Showing 6 results for Bashari
R. Roghani, S. Soltani, H. Bashari,
Volume 16, Issue 61 (fall 2012)
Abstract
Southern Oscillation Index (SOI) and Sea Surface Temperature (SST) patterns affect rainfall in many parts of the world. This study aimed to investigate the relationship between monthly and seasonal rainfall of Iran versus SOI and Pacific and Indian sea surface temperature. Monthly rainfall data, from 50 synoptic stations with at least 30 years of records up to the end of 2007, were used. Monthly and seasonal time series of each station were divided to several groups by four methods (Average SOI, SOI Phases, Indian SST Phases and Pacific SST Phases) using Rainman software and with regard to 0-3 months lead-time. Significant differences among rainfall groups in each method were assessed by the non-parametric Kruskal-Wallis and Kolmogorov-Smirnov tests, and the significant relationship was validated using Linear Error in Probability Space (LEPS) test. The results showed that SOI during summer (July-September) was related to autumn (October-December) and October rainfall in the west and northwest of Iran and the west Caspian Sea coast. The El Niño (negative) phase was associated with an increase in rainfall and the La Niña (positive) phase was associated with a decrease in rainfall in these regions. Average SOI is a useful index for rainfall forecasting in the above-mentioned areas. However, Indian and Pacific SST phases are not suggested for rainfall forecasting in Iran, duo to weak or non-persistence relationships. In conclusion, Iran rainfall is not limited to SOI, Pacific and Indian SST therefore, Rainman could not be used as an aid to water resources management over a year in Iran. It is suggested that we study the teleconnection between Iran rainfall and other ocean-atmospheric oscillations developing a model similar to Rainman in order to that we investigating the variation in Iran rainfall with aid of other effective ocean-atmospheric indicators
M. Mollaei, H. Bashari, M. Basiri, M. R. Mosaddeghi,
Volume 18, Issue 70 (winter 2015)
Abstract
Soil aggregate stability is considered as a key indicator of soil quality and health assessments in rangelands. Many factors and properties such as soil texture, organic carbon, calcium carbonate, sodium adsorption ratio, and electrical conductivity might affect soil aggregate stability. The effects of these factors on aggregate stability of 71 soil samples collected from 4 rangeland sites (2 in semi-arid and 2 in arid lands) in Isfahan province were investigated. Aggregate stability was measured using the wet-sieving method. To optimize the trial conditions for the investigated soils, three shaking times (5, 10 and 15 minutes) were used to impose different hydromechanical stresses on the aggregates of ten soils selected out of the studied soils. The structural stability was assessed using mean weight diameter (MWD) and geometric mean diameter (GMD) of the water-stable aggregates. Significant differences of MWD were observed between the shaking times. The 10-min shaking was selected as best for structural stability assessment in the studied regions because it resulted in better differentiation of soils on the basis of structural stability. Among the intrinsic properties, soil organic carbon content had the most important role in aggregate stability in all zones. However, electrical conductivity (in addition to organic carbon content) had an important role in aggregate stability in the arid rangelands. Log-normal distribution and GMD could represent better the aggregate size distribution when compared with normal distribution and MWD in the studied regions. Overall, wet-sieving method with shaking time of 10 min is suggested to assess the soil structural stability in rangelands of Isfahan province. Therefore, soil aggregate stability and the factors affecting this vital indicator can be used efficiently for assessing and monitoring management effectiveness and rangeland functionality trend.
A. H. Boali, H. Bashari, R. Jafari, M. Soleimani,
Volume 21, Issue 2 (Summer 2017)
Abstract
Appropriate criteria and methods are required to assess desertification potential in various ecosystems. This paper aimed to assess desertification levels in Segzi plain located in east part of Isfahan, with a focus on soil quality criteria used in MEDALUS model. Bayesian Belief Networks (BBNs) were also used to convert MEDALUS model into a predictive, cause and effects model. Soil samples were collected from 17 soil profiles in all land units and some of their characteristics such as texture, soluble sodium and chlorine, organic material, Sodium Absorption Ratio (SAR), Electrical Conductivity (EC) and CaSo4 of all soil samples were determined in soil laboratory. The effects of measured soil quality indicators on desertification intensity levels were assessed using sensitivity and scenario analysis in BBNs. Results showed that the used integrated method can appropriately accommodate uncertainty in the desertification assessments approaches created as a result of the influence of different soil characteristics on desertification. According to the results of MEDALUS model, 28.28 % and 71.72 % of the study area were classified as poor and moderate areas in terms of soil quality respectively. Sensitivity analysis by both models showed that soil organic matter, SAR and EC were identified as the most important edaphic variables responsible for desertification in the study area. Evaluating the effects of various management practices on these variables can assist managers to achieve sound management strategies for controlling desertification.
A. H. Boali, R. Jafari, H. Bashari,
Volume 21, Issue 3 (Fall 2017)
Abstract
This paper aimed to assess the severity of desertification in Segzi plain located in the eastern part of Isfahan city, focusing on groundwater quality criteria used in MEDALUS model. Bayesian Belief networks (BBNs) were also used to convert MEDALUS model into a predictive, cause and effects model. Different techniques such as Kriging and IDW were applied to water quality data of 12 groundwater wells to map continuous variations of the CL, SAR, EC, TDS, pH and decline in water table indices in GIS environment. The effects of measured water quality indicators on desertification severity levels were assessed using sensitivity and scenario analysis in BBNs model. According to the results of the MEDALUS, the desertification of the study area was classified as severe class due to its low quality of groundwater. Sensitivity analysis by the both models showed that decline in waater table, water chloride content and electrical conductivity were the most important parameters responsible for desertification in the region from ground water condition standpoint. The determination coefficient between the outputs of the MEDALUS and BBNs models (R2>0.63) indicated that the results of both models were significantly correlated (α=5 %). These results indicate that the application of BBNs model in desertification assessment can appropriately accommodate the uncertainty of desertification methods and can help managers to make better decision for upcoming land management projects.
M. Kazemi, H. Karimzadeh, M. Tarkesh Esfahani, H. Bashari,
Volume 22, Issue 4 (Winter 2019)
Abstract
Evaluating the possible relationships between vegetation and environmental characteristics can assist managers to identify effective factors influencing plants establishment and to characterize various vegetation communities. This study was aimed to evaluate the effects of long term grazing exclusion ( more than 33 years) and the controlled grazing system (resting – rotation grazing system) on the vegetation distribution and some soil properties in the Hamzavi research station in Hanna area-Semirom, Isfahan. Six transects (three parallel transects and three transects perpendicular to the general slope of the area) were established in each area and 10 square plots with the size of 2m2 were placed along each transect; then, the cover percentage, production and list of all plant species were recorded. In each area, eighteen plots were collected randomly and in each plot, five soil samples were collected from 0-30 cm of the soil and then the samples were mixed and one sample of the compound was selected as an evidence plot. Soil properties such as pH, EC, CaCO3, organic carbon, absorbable phosphor, total nitrogen, K, Ca, Mg, soil saturated percentage, cation exchange capacity, soil clay, silt, sand and fine sand contents were measured in the soil laboratory. The independent t test was used to compare the vegetation characteristics in two areas. Cation exchange capacity, CaCO3, gravel percentage, soil phosphor content and grazing management were identified as the most discriminative factors in separating vegetation communities based on Canonical correspondence analysis (CCA) and cluster analysis. Controlled grazing management significantly modified some soil characteristics and increased the production (352 versus 184.2 kg/ha) and vegetation cover percentage (25.46 versus 18.37), as compared to the exclusion area (α= 5%). The vegetation density was increased significantly in the exclusion rather than controlled grazing area (3.03 versus 2.02 plant/m2). This study, therefore, revealed that controlled grazing management was more effective on improving some soil quality and vegetation characteristics rather than p long term grazing exclusion in the semi-arid ecosystems. So, avoiding long term grazing exclusion in semi-arid rangelands is suggested.
F. Hadian, R. Jafari, H. Bashari, M. Tarkesh,
Volume 23, Issue 4 (Special Issue of Flood and Soil Erosion, Winter 2019)
Abstract
Soil moisture is one of the most important factors that can affect productivity in ecosystems in arid and semiarid regions. The aim of this study was to investigate soil moisture and vegetation changes in the Isfahan province at the seasonal scale. For this purpose, MODIS Land Surface Temperature (LST) and NDVI data were used to calculate the TVDI index, and the rate of soil moisture content was also measured at several soil depths including 5, 10, 20, 30 cm. in the growing season. Seasonal changes of LST and NDVI indices were also studied in different climate regions ranging from humid to hyperarid. The results showed that the changes in NDVI and LST in this region were different, depending on the climate type and soil conditions; the LST and its changes mostly depended on the amount of vegetation cover NDVI changes based on the plant phenology in humid regions, which was were greater than that in arid and semi-arid climates. Soil moisture monitoring indicated that the relationships between TDVI and different soil depths varied based on the seasonal conditions. In the early growing season, the soil moisture at the depth of 0-5 cm had a higher correlation with TVDI, but in the middle of growing season, the deeper soil moisture (10-30 cm) showed the highest correlation. Therefore, the findings of this research indicated the importance of the growing season, soil conditions and vegetation percentage and types in the soil moisture studies by using satellite data.
