K. Kamali; Gh. Zehtabian; tayybe Mesbahzadeh; M. Arabkhedri; Hossain Shohab Arkhazloo; A. Moghadamnia
Abstract
Introduction: Soil quality is an essential indicator for sustainable land management that generally depends on soil physical, chemical and biological properties. Due to the multiplicity of soil properties, the number of variables is usually reduced to a minimum set by statistical methods, which reduces ...
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Introduction: Soil quality is an essential indicator for sustainable land management that generally depends on soil physical, chemical and biological properties. Due to the multiplicity of soil properties, the number of variables is usually reduced to a minimum set by statistical methods, which reduces study time, decreases monitoring cost for sustainable use of agricultural lands. The aim of this study was to introduce the most effective soil characteristics of agricultural lands in Mohammadshahr plain, Karaj, to prevent the descending trend of soil quality.
Materials and Methods: In this study, four farms and orchards which were different in terms of crop type and irrigation system were selected and evaluated with Integrated Quality Index (IQI) and Nemero Quality Index (NQI). In both indicators, the characteristics affecting soil quality are combined in the form of a mathematical model and presented as a numerical quantity. For this purpose, first 12 soil profiles were described, followed by sampling from topsoil (surface layer) and sublayers (weighting average for the depths) and testing 17 soil characteristics affecting its quality. In the next step, both indicators were calculated using two different sets of soil properties. The first category, the Total Data Set (TDS), included all measured soil characteristics, and the second group, the Minimum Data Set (MDS), included the most important properties affecting soil quality. The Principle Component Analysis was implemented to select the MDS. Soil properties were scored to calculate IQI and NQI. For this purpose, a function was defined for each soil feature to standardize all scores between zero and one. Weighting various soil quality properties was also performed by calculating the common variance of the variables, which was obtained by factor analysis method.
Results and Discussion: Calculation of IQI and NQI indices showed that the topsoil samples were in grade III and sublayer samples belonged to grade IV with major limitations due to lack of profile development, organic carbon deficiency, salinity and high gravel. Four and six items out of 16 variables were identified effective for topsoil and sublayers, respectively. The IQI index based on TDS was more accurate and sensitive than the NQI index for soil quality assessment, as more features are considered for TDS. In the IQI index, both the weight of attributes and their scores are effective, while in the NQI index, only the attribute score is considered. On the other hand, the coefficient of determination between the TDS and MDS for topsoil and sublayer samples was 0.55 and 0.56% for IQI model, respectively, and 0.48 and 0.16% for NQI model, respectively. In other words, the determination coefficients showed the reliability of using the MDS instead of TDS in both IQI and NQI models. In the MDS, mean weight diameter (MWD) showed the highest effect on the surface layer and percentage of gravel had the greatest impact on the soil quality of the sublayer.
Conclusion: Although TDS took into account all soil properties and showed a slightly higher coefficient of determinations with both soil quality indicators, the MDS obtained similar results to the TDS with only about half of the properties. In the MDS, the features with an internal correlation is eliminated rendering it more cost effective. The results of this study assist decision-makers to choose better quality management and soil sustainability strategies while decreasing the monitoring cost.
M. Rezaei; M. Nohtani; A. Moghaddamnia; A. Abkar; M. Rezaei
Abstract
One of the most important problems in the management and planning of water resources is to forecast long-term precipitation in arid region and hyper arid regions. In this study, statistical downscaling model (SDSM) is used for study of climate change effects on precipitation. The data used as input to ...
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One of the most important problems in the management and planning of water resources is to forecast long-term precipitation in arid region and hyper arid regions. In this study, statistical downscaling model (SDSM) is used for study of climate change effects on precipitation. The data used as input to the Model are daily precipitation of Kerman and Bam synoptic stations, NCEP (National Centers for Environmental Prediction) data and the A2 and B2 emission scenarios HadCM3 for the reference period (1971-2001). Using HadCM3 A2, B2 data the precipitation for three period (2010-2039), (2040-2069) and (2070-2099) are predicted and compared with the reference period. We used the first 15 years data (1971-1985) for the calibration and the second 15 years data (1986-2001) for model validation. Research results showed that the precipitation will change and Change directions are positive in some months and negative in other months. After the examination function Indexes results from SDSM model shown that this model has better accuracy and a high ability to predict precipitation in arid region than hyper arid region.
J. Soltani; A. Moghaddamnia; J. Piri; J. Mirmoradzehi
Abstract
Nowadays, accurate estimation of evaporation as one of the important elements of hydrological cycle can play an important role in sustainable development and optimal water resources management of the countries facing water crisis. Up to now, empirical methods and formulas on estimation of non-linear ...
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Nowadays, accurate estimation of evaporation as one of the important elements of hydrological cycle can play an important role in sustainable development and optimal water resources management of the countries facing water crisis. Up to now, empirical methods and formulas on estimation of non-linear and complex process of daily pan evaporation have been developed that is of uncertainty. These methods and formulas do not have high accuracy and also access to their input parameters is difficult or their measurement requires high cost and time. In this study, performances of two non-linear models of NN-ARX and ANFIS have been evaluated to estimate daily pan evaporation under arid and hot climate conditions including dry and warm climate (Iranshahr), dry and coastal warm (Chahbahar), and semi-arid and warm temperate (Saravan). For this purpose, the best combination of model inputs was selected by using Genetic Algorithm embedded in Gama Test software for each of Synoptic stations located in these regions for the 5years period(2005-2010), then daily pan evaporation was estimated by using NN-ARX and ANFIS models. By employing the statistical criteria including R2، RMSE and MAE, performances of ANFIS model with three Gaussian membership functions and NN-ARX model were evaluated for each of the selective Synoptic stations. The obtained results indicate the accuracy of ANFIS model is higher than the one of NN-ARX model in estimating daily pan evaporation in different climatic conditions.
F. Bahreini; A. Pahlavanravi; A. Moghaddamnia; Gh. Rahi
Abstract
Desertification and land degradation in arid, semi-arid and sub-humid dry regions, are a global environmental problem. Therefore, accurate assessment of desertification trend will be useful to prevent and eradicate these problems. The study area is located in Daiyer city of Boushehr province. In this ...
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Desertification and land degradation in arid, semi-arid and sub-humid dry regions, are a global environmental problem. Therefore, accurate assessment of desertification trend will be useful to prevent and eradicate these problems. The study area is located in Daiyer city of Boushehr province. In this study, in order to assess land degradation according to local conditions of the study area, two factors of wind erosion and climate were selected as the main factors affecting desertification. Assessment of desertification status in the study area was conducted on basis of these two factors and weighting indices according to the IMDPA model. After separating work units (Geomorphologic facies), numerical value of each index was determined for each work unit, a data layer for each index was prepared and the layer related to each factor was specified by calculating the geometric mean of its indices score. Then, desertification intensity map was created by combination and determination of geometric mean of factors. The results indicated that 31.74% of the studied area falls within the medium class, 62.62% in the severe class and 4.65% in very severe desertification intensity class. Work units 6 and 8 with maximum quantitative values were placed in first priority of degradation. The work units 9, 13, 12, 10, 14, 15, 2, 7, 4, 3, 1, 11 and 5 with minimum quantitative values had lower priorities, respectively. Among the studied indices, two indices of dryness and non- living cover density percentage were the most important factors causing the desertification in this region.
