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Short-Term Basin-Scale Streamflow Forecasting Using Large-Scale Coupled Atmospheric–Oceanic Circulation and Local Outgoing Longwave Radiation

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  • 1 Department of Civil Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India
  • | 2 Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, India
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Abstract

This paper investigates the use of large-scale circulation patterns (El Niño–Southern Oscillation and the equatorial Indian Ocean Oscillation), local outgoing longwave radiation (OLR), and previous streamflow information for short-term (weekly) basin-scale streamflow forecasting. To model the complex relationship between these inputs and basin-scale streamflow, an artificial intelligence approach—genetic programming (GP)—has been employed. Research findings of this study indicate that the use of large-scale atmospheric circulation information and streamflow at previous time steps, along with OLR as a local meteorological input, potentially improves the performance of weekly basin-scale streamflow prediction. The genetic programming approach is found to capture the complex relationship between the weekly streamflow and various inputs. Different input variable combinations were explored to come up with the best one. The observed and predicted streamflows were found to correspond well with each other with a coefficient of determination of 0.653 (correlation coefficient r = 0.808), which may appear attractive for such a complex system.

Corresponding author address: Dr. Rajib Maity, Assistant Professor, Department of Civil Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India. Email: rajib@civil.iitkgp.ac.in

Abstract

This paper investigates the use of large-scale circulation patterns (El Niño–Southern Oscillation and the equatorial Indian Ocean Oscillation), local outgoing longwave radiation (OLR), and previous streamflow information for short-term (weekly) basin-scale streamflow forecasting. To model the complex relationship between these inputs and basin-scale streamflow, an artificial intelligence approach—genetic programming (GP)—has been employed. Research findings of this study indicate that the use of large-scale atmospheric circulation information and streamflow at previous time steps, along with OLR as a local meteorological input, potentially improves the performance of weekly basin-scale streamflow prediction. The genetic programming approach is found to capture the complex relationship between the weekly streamflow and various inputs. Different input variable combinations were explored to come up with the best one. The observed and predicted streamflows were found to correspond well with each other with a coefficient of determination of 0.653 (correlation coefficient r = 0.808), which may appear attractive for such a complex system.

Corresponding author address: Dr. Rajib Maity, Assistant Professor, Department of Civil Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India. Email: rajib@civil.iitkgp.ac.in

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