Keywords 
Causal modeling; SEMPLSGIS model; Temperature
effects; Precipitation spatial variations 
Introduction 
Within the context of zonal changes, extreme precipitation
events have recently received much attention because they are more
sensitive to precipitation changes than mean values [1]. Particularly,
many researchers have placed special emphasis on precipitation
variations and their impact on the environment [2]. The importance
of temperature variations and anomalies for temporal variations in
the precipitation has been well recognized [3]. Temperature variations
and anomalies can enhance rainfall through hydrological process. On
the other hand, temperature changes which are associated with other
climatic elements can highly intensify the formation of precipitation.
Due to the close relationship between temperature variations and
precipitation variations, the temperature anomalies in one zone can
impact the other zones through climatic changes in precipitation
processes [4]. Temperature extremes receive much attention as trends
in extreme events react more sensitively to climate change than mean
climate, and therefore have a more intense impact on precipitation
and precipitation formation [5]. Subtropical temperature patterns are
the key mechanisms of precipitation in the subtropics and have been
reported to play a significant role in midlatitude extreme precipitation
events [6,7]. Causal explanation and climatic prediction are distinct
challenges facing climatic modelling. Structural modelling draws
on hypotheses from a priori theory to specify causal relationships
between temperature and precipitation variables [8]. SEM is used to
estimate the interrelationships among temperature and precipitation
in the study. Although the focus was on the three core elements of
temperature (i.e. maximum temperatures, minimum temperatures
and temperature indexes), the influences of temporal and spatial
temperature on precipitation intention were also investigated [9,10].
The development of SEM in climate study was regarded as an important
statistical development in the recent decades. The SEM simultaneously
carries out causal analysis and path analysis; it can (1) measure errors
of observed variables, (2) represent ambiguous constructs in the form of unobserved variables by using several manifest variables and (3)
estimate causal relationships among both latent and manifest variables
[11].That is why SEM examines the precipitation changes model by
comparing it with multiple regressions and with regard to the nature
to paths, separate elements and distinct coefficients. The SEM method
permits checking and examining a complete model by generating
goodnessoffit statistics and assessing the overall fit. SEM also allows
for the expansion of statistical estimation by assessing and estimating
terms of error and fit indexes for temperature and precipitation
variables [12,13]. However, SEM is similar to using multiple regression
analysis in precipitation changes analysis [11,14]. The partial least
squares structural equation modeling method has recently gained
increasing attention not only in studying the climate and climatic
changes but also in precipitation variations modeling, temperature
effective research, temperature variation patterns and other climatic
research [6,1517]. One of the approaches which examine the link
from the construct to the indicators based on reflective measurement
is partial least squares structural equation modeling [18]. The other
approach examining the link from the indicators to the construct is
called formative measurement [1921]. However, recent advances in
explanatory modelling of climatic analysis now enable a consistent,
statistical evaluation of expert knowledge as the basis for modeling
specifically through the SEM framework [22]. The regional responses
of precipitation extremes to climate changes are diverse. In this study, the joint temperature behaviors of thirteen elements of temperature indices in precipitation variations in Iran are studied. We also investigated
the direct and indirect effects of temperature elements change on precipitation. Unlike most previous research where each indicator is evaluated
separately, this study analyzes three latent variables as representing overall precipitation. The same approach is employed to define the conditions
of precipitation changes and internal operation constructs. The results could be used by environmental management to study the following two
questions: a) Does the precipitation situation of a country affect the climatic condition and environment management? b) Does the precipitation
condition affect the overall internal operation and will it eventually affect the overall precipitation of Iran? While the answer is quite obvious, this
analysis takes a different approach by examining the effect of a combination of temperature indicators regarding overall precipitation and, hence,
giving environmental managers a clearer picture of the situation as they make decisions. To answer these questions, we need a unified model to
investigate the relationships between and among temperature and precipitation variables. SEM can be employed as a versatile statistical technique
to provide a suitable framework for the causal analysis of precipitation patterns and precipitation variations. The rest of the paper is organized
as follows: in Section 2, we briefly introduced the SEM and the PLS methods to provide a description of our proposed temperature effects on
framework of precipitation. Section 3 presents the new modeling of precipitation variations in detail. The experimental results and conclusions are
presented in Sections 4 and 5 respectively. 
Background 
This paper employs the SEMPLSGIS methods for investigating the variations in precipitation patterns. The next subsections provide a brief
introduction to these three algorithms to make this paper more readerfriendly and selfcontained. 
SEM as a means to study precipitation variations 
¨ 
The SEM has been regarded as an inclusive and valuable statistical method in climatology in recent years. This multivariate analysis method has
been widely applied for theoretical explorations and empirical analysis in environmental disciplines [23,24]. It describes and tests the relationships
between latent and manifest variables [25]. As presented in Figure 1, a structural equation model usually consists of two main components, a
structural model and several measurement models. The measurement model includes a latent variable, a few associated observed variables and
their corresponding measurement errors. The structural model, however, consists of all latent variables and their interrelationships [26]. Figure
1 provides a simple representation of a structural equation model investigating the effect of latent variables (Maximum Temperatures, Minimum
Temperatures and Temperature Indexes) and the manifest variables which are used to represent the latent variables. The manifest variables are
shown in rectangles, the latent variables in ellipses, measurement errors in circles with arrows indicating the direction of the effects Figure 1 shows
the manifest variables modeled based on a reflective measurement model. To establish a common terminology for the discussion that follows,
we will briefly review some special issues. Using the AMOS (It is available from the http://www03.ibm.com/software/products/en/spssamos),
SmartPLS (It is available from the http://CRAN.Rproject.org/package=semPLS) and ArcGIS software (It is available from the http://www.esri.
com/software/arcgis), a structural equation model can be stated as follow [25,2729]: 
(1) 
(2) 
(3) 
(4) 
where x represents the independent variable,E represents error,η represents internal latent variable, ξ represents external latent variable, β
represents internal variables coefficients, γ represents external variables coefficients, ζ represents error of internal variables, δ represents error
of external indicators, λ_{x} represents coefficient of external variables for external indicators,λ_{y} represents coefficient of internal variables for
internal indicators and e represents error to internal indicators. The values are equations coefficients. Equation (1) is called the latent variable or structural model and expresses the hypothesized relationships among the constructs. Equations (2), (3) and (4) are factoranalytic measurement
models which tie the constructs to observable indicators [30]. However, structural equation models comprise both a measurement model and a
structural model. The measurement model relates observed responses or ‘indicators’ to latent variables and sometimes to observed covariates. The
structural model then specifies relations among latent variables and regressions of latent variables on observed variables [31]. In SEM, three types
of effects, that is, direct, indirect and total effects are estimated. Direct effects, shown by singledirectional straight arrows, represent the relationship
between one latent variable to another. It should be noted that the arrows used in SEM indicate directionality and not causality. Indirect effects, on
the other hand, reflect the relationship between an independent latent variable (exogenous variable) and a dependent latent variable (endogenous
variable) that is mediated by one or more latent variables. The total effect is the sum of direct and indirect effects [32]. Algorithm 1 represents an
SEM algorithm which consists of six stages and various parameters of goodness fit: AFI, CFI, PFI,X^{2},GFI,ACFI,RMR,TLI,NFI, NC,IFI. PRATIO,
PNFI and …. and control how much fitting of analysis model is imposed in each iteration. The SEM algorithm includes the following six steps in
Algorithm 1: 

Partial least squares (PLS) method 
The concept of partial least squares was introduced by S. Wold, KettanehWold, and Skagerberg [33]. PLS a multivariate linear regression
method, has established itself as an important analytical tool in climatic analysis [34]. A PLS path model consists of two elements. First, there is a
structural model that represents the constructs (circles or ovals). The structural model also displays the relationships (paths) between the constructs.
Second, there are the measurement models of the constructs and the weighting scheme that display the relationships between the constructs and
the indicator variables (rectangles) [35,36]. In PLS, the measurement model specifies how the latent variables (constructs) are measured [27].
Generally, there are two different ways to measure unobservable variables. One approach is referred to as reflective measurement, and the other is
called formative measurement. Structural model shows how the latent variables are related to each other (i.e., it shows the constructs and the path
relationships between them). The location and sequence of the constructs are based on a model or the researcher’s experience and accumulated
knowledge [37,38]. Reflective measurement can be stated in the following form [39]: 
(5) 
Equation (5) is called the reflective method in PLS where ξ is the latent variable and εis the error. Structural model can be formulated in the
following form [39,40]: 
(6) 
However, there are two approaches to estimate the relationships in a structural equation model. The more widely applied one is the SEM
approach and the other is PLSSEM which is the focus of this paper. Algorithm 2 displays the full explanation of the PLS method. The PLS path
modeling method was developed by Wold and the PLS algorithm is essentially a sequence of regressions in terms of weight vectors [41]. The weight
vectors obtained at convergence satisfy fixed point equations [27]. The basic PLS algorithm, as suggested by Joseph [39,40], includes the following
three steps in Algorithm 2. 

New SEMPLSGIS method 
In this study, a new SEM method is proposed based on PLSSEMGIS, which combines the principles of SEM measurement model and PLS,
SEM structural model and PLS, and trend spatial analysis by GIS. Thus, the new mathematical model can not only generate factors to form a
structural model but also ensure the optimum relationship to the objective dependent variables .This method consists of ten steps: 1) the concept
formation in SEM model, 2) examining the data normality and evaluation of the missing data, 3) examining the outliers in the data to more
precisely forecast the precipitation changes 4) selection and initialization of the goodness of fit test in measuring models, 5) studying the reliability
of measurement model, 6) studying the reliability of structural model, 7) determining the distribution of data in the SEMPLS measurement
and structural models by GIS, 8) measuring the standard distance (spatial statistics), 9) examining the geographical weighted regression and 10)
drawing the maps of direct, indirect and total effects. The maps of the effects of the selected climatic elements in Iran were provided with a higher
accuracy using this method. Finally, the trend maps of temperatures effects on precipitation were drawn. Trend spatial analysis of the effects
allows us to model, examine, and explore spatial relationships and helps explain the factors behind observed spatial patterns [42]. By modeling
spatial relationships, however, spatial trend analysis and prediction of the temperature effects on precipitation have also been done. Modeling the
factors that contribute to precipitation, enables us to make predictions about precipitation and climatic changes [42,43]. In this paper the new
mathematical model of SEMPLSGIS is introduced and its applicability in climatic data processing is validated by a case study for identifying the
factors associated with precipitation change patterns in Iran. The case study uses concentration values of 14 elements obtained from 140 station samples in the study area. For comparison purposes, all three methods, i.e. SEM, PLS and GIS were used for analyzing the dataset. To implement the
GIS and the new SEM –PLSGIS method, precipitation was taken as the dependent variable and the other elements as the independent variables. In
order to interpret the climatic associations of latent variables from the precipitation point of view, the loadings and regression coefficients of latent
variables are important [44,45]. Algorithm 3 explains the SEMPLSGIS method; the steps of the proposed algorithm are as follows. 

Materials and Methods 
Materials 
Iran, situated in the southwest of Asia, ranges from 25°3’ to 39°47’ N and from 44°5’ to 63°18’ E. The case study dealt with the identification of
temperatures effect on precipitation in order to better forecast the precipitation variations in Iran. Precipitation variations can be defined from
various aspects, such as effectiveness of precipitation formation factors or even climatic events such as drought. With regard to precipitation changes
in Iran, we chose thirteen temperature indices, namely: 1. Annual average temperature (T.AAN) 2. Average minimum temperature (T.MIN) 3.
Average maximum temperature (T.MAX) 4. Dew point temperature (T.D.P) 5. Temperature range (T. Range) 6. Daily temperature (T.Daily) 7.
Recorded absolute maximum (T. MAXA) and minimum temperature (T. MINA) 8. Number of days with maximum temperature equal and lower
than 0°C (T.MAXB) 9. Number of days with maximum temperature equal and higher than 30°C (T.HIGH) 10. Number of days with minimum
temperature 11. Number of days with minimum temperature equal and lower than 4°C 12) number of days with minimum temperature equal
and lower than 0°C (T. MINC) 13. Number of days with minimum temperature equal and higher than 21°C (T. LOW). The chosen indices were
obtained from meteorological organization and included the monthly, seasonal and annual information of 140 stations in Iran for the period of
19752012 (Figure 2). All the observed precipitation data have been subject to strict quality control obtained from http://www.irimo.ir/eng/wd/720
ProductsServices.html. The study focused on monthly and seasonal variations. For this purpose, a harmonic analysis was applied to all data, and
data were studied with respect to the time defaults, linearity, normality, missing data, outliers etc. at different phases of the research. 
Methods 
Prediction of temperatures effect on precipitation changes are based on exploratory and confirmatory analyses with the incorporation of direct,
indirect and total effects of predictor variables on response variables. SEMPLSGIS is a multivariate statistical technique that uses factor analysis,
PLS analysis, path analysis and spatial analysis to evaluate the relationship between temperatures and precipitation [28,46]. Application of this
technique in climatology leads to this question of how the causal relationships among latent variables and the climatic factors are studied. Path
analysis can be used to answer this question. In organizing this paper, the following phases were considered: 
Concept formation model: To test the absolute effects of temperature elements on precipitation, we performed a confirmatory and exploratory
path analysis, which identifies the most likely causal links among correlated variables [47]. We studied the possible causal paths between temperature
and precipitation. Direct, indirect and the total effects of temperatures on precipitation is highly related to the causal link between temperatures and
precipitation. Nevertheless, temperature could influence precipitation, which in turn affects amount, with only no direct link between temperatures
and precipitation. Moreover, paths differences could also directly influence temperatures and precipitation. We studied ten possible cause–effect 
linkage models for each measure of temperatures at each stage (Figure 3). For causal analysis of temperature factors, different symbols were used
in order to show the relationships among temperatures elements and precipitation. Formulating a conceptual model, designing path diagram,
assessment of the model details and forecasting the temperature factors affecting Iran’s precipitation were done. In formulating the conceptual
model, the relationships between the temperatures and precipitation have been taken into account. We also explored the relationships between each
measure of temperature and precipitation because primary analyses showed different relationships within temperature factors. When analyzing,
the effect of factors on precipitation and the rate of this impact were studied. Standard series of covariance values among exogenous variables and
correlation coefficients among the series were also examined to determine the relationships among the conceptual model paths of the research [48].
Through estimating the standard of the series, the regression weights of the series were identified. Through estimating simple moments in addition
to variancecovariance evaluation, the typical covariance of the series and typical correlation matrix of the series were estimated. By estimating
implied moments or all implied moments, covariance was identified based on the temperature factors and the reconstructed correlation matrix
was based on the temperature factors; the variancecovariance reconstruction was also identified. Moreover, direct, indirect and total effects of
the series were determined as standardized and nonstandardized values and squared multiple correlation coefficients between each endogenous
variable and other variables of the model were calculated. To assess the relative importance of direct, indirect and total effects of temperature on
precipitation, we compared standardized and nonstandardized coefficients. By estimating factor score weights, the capability of each variable in
predicting the studied series was revealed. Through estimating threshold ratio or critical ratio for the difference between the factors, other factors
are carefully compared. As for the temperature variables, we used the calculated minimum temperature, which was found to be the best predictor
of precipitation among temperature factors. 
Data normality assumption and evaluation of missing data: We examined graphically and statistically the relationship between the
temperature and precipitation variables; temperature and precipitation variables were normally fitted. We did not use data from one single station
because the number of data points varied widely in Iran. In this paper, some of the stations were omitted due to the lack of sufficient data.
Reconstruction of variables was done using least squares and kriging methods. Kriging models are very valuably in identifying ambiguous points
and their fittings. These two methods may be calculated using the following relations: 
(7) 
(8) 
Where: P is the prediction period, Z (si) the value of the data measurement at its position, λi weight for measuring value, so the prediction value
at that position and N the number of measured values. We used least square and kriging to explore whether there were significant relationships
between temperature factors and precipitation. 
Recognition of outliers in the data: Outliers are scores that are different from the rest. One of the factors influencing the data normality is the
presence of the data whose values are substantially different from the other data and their distance is high to the extremes. Deleting these data is
effective for providing better results on research plan. To detect singlevariable port data, it is enough to standardize the total data. Accordingly, the
data must demonstrate the following characteristics: there should be no outliers in the data [12]. To detect multivariable port data, Mahalanobis
Distinct Index was used: 
(9) 
Where, a_{if} is the mean for ith variable in group, f w_{ij} is an element from the inverse of the pooled within groups covariance matrix (down
weights correlated variables), n is the number of sample units, g is the number of groups, and i ≠ j. This index is a typical average for the whole
variables and its higher values indicate that the variables are placed at a further distance from the general distribution of the sample and viceversa.
To measure latent and observed variables, the variables were studied individually. In the first phase, regressive weights of the series were studied.
Also, to make the series scales homogenous, standard regressive weights of the series were studied and the effect sizes of the variables were also
calculated. Moreover, to study the significance level of the model, the variables were studied using their significance level considering the critical
ratio of variables. 
Goodness of fit test in measuring models 
Goodness of fit index was used in this study. Hence, absolute fit indices (AFI), comparative fit indices (CFI) and parsimonious fit indices (PFI)
were considered. Comparative fit index is based on comparing the model with competitor models and parsimonious fit index is dependent on
the desired parameters of the researcher. Absolute fit indices include chisquare index (X2), goodness of fit index (GFI), adjusted goodness of fit
index (AGFI) and root mean squared residual (RMR), (Fernando Gutiérrez, 2014). Comparative or relative fit index includes TuckerLewis index
(TLI), BentlerBonnet index (NFI), comparative fit index (CFI), relative fit index (RFI), and incremental fit index (IFI). Parsimonious fit indices
include; normed chisquare (NC), parsimony ratio (PRATIO), parsimonious normed fit index (PNFI), parsimonious goodness fit index (PGFI),
root average squared error of approximation (RMSEA) and normed chisquare (CMIN/DF). To compare the models, Akaike information criterion
indices (AIC), BrowneCudeck criterion (BCC), Bayes information criterion (BIS), consistent version of Akaike information criterion (CAIC),
noncentral parameter (NCP), Hoelter noncentral parameter (NCP), Hoelter’s index, (HOELTER), expected crossvalidation index (ECVI) and
modified expected crossvalidation index (MECVI) are used (Table 1). 
Measurement model 
The measurement model of SEM allows the researcher to evaluate how well his or her observed (measured) variables were combined to identify
underlying hypothesized constructs. Confirmatory factor analysis is used in testing the measurement model, and the hypothesized factors are
referred to as latent variables. A measurement model in SEM defines the association between the variables of interest. It provides the link between
scores on a measuring instrument and the underlying constructs they are designed to measure. The measurement model is tested to validate the
measurement instruments. The basic equations of the structural and measurement models are the following: 
(10) 
Where η is an mdimensional vector of latent variables, x is a qdimensional vector of covariates, ε is a pdimensional vector of residuals or
measurement errors which are uncorrelated with other variables, v is a pdimensional parameter vector of measurement intercepts, L is a p × m
parameter matrix of measurement slopes or factor loadings, and K is a p × q parameter matrix of regression slopes. 
Structural model 
Equations in the structural portion of the model specify the hypothesized relationships among latent variables. The structural model is defined
in terms of the latent variables regressed on each other and the qdimensional vector x of independent variables [4951]: 
(11) 
where ξ_{j} is the generic endogenous latent variable, β is the generic path coefficient interrelating the qth exogenous latent variable to the jth
endogenous one, and ε_{j} is the error in the inner relation. We include one hypothesized structural model in the composite model in Figure 2. In
this model, we hypothesize that conceptual elements provide a function of particular concepts for several issues. We can describe relationships
among latent variables as covariance, direct effects, or indirect effects. We showed those pictorially using doubleheaded arrows. Because we did
not anticipate any nondirectional relationships between the latent variables, we specified no covariance in the structural model in Figure 3. Direct
effects are relationships among measured and latent variables. We showed those pictorially using singledirectional arrows. An indirect effect is the
relationship between an independent latent variable and a dependent latent variable that is mediated by one or more latent variables. Mediation
may be full or partial. Figure 3 shows the assumed relations with regard to the direct, indirect and total effects. 
Results and Discussion 
Measurement model test 
A pointed out, SEM studies the effect of latent variables or the variables which are not directly observable [52]. These latent variables are measured
by several observed variables. Concerning the effects of temperature variables on precipitation, three indicators that is, maximum temperatures,
minimum temperatures and temperature indexes were found to influence precipitation (Figure 1). With respect to designing conceptual model,
testing the model is done along with testing the measurement model and structural model. Studies on the reliability and validity of measuring tool,
on testing the structural models, and on the effects of temperature variables on precipitation in Iran have been conducted [53]. Hence, the PLS
method includes two measuring model and structural model tests [54]. Measurement model test includes the study of reliability and validity for
identifying the model. To study the reliability, three criteria including the reliability of observed variables, composition validity of the constructs
and average variance extracted (AVE) were calculated [55] and the results are shown in Table 2. The results show that the data are significant for
the final model (Figure 4). To study the diagnostic validity, two criteria were suggested; first, the indicators of a construct should have higher factor
loadings for their construct, that is, the lowest crossloading to other constructs. Tenenhaus et al. [56] suggest that factor loading of each indicator
on the related construct should be at least 0.1 higher than the factor loading of the same indicator on other constructs. Secondly, AVE nth root of
a construct should be more than the correlation of that construct with other constructs. In addition, the positive value of this index indicates the
suitable quality of measuring tools while negative values show the low quality of tools in measuring the target hidden variable. As shown in Table
2, values higher than 0.7 are the indicators of suitable composite reliability. Hence, the constructs showed a suitable composite reliability. To study
the second criterion, it is necessary that nth root series of AVE be taken and substituted by the mentioned matrix diameter instead of correlation
matrix of latent variables [56], so that the value of the mentioned matrix diameter should be higher than 0.7. Hence, the results in Figure 5 show that the series have an acceptable condition and the mentioned constructs of research have a suitable diagnostic validity. To study validity, Cross
Loading study was used to see if the factor loading on the target construct is 0.1 higher than that on the other construct [57]. As Table 3 shows, the
constructs are with an appropriate validity. Finally, to study the quality of measuring instruments (tools), Construct Cross Validated Communality
was used [58]. Table 4 shows the sum of squared of the observations for each block (SSO) and for each latent variable block, and the sum of square
of prediction errors for block communality (SSE) for each hidden block, as well as the inspection index of communality validity (CV) [59]. Since
the coefficient of the mentioned index is positive in Table 4, the measuring model has an appropriate quality. 
Structural model test 
After studying the measuring model, structural model should be studied and tested. To test the structural model, path coefficients (β),
significance of path coefficients and coefficient of determination values should be studied. In addition, to examining the quality of the structural
model, StoneGiser coefficient, Q2, or CVredundancy study should also be used [60]. t values of 1.96 at significance level of 0.05 were set for
studying the path coefficients. Figure 6 shows coefficient of determination values or clarified variance of each latent dependent variable. Hence,
the variance for minimum temperature is 0.903, that is, minimum temperature predicts 90 percent of the precipitation changes. To study the significance of the path, ttest (outer model Tstatistic) may be used. As Table 5 shows, all paths were higher than 2.66 at 0.01 level of significance.
Table 6 shows the significance values of the total effects (total effects, average, STDEV, Tvalues). Since the t values of variables are higher than 2.66,
the total effects of all variables are at 0.01 level with low significance. In addition, to study the quality of structural model, construct cross validated
redundancy coefficient may be utilized [18]. As shown in Table 7, the values of the mentioned coefficient or Q2 coefficient are positive, so the
structural model enjoys an appropriate quality. 
Spatial distribution of effects 
The findings of this research showed that among the three components, minimum temperature had the largest direct effect on precipitation
with an average effect coefficient of 2.67, and maximum temperature had the smallest effect with the average effect coefficient of 0.0038 (Figure
7) . The effect of spatial extension of temperature components on the rate of precipitation shows that around 55.84 percent of the areas in Iran
have been under the effect of minimum temperature components, 33.55 percent under the effect of temperature indices and around 11.61 percent
under the effect of maximum temperature components (Figure 8). Among the minimum temperature components, the highest effect of trend was
related to average minimum temperature with average effect coefficient of 0.986 (Figure 9). The effect trend of maximum temperatures on the rate
of precipitation shows that northeast and east of Iran have been under the effect of maximum temperature components (Figure 10). The effect
trend of temperature indexes on the rate of precipitation shows that west and southwest of Iran have been under the effect of temperature indexes
components (Figure 11). In studying the indirect effects of temperature components on Iran precipitation, two of the elements, namely annual
average temperature and daily temperature had significant effects in view of the three components, that is, maximum temperature components,
minimum temperature components and temperature indices. The highest rate of variations in the indirect effects of temperature components on
Iran precipitation is related to maximum temperature components with variations effect coefficient of 3.256 related to daily temperature and its
minimum rate is related to daily temperature component with variations effect coefficient of 0.863 and the annual average temperature (Figure
12). About 1 percent of the areas in Iran is under indirect effect of maximum temperature components, 13.6 percent under the indirect effect of
temperature indices and 85.4 percent under the indirect effect of minimum temperature components [61,62]. 
Conclusions 
Using an SEMPLSGIS tool – especially one with a graphical quantitative editor that allows the user to specify the model by drawing it on
curtain – it is quite easy to add climatic elements to a structural equation model. A primary purpose of our study was zoning of temperature
effects on precipitation. Our other aim was to examine the effects of the three causal indicators, composite indicators, and covariates. In this study, the direct, indirect and total effects of temperature on precipitation in Iran were studied. 14 variables were investigated using a structural
model. Results indicated that there are six major temperature variables that affect precipitation in Iran .The findings of this study also indicated
that among the three temperature factors which were influential on precipitation, the minimum temperatures factor had the highest effect on the
rate of precipitation .The hypothesis for the effects of the minimum temperature mean, lowest temperature records, maximum temperature mean, highest temperature records, dew point temperature and daily temperature on the rate of rainfall in Iran is also accepted. After all, the analysis of
the temperature effects on the rate of precipitation in Iran through the SEMPLSGIS method can show the magnitude of these effects on the rate
of precipitation changes and can well examine the variation patterns. 
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