JWSS - Journal of Water and Soil Science

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The analysis of the EC data set indicated that the spatial distribution of EC data of different depths are closely related to one another. It means that they are spatially cross correlated on one another and can be considered to be co-regionalized. It also implies that EC values at a particular depth contain useful information about the other depths which can be used to improve their estimation. In this research, we aimed to investigate the effects of using relevant ancillary information in the estimation procedure. To do this, cokriging was used. To evaluate this algorithm as a potential tool for mapping EC, its performance on the independent test data was evaluated and compared with the results obtained from studies using kriging. The results of the co-regionalization of EC at different depths indicated that cokriging the salinity data, although more rigorous from theoretical point of view, displayed no advantage over independent ordinary kriging at each depth. The results confirmed that cokriging improves little over ordinary kriging if the primary and auxiliary variables are almost equally sampled and all the variograms are identical. Also, ordinary kriging showed to be quite self-consistent since the predicted average salinity profile over the three depths was almost identical to the one predicted by cokriging. Considering the complexity of the cokriging and the LMC modeling, it is clear that there is no gain in using co-regionalization.

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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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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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