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‘’Water now and in the future’’ Modelling water availability in Noord-‐Brabant.
Authors: Carlo Cuijpers, Merla Kubli, Lissette Vanessa Armendáriz Student ID: s3022250, s4375726, s4375505 Course: Group Model Building II (MAN-‐MBAM003-‐2013-‐2-‐V) Prof.: V. de Gooyert & E. Rouwette Contact person: Merla Kubli, [email protected].
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This work is licensed under a Creative Commons Attribution-‐NoDerivs 3.0 Netherlands License.
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A collaboration of: Main partners:
Partner in expertise:
Associated partners:
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Contents A collaboration of: ................................................................................................................................... 3
Introduction ............................................................................................................................................ 5
1. Modelling process ............................................................................................................................ 7
a) Group Model Building technique ............................................................................................ 7
b) Interview process .................................................................................................................... 7
c) Group Model Building sessions ............................................................................................... 9
2. The Model ...................................................................................................................................... 10
3. Policies ........................................................................................................................................... 16
4. Simulation results .......................................................................................................................... 18
5. Access to the model ....................................................................................................................... 19
6. Value and limitations of a GMB process ........................................................................................ 20
7. Model Limitations .......................................................................................................................... 21
8. Further Model Developments ........................................................................................................ 22
9. Learning Points .............................................................................................................................. 23
10. Conclusion ................................................................................................................................... 25
References ............................................................................................................................................. 26
Appendix A: Detailed planning of the sessions ..................................................................................... 27
Appendix B: Data sources ...................................................................................................................... 31
Appendix C: Equations of the model ..................................................................................................... 33
Appendix D: Project Timeline ................................................................................................................ 39
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Introduction
This report is the final document on a collaborative project of the province of Noord-‐Brabant and students from the ‘European Master in System Dynamics’ and major stakeholders on the issue of water scarcity in this province. This report is meant to provide a total overview on the project as a basis for evaluation. Therefore this report will address the background of the project, describe the process behind the project, present and discuss the resulting model constructed in the project, share the most important insights which can be drawn from this model, and share the evaluation of the project. To provide a complete overview other relevant additional information is included in the appendix. Water scarcity is a recognized issue in Noord-‐Brabant also addressed in previous projects like the ‘Deltaplan Hoge Zandgronden’ (DHZ) (Deltaplan Hoge Zandgronden, 2009), the ‘WaterCore project’(EU, 2012), and the Telos project ‘waardecreatie met water’ (Telos, 2010). DHZ focussed on specific areas in Noord-‐Brabant with sandy soils through which the water drains faster, therefore in these areas water scarcity is a more pressing issue. Continuing intensification of agricultural activities together with effects from climate change asked for solutions to secure water availability in the future for these areas. The WaterCore project was a collaboration of different EU-‐regions including the province of Noord-‐Brabant to share policies-‐ and practical experiences related to the management of water scarcity. It is recognized that to effectively – and efficiently manage water scarcity on a regional level an integrated systems view-‐ and stakeholder participation is required (EU, 2012, p.23). The Telos project focussed on an in-‐ and output analysis of water in the natural system in Noord-‐Brabant. Hydrological models were used to map and analyse the different ‘’flows’’ of water. This project brings an additional value to these previous projects by analysing water scarcity in Noord-‐Brabant based on an integrated model of natural-‐ and human systems, which is developed together with major stakeholders. Through the use of Group Model Building (GMB) a joint understanding is achieved on the basis of a System Dynamics (SD) model is achieved, leading to more consensus about the nature of the issue (Größler, 2007; Vennix, 1996). The resulting model additionally provides the opportunity to test policies aimed on managing the issue and supporting policy design. Methods of constructing SD models together with groups of stakeholders have been widely applied for environmental issues (Van den Belt, 2004). The complexity of environmental issues-‐ in combination with a generally high level of uncertainty asks for an integrated analysis combining a plurality of views (from different relevant stakeholders). SD modelling with groups is a tool do integrate-‐ and test these perspectives (Videira, Antunes, Santos, & Lopes, 2010). To consult other cases on SD modelling addressing water management together with stakeholder groups see P.E.: Kallis, Videira, Antunes, Pereira, Spash, Coccossis, & Santos (2006). The goal of this project therefore was to: ‘’to increase dynamic understanding of the water scarcity issue in Noord-‐Brabant from 1990 to 2040, and test different policy options addressing this issue providing guidance on which solutions to implement by developing a System Dynamics model in collaboration with major stakeholders’’. The model was constructed based on literature on previous projects, -‐ interviews with stakeholders, -‐ and focus groups with stakeholders and GMB meetings. Interviews were conducted with representatives of: province Noord-‐Brabant, Waterschap Aa & Maas, ZLTO, Brabant Water, and Staatsbosbeheer. Representatives of these organizations were also invited to the focus groups/GMB meetings but the response was low. Two of these meetings were organized, the first in December 2013 and the second in January 2014, in which the model was constructed and validated.
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This report is organized as follows: first more information will be provided on the process of the project including reports of the interviews and both GMB sessions, second the model-‐ and the analysed polices will be presented and explained, third simulation results and insights will be shared, fourth the additional value of a SD model constructed using GMB over hydrological models will be discussed, fifth model limitations and points for further improvement of the model will be addressed and finalized in a conclusion. In the appendix other relevant information regarding this project is included.
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1. Modelling process
In the following section the process of the project is described. This includes an explanation of the Group Model Building technique, a summary of the conducted interviews and a report on the two sessions held. An overview on the relevant steps and meetings in the course of the project can be found in the appendix (Appendix D).
a) Group Model Building technique Group Model Building (GMB) is considered a form of group decision making support where stakeholders work together in order to solve a specific problem within a complex system (Hovmand et al. 2001). This process allows having an exceptional space of dialogue where different entities are capable of analysing collectively a situation, create a shared vision of a problem, and get information that will be used to create a computer simulation to produce scenarios and test policies assessing their effects.
The analysis and model will be based on the fundamentals of the System Dynamics (SD) discipline using specialized computer software. The formal steps of the GMB process are i) the problem definition where stakeholders will defined the time horizon and reference mode to focus on the boundaries of the model; ii) the conceptualization stage where the relevant variables, the interaction among them, identification of feedback loops and important flows and stocks will be done; iii) the formulation stage where the mathematical equations and parameters will be set; iv) analysis which is mainly a validation process: and policy evaluations stage where the model is ready to conduct policy experiments and make an analysis out of it (Vennix et al., 1992). Capability building during the period of the sessions and after is possible. Mental models from stakeholders can be modified during and after discussions and interactions with other participants and from the SD methodology use. Also it can increase the coordination and interaction among parties to resolve shared problems and provide a space for consensus or agreements.
In the case of the water scarcity in Noord-‐Brabant GMB Project, the stakeholder composition is very diverse. National, local and neighbouring authorities on water, agriculture, and forestry issues are involved but there are also private entities such as farmer organizations. The entity holding the leadership of the process is the Noord-‐Brabant Government. The call for participation, selection of the stakeholders, and communication with the University staff are functions developed by the gatekeeper: Frank van Lamoen, ecology department of the province of Noord-‐Brabant.
b) Interview process From 20th of November to December 4th, five semi-‐structured, interviews with relevant stakeholders were planned and conducted. The interview process supported the first step of the modelling activity: the problem definition, the time horizon selection, identification of the reference mode and model boundary selection. Those were 1-‐1 interviews, i.e., one of the members of the project team will interview one stakeholder each time. The interview content responded to the following topics: details about the organization, problem definition according the organization, perception and interaction of stakeholders, knowledge on SD in the entity, interest on participation in the sessions.
The entities interviewed were: Brabant Water Company, Staatsbosbeheer regio Zuid, ZLTO and the Water Board. The most relevant information elicited from the interviews is the following:
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ü ZLTO is a collaboration platform and –lobbying organization for the agricultural industry in the South of the Netherlands which is part of the national federation LTO. Agricultural activities are considered to be the main water consumers and –polluters, especially in Noord-‐Brabant were a lot of intensive agricultural industry is located. ZLTO (partially) recognises this and supports a transition to a more sustainable agriculture. The interviewee helped to create a macro picture of the water system in Noord-‐Brabant by together drawing a qualitative model, additionally the interviewee helped by illustrating the system with different facts. This interview formed the basis for the later interviews with other stakeholders.
ü ‘Waterschap Aa & Maas’, is the local water board, which was also the main institution behind
the DHZ. The system of water boards in the Netherlands is the oldest still existing political system in the country, and still is a separate political system responsible for water management with the right to collect a specific type of taxes. Because of their mission the water board is also the most important local knowledge institution on water therefore it was important to include them in the project. The interview helped to validate the initial model which was used to start the modelling process with stakeholders in the session, and – to gain specific knowledge on the water system in Noord-‐Brabant.
ü Brabant Water Company (BWC): Their main activities are extracting the water, purifying and
distributing it among the province. They pointed out that farmers and households pollute the water which increases the cost of purifying it back because the Water Board in the treatment plans uses a very simple way to clean the water before putting it again back in the rivers. In their view there are two main problems: 1) climate change, because it will condense the water available thus pollutants concentration at the same time will increase the cost of purifying 2) The competition for water, increasing mainly because industries from energy sector. Specific activities that affect the underground water are: extraction of oil, geo-‐thermal heat (to heat houses or use air conditioning during the summer), and the extraction of shale gas. The legislation regulating the shale gas extraction is 100 years old, it should be adapted because those activities are heavily polluting. Regarding the main situation, ideas looking for solutions mentioned in the interview: farmers should stop using pesticides and other toxics and moderate their livestock; law should be stricter, reallocation of their extraction places. BMF environmentalist groups and Limburg province are considered by BWC as relevant stakeholders. BWC already uses simulation models to measure the water availability.
ü Staatsbosbeheer – National Forestry Agency: Their main concern mentioned was the need of restoration of the hydrological systems; the scarcity consequence for the wetlands is the reduction in the amount of water needed for their natural and healthy development. Differences in the water demand of the wetlands regarding the season and type of land are fundamental to consider while assessing the water scarcity project. Biodiversity loss is one of the main current problems related with the water scarcity situation. Nowadays, efforts to buy agricultural land to restore hydrological system in those areas are being made by the Agency but it is considered a hard task due the economic interest of farmers and the complexity of the environmental engineering works.
An initial model was built on the information gathered from the preliminary interviews. This preliminary model will be called a seed model in the next sections, since it is a model that is supposed to grow and become more elaborated.
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c) Group Model Building sessions Two sessions conducted during the modelling process; the number of sessions and location was agreed between the gatekeeper and the modelling team. The sessions were held at the province of Noord-‐Brabant in 's-‐Hertogenbosch. In order to select the date of the sessions a doodle survey was sent to all participants. One session was held in December and the other on the beginning of January due to participant's agenda constrains.
1st Session: December 16th, 2013, from 10:00 am-‐12:00 am
Participants: Marteen Verkerk from the Waterboard, Frank Van Lamoen from Noord Brabant Province.
Facilitating team: Merla Kubli, Carlo Cuijpers and Vanessa Armendáriz.
During this session the fundamentals of System Dynamics were explained and the seed model developed from the interviews was exposed. Participants shared some references modes on how they thought the system was expected to behave regarding key issues. The reference modes are the following:
Water supply 1990-‐2040, GMB Session 1
Afterwards, with the participant's input and knowledge a model structure aiming to represent the problematic behaviour was built on the basis of the seed model. Information sources on the main variables needed to run the model were suggested and some policies were elicited to develop and test during the next session.
Water consumption by agriculture 1990-‐2040 GMB Session 1
Ground water level 1940 – 2040, GMB Session 1
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2nd Session: 7th January, 2014, 9am-‐12pm
Participants: Frank Van Lamoen from the province of Noord-‐Brabant.
Facilitating team: Merla Kubli, Carlo Cuijpers and Vanessa Armendáriz.
The model structured improved by the facilitating team after the first session was exposed in order to get feedback from the gatekeeper. This included: data collection explanation and limitations, corrections in variables and structure details developments. The simulation results were shown and discussed. One of the main limitations was pointed out as the lack of data on the reference modes and other variables (details on the model limitations are discussed in section 8. Model limitations) affecting the reliability on the simulation results. An exercise to evaluate the policies elicited during the first session was made; the evaluation consisted of an assessment regarding impact and effort to implement each policy in particular. Possible structure incorporations on the model with its relevance were discussed, i.e. climate change effect, new permits policy. Format and basic information to include in the project report was agreed on in order to make the project outcomes understandable and manageable in the short future.
For a richer model the participation of more stakeholders would be necessary. Participation in the modelling sessions was considered very low: 2 out of 12 called people were attending the first session and 1 person at the second session. The main reason identified for this was agenda constrains of different stakeholders during the time assigned to develop this project: mid of November 2013 until January 2014. This issue can be solved with the coordination of University and governmental offices times; also, developing incentives for both sides could increase participation and commitment to the project.
2. The Model
The model consists of tree major stocks of water. These are the water on surface, including lakes and rivers, the ground water in the upper layer of the soil and the deep ground water. The major consumers are the households, the industry and agriculture affecting the availability of water. The general framework of the model is represented in the following picture.
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The actual simulation model is more complex and addresses these areas in more detail. In the following section a more detailed description of the model is provided. The variables in italic are variables considered in the model.
The stock of water on surface is increased through the amount of rain, the water entering the area as a river stream and consumption of water that is fed back into the system after some treatment. The water on surface is decreased through the evapotranspiration, the water leaving the area with the stream and the water which is extracted for agricultural or industrial use and the drainage to the ground water. The ground water in the upper soil is increased through the drainage water from the water on surface. It is decreased by: the flows of the natural extraction for agriculture, meaning the water extracted by the plants, the water that is flowing into the stream back to surface and the drainage to the deep ground water level. The deep ground water shows also the pattern of the entering flow and the leaving stream. Additionally large-‐scale extractions and agriculture extractions are considered as major determinants of the stock. The stock and flow structure is represented in the model in the following manner.
In the next steps we will look at how the major flows are governed. The evapotranspiration is governed by the amount of what that is extracted for agricultural purposes and the share of how much what is evaporated and transpirated. This factor is influenced by the efficiency of the irrigation
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system in use. In this model the flow of leaving area is defined as residual between water on surface and the flows evapotranspiration and extraction for agricultural and industrial use.
The extraction of water from surface for agriculture and industrial use is defined by a permit system. Permits are given for an area of farmland and allowing for the extraction of a certain amount of water. For extreme cases a policy exists that allows to withdrawal the policies for a short period of time to reduce the stress on the water system. Low levels of the water on surface lead to that the extraction permits are intermediately withdrawn, leading to the effect that the farmers are not allowed to use the water anymore from the respective source. This is represented in the variable short term policy permit reduction. This policy works as a balancing feedback loop in the system.
In cases when the surface water permits are withdrawn it is observed that the consumers simply move to the deep ground water and apply for these permits. This is represented by the next structure piece. The higher the gap in surface permits the more application will be there for the deep ground water. This effect is combined with the available data, meaning that the effect is simply reinforcing or lowering the projections based on the calculations of the model.
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The consumption of the deep ground water is governed by the household water consumption and the effective industrial water use. The household consumption is defined as the number of households and the effective water use per household.
The effective water use per household and the water price households defines the spent costs of a household for the water consumption. When these costs are raising the pressure to invest into water saving technology increases. This pressure can be interpreted as an incentive. The technological means are in use for a certain time and will then be replaced. This is represented in the stock with
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the inflow “increase” and the outflow “ decrease”. The higher the efficient technology water saving for households the lower the amount of water used per household. In System Dynamics language we call this a feedback loop. This feedback loop is of a balancing nature. This means that an initially high amount of water per households is corrected to lower levels by the power of the feedback loop.
The same principle is applied for the water saving incentive in the industrial usage. The major difference is here that not the consumption per industrial unit is considered, but the total industrial water use and the total costs for the industry in the region to use water.
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To conclude we come to the full model. The full model integrates all the parts that were discussed in more detail in the above sections. We can see in the centre the flow structure between the three main stocks of water -‐ water on surface, ground water upper soil and the deep ground water. On the right side is the feedback loop of the permit system (B permit withdrawal policy). On the left side we have the household and industrial water consumption. For both forms of consumption there is the feedback loop of the saving technology, named “B saving technology households” respectively “ B saving technology industry”.
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3. Policies During the first session the following policies were elicited and discussed:
1) Putting the right price tag on water; increase the price of water for specific industrial sectors.
2) Fostering water efficient technologies and practices for industrial, agriculture and households use
a. Increase efficiency on water saving technologies: in the model we have structures per sectors that show how the increase in consumption can boost the pressure to save water and the investment on water saving technologies.
3) Ending the permits of extraction of agriculture use of surface and ground water.
4) Restriction to households and industrial consumption when the level of groundwater is critically low
5) Allocating water; improving land-‐use planning (not feasible to be included in this model).
In the following model representation you can see where the considered policies attack in the system. The policies are numbered in the same order as above.
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Policies were discussed with the client in the second session by the use of an effort-‐impact matrix. This is a tool to help the client think systematically about the policy options. Additionally this tool helps narrowing the gap between policy options towards implementation, because the matrix adds effort as a factor to the analysis as a combination of costs/time required/political feasibility/etcetera. Before simulation demonstrated the effectiveness of policies, the client assessed the impact-‐effort matrix as the following:
Following the theory by Meadows (1999) parameters which influence the strength of a feedback in a model have a relatively high impact, which is backed-‐up by the simulation runs of the model.
Putting the right price on water
Household water saving incentive
Industrial water saving incentive
New regulation
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4. Simulation results
The current state of the model doesn't allow a simulation of reliable results in numeric terms, since a couple a limitations and missing data (more about that in section “Model limitations”). The simulation results presented here are to be understood as an indication of where the current development of this model is and what needs to be improved. Nevertheless, the model allows drawing certain conclusion on this basis. This will be done here. The sources of the data are documented in the appendix. Additionally you can also find the full set of equations of which the model built on.
The graph on the major stocks “ground water upper soil” and “water on surface” (see below) highlights the enormous dependence of the water on surface on the amount of rainfall. The amount of water in ground water upper soil is increasing over time, same as the water on surface. Although there is no data available for the past and future development of theses stocks we can derive that the model is currently not able to explain the problematic behaviour in that part of the model, since the stock of “water on surface” as well as “ground water upper soil” is increasing.
In the next graph the deep ground water is represented. Here we can see that the amount of water in this layer of ground water is decreasing over time (note that the scale is not to zero though). This fact highlights that the deep ground water still can be heavily threatened by exhaustive water consumption although it is not a non-‐renewable resource per definition, since there are in-‐ and outflows to the stock. In the case of the deep ground water the model represent the problematic behaviour observed in reality.
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To give an example how a more sophisticated model could examine a policy evaluation with the model we make a test with the price for industrial water use. The effect of current price of 2.5 euros per cubic meter of water on the system is compared with a hypothetical price of 5 euros per cubic meter of water. The blue line in the graph shows the effective industrial water use with a price of 2.5 euros, the red line comes from a price of 5 euros.
We can see that the price increase has a significant impact on the system. The effective industrial water use is strongly reduced and stabilizes. The strong effect of this policy comes through the fact that it alters the strength of a feedback loop. Policies that function within a feedback loop have proven to be the most effective ones in general (Meadows, 1999).
5. Access to the model
The model is built in a specific software designed for System Dynamics models called iThink. The software issuer isee systems provides a free player version that allows running the model. The isee Player can be downloaded under this link (a registration is necessary): http://www.iseesystems.com/softwares/player/iseeplayer.aspx
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6. Value and limitations of a GMB process
Treating water issues is a highly complex process that normally requires much elaborated data with high details, which increase the complexity of analysing issues in a numerical and integrated way. There are mainly two kinds of hydrological models, i) the stochastic models with a less explanatory capacity due the seldom use of mathematic and statistical concepts and ii) the process-‐based models, considered deterministic models, where the flows of water and the main physical components of the region where the hydrological cycle is taking place are analysed. These models can be discrete (single events) or continuous simulation models, as the model explained in this report. Nevertheless, most continuous hydrological models do not develop other important systems or sectors (economy, government, environment, households) interconnected with the hydrological system. Instead, they are focused on a specific application area, thus, they offer high details and strong delimitation on the choice of dimension, scale, process delimitation and discretization. The more narrow boundaries of hydrological models, which are aimed to provide a realistic simulation of the area of application within uncertain situations, increase the reliability of their results.
According to Frank Van Lamoen, in the province there are entities developing projections on the issue. Within any model building process, information and quality of data will define the reliability of the scenarios. In this case, the technique used in this project corresponds to a participatory process. In a GMB process information on how the system actually work and the data gathering process is subjected to a public scrutiny, which results, after a validation process in a higher quality of information inputs.
The level of aggregation and scale of this System Dynamics Model makes easier to see the system as a whole because it considers the most important variables or processes in its performance specially when there are different players tackling different parts of a shared complex situation and each of them has different information related to the water scarcity in the region. The sessions and the model itself represent a common space of dialogue, mapping and structure building of the current situation. This same scale of analysis makes the topic manageable and the simulation feature allows the assessment of possible policies and their impact. Nevertheless, since the primary application area is one of the most important factors to consider in the model building process of a traditional hydrological model, the integration of all areas would simply not be desired and will decreases the level of analysis of the specific issues the experts aim to get at.
Rather than a static view of net amounts of water flows and a static division of its composition, an SD model gives the opportunity to model the natural flows of the water in the region dynamically through the different land layers, channels and rivers. The interactions of this amounts of water with the most relevant “sectors” (civil, industrial, governmental activities or environmental phenomena) provoke the major impact in the hydrological situation of a region. The individual or aggregated assessment on sectors impacts on the whole system is achievable with this tool. Of course, this would require an effort of having updated in the model structure the changes in the systems or the tendencies of these changes in order to keep the predictive capacity of the model in shape.
The structure developed can be adapted to different regions if the institutional dynamics work in a similar way. Also, the structure presented can be used as a base to keep modelling the further developments named in this document.
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We can conclude that the application area and the purpose of the model should define the type of techniques and the type of hydrological model applied. While SD and GMB can provide an integral view of the system as a whole and a dynamic projection within a space of high legitimacy because of the consideration of different stakeholders perspective, the traditional hydrological models give more detail on how a specific application area of the hydrological system works and more accurate implementation insights regarding a specific area.
7. Model Limitations
The model presented corresponds to the elicited knowledge on what the important issues regarding the water scarcity situation in the Province are. The number of stakeholders in the sessions and during the interview period is determining for the information captured in a model; at a bigger number of stakeholders the model is more likely to be comprehensive and useful. In this case, the participation was low. The model structure was built that has the potential to be improved in further sessions with other participants.
The level of aggregation it possesses is the result of the need to address the most relevant issues in a manageable way. During the last session the importance of the characterization of seasonal differences in the structure was mentioned because the water needed and available heavily relies on this. In the same way, the regional difference in the province such as weather, land and crop types affect the water needed for wetlands, agriculture or households.
A development on the regional differences or seasonal stages and its consequences for the water cycle in the province can be considered as potential development on the model. The current structure can be adapted to a possible phenomenon under study or specific scenarios making.
In order to increase the reliability of the current model structure to address policy effects, the availability and quality of the following data information is essential, but currently missing.
ü Flow “leaving area” ü Initial value stocks ü References modes ü Rain ü Review of structure of industrial water use efficiency and households water use efficiency ü Drainage time ü Fraction up ü Fraction drainage Deep Ground Water ü Water usage per agricultural ü Representation of the area water use of wetlands ü System responses when deep water level in a critical low level
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8. Further Model Developments
According to stakeholder's opinions, further potential steps to be developed on the model, either singularly or in a joint further development and its relevance are the following:
Climate change effect: It is considered to be one of the most influential phenomena by the WaterCore Project. Before a managerial strategy is developed to tackle the consequences of it, the need to address the effects of the climate change on the water availability in the region is fundamental. Predicting these effects is very complex due the diversity of consequences and events it can provoked. The expected impacts on the hydrological cycle are related to the rain rates per season and the condensation of superficial waters, which will eventually reduce the availability of water in a context of increasing competition for this resource. Nevertheless, other kind of extreme events could be expected as climate change consequences such as sea level raising, soil subsidence, severity and frequency of heat waves, droughts, forest fires and storminess and flooding, that at the same time will impact the water cycle. In order to assess the climate change effects by using the SD model structure, a selection on the kind of effects would have to be done because not every single consequence could be worthy to be modelled.
New permit system: There are already some ideas in the WaterCore project being studied to update the mechanism of the permits issuing for water extraction. They required institutional challenges. In the model structure a new consensual scheme for these permits can be tested.
Policy for households with similar mechanism to recycling processes at industrial level: An exploration of a policy that produces the same effects of water saving due to re-‐utilization for certain kind of households activities was suggested.
Area adaptation: In order to have a more accurate model and make scenarios, the calibration of variables according to the local levels, for instance, specific information on water use by the local household, industrial, farming companies and natural wetlands of an specific region and their interaction mechanisms are needed.
Restoration of the hydrological system: The restoration of hydrological systems was expressed during the interviews to be a powerful mechanism to re-‐balance the hydrological systems but it is considered to be high costly, since agricultural land of current needs to be purchased. Environmental engineering efforts should be performed. According to the Forestry Agency representative this kind of policy has a high long-‐term integral impact on the whole hydrological system.
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9. Learning Points During the project the team gained experience with the facilitation of a GMB process. This resulted in practical lessons, which could be considered for upcoming projects. Note that these lessons are not meant as critique. Lessons are categorized on: ‘organization/administration/communication’, ‘facilitation’, and ‘stakeholder participation’. Organization/Administration / Communication In general, we experienced that the organization of the process can be considered as almost half of the total workload. This was underestimated, although it was mentioned by the supervisor in the beginning of the project. Because it was the first time this team facilitated a whole project in collaboration with a client such seemingly basic lessons were learned to keep in mind for later projects. These lessons include:
• From the first contact and meeting with the gatekeeper who represents a/part of a ‘’client group’’ test wether there is demand for a certain project within the larger group. This will provide an indication of the participation of stakeholders throughout the project.
• In the same meeting one should focus on the definition of project goals, -‐system boundaries, and discuss ‘deliverables’. This is an important beginning towards shaping the project and setting up a project planning. Both are important to know when addressing other members of the client group or – stakeholders and with whom it is important to plan the project as soon as possible.
• The planning of the sessions remained a problem throughout the collaboration with the
stakeholders within this project. Because the time horizon for the total project, due to the deadline for the course, was rather short and – overlapped with the busy period in December and the holidays. From this experience it is advised for future projects, when possible, to take a longer planning horizon for GMB exercises in which stakeholders from multiple organizations are involved.
• Regarding the invitation of the participants it is reasonable to have a focus on having a
representative per interest group and not necessarily per organization. Because this may result in an overrepresentation of a certain background. Additionally this keeps the total number of initial invitations lower. Making the invitation more of a personal contact to ask stakeholders for their specific expertise might help increasing the participation. This is also a solution to avoid negative group effects, such as the cancellation of further participation when certain people are not attending, which was experienced in this project.
• It has proven useful and efficient that all e-‐mails to be sent out by the gatekeeper were
prepared by the facilitation team. This allowed for a faster communication procedure.
• Regarding the communication with stakeholders we learnt that the theme of the project should be more clearly the central subject, and that the method used in the project only facilitates a dialogue on this theme. This makes the project appear less technical, and lowers the step for stakeholders to participate.
Facilitation
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This project was the first time this team worked together, and – facilitated GMB sessions in which actual stakeholders were present. This experience will therefore form a basis for many projects-‐ or other experiences to come.
• In general in this project it is experienced as valuable to know your fellow (facilitating) team members and to have a certain connection and – shared background. This eases the shaping – and planning of the project, and dampens personal-‐ and cognitive conflict.
• For during the sessions a solution was found to represent the interest groups which were not
present that seemed to work pretty well. A person of the facilitating team was added to the group of present participants as a representative of the knowledge gained during the interviews and participated in the discussion. This seemed a good practice to minimize the loss of a diversity of backgrounds (cognitive conflict) in the session. It may be developed into a formal script for others to try.
• In any project like this it is uncertain how sessions will turnout to be: which participants will
be present, which scripts work for the group, etcetera. Therefore it is important to be able to anticipate on the developments during the session. Because it was the first project with actual stakeholders for this team it felt save to remain with a certain flexibility and prepare different agendas for the sessions allowing to easily make a different try. This was done in this project for the second GMB session.
Stakeholder participation The most pressing problem in the project was the low participation of relevant stakeholders. Most interest groups were represented in the interviews, but only two of these groups, one of which was the client, were present in the sessions. Therefore only two of a plurality of views is directly represented in the model. The model can therefore not be considered a full representation of the problem of increasing water scarcity in Noord-‐Brabant. This limits the accomplishment of the goal of this project. Several of the lessons mentioned in the other categories already addressed this problem, -‐ in this category lessons specifically on this issue are mentioned.
• To come to a successful collaboration with stakeholders a strong commitment is required from the start of the project, -‐ of both sides. Which may be enforced by a set of incentives. This was lacking in this project, and felt like missing. Incentives could be: symbolic rewards, public acknowledgement, internship possibilities, etcetera.
25
10. Conclusion This projects focussed on water scarcity in Noord-‐Brabant also addressed in previous projects like the ‘Deltaplan Hoge Zandgronden’ (DHZ)(Deltaplan Hoge Zandgronden, 2009), -‐ ‘WaterCore project’(EU, 2012), and -‐Telos project ‘waardecreatie met water’(Telos, 2010). This project added to these previous projects by analysing water scarcity in Noord-‐Brabant based on an integrated SD model of natural-‐ and human systems. A System Dynamics model was developed together with major stakeholders through interviews and -‐ the use of GMB. Unfortunately not much stakeholders participated in the GMB sessions, limiting: the inclusion of different viewpoints on the problem in the model, -‐ amount of holistic insights, and – the possibility to build a joint understanding of the problem which creates commitment and – consensus among the stakeholders on solving the problem. Additionally the validation of the resulting model is problematic because a formal reference mode was not available. Nevertheless the project was partially successful in the achieving the goal of ‘’increasing dynamic understanding of the water scarcity issue in Noord-‐Brabant’’. The constructed SD model integrated natural-‐ and human systems which was new for the participants and resulted in insights in: the interrelations between water management interventions and the functioning of the natural system, and the effectiveness of intervening in feedback loops. Additional insights could be derived in follow-‐ups on this project by addressing the lessons we learned during this project, and – by continuing to develop the model with a larger group of stakeholders which special attention to: the effects of climate change, alternatives for the existing water extraction permit scheme, regional differences, future developments in water saving by households, and the effects of restoring natural water systems. For which this project and the resulting model could serve as a basis to interest stakeholders and for a conceptual model.
26
References
• Deltaplan Hoge Zandgronden, (2009). Een Deltaplan Hoge Zandgronden: Naar een klimaatbestendige watervoorziening voor hoog Nederland, retrieved on: 12-‐01-‐2014, from: http://www.deltaplanhogezandgronden.nl/
• Telos, (2010). Waardecreatie met water multi-‐input multi-‐output analyse van water in Noord-‐Brabant.
• EU, (2012). Watercore: Good practices Guide, EU Regional Development Fund. • Größler, A. (2007). System dynamics projects that failed to make an impact.System Dynamics
Review, 23(4), 437-‐452. • Vennix, J. A. M. (1996). Group model building: Facilitating team learning using system
dynamics. J. Wiley. • Videira, N., Antunes, P., Santos, R., & Lopes, R. (2010). A participatory modelling approach to
support integrated sustainability assessment processes.Systems Research and Behavioral Science, 27(4), 446-‐460.
• Van den Belt, M. (2004). Mediated modeling: a system dynamics approach to environmental consensus building. Island press.
• Kallis, G., Videira, N., Antunes, P., Pereira, A. G., Spash, C. L., Coccossis, H. & Santos, R. (2006). Participatory methods for water resources planning. Environment and Planning C, 24(2), 215.
• Meadows, D. (1999). Leverage points: Places to intervene in a system. The Sustainability Institute.
27
Appendix A: Detailed planning of the sessions
WATER&SCARCITY&IN&NOORDBRABANT&GMB&PROJECT!
Session&1&9&approx.&3hrs,&planned&170min&
December&16h,&2013&
!
1) Introductions&of&participants&9&5min&2) Project&Background&and&Explanation&of&the&problem&9&&5&min&
a. Explain!goals!of!the!session!i. Understand!how!the!problem!arises!ii. Work!together!on!a!model!that!represent!the!problem!
Second!part!of!the!session!iii. Elicit!policies!that!can!be!integrated!to!the!model!
3) Graphs&over&time&–&10&min&Ask!the!participants!to!draw!in!a!graph:!How!do!they!think!water!consumption!and!water!availability!has!developed!during!past!20!years!and!will!in!the!30!years.!
4) Explanation&of&SD&with&the&conceptual&model&9&20&min&5) What&is&missing&and&what&is&wrong&&9&10&min&
a. Write!a!list!!b. Ask!to!write!the!variables!that!are!missing!on!the!conceptual!model!c. Ask!each!participant!to!express!their!variable,!make!rounds!until!all!variables!are!
expressed.!d. List!the!variables!
Think&how&those&variables&can&be&integrated&in&the&model&
Break&9&15min&
Remember&the&goal&of&the&second&part&of&the&session&
6) Expanding&model–&25&min&a. Retake!the!ideas!of!what!is!wrong!
i. Modify!them!in!the!model!b. Retake!the!ideas!that!are!missing!from!the!model!
i. Moment!to!add!all!of!this!using!ratio!exercise!7) “Extreme&values”&exercise&–&20&min&
a. Work!in!pairs!and!think!what!would!happen!in!relevant!stock!go!to!extreme!values!b. Report!to!the!group!
Break&9&15min&
8) Data&script&–&10&min&a. Show!the!model!and!identify!the!relevance!of!this!information!to!run!the!model!
28
b. Show!a!excel!chart!with!all!variables!and!assign!who!has!that!information!c. Fill!the!information!if!the!participants!present!know!it!d. Ask!to!send!it!to!emails.!
!9) Policy&Proposals&&9&25min&&
a. Individually!ask!to!think!on!the!policies!proposals,!which!policies!are!considering!right!now!and!what!would!you!suggest!(new!policies)&
b. Express!the!policy!proposals&c. List!the!policies&d. Ask!them!if!those!fit!into!the!model!and!ask!them!which!the!most!relevant!ones!
are&e. Next!session!we!would!simulate!that&
!10) Summary&by&the&observer:&what&we&did&and&what&will&happen&next&–&5&min&&
&&Remember&to&ask&to&contact&them&for&data,&Recorder&make&a&list&of&the&information&needed&and&&
!
Material&
1. Papers!for!exercise!5!and!9.!!2. Paper!already!with!the!graphs!to!the!session.!3. Tape!4. Laptop!with!I!think!5. Projector!6. Papers!7. Pens!
!Make!sure!the!room!has!a!screen!to!project!and!a!board.!!!Work!on!week!after:!Improve!the!model!
! Integrate!the!policies!! Gather!data!! Prepare!the!policy!analysis!
Develop!the!Workbook!Send!to!participants!and!ask!for!additional!feedback!Integrate!the!feedback!in!the!workbook!and!model!Call!for!the!second!session!(can!be!by!phone!calls!or!e!mails)!!!
29
WATER&SCARCITY&IN&NOORDBRABANT&GMB&PROJECT&
Session&2&–&January&7th,&2013&
Agenda&for&Session&with&Frank&van&Lamoen&
!I.&Introduction&round&(5min)&
II.&Recall&of&the&first&session&and&Workbook&(5min)&
III.&Explanation&of&SD&and&Conceptual&Model&&(10min)&
IV.&Explanation&of&the&whole&model&(20min)&
V.&Run&the&model&and&elaborate&on&the&behavior&of&most&important&variables&and&stocks&&
! 1)!Deep!ground!water!and!main!flows!! 2)!Surface!water!and!main!flows!! 3)!Upper!soil!water!and!main!flows!! 4)!Permits!mechanism!! 5)!Efficiency!technology!VI.&Validation&and&elaboration&of&the&model:&What&is&missing&and&what&is&wrong?&
@ Use!the!printed!model!to!identify!missing!things!!and!make!a!nominal!group!technique!!@ Bring!to!the!discussion!when!it’s!proper!the!variables!where!we!have!identified.!Mention!
those!that!were!absent!of!the!discussion.!!Variables&to&validate:&
Flow!“leaving!area”!Initial!value!stocks!References!modes!Rain!Graphical!functions!OR!logical!structure!of:!Ind.!water!use!efficiency!and!Households!water!use!efficiency!Drainage!time!Fraction!up!Fraction!drainage!DGW!Water!usage!per!agricultural!!Representation!of!the!area!water!use!of!wetlands!!What!happens!when!the!deep!water!level!reaches!a!critical!low!level!!Further&steps&to&keep&developing&the&model&
Global!warming!!Forestry!Area!!!VI.&Policies&elaboration&and&analysis&
! Right!pricing!on!water;!increase!the!price!of!water!for!specific!industrial!sectors.!!! Fostering!water!efficient!technologies!and!practices!for!industrial,!agriculture!and!households!use!! Increase!efficiency!on!water!saving!technologies:!in!the!model!we!have!structures!per!sectors!that!show!how!the!increase!in!
consumption!can!boost!the!pressure!to!save!water!and!the!investment!on!water!saving!technologies.!! Ending!the!permits!of!extraction!of!agriculture!use!of!surface,!upper!soil!and!ground!water.!! Restriction!to!households!and!industrial!consumption!when!the!level!of!groundwater!is!critically!low!! Allocating!water;!improving!land@use!planning!(not!feasible!to!develop!!in!this!model).!
1)!Ask!for!new!policies!2)!Matrix!of!effort!and!impact!!3)!Plot!the!most!relevant!policies!on!the!model!
Example:!amount!of!the!water!per!permit,!check!how!does!it!can!make!an!impact!*!If!the!policy!is!not!runnable!in!that!model!in!that!specific!moment!it!will!be!just!drawn!in!the!model.!
!VI.&Evaluation& &
30
WATER&SCARCITY&IN&NOORDBRABANT&GMB&PROJECT&
Session&2&–&January&7th,&2013&
Agenda&for&Session&with&Frank&van&Lamoen&
&
I.&Recall&of&the&first&session&and&Workbook&(5min)&
II.&Explain&changes&on&the&model&
III.&Run&the&model&and&elaborate&on&the&behavior&of&most&important&variables&and&stocks&&! 1)!Deep!ground!water!and!main!flows!! 2)!Surface!water!and!main!flows!! 3)!Upper!soil!water!and!main!flows!! 4)!Permits!mechanism!! 5)!Efficiency!technology!
!VI.&Policies&elaboration&and&analysis&
! Right!pricing!on!water;!increase!the!price!of!water!for!specific!industrial!sectors.!!! Fostering!water!efficient!technologies!and!practices!for!industrial,!agriculture!and!households!use!
a. Increase!efficiency!on!water!saving!technologies:!in!the!model!we!have!structures!per!sectors!that!show!how!the!
increase!in!consumption!can!boost!the!pressure!to!save!water!and!the!investment!on!water!saving!technologies.!
! Ending!the!permits!of!extraction!of!agriculture!use!of!surface,!upper!soil!and!ground!water.!
! Restriction!to!households!and!industrial!consumption!when!the!level!of!groundwater!is!critically!low!
! Allocating!water;!improving!land@use!planning!(not!feasible!to!develop!!in!this!model).!
1) Ask!for!new!policies!2) Matrix!of!effort!and!impact!!3) Plot!the!most!relevant!policies!on!the!model!
Example:!amount!of!the!water!per!permit,!check!how!does!it!can!make!an!impact!*!If!the!policy!is!not!runnable!in!that!model!in!that!specific!moment!it!will!be!just!drawn!in!the!model.!
!VI.&Validation&and&elaboration&of&the&model:&what&is&missing&and&what&is&wrong?&
@ Ask!Frank!his!points!of!concerns!on!the!model,!other!issues!to!keep!developing.!@ Express!the!variables!where!we!have!identified!that!needs!validation!
&
Variables&to&validate:&
Flow!“leaving!area”!Initial!value!stocks!References!modes!Rain!Graphical!functions!OR!logical!structure!of:!Ind.!water!use!efficiency!and!Households!water!use!efficiency!Drainage!time!Fraction!up!Fraction!drainage!DGW!Water!usage!per!agricultural!!Representation!of!the!area!water!use!of!wetlands!!What!happens!when!the!deep!water!level!reaches!a!critical!low!level!!
Further&steps&to&keep&developing&the&model&
Global!warming!process!and!consequences!Forestry!Area!!!
VI.&Evaluation&
31
Appendix B: Data sources Parameter/stock: Data required from: Households Untill 2010: (CBS, 2013)
From 2010: Projection provincie Noord-‐Brabant (2009)
km2 agricultural land Untill 2010: (CBS, 2013) From 2010: Linear projection
Price water households, euro/m3 Untill 2012: (CBS, 2013) From 2012: Linear projection
Water consumption total households 1000 m3 Untill 2012: (Brabantwater, 2013) From 2012: (linear projection)
Non retuning consumption, % of total household consumption
Untill 2007: (Brabantwater, 2013) From 2007: (remains on 2012 level)
Maximum water usage permit groundwater agriculture, m3 water per year
(Waterschap Aa & Maas, 2013)(assumed to stay in on the same level)
#permits groundwater Untill 2012: (CBS, 2013) From 2012: (linear projection)
#permits surface water Untill 2012: (CBS, 2013) From 2012: (linear projection)
Industrial water usage, m3 Untill 2012: (INM, 2013) From 2012: (linear projection)
Industrial water lozingen, 1000m3 data on 2006: (VEMW, 2013) Price industrial water usage, euro per m3 Untill 2012: (INM, 2013)
From 2012: (linear projection)
Price industrial water lozing. Euro per m3 Untill 2012: (INM, 2013) From 2012: (linear projection)
Rainfall Noord-‐brabant, m3 (KNMI, 2013, only to 2011, after which -‐ linear projection)
Variable/stock Value Source Surface water inflow 1,2 m3/s
37,843 millions of m3/year
Calculations for 2007* Source: Tabel 2 Debieten beken, kanalen, gemalen en grote rivieren Waardecreatie met water multi-‐input multi-‐output analyse van water in Noord-‐Brabant, 2010
Surface water outflow 27,9 m3/s 879,85 millions of m3/year
Calculations for 2007* Source: Tabel 2 Debieten beken, kanalen, gemalen en grote rivieren Waardecreatie met water multi-‐input multi-‐output analyse van water in Noord-‐Brabant, 2010
Surface water level 1990
-‐ Not available
Deep ground water inflow
132 millions m3/ year
Tabel 9 Waterbalans Noord-‐Brabant in miljoen m3 per jaar, Waardecreatie met water multi-‐input multi-‐output analyse van water in Noord-‐Brabant, 2010
Deep ground water outflow
298 millions m3/ year
Tabel 9 Waterbalans Noord-‐Brabant in miljoen m3 per jaar, Waardecreatie met water
32
multi-‐input multi-‐output analyse van water in Noord-‐Brabant, 2010
Deep ground water level 1990
-‐ Not available
Ground water upper soil level 1990
-‐ Not available
Evaporation 3100 millions m3/ year
Tabel 9 Waterbalans Noord-‐Brabant in miljoen m3 per jaar, Waardecreatie met water multi-‐input multi-‐output analyse van water in Noord-‐Brabant, 2010
Evapotranspiration total water (considers groundwater)
2.620 mln. m3
Tabel 1 Hoeveelheden atmosferisch water in 2007, Waardecreatie met water multi-‐input multi-‐output analyse van water in Noord-‐Brabant, 2010
Evaportranspiration 0.3 % for liter of water 0.1% lost and 0.6% goes back to the uppersoil
Information elicited during the first Group Model Building session on December 16th, 2013.
Drainage 46,3 m3/s 1,460,117 millions m3/ year
Calculations for 2007* Source: Tabel 2 Debieten beken, kanalen, gemalen en grote rivieren Waardecreatie met water multi-‐input multi-‐output analyse van water in Noord-‐Brabant, 2010
Water flowing back to the stream
19,7 m3/s 621,25 millions of m3/s
Calculations for 2007* Source: Tabel 2 Debieten beken, kanalen, gemalen en grote rivieren Waardecreatie met water multi-‐input multi-‐output analyse van water in Noord-‐Brabant, 2010
33
Appendix C: Equations of the model
Agriculture_Land(t) = Agriculture_Land(t - dt) + (change_in_agriculture_land) * dtINIT Agriculture_Land = 3262INFLOWS:
change_in_agriculture_land = GRAPH(3262)(1990, 0.00), (1991, 0.00), (1992, 0.00), (1993, -6.00), (1994, 0.00), (1995, 0.00), (1996, -30.0), (1997, 0.00), (1998, 0.00), (1999, 0.00), (2000, -56.0), (2001, 0.00), (2002, 0.00), (2003, -28.0), (2004, 0.00), (2005, 0.00), (2006, -19.0), (2007, 0.00), (2008, -18.0), (2009, 0.00), (2010, -19.0), (2011, -12.2), (2012, -10.7), (2013, -10.7), (2014, -10.7), (2015, -10.7), (2016, -10.7), (2017, -10.7), (2018, -10.7), (2019, -10.7), (2020, -10.7), (2021, -10.7), (2022, -10.7), (2023, -10.7), (2024, -10.7), (2025, -10.7), (2026, -10.7), (2027, -10.7), (2028, -10.7), (2029, -10.7), (2030, -10.7), (2031, -10.7), (2032, -10.7), (2033, -10.7), (2034, -10.7), (2035, -10.7), (2036, -10.7), (2037, -10.7), (2038, -10.7), (2039, -10.7), (2040, -10.7)
Deep_ground__water(t) = Deep_ground__water(t - dt) + (entering_through_flow + Drainage_to_deep_ground_water - Large_scale_extraction - Consumption - Agriculture_extraction_DGW - DGW__leaving) * dtINIT Deep_ground__water = 20000000000000
INFLOWS:entering_through_flow = 132000000Drainage_to_deep_ground_water = Ground_water_upper_soil*fraction_drainage_DGW
OUTFLOWS:Large_scale_extraction = 5000000Consumption = houshold_water_consumption+effective_industrial_water_useAgriculture_extraction_DGW = permits_DGW*Amount_of_water_per_permit_per_yearDGW__leaving = 298000000
Efiicient_technology_water_saving_households(t) = Efiicient_technology_water_saving_households(t - dt) + (increse_et - decrease) * dtINIT Efiicient_technology_water_saving_households = 1
INFLOWS:increse_et = (Efiicient_technology_water_saving_households*pressure_to_invest_into_saving_technnology)/Investment_AT
OUTFLOWS:decrease = Efiicient_technology_water_saving_households/average_time_of_use
Efiicient_technology_water_saving_industry(t) = Efiicient_technology_water_saving_industry(t - dt) + (increse_1 - decrease_1) * dtINIT Efiicient_technology_water_saving_industry = 1
INFLOWS:increse_1 = (Efiicient_technology_water_saving_industry*pressure_to_invest_into_saving_technnology_1)/Investment_AT_industry
OUTFLOWS:decrease_1 = Efiicient_technology_water_saving_industry/average_time_of_use_1
Ground_water_upper_soil(t) = Ground_water_upper_soil(t - dt) + (Drainage - water_flowing__into_stream - natural_extraction_for_agriculture - Drainage_to_deep_ground_water) * dtINIT Ground_water_upper_soil = 600000
34
INFLOWS:Drainage = Water_on__surface/drainage_time
OUTFLOWS:water_flowing__into_stream = Ground_water_upper_soil*fraction_upnatural_extraction_for_agriculture = Agriculture_Land*natural_groundwater_use_agricultureDrainage_to_deep_ground_water = Ground_water_upper_soil*fraction_drainage_DGW
Households(t) = Households(t - dt) + (Change_in_households) * dtINIT Households = 950123INFLOWS:
Change_in_households = Change_in_households_dataWater_on__surface(t) = Water_on__surface(t - dt) + (water_entring_area + water_flowing__into_stream + Consumption + Rain - leaving_area - Extraction_for_agricultural_or_industrial_use - Evaportranspiration - Drainage) * dtINIT Water_on__surface = 500000
INFLOWS:water_entring_area = 37843000water_flowing__into_stream = Ground_water_upper_soil*fraction_upConsumption = houshold_water_consumption+effective_industrial_water_useRain = GRAPH(TIME)(1990, 3.4e+09), (1991, 2.7e+09), (1992, 4.7e+09), (1993, 4.3e+09), (1994, 3.9e+09), (1995, 3.7e+09), (1996, 2.9e+09), (1997, 3.8e+09), (1998, 6.3e+09), (1999, 4.6e+09), (2000, 4.6e+09), (2001, 4.7e+09), (2002, 5.3e+09), (2003, 4.7e+09), (2004, 3.1e+09), (2005, 4.4e+09), (2006, 4.4e+09), (2007, 4.1e+09), (2008, 4.8e+09), (2009, 4.5e+09), (2010, 3.9e+09), (2011, 4e+09), (2012, 4.6e+09), (2013, 4.4e+09), (2014, 4.4e+09), (2015, 4.5e+09), (2016, 4.5e+09), (2017, 4.5e+09), (2018, 4.5e+09), (2019, 4.6e+09), (2020, 4.6e+09), (2021, 4.6e+09), (2022, 4.6e+09), (2023, 4.7e+09), (2024, 4.7e+09), (2025, 4.7e+09), (2026, 4.8e+09), (2027, 4.8e+09), (2028, 4.8e+09), (2029, 4.8e+09), (2030, 4.9e+09), (2031, 4.9e+09), (2032, 4.9e+09), (2033, 4.9e+09), (2034, 5e+09), (2035, 5e+09), (2036, 5e+09), (2037, 5e+09), (2038, 5.1e+09), (2039, 5.1e+09), (2040, 5.1e+09)
OUTFLOWS:leaving_area = Water_on__surface-Extraction_for_agricultural_or_industrial_use-EvaportranspirationExtraction_for_agricultural_or_industrial_use = (permits_surface_water*Amount_of_water_per_permit)*(1-evaportation_share)Evaportranspiration = Extraction_for_agricultural_or_industrial_use*((1-evaportation_share)/evaportation_share)Drainage = Water_on__surface/drainage_time
additional_applications_for_DGW_permits = gap_surface_permits
35
agriculture_land_data = GRAPH(TIME)(1990, 3262), (1991, 3262), (1992, 3262), (1993, 3256), (1994, 3256), (1995, 3256), (1996, 3226), (1997, 3226), (1998, 3226), (1999, 3226), (2000, 3170), (2001, 3170), (2002, 3170), (2003, 3142), (2004, 3142), (2005, 3142), (2006, 3123), (2007, 3123), (2008, 3105), (2009, 3105), (2010, 3086), (2011, 3074), (2012, 3063), (2013, 3053), (2014, 3042), (2015, 3031), (2016, 3021), (2017, 3010), (2018, 2999), (2019, 2989), (2020, 2978), (2021, 2967), (2022, 2957), (2023, 2946), (2024, 2935), (2025, 2925), (2026, 2914), (2027, 2903), (2028, 2893), (2029, 2882), (2030, 2871), (2031, 2861), (2032, 2850), (2033, 2839), (2034, 2829), (2035, 2818), (2036, 2807), (2037, 2797), (2038, 2786), (2039, 2775), (2040, 2765)
Amount_of_water_per_permit = 600000Amount_of_water_per_permit_per_year = 600000Amount_of_water__per_household_data = GRAPH(TIME)(1990, 181), (1991, 180), (1992, 180), (1993, 179), (1994, 179), (1995, 178), (1996, 178), (1997, 177), (1998, 177), (1999, 176), (2000, 175), (2001, 173), (2002, 171), (2003, 169), (2004, 168), (2005, 166), (2006, 164), (2007, 163), (2008, 161), (2009, 159), (2010, 157), (2011, 156), (2012, 153), (2013, 152), (2014, 150), (2015, 148), (2016, 146), (2017, 145), (2018, 143), (2019, 142), (2020, 141), (2021, 139), (2022, 138), (2023, 137), (2024, 136), (2025, 134), (2026, 133), (2027, 132), (2028, 132), (2029, 131), (2030, 130), (2031, 128), (2032, 127), (2033, 126), (2034, 125), (2035, 124), (2036, 123), (2037, 122), (2038, 121), (2039, 120), (2040, 119)
Area_per_permit = 1.2average_time_of_use = 5average_time_of_use_1 = 5Change_in_households_data = GRAPH(TIME)(1990, 1000), (1991, 1335), (1992, 2033), (1993, 1611), (1994, 854), (1995, 393), (1996, 907), (1997, 854), (1998, 1002), (1999, 3387), (2000, 1661), (2001, 12118), (2002, 9419), (2003, 7644), (2004, 8052), (2005, 7628), (2006, 10097), (2007, 8733), (2008, 8972), (2009, 10773), (2010, 10649), (2011, 8755), (2012, 9450), (2013, 9430), (2014, 9365), (2015, 8875), (2016, 8430), (2017, 8270), (2018, 7810), (2019, 7460), (2020, 7145), (2021, 6950), (2022, 6620), (2023, 6220), (2024, 5860), (2025, 5230), (2026, 4555), (2027, 4290), (2028, 3800), (2029, 2990), (2030, 2750), (2031, 10653), (2032, 5378), (2033, 5378), (2034, 5378), (2035, 5378), (2036, 5378), (2037, 5378), (2038, 5378), (2039, 5378), (2040, 5378)
costs_industrial_water_use = price_for_industrial_water_use*effective_industrial_water_usecosts_water_use_per_household = effective_water_use_per_household*water_price_housholds_datadrainage_time = 200effective_industrial_water_use = indutrial_water_use_data*effect_of_saving_technology_on_water_use_1effective_water_use_per_household = Amount_of_water__per_household_data*effect_of_saving_technology_on_water_useeffect_of_saving_technology_on_water_use = GRAPH(Efiicient_technology_water_saving_households)(0.00, 1.61), (0.5, 1.33), (1.00, 0.923), (1.50, 0.711), (2.00, 0.513), (2.50, 0.388), (3.00, 0.315), (3.50, 0.249), (4.00, 0.176), (4.50, 0.125), (5.00, 0.0806)
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effect_of_saving_technology_on_water_use_1 = GRAPH(Efiicient_technology_water_saving_industry)(0.00, 0.842), (0.5, 0.593), (1.00, 0.447), (1.50, 0.344), (2.00, 0.278), (2.50, 0.234), (3.00, 0.198), (3.50, 0.168), (4.00, 0.139), (4.50, 0.125), (5.00, 0.139)
efficiency_of_irrigation_system = 1evaportation_share = 0.6*efficiency_of_irrigation_systemfraction_drainage_DGW = 0.05fraction_up = 0.02gap_surface_permits = permits_surface_water-(Agriculture_Land/Area_per_permit)houshold_water_consumption = Households*effective_water_use_per_householdindutrial_water_use_data = GRAPH(TIME)(1990, 3.6e+08), (1991, 3.7e+08), (1992, 3.8e+08), (1993, 3.8e+08), (1994, 3.9e+08), (1995, 4e+08), (1996, 4e+08), (1997, 4.1e+08), (1998, 4.1e+08), (1999, 4.2e+08), (2000, 4.3e+08), (2001, 4.3e+08), (2002, 4.4e+08), (2003, 4.5e+08), (2004, 4.5e+08), (2005, 4.6e+08), (2006, 4.6e+08), (2007, 4.7e+08), (2008, 4.8e+08), (2009, 4.7e+08), (2010, 5e+08), (2011, 5.1e+08), (2012, 4.9e+08), (2013, 5.1e+08), (2014, 5.1e+08), (2015, 5.2e+08), (2016, 5.3e+08), (2017, 5.3e+08), (2018, 5.4e+08), (2019, 5.5e+08), (2020, 5.5e+08), (2021, 5.6e+08), (2022, 5.6e+08), (2023, 5.7e+08), (2024, 5.8e+08), (2025, 5.8e+08), (2026, 5.9e+08), (2027, 6e+08), (2028, 6e+08), (2029, 6.1e+08), (2030, 6.1e+08), (2031, 6.2e+08), (2032, 6.3e+08), (2033, 6.3e+08), (2034, 6.4e+08), (2035, 6.5e+08), (2036, 6.5e+08), (2037, 6.6e+08), (2038, 6.6e+08), (2039, 6.7e+08), (2040, 6.8e+08)
Investment_AT = 3Investment_AT_industry = 2natural_groundwater_use_agriculture = 3permits_DGW = permits_DGW_data+additional_applications_for_DGW_permitspermits_DGW_data = GRAPH(TIME)(1990, 14052), (1991, 13781), (1992, 13510), (1993, 13239), (1994, 12969), (1995, 12698), (1996, 12427), (1997, 12156), (1998, 11885), (1999, 11614), (2000, 11752), (2001, 11186), (2002, 10796), (2003, 10288), (2004, 10059), (2005, 9870), (2006, 9642), (2007, 9287), (2008, 9078), (2009, 8830), (2010, 8773), (2011, 8501), (2012, 8274), (2013, 8105), (2014, 7598), (2015, 7330), (2016, 7062), (2017, 6794), (2018, 6526), (2019, 6258), (2020, 5990), (2021, 5722), (2022, 5454), (2023, 5186), (2024, 4918), (2025, 4650), (2026, 4382), (2027, 4114), (2028, 3846), (2029, 3578), (2030, 3310), (2031, 3042), (2032, 2774), (2033, 2505), (2034, 2237), (2035, 1969), (2036, 1701), (2037, 1433), (2038, 1165), (2039, 897), (2040, 629)
permits_surface_water = (Agriculture_Land/Area_per_permit)*short_term_policy_permit_reductionpermits_surface_water_data = GRAPH(TIME)(1990, 3513), (1991, 3445), (1992, 3378), (1993, 3310), (1994, 3242), (1995, 3174), (1996, 3107), (1997, 3039), (1998, 2971), (1999, 2904), (2000, 2938), (2001, 2797), (2002, 2699), (2003, 2572), (2004, 2515), (2005, 2467), (2006, 2411), (2007, 2322), (2008, 2270), (2009, 2207), (2010, 2193), (2011, 2125), (2012, 2068), (2013, 2026), (2014, 1900), (2015, 1833), (2016, 1766), (2017, 1699), (2018, 1632), (2019, 1565), (2020, 1498), (2021, 1430), (2022, 1363), (2023, 1296), (2024, 1229), (2025, 1162), (2026, 1095), (2027, 1028), (2028, 961), (2029, 894), (2030, 827), (2031, 760), (2032, 693), (2033, 626), (2034, 559), (2035, 492), (2036, 425), (2037, 358), (2038, 291), (2039, 224), (2040, 157)
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pressure_to_invest_into_saving_technnology = GRAPH(costs_water_use_per_household)(0.00, 0.271), (35.0, 0.344), (70.0, 0.418), (105, 0.491), (140, 0.608), (175, 0.696), (210, 0.828), (245, 0.974), (280, 1.27), (315, 1.60), (350, 1.93)
pressure_to_invest_into_saving_technnology_1 = GRAPH(costs_industrial_water_use)(0.00, 0.00), (2.2e+08, 0.00), (4.4e+08, 0.00733), (6.6e+08, 0.0733), (8.8e+08, 0.22), (1.1e+09, 0.337), (1.3e+09, 0.513), (1.5e+09, 0.725), (1.8e+09, 0.996), (2e+09, 1.27), (2.2e+09, 1.80)
price_for_industrial_water_use = 2.5rule_of_thumb_evapotranspiration = 0.2share_of_non_returning_consumption = 0.027short_term_policy_permit_reduction = GRAPH(Water_on__surface)(0.00, 0.00), (1.6e+08, 0.00351), (3.2e+08, 0.00702), (4.8e+08, 0.0175), (6.3e+08, 0.0281), (7.9e+08, 0.0456), (9.5e+08, 0.0702), (1.1e+09, 0.103), (1.3e+09, 0.127), (1.4e+09, 0.134), (1.6e+09, 0.186), (1.7e+09, 0.211), (1.9e+09, 0.235), (2.1e+09, 0.254), (2.2e+09, 0.274), (2.4e+09, 0.312), (2.5e+09, 0.34), (2.7e+09, 0.365), (2.9e+09, 0.404), (3e+09, 0.428), (3.2e+09, 0.456), (3.3e+09, 0.488), (3.5e+09, 0.537), (3.6e+09, 0.565), (3.8e+09, 0.596), (4e+09, 0.642), (4.1e+09, 0.688), (4.3e+09, 0.761), (4.4e+09, 0.818), (4.6e+09, 1.00)
water_price_housholds_data = GRAPH(TIME)(1990, 1.43), (1991, 1.34), (1992, 1.68), (1993, 1.73), (1994, 1.54), (1995, 1.38), (1996, 1.36), (1997, 1.34), (1998, 1.36), (1999, 1.42), (2000, 1.59), (2001, 1.63), (2002, 1.59), (2003, 1.60), (2004, 1.61), (2005, 1.61), (2006, 1.63), (2007, 1.62), (2008, 1.60), (2009, 1.61), (2010, 1.63), (2011, 1.65), (2012, 1.53), (2013, 1.68), (2014, 1.69), (2015, 1.70), (2016, 1.72), (2017, 1.73), (2018, 1.75), (2019, 1.76), (2020, 1.77), (2021, 1.79), (2022, 1.80), (2023, 1.81), (2024, 1.83), (2025, 1.84), (2026, 1.85), (2027, 1.87), (2028, 1.88), (2029, 1.89), (2030, 1.91), (2031, 1.92), (2032, 1.93), (2033, 1.95), (2034, 1.96), (2035, 1.97), (2036, 1.99), (2037, 2.00), (2038, 2.01), (2039, 2.03), (2040, 2.04)
SD explanation:
Ground_water__in_upper_soil_1(t) = Ground_water__in_upper_soil_1(t - dt) + (Drainage_1) * dtINIT Ground_water__in_upper_soil_1 = 200INFLOWS:
Drainage_1 = Water_on__surface_2*drainage_fractionWater_on__surface_1(t) = Water_on__surface_1(t - dt) + (Rain_1 - Evaporation_1) * dtINIT Water_on__surface_1 = 100INFLOWS:
Rain_1 = 10OUTFLOWS:
Evaporation_1 = Water_on__surface_1*evaportation_fractionWater_on__surface_2(t) = Water_on__surface_2(t - dt) + (Rain_2 - Evaporation_2 - Drainage_1) * dtINIT Water_on__surface_2 = 100
INFLOWS:
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Rain_2 = 10OUTFLOWS:
Evaporation_2 = Water_on__surface_2*evaportation_fraction_1Drainage_1 = Water_on__surface_2*drainage_fraction
Water_on__surface_3(t) = Water_on__surface_3(t - dt) + (Rain_3) * dtINIT Water_on__surface_3 = 100INFLOWS:
Rain_3 = 10drainage_fraction = 0.05evaportation_fraction = 0.1evaportation_fraction_1 = 0.1
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Appendix D: Project Timeline
30/10/2013 First meeting with Frank van Lamoen, Gatekeeper/Client
12/11/2013 Finished project proposal for Client & Radboud University
26/11/2013 Interview at ZLTO
02/12/2013 Interview at Waterschap Aa & Maas
07/01/2014 Second GMB Session at the province of Noord-‐Brabant
09/12/2013 First GMB Session at the province of Noord-‐Brabant
07/02/2014 Communicated project results
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