Optimisation of hedging-integrated rule curves for reservoir operation

Reservoir managers use operational rule curves as guides for managing and operating reservoir systems. However, this approach saves no water for impending droughts, resulting in large shortages during such droughts. This problem can be tempered by integrating hedging with the rule curves to curtail the water releases during normal periods of operation and use the saved water to limit the amount and impact of water shortages during droughts. However, determining the timing and amount of hedging is a challenge. This thesis presents the application of genetic algorithms (GA) for the optimisation of hedging-integrated reservoir rule curves. However, due to the challenge of establishing the boundary of feasible region in standard GA (SGA), a new development of the GA i.e. the dynamic GA (DGA), is proposed. Both the new development and its hedging policies were tested through extensive simulations of the Ubonratana reservoir (Thailand). The first observation was that the new DGA was faster and more efficient than the SGA in arriving at an optimal solution. Additionally, the derived hedging policies produced significant changes in reservoir performance when compared to no-hedging policies. The performance indices analysed were reliability (time and volume), resilience, vulnerability and sustainability; the results showed that the vulnerability (i.e. average single periods shortage) in particular was significantly reduced with the optimised hedging rules as compared to using the no-hedging rule curves. This study also developed a monthly inflow forecasting model using artificial neural networks (ANN) to aid reservoir operational decision-making. Extensive testing of the model showed that it was able to provide inflow forecasts with reasonable accuracy. The simulated effect on reservoir performance of forecasted inflows vis-à-vis other assumed reservoir inflow knowledge situations showed that the ANN forecasts were superior, further reinforcing the importance of good inflow information for reservoir operation. The ability of hedging to harness the inherent buffering capacity of existing water resources systems for tempering water shortage (or vulnerability) without the need for expensive new-builds is a major outcome of this study. Although applied to Ubonratana, the study has utility for other regions of the world, where e.g. climate and other environmental changes are stressing the water availability situation

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 competing policies at Ubonratana reservoir, Thailand

As the main water resources infrastructure in the region, the Ubonratana reservoir has played and continues to play a significant role in the socio-economic well-being of north-eastern Thailand. For such a multi-purpose system serving flood protection and various water demand needs, it is important that the reservoir is effectively operated to ensure that the overall performance of the system is enhanced. Consequently, this study has evaluated the performance of the Ubonratana reservoir with four competing operating policies, namely: (a) the pre-2002 policy (P1); (b) a post-2002 policy, following the catastrophic flood of 2002 (P2); (c) a policy derived in the current study to address the limitations of P2 in relation to water shortages (P3); and (d) the standard operating policy, SOP (P4). The simulation analyses were implemented using a water evaluation and planning system model of the reservoir meeting domestic (first priority), industrial (second priority), irrigation (third priority) and in-stream (fourth priority) needs. The performance was summarised in terms of reliability, vulnerability, resilience and sustainability. The results showed that overall, P4 was the best, followed by P3, P1 and P2 in that order. This is a useful demonstration of how rule curves can successfully guide the operation of multi-purpose reservoir systems. </jats:p

Heavy metal pollution index for assessment of seasonal groundwater supply quality in hillside area, Kalasin, Thailand

AbstractAgriculture is a major activity in most rural areas in northern Thailand. The aim of this study was to assess the heavy metal pollution index (HPI) for water supply quality in a rural village in Kalasin Province named Kaeng Ka-am village located in the hillside area of Phu Phan mountain. The concentration of heavy metals including iron (Fe), manganese (Mn), and zinc (Zn) in groundwater supply has been analyzed by the atomic absorption spectrometer. The groundwater supplied samples were collected from eight different locations in and around the region which covers agricultural and municipal area during the monsoon and post-monsoon seasons. The results were evaluated in accordance with the drinking water quality standards suggested by the World Health Organization and Thailand Department of Health Standards. Most of the samples were found within limit except for Fe and Mn contents during the monsoon season at three sampling locations which is above the desirable limit, i.e., 0.3 mg/L. The mean values of HPI were 70 and 46 in the monsoon and the post-monsoon season, respectively, and these values are well below the critical index limit of 100.</jats:p

Stochastic assessment of Phien generalized reservoir storage–yield–probability models using global runoff data records

SummaryThis study has carried out an assessment of Phien generalised storage–yield–probability (S–Y–P) models using recorded runoff data of six global rivers that were carefully selected such that they satisfy the criteria specified for the models. Using stochastic hydrology, 2000 replicates of the historic records were generated and used to drive the sequent peak algorithm (SPA) for estimating capacity of hypothetical reservoirs at the respective sites. The resulting ensembles of reservoir capacity estimates were then analysed to determine the mean, standard deviation and quantiles, which were then compared with corresponding estimates produced by the Phien models. The results showed that Phien models produced a mix of significant under- and over-predictions of the mean and standard deviation of capacity, with the under-prediction situations occurring as the level of development reduces. On the other hand, consistent over-prediction was obtained for full regulation for all the rivers analysed. The biases in the reservoir capacity quantiles were equally high, implying that the limitations of the Phien models affect the entire distribution function of reservoir capacity. Due to very high values of these errors, it is recommended that the Phien relationships should be avoided for reservoir planning