To meet the objectives of this study we depended on several tools such as a literature review, power plant survey, stakeholder consultation, three different types of assessment models dealing with issues like water, energy and climate change and their correlations. We used an energy systems model, climate forecasting model (circulation model) and hydrological model in a predetermined sequence to obtain an integrated assessment output.
Nonetheless, in this study the three models are not endogenously integrated but manually linked to each other. For some of the analyses we used different methodologies for each of the case study countries (India and Thailand), particularly for water availability. We used the hydrological model together with the climate circulation model to estimate future water availability in the major river basin of Thailand. On the other hand, we relied on available literature for analysing the state of water resources in India.
3.1 Description of the MESSAGE model and water demand assessment for the energy sector
Besides integrating different models, a major methodological advancement has been made in this study by integrating the water demand for energy generation assessment module with the energy systems model. So far there is no global energy systems model available which can endogenously determine the water demand for the entire energy system. In this study this was the first methodological challenge which we overcame by developing a water module for the MESSAGE Model (Model for Energy Supply Strategy Alternatives and their General Environmental Impact) developed by Messner and Strubegger in 1995. MESSAGE is a multi-region energy system model capable of estimating the cheapest supply option for energy in the long-term under different constraints including climate, resources and costs. In the process of estimating the water demand exclusively for energy supply in the system, we used a newly developed water module, synchronised with the rest of the model. This module
7 endogenously determines the total water demand for total energy that needs to be supplied to the system under optimal conditions. For each energy technology that needs water, a unique water use coefficient is assigned in the model which internally interacts with the corresponding technological output in terms of energy units and derives the total water demand for that particular technology in the system. Finally, each technology based water demand gets aggregated over a period of time (here we derived water demand on an annual basis). For assessing water demand we used a water use coefficient for each eligible technology and data was collected from surveys of power plants in the country. Figure 3 below shows the schematic diagram of the MESSAGE-Water model that is the basis of our water-energy Nexus assessment.
Figure 3: Schematic diagram of MESSAGE Model with water module
3.2 Selection of the Global Circulation Method (GCM) and the downscaling of GCM data
In the context of estimating long-term water availability in the region we used two different models, namely the global circulation model and hydrological model respectively. The main
Input data GDP、POP、sectoralactivity drivers (base year &
8 purpose of adopting two models was to estimate the impacts of climate variation on long term surface water availability which is the major source for energy production. Based on the regional performance and acceptance of Global Circulation Models (GCMs), climate change projections were obtained from ECHAM4. ECHAM4 was used by several regional level and river basin level studies in Southeast Asia (Chinvanno, 2009; Sharma et al., 2007; Khattak et al., 2011). The most popular two SRES scenarios A2 and B2 were chosen for this study.
Figure 4 below shows the schematic diagram of the flow of the modelling analysis for the water demand assessment.
Figure 4: Flow chart for the water availability assessment exercise
3.3 Activity sequence
There were four major steps taken to complete the entire quantitative assessment part of this study.
In the first step, we compiled a list of all the energy technologies that use water as one input for process activities. We mainly identified around 70 different energy technologies that are in use in the energy systems in this region. This covers the technologies used for energy extraction, refining and use. Power generating technologies are given priority here as they are the major water consumers in the South and South East Asia region. Our next task was to estimate the water use coefficients for each selected energy technology. Here we only considered how much water is withdrawn from the source for energy extraction, refining and conversion (electricity generation). The major problem was the availability of region specific data. The only source of secondary data was available from the USA Department of Energy, which was based on the US power plant and energy sector. To overcome this problem, we conducted power plant surveys in both India and Thailand and collected water use data which was finally converted into water coefficients that could be used in the model.
9 In the second step, we developed the water module for the MESSAGE global model and ran a reference scenario for energy systems to estimate the base water demand.
The third step was to estimate the long term water availability for the energy sector. There were hardly any projections available from a reliable source on the energy sector’s future water demands. The major classifications of water demand categories are agricultural, residential and industrial. In most of the cases the energy sector is aggregated under either the industrial category or agricultural demand category. In this study we first conducted a literature survey to assess the water demand in different sectors and then performed certain statistical analyses using our model to determine an estimate of the water demand in the energy sector.. As we were also observing the impacts of climate change on water availability, it was assumed that climate change will also impact on the water that is available for energy generation in the future. Therefore, we conducted a hydrological simulation of net utilisable water in the study region under no climate impact, IPCC A2 and IPCC B2 scenarios. However, we could only conduct this assessment for Thailand at this time due to a lack of time. For India we used a purely statistical method to project the energy sectors’
water availability until 2050.
Figure 5: Steps in the process of analysis Step-I
•Identification of energy technologies that use water for activities
•Estimating the water use coefficients for all selected technologies ( MCM/GJ or MCM/Gwh)
Step-II •Developing the water module of the MESSAGE Model
•Running a scenario to estimate the total water demand for the energy sector.
Step-III
•Estimating long term water availability for the energy sector using proportional sharing of water among different sectors and econometric analysis
•Estimating the impact on water availability due to climate change using RGCM and Regional Hydrological Model.
Step-IV
•Identifying the water constraint mitigating technologies for the energy sector.
•Running the water constrained scenario
•Analysis
10 In the last step of this assessment, we used these water availability constraints to estimate the impacts on the long-term energy scenario in the study region in terms of technology variation, investment patterns and environmental issues. We also investigated the supply- demand management options to mitigate the water shortage problem in the region. Figure 5 describes the steps of analysis in sequence.
In this study, we conducted the above mentioned analysis separately for two countries: India and Thailand. Due to certain methodological constraints we could not link the regions under the same model. However, this juxtaposed assessment brings out some common messages which are, indeed, relevant to policies for the entire region. The following diagram (Figure 6) shows how this integrated assessment model was developed and how each part is linked.
Figure 6: Links between the different models and tools used in this study