JWSS - Journal of Water and Soil Science

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Point data of weather stations are not important in and by themselves. Therefore, it is necessary to change these point data into regional information. Undesirable distribution of weather stations and their data deficiency hinder the direct determination of the regional information, unless sufficient data in the study area could be provided. Providing extra data using the geostatistical methods is practical, scientific, simple and quick, but adopting a suitable method is the basic question. The objective of the present study is to find a suitable method to estimate monthly rainfall in the central region of Iran. In this regard, the methods of kriging (ordinary kriging, log-kriging, co-kriging), weighted moving average (WMA, with the power of 1 to 5), thin plate smoothing splines (TPSS, with the power of 2 and 3 and with covariable) were used. Cross validation technique was used to compare these methods. Based on the variography analysis, the range of influence of monthly rainfall in the central region is about 450 km. The results show that TPSS, with the power of 2 and with elevation as a covariable, was the most accurate method to estimate monthly rainfall. In addition, it is preferable to use the selected interpolation method in the sub-basins with homogeneous climates instead of considering the whole region.

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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.

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The model in this research was created based on the Artificial Neural Network (ANN) and calibrated in the Sefid-rood dam basin (excluding Khazar zone). This research was done by gathering and selecting peak flows of hydrographs from 12 sub basins, the concentration time of which was equal to or less than 24 hours and was caused only by rainfall. From all the selected sub basins, totally 661 hydrographs were prepared and their peak flows data wes used to make prediction model. The input variables of the model consisted of the depth of daily flooding rainfalls, and so the five days before rainfall of every peak flow, the area of sub basins, the main stream length, the slope of 10-85 percent of main stream, the median height of sub basins, the area of geological formations and rock units, classified at three hydrological groups of I, II, III, the base flow, and output variable was only peak flow. By using Feed Forward Artificial Neural Network with training method of back propagation error the function approximation of inputs to output was created by passing the three processes of training (learning), testing and validation. So based on that data and variables, the Multivariable Linear Regression model was created. The comparison of observed peak flows, based on validation data package, showed that the statistical parameters of (R²) coefficient and Fisher’s test parameter coefficient (F) for ANN model and MLR respectively were 0.84, 33.66 and 0.33, 3.60, indicating the superiority of ANN to traditional methods.

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Rainfall erosivity force as on important factor in soil erosion and sediment yield has been introduced in different indexes. The objective of this study was to determine suitable rainfall erosivity indices for two climates of semi-arid in Maravetape and very humid in Sangdeh, both in Khazar watershed, by correlation between rainfall erosivity indices and sediment outflow from erosion plots. For this purpose, the rainfall intensities in different time steps and the amount of rainfalls of 12 events in Maravetape and 11 events in Sangdeh have been used. Twonty five rainfall erosivity indexes were calculated based on rainfall intensity. The amount of soil loss measured after each rainfall event in 1.8×22.1 m2 erosion plots. The results of the study revealed that in very humid climate of Sangdeh and in semi-arid climate of Maravetape had high correlation of 0.803 and 0.727 (at the level of 99 percent) with sediment yield and they were applied indices in these climates of Khazar watershed. In general, the groups of 10 and 30 minutes are better than other erosivity indices in the study areas.