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.

Abstract. 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.

Agriculture 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.

This study has developed optimal hedging policies for the multi-purpose Ubonratana Reservoir in northeastern Thailand based on its existing rule curves. The hedging policy was applied whenever the reservoir storage falls below a critical level for each month of the year. The decision variables, i.e. the set of monthly storages defining the critical rule curve that triggers rationing and the rationing ratio, were optimized by genetic algorithm (GA). Both single stage (i.e. with one critical rule curve and one rationing ratio) and two-stage (with two critical rule curves and ratios) of the hedging policy were considered in the optimization. To test the effect of the optimized hedging policies on reservoir performance, simulations were carried out, forced alternatively with the existing rule curves (i.e. without hedging) and the two optimized hedging policies. Performance was summarized in terms of reliability (time-and volume-based) and vulnerability. The results showed that the vulnerability was significantly reduced by using the optimized hedging rules. However, the number of water shortages increased with the optimized rules, causing the time-based reliability to worsen significantly. This should not be of concern since, although the number of shortages increased, the associated shortage quantities on most of these additional occasions were small, leaving the volumetric reliability largely unchanged.

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