A Framework to Support the Selection of an Appropriate Water Allocation Planning and Decision Support Scheme
Abstract
:1. Introduction
2. Materials and Methods
3. Literature Review
- The water allocation schemes described in the literature;
- The associated water allocation decision support systems;
- The specific water management situation’s characteristics and the associated water allocation drivers and priorities.
3.1. Water Allocation Schemes
- Strategic allocation is a public allocation method, whereby the government has very specific strategic objectives that it aims to achieve [1].
- User-based allocation is aimed specifically at delegating the decision-making on a local level and involving stakeholders to such an extent that they regard the outcomes as fair and equitable [16].
- Multiple-criteria-based allocation is based on evaluating and scoring different allocation scenarios against multiple criteria which may include social, economic and other factors. In general, this multi-criteria analysis is aimed at selecting an allocation scenario which is more equitable and acceptable to all stakeholders [44,45,46,47].
- Price-driven allocation is based on the principle of the willingness of users to pay for additional water allocated to them. In this scheme, the water pricing has to be at a level which covers the marginal cost of supplying each additional unit of water [48].
- Market-based allocation relies purely on market forces, with market instruments such as water markets, water trading or auctions determining the water allocation. In practice, this kind of allocation has to operate in parallel with another allocation scheme and under certain governmentally controlled rules and regulations [48].
3.2. Decision Support Systems
- Rule-based and hierarchy decision support is normally based on expert knowledge that is translated into rules and relationships to guide operation. It is also possible to use computer simulations as input to develop these rules and relationships [1]. Hierarchy or priority-based decision systems can be seen as a type of rule-based decision system with the rules based on legislation or strategic priorities [38,50,51].
- Economic benefit models are based on maximizing the combined economic benefit for all water users and/or the community and are aimed at ensuring the economic sustainability of the allocation system [52,53,54]. Cost-benefit analysis is used to evaluate the benefits relative to costs in terms of water allocation and the system is aimed at maximizing the sum of the benefits. In order to address social and other non-economic aspects in water allocation, the economic benefit principle is frequently combined with multi-criteria systems [52,55,56].
- The computable general equilibrium (CGE) method is also mainly economically orientated, but it combines economic theory with actual economic data to describe a whole economic unit and the interactions between the different parts (sectors, companies, households, government, markets) within it [57,58,59].
- Game theory is based on actively involving stakeholders in the process of decision-making and conflict resolution in order to reach a well-balanced allocation situation [60,61,62,63,64,65,66,67]. Either cooperative or non-cooperative game theory approaches can be applied. The advantage of the cooperative methods lies in the way it motivates stakeholders to participate for the mutual benefit of all participants.
- Multi-objective analysis can effectively be classified as a multi-criteria analysis technique. Some authors, however, handle it as a separate technique, and specifically as an optimization method, that solves a set of multiple objectives which are to be satisfied simultaneously [39,41,70,71,72,73,74].
- System dynamics (SD): Mirchi et al. [75] proposed the use of systems thinking in the form of SD to arrive at improved integrated solutions. Zomorodian et al. [23] executed a comprehensive review of the application of SD in water resource modelling. They found the technique to be appropriate for the solving of very complex multi-dimensional (watershed) problems but that it is also limited by a number of constraints.
3.3. Water Allocation Drivers and Priorities
- Environmental dimensions: These include the ecological and environmental priorities and the relevant orientation of the society impacted [39,81,84,85]. The level of water conservation, demand management and water efficiency that will have to be implemented in the area are also included [1,53,86,87,88]. Furthermore, the interlinkage between the different resources (e.g., the food–water–energy nexus) and the resulting influence of water allocation on the other resources have to be taken into account [89,90,91,92,93,94].
- Stakeholders: These would include the different categories of water users in a watershed relative to the water supply available [51,83,95,96,97], as well as the level of stakeholder participation expected [46,47,98,99]. The complexity of the catchment area and the range of challenges, objectives and issues that the allocation system needs to address also play an important role [39,47,68].
- Technical and knowledge base factors: These entail the availability of water management expertise amongst the decision-makers [47,49,51,68], the quantity, quality and uncertainty of the data available to support the decision-making process [102] as well as the availability of and need for computing power to support the requirements of the software [21,33,41,72,103].
- Uncertainties and change: This element refers to the level of uncertainty and the sensitivity of the system to such uncertainties and changes [20,37]. Also included are the annual and seasonal variabilities that occur in the region and the flexibility required in the allocation system to provide for them [1,60,104].
4. Results and Framework Development
Framework Development Process
- System boundaries: Define the boundaries of the allocation problem, covering the temporal and spatial dimensions of the water management area.
- Evaluate the water management situation: Identify the external factors impacting the water management area, the water users and other stakeholders included within the system, as well as the cross-boundary water transfers required. Identify the available water sources within the system boundary, including possible inward transfers.
- Develop and refine an inventory of priorities, drivers and assessment criteria: Evaluate the water management situation in the region or water management area, in order to identify the situation characteristics and water allocation priorities. Based on these, identify and decide on the water allocation drivers for this water management situation. Ensure that these priorities cover economic, social, environmental, legal, technological and change factors.
- Evaluate alternative water allocation schemes based on refined priorities: Use the water allocation and decision support system matrices and figures developed earlier to identify those allocation schemes and decision support systems that are the most applicable or have elements that would be applicable to the situation. Also identify those schemes and support systems that are definitely not applicable to ensure that they are avoided. A combination of different water allocation schemes (i.e., hybrid schemes) also needs to be considered.
- Interpret the allocation systems and align with priorities: The next step entails the development of the alpha version water allocation decision support system, by basing it on the elements identified. This step has to address both overarching (long-term) water allocation as well as annual and seasonal variations and uncertainties. It is important to note that most water management situations would require an approach that integrates elements from more than one water allocation scheme and/or decision support system (a hybrid system) in order to cover all the relevant priorities and allocation drivers.
- Refinement and improvement: Finally, during operation, the water allocation results must be continuously evaluated according to the objectives, as this can inform and promote the future improvement of the allocation system.
- Feedback loops: Although the flow of the framework developing process is mainly progressive in following the steps, as indicated in Figure 6, the feedback arrows indicate that information that becomes available in a certain step may have an influence on a previous step. The arrows labelled “A” indicate the refinement and improvement introduced during the definition of the water management situation, while the arrows labelled “B” indicate the flow of refinement and improvement data during the development of the water allocation scheme.
5. Implications for Framework Application in South African Water Management Areas
6. Discussion
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A. Detailed Tables Presenting the Characteristics of Water Allocation Schemes and Associated Decision Support Systems
Evaluation Area | Hierarchy/Priority Allocation | Strategic Allocation | User-Based Allocation | Optimization Approaches | Multi-Criteria Approaches | Price-Based Allocation | Market-Based Allocation | |
---|---|---|---|---|---|---|---|---|
Social/equity orientation | Depending on priorities, can be medium to high | Depending on priorities, probably medium | High—more socially-orientated | Medium—balanced | Medium—balanced | Relatively low | Low | |
Economic orientation | Depending on priorities, probably medium-low | Depending on priorities, probably medium-high | Relatively low | Medium—balanced | Medium—balanced | Relatively high | High | |
Environmental dimensions | Environmental/ ecological orientation | Depending on priorities, can be high | Depending on priorities, probably medium | Depending on user understanding, probably medium | Medium—balanced | Medium—balanced | Medium, can be worked into pricing | Low |
Promotion of water conservation, demand management and improved water productivity | Low/medium—can be included as not strongly promoting these objectives | Low—can be included as not aimed at promoting these objectives | Medium—can be included as user group may regard these objectives as important | Medium/high—can be included as part of objective functions | Medium/high—can be included, as can be included as part of criteria to promote positive behaviour | Medium—can be included as address through pricing as users would want to save on water costs | Will promote higher water productivity but not necessarily conservation or demand management | |
Stakeholders | Stakeholder participation | Relatively small group and low participation | Relatively small group and low participation | Good participation of relevant stakeholders | Can be high | Can be high | Relatively low | Relatively small group, high participation |
Complexity of catchment area that can be handled | Low to medium | Medium | Low to medium | Medium to high | Medium to high | Medium | Medium to high | |
Range of challenges/ goals/issues handled | Allocation in line with priorities, limited other issues/challenges | Allocation in line with priorities, limited other issues/challenges | Reasonable number of issues/challenges can be resolved | Challenges/issues built into objectives; solution becomes complex | Broad range of challenges/issues can be built into criteria and weighting; expert inputs necessary | Only challenges/issues that can be linked to price can be addressed effectively | Only challenges/issues that can be linked to market forces can be addressed effectively | |
Categories of water users | Limited by priority list | Strategic user focus | Mainly social types | Multiple | Multiple | Multiple | Mainly economic driven | |
Categories of water supply | Applied to any water supply category | Linked to specific strategic priorities | Can include what is locally available | Can handle complex combinations | Can handle complex combinations | Pricing will differ from source to source (complex) | Applied to relevant supply source considered | |
Legal framework | Implementing legal framework | Implemented through priority levels by public administrators | Implemented through strategic priorities by public administrators | Users will have to work within legal framework; could be difficult to enforce | Legal framework worked into objectives and constraints | Legal framework worked into criteria, weighting and constraints | Implemented through pricing in line with legal framework by public administrators | Market will have to operate within legal framework; could be difficult to enforce |
Level of water management and decision-making | In general, decision-making centralized but implementation can be at lower (local) level | Decision-making as well as implementation tend to be centralized | Decentralized implementation with some centralized guidance/policies/rules possible | Can work at centralized as well as decentralized levels of management | Can work at centralized as well as decentralized levels of management | Guidelines and setting of prices probably from a centralized level with decentralized implementation | Mainly at a decentralized level where users and user organizations make decisions on water trading | |
Uncertainties/change | Overarching allocation vs. seasonal and annual variability | Priorities determine all allocations—in times of lower water availability, only higher priorities will be serviced | Strategic allocation will determine overarching allocation; handling of variability will depend on negotiated user ability to cope with variations | Allocation system will be used to handle both long- and short-term allocation; social needs will receive priority during times of low availability | Can handle both, may be necessary to handle with two different sets of objective functions and constraints | Can handle both, may be necessary to handle with two different sets of criteria and weightings | Normally used together with another allocation scheme to ensure cost recovery; seasonal and annual fluctuations through price premium | Limited application in overarching water allocation—rather used for reallocation and market forces will determine allocation levels during variability |
Handling of uncertainties | Not equipped to deal with much uncertainty | Strategic users covered, others not | Work out solutions to limit overall impact | Somewhat complex to work into objectives | Work out alternative scenarios as fall-back | Pricing levels based on certainty with limited variation | Covered in terms of market forces |
Evaluation Areas | Rule-Based and Hierarchy Type | Economic Benefit Models | Computable General Equilibrium (CGE) Method | Game Theory | Multi-Criteria Analysis | Multi-Objective Analysis | System Dynamics |
---|---|---|---|---|---|---|---|
Water allocation schemes to which it can be applied | Applicable mainly to hierarchy allocation system and to a degree to strategic allocation | Can provide inputs to market-based schemes; frequently part of broader multi-criteria and multi-objective schemes; can add value to price- and user-based schemes | Can provide inputs to strategic schemes; can form part of broader multi-criteria and multi-objective schemes; can add value to price- and user-based schemes | Specifically linked to user-based schemes, but can be used in combination with multi-criteria and multiple objectives in other schemes | Specifically linked to multi-criteria allocation; can provide valuable inputs on user-based and optimization schemes | Specifically linked to optimization and multi-criteria allocation schemes; can provide valuable inputs on user-based schemes | Specifically applicable in complex systems and situations, i.e., linked mainly to optimization and multi-criteria allocation schemes |
Handling social/economic/ environmental balance | Rules will address the balance; normally more socially and environmentally orientated | Mainly economically oriented but can include social and environmental aspects in terms of benefits and costs specifically | Mainly economically oriented but can be used to evaluate the impact of environmental water allocation on the wider economy | To ensure balance, stakeholders from all areas must be present—especially those from environmental areas | A well-balanced solution can be achieved by incorporating balanced criteria | A well-balanced solution can be achieved by incorporating balanced objectives | A well-balanced solution can be achieved by ensuring that the system is comprehensively modelled |
Water conservation, demand management and improved water productivity | Could be included in rule development, but not for specific application | Can promote these initiatives as they are linked to economic value of water | Can promote these initiatives as they are linked to economic value of water | These objectives are not specifically addressed, but can be introduced by facilitator or manager | Can specifically be incorporated in selected criteria and weighting | Can specifically be incorporated in objective and constraint sets | Can specifically be incorporated in model setup |
Level of stakeholder participation | Very limited except if forms part of expert knowledge base | Relatively low level of stakeholder participation—for determining benefiting groups and levels only | Limited stakeholder participation; can be used to help stimulate dialogue between decision-makers from governmental and economic backgrounds | Works best when all stakeholders are actively working together to reach a solution | Stakeholder participation can vary—good to involve in criteria identification | Stakeholder participation can vary—good to involve them in objective and constraint identification | Stakeholder participation is an important aspect, and it is important to draw from broad knowledge base |
Range of criteria incorporated | Limited range of criteria to avoid complexity | Only benefits and costs, both economic, broaden by combination with multi-criteria or multi-objectives | Orientated towards broader economic aspects only | Covers a broad range of criteria through the priorities of participating stakeholders | Aims specifically to cover a broad range of criteria to address complex allocation problems | Aims specifically to cover a broad range of criteria through multiple objectives and constraints | Specifically aimed at covering a broad, complex range of criteria, objectives and constraints |
Reliance on expert knowledge | Relies heavily on expert knowledge; rules also strategic and legislative based | Rely to a degree on expert knowledge, specifically from an economic background | Combine economic theory with actual economic data—economic expertise necessary in setting up the model | Aimed at reaching a negotiated compromise—expert facilitator can help | Expert knowledge contributes to criteria selection and weighting as well as performance scoring | Expert knowledge required in identifying specific objective functions and constraints | Expert knowledge required for accurate and comprehensive understanding of the system |
Reliance on computerized calculations | Limited computer calculations except if simulations are needed | Limited computer calculation except if complex economic model is used | Relies heavily on computer modelling of the economic units and the other interacting sectors and parts of the economy | Not much reliance on any computerized work—simulations of scenarios can be valuable inputs | Computerized calculations not critical, but used in some multi-criteria analysis techniques | Similar to multi-criteria analysis, but specifically computer-based | Substantial computational capacity needed, but running time shorter than some simulation methods |
Type of computer use | Recording and possibly simulation | Some calculations and recording | Needed to run the comprehensive economic model | Recording and possibly some simulated inputs | Recording and some calculations | Used in solving set of multiple objective functions | Used in solving the set of relationships, feedback loops and flow diagrams |
Overarching allocation vs. seasonal and annual variability | Can be applied in both, frequently used together with others to address specifically seasonal variations via rules | In general, applied more in overarching (long-term) allocation, but can add value in seasonal and annual allocation | In general, applied more in overarching (long-term) allocation, but can add value in analysis of seasonal and annual allocation | Aimed more at optimization of allocation, i.e., use another decision support tool such as economic analysis for initial allocation and game theory to optimize | Can be set up to cover both overarching seasonal/annual allocations—best used with separate criteria/weighting sets | Can be set up to cover both overarching seasonal/annual allocations—best used with separate objective sets | Can be set up to cover overarching, seasonal and annual allocations |
Applicable for sensitivity analysis | Not suitable, adjust rules for scenarios | Limited application to evaluate scenarios | Good tool to use in evaluating impacts on broader economy | Not specifically suited for sensitivity analysis; scenarios can be inputs | Sensitivity analysis on weighting and performance scoring important | Good for sensitivity analysis | Can be used for sensitivity analysis but not a specific strength |
References
- Speed, R.; Yuanyuan, L.; Le Quesne, T.; Pegram, G.; Zhiwei, Z. Basin Water Allocation Planning: Principles, Procedures and Approaches for Basin Allocation Planning; 2013; 143p, Available online: https://think-asia.org/handle/11540/82. (accessed on 16 June 2020).
- Tian, J.; Guo, S.; Liu, D.; Pan, Z.; Hong, X. A fair approach for multi-objective water resources allocation. Water Resour. Manag. 2019, 33, 3633–3653. [Google Scholar] [CrossRef]
- Khare, D.; Jat, M.K.; Sunder, J.D. Assessment of water resources allocation options: Conjunctive use planning in a link canal command. Resour. Conserv. Recycl. 2007, 51, 487–506. [Google Scholar] [CrossRef]
- Hipel, K.W.; Fang, L.; Wang, L. Fair water resources allocation with application to the South Saskatchewan river basin. Can. Water Resour. J. 2013, 38, 47–60. [Google Scholar] [CrossRef] [Green Version]
- Mendelsohn, R. Adaptation, Climate Change, Agriculture, and Water. Choices—A Publ. Agric. Appl. Econ. Assoc. 2016, 31, 1–7. Available online: https://www.jstor.org/stable/choices.31.3.03?seq=1 (accessed on 25 June 2020).
- Yin, J.; Gentine, P.; Zhou, S.; Sullivan, S.C.; Wang, R.; Zhang, Y.; Guo, S. Large increase in global storm runoff extremes driven by climate and anthropogenic changes. Nat. Commun. 2018, 9, 4389. [Google Scholar] [CrossRef] [Green Version]
- Gleick, P.H. Water in crisis: Paths to sustainable water use. Ecol. Appl. 1998, 8, 571–579. [Google Scholar] [CrossRef]
- Fletcher, S.M.; Miotti, M.; Swaminathan, J.; Klemun, M.M.; Strzepek, K.; Siddiqi, A. Water supply infrastructure planning: Decision-making framework to classify multiple uncertainties and evaluate flexible design. J. Water Resour. Plan. Manag. 2017, 143, 04017061. [Google Scholar] [CrossRef]
- Wang, S.; Huang, G.H. Identifying optimal water resources allocation strategies through an interactive multi-stage stochastic fuzzy programming approach. Water Resour. Manag. 2012, 26, 2015–2038. [Google Scholar] [CrossRef]
- Meinzen-Dick, R.; Ringler, C. Water Reallocation: Drivers, Challenges, Threats, and Solutions for the Poor. J. Hum. Dev. 2008, 9, 47–64. [Google Scholar] [CrossRef]
- Gleick, P.H. Transitions to freshwater sustainability. Proc. Natl. Acad. Sci. USA 2018, 115, 8863–8871. [Google Scholar] [CrossRef] [Green Version]
- Gong, X.; Zhang, H.; Ren, C.; Sun, D.; Yang, J. Optimization allocation of irrigation water resources based on crop water requirement under considering effective precipitation and uncertainty. Agric. Water Manag. 2020, 239, 106264. [Google Scholar] [CrossRef]
- Bijl, D.L.; Biemans, H.; Bogaart, P.W.; Dekker, S.C.; Doelman, J.C.; Stehfest, E.; van Vuuren, D.P. A global analysis of future water deficit based on different allocation mechanisms. Water Resour. Res. 2018, 54, 5803–5824. [Google Scholar] [CrossRef] [Green Version]
- Liu, D.; Guo, S.; Shao, Q.; Liu, P.; Xiong, L.; Wang, L.; Hong, X.; Xu, Y.; Wang, Z. Assessing the effects of adaptation measures on optimal water resources allocation under varied water availability conditions. J. Hydrol. 2018, 556, 759–774. [Google Scholar] [CrossRef]
- Hellegers, P.; Leflaive, X. Water allocation reform: What makes it so difficult? Water Int. 2015, 40, 273–285. [Google Scholar] [CrossRef]
- Dinar, A.; Rosegrant, M.W.; Meinzen-Dick, R. Water Allocation Mechanisms: Principles and Examples; World Bank Publications: Washington, DC, USA, 1997. [Google Scholar] [CrossRef]
- Pegram, G.; Yuanyuan, L.; Le Quesne, T.; Speed, R.; Jianqiang, L.; Fuxin, S. River Basin Planning: Principles, Procedures and Approaches for Strategic Basin Planning. 2013. Available online: https://www.adb.org/publications/river-basin-planning-principles (accessed on 16 June 2020).
- Shim, J.P.; Warkentin, M.; Courtney, J.F.; Power, D.J.; Sharda, R.; Carlsson, C. Past, present, and future of decision support technology. Decis. Support Syst. 2002, 33, 111–126. [Google Scholar] [CrossRef]
- Giupponi, C.; Sgobbi, A. Decision support systems for water resources management in developing countries: Learning from experiences in Africa. Water 2013, 5, 798–818. [Google Scholar] [CrossRef] [Green Version]
- Mabaya, G. Decision Support Systems for Water Environment Management in Rural Areas under Hydrological and Socio-Economic Uncertainties; Kyoto University: Kyoto, Japan, 2016. [Google Scholar]
- Westphal, K.S.; Vogel, R.M.; Kirshen, P.; Chapra, S.C. Decision Support System for Adaptive Water Supply Management. J. water Resour. Plan. Manag. 2003, 129, 165–177. [Google Scholar] [CrossRef] [Green Version]
- Mirchi, A.; Watkins, D.; Madani, K. Modeling for watershed planning, management, and decision making. In Watersheds: Management, Restoration and Environmental Impact; Vaughn, J.C., Ed.; Nova Science Publishers, Inc.: Hauppauge, NY, USA, 2010; pp. 221–244. ISBN 9781616686673. [Google Scholar]
- Zomorodian, M.; Lai, S.H.; Homayounfar, M.; Ibrahim, S.; Fatemi, S.E.; El-Shafie, A. The state-of-the-art system dynamics application in integrated water resources modeling. J. Environ. Manage. 2018, 227, 294–304. [Google Scholar] [CrossRef]
- Balsam, G. Decision Support Systems for Water Management: Investigating Stakeholder Perceptions of System Use. 2016. Available online: http://libproxy.lib.unc.edu/ (accessed on 1 July 2020).
- Junier, S.; Mostert, E. A decision support system for the implementation of the Water Framework Directive in the Netherlands: Process, validity and useful information. Environ. Sci. Policy 2014, 40, 49–56. [Google Scholar] [CrossRef]
- Borowski, I.; Hare, M. Exploring the gap between water managers and researchers: Difficulties of model-based tools to support practical water management. Water Resour. Manag. 2007, 21, 1049–1074. [Google Scholar] [CrossRef]
- Van Delden, H.; Seppelt, R.; White, R.; Jakeman, A.J. A methodology for the design and development of integrated models for policy support. Environ. Model. Softw. 2011, 26, 266–279. [Google Scholar] [CrossRef]
- Serrat-Capdevila, A.; Valdes, J.B.; Gupta, H.V. Decision Support Systems in Water Resources Planning and Management: Stakeholder participation and the sustainable path to science-based decision making. In Efficient Decision Support Systems—Practice and Challenges From Current to Future; Jao, C., Ed.; IntechOpen: London, UK, 2011. [Google Scholar]
- Snyder, H. Literature review as a research methodology: An overview and guidelines. J. Bus. Res. 2019, 104, 333–339. [Google Scholar] [CrossRef]
- Webster, J.; Watson, R.T. Analyzing the past to prepare for the future: Writing a literature review. MIS Q. 2002, 26, xiii–xxiii. [Google Scholar] [CrossRef]
- Torraco, R.J. Writing integrative literature reviews: Guidelines and examples. Hum. Resour. Dev. Rev. 2005, 4, 356–367. [Google Scholar] [CrossRef]
- Devi, G.K.; Ganasri, B.P.; Dwarakish, G.S. A Review on Hydrological Models. Aquat. Procedia 2015, 4, 1001–1007. [Google Scholar] [CrossRef]
- Golmohammadi, G.; Prasher, S.; Madani, A.; Rudra, R. Evaluating Three Hydrological Distributed Watershed Models: MIKE-SHE, APEX, SWAT. Hydrology 2014, 1, 20–39. [Google Scholar] [CrossRef] [Green Version]
- Seago, C. A Comparison of the South African Approach to Water Resources Management and Planning with four International Countries (Report to the Water Research Commission). 2016. Available online: http://wrcwebsite.azurewebsites.net/wp-content/uploads/mdocs/KV%20341-15.pdf (accessed on 7 October 2020).
- World Bank. Water Resources Management: A World Bank Policy Paper; World Bank: Washington, DC, USA, 1993. [Google Scholar]
- Jiang, M. Towards Tradable Water Rights: Water Law and Policy Reform in China; Dinar, A., Ed.; Springer Nature, Springer International Publishing: Cham, Switzerland, 2018; ISBN 9783319670850. [Google Scholar]
- Pyke, C.R.; Bierwagen, B.G.; Furlow, J.; Gamble, J.; Johnson, T.; Julius, S.; West, J. A decision inventory approach for improving decision support for climate change impact assessment and adaptation. Environ. Sci. Policy 2007, 10, 610–621. [Google Scholar] [CrossRef]
- Tyagi, A.; Shortle, J.S. Modeling Endogenous Change in Water Allocation Mechanisms: A Non-Cooperative Bargaining Approach. In Proceedings of the 2016 Annual Meeting—Agricultural and Applied Economics Association (AAEA) Conferences, Boston, MA, USA, 31 July–2 August 2016; p. 28. Available online: http://purl.umn.edu/235571 (accessed on 17 August 2020).
- Roozbahani, R.; Schreider, S.; Abbasi, B. Multi-objective decision making for basin water allocation. In Proceedings of the 20th International Congress on Modelling and Simulation, Adelaide, Australia, 1–6 December 2013; pp. 2973–2979. [Google Scholar] [CrossRef]
- Tu, Y.; Zhou, X.; Gang, J.; Liechty, M.; Xu, J.; Lev, B. Administrative and market-based allocation mechanism for regional water resources planning. Resour. Conserv. Recycl. 2015, 95, 156–173. [Google Scholar] [CrossRef]
- Kiani-Moghaddam, M.; Shivaie, M.; Weinsier, P.D. Introduction to Multi-objective Optimization and Decision-Making Analysis. In Modern Music-Inspired Optimization Algorithms for Electric Power Systems; Springer: Cham, Switzerland, 2019; pp. 21–45. Available online: http://link.springer.com/10.1007/978-3-030-12044-3 (accessed on 21 August 2020).
- Yan, D.; Ludwig, F.; Huang, H.Q.; Werners, S.E. Many-objective robust decision making for water allocation under climate change. Sci. Total Environ. 2017, 607–608, 294–303. [Google Scholar] [CrossRef]
- Zhao, S.; Liu, W.; Zhu, M.; Ma, Y.; Li, Z. A priority-based multi-objective framework for water resources diversion and allocation in the middle route of the South-to-North Water Diversion Project. Socioecon. Plann. Sci. 2021, 78, 101085. [Google Scholar] [CrossRef]
- Hajkowicz, S.; Higgins, A. A comparison of multiple criteria analysis techniques for water resource management. Eur. J. Oper. Res. 2008, 184, 255–265. [Google Scholar] [CrossRef]
- Elleuch, M.A.; Anane, M.; Euchi, J.; Frikha, A. Hybrid fuzzy multi-criteria decision making to solve the irrigation water allocation problem in the Tunisian case. Agric. Syst. 2019, 176, 102644. [Google Scholar] [CrossRef]
- Kapetas, L.; Kazakis, N.; Voudouris, K.; McNicholl, D. Water allocation and governance in multi-stakeholder environments: Insight from Axios Delta, Greece. Sci. Total Environ. 2019, 695, 133831. [Google Scholar] [CrossRef]
- Hajkowicz, S.; Collins, K. A review of multiple criteria analysis for water resource planning and management. Water Resour. Manag. 2007, 21, 1553–1566. [Google Scholar] [CrossRef]
- Rey, D.; Pérez-Blanco, C.D.; Escriva-Bou, A.; Girard, C.; Veldkamp, T.I.E. Role of economic instruments in water allocation reform: Lessons from Europe. Int. J. Water Resour. Dev. 2019, 35, 206–239. [Google Scholar] [CrossRef] [Green Version]
- Power, D.J. Decision Support Systems: Concepts and Resources for Managers; 2002; 251p, Available online: https://scholarworks.uni.edu/facbook/67 (accessed on 27 July 2020).
- Cheong, T.S.; Ko, I.; Labadie, J.W. Development of multi-objective reservoir operation rules for integrated water resources management. J. Hydroinform. 2010, 12, 185–200. [Google Scholar] [CrossRef] [Green Version]
- Song, W.Z.; Yuan, Y.; Jiang, Y.Z.; Lei, X.H.; Shu, D.C. Rule-based water resource allocation in the Central Guizhou Province, China. Ecol. Eng. 2016, 87, 194–202. [Google Scholar] [CrossRef]
- Divakar, L.; Babel, M.S.; Perret, S.R.; Gupta, A. Das Optimal allocation of bulk water supplies to competing use sectors based on economic criterion—An application to the Chao Phraya River Basin, Thailand. J. Hydrol. 2011, 401, 22–35. [Google Scholar] [CrossRef]
- Muller, J. Estimating the Marginal Value of Agricultural Irrigation Water; University of Cape Town: Cape Town, South Africa, 2016; Available online: https://open.uct.ac.za/handle/11427/25409 (accessed on 19 August 2020).
- Oxley, R.L.; Mays, L.W. Application of an Optimization Model for the Sustainable Water Resource Management of River Basins. Water Resour. Manag. 2016, 30, 4883–4898. [Google Scholar] [CrossRef]
- Sjöstrand, K.; Lindhe, A.; Söderqvist, T.; Rosén, L. Sustainability assessments of regional water supply interventions—Combining cost-benefit and multi-criteria decision analyses. J. Environ. Manage. 2018, 225, 313–324. [Google Scholar] [CrossRef]
- Sjöstrand, K. Water management—Decision support for informed prioritizations. In Proceedings of the 4th Water Research Commission Symposium, Johannesburg, South Africa, 11–13 September 2019. [Google Scholar]
- The Office of the Chief Economic Adviser: Scottish Government. Computable General Equilibrium (CGE) Modelling and SG’s CGE Model. 2015. Available online: https://www.gov.scot/publications/cge-modelling-introduction/ (accessed on 7 August 2020).
- Burfisher, M.E. Introduction to Computable General Equilibrium Models. In Introduction to Computable General Equilibrium Models; Cambridge University Press: Cambridge, UK, 2017; pp. 8–23. Available online: https://www.cambridge.org/core/product/identifier/CBO9781316450741A014/type/book_part (accessed on 10 March 2021).
- Zhang, Y.; Lu, Y.; Zhou, Q.; Wu, F. Optimal water allocation scheme based on trade-offs between economic and ecological water demands in the Heihe River Basin of Northwest China. Sci. Total Environ. 2020, 703, 134958. [Google Scholar] [CrossRef] [PubMed]
- Degefu, D.M.; He, W.; Yuan, L.; Zhao, J.H. Water Allocation in Transboundary River Basins under Water Scarcity: A Cooperative Bargaining Approach. Water Resour. Manag. 2016, 30, 4451–4466. [Google Scholar] [CrossRef]
- Mehrparvar, M.; Ahmadi, A.; Safavi, H.R. Social resolution of conflicts over water resources allocation in a river basin using cooperative game theory approaches: A case study. Int. J. River Basin Manag. 2016, 14, 33–45. [Google Scholar] [CrossRef]
- Etro, F. Research in economics and game theory. A 70th anniversary. Res. Econ. 2017, 71, 1–7. [Google Scholar] [CrossRef]
- Oftadeh, E.; Shourian, M.; Saghafian, B. An Ultimatum Game Theory Based Approach for Basin Scale Water Allocation Conflict Resolution. Water Resour. Manag. 2017, 31, 4293–4308. [Google Scholar] [CrossRef]
- Yuan, L.; He, W.; Degefu, D.M.; Liao, Z.; Wu, X. Water allocation model in the lancing-mekong river basin based on bankruptcy theory and bargaining game. In Proceedings of the World Environmental and Water Resources Congress 2017, Sacramento, CA, USA, 21–25 May 2017; pp. 628–642. [Google Scholar] [CrossRef]
- Madani, K.; Pierce, T.W.; Mirchi, A. Serious games on environmental management. Sustain. Cities Soc. 2017, 29, 1–11. [Google Scholar] [CrossRef] [Green Version]
- Madani, K. Game theory and water resources. J. Hydrol. 2010, 381, 225–238. [Google Scholar] [CrossRef]
- Bahrini, A.; Riggs, R.J.; Esmaeili, M. Social choice rules, fallback bargaining, and related games in common resource conflicts. J. Hydrol. 2021, 602, 126663. [Google Scholar] [CrossRef]
- Ananda, J.; Herath, G. A critical review of multi-criteria decision making methods with special reference to forest management and planning. Ecol. Econ. 2009, 68, 2535–2548. [Google Scholar] [CrossRef]
- Sarband, E.M.; Araghinejad, S.; Attari, J. Developing an Interactive Spatial Multi-Attribute Decision Support System for Assessing Water Resources Allocation Scenarios. Water Resour. Manag. 2020, 34, 447–462. [Google Scholar] [CrossRef]
- Cai, X.; Lasdon, L.; Michelsen, A.M. Group decision making in water resources planning using multiple objective analysis. J. Water Resour. Plan. Manag. 2004, 130, 4–14. [Google Scholar] [CrossRef]
- Ahmad, I.; Tang, D. Multi-objective Linear Programming for Optimal Water Allocation Based on Satisfaction and Economic Criterion. Arab. J. Sci. Eng. 2016, 41, 1421–1433. [Google Scholar] [CrossRef]
- Gunantara, N. A review of multi-objective optimization: Methods and its applications. Cogent Eng. 2018, 5, 1502242. [Google Scholar] [CrossRef]
- Nasiri-Gheidari, O.; Marofi, S.; Adabi, F. A robust multi-objective bargaining methodology for inter-basin water resource allocation: A case study. Environ. Sci. Pollut. Res. 2018, 25, 2726–2737. [Google Scholar] [CrossRef]
- Dadmand, F.; Naji-Azimi, Z.; Motahari Farimani, N.; Davary, K. Sustainable allocation of water resources in water-scarcity conditions using robust fuzzy stochastic programming. J. Clean. Prod. 2020, 276, 123812. [Google Scholar] [CrossRef]
- Mirchi, A.; Madani, K.; Watkins, D.; Ahmad, S. Synthesis of system dynamics tools for holistic conceptualization of water resources problems. Water Resour. Manag. 2012, 26, 2421–2442. [Google Scholar] [CrossRef]
- Cunha, A.; Morais, D. Decision Support Model for Participatory Management of Water Resource. Springer Int. Publ. Switz. 2015, 216, 85–97. [Google Scholar] [CrossRef]
- Smith, C.M.; Shaw, D. The characteristics of problem structuring methods: A literature review. Eur. J. Oper. Res. 2019, 274, 403–416. [Google Scholar] [CrossRef]
- Martínez-Santos, P.; Henriksen, H.J.; Zorrilla, P.; Martínez-Alfaro, P.E. Comparative reflections on the use of modelling tools in conflictive water management settings: The Mancha Occidental aquifer, Spain. Environ. Model. Softw. 2010, 25, 1439–1449. [Google Scholar] [CrossRef]
- He, Y.; Chen, X.; Sheng, Z.; Lin, K.; Gui, F. Water allocation under the constraint of total water-use quota: A case from Dongjiang River Basin, South China. Hydrol. Sci. J. 2018, 63, 154–167. [Google Scholar] [CrossRef]
- Keskinen, M.; Käkönen, M.; Tola, P.; Varis, O. The Tonle Sap Lake, Cambodia: Conflicts With Abundance of Water. Econ. Peace Secur. J. 2007, 2, 49–59. Available online: https://www.epsjournal.org.uk (accessed on 12 November 2019).
- Wang, Z.; Zheng, H.; Wang, X. A Harmonious Water Rights Allocation model for Shiyang River Basin, Gansu Province, China. Int. J. Water Resour. Dev. 2009, 25, 355–371. [Google Scholar] [CrossRef]
- Gallego-Ayala, J. Trends in integrated water resources management research: A literature review. Water Policy 2013, 15, 628–647. [Google Scholar] [CrossRef]
- Fu, Q.; Li, T.; Cui, S.; Liu, D.; Lu, X. Agricultural Multi-Water Source Allocation Model Based on Interval Two-Stage Stochastic Robust Programming under Uncertainty. Water Resour. Manag. 2018, 32, 1261–1274. [Google Scholar] [CrossRef]
- Furlong, C.; Dobbie, M.; Morison, P.; Dodson, J.; Pendergast, M. Infrastructure and Urban Planning Context for Achieving the Visions of Integrated Urban Water Management and Water Sensitive Urban Design; Approaches to Water Sensitive Urban Design; Elsevier Inc.: Oxford, UK, 2018; pp. 329–350. [Google Scholar] [CrossRef]
- Furlong, C.; Brotchie, R.; Considine, R.; Finlayson, G.; Guthrie, L. Key concepts for Integrated Urban Water Management infrastructure planning: Lessons from Melbourne. Util. Policy 2017, 45, 84–96. [Google Scholar] [CrossRef]
- Nieuwoudt, W.L.; Backeberg, G.R. A review of the modelling of water values in different use sectors in South Africa. Water SA 2011, 37, 703–710. [Google Scholar] [CrossRef] [Green Version]
- Li, M.; Guo, P.; Singh, V.P. An efficient irrigation water allocation model under uncertainty. Agric. Syst. 2016, 144, 46–57. [Google Scholar] [CrossRef]
- Mathieu, L.; Tinch, R.; Provins, A. Catchment management in England and Wales: The role of arguments for ecosystems and their services. Biodivers. Conserv. 2018, 27, 1639–1658. [Google Scholar] [CrossRef]
- Zhang, Y.F.; Li, Y.P.; Huang, G.H.; Ma, Y. A copula-based stochastic fractional programming method for optimizing water-food-energy nexus system under uncertainty in the Aral Sea basin. J. Clean. Prod. 2021, 292, 126037. [Google Scholar] [CrossRef]
- Mannan, M.; Al-Ansari, T.; Mackey, H.R.; Al-Ghamdi, S.G. Quantifying the energy, water and food nexus: A review of the latest developments based on life-cycle assessment. J. Clean. Prod. 2018, 193, 300–314. [Google Scholar] [CrossRef]
- Ren, C.; Xie, Z.; Zhang, Y.; Wei, X.; Wang, Y.; Sun, D. An improved interval multi-objective programming model for irrigation water allocation by considering energy consumption under multiple uncertainties. J. Hydrol. 2021, 602, 126699. [Google Scholar] [CrossRef]
- Fan, J.L.; Kong, L.S.; Wang, H.; Zhang, X. A water-energy nexus review from the perspective of urban metabolism. Ecol. Modell. 2019, 392, 128–136. [Google Scholar] [CrossRef]
- Mabhaudhi, T.; Nhamo, L.; Mpandeli, S.; Nhemachena, C.; Senzanje, A.; Sobratee, N.; Chivenge, P.P.; Slotow, R.; Naidoo, D.; Liphadzi, S.; et al. The water–energy–food nexus as a tool to transform rural livelihoods and well-being in Southern Africa. Int. J. Environ. Res. Public Health 2019, 16, 2970. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zeng, X.; Zhao, J.; Wang, D.; Kong, X.; Zhu, Y.; Liu, Z.; Dai, W.; Huang, G. Scenario analysis of a sustainable water-food nexus optimization with consideration of population-economy regulation in Beijing-Tianjin-Hebei region. J. Clean. Prod. 2019, 228, 927–940. [Google Scholar] [CrossRef]
- Kondili, E.; Kaldellis, J.K.; Papapostolou, C. A novel systemic approach to water resources optimisation in areas with limited water resources. Desalination 2010, 250, 297–301. [Google Scholar] [CrossRef]
- Abdulbaki, D.; Al-Hindi, M.; Yassine, A.; Abou Najm, M. An optimization model for the allocation of water resources. J. Clean. Prod. 2017, 164, 994–1006. [Google Scholar] [CrossRef]
- Su, D.; Zhang, Q.H.; Ngo, H.H.; Dzakpasu, M.; Guo, W.S.; Wang, X.C. Development of a water cycle management approach to Sponge City construction in Xi’an, China. Sci. Total Environ. 2019, 685, 490–496. [Google Scholar] [CrossRef]
- Ben-Daoud, M.; Mahrad, B.E.; Elhassnaoui, I.; Moumen, A.; Sayad, A.; ELbouhadioui, M.; Moroșanu, G.A.; El Mezouary, L.; Essahlaoui, A.; Eljaafari, S. Integrated water resources management: An indicator framework for water management system assessment in the R’Dom Sub-basin, Morocco. Environ. Chall. 2021, 3, 100062. [Google Scholar] [CrossRef]
- Rouillard, J.; Rinaudo, J.D. From state to user-based water allocations: An empirical analysis of institutions developed by agricultural user associations in France. Agric. Water Manag. 2020, 239, 106269. [Google Scholar] [CrossRef]
- Toxopeüs, M.; Helen, S.F. Water Governance I: A Broad Outline of the Legislative Framework in South Africa. 2019. Available online: https://hsf.org.za/publications/hsf-briefs/water-governance-i-a-broad-outline-of-the-legislative-framework-in-south-africa (accessed on 13 March 2020).
- Schreiner, B. Viewpoint—Why has the South African National Water Act been so difficult to implement? Water Altern. 2013, 12, 38–41. Available online: https://www.wrc.org.za/wp-content/uploads/mdocs/14%20Water%20law%20p%2038-41.pdf (accessed on 15 March 2020).
- Pienaar, G.W.; Hughes, D.A. Linking Hydrological Uncertainty with Equitable Allocation for Water Resources Decision-Making. Water Resour. Manag. 2017, 31, 269–282. [Google Scholar] [CrossRef]
- Dixon, P.B.; Jorgenson, D.W. Handbook of Computable General Equilibrium Modeling; Elsevier B.V.: Oxford, UK, 2013; ISBN 978-0-444-53634-1. [Google Scholar]
- Roffe, S.J.; Fitchett, J.M.; Curtis, C.J. Classifying and mapping rainfall seasonality in South Africa: A review. S. Afr. Geogr. J. 2019, 101, 158–174. [Google Scholar] [CrossRef]
- Di, D.; Wu, Z.; Wang, H.; Huang, S. Multi-objective optimization for water allocation of the Yellow River basin based on fluid mechanics, emergy theory, and dynamic differential game. J. Clean. Prod. 2021, 312, 127643. [Google Scholar] [CrossRef]
- Yao, L.; Xu, Z.; Chen, X. Sustainable water allocation strategies under various climate scenarios: A case study in China. J. Hydrol. 2019, 574, 529–543. [Google Scholar] [CrossRef]
- ISO 14040:2006. Environmental Management—Life Cycle Assessment—Principles and Framework. ISO: Geneva, Switzerland, 2006. Available online: https://www.iso.org/obp/ui/#iso:std:iso:14040:%0Aed-2:v1:en (accessed on 19 August 2020).
- Department of Water Affairs and Forestry. National Water Resource Strategy, 1st ed.; Department of Water Affairs and Forestry: Pretoria, South Africa, 2004. Available online: https://cer.org.za/wp-content/uploads/2017/10/NWRS-2004.pdf (accessed on 19 August 2020).
- Basson, M.S.; Allen, R.B.; Pegram, G.G.S.; van Rooyen, J.A. Probablistic Management of Water Resource and Hydropower Systems; Water Resources Publications: Highlands Ranch, CO, USA, 1994. [Google Scholar]
- Republic of South Africa. National Water Act, No 36 of 1998. Government Gazette 1998. Available online: https://www.gov.za/sites/default/files/gcis_document/201409/a36-98.pdf (accessed on 12 August 2020).
- Tempelhoff, J. The Water Act, No. 54 of 1956 and the first phase of apartheid in South Africa (1948–1960). Water Hist. 2017, 9, 189–213. [Google Scholar] [CrossRef] [Green Version]
- Rawlins, J. Political economy of water reallocation in South Africa: Insights from the Western Cape water crisis. Water Secur. 2019, 6, 100029. [Google Scholar] [CrossRef]
- Muller, M. South Africa needs good water management—Not new water laws. Conversation. 2018. Available online: https://theconversation.com (accessed on 19 August 2019).
- Herrfahrdt-Pähle, E. Applying the concept of fit to water governance reforms in South Africa. Ecol. Soc. 2014, 19, 25. [Google Scholar] [CrossRef] [Green Version]
- Department of Water Affairs and Forestry. Water Allocation Reform Strategy. 2008. Available online: https://www.dws.gov.za/WAR/beneficial.aspx (accessed on 19 August 2019).
- du Plessis, A. Freshwater Challenges of South Africa and Its Upper Vaal River: Current State and Outlook; Springer: Cham, Switzerland, 2017; pp. 65–76. [Google Scholar] [CrossRef]
- Mclachlan, A. The balancing act of Gauteng’s water security. Water Wheel 2020, 19, 38–42. [Google Scholar]
- Coleman, T.J.; Mckenzie, R.S.; Rademeyer, J.I.; Van Rooyen, P.G. Lessons learned from the Vaal river system reconciliation strategy study. In Proceedings of the 13th SANCIAHS Symposium, Cape Town, South Africa, 5–7 September 2007. [Google Scholar]
- Munnik, V. The Reluctant Roll-Out of Catchment Management Agencies (Report to Water Research Commission); Water Research Commission: Pretoria, South Africa, 2020. [Google Scholar]
- Paterson, M.N. Exploring the role of Cooperative Governance in Water Resource Management: A study of Catchment Management Agencies in South Africa. 2022. Available online: https://scholar.sun.ac.za (accessed on 12 May 2022).
- Van Rooyen, P.G.; Mckenzie, R.S.; Rademeyer, J.I. Lessons Learned from Three Decades of Water Resource Planning of the Integrated Vaal River System; WRP Consulting Engineers (Pty) Ltd.: Pretoria, South Africa, 2018. [Google Scholar]
Evaluation Area | Hierarchy/Priority Allocation | Strategic Allocation | User-Based Allocation | Optimization Approaches | Multi-Criteria Approaches | Price-Based Allocation | Market-Based Allocation | |
---|---|---|---|---|---|---|---|---|
Social/equity orientation | Medium/high | Medium | High | Medium | Medium | Low | Low | |
Economic orientation | Medium/low | Medium/high | Low | Medium | Medium | Medium/high | High | |
Environmental dimensions | Environmental orientation | Medium/high | Medium | Medium | Medium | Medium | Medium | Low |
Promotion of water conservation and efficiency | Low/medium | Low | Medium | Medium/high | Medium/high | Medium | Low/medium | |
Stakeholders | Stakeholder participation | Low | Low | High | Medium/high | Medium/high | Low | Low/medium |
Complexity of catchment area that can be handled | Low/medium | Medium | Low/medium | Medium/high | Medium/high | Medium | Medium/high | |
Range of challenges/ goals/issues handled | Limited range | Limited range | Reasonable range | Broad range | Broad range | Limited range | Limited range | |
Categories of water users | Limited | Strategic only | Mostly socially driven | Multiple | Multiple | Multiple | Economically driven | |
Categories of water supply | Any | Mostly strategic | Any | Complex combinations | Complex combinations | Source driven | Source driven | |
Legal framework | Implementing legal framework | Easy to enforce | Easy to enforce | Difficult to enforce | Work into objectives and constraints | Work into criteria, weighting and constraints | Complex to enforce | Difficult to enforce |
Level of water management and decision-making | Centralized decision-making; decentralized implementation | Decision-making and implementation centralized | Centralized policies/rules Decentralized implementation | Centralized or decentralized | Centralized or decentralized | Centralized guidelines; decentralized implementation | Centralized guidelines; decentralized implementation | |
Uncertainties/ change | Overarching allocation vs. seasonal/annual variability | Priorities determine all allocations | Strategic allocation will determine allocation | Allocation system will be used to handle both | Can handle both, two sets of objective functions | Can handle both, two sets of criteria and weightings | Seasonal and annual fluctuations through price premium | Market forces will determine allocation during variability |
Handling of uncertainties | Limited | Strategic users only | Work out solutions to limit overall impact | Work into objectives | Alternative scenarios as fall-back | Limited | Market forces |
Evaluation Areas | Rule-Based and Hierarchy Type | Economic Benefit Models | Computable General Equilibrium (CGE) Method | Game Theory | Multi-Criteria Analysis | Multi-Objective Analysis | System Dynamics |
---|---|---|---|---|---|---|---|
Water allocation schemes to which it can be applied | Hierarchy allocation system; partly strategic allocation | Market-based schemes; partly multi-criteria/ objective, price- and user-based schemes | Market-based schemes; partly multi-criteria/ objective, price- and user-based schemes | User-based schemes; Partly multi-criteria and multiple objective schemes | Multi-criteria allocation; partly user-based and optimization schemes | Multi-objective/ criteria schemes; partly user-based schemes | For complex systems and situations, multi-criteria/ objective schemes |
Handling social/economic/ environmental balance | Through rules | Economic oriented; social and environmental through benefits and costs | Economic oriented; can evaluate environmental impact of on wider economy | Stakeholders from all areas must be presented | Incorporate balanced criteria | Incorporate balanced objectives | Through comprehensive modelling |
Water conservation, improved water productivity | Through rule development | Linked to economic value of water | Linked to economic value of water | Introduce by facilitator or manager | Incorporate in selected criteria and weighting | Incorporate in objective and constraint sets | Incorporate in model setup |
Stakeholder participation | Limited | Limited | Limited | Medium to high | Medium to high | Medium to high | High |
Range of criteria incorporated | Limited range | Limited range | Limited range | Broad range | Broad range | Broad range | Broad, complex range |
Reliance on expert knowledge | High to compile rules | Medium to high (economic) | Low to medium (economic expertise to set up model) | Low with expert facilitator | Expert knowledge to set up | Expert knowledge to set up | Expert knowledge to understand system |
Reliance on computerized calculations | Low | Low to medium | High | Low | Medium | Medium to high | High |
Type of computer use | Recording | Some calculations and recording | Comprehensive economic modelling | Recording | Recording and some calculations | Solving set of multiple objective functions | Solving the system dynamic setup |
Overarching allocation vs. seasonal/annual variability | Rules for both | Mainly for overarching (long-term) allocation, other partly | Mainly for overarching (long-term) allocation, other partly | Aimed more at optimization of allocation | Through separate criteria/weighting sets | Through separate objective/constraint sets | Set up to cover all |
Applicable for sensitivity analysis | Not suitable | To evaluate scenarios | Impacts on broader economy | Limited, scenarios can be inputs | Sensitivity analysis on weighting and performance | Good for sensitivity analysis | Limited use for sensitivity analysis |
Evaluation Elements | Priority/ Hierarchy | Strategically Focussed | User-Based | Optimized Objectives | Multi-Criteria | Price-Based | Market-Based | |
---|---|---|---|---|---|---|---|---|
Critical implementation | Legal framework | 4 | 2 | 1 | 2 | 2 | 1 | 1 |
Equity | 3 | 2 | 4 | 2 | 2 | 1 | 1 | |
Ecological objectives | 4 | 2 | 2 | 3 | 3 | 2 | 1 | |
Total | 11 | 6 | 7 | 7 | 7 | 4 | 3 | |
Important elements to address | Economic development | 2 | 3 | 2 | 3 | 3 | 3 | 4 |
Stakeholder participation | 2 | 1 | 4 | 3 | 3 | 1 | 2 | |
Multiple objectives | 1 | 1 | 2 | 3 | 4 | 2 | 1 | |
Conservation/efficiency focus | 2 | 2 | 2 | 4 | 4 | 2 | 2 | |
Total | 7 | 7 | 10 | 13 | 14 | 8 | 9 |
Evaluation Elements | Rule-Based | Economic Benefit | CGE Method | Game Theory | Multi-Criteria | Multi-Objective | System Dynamics | |
---|---|---|---|---|---|---|---|---|
Support priority/ hierarchy WA | Applicability level | 4 | 1 | 1 | 2 | 2 | 2 | 2 |
Supporting balance | 2 | 1 | 1 | 2 | 3 | 3 | 3 | |
Ease of integration | 4 | 1 | 1 | 2 | 2 | 1 | 1 | |
Total | 10 | 3 | 3 | 6 | 7 | 6 | 6 | |
Support multi-criteria/ objectives WA | Applicability level | 1 | 2 | 2 | 2 | 4 | 4 | 4 |
Supporting balance | 2 | 2 | 2 | 3 | 4 | 4 | 3 | |
Computer dependence | 3 | 3 | 1 | 4 | 3 | 2 | 1 | |
Expert dependence | 1 | 2 | 3 | 3 | 2 | 2 | 1 | |
Conservation/efficiency focus | 2 | 2 | 2 | 2 | 4 | 4 | 3 | |
Ease of integration | 3 | 3 | 2 | 3 | 3 | 3 | 2 | |
Total | 12 | 14 | 12 | 17 | 20 | 19 | 14 |
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Nel, J.B.; Mativenga, P.T.; Marnewick, A.L. A Framework to Support the Selection of an Appropriate Water Allocation Planning and Decision Support Scheme. Water 2022, 14, 1854. https://doi.org/10.3390/w14121854
Nel JB, Mativenga PT, Marnewick AL. A Framework to Support the Selection of an Appropriate Water Allocation Planning and Decision Support Scheme. Water. 2022; 14(12):1854. https://doi.org/10.3390/w14121854
Chicago/Turabian StyleNel, Johannes B., Paul T. Mativenga, and Annlizé L. Marnewick. 2022. "A Framework to Support the Selection of an Appropriate Water Allocation Planning and Decision Support Scheme" Water 14, no. 12: 1854. https://doi.org/10.3390/w14121854
APA StyleNel, J. B., Mativenga, P. T., & Marnewick, A. L. (2022). A Framework to Support the Selection of an Appropriate Water Allocation Planning and Decision Support Scheme. Water, 14(12), 1854. https://doi.org/10.3390/w14121854