جستجوی مقالات مرتبط با کلیدواژه « run » در نشریات گروه « آبخیزداری، بیابان، محیط زیست، مرتع »

The performance of any water projects depends on forecasting hydrological events in the future. In hydrology using physical models is not feasible to predict future events and often used from abstract model that describes the system based on mathematical concepts. The use of statistical distributions to estimate annual average discharge in different return periods to design the hydraulic structures is one of the most common methods. In this research in order to obtain the best statistical distributions and estimate annual average discharge in different return periods, annual average discharge data from 6 hydrometric stations Gilan province named Punel, Rudbarsera, Toollat, Shalman, Mashinkhaneh and Haratbar were collected during the 50-year statistical periods (1965 to 2016). The randomness of the data was tested with Run Test method and then fitted with a variety of relevant statistical distributions using SMADA software based on the Method of Moments. Finally, the best distribution was determined using statistical indicators root mean square error (RMSE) and mean absolute error percentage (MAPE) for all hydrometric stations. The results showed that the mean annual discharge statistics of Punel, Shalman, Toollat, Mashinkhaneh and Haratbar stations with Log Pearson type III distribution and Rudbarsera station with 3Parameter Log Normal distribution Show the most fit and compliance. Therefore, using the best distributions mentioned above, return periods of 2, 3, 5, 10, 25, 50, 100 and 200 years were estimated.

Climate change has a critical impact on water resources, especially in arid regions. In the first part of the study, the LARS-WG was used for downscaling of climatic variables including rainfall, solar radiation, minimum and maximum temperature over the Ghareh-Chay basin in Markazi province for a 31 year historical period (1983-2013). Results showed that LARS-WG can be applied successfully to downscale the climatic variables. By using the three emission scenarios, A1B, A2 and B1 in different future horizon, decreasing and increasing trends were seen for rainfall and temperature, respectively. In the second part of study, adaptive inference neuro fuzzy system (ANFIS) was applied for run-off simulation over the basin for three different horizons, 1400, 1435 and 1470. Two different identification methods namely, grid partitioning and subtractive clustering were used for ANFIS model. Performance of the subtractive clustering was slightly better than the grid partitioning in run-off modeling process. The results indicated that in comparison to the baseline period, the maximum discharge will be reduced by14, 27 and 43 percent in 2020, 2055and 2090 horizons, respectively. Based on the occurrence of climate change over the basin and intensification during the next years, it seems necessary to consider some ways for adaptation and coping with climate change for protecting the water resources.

Estimate of erosion, sediment and runoff in a watershed are an important target of researchers in areas without data. Also aware of the model sensitivity to the input factors can be used in cases where there is no time or resources to measure all the parameters, will help in selecting the most effective parameters. In this study because the high efficiency and a wide range of input parameters of the WEPP model the sensitivity of soil parameters WEPP model were investigated. In order to run this model, information on soil type, management, topographic and climatic characteristics and properties of waterways of the area studied is required. Climate data were extracted from the station Birjand. The topographical properties via GeoWEPP model and using DEM and factors of management and channel properties measured in the field and were entered into the model. Finally, the parameters of the soil contains clay, silt, gravel, sand, organic matter, cation exchange capacity and soil depth was entered into the model. Depending on the type of soil in the area, to determine the sensitivity, each soil factor was changed separately and the effect on the mean runoff, sediment yield and soil loss were calculated. The results showed that the amount of silt and sand have had the greatest and cation exchange capacity and soil depth the least impact on the amount of runoff, erosion and sediment. Also amount of runoff and sediment yield increased with increasing clay. But impact of sand, organic matter, cation exchange capacity and soil depth on the amount of sediment and soil loss was not clear.