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Thư viện số Văn Lang: Integrated Groundwater Management: Concepts, Approaches and Challenges

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Nguyễn Gia Hào

Academic year: 2023

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First, the invisible nature of groundwater resources, compounded by a lack of scientific understanding of the system, breeds misconceptions among lay people about the nature of the resource and how it changes (eg, the myth of underground rivers). The victims have been the most marginalized in society and ultimately the ecosystem in which these processes are embedded.” The distributed nature of groundwater pumping and use can lead to similar self-organizing behavior at multiple scales.

Table 24.2 Summary of case studies reviewed as part of this chapter
Table 24.2 Summary of case studies reviewed as part of this chapter

Lessons Learnt

The researcher reflected on the process and concluded that the success of any method is highly dependent on context and implementation. Addressing the complexity of the human dimension cannot be a simple recipe: “a continuous reconstruction of the process and its assumptions was necessary.”.

Conclusions

University of New South Wales, Canberra Flood RL (2010) The relationship of 'systems thinking' to action research. Mingers J, Leroy W (2010) A review of the recent contribution of systems thinking to operations research and management science.

Introduction

Decision Support Systems in Relation to Groundwater

Hydrogeological features (shown in Table 25.1, Section 25.3.1 of this chapter) are the most fundamental unit of information for describing groundwater systems. DSS that combine aquifer and groundwater management performance, as shown in Figure 25.1, create a more transparent lens.

Table 25.1 Natural attributes for a hydrogeologic system a State
Table 25.1 Natural attributes for a hydrogeologic system a State

Data and Modeled Attributes for Aquifer Performance

Decision contexts related to groundwater monitoring stations, sampling sites or waste management are all good examples of the many sets of decision contexts that cross sectors, from industrial to environmental management or household and agricultural use cases. Determining the response of the aquifer to variations in any of the variables for this equation is key to defining the volumes of groundwater that may be available for pumping.

Addressing Stakeholder Perspectives for Groundwater Governance

DSS can help decision makers conceptualize a problem in a new way, as well as allow the rapid conversion of vast data sets typically associated with groundwater problems into understandable reports that can provide guidance and insight (Kersten2000 ). Informational approaches attempt to improve the quality of a decision by providing information to help decision makers analyze a situation and evaluate alternatives.

Decision Support Systems: Background and Types

Moura et al.(2011)Assess groundwater quality control with uncertainty Local to Regional: farmanda aquifer for case studies;Upper Guadiana Basin; Altiplano, Spain Lump parameter representation of aquifers with a coupled hydro-economic model General Algebraic Modeling System (GAMS) and Object Oriented Bayesian Network (OOBN) for stochastic modeling Maximize farm-level gross profit as a function of crop prices and yields; the level of OOBN added water response. Quintana et al.(2005)bGroundwater management Local-regional: HeraultMiddle Valley, France Not clear but indicates that a groundwater module included GOUVERNeor TIDDD(Toolto InformDebates, Dialogues & Deliberations) Investigative decision support with stakeholder participants.

Fig. 25.3 Evolution of decision support systems for proactive support of science-based delibera- delibera-tion and negotiadelibera-tion (Modified from Kersten and Lai 2008; Pereira and Quintana 2002)
Fig. 25.3 Evolution of decision support systems for proactive support of science-based delibera- delibera-tion and negotiadelibera-tion (Modified from Kersten and Lai 2008; Pereira and Quintana 2002)

Factors Related to Adoption of DSS

The application of Bayesian networks (Moura et al. 2011; Molina et al.. 2013a, b; Fienen et al. 2013) over multiple cases demonstrates a replicable methodology, and the WEAP-MODFLOW software tool (Le Page et al. 2012; Hadded et al. 2013) is gaining traction across various applications. Successful DSS for groundwater management will need to remain flexible and simple enough to explain to various user and decision-making groups, while addressing key barriers to adoption.

Conclusions

Andreu J, Capilla J, Sanchis E (1996) AQUATOOL, a general decision support system for operational planning and management of water resources. Letcher RA (2005) Implementation of a water allocation decision support system in the Namoi and Gwydir valleys. McKinney DC, Cai X, Maidment DR (1997) A prototype GIS-based decision support system for river basin management.

Introduction

Data Management Lifecycle .1 What Is Data Management?

The task for a data management model is to define the data management workflow and process. It is worth noting that this data management model can be modified depending on the purpose of the study and is provided as a general-purpose model. Integrated groundwater studies have a specific set of requirements for data types and their specific data management needs.

Fig. 26.1 WMO data management scheme
Fig. 26.1 WMO data management scheme

Time Series Data Management

This is complemented by surface geophysical data, which may include seismic, electromagnetic and electrical data, on the basis of which the hydrogeology and conceptual models of the groundwater systems can be developed. Most groundwater data management systems have separate tools, processes and mechanisms for storing time series, GIS and spatial data, metadata and conceptual models.

GIS toolsets

Examples of GIS Data models

There are many studies in which these types of models have been successfully used (Whiteaker et al. 2006) in combination with GIS toolsets. One problem that arises concerns unique identifiers in these types of systems (called HydroID in ARCHydro-GW), which identify features in the geospatial databases. This has led to the development of the conceptual model Hy-Features (Atkinson et al.2012), in which the features are defined independently of representation.

Metadata Requirements

Usually these identifiers are locally scoped, meaning they are assigned to be unique within a GeoDatabase, and they are most often non-unique when combining or integrating databases. For example, a borehole may be represented by a point, in one particular GeoDatabase, and by a line in another GeoDatabase. The difference may seem esoteric, but defining features in this way allows for easier integration of data for a particular feature type and greatly facilitates integrated studies.

Conceptual Models

Web-Based Data Management and Modeling

Groundwater Data Networks

Challenges: Data Interoperability in Groundwater Data Networks

The syntax level includes using standard data languages, such as GML (Geographical MarkUp Language; Portele 2007), which can be used to encode data. Standard schemas are usually diagrammed using well-constrained methods, such as UML, and can be expressed in various formats, such as XML. Both can be applied to (1) data content, such as common rock type terms and their definitions, and (2) data structure, such as a generally defined lithology field containing rock type terms.

Fig. 26.5 Heterogeneous water well data from the Canadian Groundwater Information Network (www.gw-info.net)
Fig. 26.5 Heterogeneous water well data from the Canadian Groundwater Information Network (www.gw-info.net)

Examples

They also significantly demonstrated that key organizational mandates can be improved through the deployment of open standards and the resulting interoperability of the data networks. However, unlike previous data networks described herein, which were web-centric, HIS emphasizes desktop tools as primary interfaces to the data network. A web-based data network would greatly ease the burden of the data management challenge for integrated modeling studies such as AWRA.

Fig. 26.6 Architecture for GIN and NGWMN – local data sources in the lowest tier, central data caches, catalogs, and transformations in the middle tier, and distributed web portals in the upper tier
Fig. 26.6 Architecture for GIN and NGWMN – local data sources in the lowest tier, central data caches, catalogs, and transformations in the middle tier, and distributed web portals in the upper tier

Discussion of Future Trends

Open Access This chapter is distributed under the terms of the Creative Commons Attribution-Noncommercial 2.5 License (http://creativecommons.org/licenses/by-nc/2.5/) which permits any noncommercial use, distribution and reproduction in any medium, provided the original author(s) and source are credited. Open Geospatial Consortium, OGC 10025r1, version 2.0, 77 pp.http://portal.opengeospatial.org/files/?artifact_id=41510 Croke BFW et al (2006) Integrated assessment of water resources: Australian experiences. In: Proceedings of the Environmental Information Management Conference 2011, pp 132–137, http://eim.ecoinformatics.org/eim2011/eim- procedures-2011/view.

Introduction: Conjunctive Use Overview

Conjunctive use can also facilitate the integration of reclaimed water to meet urban landscape irrigation requirements (green strips and public gardens). Conjunctive use operations involve diverse environmental, economic and social aspects, as changes in the natural cycle of surface water and groundwater are likely to cause costs and benefits not only for the direct users, but also the neighboring uses. Examples of these opportunities are found in California, where complex surface and groundwater problems have stimulated the development of new approaches for conjunctive use.

Economic and Hydrologic Tradeoffs of Conjunctive Use

The overdraft increased future groundwater pumping costs, with potentially large economic impacts and risk to the viability of conjunctive use operations. Flexible management of additional conjunctive use facilities and groundwater storage capacity under flexible water allocation can generate significant economic benefits. CU adds operational flexibility to take better advantage of water transfers, and transfers provide the allocation flexibility to take better advantage of conjunctive use (Pulido-Velazquez et al.2004).

Hydroeconomic Models Applied to Conjunctive Use

Network flow programming has been applied to large systems that assume linear or piecewise linearized responses (Jenkins et al. 2004). Black-box" neural network approaches have also been used to simulate groundwater response functions (Karamoutz et al. 2007). The nested multireservoir method also allows quantification of stream-aquifer interaction by simple and operational explicit equations of state (Pulido-Velazquez et al. 2005).

Fig. 27.1 Conceptual representation of HEM for conjunctive use management
Fig. 27.1 Conceptual representation of HEM for conjunctive use management

Selected Applications

Interrelated use operations are represented by additional decision variables for artificial recharge area, recharged and pumped volumes. Applying the model in Marques et al. 2010) showed that groundwater availability, price and associated use operations influence crop and irrigation technology decisions. With cross-utilization operations, gains in revenue reliability were greater than gains in expected net profitability, with a trade-off between the two.

Challenges, Benefits and Future Directions

Bredehoeft JD, Young RA (1983) Interrelated use of groundwater and surface water for irrigated agriculture: risk aversion. Rao SVN, Bhallamudi SM, Thandaveswara BS, Mishra GC (2004) Interrelated use of surface and groundwater for coastal and deltaic systems. Safavi H, Alijanian M (2011) Optimal crop planning and interrelated use of surface and groundwater resources using fuzzy dynamic programming.

Introduction

This chapter provides an overview of commonly used methods to explore uncertainty in groundwater management predictions. They may be better prepared to participate in the judgment of the value of the information that was put into the modelling, and thus increase their confidence in the predictions of the uncertain results produced. To describe methods for exploring uncertainty in a groundwater model, the chapter first sets the stage by discussing the construction of a clear model.

Starting from a Clear Problem Definition

Different users may have different conflicting goals or conflicting understandings of the problem (Brugnach et al. 2008). Modelers and stakeholders must work together to define a problem, in a way that is aware of the uncertainty involved. Using visualization of the solution and its impact, the user is asked to identify desired or undesirable outcomes of the current best scenario.

Fig. 28.3 The iterative discovery method. Starting from the current best scenario, potential desirable and undesirable outcomes are identified which prompt the three cycles (assumption, intervention and management target) in order to identify achievable an
Fig. 28.3 The iterative discovery method. Starting from the current best scenario, potential desirable and undesirable outcomes are identified which prompt the three cycles (assumption, intervention and management target) in order to identify achievable an

Communicating and Using Predictions of Uncertain Outcomes

Rather than providing tables of probabilities, they can be better visualized (Barnett et al. 2012) using maps or graphs. There is an extensive literature on the presentation and interpretation of uncertainty estimates (Wardekker et al. 2008; One way to approach this is through quality assurance of the modeling process and its constituent assumptions (Refsgaard et al. 2005; Guillaume2011).

Methods for Generating Alternative Models

To capture historical variability, observations can be sampled from existing time series (e.g. Guillaume et al. 2012). Time series corrections can also be made using parameters that can be estimated together with other parts of the model (Vrugt et al. 2008). Depending on the mathematical form of the model, 'linear methods' can be used to provide fast estimates (e.g. Doherty et al. 2010b).

Conclusions

Fu B, Guillaume JHA (2014) Assessing certainty and uncertainty in riparian habitat suitability models by identifying parameters with extreme outputs. Matott LS, Babendreier JE, Purucker ST (2009) Assessing uncertainty in integrated environmental models: a review of concepts and tools. Refsgaard JC, van der Sluijs JP, Højberg AL, Vanrolleghem PA (2007) Uncertainty in the environmental modeling process – a framework and guidance.

Hình ảnh

Table 24.1 Summary of the three waves of development in systems thinking approaches Point of
Table 24.2 Summary of case studies reviewed as part of this chapter
Fig. 25.1 The conceptual relationship between decision support, aquifer performance, and groundwater governance in integrated groundwater management (Modified from Hamilton et al
Table 25.1 Natural attributes for a hydrogeologic system a State
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