A general operating policy for a multiple reservoir system operation is derived in this study using the combination of a discrete differential dynamic programming (DDDP) and a neural network (NN). The combination model is divided into three stages that consist of a DDDP algorithm, a NN algorithm, and a simulation model. In the first stage the optimal operating policies are derived from the DDDP in association of the genetic algorithm (GA) using the first set of data. The obtained optimization results are then supplied as the training patterns to the NN to derive a general operating policy. During the training process proceeds, the networks are evaluated by a simulation model to investigate their performance for another set of data. The demonstration is carried out through application to the Mae Klong system in Thailand. The objective of the optimization is to minimize the total irrigation deficits during the operation period. The results obtained in this study show that the combination model performs satisfactorily on deriving a reservoir general operating policy.

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A deterministric optimization model is presented to allocate irrigation waters to blocks of crop fields in an agricultural district. In order to prevent excessive agricultural water use and enhance water productivity, two competing objectives, i.e., minimization of water withdrawal from a river and maximization of crop yield, are formulated. Water balances in a river, a field block and a channel network connecting some field blocks are considered as constraints. Adjusting a weight in a synthesized objective function results in producing a set of noninferior solutions, which can be used to analyze a trade-off between the two objectives. The optimization model is applied to a hypothetical irrigation district for rice production, successfully generating optimal allotments of irrigation waters to blocks of paddy fields and optimal water withdrawals.

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Water as a limiting factor in agricultural production in semi-arid Savannah could be developed on a small scale by constructing self-reliant irrigation tanks (micro-dams) that directly catch and store rainwater from prepared catchment, having less impact on the environment. The tank could also be a core of village-level rural community to encourage agricultural activities as well as farmers' communication. The paper describes the design and construction practices of a prototype irrigation tank for harvesting rainwater in Ghana. Topographical survey and geotechnical investigation at the project site provided data for the appropriate location and design of the micro-dam. Representative soil samples from soil profiles to the parent material showed that the subsoil condition was suitable for the foundation structure and as core material. Prudent and meticulous construction process of the irrigation tank proceeded with the removal of organic materials from the surface soil before starting the embanking. Apart for filter materials, clay core and rock materials were appropriately identified near the dam site and mined for the embanking process to cut down on the cost of construction of the dam reservoir. Embanking was implemented in two phases in order to release floods smoothly through the central watercourse route zone and the spillway section of the dam in the event of rains during the construction period. A steel pipe (draw-off pipe) appropriately fitted with an anti-seepage collar and a spindle fitted valve was laid in the transverse of the dam body, for withdrawing water from the reservoir to the farmland. Due diligence was taken in the design and construction of the spillway structure. Design flood discharge was taken on 100-year flood discharge from a viewpoint of sustainable water source development. With the installation of soil tensiometers within the farmland, the soil moisture status is monitored in order to help optimally decide the timing and amount of water supply from the irrigation tank and therefore to increase crop yield while increasing water use efficiency. With the aid of an automatic water level sensor and an automatic rain gauge, the time-varying reservoir water level and rainfall are also monitored for future research use.

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Water is most dense at 4℃ and becomes less dense at both higher and lower temperatures. Because of the density-temperature relationship, many reservoirs in temperate climates tend to stratify. The water warmed by the sun will remain at the surface (epilimnion), while the dense and cold water sinks to the bottom (hypolimnion). The layer separating the two layers is the thermocline. These layers do not mix, and the hypolimnion is cut off from atmospheric oxygen and suffers from environmental degradation such as eutrophication. Organic sludge is removed by dredging and self-purification of reservoir is produced by supplying oxygen to a hypolimnion by aeration to improve this environmental degradation. In this paper, we suggest new water purification system without aeration or dredging. The system promotes photosynthesis of phytoplankton at a disphotic and hypoxic bottom layer by irradiating artificial light directly. Our concern is to inspect that this system can supply oxygen with phytoplankton living in a hypoxic hypolimnion.

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Water stress is a major constraint to increasing soybean yields in the tropical regions such as Indonesia. Therefore, the present study was conducted to investigate the critical water content and water stress coefficient of soybean subjected to deficit irrigation. This research was conducted in a plastic house at the University of Lampung, Sumatra, Indonesia, from June to September, 2003. The soil moisture treatments imposed were 0-20%, 20-40%, 40-60%, 60-80%, and 80-100% of available water (AW) deficit, arranged in RCB (Randomize Complete Block) design with four replications. The results showed that critical water content has been changing through the total growth stage. Furthermore, soybean plant was suffered from water stress most strongly at the growth stages of pod filling and initial maturing, and the critical water content at these stages was 0.369m^3/m^3, at 30% of AW deficit (51kPa). The average yield response factor (Ky) of soybean for the total growing period was 0.507. The optimum yield of soybean with the highest yield efficiency was 2.77t/ha, crop water requirement was 587mm and could conserve 24.6% of water compared to full irrigation, which was reached by the water management that maintained the soil moisture at 40-60% of AW deficit, and the average value of Ks was 0.68.

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An experiment was conducted to determine the effect of available water deficit at different growth stages on the yield and crop water requirement of soybean. This research was conducted in a plastic greenhouse of Agricultural Faculty, the University of Lampung from August to November, 2001. A factorial experiment was arranged in randomized block design with three replications. The period of available water deficit (S) was the first factor with four different growth stages, namely: early vegetative phase (S1), advance vegetative phase (S2), flowering phase (S3), and pod filling to maturity phase (S4). Available water deficit (D) was the second factor with four levels including: D2(20-40%), D3(40-60%), D4(60-80%), and D5(80-100%). The results showed soybean to be sensitive to water stress especially at S3 and S4, and the critical water content of these stages was around 50% of available water deficit or 0.26m^3/m^3(soil moisture suction was 120kPa). The values of water stress coeffisient (Ks) of the soybean at S2, S3, and S4 were 0.55, 0.57, and 0.27 respectively. The values of yield response factor (Ky) of SI, S2, S3, and S4 were 0.96, 0.42, 0.46, and 1.20 respectively. The water deficit at early vegetative (S1) and pod filling to early maturing (S4) was sensitive to the relative decrease of yield. The damage caused by water defict at early vegetative stage (S1) could recover by full irrigation (D1) after advance vegetative stage (S2). However, even small water deficit during pod filling to early maturing stage (S4) could strongly damage the yield of soybean. All the levels of water deficit (D1〜D4) at pod filling to early maturing stage (S4) decreased yield efficiency extremely, which means that the deficit irrigation applied after the pod filling stage could not be tolerated.

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Lake Koyama, located in the eastern part of Tottori Prefecture, has various water environmental problems due to organic pollution and eutrophication. Anoxic water is especially generated in the deepest part of the pond in summer, and there is a strong suspicion that the progression of anoxia will cause further deterioration of the water environment. Therefore, predicting dissolved oxygen (DO) variations is an important subject to plan the preservation and improvement of water environments. In this study, we tried to predict in short-term the time transition of DO at the water surface and the lake bottom at the deepest site using a Local Approximation (LA) method of forecast technique developed in the chaos engineering field. We examined the presence of chaos in both DO time-series. As a result, a DO time-series in the surface didn't show low-dimensionality chaos in either summer or autumn, but could be predicted by the LA method three hours in advance. On the other hand, in summer a DO time-series at the bottom showed low-dimensionality chaos, but not in autumn. Also, DO in summer and autumn could be respectively predicted one hour and four hours in advance. In conclusion, the LA method that predicts time-series through the orbit information of historical data was effective for the short-term forecast of DO, regardless of the presence of chaoticity in its time-series data

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