Journal of Range and Watershed Managment

Abstract Over the past few decades due to population growth and urban development, urban runoff has increased and led to different problems such as inundation of urban pathways, dissemination of environmental pollutions and flood hazards. In order to urban runoff management, it is necessary to estimate runoff rate correctly. SWMM is one of the most widely used models in estimating urban runoff. The goal of this research is to evaluate the performance of SWMM model in simulating flow rate in an urban watershed in District 22 of Tehran. At first, model required parameters were calculated. For model evaluation and validation, in three events, runoff was measured in the watershed outlet and was compared with simulated runoff. The model validation results showed that the simulated flow rates had good adaptation with the observed ones. The validation results were used for estimating optimum values of model input parameters. The results of SWMM model evaluation confirm model accuracy with NS= 0.72 and RSR= 0.53 and indicate the model ability in simulating urban runoff. So, SWMM model can be used for urban runoff management plans and designing urban runoff drainage networks in this area.

Abstract Knowing the amount of runoff and sediment generated from different lands is an important step in land use management. Since it is not always possible to measure these values, modeling parameters, will be a way to achieve watershed comprehensive management. Beheshtabad watershed in Chaharmahal & Bakhtiari province with different land uses such as range land, agriculture, gardens and urban area is an important part of Northern Karun Basin. In order to simulate the amount of runoff and sediment generated in different land uses, SWAT model was used. Discharge and sediment data of weather and hydrometric stations located in the area was used for sensitivity analyses and then to calibrate and validate model by SUFI-2 algorithm. NS and R² coefficients obtained for runoff calibration are respectively 0.69, 0.71 and for runoff validation are 0.65, 0.67 and for sediment are 0.72, 0.73 in calibration, and 0.66, 0.71 in validation, that confirmed model ability to accurately estimate of flow, runoff and sediment in the study area. The results showed that dry farming lands with average runoff and sediment of 190 mm, 24.5 tons per hectare per year respectively, has the highest and gardens with an average of 80 mm runoff has the lowest sediment yield of 1.63 tons per hectare in the year.

Abstract Watershed outflow has influenced by different factors such as climatic, human and physical aspects and this Variability of effective factors can cause complex conditions, difficulty of flow forecasting and it mainly originates by different local and temporal scales of these factors. Also, some remote meteorological signals can cause changes in meteorological conditions in different regions. Hablehrud river flow has a vital role in regional development, especially for agricultural section. Thus research of river flow forecasting should be done for water resources management especially when there are drought and climate change conditions in order to facilitate sustainable development. In this study four nonlinear models of artificial neural networks including Generalized Feed Foreward Networks (JFNNs), Jordan/Elman Networks(JENs), Time Lag Recurrent Networks(TLRNs) and Radius Basis Function Networks(RBF) was used to modeling Hablehrud river flow(Bonkuh station) during 1982 to 2011. Input variables after sensitivity analysis were used in 4 models and 4 scenarios. Ten teleconnection indexes were used as input of the model to evaluate their roles in model capability. Results indicated that in the test stage Jordan/Elman Networks represented lower error compared with selected models (RMSE for 4 scenarios are5.57, 4.9, 5.35 and 4.62 respectively). In general error showed decreasing trend from first scenario to the last. Error was decreased of 15 to 31 percent by using teleconnection patterns as inputs (GFFN=%26, JEN=%15.8, TLRN=%25.5 and RBF=%31.7). Totally using teleconnection indexes as inputs in the modeling stage can diminish error of flow forecasting, although selected models indicated different results due to its variable topologies.

Abstract WEPP model needs a great deal of input data. Identifying the model’s sensitive parameters and
their prioritization increases the accuracy and efficiency of the model. On the other hand, WEPP
model can simulate processes affecting on runoff, erosion and sediment throughout the year. Thus,
model sensitivity must vary based on the storm occurrence time and parameters value in different
sections of the year. To prove this assumption, two spring and autumn storm events related to 2008
were selected and sensitivity analysis of the WEPP model was done in three plots with different
conditions in Sanganeh watershed. For sensitivity analysis, the OAT method was used and
sensitivity degree of parameters was calculated. Obtained results show that the rate of sand is the
most sensitive parameter of WEPP model. This parameter was followed by other parameters like
clay percent, effective hydraulic conductivity, height and intensity of rainfall, day degree of
growing, growing season and percent of growing season when leaf area index decreases. Most
variations are observed in prioritization of sensitive parameter in the plant/ management file. In
most cases, sensitivity degree of these parameters in autumn event comparing to the spring event
has significantly reduced in all plots. In general, obtained results show that the rate of sensitivity of
the WEPP model to different parameters varies during the time. Hence, for using this complex
model in regions with data limitation, the user must be aware to this issue that regarding storm time,
which parameter is more sensitive in the pilot area and need to be carefully measured in the field.