Estimating the Available Water in the Watershed using System Dynamics Hydrological Model (Case Study: Ilam Watershed)

Authors

  • Amin Sarang School of Environment, College of Engineering, University of Tehran, Tehran, Iran
  • Hamid Abdolabadi School of Environment, College of Engineering, University of Tehran, Tehran, Iran
  • John Little Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, USA
  • Mojtaba Ardestani School of Environment, College of Engineering, University of Tehran, Tehran, Iran
Abstract:

Hydrological models provide water managers with the available amount of water in the watershed. In this paper, we firstly developed a system dynamics model to calculate the available amount of water in the watershed. Then, we defined two scenarios one of which is the development scenario describing how land use changes can affect water availability in the watershed. Next, we divided the watershed into upstream and downstream assuming that these two sub-watersheds have different physical features including soil type and land cover to find out how the available water changes. The system dynamics model includes three main components of the hydrologic processes: rainfall-runoff model, snow accumulation, and groundwater. The model calculates runoff at the outlet of a watershed and sub-watersheds with monthly time step. We applied the developed model to the Ilam watershed to demonstrate the capability of the model in estimating runoff volume and available water. We calibrated model coefficients based on minimizing the model’s error in estimating the out flow of the watershed. The error was measured using the Nash-Sutcliff efficiency coefficient, the Pierson correlation coefficient, and the standard error. Specific tests such as the dimensional analysis test, and extreme conditions test were utilized to assess the structural accuracy of the system dynamics model. Results showed the appropriate accordance of the model’s output with the observed data by a value of the Nash-Sutcliff coefficient (Ens) close to 1, the rather high data correlation coefficient (R), and also a low standard error for the model’s calibration and verification periods.

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Journal title

volume 2  issue 4

pages  265- 280

publication date 2018-11-01

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