Spatial-temporal Variability of Seasonal Precipitation in Iran

Abstract

Spatial-seasonal variability and temporal trends has essential importance to climatic prediction and analysis. The aim of this research is the seasonal variations and temporal trends in the Iran were predicted by using rainfall series. The exploratoryconfirmatory method, and seasonal time series procedure (STSP), temporal trend (TT), seasonal least squares (SLS) and spatial (GIS) methods (STSP¬-SLS-GIS) were employed to bring to light rainfall spatial-seasonal variability and temporal trends (SSVTT). To explore the spatial-seasonal variability and temporal trends during the period over 1975 to 2014 at 140 stations. To investigate the spatial-seasonal variability and temporal trends amount of each series was studied using ArcGIS 10.3 on different time scale. New climatic findings for the region: the investigates and predictions revealed that: (a) range of monthly and seasonal changes of rainfall tends to be highest (increasing trend) during winter (Winter Seasonal Index or WUSI=137.83 mm); (b) lowest (decreasing trend) during summer (Summer Seasonal Index or SUSI=20.8l mm) and (c) the coefficient of rainfall seasonal pattern variations in winter to 5.94 mm, in spring to 11.13 mm, in summer to 4.44 mm and in autumn to 8.05 mm with seasonality being the most effective of all. Mean annual rainfall changed from 51.45 mm (at Bafgh) to 1834.9 mm (at Bandar Anzali). Maximum decrease in annual rainfall was obtained at Miandeh Jiroft (-143.83%) and minimum at Abali (-0.013%) station. The most apparent year of variation was 2007 in annual rainfall.

Effect of Different Soil Treatments with Hydrogel on the Performance of Drought-Sensitive and Tolerant Tree Species in a Semi-Arid Region

“…The average annual precipitation was 208 mm, and most precipitation occurred in the winter. Mean annual rainfall ranged from 51 to 1835 mm [18]. The soil was a sandy loam with a pH of 7.5 and an extremely low N content (0.02% on a mass base).…”

Comparing causal techniques for rainfall variability analysis using causality algorithms in Iran

“…Iran is located in the southwest of Asia with a 25° 3′–39° 47′ N latitude and 44° 5′–63° 18′E longitude, and it is a mainly diverse landforms realm where two major landforms, the mountainous (highlands) realm and the lowlands (plains) realm (has an elevation range from −26 to 5671 m) ( Javari, 2016b ), separate different climatic zones (relatively wet climate to dry climate) ( Alizadeh-Choobari and Najafi, 2017 ). Climatic data series during the period from 1975 to 2014 collected from the Meteorological Organization of Iran ( http://www.irimo.ir ) were employed to predict the climatic effect variability (CEV).…”

A Review on the Relevant Applications of Machine Learning in Agriculture

“…The ARCH family include ARCH, GARCH, ARCH in Mean (ARCH-M), Exponential GARCH (EGARCH), Glosten, Jagannathan and Runkle (GJR) and Threshold Autoregressive Conditional Heteroskedasticity (TARCH). The favorable choice of ARCH family models are the spatial distributions in the characters and designs of rainfall, but these also can be nonlinear as they can response in very different rainfall variability (Javari, 2016) [117] among stations (Gouriéroux, 2012[118]; Hafner et al , 2015 [119]; Shimizu, 2014[120]).…”

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