WATERSHED ENGINEERING AND MANAGEMENT
Year:2017 | Volume:9 | Issue:3 |Papers Count:10

Mountainous rivers have characterized by steep slope, roughness due to bedrock and coarse aggregation, bed and marginal vegetation as well as severe and rapid spatial displacement of the surface substrate morphology. The protection role of vegetation in the river bank is known as an economic and environmental option that decreases hydraulic conductivity power of the river through the rising of roughness coefficient and rising water level. This study aimed to estimate the flow resistance and roughness coefficient of the river that is affected by the physical and biophysical factors of the Shahr-chay River bed in West Azerbaijan in three selected river reach. Results showed that none of the proposed equations for estimation of roughness coefficient depend on energy slope, but in general, without affecting flow, roughness coefficient in mountain streams depends only on the degree of submergence. Marginal trees can affect Froude number by increasing margin pressure and friction forces, also, enhancing the hydraulic depth, less than critical depth. It was also found the equation for estimating roughness coefficient both with and without marginal trees, have different coefficients and can provides roughness coefficient prediction possibility for mountainous rivers. Comparing the obtained relations both with and without its fringe of trees showed that fringe trees can increase the flow depth and roughness coefficient. Coefficient of determination of all mathematical relations was above 0. 99 and calculated relative error was 0. 001-0. 008 that demonstrates the suitability of the model to estimate the roughness coefficient.

Evaporation is one of the most important and effective factors in water resources planning and management in arid and semi-arid areas and examining it's changes in time scales and different years as one of the most important climatic parameters, has an important role in planning and water resource management in agriculture section and determining cultivation pattern and proper water resource management. One of the methods to assess and forecast changes in evaporation is time series models by the generic name of ARIMA models. Therefore, in order to determine the best model to predict pan evaporation, after considering the climate using improved Domarton climatic classification method, in each climatic sample, one evaporation station was selected and standardized pan evaporation index (SPEI) was calculated for each of the stochastic model for estimation the amount of future monthly time series SPEI in the period of 1954-1955 to 2009-2010 over the next 12 months. Results showed that the Auto Regressive Moving Average model (ARMA) and Auto Regressive (AR) had the best performance, in Hormozgan province, so that, in the hot and dry, hyper hot arid and hyper hot hyper arid climates, in the next month, had the highest standard coefficient of determination (R) of 0. 83, 0. 71, 0. 7 and the lowest value RMSE of 0. 59, 0. 8, 0. 88, respectively and the AR model was able to predict the next 11 months, well. The results showed that AR model has better performance compared to ARMA model in estimating monthly pan evaporation in hot and dry climates in coastal wilderness areas.

Flood forecasting in a sound way leading to correct results has been a challenge for all researchers and engineers for many decades, which is the basic reason for developing many different types of mathematical rainfall runoff models. Correct estimation of infiltration during a storm is essential to a justified and rational modeling of runoff at watershed scale. There are many experimental or mathematical models for simulating infiltration and deriving net rainfall with pros and cons. In the present research, instead of evaluating infiltration simulation methods solely, four more widely used methods; namely, ɸ index, Horton, Green-Ampt and SCS methods were selected to find out their effects on the performance of a kinematic wave based geomorphological model called KW-GIUH. Furthermore, a sensitivity analysis with respect to different infiltration methods and different overland and channel roughness coefficient were performed. Horton and ɸ method led to a better performance of the model in terms of observed and simulated hydrographs in the study watershed. In this regard, Nash-Sutcliffe efficiency indices for Horton and ɸ index methods were obtained as 94. 9 and 90. 3, respectively, while it was 77. 7 for Green– Ampt and 76. 3 for SCS infiltration method. According to results of sensitivity analysis, KW-GIUH performance has the most and least sensitivity when using Green– Ampt and ɸ index as the infiltration method, respectively. Although studying in a steep watershed with an overland slope of about 17% and a small area of about 38 km2, changing the overland roughness coefficient has more effect on the model performance comparing with the change of channel roughness coefficient. Simulated flood peaks changed about 64 % due to changing the overland roughness coefficient while this value amounted to 25% for channel one. Shortly, it is concluded that KW-GIUH is highly sensitive to infiltration simulation method and overland roughness coefficient.

Urban inundation is a serious challenge that increasingly face the residents of many cities, as well as policymakers. Accordingly, inundation assessment is becoming increasingly momentous around the world. This comprehensive evaluation involves numerous indices in urban catchments, but the high-dimensional and non-linear relationship between the indices and the risk presents a tremendous challenge for accurate evaluation. In this study a project based on Stormwater Management Model (SWMM), Kriging and k-means clustering was used for quantitative and qualitative flooding risk assessment in urban drainage system over the western part of Kerman city. Flooding risk in the drainage system over the study area was evaluated by various precipitation scenarios and required parameters were calculated by land use map, DEM and field study. For calibration and assessment of model, calculated runoff in 3 point was measured and was compared with simulated runoff. Results show that, the proposed approach can identify the main driving factors, i. e., inundation duration, largest water flow and total flood amount in this study area. The inundation risk of each node can be qualitatively analyzed and quantitatively calculated. There are 3, 10, 13, 23, 30, and 33 node at risk under the return periods of 1-year, 5-years, 10-years, 20-years, 50-years and 100-years, respectively. This study proposes a novel and successful approach to assessing risk in urban drainage systems and provides guidance for improving urban drainage systems and inundation preparedness.

The swelling potential of fine-grained soils is one of effective parameters on soil mechanical behavior and erosion and fundamental data required for the design, construction and choosing construction materials. This paper presents a multi-layer perceptron (MLP) artificial neural network (ANN) model to prediction of the swelling potential of marl soils. Marl soil is a fine-grained soil. The Levenberg-Marquadt learning algorithm was used to train the networks. Existing models prediction of soil swelling potential based on physical and soil index parameters. The present study considers the effects of chemical factors on the behavior and characteristics of fine-grained soils along with the common soil index parameters. The model used physicochemical and mechanical test results from 60 marl soil samples taken from marl formations in the Neogene basin in central Iran (Tehran, Qom and Saveh regions). The models were designed to use different input data sets and structures to determine which soil properties and ANN structures correlate well with the swelling potential parameter. Electrical conductivity (EC) of saturated soil was a new input parameter used in addition to the physical and soil index parameters that include the atterberg limit, activity, content of the clay and silt, initial of porosity ratio and dry density. Values of RMSE, R2 and MCE (evaluation criteria) related to the best model with the physical parameters LL, PI, A, M, C and Yd0 are respectively 0. 89, 2. 3, 0. 84, and for the best model with the physical parameters LL, PI, M, C, Yd0 and EC are respectively 0. 92, 1. 7, and 0. 91. The results of the evaluation criteria models show that inclusion of EC improved the accuracy of the model. It was found that the accuracy of the generalizations and estimations of the ANN models was further increased by clustering data before the data division stage by k-means method to Compared with hierarchical method.

The aim of this research was to compare the soil physico-chemical characteristics and the morphology of two types of gullies in dry areas. In this research, places with gully erosion were recognized. Then, two trapezoidal and V-shaped gullies were selected based on soil physico-chemical characteristics and gully morphology. Gullies’ length and their wide at the head and 25, 50 and 75 percent of the length were measured. The area, mean depth, mean wide, soil depth, head plan, overall plan, slope cross section shape, also, gully erosion reasons were investigated. Soil samples were taken from 25, 50 and 75 percent of the length and from the end of gulliesThe data were analyzed using SPSS software. Results showed that soil parameters such as EC, clay, CEC, SAR, Ca++ Mg++ and So4--at the 0. 05 level (P<0. 05) and SP at the 0. 01 level (P<0. 01) have significant difference in two gully shapes. But, there is no significant difference between Na+, Cl-, Hco3-, C, pH, N, Silt and Sand in two gullies.

Generally, satellite images and climatological and environmental data are used simultaneously in dust storm studies. Most of the processing techniques are limited to a few indices such as brightness temperature differences, normalized dust density index, aerosol optical depth, false color composite and visual image interpretation. Since the reliability of the above-mentioned methods are varied, so an attempt was made to increase the reliability of automatic spatial image clustering by contributing the expert knowledge via visual image interpretation. Therefore, the required satellite images were collected for a period of 2005 to 2008, in Mesopotamian, Syria and south-western provinces of Iran. For doing this, the MODIS color composite images were decomposed into their ordinary bands and then used for further analysis. In this research, spatial image clustering followed by visual image interpretation (hybrid classification) was applied for accurate mapping of geographical extents of dust storm with different intensities. The sources of dust storms were also identified through visual image interpretation by considering the interpretation criteria such as shapes and patterns. Comparison of the previous results and obtained by this research, indicated critical conditions in terms of dust storm occurrence in the region. Image clustering and hybrid classification led to daily dust persistence over the region and then those images compiled in order to prepare annual total heavy dust persistence map. The result of using brightness temperature difference method verified similarity and reliability of obtained results. Therefore, this simple method could be proposed for identification of dust sources, related plumes and their affecting areas, when raw image data are not available.

Nowadays, the impacts of climate change evaluation on watershed hydrological resources is the most important task before any water resources planning. Stream flow trend was analyzed using non parametric Man-Kendall test conducted for 30, 40, 50 and 60 year periods in three, monthly, seasonal and annual time-scales and also peak discharge in Mazandaran Province watersheds. Results showed that the increase in Greenhouse gases and consequently climate change influenced severely on river streamflow which led to reduced river stream flows, especially in the eastern parts of the region the trend analysis results showed that most of hydrometric stations had significant downward trend especially in summer season and only they experienced positive trend in winter. It seems that increase in winter season is due to temperature rise and consequently melting of snow which led to increase of base-flow and upward trend during the studied period. Results of peak flow trend analysis showed that upward trend for recent four decades is due to changes in precipitation pattern. The research achievement may considerably help in forecasting the upcoming drought, irrigation planning and water resources management.

The process of water diversion from the rivers always involve with sediment diversion in different sizes. The sediments that transported to the branch channel cause the expenditure to the water conveyance system and power-generation installations. One of the common methods to control the bed load and water diversion increasing, is to modify the approach flow pattern by the control structures. In this study, the efficiency of spur dikes in modifying of diversion flow to the intakes that located at curved channels are investigated. Experiments carried out in ten main groups in a 180° curve channel with a diversion located at the 118° on the external curve. Results of this study show that the sediment discharge ratio increased by discharge ratio increasing and severely related to it. The spur dike at upstream of the intake decreases the sediment discharge ratio by modifying the approaching flow pattern such that in C2 experiments, the sediment discharge ratio until the discharge ratio is equal to 0. 05, 0. 15, 0. 25, 0. 35 decreased up to 86. 6, 73. 3, 64. 7, 46. 7 respectively. But this effectiveness limited to Qr=0. 4, because the strength of secondary flow diminished due to flow diversion increasing. Also, study of the effect of spur dike on sediment volume fraction in diversion channel show that, the maximum value of Vr diminished from 0. 4 in type A experiments to 0. 18 in type D experiments by reduction of the distance between the spur dike and the intake centerline. According to the experiments, positioning the spur dike at upstream of the intake change the dimensions of stream tube and decrease the diversion from near bed high-sediment flow and increase the diversion from near surface low-sediment flow. The dimensions of the separation zone severely decreased in type D experiments by presence of spur dike and discharge ratio increasing.

Run off simulation is one of the most important topics in hydrology And its study is based on rainfall-run off models. Several rain fall and run off models have been developed and the most appropriate model should be selected for each catchment. By applying the appropriate model the water consumption will be optimized. The model should be selected for each catchment based on the model abilities and limits. In this study, the performance of two rain fall and runoff models, GR2M and GR4J were compared in Darehtakht Basin in Lorestan Province during 1379 to 1392. The Nash coefficient was used as a decision criteria for comparing two model performances. Nash coefficient for GR4J and GR2M were 42. 7 and 65. 5, respectively. Results showed that both models can predict the performance of the catchment accurately, but, based on Nash coefficient the GR2M is more accurate than the GR4M.