Incorporating CO2 net flux in multipurpose reservoir water allocation optimization

Many water planning, researches have been done but water pricing has not been considered as the prime factor. Cost allocation is required whenever a project deals with multi-purpose groups. An optimization model which is accommodating the water allocation and water price must be developed in Indonesia. The new linear optimization model is developed to present a method for the determination of equitable impact fees and optimal water allocation for single reservoir. The proposed method is demonstrated on a river system with 4 major reservoirs. Each reservoir system serves 5 uses (irrigation, hydroelectric and flood control, industrial and domestic need). Using optimization with the cost of the reservoir and its facilities as targets and the objective function is maximization of total net benefit of user income, water allocation as x variable and water price as the y variable will produce the optimal result. The result is the optimal water allocation with a minimal water price which is present on 3 simulation analysis. The result of the model is in a graphic and table presentation, which can be used easily to determine the water allocation and water price per m3 in all reservoir systems

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Water Allocation Using the Bankruptcy Model: A Case Study of the Missouri River

Water ◽

10.3390/w12030619 ◽

2020 ◽

Vol 12 (3) ◽

pp. 619 ◽

Cited By ~ 1

Author(s):

Heshani Wickramage ◽

David Roberts ◽

Robert Hearne

Keyword(s):

Water Allocation ◽

Missouri River ◽

Water Levels ◽

Sharing Rule ◽

Reservoir Water ◽

Allocation Rules ◽

Navigation Channel ◽

Constrained Equal Awards Rule ◽

Constrained Equal Awards

This research applies cooperative game theory—specifically, the bankruptcy model—to address conflicts arising from the scarcity of water resources shared by multiple agents. This case study addresses potential outcomes of five allocation rules applied to the apportionment of water between two agents in the Missouri River. Currently, there is no interstate compact to apportion Missouri River and frequent disputes between upstream and downstream states occur. Upstream states favor managing reservoir water levels to support reservoir recreation and downstream states want water for the downstream navigation channel. The five allocation rules studied are (1) Proportional Sharing, (2) Constrained Equal Awards, (3) Constrained Equal Losses, (4) Sequential Sharing based on Proportional Sharing, and (5) a proposed Modified Constrained Equal Awards rule. The results of the analysis of apportionment during four dry years in the 2000s show that the best approaches are the proposed Modified Constrained Equal Awards Rule and the Proportional Sharing Rule.

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Reservoir Water Allocation under Abrupt Pollution Condition

Journal of Irrigation and Drainage Engineering ◽

10.1061/(asce)ir.1943-4774.0000679 ◽

2014 ◽

Vol 140 (3) ◽

pp. 04013017 ◽

Cited By ~ 4

Author(s):

Omid Bozorg Haddad ◽

Soheila Beygi ◽

Miguel A. Mariño

Keyword(s):

Water Allocation ◽

Reservoir Water

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Evaluating Alternative Water Allocation Strategies for Maximizing Returns to Irrigation for Wheat in the Texas Panhandle under Declining Water Availability

World Environmental and Water Resources Congress 2007 ◽

10.1061/40927(243)241 ◽

2007 ◽

Author(s):

W. Arden Colette ◽

Lai K. Almas

Keyword(s):

Water Availability ◽

Water Allocation ◽

Texas Panhandle ◽

Alternative Water ◽

Allocation Strategies

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Drinking water quality and protection of resources

International Review of Applied Sciences and Engineering ◽

10.1556/irase.1.2010.1-2.8 ◽

2010 ◽

Vol 1 (1-2) ◽

pp. 51-54

Author(s):

J. Fettig

Keyword(s):

Water Quality ◽

Drinking Water ◽

Water Resources ◽

River Water ◽

Spring Water ◽

Public Water Supply ◽

Legal Requirements ◽

Reservoir Water ◽

Water Lake ◽

Treatment Step

Abstract The structure of public water supply in Germany and the water resources used are briefly described. An overview over the legal requirements for drinking water is given, and the sources for contaminants are outlined. Then the multiple-barrier approach is discussed with respect to the resources groundwater and spring water, lake and reservoir water, and river water. Examples for treatment schemes are given and the principle of subsurface transport of river water as a first treatment step is described.

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Evaluation of Capacity of Salandi Reservoir Using Remote Sensing Data Along With Qgis Software

International Journal of Emerging Research in Management and Technology ◽

10.23956/ijermt.v6i8.164 ◽

2018 ◽

Vol 6 (8) ◽

pp. 339

Author(s):

Rupali Dhal ◽

D. P. Satapathy

Keyword(s):

Remote Sensing ◽

Satellite Data ◽

Remote Sensing Data ◽

Project Work ◽

Space Technology ◽

Reservoir Water ◽

Sediment Distribution ◽

Sensing Data ◽

Spread Area ◽

Water Spread

The dynamic aspects of the reservoir which are water spread, suspended sediment distribution and concentration requires regular and periodical mapping and monitoring. Sedimentation in a reservoir affects the capacity of the reservoir by affecting both life and dead storages. The life of a reservoir depends on the rate of siltation. The various aspects and behavior of the reservoir sedimentation, like the process of sedimentation in the reservoir, sources of sediments, measures to check the sediment and limitations of space technology have been discussed in this report. Multi satellite remote sensing data provide information on elevation contours in the form of water spread area. Any reduction in reservoir water spread area at a specified elevation corresponding to the date of satellite data is an indication of sediment deposition. Thus the quality of sediment load that is settled down over a period of time can be determined by evaluating the change in the aerial spread of the reservoir at various elevations. Salandi reservoir project work was completed in 1982 and the same is taken as the year of first impounding. The original gross and live storages capacities were 565 MCM& 556.50 MCM respectively. In SRS CWC (2009), they found that live storage capacity of the Salandi reservoir is 518.61 MCM witnessing a loss of 37.89 MCM (i.e. 6.81%) in a period of 27 years.The data obtained through satellite enables us to study the aspects on various scales and at different stages. This report comprises of the use of satellite to obtain data for the years 2009-2013 through remote sensing in the sedimentation study of Salandi reservoir. After analysis of the satellite data in the present study(2017), it is found that live capacity of the reservoir of the Salandi reservoir in 2017 is 524.19MCM witnessing a loss of 32.31 MCM (i.e. 5.80%)in a period of 35 years. This accounts for live capacity loss of 0.16 % per annum since 1982. The trap efficiencies of this reservoir evaluated by using Brown’s, Brune’s and Gill’s methods are 94.03%, 98.01and 99.94% respectively. Thus, the average trap efficiency of the Salandi Reservoir is obtained as 97.32%.

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