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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 22, issue 6
Hydrol. Earth Syst. Sci., 22, 3533-3549, 2018
https://doi.org/10.5194/hess-22-3533-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue: Sub-seasonal to seasonal hydrological forecasting

Hydrol. Earth Syst. Sci., 22, 3533-3549, 2018
https://doi.org/10.5194/hess-22-3533-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 29 Jun 2018

Research article | 29 Jun 2018

Seasonal streamflow forecasting in the upper Indus Basin of Pakistan: an assessment of methods

Stephen P. Charles1, Quan J. Wang2, Mobin-ud-Din Ahmad3, Danial Hashmi4, Andrew Schepen5, Geoff Podger3, and David E. Robertson6 Stephen P. Charles et al.
  • 1CSIRO Land and Water, Floreat, 6014, Australia
  • 2The University of Melbourne, Parkville, 3010, Australia
  • 3CSIRO Land and Water, Canberra, 2601, Australia
  • 4Water and Power Development Authority, Lahore, Pakistan
  • 5CSIRO Land and Water, Dutton Park, 4102, Australia
  • 6CSIRO Land and Water, Clayton, 3168, Australia

Abstract. Timely and skilful seasonal streamflow forecasts are used by water managers in many regions of the world for seasonal water allocation outlooks for irrigators, reservoir operations, environmental flow management, water markets and drought response strategies. In Australia, the Bayesian joint probability (BJP) statistical approach has been deployed by the Australian Bureau of Meteorology to provide seasonal streamflow forecasts across the country since 2010. Here we assess the BJP approach, using antecedent conditions and climate indices as predictors, to produce Kharif season (April–September) streamflow forecasts for inflow to Pakistan's two largest upper Indus Basin (UIB) water supply dams, Tarbela (on the Indus) and Mangla (on the Jhelum). For Mangla, we compare these BJP forecasts to (i) ensemble streamflow predictions (ESPs) from the snowmelt runoff model (SRM) and (ii) a hybrid approach using the BJP with SRM–ESP forecast means as an additional predictor. For Tarbela, we only assess BJP forecasts using antecedent and climate predictors as we did not have access to SRM for this location. Cross validation of the streamflow forecasts shows that the BJP approach using two predictors (March flow and an El Niño Southern Oscillation, ENSO, climate index) provides skilful probabilistic forecasts that are reliable in uncertainty spread for both Mangla and Tarbela. For Mangla, the SRM approach leads to forecasts that exhibit some bias and are unreliable in uncertainty spread, and the hybrid approach does not result in better forecast skill. Skill levels for Kharif (April–September), early Kharif (April–June) and late Kharif (July–September) BJP forecasts vary between the two locations. Forecasts for Mangla show high skill for early Kharif and moderate skill for all Kharif and late Kharif, whereas forecasts for Tarbela also show moderate skill for all Kharif and late Kharif, but low skill for early Kharif. The BJP approach is simple to apply, with small input data requirements and automated calibration and forecast generation. It offers a tool for rapid deployment at many locations across the UIB to provide probabilistic seasonal streamflow forecasts that can inform Pakistan's basin water management.

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Predictions of irrigation-season water availability are important for water-limited Pakistan. We assess a Bayesian joint probability approach, using flow and climate indices as predictors, to produce streamflow forecasts for inflow to Pakistan's two largest dams. The approach produces skilful and reliable forecasts. As it is simple and quick to apply, it can be used to provide probabilistic seasonal streamflow forecasts that can inform Pakistan's water management.
Predictions of irrigation-season water availability are important for water-limited Pakistan. We...
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