There are a number of different parameters like discharge which affect water quality and conditions. Typically, water quality is expected to improve by the increase in discharge and significantly influenced by the runoff water quality entering the river system. As a result this research subjected to address the increase of salinity in the wet season. To reach this point, a multivariate statistical technique, namely factor analysis (FA), has been used to assess the spatial variability of water quality parameters and conditions in the Karoon River basin located in southwest Iran. The various water quality parameters (10 variables) and discharge were incorporated to FA to better interpret the processes (natural) and specific source of water quality deterioration. The results revealed that water quality variations are affected mostly by dissolved mineral salts along the entire Karoon River. Furthermore, major contamination threat is caused by geological situation for over the year which is defined as nonpoint pollution source and may explain most part of the observed variances (50 %) in the data. Then as a general result of this study it can be claimed that spatially and temporally management of water use in different parts can be carried out effectively by FA technique.

This work aims to assess the capability of co-active neuro-fuzzy inference system (CANFIS) for drought forecasting of Birjand, Iran through the combination of global climatic signals with rainfall and lagged values of Standardized Precipitation Index (SPI) index. Using stepwise regression and correlation analyses, the signals NINO 1+2, NINO 3, Multivariate Enso Index, Tropical Southern Atlantic index, Atlantic Multi-decadal Oscillation index, and NINO 3.4 were recognized as the effective signals on the drought event in Birjand. Based on the results from stepwise regression analysis and regarding the processor limitations, eight models were extracted for further processing by CANFIS. The metrics P-factor and D-factor were utilized for uncertainty analysis, based on the sequential uncertainty fitting algorithm. Sensitivity analysis showed that for all models, NINO indices and rainfall variable had the largest impact on network performance. In model 4 (as the model with the lowest error during training and testing processes), NINO 1+2(t-5) with an average sensitivity of 0.7 showed the highest impact on network performance. Next, the variables rainfall, NINO 1+2(t), and NINO 3(t-6) with the average sensitivity of 0.59, 0.28, and 0.28, respectively, could have the highest effect on network performance. The findings based on network performance metrics indicated that the global indices with a time lag represented a better correlation with El Niño Southern Oscillation (ENSO). Uncertainty analysis of the model 4 demonstrated that 68 % of the observed data were bracketed by the 95PPU and D-Factor value (0.79) was also within a reasonable range. Therefore, the fourth model with a combination of the input variables NINO 1+2 (with 5 months of lag and without any lag), monthly rainfall, and NINO 3 (with 6 months of lag) and correlation coefficient of 0.903 (between observed and simulated SPI) was selected as the most accurate model for drought forecasting using CANFIS in the climatic region of Birjand.

Uncertainty assessment of groundwater modeling is important for sustainable groundwater management and planning. The purpose of this study is to assess parameter uncertainty of groundwater modeling in the Birjand plain, Iran. This arid aquifer was modeled using MATLAB-based MODFLOW to avoid propagating uncertainty associated with hydraulic conductivity and recharge parameters. So, the aquifer was divided into 17 hydraulic conductivity homogenous zones; besides, 9 recharge zones were considered separately. Parameter uncertainty was evaluated using the Monte Carlo (MC) sampling technique, namely, the generalized likelihood uncertainty estimation (GLUE). The results indicated that the performance of the GLUE based on the inverse error variance likelihood function was satisfied, because it gave the higher bracketing of observations equal to 86 %. Parameter uncertainty is well defined in the zones where they are not influenced directly by an inflow or outflow stream while hydraulic conductivity parameters of these zones follow approximately a normal distribution. In addition, groundwater modeling leads to a uniform exponential distribution in the zones with inflow or outflow streams.

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