IRAN-WATER RESOURCES RESEARCH
Year:2009 | Volume:5 | Issue:2 (14)|Papers Count:17

The Geomorphologic Instantaneous Unit Hydrograph utilizes Horton's law and the drainage characteristics of the watershed. This is a simple approach to direct runoff computations in ungagged watersheds. Hydrologists have increasingly attempted to relate the watershed’s hydrological responses to watershed topographical characteristics. In this study three different categories of rainfall-runoff models proposed for ungagged watersheds, including a black-box model equipped with Geomorphologic characteristics called: the Geomorphologic 1-Artificial Neural Network (GANN) model, 2-a conceptual two parameter model (Nash model), and 3-Geomorphology Instantaneous Unit Hydrograph (GIUH) were evaluated in a middle size watershed. The applicability of these models were studied for ten rainfall runoff events of the Kassilian representative watershed located in the north of Iran. The results indicated that GANN model in runoff estimation is more powerful than the other two models. It can also be concluded that adopting the geomorphologic characteristics of watershed in the ANN model can promote this model from a pure black-box model to a model with more capabilities in simulation of a rainfall runoff relationship.

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 predetermined 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.

The value of irrigation efficiency cannot be precisely known. Therefore, water resources planning and irrigation network design are normally based on uncertain values of irrigation efficiency which ends up with disappointing results in practice. This research used "system" and "non system" approaches to analyze the data obtained from Pasha-Kola irrigation network in Mazandaran Province in northern Iran. This network is cultivated with rice and has a shallow water table condition. Furthermore, reported data for multiple cropping projects were obtained for Dez project in the Khuzestan province and Doroodzan project in the Fars province from other investigators and used to determine the "system" efficiency. In the "system" approach the deep percolation and surface runoff were not considered as water loss. However, these were considered as water losses in the "non system" approach. The project efficiency for "system" and "non system" approaches considering the deep percolation as water loss were obtained as 0.87 and 0.51, respectively. However, the project efficiency for the "non system" approach in which deep percolation was ignored was 0.85 which is similar to that obtained by the “system” approach. It may be concluded that, for irrigation projects with single crop (rice) and shallow water table, the project efficiency (either "system" or "non system") is generally higher than that of no shallow water multiple cropping networks. Furthermore, for rice irrigation projects, deep percolation of water may not be considered as loss due to its potential of being reused as groundwater supply and the "system" irrigation project efficiency is similar to the "non system" project efficiency. In general, it is more reliable that the "system" approach be used for evaluation of irrigation projects. Furthermore, in a "non system" approach the deep percolation may not be considered as water loss.

Surface water management practices are directly influenced by the stream flow 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 stream flow. The performance of the stochastic model was compared to the neuro-fuzzy model through application to the stream flow 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.

Introduction: Due to the variation of rainfall and inflow regimes in different years, it is necessary to adopt a suitable management model for reservoir operation. Water requirement may differ at different demand sites and therefore it can be satisfied with one reservoir or a network of reservoirs. Different methodologies are adopted for optimal operation of a system of reservoirs. These may be classified in different groups, Stochastic Dynamic Programming (Braga et al., 1991, Ghahraman and Sepaskhah, 2002), Fuzzy Rule Based Programming (Faye et al., 1991), Linear Programming (Mohan and Raipure, 1992) Deterministic Dynamic Programming- Regression Based (Karamouz et al., 1992), Mixed Integer Non-Linear Programming (Teegavarapu and Simonovic, 2000), and Dynamic Programming And Artificial Neural Network (Chnddramouli and Raman, 2001),. Since nearly all dams in Iran regulate water for agricultural uses, a wise discipline should be adopted for best allocation of limited water (especially in droughts) considering the uncertain future river yield.

Introduction: Water resources systems management is directly influenced by stream flow forecasting. It is therefore necessary to develop appropriate and applicable models for stream flow forecasting, especially in the low-flow context. Both stochastic models and artificial intelligence based models are widely used for simulation and forecasting of hydrologic time series. The literature shows that both models have performed well in different cases (Mishra et al., 2007) and thus it is difficult to know a priori which particular model would be better suited for a given problem.

Introduction: The intensified human activity and the growing population have changed the climate and the land biosphere. The linear warming trend over the last 50 years is 0.13oC (0.10oC to 0.16oC) per decade which is nearly twice that of the last 100 years. Other climatological parameters, such as precipitation, cloudiness, and evaporation have also showed strongly varying trends on both global and regional scales (IPCC, 2007). Anthropogenic climate change has been affecting not only the climate variables but also the extreme events e.g. drought, and flood. About the flood especially the trends in fluvial flooding are difficult to detect affected by factors such as land use, reservoir operations, drainage, or flood alleviation schemes in addition to changes due to the climate (Prudhomme, et. al. 2003).

Introduction: Fuzzy logic and ANN were previously used in rainfall prediction. Halid and Ridd (2002) had used Fuzzy logic technique to make a model for forecasting local precipitation in June in Hazanodin airport region, the biggest rice production area in Indonesia. Maria et al. (2005) had used Neural Networks and regression models to predict the amount of rainfall in Sao Paolo regions in Brazil. Cavazos (2000) had used Neural Networks for forecasting daily precipitation. Choi (1998) had used Neural Networks and Geographic Information Systems for forecasting precipitation.

Introduction Due to the seasonal variabilities of the hydro climatological data influenced by random variables, the stochastic models are among the best methods of prediction of these data.The hydrological models represent an approximation of the real processes. The predicted values by these models are never completely certain but the important matter in the modeling process is to reach an acceptable agreement to the real records.

Introduction Simulation of fluid flow and transport phenomena in porous media has a wide range of applications in science and engineering including design of rock fill embankments and sand filters, design of gabion spillways, ground water utilization, and efficient management of oil reservoirs. In the past one hundred years, various efforts have been made to simulate such flow using both Darcy and non-Darcy laws. In recent years, research has been done on converting original porous media into a 2-D and/or 3-D pore network model to address the challenging and complex issues in these media (Acharya et al., 2004; Al-Raoush et al., 2003; Wang et al., 1999; Thauvin and Mohanty, 1998).Such a pore network can be conceptualized by a rectangular lattice having cylindrical pore throats connecting to pore bodies on all sides. The pore body can be spherical or simply the shape created by all connecting pore throats. The pore network is composed of the voids between grains and the pore throat. The pore throat represents the channel connecting two pore bodies. As the flow is exposed to the atmosphere on three sides, the existing methodology in pipe network analysis cannot be effectively used to analyze the fluid flow. This study proposed a modified methodology to analyze such a network for pressure and velocity field.

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.

Introduction Hamoon Lake is not only the widest and the most important sweet lake of the country, but it also possesses bioenvironmental potentials as well as unique ecological values in Sistan.It also possess a special position in supplying the highest protein source in the region, The survival of the vast majority of the inhabitants in the area including hunters, fishers, and cattle owners, largely depend on this lake. These functions have been entirely damaged as a result of the recent drought. It is thus essential to investigate the causes and to seek the most promising solutions to overcome this situation. It is also needed that the policy makers and planning organizations think of rehabilitation of the socio economical and ecological state.

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 31o-38o N, 74o- 80oE. 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 (DSLP), Sea Surface Temperature (DSST) 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 rock fill 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.