Modeling Watershed Operation: Case Study of the Chaffia Dam (Algerian Extreme North East) (original) (raw)
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Mathematical Modeling of Watershed Hydrology
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Watershed models for assessment of hydrological behavior of the catchments: a comparative study
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Hydrological parameters like overland flow, soil loss and nutrient losses can be studied by using different watershed models. However, all these models vary significantly in their analysis of parameters, input and output flexibility, scale accountability, processing ability, computational efficiency and capability of modeling the changes in catchments. This paper reviews different watershed models used for analyzing overland flow, soil loss and sediment yield with their shortcomings and strengths. These watershed models are described briefly along with their capabilities and shortcomings with their examples of applications, results and comparisons. An outcome of these discussions is presented in tabular format as a screening tool to allow the researchers and decision makers to choose the appropriate watershed model for the specific purpose.
AN EXPERIMENT IN DETERMINISTIC WATERSHED MODELING
Journal of The American Water Resources Association, 1971
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11.[35-42]Rainfall-runoff modelling of a watershed
In this study an adaptive neuro-fuzzy inference system was used for rainfall-runoff modelling for the Nagwan watershed in the Hazaribagh District of Jharkhand, India. Different combinations of rainfall and runoff were considered as the inputs to the model, and runoff of the current day was considered as the output. Input space partitioning for model structure identification was done by grid partitioning. A hybrid learning algorithm consisting of back-propagation and least-squares estimation was used to train the model for runoff estimation. The optimal learning parameters were determined by trial and error using gaussian membership functions. Root mean square error and correlation coefficient were used for selecting the best performing model. Model with one input and 91 gauss membership function outperformed and used for runoff prediction.
Arabian Journal of Geosciences, 2019
Determining the link between rainfall and flow for a watershed is one of the most imperative problems and challenging tasks faced by hydrologists and engineers. Conceptual and Box-Jenkins hydrological models represent suitable tools for this purpose in circumstance of data Scarce and climate complexity. This research consists in a comparative study between conceptual models and Box-Jenkins model, namely, GR2M, ABCD, and the autoregressive moving average (ARIMA) which has a numerical design. The three models were applied to three catchments located in the northwest of Algeria. Basins have been selected according to the availability of long-time series of hydrological and climatic data (more than 30 years) to calibrate parsimonious models, taking into account the climatic variables and the stochastic behavior of the natural stream flow. Overall, the conceptual models perform similarly; whereas the results show that the GR2M model performed better than the ABCD in the validation stage, the stochastic model shows better results as opposed to conceptual models in the case of the Mellah Wadi which presents high permeability in its behavior. This is due to the simplicity of the model needed for data (only runoff data) and the ability of the stochastic model to produce stream flow in complex catchments. Such circumstance could be caused by different motivations. On the one hand, the diverse number of model parameters that make the ABCD the less parsimonious approach, with four parameters to be calibrated. On the other hand, the inability of the ABCD and the ARIMA model to capture and describe the groundwater processes, important for the cases study. Moreover, the validation period includes a large drought period, started in the late 1980s, which makes difficult model adaptation to different hydrological regimes.
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