M. Teimouri, M.r. Ghanbarpour, M. Bashirgonbad, M. Zolfaghari, S. Kazemikia,
Volume 15, Issue 57 (fall 2011)
Abstract
Baseflow separation has long been an important topic in hydrology and has a crucial role in water resources management in arid and semi arid regions like Iran. In this paper, a comparison among commonly used automated techniques for hydrograph separation including theoretical method of local minimum and digital filter of one parameter with different filtering parameters of 0.9 to 0.975 and two parameter methods was done to estimate baseflow using baseflow index. For this purpose, daily flow data in some stream gauging stations in west Azarbaijan province were used. For comparison, in addition to baseflow index the graphical method based on the observed daily flow data and correlation coefficient among them was utilized. The main aim of this research is distinguishing the most suitable method in hydrograph separation and estimating the baseflow. Results showed that in different methods baseflow largely contributes to streamflow and also has high fluctuations. However, the results of the digital filter with two parameters appear to be hydrologically more plausible than those of the other methods, but the results of digital filter with proper parameter - in this region one parameter method with filter of 0.925- has proper estimation accuracy. Also, the baseflow index based on method of two parameter digital filtering varies from 0.54 to 0.78 in this study area.
S. Ayoubi, R. Taghizadeh, Z. Namazi, A. Zolfaghari, F. Roustaee Sadrabadi,
Volume 20, Issue 76 (Summer 2016)
Abstract
Digital soil mapping techniques which incorporate the digital auxiliary environmental data to field observation data using software are more reliable and efficient compared to conventional surveys. Therefore, this study has been conducted to use k- Nearest Neighbors (k-NN) and artificial neural network (ANN) to predict spatial variability of soil salinity in Ardekan district in an area of 700 km2, in Yazd province. In this study, 180 soil samples were collected in a grid sampling manner and then soil chemical and physical properties were measured in laboratory. Environmental auxiliary variables were included topographic attributes, remote sensing data (ETM+) and apparent electrical conductivity (ECa). The result of the study showed that the K-mean nearest neighborhood had higher accuracy than ANN models for predicting soil electrical conductivity (ECe). Overall, k-NN models could provide significant relationships between soil salinity data and environmental auxiliary variables. The k-NN model had the root mean square and coefficient of determination of 12.10 and 0.92, respectively, between predicted and observed ECe data. Also, apparent EC, and remotely sensed indices and wetness index were identified as the most important factors for predicating the soil salinity in the studied area.
