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Showing 4 results for Faramarzi
Computation and Regionalization of Low Flow Indices and Determination of Hydrological Drought Durations in Karkhe Watershed
S. S. Eslamian, M. Ghasemi, S. Soltani Gerdefaramarzi,
Volume 16, Issue 59 (spring 2012)
Abstract
In this study, in order to determe low flow conditions in Karkhe watershed, 5 indices of Q7,10, Q7,20, Q30,10, Q4,3 and Q95 were used for analyzing 12 hydrometric station data in the years of 1345-46 to 1380-81. Discharge data homogeneity was performed by Run Test. The Q95 index was determined by flow duration curve (FDC) and other indices were determined using 4, 7 and 30-day low flow frequency analysis. After calculating the indices, periods of low flows were determined. The indices were regionalized by Kriging method. The results showed that for the most stations, low stream flows happened in the years of 1345-46, 1377-78, 1378-79, 1379-80 and 1380-81 and the percentages of stations having low flows in these years were 68, 92, 84, 75 and 59, respectively. According to the regional maps of low flows in Karkhe watershed, maximum low flows are located in central and southern areas and all of the mentioned indices decrease from south to the north of this watershed.
Evaluation of Palmer Drought Severity Index in Central Iran
S. Azadi, S. Soltani Kopaei, M. Faramarzi, A. Soltani Tudeshki, S. Pourmanafi,
Volume 19, Issue 72 (summer 2015)
Abstract

The Palmer Drought Severity Index (PDSI), which uses hydrometeorological variables to solve a simple water balance equation in the soil and considers the drought or wet conditions as dynamic phenomena, is used for the assessment of drought conditions in many parts of the world. The main goal of this study was to assess the PDSI based on its original assumptions, its regionalized status, using the outputs of already calibrated and validated SWAT model in central regions of Iran. The PDSI was assessed through five methods: 1) original Palmer Index without calibration in which the climate coefficients and the severity equation were derived for Kansas and central Iowa 2) original Palmer Index in which the coefficients of severity equations were adjusted 3) the Palmer Index with the calibration of equations in central areas of Iran 4) the Palmer Index using the soil moisture and potential evapotranspiration from SWAT model and 5) the Palmer Index using the soil moisture, potential evapotranspiration and runoff from SWAT model. The evaluation was conducted for 17 major basins covering the entire country with a monthly time step for the period 1990-2002. Then, using all five methods, the severity of the drought for 160 sub basins located in central Iran was calculated and evaluated. The results of this study indicated that method 4 provides more acceptable results. Also, the results of this research showed these methods clearly demonstrated (1992) as the wettest year and (2001) as the driest year. The approach used in this study is applicable to regional calibration of Palmer Index and the outputs of other hydrological models.


Digital Mapping of Soil Family Class Using the Machine Learning Approach (A Case Study: Semi-Arid lands in the West of IRAN)
Z. Maghsodi, M. Rostaminia, M. Faramarzi, A Keshavarzi, A. Rahmani, S. R. Mousavi,
Volume 24, Issue 2 (Summer 2020)
Abstract
Digital soil mapping plays an important role in upgrading the knowledge of soil survey in line with the advances in the spatial data of infrastructure development. The main aim of this study was to provide a digital map of the soil family classes using the random forest (RF) models and boosting regression tree (BRT) in a semi-arid region of Ilam province. Environmental covariates were extracted from a digital elevation model with 30 m spatial resolution, using the SAGAGIS7.3 software. In this study area, 46 soil profiles were dug and sampled; after physico-chemical analysis, the soils were classified based on key to soil taxonomy (2014). In the studied area, three orders were recognized: Mollisols, Inceptisols, and Entisols. Based on the results of the environmental covariate data mining with variance inflation factor (VIF), some parameters including DEM, standard height and terrain ruggedness index were the most important variables. The best spatial prediction of soil classes belonged to Fine, carbonatic, thermic, Typic Haploxerolls. Also, the results showed that RF and BRT models had an overall accuracy and of 0.80, 0.64 and Kappa index 0.70, 0.55, respectively. Therefore, the RF method could serve as a reliable and accurate method to provide a reasonable prediction with a low sampling density.

Frequency Analysis of Maximum Daily Rainfall and Determining the Best Appropriate Distribution Functions in the Bostan Watershed, Golestan Province
S. Yaghobi, Ch.b. Komaki, M. Hosseinalizadeh, A. Najafinejad, H.r. Pourghasemi, M. Faramarzi,
Volume 27, Issue 1 (Spring 2023)
Abstract
Frequency analysis of daily rainfall or return period of rainfall and flooding events is very important considering the behavioral complexity in water resources management; because ignoring it can lead to urban destructive floods. In the present research, three distribution functions of Pearson, Beta, and Gamma were compared to investigate and select the most appropriate distribution function for the precipitation data acquired from meteorology stations and CHIRPS satellite in seven stations in the watershed of Bustan Dam. Statistical analyses showed that satellite data were ineffective to estimate daily precipitation due to high errors in RMSE, MAD, and NASH. Meteorological data were used to spot the best distribution. Google Earth Engine and Python programming language were used. Then, the selected distribution function was used to determine the maximum daily rainfall, frequency probability, and return period of 2, 10, 50, 100, and 200 years. The results of the goodness of fit test, Error Sum of Squares, Bayesian Information Criterion, Akaike Information Criteria well as Kullback-Leibler Divergence showed that in five stations of Kalaleh, Qarnaq, Golestan National Park, Golestan Dam, and Glidagh, the Pearson function is the most suitable distribution function. Also, in the other two stations (Gonbad and Tamar), the Beta function was recognized as a suitable function. However, Gamma distribution in the study area is not efficient. So, it can be concluded that heavy and irregular rainfall can be effective in choosing the best distribution function at each station. Therefore, it is recommended to consider the maximum possible rainfall and as a result of the possible occurrence of floods with principled and accurate management to prevent human and financial losses in susceptible areas, especially in the study area.

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