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Daily Reservoir Inflow Forecasting Using Weather Forecast Downscaling and Rainfall-runoff Modeling (Case Study: Upstream of Shahid Kazemi Reservoir)

Meydani, Amir Reza | 2022

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 54967 (09)
  4. University: Sharif University of Technology
  5. Department: Civil Engineering
  6. Advisor(s): Tajrishy, Massoud; Dehghanipour, Amir Hossein
  7. Abstract:
  8. Shahid Kazemi reservoir is the largest, and the most strategic dam of Urmia Lake basin is located on the Zarrineh Rood River in the south of this basin. This reservoir is responsible for supplying drinking water to large cities such as Tabriz, the agricultural demands of significant plains, including the Miandoab plain, and most importantly, the environmental demands of Urmia Lake and downstream rivers. In recent years, due to increased consumption upstream of the dam and agricultural development downstream of the, Urmia Lake has faced water shortages. This study develops the first daily runoff forecast system for Shahid Kazemi reservoir in Urmia Lake basin (ULB), Iran, an important region for managers and water resources policymakers.A weather forecast model is developed to downscale large-scale raw weather forecasts of ECMWF and NCEP to small-scale spatial resolutions. Various downscaling methods, including deterministic Artificial Intelligence (AI) techniques and a Bayesian Belief Network (BBN), are compared. GMDH, SVR and SVR-GMDH hybrid methods were used to downscale weather forecast parameters in the deterministic approach. Downscaled precipitation and temperature forecasts are then fed into a rainfall-runoff model that accounts for daily snow and soil moisture dynamics in the sub-basins upstream of Shahid Kazemi reservoir. The multi-objective Particle Swarm Optimization (MOPSO) method is used to estimate hydrological model parameters by maximizing simulation accuracy of observed river flow (NSEQ) and the logarithm of river flow (NSELogQ) in each sub-basin. Results of the weather forecast downscaling model show that the accuracy of the BBN is greater than the various deterministic AI methods tested. Calibration results of the rainfall-runoff model indicate no significant trade-off between fitting daily high and low flows, with an average NSEQ and NSELogQ of 0.43 and 0.63 for the calibration period, and 0.54 and 0.57 for the validation period. The application of the developed system has been used in simulating historical periods and forecasting in real-time. Water balance modeling historical severe drought (1999, 2000, 2001, and 2008) indicates that precipitation and inflow to the reservoir were decreased by 32% and 40% in severe drought years, respectively, and soil moisture and snow accumulation were reduced by 32% and 158%, respectively, compared to the long-term period. The entire forecasting system was evaluated using inflow observations for the year 2020 and 2021, resulting in an NSE of 0.67 for forecasting daily inflow into Shahid Kazemi reservoir. These results demonstrate the accuracy of the daily runoff forecast system, which can help policymakers in the real-time optimal water allocation between agricultural and environmental water demands
  9. Keywords:
  10. Downscaling ; Runoff Forecasting ; Lake Urmia Watershed ; National Center for Environmental Prediction (NCEP) ; Shahid Kazemi Dam ; National Center for Environmental Prediction (NCEP) ; Rainfall-Runoff Modeling

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