JOURNAL OF WATERSHED MANAGEMENT RESEARCH
Year:2010 | Volume:1 | Issue:1|Papers Count:7

In recent years, landslides occurrence has been increased in different regions of Iran because of different agents, especially road construction. Therefore, inspection of this phenomenon including effective agents in landslide occurrence, determination of susceptible area … is necessary to prevent the landslides formation. For this purpose, the Ilam dam basin having 476 (km2) area was selected for investigations. At the beginning of this research, after field investigations, seven factors including: geology, slope, aspect, fault, drainage network and land use were recognized as primary effective parameters in landslide occurrence. In the next stage, the information layers of above parameters were prepared in Geographic Information System (GIS) environment using Arc view 3.2 software and these maps were overlaid with the landslide inventory map. Analysis of the results indicated that aspect factor has minimum effect on landslide occurrence in the area. Then, using the six remainder factors, homogeneous units map is prepared. In order to quantify the factors and give an appropriate weight to the parameters, the slide area percentage in each unit was calculated. Multiple regressions were used for statistical analysis. The distance from faults and land use parameters were put aside by the model. The results showed that the four factors including road, slop, drainage network and geology have the highest affect on landslide occurrence respectively.

Road construction in forest area is interfering in natural environment of forest and it is caused to derange it. So, studying of soil mechanic and its characteristics is a need. It is useful to choose the suitable road construction technique. This research was carried out because of landslide problem in forest road. Investigation of some mechanic characteristics of soil and its effect on mass movement was studied. For this research two zones of road side were selected in two parcels (No. 105 and 107) were located on Chafroud district in Guilan Province. Then surveying, Length measuring, Min. and Max. Width, height and slope of soil movement in two zones were measured. Four boreholes were dug (two in moved area and two in stable area) and the soil samples were sent to soil mechanic laboratory. Some mechanical testes like soil texture, liquid and plastic limits (Aterberg Limits) were done and so the results show that in moved soil the texture was SC and for stable area were CL and CH. The process of moisture changing in soil was investigated. Absorption of moisture with fine soil, undesirable drainage and stimulation of road construction cause landslide in study area.

The application of suitable empirical models for estimation of soil erosion and sediment yield is essential owing to nonexistence or lack of associated data in many watersheds. In the present study, the calibration of different versions of universal soill loss equation Viz. EUSLE, USLE-M, AOF, MUSLE, MUSLT and TAM was considered in estimation of storm-wise sediment yield in Chehelgazi of Gheshlagh Dam Watershed in Kurdistan province. To conduct the study, the entire input data were collected for the Chehelgazi watershed in sanandaj Township. The models’ estimates were then compared with observed sediment data for 11 storm events. In contrary to high correlation coffeicenty among different models’ estimates, the inappliability of the study models except calibrated TAM and MUSLE-S was proved in estimation of measured sediment data with the help of correlation matrix and t-test analyses.

Knowledge of frequency ratings of low flows is important to understand the phenomenon due to damage producing effects of drought. Low flow Analysis is a method of hydrologic estimation of drought. One of the methods in the regions without adequate data is the regional analysis of low flows, based on real data and its results have the higher reliability coefficients. In this study, the suitable stations for analysis of these flows were selected, low annual flow series with duration of 7, 15 and 30 days were calculated, 8 statistical distributions functions were fitted with the data and the Log-Pearson Type II distribution was selected as the suitable regional distribution. Also 16 physiographical, climatic and geological parameters were determined for the selected watersheds. To choose the independent variables, factor analysis was used. The results showed that among 16 input parameters, the area, mean annual rainfall and percentage of permeable formation were the most important factors, which explain 79% of variance. Two homogeneous regions were recognized in the study area with cluster analysis and the two regions were completely separated by distinguishing analysis. The regional analysis of low flows was carried out using step-wise regression and multiple regression methods for each homogeneous region and the whole study area, considering r factors of the area, mean annual rainfall and percentage of permeable formation. The low flow rates with duration of 7, 15 and 30 days and return periods of 2, 5, 10, 20 and 50 years were finally estimated. The study of the models showed that the models used for homogeneous regions have higher correlation coefficients and lower standard errors with respect to the models used for the whole area.

Acceptable statistical data is the main basis for hydrological studies. Because there are lots of continuous and disperse blanks in most of hydrological data such as river discharge, it is necessary to estimate and forecast these data by suitable methods. These blanks are caused by different factors such as loss of data record, elimination of incorrect data and disordered function of measurement instruments. There are many procedures to estimate and regenerate these data, and depending on the condition of a given station, a particular procedure may produce the best results. In this study, the method of artificial neural networks has been compared to other methods including normal ratio, graphical, simple linear regression, multivariate linear regression and time series (auto regression) methods, in order to regenerate the monthly and annual discharge data for hydrometric stations in Grand Karoon river basin. After eliminating observed data, their values were estimated using mentioned procedures. Then, the priority of each procedure was assessed by means of rooted mean square of errors (RMSE). The results of monthly data regeneration indicated that artificial neural networks was the best procedure in most stations with a frequency value of 59.26%.

The process of precipitation-runoff of each basin, is mostly influenced by hydrologic, geomorphology conditions and zone climate. The measurement, distribution and pattern of precipitation, canopy cover, snow melt and accumulation process characteristics, soil layer characteristics and geology formation are the most important factors which impact the runoff measurement and basin water balance components. One of the general procedures for acknowledgement of precipitation-runoff process is its simulation by using the hydrologic models and analysis of the results. In this study, it was tried by using BROOK90, the basin process of precipitation-runoff, snow accumulation and melt model and also the basin water balance components of Korkorsar are simulated and calibrated then the effect of hydrologic conditions and also climate changes on basin precipitation-runoff process and water balance components are studied and analyzed.

In order to assess of suitable distribution along with statistical period length changes as well as to select the best frequency distribution to estimate peak discharge with certain occurance probability, 23 gauging station wich have the more complete data, were selected among hydrometrical gauge stations of central Alborz region. A 20 years statical period has been selected after analyzing scatter data. complete the missing data and divided the prepared data in to 10, 15 and 20 years period. in every station 11 series of 10 years’ time period, 6 series of 15 years’ time period and A sery of 20 years’ time period and in all stations 414 series were analyzed. Then the best frequency distribution was selected for statistical series by data analysis in time series (according to HYFA output) and goodness of fit of relative residual square mean (RMS). trend of statistical distribution changes with change of statistical period length is in such way that no distribution can be selected definitely and based on the best fitted distribution in 10, 15 and 20 years’ time period, three parameters log pearson (moment) with 45.4 percent, three parameters log pearson (moment) with 35.5 percent and three parameters log pearson (moment) with 47.8 percent were the best distributions respectively. Second disribution for 10, 15 and 20 years’ time periods were three parameters Pearson (moment), two parameters Gama (maximum likelihood) and two parmeters log normal (maximum likelihood), respectively. on based on selecting best distribution with moment and maximum likelihood methods separately. three parameters log pearson was the best in moment method and based on maximumlikelihood in 10 and 15 years time Periods two parameters gama distribution and in 20 years time period three parameters log normal were the best distributions.