S. M. J. Nazemosadat, A. Shirvani,
Volume 8, Issue 1 (4-2004)
Abstract
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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A. Fatehi Marj, A. Borhani Darian, M. H. Mahdian,
Volume 10, Issue 3 (10-2006)
Abstract
Orumiyeh Lake basin is one of the important regions in Iran from water resources and environment standpoints. In this basin, substantial part of the annual precipitation occurrs in spring, winter, and fall seasons. Due to semi-arid climate of the basin, rainfall forecasting is an important issue for proper water resources planning and management, particularly in drought years. On the other hand, investigations around the world show that there is a good conection between climatic signals and the amount of precipitation. In this paper, the relationship between climatic signals and seasonal rainfall was investigated in Orumiyeh Lake basin. For this purpose, monthly SPI (Standard Precipitation Index) was calculated and used along with six climatic signals including SOI (Southern Oscillation Index), PDO (Pacific Decadal Oscillation), PNA (Pacific North America), NAO (North Atlantic Oscillation), NINO3.4 and, NOI (North Oscillation Index). A new method employing the negative and positive phases of signals was proposed and tested to distinguish the relationship between the climatic signals and the individual stations rainfall in the basin. Furthermore, it was found that using joint signals substantially improves the precision of the forcast rainfalls. The results showed that fall and winter rainfalls had the highest correlatetions with SOI and NAO, respectively. Therefore, it would be possible to forecast the basin rainfall using climatic signals of the previous seasons.
. A. A. Sabziparvar1, S. Ebrahimzadeh2, M. Khodamoradpour3,
Volume 21, Issue 4 (2-2018)
Abstract
The most important factor in determining crop water requirement is estimation of evapotranspiration (ET). Majority of the methodsestimate ET apply series of relatively complex formula,which is then used to determine crop evapotranspiration (ETc). The parameters used in aforesaid methods are: Solar radiation, wind speed, humidity, etc. Unfortunately, in Iran and many countries, long-term records of these parameters are not readily available. The purpose of this study is to calculate the Selianinov Hydrothermic Index that merely requires daily temperature and precipitation data in order to determine correlation coefficients (r) versus ET and Crop Water Requirement (CWR) of some agricultural crops of Iran. First, the Selianinov index is calculated from daily precipitation and temperature during the growth season. Further, the results are correlated against both ETc and CWR. The model results indicate inverse (negative) strong exponential and polynomial relations between the dependent and independent variables. Coefficient of determination (R2) for polynomial equations (on average 0.84) in all crops was better than exponential equations (on average 0.72). Correlation between Selianinov index and CWR indicates that coefficient of determination in both equations was close together (0.83 for polynomial equations and 0.82 for exponential equations).
