23 research outputs found
Assessing climate change impacts on operation and planning characteristics of Pong Reservoir, Beas (India)
Stochastic assessment of Phien generalized reservoir storage-yield-probability models using global runoff data records
Effect of reservoir zones and hedging factor dynamism on reservoir adaptive capacity for climate change impacts
When based on the zones of available water in storage, hedging has
traditionally used a single hedged zone and a constant rationing ratio for
constraining supply during droughts. Given the usual seasonality of
reservoir inflows, it is also possible that hedging could feature multiple
hedged zones and temporally varying rationing ratios but very few studies
addressing this have been reported especially in relation to adaptation to
projected climate change. This study developed and tested Genetic Algorithms
(GA) optimised zone-based operating policies of various configurations using
data for the Pong reservoir, Himachal Pradesh, India. The results show that
hedging does lessen vulnerability, which dropped from  ≥  60 % without
hedging to below 25 % with the single stage hedging. More complex hedging
policies, e.g. two stage and/or temporally varying rationing ratios only
produced marginal improvements in performance. All this shows that water
hedging policies do not have to be overly complex to effectively offset
reservoir vulnerability caused by water shortage resulting from e.g.
projected climate change
Inflow forecasting using artificial neural networks for reservoir operation
In this study, multi-layer perceptron (MLP) artificial neural networks have
been applied to forecast one-month-ahead inflow for the Ubonratana reservoir,
Thailand. To assess how well the forecast inflows have performed in the
operation of the reservoir, simulations were carried out guided by the
systems rule curves. As basis of comparison, four inflow situations were
considered: (1) inflow known and assumed to be the historic (Type A);
(2) inflow known and assumed to be the forecast (Type F); (3) inflow known
and assumed to be the historic mean for month (Type M); and (4) inflow is
unknown with release decision only conditioned on the starting reservoir
storage (Type N). Reservoir performance was summarised in terms of
reliability, resilience, vulnerability and sustainability. It was found that
Type F inflow situation produced the best performance while Type N was the
worst performing. This clearly demonstrates the importance of good inflow
information for effective reservoir operation
Assessing climate change impacts on operation and planning characteristics of Pong Reservoir, Beas (India)
Evaluating the variability in surface water reservoir planning characteristics during climate change impacts assessment
This study employed a Monte-Carlo simulation approach to characterise the uncertainties in climate change induced variations in storage requirements and performance (reliability (time- and volume-based), resilience, vulnerability and sustainability) of surface water reservoirs. 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 climate-change-perturbed future runoff scenarios. The resulting runoff ensembles were used to force simulation models of the behaviour of the reservoir to produce ‘populations’ of required reservoir storage capacity to meet demands, and the performance. Comparing these parameters between the current and the perturbed provided the population of climate change effects which was then analysed to determine the variability in the impacts. The methodology was applied to the Pong reservoir on the Beas River in northern India. The reservoir serves irrigation and hydropower needs and the hydrology of the catchment is highly influenced by Himalayan seasonal snow and glaciers, and Monsoon rainfall, both of which are predicted to change due to climate change. The results show that required reservoir capacity is highly variable with a coefficient of variation (CV) as high as 0.3 as the future climate becomes drier. Of the performance indices, the vulnerability recorded the highest variability (CV up to 0.5) while the volume-based reliability was the least variable. Such variabilities or uncertainties will, no doubt, complicate the development of climate change adaptation measures; however, knowledge of their sheer magnitudes as obtained 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
Effect of Hedging-Integrated Rule Curves on the Performance of the Pong Reservoir (India) During Scenario-Neutral Climate Change Perturbations
This study has evaluated the effects of improved, hedging-integrated reservoir rule
curves on the current and climate-change-perturbed future performances of the Pong reservoir,
India. The Pong reservoir was formed by impounding the snow- and glacial-dominated Beas
River in Himachal Pradesh. Simulated historic and climate-change runoff series by the
HYSIM rainfall-runoff model formed the basis of the analysis. The climate perturbations used
delta changes in temperature (from 0° to +2 °C) and rainfall (from −10 to +10 % of annual
rainfall). Reservoir simulations were then carried out, forced with the simulated runoff
scenarios, guided by rule curves derived by a coupled sequent peak algorithm and genetic
algorithms optimiser. Reservoir performance was summarised in terms of reliability, resilience,
vulnerability and sustainability. The results show that the historic vulnerability reduced from
61 % (no hedging) to 20 % (with hedging), i.e., better than the 25 % vulnerability often
assumed tolerable for most water consumers. Climate change perturbations in the rainfall
produced the expected outcomes for the runoff, with higher rainfall resulting in more runoff
inflow and vice-versa. Reduced runoff caused the vulnerability to worsen to 66 % without
hedging; this was improved to 26 % with hedging. The fact that improved operational practices
involving hedging can effectively eliminate the impacts of water shortage caused by climate
change is a significant outcome of this study
Associating Climatic Trends with Stochastic Modelling of Flow Sequences
patterns are highly sensitive to temperature (T) variation and thus also affect natural streamflow processes. This paper presents a novel suite of stochastic modelling approaches for associating streamflow sequences with climatic trends. The present work is built upon a stochastic modelling framework (HMM_GP) that integrates a hidden Markov model (HMM) with a generalised Pareto (GP) distribution for simulating synthetic flow sequences. The GP distribution within the HMM_GP model aims to improve the model’s efficiency in effectively simulating extreme events. This paper further investigated the potential of generalised extreme value distribution (GEV) coupled with an HMM model within a regression-based scheme for associating the impacts of precipitation and evapotranspiration processes on streamflow. The statistical characteristic of the pioneering modelling schematic was thoroughly assessed for its suitability to generate and predict synthetic river flow sequences for a set of future climatic projections, specifically during ENSO events. The new modelling schematic can be adapted for a range of applications in hydrology, agriculture, and climate change