IRAN-WATER RESOURCES RESEARCH
Year:2015 | Volume:10 | Issue:3 (31)|Papers Count:12

In Iran, the history of the scientific study of flood loss is not so long and is limited to structural losses. The calculation of agricultural losses due to flood are more difficult to assess, due to its dependence on plant type, plant's strength during flood period, and the coincidence of the flood occurrence with the plant growing stages. In this article, the required functions for determining the expected loss with respect to the most important hydraulic parameters and the plant's growing stages are presented for rice. In the case study the two factors of loss percent per growing stage (b) and the probability of the coincidence of flood occurrence with different growing stages (a) were used to calculate the expected loss. The CCHE was used as the hydraulic model and all calculations were performed in GIS environment. Currently for any flood prone area, the flood loss is considered as 100%. While the outcomes of this research illustrates that flooding may even cause no loss and one may find points in the flood prone area where the loss is zero. The presented procedure can be used as a basis for determining the required criteria for flood risk management and developing a comprehensive insurance program against flood.

Potable water in Iran, like most parts of the world, is scarcer every day under the effect of factors such as the drought, population growth, and increasing per capita consumption. In these conditions human effort for optimum use of this non-replacable resource seems necessary. In this regard, estimates of water demand in the future, provides the opportunity for decision makers to consideri the constraints and leading disasters to adopt of the necessary measures. In this study, the per capita water demand forecasts from both point and interval approach is used. After estimating water demand function, the possible scenarios for the future of the independent variables are predicted. Then effects of changes in economic parameters in the water use for domestic water demand are discussed. The results emphasize the importance of commodity water. In order to point prediction, four scenarios were defined and independent variables were predicted. Percent of the first three scenarios of water demand (for the case that the subsidies plan is not implemented) for the period 2011 to 2032 is 40 to 57 percent. In the fourth scenario that assumes subsidies plan, amounts of per capita water demand decreases 3 and 2 cubic meters, resepectively for years 2011 and 2013 compared to the situation that the subsidies were not implemented. Finally, by use of neural networks, the long-term water demand is predicted. The results showed that the use of indecisive part of time series in demand prediction by neural networks can solve the introspection problem of these models to some extent.

In recent years, many empirical methods and mathematical models have been developed to estimate runoff, among which the SCS curve number is the most important and widely used method. In this method a parameter called Curve Number (CN) is used for transforming rainfall to runoff. Currently, different techniques such as RS and GIS in combination with filed surveying are used to estimate this parameter. These methods due to the huge amount of required data, field investigation, and computation burden are costly and time consuming. In this research based on the saturation excess concept, which is used in the structure of a semi distributed model called TOPMODEL, a new efficient method with a minimum data requirement was developed to estimate the SCS curve number. The proposed method depends on three parameters: soil porosity, average distance to the catchment water table, and the parameter that controls the effective depth of the saturated soil. The Kasilian catchment in northern Iran is used to investigate the proposed method. In this study, using lansat7 ETM+ images and performing some modification on the images the landuse map was obtained. Finally, by combining this map and the watershed soil and slope maps and also the results of an extensive filed survey, the SCS curve number map was developed for the entire watershed. Also, TOPMODEL was calibrated in the Kasilian catchment for the estimation of proposed method parameters. Results indicate that the watershed's average curve number based on RS/GIS and the proposed methods are about 59.6 and 62.8, respectively. Furthermore, the results showed that the minimum and maximum differences between the Curve Number obtained from RS and GIS techniques and from the proposed method for the whole range of the used parameters are about 0.3% and 12% respectively.

Helmand River basin upstream Kajakai dam (Afghanistan), has a considerable role in its annual water yield. In order to deal with the ever going water conflict between Iran and Afghanistan, the knowledge about this basin’s long term hydro-meteorological conditions can be a useful measure for exploitation of the basin’s water resources. It is obvious that such an evaluation needs long term data that is a serious obstacle in Afghanistan due to the poor hydro-infrastructures. For such region with serious scarcity of data, application of the global databases and rainfall-runoff models can be a good alternative. The CRU database is one of these databases with relevant temporal and spatial resolution, which contains these climate data since 1901. SWAT model is also a well know rainfall-runoff model that has been applied for a few trans-boundary basins. Nevertheless, any application of CRU data and SWAT needs pre-assessments, which construct the main objectives of this research work. For this, the paper compared CRU data with the observed data of 17 meteorological stations in Afghanistan that resulted 313 mm compared to 323 mm as annual precipitation for the study area (the upper Helmand). The next step relates to calibration and validation of SWAT, which were done by a limited observed discharge data (1969 to 1979). Furthermore, the model was run using measured and CRU climate data, which the latter performed better. The results of SWAT for the entire period of CRU data set (1913-2012) revealed that there is heterogeneity between the time series before and after 1940, such that the annual rainfall decreases and annual temperature increases. Same behavior was seen for the time series of discharges and its annual average of discharges decreases from 8.1 BMC to 6.07 BMS for the same period. One of the considerable points of this research work is its methodology that can be applied for other transboundary river basins.

Wastewater Treatment Plants (WWTPs) are vital infrastructures for ensuring the human and environmental health. Environmental protection, preventing contamination of underground water resources and promoting public health, are the reasons that the WWTPs systems are one of the most important elements in the civilized societies. Failures in WWTPs operation may lead to adverse outcomes such as untreated wastes exclusion from WWTPs containing various chemical and biological pollutants and their entry into the urban environments and agricultural lands. That can lead to serious crises such as outbreaks of contagious diseases in the community. Hence, assessing the potential risks in WWTPs and designing the prevention and mitigation plans are necessary. In this paper, an algorithm has been proposed for risk assessment and management of WWTPs. In the proposed algorithm, the risk of a specific element of a WWTP is calculated by multiplying three parameters comprising probability of failure, intensity of probable damages, and vulnerability of element. The aforementioned parameters is quantified by means of experts and policy-makers’ viewpoints which are elicited from questionnaires. Based on the values of operational risk elements (probability, intensity, and vulnerability), different mitigation or prevention plans could be proposed to reduce risks. The proposed algorithm has been implemented on the Tehran South WWTP as a case study and earthquake risk in "biogas tanks" and the explosion risk in "disinfection units" gained first and second rank in the results, respectively. Result of this study shows that the proposed algorithm can be employed by managers and policy-makers of WWTPs as a robust and practical decision-making tool.

Domestic, industrial and agricultural water supply has become a formidable challenge in light of population growth. Various water stakeholders, particularly in arid and semiarid basins, compete for profit that brings about severe conflicts in water allocation. Upstream stakeholders usually take their increasing share such that downstream water demands are becoming more difficult to meet. In this respect, environmental demands, such those of lakes and wetlands, are increasingly neglected. Urmia Lake in northwestern Iran, known as the second largest saline lake in the world, is a vivid example of negligence in providing environmental water rights. Urmia Lake is currently under the environmental threat due to prolonged droughts which causes reduction in rainfall, inflow, and water level in the lake. For a realistic water resources management in such situation, the estimation of inflow threshold for each drought severity state is the first and most important step. In this study, based on the analysis of lake’s historical inflow time series, environmental requirement subject to various drought severity conditions was estimated as thresholds for decision making in the future. Then analysis of existing condition (current inflow with regard to in operation dams and water withdrawals) was considered by system dynamic approach and water inflow to the lake was calculated under each scenario and compared by determined thresholds. Results of basin simulation revealed that if the water inflow to the lake remained the same as the current condition and water withdrawals were continued as previous, the lake will remain in moderate hydrological drought condition even with no dam construction or development in the basin. Therefore we need a trade-off between the current development in the basin due to water withdrawals and water resources projects and maintain required lake inflow for surviving it.

In this study, the in-situ application of nano zero valent Iron/Nickel particles for Nitrate removal in groundwater resources was investigated. First, nano-particles were produced and stabilized with starch. Second, it was employed in batch and continuous experiments, using transparent column and bench-scale models. Based on batch experiments, the reaction kinetics was consistent with the adsorption model by the order of 1 to 1.3. The variation of the kinetics order depends on pH and Nickel content. So that highest reactivity was observed for Iron nano particles with 3% of Nickel. Based on continuous experiments, a seepage velocity of 10 m/d yielded the maximum removal efficiency. Furthermore, Nitrate remediation in a continuous system was mostly influenced by seepage velocity, quantity and freshness of nano particles, and grain size of porous media. In the batch mode, the maximum Nitrate removal was 99% while in the continuous mode it did not exceed 70%.

Self-Organizing Feature Maps (SOFM) are a category of artificial neural networks with a special ability in pattern recognition and data clustering using attributes of data points. In this study, ability of Kohonen self-organizing feature maps has been assessed in regionalization of Aras watershed in order to perform regional flood frequency analysis. Results showed SOFM can be used as an efficient tool for data clustering and regionalization of watersheds. Furthermore the results showed that the cluster validity measure is equal to optimum value for regionalization of Aras watershed, when the number of regions is equal to 4. Also heterogeneity measures are equal to optimum values, when number of regions is equal to 2 or 4. However, when number of regions is equal to 2, largest regions can be formed.

A group of sites with sufficient homogeneity in producing flood mechanisms form a homogeneous region for regional flood frequency analysis. Cluster analysis is the generic name of a variety of multivariate statistical procedures that are used to classify given data into similar groups or clusters. This procedure is an efficient method for regional flood frequency analysis. In this article, cluster analysis of annual maximum flow data of 34 gauging stations located in Lorestan province in Roodbar Dam and Bakhtiari Dam basins for 2 to 6 clusters has been performed using R programming language. K-means algorithm that is a type of partitional clustering has been used for cluster analysis. Merged groups of sites have been formed and homogeneous groups of Roodbar and Bakhtiari dam basins has then been identified by performing homogeneity test.

Seawater desalination is recently considered as an important source for providing drinking water largely due to the increased water consumption as well as depletion of available and accessible water resources. This is especially important in case of Iran which is located in an arid region facing the challenges of water resources shortage. Due to the progressive development of seawater desalination processes, the discharge of brine can potentially bring about physical, chemical, and ecological effects on the environment of receiving water resources. Therefore, the necessity of more accurate and better understanding of such effects and different aspects of desalination processes as well as the brine as the by-product is undeniable. The focus in the present paper is investigation on the effects of brine discharge on the quality of various parameters in the receiving water and providing solutions for minimizing the side-effects. Techniques such as use of cooling water in power plants, combination of brine with municipal sewage, groundwater desalination, and proper selection of sites and discharge procedures are among the practical proposed strategies.