JWSS - Journal of Water and Soil Science

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The study of geographical extent of precipitation pattern is important because of its impact on agriculture, water resources, tourism, industry, dams, and irrigation. The principal component analysis (PCA), as an elegant mathematical tool, was applied for the regionalization of winter precipitation in central south Iran (Fars, Boushehr, and Kohgiloye and Boyerahmad Provinces). Averaging monthly rainfall data of Dey, Bahman and Esfand (20 December to 20 March) produced the time series of winter rainfall. In each individual station, correlation matrix of the normalized data was then performed for the computation of the standard PCA. Eigenvalues, eigenvectors, PC time series and the loading of the principal components were then computed. The Screet test technique was applied as a trial for addressing the problem of determining the number of PC modes that should be retained. Two of the first PCs, which account for 68.1% of total variance in the rainfall data, were kept and used for the regionalization of rainfall data. The rotation solution was then selected as a suitable tool for delineating the rainfall region associated with the retained PCs. The results indicated that for the first PC, loading became high over most part of the study area. Therefore, the time series of PC1 that accounts for about 60.4% of the variance in raw data, could be used as the regional time series of winter rainfall over most parts of the provinces studied. The second PC revealed a high loading over a small area in northern part of the regions studied (Bavanat in Fars Province). Rainfall in this station showed poor correlation with the precipitation over the neighboring station in Fars Province. It seems that the rainfall in Bavanat is mostly influenced by the Mediterranean air masses entering the area through the northern and western districts. For the other parts of the regions studied, Sudan current which encroaches the country through southwestern borders (Persian Gulf regions) make up an essential portion of winter rainfall.

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The present study evaluates the influence of the El Ninio Southern Oscillation (ENSO) phenomenon on the cold season precipitation over Isfahan, Fars, Khuzestan, Chaharmahal-Bakhtyari, Bushehr and Kohgiluyeh-Boyerahmad provinces. The results indicate that the occurrence of La Nina events caused a 20% to 50% reduction in precipitation over Bushehr, Chaharmahal-Bakhtyari and southern Fars. The cold event did not change the total precipitation over the other parts of the region. In contrast to La Nina episodes, the occurrence of El Ninio events caused a 20% to 70% increase in rainfall in most of the study area. While the most highly wet conditions are related to the El Ninio events, the occurrence probability of the severe droughts has found to be low during such events. In association with La Nina events, the occurrence probability of severe drought was found to be low. Only in Khuzestan and southern parts of the Fars Provinces, this probability has increased to about 0.5.

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In Iran, about 75% of national rice production is supplied in Gilan and Mazandaran proviences which have the highest amount of precipitation. Seasonal prediction of rainfall induces significant improvement on yield production and on preventing climate hazardz over these feritle areas. Canonical correlation analysis (CCA) model was carried out evaluates the possibility of the prediction of winter rainfall according to the states of ENSO events. The time series of (southern oscilation index (SOI) and SST (sea surface temperature) over Nino's area (Nino's SST) are used as the predictors, and precipitation in Bandar Anzali and Noushahr are used as the predictands. Emperical orthogonal functions (EOF) were applied for reducing the number of original predictors variables to fewer presumably essential orthogonal variables. Four modes of variations (EOF1, EOF2, EOF3, EOF4) which account for about 92% of total variance in predictors field were retained and the others were considered as noise. Based on the retained EOFs and precipitation time series, the canonical correlation analysis (CCA) was carried out to predict winter precipitation in Noushahr and Bandar Anzali. The results indicated that the predictors considered account for about 45% of total variance in the rainfall time series. The correlation coefficents between the simulated and observed time series were significant at 5% significant level. For 70% of events the anomalies of observed and simulated values have the same sign indicating the ability of the model for reasonable prediction of above or below normal values of precipitation. For rainfall prediction, the role of Nino's SST (Nino4 in particular) was found to be around 10% more influential than SOI. .

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The influence of the Sea Surface Temperatures (SSTs) on the seasonal precipitation over northern and southwestern parts of Iran was investigated. The warm, cold and base phases of the SSTs were defined and the median of precipitation during each of these phases (R_w, R_c and R_b, respectively) was determined. The magnitude of R_w/R_b, R_c/R_b and R_c/R_w were used as criteria for the assessment of the effects of the alternation of SST phases on seasonal precipitation. The results indicate that in association with cold SST phase, winter rainfall is above median over western and central parts of the coastal region, central and southern parts of Fars Province and all the stations studied in Khozestan Province. On the other hand, the prevalence of warm SST phase has caused about 20% decrease in winter precipitation over the Caspian Sea coastal area and northern parts of both Fars and Khozestan provinces. In association with warm SST phase in winter, precipitation during the following spring was found to be above normal for all the stations studied in the coastal region of the Caspian Sea. The highest sensitivity levels were found in Bandar- Anzali and Astara for which spring precipitation has increased by 80% due to the dominance of warm winter phase. However, the occurrence of boreal cold SST events causes shortage of precipitation in the eastern parts of the coastal areas along the Caspian Sea. A Possible Physical mechanisem justifying the influence of the Caspian Sea SST on the Precipitation variability was introduced. According to this mechanisem, temporal and spatial variability of the Siberian High is forced by the fluctuations in these SSTs.

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Since the fluctuations of the Persian Gulf Sea Surface Temperature (PGSST) have a significant effect on the winter precipitation and water resources and agricultural productions of the south western parts of Iran, the possibility of the Winter SST prediction was evaluated by multiple regression model. The time series of PGSSTs for all seasons, during 1947-1992, were considered as predictors, and the time series of MSSTs during 1948-1993, as the prrdictand. For the purpose of data reduction and principal components extraction, the principal components analysis was applied. Just the scores of the first four PCs (PC1 to PC4) that accounted for the total variance in predictor field were considered as the input file for the regression analysis. For finding the dependency of each principal component to the first time series of the PGSST, the Varimax rotation analysis was applied. The results have indicated that PC1 to PC4 respectively are the indicator of temperature changes during winter, autumn, Spring and Summer. According to the regression model, the components of PC1, PC2 and PC4 were significant at 5% level. But the components of PC3 was insignificant. The results indicated that the significant variables are held accountable for the 33.5% of the total variance in the winter PGSSTs. It became obvious that for the prediction of the winter PGSST, the PGSST during the winter of the last year has a particular importance. At the next stage, autumn and summer temperature have also a role in prediction of winter PGSST.

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The Madden Julian oscillation (MJO) is known as the primary mode of large-scale inter-seasonal variability in tropical regions, affectimg equatorial and sub-tropical climates. This study investigated the effects of the MJO on the occurrence of wet and dry spells in Fars province, central southern part of Iran, during November-April. Monthly precipitation data of nine stations spread over various parts of the province was analyzed during 1979-2005. Using two well-known MJO indices: MK and WH, the positive and negative phases of the MJO phases (enhanced and suppressed convective activity over the equatorial Indonesian region, respectively) were identified for monthly and seasonal scales. Precipitation-MJO composites were then constructed for the opposite phases. It was shown that for all the considered stations, seasonal precipitation during negative MJO phase was significantly greater (from about 2.5 to 6.0 folds) than the corresponding values during the positive phase. Moreover, the applied statistical tests proved that the frequency of wet or dry events was related to the prevalence of negative or positive MJO phase, respectively. As the positive MJO phase was engulfed, the probability of dry events varied from 60% to 84%. On the other hand, the probability of wet events was found to vary from 60% to 76% during the MJO negative phase.