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

Abstract

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 (AprilSeptember) 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.

Co-designing Indus Water-Energy-Land Futures

“…59 In Pakistan, 45% of the annual flows come from snow and glacial ice melt, 60,61 and although uncertain, climate-change projections indicate an increase in the annual water flow in the near term (as a result of glacier melting) but a sharp decrease in the medium run, which will heavily affect water availability in the country. [62][63][64][65] Furthermore, regional stakeholders also recognize the imminent threat to groundwater sustainability and its link to energyrelated issues. Indian and Pakistani energy subsidies with large uncertainty in surface-water availability, for example, have contributed to unsustainable groundwater pumping.…”

Drought monitoring and prediction in climate vulnerable Pakistan: Integrating hydrologic and meteorologic perspectives

“…Our lagged monthly cross-correlation analysis showed strong correlations between SPEI and SSI in (at-least) the early Kharif season (April to June) (Charles et al, 2018), for the Chenab, Jhelum and Kabul catchments (with lags of up to two months). This is a very important outcome, since monitoring and prediction of streamflow deficits in these basins in early Kharif months is extremely critical for reservoir operations and irrigation planning in the lower Indus, and for seasonal drought forecasting and planning in the country (Young et al, 2019;Charles et al, 2018;Ministry of Water Resources, 2018). Given these strong lagged cross-correlations, we believe that SPEI could be used in operational drought forecasting and warning systems within Pakistan in future, for early streamflow deficit prediction in Kabul, Chenab and Jhelum basins in the early Kharif.…”

“…The temporal oscillations (i.e., oscillations between negative and positive SPEI-3 values) in the basin-wide SPEI-3 values, however, are higher than in corresponding SSI-3 values. A plausible reason here could be that snowmelt and glacial melt dominate the hydrology of these Upper Indus catchments (Charles et al, 2018;Lutz et al, 2016). Hence, SPEI-x with a larger accumulation period could correspond better with SSI-3, in terms of oscillatory patterns and drought duration.…”

“…Within the context of Pakistan and especially the catchments being assessed in this study, analysis of hydrological droughts over accumulation periods of 1-6 months might be most suitable (Fraz Ismail and Bogacki, 2018;Charles et al, 2018). This is because hydrological outflows from the four catchments of our study are central to seasonal planning and operations (conducted in cycles of six months) of the IBIS in lower Indus (Fraz Ismail and Bogacki, 2018;Charles et al, 2018). A major proportion of the outflows of Upper Chenab, Upper Jhelum, Upper Indus and Kabul catchments are diverted for agricultural consumption in the lower Indus via IBIS (Young et al, 2019).…”

“…Irrigation System (IBIS) (Bogacki and Ismail, 2016;Young et al, 2019). For instance, early prediction / detection of surface flow deficit in the Upper Indus catchments in April to June, also referred to as the early Kharif season, is extremely valuable for water management and operations in the lower Indus basin, including operations of storage resevoirs (Bogacki and Ismail, 2016;Charles et al, 2018;Fraz Ismail and Bogacki, 2018). SPEI values linked to Chenab, Jhelum and Kabul basins could thus be used in both seasonal drought planning and water system operations.…”