IRANIAN JOURNAL OF WATERSHED MANAGEMENT SCIENCE AND ENGINEERING
Year:2017 | Volume:11 | Issue:38 |Papers Count:8

Estimates of soil loss are important to issues of land and water management. Much of the soil loss information in erosion control is based on the use of the Revised Universal Soil Loss Equation (RUSLE). This study assessed use of RUSLE as a soil loss predictor on small drainage areas using sediment data. Toward this, twenty drainage areas covering the first order waterways were selected in the semi-arid Tahamchai Watershed, NW Zanjan, Iran. Sediment yield was measured using sediment volume deposited behind the check dam at the outlet of each drainage area for a 16-year study period (1995-2011). Soil erosion factors including rainfall erosivity, soil erodibility, slope, vegetation cover and conservation practices were determined for each drainage area. Soil loss was estimated using the RUSLE model integrated with RS and GIS techniques based on the maps of rainfall erosivity, soil erodibility, slope, cover crop, and conservation practices factors. Based on the results, mean annual estimated soil loss was 43. 68 Mg ha-1 year-1 which was 6. 58 times bigger than the mean annual sediment yield (8. 67 Mg. ha-1. year-1) in the area. The model efficiency was-6. 83, indicating the inability of the model to correctly predict net soil loss in the small drainage areas. The estimation error was directly related to overestimation of soil erosion factors in the area. It seems soil erodibility, slope and conservation factors to have the exaggerated estimates in the area on one hand, and some other variables such as slope shape and waterway characteristics to be also effective in controlling the soil loss in the area on the other hand. This study is an important step forward in conducting more accurate erosion evaluation of the drainage areas using the RUSLE.

The main requirement of regional frequency analysis is the homogeneity of the study area. The results of regional frequency analysis will be valid while the study area is homogeneous. Classifying an area into homogeneous regions causes more careful predicting of flood for different return periods at areas with no data or low-recorded length. Numerous methods are used for identifying of the Homogeneous Regions that the efficiency of these methods is dependent on identifying the effective basin variables and their weights. In this study, the canonical correlation analysis method using the pair canonical variables and its relations are able to determine the region of ungaged station. Based on the results, the regional homogeneity of ungaged station obtained by canonical correlation analysis method has more stations and the RMSE of flood estimation in ungauged sites is low in compare the Mahalanobis distance (MD) that it is caused increasing the length of the time record and the reliability in regional frequency analysis. As well as, the canonical correlation analysis method showed that there is an effective correlation between the watershed variables and flow variables and indicated the effectiveness of linear combination for area and p24 on flow variables. These variables can be used to model the flow variables in this area.

Over exploitation of groundwater and exist of land subsidence evidences in Ardabil plain, demonstrate necessity of land subsidence risk zoning in this plain. The purpose of this study was to preparation land subsidence probability map and determines the high-risk areas with using fuzzy analytical hierarchy process (FAHP) model. Information layers including; geological formation, distance from the fault, rate of groundwater level decline, slope and thickness of alluvium were used as effective factors. The pairwise comparisons for criteria were done by using experts’ opinions. Obtained weights were assigned to each layer in the Geographic Information System (GIS) and land subsidence risks were calculated. The results show that this plain has 13 percent of very high risk, 20 percent of high risk, 45 percent of moderate risk, 16 percent of low risk and 6 percent of very low risk of land subsidence. Also, about 67. 5 percent of the southern regions of area, due to excessive use of groundwater, have very high and high class of land subsidence risk. For verification of the results, field data of fractured pipe wells were used. The verification results show that almost 80 percent of model outputs (moderate to high risk) are consistent with field data.

This paper addressed variations in scour and flow patterns around T-shaped spur dikes located at a 90 degree mild bend. Simulation of flow and scour patterns was conducted through 7 distinct modeling for a single and non-submerged spur dike and also with series spur dikes at distances of 2. 5, 3. 5, and 5 times the spur dike length from each other in non-submerged and submerged (25% submergence) modes using a numerical model. A comparison between the experimental and numerical results was carried out in nonsubmerged and single mode. Accommodation of experimental and numerical data was indicative of the efficiency of SSIIM numerical model in scour and flow pattern simulation in bend channels. The results of this study demonstrated that the dimensions and location of vortices, as well as the degree of scour, would change by varying the distance between the spur dikes, and spur dikes’ submergence ratio; hence, in a submerged mode, increasing the distance between the spur dikes would increase the degree of scour and the maximum sedimentation, while increasing spur submergence would decrease the maximum scour and sedimentation, and the maximum secondary flow strength occurring at the upstream end of the first spur dike would decrease by 34. 55%. In addition, a 25% submergence of spur dikes would lead the maximum scour to occur at the distance from the third spur dike to the bend exit.

In recent decades, the water level of the Urmia Lake has decreased due to over utilization of surface and groundwater resources, prevention of surface water resources discharges to the Urmia Lake and also climate changes, which caused water and environmental crisis in the region. Therefore, modeling Lake level fluctuations is essential for its water resources planning and management. The aim of the study is to forecast the Urmia Lake water level fluctuations for one, three and seven days ahead using extreme learning machine (ELM). Also, the artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) models used to compare the ability of ELM. For this purpose, the daily water level (Dec 1965-Nov 2012) of the lake was used. To develop the models, the current day and six days water level lags as the inputs used for forecasting the one [h(t+1)], three [h(t+3)] and seven [h(t+7)] days ahead water levels. The data sets were divided into two subsets training/validation (85%) and testing (15%) and after modeling, the performance of the models were evaluated based on coefficient of determination (R2), root mean square errors (RMSE) and Nash– Sutcliffe coefficient (NSC). The results showed that the ELM model for onestep-ahead water level modeling with R2 = 0. 9995, RMSE = 0. 0151 m and NSC = 0. 9995 respectively, outperformed in comparison with ANN and ANFIS models. Also, it was observed that ELM models learned faster than the other models during model development trials whereas the ANFIS models had the highest computation time.

One of the most important consequences of soil erosion is sedimentation. The detection and surveying the type and amount of sediment is important because of its effects on irrigation networks, environment, and reservoirs. This study was performed in order to investigate the suspended sediment changes under the influence of rainfall erosivity cycle in Sorkhab-Keshvar watershed in the Lorestan province. For estimating the suspended sediment, we used a combination of sediment rating curve of mean loads within discharge classes and average daily discharge data. Monthly and semi-monthly EI30-based erosivity indices were calculated in 13 rain gauging stations inside and around of the watershead. In the next stage, erosivity maps based on kriging interpolation were produced in order to calculate the average monthly and semimonthly rain erosivities. After calculating monthly and semi-monthly sediment loads, their correlations with corresponding erosivities were investigated. The results show that the maximum and minimum of suspended sediment occured in Azar and Shahrivar (Iranian month equal to Nov. 22 to Dec. 21 and Agu. 23 to Sep. 22) with the values of 246216 and 1272 ton, respectively. Also, the maximum and minimum of rainfall erosivity occured in Dey (Iranian month equal to Dec. 22 to Jan. 20) and summer respectively with the values of 182 and 0. 01 MJmm ha-1 h-1. The highest values of semi-monthly erosivity and sediment observed in second half of Dey and Azar (Iranian month equal to Dec. 22 to Jan. 20 and Nov. 22 to Dec. 21) with the values of 104 MJmm ha-1 h-1 and 148241 ton, respectively. Sediment and erosivity showed two peaks in the watershed, two peaks of sediment occured in Azar and Farvardin (Iranian month equal to Nov. 22 to Dec. 21 and Mar. 21 to Apr. 20) and for the reosivity occurred in Dey and Farvardin (Iranian month equal to Dec. 22 to Jan. 20 and Mar. 21 to Apr. 20).

Attention to the suspended sediment issue is a fundamental subject in the regional and strategic programming. In the other hand, lack of proper database of this phenomenon has been causing serious problems. By developing of the societies, increasing pressure to the natural resources, causing more flooding events and sediment removal in the near future is expected. Therefore trying to enhance sediment databases is essential. In the present study, in order to enhance sediment database in hydrometric stations of Iran, optical suspended sediment estimator was constructed and calibrated in the laboratory and also in the field conditions. Laboratory calibration was carried out in two stages including, high and low sediment yield flows. Validation curves drawn for this process, represents a very good correlation between the actual and the estimated suspended sediment data. The R2 statistic for low and high sediment concentration is 0. 995 and 0. 98 respectively. The field validation test of device was carried out in Mehriz River in the Yazd province of Iran, and indicated the appropriate performance of this instrument.

Attention to the suspended sediment issue is a fundamental subject in the regional and strategic programming. In the other hand, lack of proper database of this phenomenon has been causing serious problems. By developing of the societies, increasing pressure to the natural resources, causing more flooding events and sediment removal in the near future is expected. Therefore trying to enhance sediment databases is essential. In the present study, in order to enhance sediment database in hydrometric stations of Iran, optical suspended sediment estimator was constructed and calibrated in the laboratory and also in the field conditions. Laboratory calibration was carried out in two stages including, high and low sediment yield flows. Validation curves drawn for this process, represents a very good correlation between the actual and the estimated suspended sediment data. The R2 statistic for low and high sediment concentration is 0. 995 and 0. 98 respectively. The field validation test of device was carried out in Mehriz River in the Yazd province of Iran, and indicated the appropriate performance of this instrument.