Developing decision support tools for optimum domestic management by Bayesian belief networks in Tehran, Iran.
Water resource systems have benefited both people and their economies for many centuries. The services provided by such systems are multiple. Yet in many regions water resource systems are not able to meet the demands, and sometimes even the basic needs, for clean fresh water. Inadequate water resource systems reflect failures in planning, management, and decision making and at levels broader than water.
Over the centuries surface and ground waters have been a source of water supply for agricultural, municipal, and industrial consumers. They have provided people water-based recreational opportunities and have been a source of water for wildlife and their habitat. They have also served as a means of transporting and transforming waste products that are discharged into them.
The use of groundwater to supply large surfaces of irrigated land has been the key to agricultural development in a large number of countries over the past few years. In arid and semiarid zones, irrigation using groundwater has transformed good quality dry land with low productivity into high productive areas. Consequently, the income level for the farmers has increased and the rural population base has been maintained. .
Groundwater extraction should be kept sustainable by balancing abstractions with the recharge of the aquifer. Overexploitation of the systems has often been a risk in the past, leading to serious environmental damage and contributing to the desertification process. Moreover, the exploitation of this land will eventually prove to be uneconomical in the future .
In addition, it is necessary to have participation by the stakeholders in the water situation to management of water resources. So that they can have the opportunity to identify the issues that are most important them; even though this may give rise to conflicts and opposing opinions, the process is enriched by it and consensus solutions may be found. The concept of integration means that the impact produced by a given type of management or by a specific decision is not limited to water availability, but may also span related aspects of the resource and its medium. In this sense, it has been remarked that "Many watershed management problems require holistic, integrated solutions" .
This paper discusses a model based on a Bayesian Network (BN) has been applied as an integrating tool for Tehran city water system supplied.
The aim of this paper is firstly to show a BN model which was developed to assess the impact of a range of management interventions identified during the study and secondly to present the results obtained through the application.
2. Integrated water resources management of Tehran domestic system:
Participation and integration are increasingly accepted by international organizations and national authorities as central principles for decision making in the environmental field .
in the study area to prepare Water Basin Management Plans in accordance with the principles of Integrated Water Resource Management (IWRM), and with the express requirement of active stakeholder participation in the planning process.
The water management challenge in this area is to develop strategies that are able to strike a balance between water resource sustainability and agricultural sector profit. At least two conditions are necessary for the development of effective strategies. The first is the active participation of stakeholders in the management and decision-making process; the second is the application of tools that enable the problem to be tackled in an integrated way using multi- disciplinary skills. Only by ensuring these conditions suitable, sustainable strategies and policies can be formulated and more importantly implemented .
Tools used to support the implementation of IWRM can be divided into two main groups, models and Decision Support Systems (DSSs) .
Models are descriptions of a real-world system that simplify calculation and prediction. However, although these models are highly useful for studying water resources and impacts on the environment, in most cases they are not designed to address and integrate widely-varying aspects such as the socioeconomic, legal and cultural issues related to water management .
DSSs are considered the best tool for approaching an integrated analysis of water management. Such systems apply reason similar to that of a human being, who is the expert in the subject . In addition, there are also several commercial software packages, specifically designed for each type of DSS, .
DSSs can be either stochastic or deterministic, depending on whether or not; they deal with processes containing a degree of uncertainty. Stochastic DSSs are further sub-divided depending on how uncertainty is dealt with, and include methods such as certainty factors , evidence theory  and probabilistic methods. In this study, the selected technique is a probabilistic method: Bayesian Networks (BNs).
In this paper we consider the Participatory and Integrated Planning (PIP) procedure described in Soncini-Sessa et al. (in press-a-b) and in Castelletti and Soncini-Sessa with some adjustment, which was developed specifically for Tehran water systems. As shown in Fig.1, the proposed methodology includes some main components namely water balance simulations, PIP procedure, and finally developing real time operating rules for groundwater management by training a BN. The model inputs are the Tehran dams operating data, inflow to dams, population, water consumption per capita and Tehran aquifer information.
The water balance model (WEAP soft) used to simulate existing condition. Afterwards structural of Tehran Bayesian Network was made. By the outputs of water balance model, the CPTs populated, i.e. filled in with probability values. These may be derived from operational dam data, elicited from experts.
The outputs of the proposed methodology are the Probability Distribution Function (PDF) of optimal fractional allocation of each dams and the PDF of optimal policies for groundwater exploitation. Therefore, a decision maker can decide optimally being aware of the variations of the total groundwater exploitation.
Bayes' theorem, developed by the Rev. Thomas Bayes, an 18th century mathematician and theologian, was first published in 1763. Mathematically it is expressed as:
P(H|E,c) = P(H|c) x P(E|H,c)/P(E|c)
Where we can update our belief in hypothesis H given the additional evidence E and the background context c. The left-hand term, P(H|E,c), is known as the "posterior probability", or the probability of H after considering the effect of E on c. The term P(H|c) is called the "prior probability" of H given c alone. The term P(E|H, c) is called the "likelihood" and gives the probability of the evidence assuming that the hypothesis H and the background information c is true. Finally, the last term P(E|c) is independent of H and can be regarded as a normalizing or scaling factor .
Bayes' theorem provides a mathematical framework for processing new data, as they become available over time, so that the current posterior distribution can then be used as the prior distribution when the next set of data becomes available. Determining the prior distribution is usually based on generic data, and the new or additional data usually involve system-specific test or operating data. The resulting posterior distribution would then be the system-specific distribution of the parameter.
The above Bayesian probability theory allows one to model uncertainty about the events and outcomes of interest by combining common-sense knowledge and observational evidence. This is done using Bayesian networks. The network is commonly represented as a graph, which is a set of nodes and arrows. The nodes represent the probabilistic state variables and the arrows represent the causal relationships between these variables.
A Bayesian Network (BN) is a type of Decision Support System (DSS) based on a probability theory which implements Bayes' rule [18,12,13,2]. BNs have gained a reputation of being powerful techniques for modeling complex problems involving uncertain knowledge and impacts of causes. The part of the net defined by variables and links is relatively easily communicated to stakeholders .
The multilateral properties of belief networks appear to allow their use in multiple ways in resource and environmental modelling . According to Cain , a BN can also be defined as follows: "Some nodes that represent random variables that interact with others. These interactions are expressed like connections between variables". The use of BNs presents a series of advantages over that of other Environmental DSSs, as mentioned in previous studies [4,5,9]. For instance, according to Borsuk et al. , the graphical structure explicitly represents a cause-effect relationship between system variables that may be obscured under other approaches.
Netica is a complete software package to work with Bayesian Belief network, decision net and influence diagrams. it is used to model the problem and perform the computations, use these to answer queries and find optimal decisions, and create probabilistic expert systems. It is suitable for applications in the areas of diagnosis, prediction, decision analysis, probabilistic modeling, risk management, expert system building, sensor fusion, reliability analysis, and certain kinds of statistical analysis and data mining. (help netica).
This paper seeks to attach greater importance of Bayesian network, which utilize the stakeholders' knowledge and real data in the design of management tools to allocate water requirement in real time operation. In our case, this applies to the construction and setting up of a Bayesian Network, which may prove to be of assistance in the decision-making process leading to proper water management policy.
4. Model building:
4.1. Study area:
This study is set mainly in Tehran the capital of Iran with a total population of about 8,000,000 covers approximately area of 790 [km.sup.2] and lies within the Tehran basin on the semiarid plains on the south side of the Caspian Sea as shown in Fig. 2. The population growth in the next decade will place immense demands on the city's water resources.
4.2. Current water supply and demands:
Five dams included of Karaj, Taleghan, Lar, Latyan and Mamlu are regulated to supply Tehran domestic demands, but they are only supply approximately 72% of demands in current condition. Groundwater has an important role in supplement of the remaining domestic demands when surface water resources are inadequate. Therefore exploitation of ground water has been increased as a safe water resource. The annual exploitation of subterranean water for supply is increasing and currently is approximately 250 MCM, which is drawn In Table 1. In 2006 the surface water resources supply Tehran domestic with 705 MCM/year while a further 250 MCM/year are pumped from 200 deep wells with an average depth of 130 m to water supply network. With regards to increasing population and water demand in next decays, Taleghan and Mamlu dams will be added to Tehran water supply cycle and supply approximately 185 MCM of Tehran domestic demands (Table 1).
On the other hand Tehran lacks an adequate wastewater collection and treatment system. In 2006, the total municipal water consumption was around 955 MCM that approximately 85% of this is returning water and 165 MCM of domestic return flow are reclaimed by waste water treatment plant. In 2035 all of the Tehran wastewater network will complete and return flow will being refined. Now most of the city's wastewater is disposed under the ground, without any treatment, through the use of injection wells. This type of disposal is unique and has caused some water supplies to be polluted, raised the water table in some aria, and degraded surface water channels. With regard to this dangerous levels of nitrates have been found in Tehran's drinking water in recent years due to contaminated sources tapped to address population overgrowth. Therefore it should be restricted the use of groundwater as a source of Tehran domestic supply and imperative strategies should be adopted to regulate the minimum volume of water abstraction in order to sustain the quality of drinking water in Tehran.
4.3. Representation in WEAP:
There is one main demand that is represented in the model: urban demand in Tehran. It is calculated by the Tehran Water and Sewage Company. For modeling Tehran water and resources, a hydrological study performed through a mass balance model using WEAP (Water Evaluation And Planning System). For this monthly water accounting was constructed for whole system. WEAP is distinguished by its integrated approach to simulating water systems and by its policy orientation.
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The outputs of these WEAP model regarding to operational dam data and water balances was the input information introduced in the DSS. Demands and supplies are represented on a monthly basis for the years 1972-2010 for purposes of calibration.
We included as much detail as was needed to properly characterize both demand and supply sources, subject to the availability of field data. The representations consist of the following main elements:
Distribution System: In the WEAP model, distribution system is either municipal supply (for Tehran city). From west to east, the active dams are Karaj (Amirkabir), Taleghan, latyan, Mamlu, and Lar. Account is taken of inflows, outflows, releases, evaporative losses, and groundwater interactions.
Problems that are facing water resources management in Tehran can be summarized as increase in demand and waste production due to population growth and socioeconomic development; decrease in availability of water per capita; high losses of urban water; and local depletion and pollution of surface and groundwater. Urban water management in this city will fail without a holistic and integrated view.
Water allocation within WEAP is carried out by using user-specified priorities for demands and sources. The WEAP algorithm is implemented as a series of linear programming (LP) problems, iterated over demand and supply priorities. After observing the patterns of dam releases and volumes over time, the WEAP outputs were specified to train CPTs of Bayesian network.
4.4.Structure of the Bayesian network:
In this paper only three uses of Bns are considered: (1) for modeling, when they are used to describe the system being studied; (2) for aiding decision making, when they include decision and utility nodes, and are employed as a decision support system (DSS). (3) A third use does exist, however: Bns can be used as a visualization tool to summarize simply the outcomes of more complex models. This tool may be part of a more complex DSS. Fig.3 shows the BN of Tehran water system in Netica.
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4.5. Variable description:
The BN used to describe each dam is divided into two main sections; one deals with the water mass balance, the other with operation data conditions. Of course, the dams are not identical, which means that the configuration of each one is slightly different to reflect the specific circumstances of each dam.
Fig. 3 and Table 2-8 reveal all variables and their states that appear in the dam networks, though not all appear in each and every dam; the variables of the networks are also shown.
The water balance variables were defined using the water balance models developed for this research. The CPTs for BN(Bayesian Network) variables were entered automatically via the Learning Wizard module, using data obtained from the output of WEAP model constructed for each dam. Automatically entered variables included "'Rainfall"', "Soil conservation", "Inflow to dam" and "Dam storage". The relationship among these variables was established within the mass water balance model.
The BN variables were trained from two ways:
1) Firstly from a mass water balance simulation model (WEAP) in which the main variables were defined and after doing simulation process the outputs used to train BN network variable and CPT.
2) Secondly, from the operational dam data in which all data included "Inflow to dam",[ "domestic, agriculture and other outflows" and "dam storage", are measured daily.
Variables are divided into four groups according to their function in the network. The groups are:
1) Parent nodes: these are not subject to changes in the states of other nodes.
2) Intermediate variables: represent simulation of the intermediate processes that take place between action and objective.
3) Partial objectives: intermediate objectives that contribute toward final objectives.
4) Final objectives: represent the variables that are of key importance to the system; it is the states of these variables that are of most concern to decision makers.
To summarize, states of the variables (Tables 2 and 3) were defined according to the outputs of the previous models made in the sectorial studies (mass balance model), as well as according to the results from the operational dam data, As in many previous studies [7,5,23], variables have been parameterised using either knowledge or data.
4.6. Validation of the DSS:
Evaluation and validation of the DSS was conducted in collaboration with stakeholders (government managers) and by using information from parallel studies being undertaken by the Tehran water company. Results obtained from the numerical part of the DSS based on BNs are in line with results of previous hydro-economic partial studies.
The whole process cannot be carried out by the analyst alone, but should be done together with the stakeholders and government managers. These water actors indicated whether the results offered by the DSS, both partial and global, are acceptable or, on the contrary, the relationships between variables, their states or their probabilities should be changed in order to make them acceptable. In order to achieve this aim, one meetings were held. Thus, the stakeholders' participation is justified. So, a "user validation" has been performed. Once the model has been validated, we can be certain that the stakeholders included Tehran regional water company and Tehran water & wastewater company managers will trust the BN further down the decision-making process.
5.Results from simulation of water management scenarios:
The existing surface potential to supply Tehran demand is totally almost 730 MCM from 3 dams. The surface water resources have been regulated to the highest capacity during the critical period. Also there are no anymore surface resources to transfer Tehran. Continue with the status quo and increasing population, water demand will increase and because of limitation in surface water, more ground water will extract. On the other hands excessive withdrawal of groundwater resources has other consequences like Nitrate increscent in drinking water and Tehran aquifer instability. For optimum management other policies in order to reduction of water consumption should be taken.
To assess the impacts of potential water management actions, a number of scenarios were simulated and compared with "current condition" scenario (CuCo) as described, by data available in the hydrological year 2009-2010. For example the result of BN for Karaj dam in current condition is shown at Fig. 4. Proposed scenarios in next Section have been based on actions carried out in Tehran water system as well as information obtained from water managers and operational data. All states and probability distributions for every intervention were, as far as possible, based on information obtained from managers (stakeholders).
5.1.Results from simulation of current condition(CuCo):
The results of simulations representing current conditions for each dam and for the overall system can be summarized as follows (Table 7-9).
Tehran has population about 8,100,000 in 2011. This population requires about 980 MCM water per year. Dams around Tehran regulate approximately 730 MCM per year and rest of demand is supplied through ground water extraction. Net demand per capita in current condition is about 260 (lit/day) and the most probability of monthly water demand of city has a 53.4% to be between 75-83.3 MCM (Table 8). There is 38.4% chance that the ground water extraction rate will be 0 (not extraction). If the probability of ground water extraction (Table 9.) be less, indicate decreases in uses of ground water and good for stakeholders (people and managers). The probability of domestic demand of Tehran in current condition is shown in Fig5.
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5.2. People Cooperation to Reduce water Consumption(PCRC):
In our case, the main use is for urban, which accounts for more than 90% of the total water consumed. Tehran people are therefore the first element to be taken into account in the management of water resources.
Urban green space and industrial use accounts less than 10% of the total resources involved. It is managed on a joint basis, as industrial consumers are connected to the urban network. The bodies who are responsible for supplying of drinking water to municipal districts are Tehran regional water company and Water and Sewage. With regard to last studies and manager opinions, people cooperation has a important role in reduction of water consumption. so that with good cooperation of people the net demand per capita will decrease and following that probability of ground water extraction at 0 (not extract state) increases to 46.1% in this scenario.
5.3. Urban Water price increases(UPWI):
After announcing the fuel rationing policy in 2008, government decided to eliminate subsidies in order to manage consumption of natural resources. Thus, a cashing subsidy policy has been applied since end of 2010. The results of subsidies elimination in last 12 month show reduction of 20% in water consumption. Therefore the role of increase in water price is unavoidably. Price increases in water prices to double in existing rate causes that net demand per capita reduce and probability of ground water extraction at 0 (not extract state) increases to 55.4% in this scenario rather that current condition.
5.4. Reduce population growth rates and prevent migration to Tehran(RPGR):
Tehran has faced critical situation regarding to population and mitigation rate in last year. On the other hand available water resources are limited. We consider effect of population growth on ground water extraction in RPGR rather than CuCo. In the near future with increase of population from A state to B state, probability of ground water extraction at 0 (not extract state) decreases to 19.8% in this scenario.
5.5.Improvement of Urban Water Network(IUWN):
Since the 1980s access to urban water supply has increased from 75.5% to 98%. However, a number of challenges remain. According to the World Bank, Tehran is suffered by "low water distribution efficiency in urban network". According to water meters information's published by Tehran water and wastewater company (WWC), water distribution efficiency is approximately 85%. With improvement of this, gross demand per capita will decrees, subsequently probability of ground water extraction at 0 (not extract state) increases to 51.1% in this scenario (table 9).
5.6.Best Condition in Water Management (BCWM):
In this scenario all the policy that helps the managers to decrease water consumption are considered. The policies are shown in table 7. with regard to all effective policies, net demand per capita will be reduced to its lowest rate and probability of ground water extraction at 0 (not extract state) increases to 70% in this scenario (table 8).
This paper shows the way in which a city water system can be modeled and integration of hydrological and social factors simulated using a Bayesian Network (BN) approach. There is a high degree of uncertainty concerning the decision-making process. For this reason and because of the large number of variables and complex nature of the system, the use of the BN technique is justified. To evaluate the possible impacts caused by future water management actions on the water system, some interventions have been selected and simulated by the model.
Results reveal that under current conditions, it is not possible to recover all demands of Tehran by surface water resources. Furthermore, results also reveal that any intervention should be taken to reduce drawdown of ground water.
This paper provides a practical demonstration of how a BN model may be used to support water resource management decision-making exemplified in Tehran. It has also been demonstrated that BN can be used to balance Tehran versus the hydrological sides of the equation. The results of the model application show the direction and the order to which the efforts should be directed. Stakeholder participation (manager and people) is the key to achieve the validation of this type of model, as well as strengthening collaboration and increasing confidence among stakeholders, managers and researchers.
The author gratefully acknowledges the contributions of the following people and organizations: Dr. Reza Karachian, from the Tehran University for his Suggestion in use of BN; Dr. K. Shiati and M. shafeefar from Yekom consulting engineering company, for their cooperation. package Netica that was the software used for this study.
[1.] Barthel, R., S. Janisch, N. Schwarz, A. Trifkovic, D. Nickel, C. Schulz, W. Mauser, 2008. An integrated modelling framework for simulating regional-scale actor responses to global change in the water domain. Environmental Modelling & Software, 23: 10951121.
[2.] Bayes, T., 1991. 1763. "An essay towards solving a problem in the doctrine of chances" Bayesian Statistics: Principles, Models, and Applications, 189-215.
[3.] Bernabe'u, R., J. Serna, 2002. Ponencia "Los grandes cultivos herba'ceos de regadi'o en Castilla-La Mancha". Libro Blanco de la Agricultura y el Desarrollo Rural. Jornada Autono'mica de la Comunidad de Castilla-La Mancha. Toledo, Spain.
[4.] Bromley, J., 2005. Guidelines for The Use of Bayesian Networks as a Participatory Tool for Water Resource Management. Based on The Results of The MERIT Project. Centre for Ecology and Hydrology, Wallingford.
[5.] Bromley, J., N. Jackson, O. Clymer, A. Giacomello, F. Jensen, 2005. The use of Hugin to develop Bayesian Network as an aid to integrated water resource planning. Environmental Modelling, Software, 20: 231-242.
[6.] Buchanan, B.G., E.H. Shortlife, 1984. Rule based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. Addison-Wesley, Reading.
[7.] Borsuk, M.E., C.A. Stow, K.H. Reckhow, 2004. A Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysis. Ecological Modelling, 173: 219-239.
[8.] Cain, J., 2001. Planning Improvements in Natural Resources Management. Centre for Ecology and Hydrology, Wallingford, UK.
[9.] Castelletti, A., R. Soncini-Sessa, 2007. Bayesian Networks and participatory modeling in water resource management. Environmental Modelling & Software, 22: 1075-1088.
[10.] Henriksen, H.J., P. Rasmussen, G. Brandt, G. von Bu low, F.V. Jensen, 2006. Public participation modelling using Bayesian networks in management of groundwater contamination. Environmental Modelling & Software, 22(8): 1101-1113.
[11.] Hisschemo ller, M., R.S.J. Tol, P. Vellinga, 2001. The relevance of participatory approaches in integrated environmental assessment. Integrated Assessment, 2: 57-72.
[12.] Jensen, F.V., 1996. An Introduction to Bayesian Networks. UCL Press, London. Jensen, F.V., 2001. Bayesian Network and Decision Graphs. Department of Computer Science, Aalborg University, Aalborg, Denmark.
[13.] Marti'n de Santa Olalla, F., A. de Juan Valero, 2001. In: Marti'n de Santa Olalla, F. (Ed.),El uso del agua en una agricultura sostenible. Agricultura y Desertificacio'n. Ediciones Mundi-Prensa, Madrid, pp: 273-303.
[14.] Marti'n de Santa Olalla, F., A. Dominguez, F. Ortega, A. Artigao, C. Fabeiro, 2007. Bayesian networks in planning a large aquifer in Eastern Mancha, Spain. Environmental Modeling & Software, 220: 1089-1100.
[15.] Marti'n de Santa Olalla, F.J., A. Domi'nguez, A. Artigao, C. Fabeiro, J.F. Ortega, Integrated hydric resource management of the Hydrogeological Unit "Eastern Mancha" using Bayesian Belief Networks. Agricultural Water Management, in press.
[16.] Molina, J.L., J. Bromley, J.L. Garci'a-Aro'stegui, C. Sullivan, J. Benavente, 2010. Integrated water resources management of overexploited hydrogeological systems using Object-Oriented Bayesian Networks. Environmental Modelling & Software, 25: 383397.
[17.] Pearl, J., 1988. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Mateo, CA.
[18.] Prato, T., C. Fulcher, 1998. Protecting soil and water resources through multi objective decision making. In: El-Swaify, S.A., Yakowitz, D.S. (Eds.), Proceedings of the First International Conference on Multiple Objective Decision Support Systems (MODSS) for Land, Water and Environmental Management. Lewis, New York, pp: 385-394.
[19.] Sapher, G., 1976. A Mathematical Theory of Evidence. Princeton University Press, Princeton.
[20.] Stevens, L., 1984. Artificial Intelligence. The Search for The Perfect Machine. Hayden Book Company, New York.
[21.] Tajrishy, M., A. Abrishamchi, 1998. Integrated Approach to Water and Wastewater Management for Tehran, Iran, Water and Sewage, 28: 2-11.
[22.] Ticehurst, J.L., D. Rissik, R.A. Letcher, L.H.T. Newham, A.J. Jakeman, 2005. Development of decision support tools to assess the sustainability of Coastal Lakes. In: Zerger, A., Argent, R.M. (Eds.), MODSIM 2005 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, Melbourne, Australia, pp: 2414-2420.
[23.] Varis, O., S. Kuikka, 1999. Learning Bayesian decision analysis by doing: lessons from environmental and natural resources management. Ecological Modelling, 119: 177195.
M. Asadilour, F. Kaveh, M. Manshuri, and A. Khosrojerdi
Department of Water Engineering, Islamic Azad University Science and Research Branch of Tehran, Iran.
M. Asadilour, Department of Water Engineering, Islamic Azad University Science and Research Branch of Tehran, Iran.
Table 1: Tehran domestic demand and regulated water to supply (Tehran Regional Water Company). Consumption Tehran domestic Tehran Other year Population per capita demand (MCM) demands (lit/day) (MCM) 2006 7,500,000 330 903.4 51.6 2035 10,000,000 298 1087.7 70.3 Tehran water supply resources(MCM) year Karadj Taleghan Latyan Lar Mamlu Ground Dam Dam Dam Dam Dam water 2006 315 -- 280 110 -- 250 2035 340 115 320 165 70 148 Table 2: Extended list of variables and their states for Karaj Dam system. state Percipitation Inflow to soil Dam storage (mm) Dam(MCM) conservation (MCM) (CN) A 0-25 0-25 90 0-50 B 25-50 25-50 70 50-100 C 50-75 50-100 -- 100-150 D 75-100 100-150 -- 150-205 E 100-125 >150 -- -- F 125-150 -- -- -- G >150 -- -- -- H -- -- -- -- state Domestic Release to Tehran (MCM) A 0-5 B 5-10 C 10-15 D 15-20 E 20-25 F 25-30 G 30-35 H -- Table 3: Extended list of variables and their states for Mamlu & Taleghan Dams system. Mamlu Dam Inflow to Dam Dam storage Domestic Release state (Latyan Spill) MCM to Tehran A 0-25 0-40 0-2 B 25-50 40-80 2-4 C 50-100 80-120 4-6 D 100-150 120-160 6-6.45 E >150 160-200 -- F -- 200-240 -- G -- 240-250.6 -- H -- -- -- Taleghan Dam state Inflow to Dam Dam storage Domestic Release MCM to Tehran A 0-25 50-100 3-6 B 25-50 100-150 6-9 C 50-75 150-200 9-12 D 75-100 200-250 12-15 E 100-125 250--300 >15 F 125-150 300-350 -- G 150-200 350-400 -- H >200 400-420 -- Table 4: Extended list of variables and their states for Lar Dam system. state Percipitation Inflow soil Dam Domestic (mm) to conservation storage Release to Dam(MCM) (CN) (MCM) Tehran (MCM) A 0-40 0-25 90 0-50 0-5 B 40-80 25-50 70 50-100 5-10 C 80-120 50-100 -- 100-150 10-15 D 120-160 100-150 -- 150-200 15-20 E 160-200 >150 -- 200-250 20-25 F 200-240 -- -- 250-300 25-30 G >240 -- -- 300-350 30-35 H -- -- -- -- 35-46 Table 5: Extended list of variables and their states for Latyan Dam system. state Percipitation Inflow to Dam soil (mm) (MCM) conservation (CN) A 0-40 0-25 90 B 40-80 25-50 70 C 80-120 50-100 -- D 120-160 100-150 -- E 160-200 >150 -- F 200-240 -- -- G >240 -- -- H -- -- -- state Dam storage Domestic Latyan Spill (MCM) Release (MCM) (MCM) A 0-50 0-5 0 B 50-100 5-10 0-20 C 100-150 10-15 20-40 D 150-200 15-20 40-60 E 200-250 20-25 60-80 F 250-300 25-30 80-105 G 300-350 30-35 -- H -- 35-46 -- Table 6: Extended list of variables and their states for Tehran city system. state Water Price (Rial/ population [m.sup.3]) A 250 7,510,000-8,490,000 B 300 8,500,000-9,490,000 C 350 9,510,000-10,490,00 D 400 10,510,000-11,490,000 E -- -- F -- -- G -- -- H -- -- I -- -- J -- -- state Net demand per capita Pepole cooperation (lit/day) A 182 [+ or -] 13% Aphatetic B 208 [+ or -] 13% Good C 234 [+ or -] 13% -- D 260 [+ or -] 13% -- E -- -- F -- -- G -- -- H -- -- I -- -- J -- -- state Urban Network Yearly Domestic demand improvement of A YES 500-600 B NO 600-700 C -- 700-800 D -- 800-900 E -- 900-1000 F -- 1000-1100 G -- 1100-1200 H -- 1200-1400 I -- 1300-1400 J -- 1400-1500 Table 7: Comparison of the impacts on net demand per capita caused by the water management implementation. variables in scenarios scenarios population Pepole Urban Network Water Price cooperation improvement CuCo A Apathetic No A PCRC A Good No A UWPI A Apathetic No D RPGR B Apathetic No A IUWN A Apathetic yes A BCWM A God yes D objective Likelihood Net demand per capita scenarios 182 [+ or -] 13% 208 [+ or -] 13% 234 [+ or -] 13% CuCo 0 0 0 PCRC 4 12 9 UWPI 25 10 5 RPGR 0 0 0 IUWN 0 0 0 BCWM 25 17 13 Net demand per capita scenarios 260 [+ or -] 13% CuCo 100 PCRC 75 UWPI 60 RPGR 100 IUWN 100 BCWM 45 Table 8: Comparison of the impacts on Monthly Domestic Demand of Tehran by the water management implementation. variables in scenarios scenarios population Pepole Urban Network Water cooperation improvement Price CuCo A Apathetic No A PCRC A Good No A UWPI A Apathetic No D RPGR B Apathetic No A IUWN A Apathetic yes A BCWM A Good yes D objective Likelihood Monthly Domestic demand of Tehran (MCM) scenarios 41.7-50 50-58.3 58.3-66.7 66.7-75 CuCo 0 0 0 24.4 PCRC 0.44 4.44 9.92 26.5 UWPI 2.8 17.7 13.1 20 RPGR 0 0 0 0 IUWN 0 0 13.3 53.3 BCWM 11.5 22.9 21.3 29 objective Likelihood Monthly Domestic demand of Tehran (MCM) scenarios 75-83.3 83.3-91.7 91.7-100 100-108.3 CuCo 53.4 22.2 0 0 PCRC 42 16.7 0 0 UWPI 33.1 13.3 0 0 RPGR 20 48.9 28.9 2.2 IUWN 31.1 2.3 0 0 BCWM 14.3 1 0 0 objective Likelihood Monthly Domestic demand of Tehran (MCM) scenarios 108.3-116.7 116.7-125 CuCo 0 0 PCRC 0 0 UWPI 0 0 RPGR 0 0 IUWN 0 0 BCWM 0 0 Table 9: Comparison of the impacts on Ground Water Extraction by the water management implementation. variables in scenarios scenarios population Pepole Urban Network Water Price cooperation improvement CuCo A Apathetic No A PCRC A Good No A UWPI A Apathetic No D RPGR B Apathetic No A IUWN A Apathetic yes A BCWM A Good yes D objective Likelihood Ground water extraction(drinking) scenarios Not extract 0 0-5 5-10 10-15 15-20 CuCo 38.4 12 12.2 11.2 9.3 PCRC 46.1 11.4 11.1 9.8 7.9 UWPI 55.4 9.94 9.31 8 6.35 RPGR 19.8 8.42 10.2 11.3 11.6 IUWN 51.1 12.2 11.3 9.25 6.76 BCWM 70 8.7 7.2 5.5 3.9 objective Likelihood Ground water extraction(drinking) scenarios 20-25 25-30 Other states CuCo 6.9 4.6 5.4 PCRC 5.7 3.8 4.2 UWPI 4.6 3 3.4 RPGR 10.8 9.22 18.66 IUWN 4.41 2.57 2.41 BCWM 2.4 1.4 0.9
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|Title Annotation:||Original Article|
|Author:||Asadilour, M.; Kaveh, F.; Manshuri, M.; Khosrojerdi, A.|
|Publication:||Advances in Environmental Biology|
|Date:||Jan 1, 2012|
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