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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/hess-2017-428
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
26 Jul 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).
Assessment of methods for seasonal streamflow forecasting in the Upper Indus Basin of Pakistan
Stephen P. Charles1, Quan J. Wang2, Mobin-ud-Din Ahmad3, Danial Hashmi4, Andrew Schepen5, and Geoff Podger3 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
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 (ESP) 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 show that the BJP approach using two predictors (March flow and an 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.

Citation: Charles, S. P., Wang, Q. J., Ahmad, M.-U.-D., Hashmi, D., Schepen, A., and Podger, G.: Assessment of methods for seasonal streamflow forecasting in the Upper Indus Basin of Pakistan, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-428, in review, 2017.
Stephen P. Charles et al.
Stephen P. Charles et al.
Stephen P. Charles et al.

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Short summary
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 stream-flow forecasts for inflow to Pakistan's two largest dams. The approach produces skillful and reliable forecasts. As it is simple and quick to apply, it can be used to provide probabilistic seasonal stream-flow 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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