات بارش را در منطقه‌ی مطالعاتی واقع در جنوب غرب ایران نشان داد. نتایج حاصل از مدل برازش شده حاکی از احتمال کاهش در میزان بارش سالیانه طی دوره‌های زمانی 5 و 10 ساله پس از سال 2017 می-باشد. با توجه به مقادیر بارش ماهیانه شبیه‌سازی شده، مقدار شاخص فرسایندگی در منطقه به دست آمد که کاهش ضریب فرسایندگی باران طی دوره های تا 10 سال آینده را بیان می‌نماید.

کلیدواژه‌ها

عنوان مقاله [English]

Forecasting of short-term and mid-term variations of rainfall erosivity index using SARIMA

نویسندگان [English]

  • Ataallah Khademalrasoul 1
  • Hadi Amerikhah 2

1 Assistant Professor of Soil Science Department, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran

2 Scientific member of Soil Science Department, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran

چکیده [English]

Introduction Climate is one of the most effective factors on soil formation, evolution and degradation. It is include different parameters which mainly based on precipitation and temperature. In the recent years the effects of global warming and climate change has extremely enhanced. Climate change as an important phenomenon is effective on precipitation parameters including volume, intensity and concentration which categorized in the temporal and spatial variations. Quantifying the effects of climate change is important for identifying critical regions prone to soil erosion under a changing environment. Land-based ecosystems are influenced by patterns of air temperature and precipitation, which include daily and seasonal changes along with humidity and wind, and the nature of the land surface. Global climate change already has observable effects on the environment. Regarding the importance and effectiveness of climate factor and climate changes during the time, it is essential to focus on climate changes on water behavior at different scales. Indeed, precipitation parameters interacting the soil parameters are influencing on runoff potential in the fields and watersheds. In this regard Rainfall-runoff erosivity (R) is one key climate factor that controls water erosion. Universal soil loss equation (USLE) is the main common equation to predict soil loss, this equation consisting 5 factors which R-Factor (Rainfall erosivity factor) is one of the effective factors in this equation.
Material and Methods Regarding the effect of climate on soil erosion processes therefore, monitoring of climate is really important. In this study in order to evaluate the climate changes based on time series, four climatological stations including, Ardal, Saman, Izeh, and Dehdez were selected. Using the statistical data of precipitation, calculation of eroding index was performed until 2017. The ACF (Auto Correlation Function) and PACF (Partial Auto Correlation Function) for precipitation data were prepared, afterwards the ADF test was performed at confidence level of 1, 5 and 10 percentage. Then the suitable parameters for p, r and q were selected and the SARIMA (Seasonal auto-regressive integrated moving average) model was provided. The statistical analyses were performed with Stata SE, Minitab 18 and SPSS 19. Moreover, the graphical trends of rainfall as an index of precipitation and the rainfall erosivity factor (R-Factor) were presented. Also, the spatial distribution of R-Factor (in the form of GIS-Maps) were provided including three separated maps based on real data, 5 year predicted and 10 year predicted data. So there was a possibility to monitor and compare the spatial distribution of R-Factor at different time periods. Then based on the area, the percentage of rainfall erosivity index was calculated for the study area based on the real data, 5 year predicted and 10 year predicted data. In addition, the statistical parameters including R-square, RMSE, P-value and so on were calculated for the best model (SAR12) regarding all climatological stations.
Results and discussion Our results depicted that to present the trend of precipitation variations as erosive factor the ARIMA (0,0,1)×(1,1,1)12 was the best model. Also, the seasonal autoregressive moving average showed the variation of precipitation in the study area which located in the southwest of Iran. The results of modeling stated that reduction of precipitation for 5 and 10 year periods after 2017. According to amount of monthly simulated of precipitation, the amount of erodibility index was obtained in the area which illustrated the declining trend until 10 year. According to ADF test for all evaluated climatological stations the probability for Ardal was 0.34, for Dehdez was 0.425, for Saman was 0.345 and for Izeh was 0.177, therefore there was difference between climatological stations. Furthermore, the statistical analyses for SAR12 model revealed that the R-square for Ardal station was 0.492, for Dehdez was 0.716, for Saman was 0651 and for Izeh was 0.576. Moreover, approximately 37 % of area has very low rate of erodibility index without previous occurrence.
Conclusion Our results clearly confirmed the importance of climate factors and climate change during the time. As results illustrated regarding the variations of precipitation the R-Factor changed. Moreover, climate change is effective on spatial variations of crop cover in the watersheds. Climate change is capable to alter the crop cover patterns in the watersheds and the changes in crop cover distribution and runoff could change the soil erosion potential. Generally, based on results has to focus on water resources conservation in the study area to preserve soil and water against erosive forces and try to improve the vegetation cover because of decreasing of precipitation. In order to manage the soil resources, we need to monitor the climate changes in the watersheds and try to enhance the vegetation covers in the critical parts on the fields.

کلیدواژه‌ها [English]

  • Rainfall erosivity index
  • SARIMA model
  • precipitation forecasting
  • climate changes
  • Dickey- Fuller test
مراجع
  1. Asakereh, H. (2008). Modeling of ARIMA for the average annual of temperature of Tabriz. Geography Researches. 756, 15601-15622.
  2. Bertoni, J., Lombardi Neto, F. (1990). Conservação do solo. ícone. São Paulo. Brazil 355 pp.
  3. Box, G.P.E.; Jenkis, G.M. (1976). Time Series Analysis: Forecasting and Control, 2nd ed.; San Francisco Press: San Francisco, CA, USA.
  4. Da Silva, A. M. (2004). Rainfall erosivity map for Brazil. Catena, 57(3), 251-259.
  5. Druce, D.J. (2001). Insights from a history of seasonal inflow forecasting with a conceptual hydrological model. J. Hydrol. 2001, 249, 102–112.
  6. Forman, S.L.; Pierson, J.; Lepper, K. (2000). Luminescence geochronology. In Quaternary Geochronology: Methods and Applications; Sowers, J.M., Noller, J.S., Lettis,W.R., Eds.; American Geophysical Union Reference Shelf: Washington, DC, USA, 2000; Volume 4, pp. 157–176.
  7. Fournier, F. (1956). The effect of climatic factors on soil erosion estimates of solids transported in suspension in runoff. Association Hydrologic Int. Public, 38.
  8. Houghton JT . (1995). Climate change: the science of climate change. Cambridge University Press. New York, 357-383.
  9. Irvine, K.N.; Eberhardt, A.J. (1992). Multiplicative, season ARIMA models for Lake Erie and Lake Ontario water levels. Water Resources. Bull. 1992, 28, 385–396.
  10. Kinnell PIA. (2010). Event soil loss, runoff and the universal soil loss equation family of models: A review. Journal of Hydrology. 2010;385(1):384-397.
  11. Lee, J. H., Heo, J. H. (2011). Evaluation of estimation methods for rainfall erosivity based on annual precipitation in Korea. Journal of Hydrology, 409(1), 30-48.
  12. Madani, K., A. AghaKouchak and A. Mirchi, (2016). “Iran’s Socio-economic Drought: Challenges of a Water Bankrupt Nation,” Iranian Studies 49, 997-1016.
  13. Masoud Masoudi, M., Elhaeesahr, M. (2016). Trend assessment of climate changes in Khuzestan Province, Iran Natural Environment Change, Vol. 2, No. 2, Summer & Autumn 2016, pp. 143- 152.
  14. Nearing MA, Yin S, Borrelli P, Polyakov VO. (2017). Rainfall erosivity: An historical review. Catena. 2017;157: 356-362.
  15. Oduro-Afriyie, K. (1996). Rainfall erosivity map for Ghana. Geoderma, 74(1), 161-166.
  16. Partal, T.; Cigizoglu, H.K. (2008). Estimation and forecasting of daily suspended sediment data using wavelet-neural networks. J. Hydrol. 2008, 358, 317–331.
  17. Renard, K. G., & Freimund, J. R., (1994). Using monthly precipitation data to estimate the R-factor in the revised USLE. Journal of Hydrology, 157(1-4), 287-306.
  18. Sharifan, H., Ghahramn, B. (2005). Evaluation of rainfall forecasting using SARIMA technique in Golestan Province. Journal of Agriculture Science and Natural Resources. 3 (14).
  19. Shentzis, I.D. (1990). Mathematical models for long-term prediction of mountainous river runoff methods, information and results. Hydrol. Sci. J. 1990, 35, 487–500.

 

 

  1. Sivakumar, B. (2006). Suspended sediments load estimation and the problem of inadequate data samplings: A fractal view. Earth Surface. Process. Landf. 2006, 31, 414–427.
  2. Sivakumar, B.; Berndtsson, R.; Persson, M. (2001). Monthly runoff prediction using phase space reconstruction. Hydrol. Sci. J. 2001, 46, 377–387.
  3. Smith, B., Ludlow, L., Brklacich, M. (1988). Implication of a global climatic warming for agriculture: A review and appraisal. J Environ Qual, 17:518-527.
  4. Smith, J.A. (1991). Long-range streamflow forecasting using non-parametric regression. Water Resources. Bull. 1991, 27,39–46.
  5. TezhAb CE, (2005). Ministry of agriculture-jahad, Multifunctional Forestry Executive Design Studies,part1, Dehdez section,2005.(In persian)
  6. Torabi, S. (2000). Forecasting of temperature and precipitation variations in Iran. PhD thesis of Natural Geography, Tabriz University. 201pp.
  7. Wischmeier, W. H., Smith, D. D. (1978). Predicting rainfall erosion losses-A guide to conservation planning. Predicting rainfall erosion losses-A guide to conservation planning.
مهندسی زراعی
دوره 45، شماره 1
خرداد 1401
صفحه 79-95
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