Journal of Range and Watershed Managment
Year:2011 | Volume:64 | Issue:1|Papers Count:8

The main objective of this study was to classify the plant communities and the most effective environmental factors on distribution of plant communities in Taleghan region. To achieve these objectives, plant sampling was done by randomized-systematic method in index region of each vegetation type. Plot and sample sizes were determined by minimum area and vegetation cover changes, respectively. Then, vegetation cover classification was performed by TWINSPAN analysis. In order to study the effects of soil properties on distribution of plant communities ,at the first and end of every transect, soil sampling was done and soil factors in depth of 0-30 centimeter including soil texture, phosphorous, potassium, nitrogen, lime, pH and EC were measured.  Principal component analysis (PCA) indicated that there is significant relation between distribution of plant communities and environment factors. Also, the result showed that aspect of slope, elevation, soil texture, lime and potassium percentages, and soil depth have meaningful impacts on distribution of plant communities.

This research was designed to evaluate applicability of some empirical methods including Fuller (1914), Sangal (1983) and Fill and Steiner (2003) approaches as well as the ANN (Artificial Neural Networks) technique to prolong instantaneous peak flow data series using daily measured flow data. To do this, after initial consideration, a number of 12 gauging stations located in arid and semi-arid regions of the north half of Iran were selected and the related data was collected. Then, the applied empirical methods were calibrated for the related stations, and the instantaneous peak flow was estimated for some specific years. To be able to have a reliable comparison, the same years were also used in testing phase of the ANN modeling. Comparing the results shows higher ability of ANN over the empirical methods for this specific application, although there was a considerable improvement of the results for empirical methods after calibration. Reliable results produced by ANN method, compared to the traditional methods, in this research showed superior abilities of new machine learning techniques to solve the problem of inadequate measured peak flow data periods and to optimize water related designs.

Most important reason of spatial changes in vegetation is spatial variability of soil properties, so knowledge about pattern distribution of variability soil properties for better management is necessary. Two study site including grassland and scrubland were selected in this study. One 500 × 400 m2 network was established in each study site and soil samples were taken from intersection points. 30 soil sample from 0-30 cm depth in scrubland. Also in grassland were taken 30 soil samples from 0-30 cm depth. Compares of means of soil properties using T-student test was shown carbonate calcium, EC, organic carbon, percent of silt and sand have significant different in two community (p< 0.05). Spatial analysis was represented; appropriate models for entire soil properties were isotropic models. Most of soil properties had moderate to high spatial relationship that shows points close to each other had similar values whereas pH & EC had low spatial relationship in scrub land that shows points close to each other had not similar values necessarily. Soil properties except percent of clay with moderate relationship, in grass land had strong spatial structure.

To perform regional analysis and rainfall estimation of the rain gauges of the river basin code of 21 located in Semnan province, multivariate geostatistical approaches were used. Elevation was introduced as a covariant and effective factor in rainfall. Thirty eight rain gauges were selected for a 30 year period. The geostatistical analysis extension module of ArcGIS 9.3 was used for analysis and development of seasonal and annual prediction map. GS+ was used for understanding the details of variogram models and statistical fitting parameters. Average and reduced mean square errors of cross validation indices were considered for prediction of rainfall. The results showed that Cokriging methods with the assumption of elevation as a covariant factor and variance reduction gives accurate rainfall estimation which is mainly due to the high correlation between elevation and rainfall. Although the seasonal correlation is not stable, the value of correlation is not less than 0.6. Therefore, as the coefficient of correlation increases the accuracy of estimation will be enhanced.

Investigation of soil hydraulic properties like soil moisture retention curve and unsaturated hydraulic conductivity plays an important role in study of environmental researches in which their spatial and temporal variability led to development of indirect methods in prediction of these soil characteristics. Therefore, in this study indirect methods have been used in order to estimate surface fractal dimension to predict soil moisture curve. One parameter linear and nonlinear regressions were developed and compared to artificial neural networks by using readily available parameters like soil clay content, water content at permanent wilting point, cation exchange capacity and soil porosity. In the training step of regression analysis and neural networks, 97 measured soil samples and in the testing step 24 rest of soil samples with Petersen et al. (1996) database were used. The calculated values of RSE and RMSE showed that neural networks with seven neurons in the hidden layer are able to estimate surface fractal dimension from the easily available parameters more accurate than the other models.

Region drought monitoring is one of the important subjects in drought risk management. For this reason, relevant rain gauge stations in Golestan province in which data had been recorded until the end of water year 2006-2007, after sufficient statistical test, were selected. Drought conditions for each station was investigated in nine different periods (including eight monthly and one annual periods) using Standardized Prediction Index (SPI) in order to determine the best interpolation method for drought monitoring in the region. Some deterministic interpolation methods such as RBF, GPI, IDW and a geostatistical method were considered. After determining the best interpolation method, according to the error factors and extending point drought information to regional one, the severity maps of this phenomena over 25 years, was plotted. Then, spatial distribution of drought conditions over the statistical period in Golestan province was plotted using ArcGIS ver9.2. According to the results, kriging and RBF methods have shown the best response as interpolation methods in monitoring of drought severity at different time periods.

Drought based on frequency of occurrence, severity, affected area, economic damages, social affects and severe long term impacts is very important and dangerous phenomenon compared to other disasters. Therefore, it needs more consideration in decision making. Drought assessment and monitoring is the most important of technical aspects in drought studies. The objectives of current study is investigating of daily Effective Drought Index (EDI) and Available Water Resources Index (AWRI) in different climates of Iran in order to compare the results with runoff data of hydrometric gauging stations to understand the validation of these indices as well as to know the relationships between the indices and runoff data. Five synoptic meteorological stations in different climatic conditions of Iran were selected for this purpose and the indices were calculated. Then, five hydrometric gauging stations in the vicinity of the meteorological stations or in the area affected by the same climatic condition were selected and the runoff data and the calculated indices were compared. The result showed that the EDI as an independent of climatic characteristics is a suitable index in determining of drought, risk assessment and drought warning system. Meanwhile the AWRI showed the drought effects in water resources as function of daily decline of rainfall.

Suspended sediment is the major part of total sediments transported by rivers. Suspended sediment measurement is an important issue for erosion and sedimentation, water resources engineering and watershed management studies. The sediment yield of the watersheds is also a major problem of watershed management activities. Statistical analysis of sediment yield is an important method for trend assessment. In this study, sediment yield of Menderjan Watershed for the period of 1979-2004 is considered. Descriptive statistics and trend of observed series (with mean and standard deviation of 59.66 and 348.47 mg/lit, respectively) showed a decreasing trend which is significant at 5% level. However, the cumulative standardized plot shows three different periods with Kendall test values of 0.265, -0.119 and -0.166 which are significant at 1, 5 and 10% levels, respectively. Comparison of three nonparametric tests of mean, variance and distribution functions showed significant difference of mean, standard deviation and frequency distribution for the studied periods. This significant difference, especially for standard deviation, shows the reduction of sediment yield in the third period due to watershed management practices in 7135 hectares area, 174 mechanical structures with 3815 cubic meter volume, and 275232.3 cubic meter sediment storage volume in the basin and its importance.