Assessment of Temperature and Elevation Controls on Spatial Variability of Rainfall in Iran

Abstract

Abstract:With rainfall changes, hydrological process variability increases. This study predicts the potential effects of temperature and topography characteristics on rainfall spatial variability. Temperature and topography were considered as two effective factors that may influence monthly rainfall. This study uses rainfall and temperature data from 174 synoptic and climatic stations and 39,055 rain, elevation and temperature points extracted by ArcGIS10.3 over the 40 years . In this study, in order to predict the relationship between temperature, topography and rainfall, a combination of statistics including spatial statistics and Geographical information System (GIS) methods were employed. It was found that the distribution and rainfall variability in some parts of Iran was regarded to be based on topography and temperature. The spatial patterns showed that the variability based on spatial autocorrelation in rainfall severity gradually increased from west to east and north to south in Iran. Temperature and topography influence rainfall spatial variability; moreover, these factors have direct, indirect and total effects on rainfall variability in temporal and spatial patterns. These research results will be useful for the regionalization of climate and rainfall formation factors, management of water sources, environmental planning and measuring environmental controls on the climate system.

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

“…Climatic effect variability (CEV) can represent itself as a direct or indirect technique for pattern scheming of causality made based on the climatic factors and variables ( Javari, 2017b ). Reproduction of CEV is the important process in climatic variables effect evaluated with the suggested CPSRM, PLS and GIS algorithms in climate ( Zheng et al., 2017 ).…”

“…However, there are studying facts displaying that climatic variables and suitable factors were not studied ( Wang et al., 2017a , b ; Hermwille et al., 2017 ; Frankel et al., 2009 ; Howarth et al., 2017 ). In addition, climatologists considered their causal and non-causal effect short time in the past ( Wang et al., 2017a ), and the application of causal and non-causal effect methods to predict the climatic temporal-spatial variability such as rainfall, temperature and humidity in various regions ( Javari, 2017b ; Mostafaeipour et al., 2017 ; Li et al., 2017 ). The number of causal and non-causal effect models applications in climatic CPSRM changes, and the progress of causal models is well used by various climatic disciplines to some exceptional studies ( Hewitt et al., 2017 ) such as that of Hewitt et al.…”

“…(2017) . However, the subject of causal models such as CPSRM, PLS and GIS has considered only partial application in the climatic CPSRM thus far, thereby diminishing to apply causality models to its detailed ability in its effect to predict the climatic effect variability (CEV) ( Hewitt et al., 2017 ; Bühlmann et al., 2014 ; Javari, 2017b ). Climatic effect variability (CEV) is structured as a multi-effect level according to CPSRM, PLS, and GIS models on climate, and FGT has been formulated a mono-sided analysis pattern on analysis of climatic changes ( Tenenhaus et al., 2005 ).…”

“…In this study, by referring to “mechanical-causal models of effect”, covariance-based analysis or causal processes and mechanical relations (CPSRM) is a generalized exploratory and confirmatory causal technique ( Lomax and Schumacker, 2012 ), as it is mainly suitable to analyze climatic effect variability (CEV) and systematic relationships of variables in climate ( Javari, 2017d ). Patterns of partial least squares (PLS) in climatic CPSRM are developed, and the applications of PLS are well applied by some climate disciplines ( Javari, 2017b ). On the other hand, the subject of PLS statistical applications has been established only to analyze RM and FT applications ( Javari et al., 2016 ; Tenenhaus et al., 2005 ).…”

Simulation of precipitation variations in Iran using Kriging interpolation methods

“…To evaluate the homogeneity of the precipitation series (Javari, 2016), the Standard Normal Homogeneity test, Alexanderson's SNHT test (Alexandersson, 1986) and the Pettitt-Whitney-Mann test (PWM) (Pettitt, 1979) were used at 5% significant level (Javari, 2017a). Finally, for each station series, trend values were analyzed using Mann -Kendall method and to analyze the precipitation spatial variability, geostatistical methods (Kriging method) were used.…”