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topographic features and hydro-meteorological characteristics and to stably supply water and effectively control flooding, water resources development projects have been carried out. Most recently, due to urbanization and industrialization, as well as population pressures, the amounts of water usage have increased, and the states of water quality in rivers have degraded. Thus, it has been requested to secure water resources abundantly enough to upgrade the water-friendliness of rivers and rehabilitate the environment and ecosystems. Also, due to the prevalence of irrigation farming, the amount of agriculture water usage has increased. Besides, in rural areas, more water needs to be supplied than in the past, because the population wants to enjoy their prosperity under civilized circumstances. According to this information about water resources, it is predicted that, in 2020, about 0.82 billion m 3 would be deficient in South Korea. To secure water resources under the aforementioned circumstances, structural and non-structural measures are to be developed, and it is apparent that they need to go into effect in the near future. Particularly, to maintain the environment and ecosystems from the perspective of sustainability, non-structural measures, such as joint-operations of multi-reservoir systems and water demand management, are to be determined and implemented consistently. Moreover, due to the changes in climate, socio-economic systems, and eco-systems, the operation rules of existing water resources facilities should be reviewed and adapted to the changes. To achieve these management objectives, tools for evaluating water supply capacities of systems, such as water-budget models, simulation models, and optimization models, are to be prepared, and appropriate scenarios are to be created to determine efficient operation rules for the systems (Yeh, 1985; Wurb, 1993). In this study, an optimal reservoir operation model is developed through integration of a nonlinear, multiple-objective function, a global optimization method, and a streamflow network model, and it is then applied to simulating the operations of two study reservoirs to verify the model’s applicability. 2. Optimal Reservoir Operation Model In general, reservoir operation revenues are evaluated using simulation models, optimization models, and simulation-optimization models (Dandy et al., 1997). Particularly, simulation-optimization models have been employed to consider peculiar objective functions and operation conditions of specific operation periods through using simulation models and optimization models alternatively. Fig. 1 shows the flowchart of the presented model, which is grouped into the simulation-optimization models category and takes one of the simulation and optimization modules, taking into account the condition of each operation period of reservoirs. 2.1 Reservoir Operation Sub-model A reservoir operation sub-model is developed to compute reservoir storage amounts and hydro-electric power outputs, considering inflows, releases, overflows during operation periods. During the simulation, in the case that the quality assurance of the data pertaining to observed inflows is confirmed, the historical inflow data is employed, and in other cases, the inflow data is generated using the SSARR hydrologic model. The amounts of municipal and industrial water usages and in-stream flow are determined before reservoir operations, considering the demand of each. On the other hand, agricultural water demands of all periods are simulated using the modified Penman method, considering the situations of rice paddy fields. Hydro-electric power outputs of
all periods are computed using Eq. (1), taking into account discharge for hydro-electric power generation, water stage of the reservoir, operation time, and turbine efficiency. (1) where GE = actually computed hydro-electric power outputs during the period t, E G = generating efficiency, O L = (1 - rate of operating loss), E t = turbine efficiency, Q t = discharge for hydro-electric power generation, H t = effective head drop, H L = (1 - rate of head loss), and T = operation time, respectively. Start Simulation model control data Input data Optimization model control data Computation of inflow and water demand No Optimization model Reservoir operation period ? Yes SCE -UA Method Reached optimum value ? No File with Variables’ values Yes Computation of reservoir storage Computation of hydropower product Computation of water requirement Computation of objective function Computation of reservoir storage Yes No. of period
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POSTER SW: SOIL AND WATER ENGINEERI
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Presenter: Jose Euclides Paterniani
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1 Faculdade de Engenharia Agricola
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Matsura Department of hydraulic and
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P-2064 MULTIVARIATE STATISTICAL OF
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temperature-based (e.g., Thornthwai
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two types of reference surfaces rep
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(d) Baft (c) Bam (b) Kerma n (a) Ji
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Estevez, J., Gavilan, P., & Berenge
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2. Materials and Methods 2.1 The hy
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Ia = n × v ec Equation 3 which: Ia
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5. References ALLEN, R.G.; PEREIRA,
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The density analysis was performed
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Figure1 - Relationship between the
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4 Conclusions • The density obtai
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characteristics resulting from of g
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Table1. Morphological characteristi
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Transpiration of Eucalyptus spp see
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The fertilization growth and harden
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Cool, J. B., Rodrigo, G. N., Garcí
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Abstract Agriculture and water sour
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In 1985 and 1986 hygienic protectio
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spring area. It is also prohibited
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Biological Nitrogen Fixation In Gen
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We used a completely randomized in
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5. References AYERS, R.S.; WESTCOT,
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2 However, the cultures are not alw
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4 TABEL 2: Mean values of radiation
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accumulated ETo (mm dia -1 ) 6 900
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only the expansion of agricultural
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FIGURE 2: Content of chlorophyll a,
Page 63 and 64: Evapotranspiration and Crop Coeffic
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Page 67 and 68: TABLE 1: Irrigation depth and actua
Page 69 and 70: NUTRIENT RETENTION IN WETLANDS USIN
Page 71 and 72: Table 2. Daily affluent concentrati
Page 73 and 74: IMPORTANCE OF DRY GEAR MASS CULTURE
Page 75 and 76: mobilizing assimilated exerted by c
Page 77 and 78: This method consists of covering th
Page 79 and 80: uncovered ones, that mixed the wate
Page 81 and 82: coliforms and E-coli that might hav
Page 83 and 84: WATER TREATMENT BY COAGULATION WITH
Page 85 and 86: in a grinder and passed through a 0
Page 87 and 88: 3.2. Determination of the required
Page 89 and 90: ANALYSIS OF LEVELS OF LAND DEGRADAT
Page 91 and 92: This methodology consists of a sequ
Page 93 and 94: FIGURE 5. A - Area of exploitation
Page 95 and 96: Water technology improvements and t
Page 97 and 98: The Multiattribute Utility Theory (
Page 99 and 100: higher demand than those of scenari
Page 101 and 102: YIELD AND BEAN SIZE OF COFFEA ARABI
Page 103 and 104: uniformity of flowering. The irriga
Page 105 and 106: Table 3 - Analysis of variance for
Page 107 and 108: SUGARCANE FERTIRRIGATED WITH MINERA
Page 109 and 110: 3. Results and Discussion The value
Page 111 and 112: espectively, compared to that obser
Page 113: Optimal Reservoir Operation Model w
Page 117 and 118: (a) Calibration (b) Verification Fi
Page 119 and 120: Characteristics of Heavy Metal Cont
Page 121 and 122: 2. Materials and Method 2.1. Study
Page 123 and 124: TABLE 2: Devices for collecting of
Page 125 and 126: Calibration of Hargreaves Equation
Page 127 and 128: Relative error (RE): Index of agree
Page 129 and 130: Stochastic modelling of Contaminant
Page 131 and 132: widely used for various fields such
Page 133 and 134: show that Extvalue and Logistic dis
Page 135 and 136: Efficiency of water and energy use
Page 137 and 138: Pressure: it was obtained by means
Page 139 and 140: them cover similar percentages. Dur
Page 141 and 142: Relationship among compaction, mois
Page 143 and 144: Cylindrical containers (191mm diame
Page 145 and 146: Figure 9 High compaction. Bulk dens
Page 147 and 148: Simulation of water flow with root
Page 149 and 150: water contents were almost greater
Page 151 and 152: Operation and Energy Optimization M
Page 153 and 154: Urmia Salt Lake Urmia FIGURE 1: Gha
Page 155 and 156: changes have been done in system. F
Page 157 and 158: Application of Surface Cover and So
Page 159 and 160: significantly lower than those from
Page 161 and 162: Choi, J. D., (1997). Effect of Rura
Page 163 and 164: 1.1. Scope and aim The growth of th
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Therefore, the remaining works are
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network makes such volumes unaccept
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Reclaimed wastewater reuse has been
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Fig. 2 shows the monitoring results
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3. Conclusions Reclaimed wastewater
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Q P Ia 2 P Ia S for P≥Ia Q 0
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data P (mm), gauged in 130 pluviogr
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TABLE 2: CN emp values obtained for
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References Chapman, T. G. & Maxwell
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This work, after applying Kennessey
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TABLE 2 - Partial runoff coefficien
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Figure 3 also reports a comparison
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2. Material and Methods The experim
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TABLE 2: Summary of variance analys
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BEZERRA, I. L.; GHEYI, H. R.; FERNA
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2. Materials and Methods The study
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Figure 3. Hourly values of ET estim
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Ortega-Farias, S.O., Cuenca, R.H.,
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2 Material end methods The wastewat
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Queiroz et al. (2004) and (Fonseca
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Reference list CEREDA, M.P. (2001)
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2. Data and Methods 2.1. Methods Ir
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3. Results The water balance model
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Acknowledgments This work was carri
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and the need to reduce costs, it be
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40 mm 65.30 59.00 8.70 8.10 60 mm 6
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REFERENCES BERTRAND, J. P. et al. L
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The mathematical modeling in the wa
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OD (mg L -1 ) OD obs (mg L -1 ) TAB
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OD (mg L -1 ) DBO (mg L -1 ) 8,00 7
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Effect of Rice Straw Mulch on Runof
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mg/L, 14.6 mg/L, and 1.2 mg/L, resp
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1 PAPAYA SEEDLINGS PRODUCTION FROM
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3 into an oven with circulating air
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5 14 12 Stem diameter (mm) 10 8 6 4
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7 Root dry matter (g plant -1 ) 8 7
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CAVALCANTE, L. F.; CORDEIRO, J. C.;
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This document was created with Win2
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extractable and non-extractable bou
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MOD-E and MOD-B, and 65.99% and 80.
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Houot, S., Barriuso, E., Bergheaud,
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measures water content and electric
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As observed, increasing the dischar
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irrigation: a comparison of point a
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field operations (STRECK et al., 20
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3. Results and discussions Table 1
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4. Conclusions It can be concluded
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water maintenance. At the same time
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TABLE 1 Stream discharge for each m
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TABLE 5 Correlation analysis of wat
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turbulent flow energy produced by w
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The numerical model was validated a
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4. Conclusion FIGURE 6: The shape o
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2.1 Case study The Zayandeh-Rud bas
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economic factors. In this is a very
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FIGURE 1. Location of the Wuliangsu
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WT( o C) (a) 30 25 20 15 10 5 0 -5
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(a) (b) (c) (d) (e) (f) (g) (h) (i)
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• The effectiveness of the crop c
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As evidenced by Rana et al. (2005),
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1.20 1.20 2010 2011 0.90 0.90 K c 0
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elationships. There is forest area,
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B = ( c − a) A − ( c − d) c
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References Choi, W.-J., Lee, S.-M.,
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of faecal bacteria (Kummerer, 2004;
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FIGURE 1 - Project tasks and links
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Oliveira, A.B. & Henriques, M. (201
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1 Introduction To irrigate is to su
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the inverter that provides a refere
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OLIVEIRA FILHO, D. ; SAMPAIO, R. P.
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1.1 Description of the Study Area T
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Refrences: Bruce J.P. (1994). Natur
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RE because it has the advantages ov
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with L 1 2 com obs J ( k ) = ∑{ f
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Relative hydraulic conductivity r 1
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surfaces requires information on th
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climate. Therefore a special coeffi
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FIGURE 2: The relation between K pa
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Coagulation using Moringa oleifera
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After the assembly of the experimen
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For the positive control test, the
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MULTIVARIATE STATISTICAL OF PRINCIP
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The multiple regression equations w
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Table 3 - Regression models that be
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Is Imaging Analysis Quantifying the
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of the computer. All additional ima
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The final enhanced images were segm
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image analysis is well suited and f
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EVALUATION OF CROP CANOPY EFFECT ON
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∂e ∂e ∂e + u + v ∂t ∂x
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4. Results and discussion 4.1. Spat
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Benchmarking of Irrigated Agricultu
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Indicators can be thought of as sta
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total area is about 13,700 km2. The
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