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Showing 3 results for Zolfaghari
Comparison of Baseflow Index in Hydrograph Separation with Different Methods in Some Rivers of West Azarbaijan Province
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.
The Comparison of k-NN and ANN for Digital Mapping of Salinity in Chahafzal, Ardekan
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.


An Assessment of Drought Severity Using Drought Indices based on Potential Evapotranspiration-Based and the TOPSIS Multi-Criteria Decision-Making Model at Selected Stations within the Zayandeh-Rud River Basin
F. Zolfaghari, S. Eslamian, A.r. Gohari, M.m. Matinzadeh, S. Azadi,
Volume 29, Issue 2 (Summer 2025)
Abstract
Drought represents one of the most critical natural disasters, exerting profound impacts on agriculture, society, the economy, and water resources. Various indices are used to monitor drought and its effects. This study aims to monitor drought in the Zayandeh-Rud Basin using the Standardized Precipitation Index (SPI), the Standardized Precipitation-Evapotranspiration Index (SPEI), the Evaporative Demand Drought Index (EDDI), the Palmer Drought Severity Index (PDSI), and the Reconnaissance Drought Index (RDI). All these indices are based on potential evapotranspiration, incorporating parameters such as precipitation, temperature, relative humidity, wind speed, and sunshine duration. These five indices were calculated and evaluated during the statistical period of 1993–2023 for meteorological stations in Isfahan, East Isfahan, Kabootarabad, Daran, Shahreza, Najafabad, and Mobarakeh. After calculating the indices and using spatial zoning maps, the studied stations were compared in terms of these indices. The continuity of dry and wet periods, as well as the intensity of droughts and wet spells, was analyzed. Subsequently, drought intensities during different years in these stations were ranked using the TOPSIS model based on factors such as precipitation, potential evapotranspiration, and station elevation. The results showed that in stations with a dry climate (such as Isfahan, East Isfahan, and Shahreza), drought occurrences (as indicated by higher rankings) have been consecutive over multiple years. Comparing the performance of the indices in the studied stations using spatial zoning maps revealed that the intensity of droughts and wet spells in regions with dry and semi-dry climates was not very significant. However, in areas with humid climates, the fluctuations in drought and wet spell intensities were quite substantial. The findings indicate that the PDSI and EDDI indices are more suitable for evaluating drought in dry climates.

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