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Performance Evaluation of Remote Sensing-based High Frequent Streamflow Estimation Models at the Bramhani River Basin Outlet
  • Bhabagrahi Sahoo,
  • Manoj Kumar Tiwari
Research Scholar

Corresponding Author:sahoodp19@gmail.com

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Bhabagrahi Sahoo
Associate Professor
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Manoj Kumar Tiwari
Assistant Professor
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For catchment-scale streamflow estimation, recently, the preference is shifting towards the use of innovative remote sensing (RS)-based approaches at the remote gauging stations. In this context, this study evaluates the performances of three RS-based models designed with the near-infrared (NIR) bands of Landsat images at the Jenapur outlet of the Brahmani River basin in eastern India. These RS-based models are designed using the spectral behavior of land (C) and water (M) pixels in the NIR region of the electromagnetic spectrum in the presence and absence of water surrounding the streamflow gauging station. Further, the computed pixel ratio (C/M) is used as a parameter for the discharge estimation, in which four years (2009-2013) of Landsat images are used during calibration and three years (2014-2016) of these images are used during validation. Model-I uses the C/M method in which a box-matrix is conceptualized to analyze the optimal location of the land pixel (C0); and subsequently, the time series of C/M is calibrated with the in-situ discharge (Q) time series. The best pixel ratio (C0/M) time series is preprocessed with an exponential smoothing filter to derive the best filtered-pixel ratio (C0/M*) time series, which is used in the regression model to estimate the river discharge. Model-II corresponds to the multi-pixel ratio (MPR) method, where a 3×3 window is used to calculate the average reflectance of both the C and M pixels within the box-matrix, and subsequently, to obtain the best pixel (C0ʹ/Mʹ) ratio as in the case of Model-I to develop the spectral relationship between C0ʹ/Mʹ and Q time series. Model-III uses both the C/M and water width-based function to estimate the streamflow. The performance evaluation of the models is carried out using the Nash-Sutcliffe efficiency, Percentage bias, and Mean absolute error, which reveals that the model performance varies in the order: Model-III > Model-II > Model-I. This proposed RS-based discharge estimation model framework has the potential to be used in many world- rivers with varying cross-sections.