مجله تحقیقات منابع آب ایران
سال پنجم شماره 2 (پیاپی 14، پاییز 1388)

Since the river flow regime is not always in harmony with the downstream water requirements, reservoir systems are constructed to regulate the natural river flow. Because of the spatial distribution of the water requirement sites, the storage system on a river may consist of several reservoirs. Due to the variable rainfall and river regime, the management policies play an important role for operation of the reservoir system. In this study, a deterministic Genetic Algoritem model is developed for optimal operation of the multireservoir water resource system in the north of Khorasan, northeastern Iran. The reservoirs are single purpose and regulate water for an irrigation project. The system is intended to maximize the total farm income. The system is made up of two reservoirs in series on Zangelanloo and Shoorkal rivers. Objective downstream farming fields are cultivated with a pre-determined multiple cropping pattern of wheat (27% and 18% in field 1 and 2, respectively), barley (30% and 26% in field 1 and 2, respectively), and sorghum (43% and 56% in field 1 and 2, respectively). The model developed in this study is used to obtain the optimal pattern of reservoir operation and water allocation among different crops for a definite combinations of state variables (reservoir storage classes at the beginning of the season and rainfall and inflow regimes). Total farm income were maximized. Running the model for 12 combinations of the state variables (4 reservoir storage classes and 3 regimes of dry, wet and average for rainfall and river inflow) showed that the results corresponding to the dry regime were sensitive to the reservoir storage class at the beginning of the season. In other regimes this sensitivity decreased. Also relative crop yield of field 2 decreased more in the dry regime, which may be due to the smaller reservoir in Shoorkal dam.

Surface water management practices are directly influenced by the streamflow forecasting, especially for the low-flow context. In this paper, the monthly low-flow time series were modeled and forecasted using a traditional stochastic model (Autoregressive Integrated Moving Average-ARIMA) and an artificial intelligence based model (Adaptive Network based Fuzzy Inference System-ANFIS). Low-flow in each month was defined as the minimum value of one, three, and seven day moving averages of daily streamflow. The performance of the stochastic model was compared to the neuro-fuzzy model through application to the streamflow data from the Navrood River basin in the Guilan state, northern Iran. The results showed that the stochastic model resulted in more accurate forecasted values than the neuro-fuzzy model for one, three, and seven day low-flow time series. Furthermore, in all neuro-fuzzy and stochastic models the error in forecasting three-day low-flow is less than those for one- and seven-day low-flow.

This research was aimed to investigate the changes of flood magnitude and frequency considering the uncertainty of AOGCM models that may occur due to the climate change predicted for the time period of 2040-2069. At first, monthly temperature and precipitation data of AOGCM models (models of TAR reports) were provided in the baseline period (1971-2000) and the target period (2040-2069) under the SRES emission scenario, namely A2. Then, these data downscaled spatially and temporally to Aidoghmoush Basin by proportional and change factor methods. Results showed temperature increase and precipitation variation in the target period compared to the baseline period. Monthly probability distribution function of temperature and precipitation in the period of 2040-2069 was constructed by weighting method; comparing observed and modeled temperature-precipitation. A semi- conceptual model (IHACRES) for simulation of daily runoff was calibrated for the basin. Using the Monte Carlo approach 2000 samples of temperature and precipitation were sampled from probability distribution functions and introduced to IHACRES. Finally 2000 series of daily runoff were simulated for the target period. Theoretical probability distribution was fitted to maximum annual flood series and the flood regime of the target period was compared to that of the baseline. Results indicated that the climate change will affect the flood regime of the basin.

Seasonal rainfall forecasts can effectively be used for resources planning and management - e.g., reservoir operations, agricultural practices, and flood emergency responses. Effective planning and management of water resources in the short term requires a general view of the upcoming season. In the long term, this needs realistic projections of scenarios for future variability and changes. In this paper, 33 years of rainfall data in the Khorasan region, northeastern Iran was analyzed. The study area is situated at 31°-38°N, 74°- 80°E. This synoptical data was trained by the Mamdani fuzzy Inference system and the artificial neural network. For performance evaluation, predicted outputs were compared with the actual rainfall data. First, synoptical relationships were investigated i.e. Sea Level Pressure (SLP), Sea Surface Temperature (SST), Sea Surface Pressure Difference (SLP), Sea Surface Temperature (SST) and Air Temperature at 850 hpa, Geopotential Height at 500 hpa, and Relative Humidity at 300 hpa. Models were then calibrated for the period of 1970 to 1992. Finally, the rainfall is predicted. Simulation results revealed that the Mamdani fuzzy Inference system and artificial neural networks are both promising and efficient. The root mean square for Mamdani fuzzy Inference system and the artificial neural network were 52 and 41 millimeters, respectively.

Stochastic models are among the most suitable methods for predicting Hydro-climatological data with seasonal variability incorporated with random processes. State space model of the second order was utilized to predict rainfall - runoff for one or more lag times. The input-output variables were modeled separately and the seasonal models of Box and Jenkins family was applied for description of both variables in Kardeh basin. This basin is located in the north-east of Mashhad city with an area of 242 square kilometers. The predicted monthly values of rainfall and runoff were calculated for the second half of the year 2005 and the beginning six months of 2006. The procedure is applicable to other similar basins.

Simulation of fluid flow in porous media has a wide range of applications including design of rockfill embankments, design of sand filters, or efficient management of ground water and oil reservoirs. Various efforts have been made during the past century to conduct such simulation modeling using both Darcy and non-Darcy laws. The nature of flow in porous media consists of a part which is under pressure and another part near the phreatic line which is exposed to atmosphere. Accordingly, a coupled pressurized-free surface flow model should be conceptualized using the network of pore body and pore throat. An effective modeling tool like an open source public domain software can then be used. In this study EPANET was modified to accommodate the nature of flow involved. For the verification purposes, a physical model was built in the Hydraulic Lab at the School of Engineering in Shiraz University. Steady state water surface profile and outflow discharge were monitored for different upstream water levels. This data were then used to calibrate and validate the developed computer model. Results showed that a satisfactory agreement between computer model and experimental records can be obtained for a wide range of upstream flow conditions. In a majority of cases, computer model captures more than 99% of variability in observed outflow discharge or water surface profile.

In areas with the relative annual precipitation of less than 100 ml, shallow waters, the underground sources of water, and other flow from the surroundings play a fundamental role on socio-economic condition of the regions. In the absence of the underground resources in Sistan province, southwestern Iran, amplified the role of Hamoon Lake. In fact, the wet and dry periods have largely influenced the socio-economical and the ecological aspects of this region. Hamoon lake consists of over 42 small islands which serves as the main food source of over 90000 cows. Besides, more than 80 villages are to a great extent dependent on this lake. Hunting and fishing is the main job in this area. Over 470000 various types of birds and more than 15000 tons of fish are hunted annually. Weaving of 30000000 square meters of mat is also a dominant job in the area. During the last 10 years the prevailing drought caused the Hamoon lake to dry up and the whole rush to be swept away. The whole socio-economic condition is significantly affected by this event. At present, not only are the inhabitants been deprived of benefiting from their natural resources, but they should also find alternative sources of income. The economical revival of the region strongly depends on the rehabilitation of Hamoon Lake. A task which to the policy makers looks almost impossible.