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Remotely sensed ET for streamflow modelling in catchments with contrasting flow characteristics: an attempt to improve efficiency

  • A. Kunnath-Poovakka
  • D. Ryu
  • L. J. Renzullo
  • B. George
Original Paper
  • 84 Downloads

Abstract

An efficient calibration with remotely sensed (RS) data is important for accurate predictions at ungauged catchments. This study investigates the advantages of streamflow-sensitive regionalization on calibration with RS evapotranspiration (ET). Regionalization experiments are performed at 28 catchments in Australia. The catchments are classified into three groups based on annual rainfall and runoff coefficients. Streamflow, RS ET, and a multi-objective RS ET-streamflow calibration are performed using the DiffeRential Evolution Adaptive Metropolis algorithm in each catchment. Simplified Australian Water Resource Assessment-Landscape model is calibrated for a selection of five parameters. Posterior probability distributions of parameters from three calibrations performed at donor catchments in each group are inspected to find the parameter for regionalization in the individual group. In group 1 of wetter catchments, regionalization of parameter FsoilEmax (soil evaporation scaling factor) helps to simplify the calibration without any deterioration in ET, soil moisture (SM) and streamflow predictions. Regionalization of parameter Beta (coefficient describing rate of hydraulic conductivity increase with water content) in group 2 assists to improve the streamflow predictions with no decrement in ET and SM predictions. However, regionalization is not able to provide satisfactory results in group 3. Group 3 includes low-yielding catchments, with average annual rainfall below 1000 mm/year and runoff coefficient less than 0.1, where traditional streamflow calibration also fails to produce accurate results. This study concludes that streamflow-sensitive regionalization is effective for improving the efficacy of RS ET calibration in wetter catchments.

Keywords

Evapotranspiration (ET) Remotely sensed (RS) data DREAM Calibration Regionalization 

Notes

Acknowledgements

This research was financially supported by International Postgraduate Research Scholarships (IPRS) and Australian Postgraduate Award (APA) and the Carlton Connect Initiative Fund Project 22514, representing a collaboration between the Melbourne School of Engineering, The University of Melbourne, and the South Australian Government Department of Environment, Water and Natural Resources. We would like to acknowledge Commonwealth Scientific and Industrial Research Organization (CSIRO) and Bureau of Meteorology (BoM) for providing necessary data.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • A. Kunnath-Poovakka
    • 1
    • 4
  • D. Ryu
    • 1
  • L. J. Renzullo
    • 2
  • B. George
    • 3
  1. 1.Department of Infrastructure EngineeringThe University of MelbourneParkvilleAustralia
  2. 2.CSIRO Land and WaterCanberraAustralia
  3. 3.Integrated Water and Land Management ProgramICARDACairoEgypt
  4. 4.Department of Civil EngineeringIndian Institute of Technology BombayPowaiIndia

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