JWSS - Journal of Water and Soil Science

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The temporal and spatial vegetation dynamics is highly dependent on many different environmental and biophysical factors. Among these, climate is one of the most important factors that influence the growth and condition of vegetation. Of the abiotic factors affecting the geographic distribution of vegetation type, climate is probably the most important. Ecological research has traditionally aimed to generalize vegetation types that are assumed to be homogenous. Most of climatic classifications related to bioclimate are focused on limited climatic factors such as temperatue, precipitation and combination of them. As climate is a compound phenomena using limited factors cannot show the climate of a region, and as a result most climatic factors must be considered in bioclimatic classification. Therefore, a climatic study using various climatic factors could reveal the effective factors in distribution of vegetation. In order to determine bioclimatic zones in Chahar-Mahal & Bakhtiari province using multivariate statistical method, 71 climatic variables, which were more important in plant ecological conditions, were selected and evaluated by the factor analysis. The factor analysis revealed that the first three factors which explain %91.8 of total variance among the selected variables were temperature, precipitation, and radiation. According to results and using hierarchical cluster analysis in Ward’s method, bioclimatic classification in Chahar-Mahal province was carried out and 5 bioclimatic zones were found. In addition, Chahar-Mahal province was classified by 4 traditional climatic classification methods (Koppen, Gaussen, Emberger and De Martonne) and those classes were compared to climatic classes obtained by multivariate statistical method. The latter comparison was suggestive of the fact that multivariate statistical method provides a more appropriate classification in comparison to the traditional methods, specially because more dominant vegetation species could be defined for each of the newly described climatic classes. Furthermore, dominant species were determined for each climatic region.

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In this research, 5 percent of normal Precipitation Index (PNPI),Deciles of Precipitation(DPI),Rainfall Anomaly Index (RAI), Bahlme & Mooley Drought Index (BMDI) and standardized Precipitation Index (SPI) were used in order to investigate drought in Yazd synoptic station and 31 non synoptic stations all around this province. For this purpose, the present statistical errors were reconstructed via correlation between the stations, after raining data collection from the considered stations. Then, calculation of PNPI, DPI, RAI, BMDI and SPI indexes were calculated on monthly and annual scales. Situation of drought was determined based on the obtained values from calculation of each index according to tables related to the considered indexes in different classes of drought during the statistical period. Then indexes were compared to each other, considering drought given situation for each station. The difference and similarity of each index with other 4 indexes were calculated and investigated. Also after determination of drought situation in each station, given percentage of drought different situations via each Index was determined on annual scales, in 33 study stations. After passing the above mentioned stages, it was found that there was the highest percentage value of similarity between the two indexes RAI & DPI, as both indexes indicated similar situation of hard drought in the stations. These two indexes are considered most efficient to investigate aerology drought. But considering that static indexes are faced with problem on monthly scales and in stations located in drought regions, it is recommended to use‌ SPI & BMDI dynamic indexes whose similarity percentages are acceptable.

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This study performs trend analysis of hydroclimatic varibles and their possible effects on the water resources variability. Nonparametric Mann-Kendall and spearman tests were used to investigate trend analysis of mean annual and 24-hr maximum rainfall, flood and low flow parameters of 23 hydrometery and 18 synoptic stations in Sefid-Roud basin. The results showed that mean annual and 24-hr rainfall parameters are decreasing in few stations while most of stations representing negative trend for low flow and flood time series. Applying Sequential Mann-Kendall test revelad that this negative trend is started from 1965 to 1970 for rainfall parameters and from 1970 to1980 for flow (low flow and flood) parameters. Results show that climate change has probability affected variability of climatic variables, while changing of land use may have aslo affeteced extreme flow trends during recent decads. Therefor it can be noted that combination of climate chanege effects and human activities on water recources have affected the negative trend of hydroclimatics variables.

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In this study, in order to determe low flow conditions in Karkhe watershed, 5 indices of Q7,10, Q7,20, Q30,10, Q4,3 and Q95 were used for analyzing 12 hydrometric station data in the years of 1345-46 to 1380-81. Discharge data homogeneity was performed by Run Test. The Q95 index was determined by flow duration curve (FDC) and other indices were determined using 4, 7 and 30-day low flow frequency analysis. After calculating the indices, periods of low flows were determined. The indices were regionalized by Kriging method. The results showed that for the most stations, low stream flows happened in the years of 1345-46, 1377-78, 1378-79, 1379-80 and 1380-81 and the percentages of stations having low flows in these years were 68, 92, 84, 75 and 59, respectively. According to the regional maps of low flows in Karkhe watershed, maximum low flows are located in central and southern areas and all of the mentioned indices decrease from south to the north of this watershed.

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Southern Oscillation Index (SOI) and Sea Surface Temperature (SST) patterns affect rainfall in many parts of the world. This study aimed to investigate the relationship between monthly and seasonal rainfall of Iran versus SOI and Pacific and Indian sea surface temperature. Monthly rainfall data, from 50 synoptic stations with at least 30 years of records up to the end of 2007, were used. Monthly and seasonal time series of each station were divided to several groups by four methods (Average SOI, SOI Phases, Indian SST Phases and Pacific SST Phases) using Rainman software and with regard to 0-3 months lead-time. Significant differences among rainfall groups in each method were assessed by the non-parametric Kruskal-Wallis and Kolmogorov-Smirnov tests, and the significant relationship was validated using Linear Error in Probability Space (LEPS) test. The results showed that SOI during summer (July-September) was related to autumn (October-December) and October rainfall in the west and northwest of Iran and the west Caspian Sea coast. The El Niño (negative) phase was associated with an increase in rainfall and the La Niña (positive) phase was associated with a decrease in rainfall in these regions. Average SOI is a useful index for rainfall forecasting in the above-mentioned areas. However, Indian and Pacific SST phases are not suggested for rainfall forecasting in Iran, duo to weak or non-persistence relationships. In conclusion, Iran rainfall is not limited to SOI, Pacific and Indian SST therefore, Rainman could not be used as an aid to water resources management over a year in Iran. It is suggested that we study the teleconnection between Iran rainfall and other ocean-atmospheric oscillations developing a model similar to Rainman in order to that we investigating the variation in Iran rainfall with aid of other effective ocean-atmospheric indicators