Journal of Crop Production and Processing

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Probable Maximum Precipitation (PMP) is the maximum possible amount of precipitation which could occur in a gauging station, a region, or in a watershed. Probable maximum precipitation is usually estimated by two general methods: the first is synoptic method in which short period (hourly) meteorological parameters such as dew point, wind speed and air pressure are used. The second is statistical method which is based on the statistical analysis of the 24-h maximum precipitations. In this study, the amount of 24-h PMP was estimated by Hershfield, Bethlahmy and modified Bethlahmy methods using date obtained from meteorological and Ministry of Energy over 15 or more years.

 The results showed that there exist large differences between statistical and synoptic methods however, there are rather smaller differences between Bethlahmy and synoptic methods. For modified Bethlahmy method, the results were multiplied by a coefficient of relative humidity. Then the calibrated 24-h PMP values were estimated for all meteorological stations of Iran and a contour map of 24-h PMP for the country was developed.

Results showed that a minimum value of 24-h PMP (110 mm) occurred in the central part of country and a maximum amount (260 mm) was found in both south and north parts of Iran.

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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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The vast pastures and agricultural development plans for dry farming and irrigated farming in Khuzestan Province depend on rain. This requires availability of annual precipitation prediction models to be used in the management decision-making process. In this research, the long-term daily precipitation data from 15 rain gauge stations in the study area were collected for study and a relationship between the early fall season precipitations of 42.5 mm (t_42.5) and the annual precipitation was obtained. The results showed that the relationship was an inverse one such that the later the fall precipitation occurred, the greater the annual precipitation would be. To increase the coefficient of determination in the models, climatic variables such as Persian Gulf sea surface temperature and geographical characteristics (longitude, latitude, altitude, and long term mean annual precipitation) were used. Except for the long term mean annual precipitation and altitude, other variables did not increase the coefficient of determination. The final simple model found is as follows: P_a=184.787-1.891t_42.5+0.855P_m , R²=0.704 where, P_a is the annual precipitation, t_42.5 is the time from beginning of fall season for 42.5 mm of precipitation, and P_m is the long term mean annual precipitation.

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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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The Madden–Julian Oscillation (MJO) known as the dominant mode of tropical and extratropical intraseasonal variability has an important role in the coupled ocean-atmosphere system. This study investigates the eastward propagation of the MJO and its impact on monthly (February-April) maximum and minimum precipitation in Fars Province. The positive and negative phases of MJO were categorized for the period 1979-2002. The maximum and minimum values of monthly precipitation was then determined for each phase as well as for the entire length of records. The given results have indicated that, in February, both maximum and minimum precipitation during negative phase were significantly greater than the corresponding values during the positive phase. This implies that the enhanced February precipitation and flood events are associated to the negative MJO phase. On the other hand, severe water shotage in February was linked with prevalence of the positive phase. The results for April were mostly found to be similar to February except that minimum precipitation was not significantly associated to the positive phase. In contrast to February, minimum monthly precipitation in March was found to coincide with the negative MJO phase. Maximum precipitation, however, could coincide with neither of extreme phases of MJO.