The prediction of groundwater levels in a well has immense importance in the management of groundwater resources, especially in arid regions. This paper investigates the abilities of neurofuzzy (NF) and artificial neural network (ANN) techniques to predict the groundwater levels. Two different NF and ANN models comprise various combinations of monthly variablities, that is, air temperature, rainfall and groundwater levels in neighboring wells. The result suggests that the NF and ANN techniques are a good choice for the prediction of groundwater levels in individual wells.Also based on comparisons, it is found that the NF computing techniques have better performance than the ANN models in this case.

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Qualitative and quantitative restrictions on water resources have given rise to large water stress on land and plants. The recognition of such stresses can be of help in crop management. Due to the large impact on yield, water stress plays an important role in planning proper irrigation, timing, and amount of water needed by plants. Crop Water Stress Index (CWSI) is used for monitoring and quantifying water stress as well as for irrigation scheduling. This study was conducted for the purpose of determining the Maize (SC-701) irrigation Scheduling, use of leaf temperature in the north of Isfahan, Iran, in the crop year 2013, with five irrigation areas, where the amount of Total Available Water (TAW) was 35, 65, 75, 85, 100% respectively, in four replications. Results revealed that in return of TAW from 35 to 100%, Leaf and air temperature difference (Tl-Ta) reached 4 °C. CWSI rose about three times. CWSI in the day before the irrigation in treatment T1 and T5 was about 0.12 and 0.46, respectively. The results revealed that non-stress equation for corn in the T3 (75%TAW), was 7105 .and stress equation was completely fixed and is equal to 2.3. CWSI index is based on irrigation planning and it was 0.24. Examining yield results revealed that irrigation scheduling in this area should be done by treatment at 75% TAW.

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This study analyzed the annual streamflow of Karkheh River in Karkheh river basin in the west of Iran for flood forecasting using stochastic models. For this purpose, we collected annual stremflow (peak and maximum discharge) during the period from 1958 to 2015 in Jelogir Majin hydrometric station (upstream of Karkheh dam reservoir). A time series model (stochastic model or ARIMA) has three stages consists of: model identification, parameter estimation and diagnostic check. Model identification was done by visual inspection on the Autocorrelation and Partial Autocorrelation Function. Three types of ARIMA(p,d,q) models (0,1,1), (1,1,1) and (4,1,1) suggested for the studied series. The suggested model parameters were computed using the Maximum Likelihood (ML), Conditional Least Square (CLS) and Unconditional Least Square (ULS) methods. In model verification, the chosen criterion for model parsimony was the Akaike Information Criteria (AIC) and the diagnostic checks include independence of residuals. The best ARIMA model for this series was (4,1,1), with their AIC values of 88.9 and 77.8 for annual peak and maximum streamflow respectively. Forecast series up to a lead time of ten years future (2006 to 2015) were generated using the accepted ARIMA models. Model accuracy was checked by comparing the predicted and observation series by coefficient of determination (R2). Results show that the ARIMA model was adequate for the flood analysis in Karkheh River and the forecast of the series in short time at future.

One of the management tools for sediment and erosion control in the different scales from plot to watershed is informing about soil displacement process that can be obtained using fallout radionuclide spectroscopy. In recent decades, use of the radionuclides for determining sedimentation rate was common, among which Cesium ( 137 Cs) is the most often used. In this research, three, 4-meter long sediment cores were collected from the western part of the Anzali Lagoon. The Anzali Lagoon is one of the sediment treated ecosystems in the north of Iran. The level of 137 Cs of the sediment samples was measured based on Spectrometry analysis in the Atomic Energy Organization of Iran. The grain size distribution showed that the sediment samples were mainly fine textured (Silt with low plasticity properties). The results represented that the highest amount of the 137 Cs was observed in the depth of 2.4-2.7 m, which can be related to the Chernobyl disaster in 1986. An overall sedimentation rate of 8.5 cm yr -1 (=119 kg m -2 yr -1 ) was obtained based on the 137 Cs calendar of the sediment cores. This sedimentation rate is considerable, and a special arrangement is necessary to save the Lagoon.