Journal of Water Resources Engineering
Year:2015 | Volume:8 | Issue:26|Papers Count:8

Drought is a complex natural phenomenon caused by the breakdown of the water balance and its affection on the environmental field is obvious. In this regard, the application of remote sensing technologies in monitoring drought becomes mandatory where a satisfactory number of climatological stations is lacking. The present study employed NDVI, RVI, VCI, DSI, VSWI, SAVI, TCI and EVI vegetation indices derived from the TM sensor of the LANDSAT satellite in 13.06.2010 and 05.16.2000 for drought monitoring. Furthermore, the amount of precipitation in a span of 14 years (1998-2012) comprising 15 grade points were extracted from the TRMM satellite-assisted collected data with a special resolution of 0.25´0.25. The wet and dry years were identified using the Standard Index of Annual Precipitation (AIAP). The results show a five-year of drought (2011-2006) period, four years with normal and five years with adequate precipitation. The application of the NDVI and SAVI indices reveal that an increase in the minimum and maximum values is expected in the wet years. In addition, as the ambient temperature greatly affects the vegetative covers conditions, the difference between the values in the wet and drought years were obtained through combining the DSI and VSWI temperature and vegetation indices.

Diversion dams prevent fish migration along rivers. Fish ladder is an appurtenance of a dam, which facilitates fish migration. Therefore, the environmental problem as result of the construction of dams is controlled by adding a flexible fish ladder, which contains an adjustable outlet for the fluctuating water levels. A fish ladder outlet is an overflow type outlet with free flow in the side channel, which is embedded perpendicular to the fish ladder. Wide fluctuations in the river flow affect the performance of the fish passage and can cause the fish ladder not to function as it was expected. The mean first order second moment (MFOSM) method was used to analyze the uncertainty of the water level profile and the crest elevation of the fish passage outlet. The optimal design of that structure was the result of applying this method.

Due to the prolonged droughts in the recent decades, the importance of predicting the flow rate of surface water in rivers for water resources management increases. In this regard, the flow rates in the natural water ways, which is the most important supplement source for water in dam storages, are considered as the most vital factors in predicting surface water. In this study, a combined powerful model, using the artificial neural networks and wavelet theory, was developed to predict the flow rate of the Kor River at the Dashtbal hydrometric station located at the upstream of the Doroudzan Dam. The comparison of the results obtained from this model with those predicted by ANFIS inculcate the superiority of the former providing that the employed parameters are adequately selected. The ANFIS model with 4 Gaussian functions, and the Daubechies 4 wavelet within the third decomposition level occupied the 2nd and 3rd positions in accurate prediction after the combined model developed in this study.

Drought is one of the major natural disasters which affect human society more than all other calamities. In order to monitor the drought conditions in Nahavand City, the province of Hamedan, the monthly and annual rainfall data of the Varayeneh and Vasaj Stations were collected for 41 years (1969-2009). The drought condition were analyzed using Rainfall Anomaly Index (RAI), Standard Index of Annual Precipitation (SLAP), Percentage of Normal Index (PNI), Deciles Index (DI), Standardized Precipitation Index (SPI), Chinese Z Index (CZI), Modified CZI (MCZI) and Z Score Index (ZSI). The results indicate that the most severe drought recorded at both the Varayeneh and Vasaj stations based on the PNI, SPI, CZI, DI and ZSI indices in the water year of 1998-1999. According to the MCZI index, a severe drought, was observed at both the Vasaj and Varayeneh stations in the water year of 1970-1971. Based on SLAP index, five severe drought cases were observed at the Varayeneh station, and seven severe drought cases were observed at the Vasaj station. Based on the RAI index, five severe droughts were observed at the Varayeneh station and seven severe droughts were observed at the Vasaj station. Moreover, based on the majority of these indices, the most severe drought of this 40-year period in Nahavand City occurred in the water year of 1998-1999.

The Sari-Ghaemshahr plain is located in the province of Mazandaran, in the southeast boarders of the Caspian Sea. The water of 44 production wells was sampled in the fall of 2011 and the spring of 2012 to determine their suitability for drinking and irrigation of the agricultural lands. The samples were analyzed for Ca+2, Mg+2, Na+, HCO3-, SO4-2, Cl- , SAR, PI, and KI. The samples were categorized as Fresh, brackish to saline from the type of CaHCO3, and mixture of (Ca, Mg, Cl) and Na-Cl based on the Piper diagram. The Gibbs diagram shows how to determine the chemical reaction and type of water. Ionic ratios reveal that the source of calcium and magnesium ions were carbonate rocks, sodium ions from (Na-rich) clay minerals and a solution of sodium chloride, and sulfate from human activities and not from the sedimentary origin. These reactions were governed by the geological formations and intrusion of saline water (sea, connate water). Although some of the water samples were categorized as hard or very hard according to the WHO drinking water standards and the Schuellers diagram, they were potable. Based on the USDA irrigation water standards, most of the wells contained very suitable to permissible water for irrigations, and only a small percentage of them had unsuitable water strict ground water management, particularly pumping reduction. The artificial recharge is recommended if the soil salinization, permeability reduction and saltwater intrusion into the freshwater aquifers are to be prevented. To avoid leaching soil, reducing the permeability and saltwater level, which means the unsuitable water, the arrangements of exploitation of water control such as raising the water table is used in the recharge province.

Improving the accuracy of the missing precipitation data, particularly in large watershed with low-density precipitation network, is one of the challenges of the hydrologists. This study investigated six different types of artificial neural networks, namely: the MLP, the TLFN, the RBF, the RNN, the TDRNN, the CFNN along with different optimization methods, and geostatistical methods namely the Kriging and the Cokriging models for infilling the missing daily precipitation. Daily precipitation records from 15 rain gaging stations located within the Karkheh Watershed in the southwest Iran, were used to evaluate the accuracy of different models for infilling data gaps of daily precipitation. The results suggest that the MLP, the TLFN the CFNN and the Cokriging can provide more accurate estimates of the missing precipitation values than the other ones. However, the MLP overall appears to be the most effective method for infilling the missing daily precipitation values. Moreover, the results show that the dynamically driven networks (RNN and TDRNN) are less suitable for infilling the daily precipitation records whereas the RBF are appeared to be fairly suitable. Also, the kriging model is less effective than the MLP, the Cokriging, the TLFN and the CFNN models, but shows better results than the RNN, TDRNN and RBF networks.

The sediment load of a river is one of the major parameters in designing a dam, as it not only affect configuration and construction on its upstream, but also due to its negative consequences resulting from sediment dredging and some environmental issues that arise in its downstream. Therefore, elucidation of sediment transport mechanisms is of utmost importance. As several independent and nonlinear factors affect this phenomenon, its randomness has been accepted by many hydrologists as an axiom. The chaos theory states that many complex systems with random behavior are limited only by the number of parameters, and their behavior is predictable in a short term. The dynamics of sediment transport, and also the influence of a dam on its patterns are studied in this project benefitting from the chaos theory, and the reconstructed phase space and correlation dimensions methods. Results indicate that the sediment time series have low-dimensional chaos, and in a period as short as 10 days, a dam affects the dynamics and converts a chaotic phenomenon in to random ones. However, due to the created information and disconnection that takes place in a chaotic system in longer times, i.e. one month, the above mentioned transformation disappears an the system resumes a chaotic behavior.

Drought management is a holistic approach applied by farmers can significantly reduce the outcomes of the drought. The farmers' knowledge about the drought management is essential in framers’ policy. This study aimed to assess the relation of the knowledge of the farmers in the City of Miandoab with the drought management using the descriptive and correlation methods. A questionnaire was the main tool to collect data. The questionnaire's robustness was confirmed by experienced faculty and experts. The reliability of the questionnaire was assessed using the Cronbach's coefficient alpha (0.73- 0.86). Farmers in Miandoab were the statistical population. Samples were selected using a two-stage cluster sampling method (n=120). The results show that a mean score of farmers' knowledge in the drought management was 9.72 out of 20. Surveying the test results also show that the knowledge of the insured farmers and those participated in extension classes was significantly more than that of the non-insured and non-participants in the extension classes. The correlation analysis shows that there is a significant relationship between knowledge score in the drought management and the age of farmers, orchard area and the annual income.