majid javari

The trend and regimes of rainfall considerably are different effects on the bio-environmental process. Therefore; climatic elements changes and changed trends and regimes of rainfall are both makers, with severe changes to bio-environmental conditions. In a more detailed analysis, it is essential to detect both trends and regimes of rainfall, distribution of monthly and annual rainfall, as well as seasonality patterns. Therefore, this study used monthly and annual rainfall series records for 1975–2019 from 140 synoptic stations and satellite data such as geopotential height, Southern Oscillation Index (SOI), Northern Oscillation Index (NOI), North Atlantic Oscillation (NAO) to detect the trend and regimes of rainfall in Iran. Statistical analysis with parametric and non-parametric tests for monthly and annual rainfall series was used to detect the rainfall patterns based on selecting the Mann–Kendall test (MK), Sen.’s slope method (SSM), and the t-student test at a 5% significance l...

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 exploratory-confirmatory 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....

Spatial Neighborhood Analysis of the Monthly Rainfall in Iran This study examines the spatial neighborhood variations (SNPV) of monthly rainfall in Iran for the period of 1975–2014. A monthly neighborhood cell variations analysis method consisting of six measurement sub-models was created on the basis of the neighborhood statistic values of monthly and seasonal precipitation; the data were the monthly and seasonal values for the period between 1975 and 2014, and were obtained from 140 stations and 38968 rainfall points. In this study, a forecast has been made to study the neighborhood pixel spatial and temporal variability of precipitation at 140 stations in Iran over the period of 39 years (1975– 2014) on monthly and seasonal basis. Mean point statistics (MPS), maximum point statistics (MXPS), minimum point statistics (MIPS), range point statistics (RPS), standard deviation point statistics (SDPS) and sum point statistics (SPS) estimate and neighborhood filter (low/high) were used to determine analysis and forecasting monthly variations.

Structural equation modeling (SEM) is a quantitative technique for evaluating the causal relations between and among a number of variables using a combination of statistical methods and assumptions. Although there have been recent developments in expanding SEM to include climatic changes, most applications have been restricted to the simulation of causal processes; this is especially true for the modelling of environmental changes. However, when SEM is applied as an exploratory technique in climate studies, the proposed model can illustrate and examine the relationships among several climatic elements in the environment. To investigate the relationship between temperature and precipitation, a combination of SEM, partial least squares (PLS) and GIS methods were employed in this study. A measurement model, a structural model and a spatial model for examining the relationships between temperature and precipitation were proposed. A SEM-PLS-GIS model consisting of three measurement sub-models was created on the basis of the concentration values of 14 climatic elements; the data were the monthly and seasonal values for the period between 1975 and 2012, and were obtained from 140 stations. The results of the new SEM-PLS-GIS model showed that seven temperature factors affected, directly or indirectly, the precipitation with minimum temperatures were being the most effective of all. It can be concluded that employing SEM-PLS-GIS model for the purpose of describing causal patterns of climate variations especially the variations of precipitation can be of great value.

Changes in rainfall and topography are two essential factors in flooding changes and flood risk management. In this study, we investigated rainfall (random changes) and topographic changes in the flooding in Iran. Rainfall series on 140 synoptic stations and topographic data at six watersheds during 1975–2017 were collected. Average error equal to zero, equal frequency, MiniMax, MinMAD, the least-squares, and equal frequency least-squares methods have been used to assess the random effects of rainfall on flood changes. In this study, we studied the random effects of rainfall on flood changes and their relationship with topographic distribution based on the increased rate of rainfall randomness and topographic distribution. Results showed that spatial analysis of flood risk has randomness-dependent relationships. When the randomness rate of rainfall increases, flood variability increases more substantially based on topographic distribution. The results of the study present risk-based...

The kernel interpolation techniques provide a suitable spatial format that computes a temperature output layer for temperature output pattern. In this study, the efficiency of kernel interpolation functions for investigating spatial variability of temperature in Iran were studied during 39 years (1975-2014) at 174 stations and 29664 temperature points. In this study, six functions (Exponential, Gaussian, Guartic, Epanechnikov, Polynomial5 and Constant) were used to analyze and forecast monthly, seasonal and annual temperature kernel function patterns variability. Among the kernel functions, the strongest effect was discovered between functions for temperature forecasting the Exponential function at all stations during 39 years. The results showed that the increases in spatial variations of the temperature were occurring mostly in mountainous regions and there are different temperature spatial variation patterns (effect factors) in Iran. In addition, the significant relationships wer...

In this paper, was studied the rainfall trends in monthly, seasonal and annual scales in the Atrak river basin for 40 years (1975-2014). A database of monthly, seasonal and annual rainfall to detect the rainfall trends in the Atrak river basin was collected from 27 climatological and synoptic stations. To study was used the impact of rainfall trends in hydrological patterns the Thiessen Polygon. This study displays monthly, seasonal and annual trend patterns and magnitude of trend slope in rainfall data series for Atrak river basin using Mann-Kendall (MK) test and Sen’s slope estimator test at 5% significance level. The results showed that there isn’t a significant trend in all seasonal and annual rainfall data series in the Atrak river basin. Accordingly, this analysis, only five stations for overall Atrak river basin shows a significant increasing trend of rainfall in annual rainfall series. In addition, based on this analysis, only July month for overall Atrak river basin shows a...

The effectiveness of temperature and rainfall on wheat production and yield is an important indicator of agriculture economy. In this paper, we present a predictable method that estimates the effects of temperature and rainfall variables on wheat production and yield with collected data from 177 synoptic and climatic stations and Wheat Production Time Series (WPTS) over the 15 years (1999–2014) to analyze the effectiveness of local climatic elements using effectiveness-based modeling (EBM) the impact of temperature and rainfall variability on wheat production and yield. We use the combination of statistical methods, including structural equation modeling (SEM), time series and geographical information System (GIS). Effectiveness-based modeling is designed to predict the impacts of temperature and rainfall elements on wheat production and yield in twenty-four models analyzing how these elements relate to wheat production regionalization processes that are effective to climatic condit...

In this study, represents a new climatic modeling of monthly rainfall for Iran (1975–2014), presented with the spatially variability, patterning monthly rainfalls series available in the 140 stations and rainfall points. Eight special interpolation methods were estimated and considered: the inverse distance weighting (IDW), the ordinary kriging (OK), the simple kriging (SK), the universal kriging (UK), the indicator kriging (IK), the probability kriging (PK), the disjunctive kriging (DK) and the empirical Bayesian kriging (EBK). The results of the several methods were studied and assessed by the validation indicators, evaluating the outcomes from the methods with the actual rainfalls series and predicting various residuals amounts. The eight methods presented suitable for IDW, OK, UK and EBK than for other methods with the least RMSE (IDW=0.497, OK=0.37, UK=0.398 and EBK=0.189), and for the spatial variability, rather than another patterns, as well at 31200 rainfall points in Januar...

The aim of this paper is to predict the rainfall trend and regimes in Iran. In the present work, the trend and regimes modeling of rainfalls in Iran are examined using monthly, seasonal, and annual rainfall data series from 140 stations for the period 1975-2014. The trend of rainfall data is analyzed using Mann-Kendall test (MK) and Sen’s slope method (SSM), and the seasonality index (SI) is used to analyze the regimes modeling. Results showed a significant SI value approximately from southern to central parts of Iran, presenting an extreme seasonality with the distribution of monthly rainfall in 1 to 2 months. Based on the SI values, four distinct zones were detected for rainfall patterns of Iran. The monthly rainfall regimes in most of central to northern parts, as the second zone, were mainly a < 3 months pattern (SI = 1-1.19). In comparison, monthly rainfall regimes in most northern to near northwest parts, as the third zone, were markedly seasonal with a long dry season patt...

The main research aims to detecting the linear and nonlinear variability modeling in analyzing the variability patterns of rainfall series. For rainfall linear and nonlinear variability modeling, the ARIMA models and ARCH family models has been used for predicting the monthly and annual rainfall series extracted from IRIMO during 1975-2014 within 140 stations in Iran. Several ARIMA and ARCH (six models) models have been used and their validity has been confirmed by evaluating different accuracy indicators, using the hybrid model for the variability modeling. The analysis of ARIMA and GARCH selective models indicates existence of random and non-random in the rainfall time series. The combination model of (1, 0, 0) and GARCH (1, 1) is applied for the estimate and prediction of monthly rainfall. With careful valuation of the hybrid model, the ARIMA (1,0,0) and GARCH(1,1)  is recognized as the significant acceptable model by determines of different accuracy indicators similar to mean sq...

The most important aim of this study was on the monitoring spatial variability in rainfall through the study of daily data in Iran. The analysis shows a pattern based on daily rainfall data, for 1951 to 2014 periods, in 170 stations. The results indicate that during the period 1951-2014, show the change of spatial autocorrelation in the eastern parts of Iran in daily rainfall data distributed in different years. Results reveals that the daily rainfall data occurred a change of squared difference in different years, in the western south part, whereas in the eastern part it was indicated in different years. For daily rainfall data in Iran, the change point of 0.5 is a good starting point where, typically, the values vary between small parts (0.2 to 0.4) and large parts (0.6 to 1). Change point in spatial variability distribution of daily rainfall series during 1951-2014 was observed at variability boundary (0.5) in Iran between the western north region (effective region) and central a...

The main objective of this study is to examine trend and homogeneity through the analysis of rainfall variability patterns in Iran. The study presents a review on the application of homogeneity and seasonal time series analysis methods for forecasting rainfall variations. Trend and homogeneity methods are applied in the time series analysis from collecting rainfall data to evaluating results in climate studies. For the homogeneity analysis of monthly, seasonal and annual rainfall, homogeneity tests were used in 140 stations in the 1975–2014 period. The homogeneity of the monthly and annual rainfall at each station was studied using the autocorrelation (ACF), and the von Neumann (VN) tests at a significance level of 0.05. In addition, the nature of the monthly and seasonal rainfall series in Iran was studied using the Kruskal-Wallis (KW) test, the Thumb test (TT), and the least squares regression (LSR) test at a significance level of 0.05. The present results indicate that the seasonal patterns of rainfall exhibit considerable diversity across Iran. Rainfall seasonality is generally less spatially coherent than temporal patterns in Iran. The seasonal variations of rainfall decreased significantly throughout eastern and central Iran, but they increased in the west and north of Iran during the studied interval. The present study comparisons among variations of patterns with the seasonal rainfall series reveal that the variability of rainfall can be predicted by the non-trended and trended patterns.

In this paper, was studied the rainfall trends in monthly, seasonal and annual scales in the Atrak river basin for 40 years (1975-2014). A database of monthly, seasonal and annual rainfall to detect the rainfall trends in the Atrak river basin was collected from 27 climatological and synoptic stations. To study was used the impact of rainfall trends in hydrological patterns the Thiessen Polygon. This study displays monthly, seasonal and annual trend patterns and magnitude of trend slope in rainfall data series for Atrak river basin using Mann-Kendall (MK) test and Sen's slope estimator test at 5% significance level. The results showed that there isn't a significant trend in all seasonal and annual rainfall data series in the Atrak river basin. Accordingly, this analysis, only five stations for overall Atrak river basin shows a significant increasing trend of rainfall in annual rainfall series. In addition, based on this analysis, only July month for overall Atrak river basin shows a significant increasing trend of rainfall in monthly rainfall series.

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 exploratory-confirmatory 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.

This study examines the spatial neighborhood variations (SNPV) of monthly rainfall in Iran for the period of 1975–2014. A monthly neighborhood cell variations analysis method consisting of six measurement sub-models was created on the basis of the neighborhood statistic values of monthly and seasonal precipitation; the data were the monthly and seasonal values for the period between 1975 and 2014, and were obtained from 140 stations and 38968 rainfall points. In this study, a forecast has been made to study the neighborhood pixel spatial and temporal variability of precipitation at 140 stations in Iran over the period of 39 years (1975– 2014) on monthly and seasonal basis. Mean point statistics (MPS), maximum point statistics (MXPS), minimum point statistics (MIPS), range point statistics (RPS), standard deviation point statistics (SDPS) and sum point statistics (SPS) estimate and neighborhood filter (low/high) were used to determine analysis and forecasting monthly variations. To explore the spatial distribution of monthly neighborhood pixel variations of each station was interpolated using ArcGIS on temporal-spatial model. We have also estimated the filter low and high method (FLHM) and found that from western mountains and western south of Khazar Sea to center of Iran has pixel numbers variations of monthly rainfall of more than 5123 pixel (42.296 km2) in February to less 391 pixel in July and that the extreme pixel numbers range with distribution of monthly rainfall in this region is almost 4733. The application of the SNPV index has shown very notable extreme variability for monthly precipitation pattern from west to east and from north to south. The SNPV index variations most for monthly rainfall in most regions of western to center in Iran are also found to be 32.118 of standard deviations for October and SNPV index variations least for monthly rainfall in most regions of western to center in Iran are also found to be 4.73 of standard deviations for July. The SNPV index variations reveals that the monthly rainfall distribution in Iran has become symmetric with changes in rainfall centralization distribution.

The most important aim of this study was on the monitoring spatial variability in rainfall through the study of daily data in Iran. The analysis shows a pattern based on daily rainfall data, for 1951 to 2014 periods, in 170 stations. The results indicate that during the period 1951-2014, show the change of spatial autocorrelation in the eastern parts of Iran in daily rainfall data distributed in different years. Results reveals that the daily rainfall data occurred a change of squared difference in different years, in the western south part, whereas in the eastern part it was indicated in different years. For daily rainfall data in Iran, the change point of 0.5 is a good starting point where, typically, the values vary between small parts (0.2 to 0.4) and large parts (0.6 to 1). Change point in spatial variability distribution of daily rainfall series during 1951-2014 was observed at variability boundary (0.5) in Iran between the western north region (effective region) and central and eastern south region (inoperative region) in Iran. Results reveals that the spatial clusters of daily rainfall data for most of the stations with low values. Hot Spot analysis shows that dissimilar is the daily rainfall data distribution patterns in Iran. In OLS analysis, significant spatial relationships (R 2 =0.72) were observed between the stations elevation distribution and daily rainfall data distribution in Iran.Statistically significant clustering of high and low residuals in daily rainfall data analysis with series period suggested that the GWR model is specified. The result of variance inflation factor (VIF) indicated {large variance inflation factor (VIF) values (> 7.5)} revealed redundancy among stations elevation distribution. In general, result of Ripley's K-function analysis was observed the observed K value is larger than the upper confidence envelope value, spatial clustering (western and southwestern of Caspian Sea) for daily rainfall data values that are

KURZFASSUNG Bufo luristanicus SCHMIDT, 1952 ist ein Endemit des Südwest-Iran. Wir untersuchten die Morphologie, Ökologie und das Verhalten adulter und juveniler Exemplare von verschiedenen Fundorten in den Provinzen Lorestan and Khuzestan. The Tiere unterschieden sich hinsichtlich der Größe und des Typs der Tuberkel auf der Dorsalseite von Rumpf und Extremitäten. Wir unterschieden folgende zwei Tuberkeltypen: (a) einfache bestachelte Tuberkel und (b) warzenförmige bestachelte Tuberkel. Alle untersuchten Exemplare zeigten Asymmetrien in Größe und Form der Ohrdrüsen. Jungtiere hatten kleine Tuberkel, Trommelfell und Ohrdrüsen waren bei ihnen nicht feststellbar. Das Taxon bevorzugt im wesentlichen zwei Lebensraumtypen, Tal-und Gebirgslandschaften, mit einer Reihe von Mikrohabitaten wie Tunneln und Gesteinsspalten. Die grobmorphologische Untersuchung der im Rahmen dieser Untersuchung aufgesammelten Tiere legt nahe, Bufo luristanicus eher als Unterart von B. surdus denn als eigenständige Art aufzufassen. Es erscheint sinn-voll, B. luristanicus als Angehörigen des B. surdus Komplexes zu betrachten, der dann drei Formen umfassen würde: B. surdus surdus, B. s. annulatus and B. s. luristanicus. ABSTRACT Bufo luristanicus SCHMIDT, 1952 is an endemic toad of southwest Iran. We studied the morphology, ecology and behavior of adult and juvenile specimens collected from several localities in the provinces of Lorestan and Khuzestan. The specimens varied in terms of the size and type of their tubercles on the dorsal side of trunk and limbs. We recognized two types as follows: (a) simple spiny tubercles and (b) nipple-shaped spiny tubercles. All specimens studied displayed some asymmetry in size and shape of the parotoid glands. The juveniles displayed small tubercles; tympanic membrane and parotoid glands were not observed in these specimens. This taxon prefers two major habitat types, valleys and mountains with several microhabitats such as tunnels and clefts of stones. Gross morphological examination of the specimens, which were collected and examined in this research, suggests that the taxon B. luristanicus should be considered a subspecies of B. surdus rather than an independent species. According to this it is more reasonable to put B. luristanicus in the B. surdus complex which, thus, would consist of three subspecies: B.surdus surdus, B. s. annulatus and B. s. luristanicus.

This study estimated spatial variability of precipitation in the monthly and annual scales in Iran for the period of 1975 to 2014 in 140 stations using kriging interpolation methods. In precipitation variability analysis three procedures were used: Mann-Kendall test, Sen's slope estimator and spatial trend patterns. Results show that there are both increasing and decreasing trends in monthly precipitation in Iran. Based on the magnitude of the significant trend, there are three patterns of the significant trend (average, lower and upper) in the monthly precipitation of Iran that vary from-0.0785 mm/month in October to 0.1536 mm/month in November. As a result, in January, February, March, May, October, and December, the magnitude of negative trends and in April and November the random and positive patterns were estimated in the precipitation in 140 stations, respectively.

The main research aims to detect the linear and nonlinear variability modeling in analyzing the variability patterns of rainfall series. For rainfall linear and nonlinear variability modeling, the Autoregressive Integrated Moving Average (ARIMA) models and Autoregressive Conditional Heteroskedasticity (ARCH) family models have been used for predicting the monthly and annual rainfall series extracted from Islamic Republic of Iran Meteorological Office (IRIMO) between 1975 and 2014 within 140 stations in Iran. Several ARIMA and ARCH family (six models) models have been used and their validity has been confirmed by evaluating different accuracy indicators, using the hybrid model for the variability modeling. The analysis of ARIMA and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) selective models indicated existence of random and non-random in the rainfall time series. The combination model of (1,0,0) and GARCH (1,1) is applied to the estimate and prediction of monthly rainfall. With careful valuation of the hybrid model, the ARIMA (1,0,0) and GARCH(1,1) is recognized as the significant acceptable model by determines of different accuracy indicators similar to mean squared error (77025.34); root mean squared error (277.53); mean absolute error (167.68); mean absolute percentage error (79.68); and Theil's U coefficient (0.365). However, the results showed that the hybrid model, as a variability model is more efficient in forecasting the rainfall variability and underlying this model can be used as variability forecast model and chaos phenomena in Iran. In addition, a nonlinear model of ARCH family, especially GARCH (1,1) provided a quantitative-analytical method to distinguish between a particular random and non-random model for rainfall variability in Iran. Citation: Majid, J. 2017. Assessment of dynamic linear and non-linear models on rainfall variations predicting of Iran. Agricultural Engineering International: CIGR Journal, 19(2): 224–240.