Abstract
Climate change is predicted to affect water resources infrastructure due to its effect on rainfall, temperature
and evapotranspiration. However, there are huge uncertainties on both the magnitude and direction of
these effects. The Pong reservoir on the Beas River in northern India serves irrigation and hydropower needs.
The hydrology of the catchment is highly influenced by Himalayan seasonal snow and glaciers, and Monsoon
rainfall; the changing pattern of the latter and the predicted disappearance of the former will have profound
effects on the performance of the reservoir. This study employed a Monte-Carlo simulation approach to characterise
the uncertainties in the future storage requirements and performance of the reservoir. Using a calibrated
rainfall-runoff (R-R) model, the baseline runoff scenario was first simulated. The R-R inputs (rainfall and temperature)
were then perturbed using plausible delta-changes to produce simulated climate change runoff scenarios.
Stochastic models of the runoff were developed and used to generate ensembles of both the current and climatechange
perturbed future scenarios. The resulting runoff ensembles were used to simulate the behaviour of the
reservoir and determine “populations” of reservoir storage capacity and performance characteristics. Comparing these parameters between the current and the perturbed provided the population of climate change effects which was then analysed to determine the uncertainties. The results show that contrary to the usual practice of using single records, there is wide variability in the assessed impacts. This variability or uncertainty will, no doubt, complicate the development of climate change adaptation measures; however, knowledge of its sheer magnitude as demonstrated in this study will help in the formulation of appropriate policy and technical interventions for sustaining and possibly enhancing water security for irrigation and other uses served by Pong reservoir.
and evapotranspiration. However, there are huge uncertainties on both the magnitude and direction of
these effects. The Pong reservoir on the Beas River in northern India serves irrigation and hydropower needs.
The hydrology of the catchment is highly influenced by Himalayan seasonal snow and glaciers, and Monsoon
rainfall; the changing pattern of the latter and the predicted disappearance of the former will have profound
effects on the performance of the reservoir. This study employed a Monte-Carlo simulation approach to characterise
the uncertainties in the future storage requirements and performance of the reservoir. Using a calibrated
rainfall-runoff (R-R) model, the baseline runoff scenario was first simulated. The R-R inputs (rainfall and temperature)
were then perturbed using plausible delta-changes to produce simulated climate change runoff scenarios.
Stochastic models of the runoff were developed and used to generate ensembles of both the current and climatechange
perturbed future scenarios. The resulting runoff ensembles were used to simulate the behaviour of the
reservoir and determine “populations” of reservoir storage capacity and performance characteristics. Comparing these parameters between the current and the perturbed provided the population of climate change effects which was then analysed to determine the uncertainties. The results show that contrary to the usual practice of using single records, there is wide variability in the assessed impacts. This variability or uncertainty will, no doubt, complicate the development of climate change adaptation measures; however, knowledge of its sheer magnitude as demonstrated in this study will help in the formulation of appropriate policy and technical interventions for sustaining and possibly enhancing water security for irrigation and other uses served by Pong reservoir.
| Original language | English |
|---|---|
| Pages (from-to) | 49-67 |
| Number of pages | 9 |
| Journal | Proceedings of the International Association of Hydrological Sciences |
| Volume | 371 |
| DOIs | |
| Publication status | Published - 12 Jun 2015 |
