Time Series Analysis of Soil Moisture Content in Loess Deposits of Hezarpich using ARIMA model

Subject Areas : Farm water management with the aim of improving irrigation management indicators

hasan Rezaii Moghadam ¹ , Mohsen Hosseinalizadeh ² , Vahedberdi Sheikh ³ , roya Jafari ⁴

1 - PhD. Student, Watershed Management Dept., Gorgan University of Agricultural Sciences
2 - Assistant Prof. Arid Zone Management Dept., Gorgan University of Agricultural Sciences and Natural Resources, Iran.
3 - Associate Prof. Watershed Management Dept., Gorgan University of Agricultural Sciences and Natural Resources, Iran.
4 - M.Sc., Watershed Management Dept., Gorgan University of Agricultural Sciences and Natural Resources, Iran.

Received: 2019-08-03 Accepted : 2020-11-10 Published : 2021-02-19

Keywords: Soil Moisture Content, Loess Deposits, TDR, Time series,

Abstract :

Soil moisture content (SMC) as a small part of water balance, nearly considered in all hydrological process and soil and atmosphere tradeoff. Therefor its prediction is useful in planning, designing and decision making. For this, purposeweekly SMC in 40 weeks was measured by Time Domain Reflectometry in 3 different location of wheat and rangeland in Loess deposits (West of Gorgan with 27 ha area) at 20cm intervals down to the 80cm depth. SMC in all considered depths and locations had trend for study period and the best model was selected regards to Akaike information criterion (AIC). The best prediction model in rangeland belongs to 60cm depth (R= 0.96). For all considered depths except 40cm in one location in wheat, Integrated Moving Averages (1,1) was selected as the best model. For the other location in the same land cover, the best prediction model devoted to 20cm depth (R= 0.86). Integrated Moving Averages (1,1) for all study locations had the highest priority. Considering tillage practices in crop land and following plough pan in 40cm depth, Autoregressive Integrated moving Average (1,1) selected as the best model for prediction.

References:

بداق جمالی، ج.، احمدیان، ج.، جوانمرد، س.، گلمکانی، ت. و ملکی زاده، ص. 1383. ضرورت پایش رطوبت خاک در افزایش بهره­وری آب کشاورزی. مجموعه مقالات یازدهمین همایش کمیته ملی آبیاری و زهکشی ایران. دانشگاه تهران.

محمدی، ج. و اسفندیارپور بروجنی، ع. 1387. پدومتری: آمار زمانی، انتشارات تهران، 370 صفحه.

شمس­نیا، س.ا.، پیرمرادیان، ن. و امیری، س.ن. 1388. مدل­سازی خشکسالی در استان فارس با استفاده از تحلیل سری های زمانی. نشریه جغرافیا و برنامه­ریزی، 14(28): 189-165.

کمالی، ج.، محمودیان شوشتری، م. و جلال کمالی، ن. 1385.   پیش­بینی جریان ماهانه ورودی به مخزن سد شهید عباسپور با استفاده از مدل­های سری زمانی Box-jenkins. هفتمین سمینار بین المللی مهندسی رودخانه، 7 صفحه.

مدرس، ر. 1382. مدل­سازی باکس و جنکینز سری زمانی بارندگی ماهانه ایستگاه قلعه شاهرخ. همایش پیش­بینی وضع هوا. 13صفحه.

مظلوم علی آبادی، ی.، واعظی، ع.ر.  و نیکبخت، ج. 1397. تغییرات زمانی رطوبت خاک تحت تأثیر بارندگی و دما در شرایط آیش و کشت در کشتزار دیم. نشریه حفاظت منابع آب و خاک. (2):8- 148-136.

Box, G.P.E., and Jenkinks, G.M., and Reinsel, G.C. 1994. Time Series Analysis: forecasting and Control, Holden-Day, 3th Edition.

Coghlan, A. 2011. A little book of R for time series. 71p.

De Lannoy, G. J., Verhoest, N. E., Houser, P. R., Gish, T. J., and Van Meirvenne, M. 2006. Spatial and temporal characteristics of soil moisture in an intensively monitored agricultural field (OPE 3). Journal of Hydrology. 331:719-730.

Gómez Plaza, A., Alvarez Rogel, J., Albaladejo, J., and Castillo, V. 2000. Spatial patterns and temporal stability of soil moisture across a range of scales in a semi‐arid environment. Hydrological Processes. 14:1261-1277.

He, H., Dyck, M. F., Horton, R., Li, M., Jin, H. and Si, B. 2018. Distributed Temperature Sensing for Soil Physical Measurements and Its Similarity to Heat Pulse Method. In Advances in Agronomy, 148:173-230.

Hyndman, R.J., Razbash, S., Schmidt, D., and Zhou, Z. 2012. Forecast: Forecasting functions for time series and linear models.R package version 3.25. http://CRAN.R-roject.org/package=forecast.

Khatar, B., and O. Bahmani. 2015. Predicted Temperature of Deep Soil Layers Using Time Series Models. Journal of Soil Research. 29(2): 200-210. (in Persian).

Lai, X., Zhu, Q., Zhou, Z. and Liao, K. 2017. Influences of sampling size and pattern on the uncertainty of correlation estimation between soil water content and its influencing factors. Journal of Hydrology, 555: 41- 50.

Martinez, C., Hancock, G., Kalma, J., and Wells, T. 2008. Spatio temporal distribution of nearsurface and root zone soil moisture at the catchment scale. Hydrological Processes. 22:2699-2714.

Mekonnen, D. F. 2009. Satellite remote sensing for soil moisture estimation: Gumara catchment, Ethiopia. ITC. Plauborg, F. 2002. Simple model for 10 cm soil temperature in different soils with short grass. Eur. J. Agron, 17:173–179.

Nasseri, A., Neyshabori M.R., and Fakheri fard A. 2013. Time series analysis of furrow infiltration. Irrigation. and Drainage, 62: 640-648.

Niroomand, H.A. 2001.Time Series Analysis. Ferdowsi University of Mashhad press. 2end edition. (in Persian).

Salajegheh, A., A. Fathabadi and M. Najafi Hajiva. 2008. Comparison of Application Time Series and Artificial Neural Network Models in Drought Forecasting (Case Study: Khorasan Razavi Provinces) Indices. jwmseir. 2 (4) :74-77. (in Persian).

Sheikh, V., Visser, S., and Stroosnijder, L. 2009. A simple model to predict soil moisture: Bridging Event and Continuous Hydrological (BEACH) modelling. Environmental Modelling & Software. 24:542-556.

Team, R.C. 2016. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/.

Vazirpour, S., H. Ebrahimian, H. Rafiee and F. Mirzaei Asl Shirkohi. 2016. Application of Time-series Modeling to Predict Infiltration of Different Soil Textures. Journal of Soil and Water. 30(1): 41-51. (in Persian).

Wang, L. and Qu, J. J. 2009. Satellite remote sensing applications for surface soil moisture monitoring: A review. Frontiers of Earth Science in China, 3(2): 237-247.

Williams, C. J., McNamara, J. P. and Chandler, D. G. 2009. Controls on the temporal and spatial variability of soil moisture in a mountainous landscape: the signature of snow and complex terrain. Hydrology and Earth System Sciences, 13(7): 13-25.

Zhu, Q. and Lin, H. 2011. Influences of soil, terrain, and crop growth on soil moisture variation from transect to farm scales. Geoderma, 163(1-2): 45-54.

Zoua, P., Yanga J., Fub J., Liu G., Li D. 2010. Artificial neural network and time series models for predicting soil salt and water content. Agricultural Water Management, 97: 2009– 2019.

Ford, C.R., Goranson, C.E., Mitchell, R.J., Will, R.E. and Teskey, R.O., 2005. Modeling canopy transpiration using time series analysis: a case study illustrating the effect of soil moisture deficit on Pinus taeda. Agricultural and Forest Meteorology, 130(3-4), pp.163-175.

Kim, S., Sun, H. and Jung, S., 2011. Configuration of the relationship of soil moistures for vertical soil profiles on a steep hillslope using a vector time series model. Journal of hydrology, 399(3-4), pp.353-363.

Kim, S., 2016. Time series modeling of soil moisture dynamics on a steep mountainous hillside. Journal of hydrology, 536, pp.37-49.