Climate change’s effect on sea surface temperature (SST) at the regional scale vary due to driving forces that include potential changes in ocean circulation and internal climate variability, ice cover, thermal stability, and ocean mixing layer depth. For a better understanding of future effects, it is important to analyze historical changes in SST at regional scales and test prediction techniques. In this study, the variation in SST across the Persian Gulf and Gulf of Oman (PG&GO) during the past four decades was analyzed and predicted to the end of 21 st century using a proper orthogonal decomposition (POD) model. As input, daily optimum interpolation SST anomaly (DOISSTA) data, available from the National Oceanic and Atmospheric Administration of the United States, were used. Descriptive analyses and POD results demonstrated a gradually increasing trend in DOISSTA in the PG&GO over the past four decades. The spatial distribution of DOISSTA indicated: (1) that shallow parts of the Persian Gulf have experienced minimum and maximum values of DOISSTA and (2) high variability in DOISSTA in shallow parts of the Persian Gulf, including some parts of southern and northwestern coasts. Prediction of future SST using the POD model revealed the highest warming during summer in the entire PG&GO by 2100 and the lowest warming during fall and winter in the Persian Gulf and Gulf of Oman, respectively. The model indicated that monthly SST in the Persian Gulf may increase by up to 4.3 °C in August by the turn of the century. Similarly, mean annual changes in SST across the PG&GO may increase by about 2.2 °C by 2100.

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This research aims to assess contamination status of water and sediment in Sabalan dam reservoir (SDR) and evaluate the impact of water withdrawal depths on the carcinogenic and non-carcinogenic risks of metals for exposed people. Results of metal pollution indices revealed some degree of pollution in water and sediment of the reservoir, especially associated with arsenic. Risk assessment of metals in water of the SDR for non-carcinogenic materials through different scenarios of water withdrawal depth revealed that consuming water from the depth of 10 m can be somewhat troublesome to human health. The carcinogenic risk of arsenic from depth of 10 m of the reservoir was about four times greater than that from water surface. Minimum carcinogenic risk of consuming water in the reservoir was found to be 1.69 × 10E-4, which is higher than the maximum limit proposed by the U.S. EPA, indicating the water consumption from the SDR can result in harmful effects on human health.

The main objective of the present investigation is to predict longitudinal dispersion coefficient (K x ) in natural streams using artificial neural network (ANN) technique based on most famous training functions such as Trainlm, Trainrp, Trainscg, Trainoss, and so on. To achieve the goal, hydraulic and geometric data (shear velocity, channel width, local flow depth, and mean longitudinal velocity) that are easily obtained in natural streams are used. First, we have tried to review the most well-known of published work in the field due to find out deficiencies of them. Second, new approach of ANN model based on the famous training functions is applied for predicting K x in natural streams and then the best architectures for each training functions is selected by trial and error. Finally, Levenberg-Marquardt training function (Trainlm) is selected as the best choice for training the network parameters. Determination coefficient (R 2 ) and mean absolute error for ANN (Trainrp) model were equal to 0.94 and 33 in the training and 0.95 and 30 in the testing steps, respectively. It is hoped that the presented methodology in the research, can be useful in river water quality management studies.

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