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Water in fuel sedimenter indicator forex

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The synthetic case is simplistic but is based on interrelations that are observed in real-world applications. The synthetic case is summarized in Figure 3. The population has water, energy, and food demands. In the base scenario, water demand is satisfied by upstream water withdrawals. Energy demand here only electricity is supplied by a hydropower turbine on the river and a thermal power plant.

Energy demand is seasonal and is higher during the last 6 months of the year. Food demand here a single crop is supplied by an irrigated field downstream of the hydropower dam. Ecosystems in the delta have specific water requirements. We consider four years of hydrological conditions, alternating between wetter and dryer years, while other parameters remain the same.

The flow in the river is seasonal, while it is larger than the water supply and irrigation demand, the seasonal trend and inter-annual variation lead to scarcity during the dry season of dry years. Energy curtailments occur during dry years as hydropower production is low. Similarly crop production and prices fluctuate when yields are low as irrigation demand is curtailed because of water shortages. Figure 3. Spatial and temporal representation of the synthetic case.

CF stands for capacity factor of solar and wind power share of capacity available in practice. Irrigation consumption, storage change, hydropower and thermal production are not parameters but outcomes of the modeling framework. A Spatial set up, B temporal dimension—water, C temporal dimension—energy. Potential investments are represented in dark red in Figure 3A. Desalinization is considered as an alternative to surface water supply, it is however energy intensive here we consider neither the source of the salt water nor environmental constraints linked to the effluents.

New power plants are considered: solar panels and wind turbines, which have different seasonal variation: solar has a lower availability when irrigation is high, while wind has a lower availability during the low flow season Figure 3C. A bioenergy facility is considered, which consumes crops not residues and produces electricity. Finally, three investments are considered to improve the agricultural sector: 1 developing irrigation downstream of the dam, 2 developing rainfed agriculture with improved moisture management, and 3 improving the irrigation efficiency reducing water losses of existing irrigated agriculture.

All investments can be combined, and none are mutually exclusive. Investments in hydropower, reservoirs, bioenergy, desalinization are assumed to be binary all or nothing , while investments in irrigation, rainfed, irrigation efficiency, solar or wind power can be scaled to any fraction of the considered project costs are assumed to be proportional to the project size. Here we do not consider budget constraints, discount rate, nor the timing of investments, but these features are available in the model.

We consider 6 uncertain exogenous parameters: environmental flow requirements, climate change runoff and precipitation , yields, crop demand, carbon tax, and electricity demand. We consider four frameworks reflecting different perspectives about the planning problem: the Nexus framework is the integrated planning approach considering all sectors and potential investments jointly; while the Water, Energy, and Food frameworks silo approaches only consider their respective sector and investments.

The silo frameworks are defined as follows. When evaluating scenarios in the silo frameworks, some uncertainties of exogenous model parameters do not directly affect the frameworks e. To make the example more realistic, alternative assumptions are taken.

In the silo energy framework: 1 uncertainty of runoff is replaced by uncertainty of hydropower production, 2 uncertainty of crop demand is replaced by uncertainty of crop price. In the silo water framework, 1 uncertainty of energy demand is replaced by uncertainty of energy price, 2 uncertainty of crop demand is replaced by uncertainty of irrigation water demand. In the nexus framework the investments are selected jointly considering the interrelations between the different systems.

In the silo frameworks, investments are selected separately considering the water, energy, and food sectors. We observe significant differences between the nexus and silo investment plans Table 1. Table 1. Investment selection for the baseline scenario with the nexus and silo framework and impact on key indicators of the water-energy-nexus.

The turbine development is perceived as profitable in the silo water framework as the price of electricity is high Table 1. For the same reason the desalinization is not considered as it is too expensive with the current electricity price.

In the nexus framework, new investments in the power system like solar and bioenergy reduce the electricity price and thus provide the opportunity to invest in a desalinization plant; at the same time, the value of building a new hydropower turbine is reduced. A similar effect is observed for the bioenergy plant: with the current crop price, it is not profitable to invest in the bioenergy plant Table 1.

Thus, in the silo energy framework, bioenergy is not perceived as a beneficial investment. In the nexus framework, the possibility of increasing the crop production through more cultivated area and water efficiency improvement in irrigation, turn the bioenergy plant into a beneficial investment. Seasonality also plays an important role in the interrelations between the sectors.

Within the energy silo approach, wind power is perceived as more profitable than solar, as it has lower investment costs Table 1. However, wind production is low when there is no irrigation demand Figure 3C , so compensating the wind power seasonal variation with hydropower releases leads to additional trade-offs with irrigation.

Solar power in turn, has a low production when irrigation demand is high, so it matches hydropower production from water releases that also serve irrigation. In the silo energy framework, hydropower is considered with an average capacity factor and trade-offs with irrigation are not perceived which leads to the investment in wind power, while in the nexus framework solar power is preferred because its seasonality creates synergies with the seasonality of irrigation water demand.

The food silo framework does not consider trade-offs between irrigation and hydropower production, so it seems more profitable to invest in irrigated agriculture. In contrast, the nexus framework invests in improved rainfed agriculture to reduce trade-offs with hydropower.

Similarly, the benefits of improving irrigation efficiency are better perceived in the nexus framework as it generates synergies with other water users. We perform investment selection for the ensemble of uncertainty scenarios. We then use various robustness metrics to quantify the benefits of each investment option across scenarios under each planning approach. Metrics including the NPV of individual investments require with-without analysis in which each investment option is either added to the optimal set if it was not selected or removed from it if it was selected.

The difference in total economic benefits between these two model runs represents the NPV of that investment option which may be negative. For investments that can be scaled, the NPV reported is for the full investment. Thus, the NPV-metric is not fully relevant as a partial investment might be more beneficial, hence the metric showing the share selected in a given percentile of scenarios is more relevant. We perform the analysis with the nexus and silo frameworks and show the investment performance indicators Table 2.

Table 2. Performance indicators for investments for different frameworks and robustness metrics. There is an important difference in the calculated robustness of investments for the silo and nexus frameworks Table 2 , similar effects to the base case are observed: the silo framework leads to the development of hydropower, irrigation and wind energy, while the nexus framework leads to more rainfed, irrigation efficiency, desalinization, bioenergy and solar power relative to the silo framework.

There is also a variation among the robustness metrics: a more risk averse metric e. Figure 4 shows the empirical distribution function of the NPV of individual optimal investments in each scenario for the nexus and silo frameworks. This provides a more detailed information on the risk-profile of each individual investment. For example, desalinization in the nexus framework, is found to have a negative or low NPV in a large share of scenarios but has a high NPV in the remaining ones.

Figure 4. Investment NPV empirical distribution function with optimal investments in each scenario for the nexus analysis solid lines and the silo analysis dashed lines. An important dimension of the scenario analysis is not only to select alternatives but also to understand the weaknesses or risk factors of investments. We evaluate the sensitivity of investment decisions to uncertain parameters for the silo and nexus frameworks Figure 5.

In both silo and nexus frameworks, investments are sensitive to parameters within their respective sectors e. We observe nexus effects when investments show sensitivity to parameters that are not linked to their respective sectors. For example, in the nexus framework desalinization is sensitive to a carbon tax as energy becomes more expensive, making desalinization less profitable , solar and wind power investments are sensitive to an environmental flow policy as it reduces hydropower production and thus increases potential for alternative power production , and the dam extension is sensitive to yields low yields lead to higher crop prices leading to more irrigation and thus justify the dam extension.

In the silo frameworks, these cross-sectoral sensitivities cannot be observed. However, this approach requires accurately representing the impact of runoff change on hydropower production and ignores other co-impacts. Here, the sensitivity of wind and solar investments to runoff is higher for the nexus framework because low runoff and precipitation also lead to lower agricultural production and thus less bioenergy production Figure 5.

Figure 5. Sensitivity of investment average selection rate performance indicator to uncertain parameters. The parameters between brackets specify how the uncertainty parameter was translated for the silo frameworks as specified in the case presentation. Investments might be particularly sensitive to the combination of multiple uncertain factors. We observe that the dam extension is sensitive to runoff and yields, wind investment is sensitive to runoff and crop demands and rainfed investment is sensitive to environmental flows and energy demand.

In the silo framework one of the parameters is often ignored, leading the investments to be perceived as less sensitive to uncertainties. Additional results illustrating this are available in the Supplementary Material. The investments' performances are also sensitive to other investments. For the nexus analysis, we show the perceived individual investment NPV profile Figure 6 , for: 1 optimal investments within each scenario solid line , and 2 ignoring all other investments dashed lines.

We observe that considering investments individually significantly affects their value: bioenergy and desalinization are much less valuable when ignoring other investments as these investments rely on new power generation and agricultural production , while hydropower development is found more beneficial as it does not have to compete with alternative power investments.

This is similar to the silo planning effect—except that in this case the decision process is only blind to other investments. This effect is expected to be important when other investments might considerably affect the current equilibrium, which is the case in this example.

Figure 6. Investment NPV empirical distribution function with and without other investments. The NPV of each investment is calculated through with-without analysis for each scenario considering 1 optimal investments within each scenario solid line , and 2 ignoring all other investments dashed lines. In a practical planning problem, the results of the previous steps would be presented to stakeholders and decision-makers, who would refine their objectives, potential solutions, and visions of potential futures.

In addition, impacts and objectives that are outside of the modeling framework would be considered. Then, a few alternative investment plans would be designed and evaluated. We re-evaluate the investment plans through the range of uncertainty scenarios. In both cases the investments plans are re-evaluated with the nexus framework, which is assumed to be the framework most representative of reality.

As expected, investment plans selected using the nexus framework perform considerably better than the investment plans selected with the silo frameworks Figure 7. Figure 7. The NPV of investments plans is calculated through with-without analysis in each scenario. The purpose of the Zambezi study case is to demonstrate the framework on a real case that involves multiple investment types with complex interrelations at variable spatial and temporal scales.

Three main reservoirs Itezhi-Tezhi, Kariba, and Cahora Bassa have an active storage capacity of 10 9 m3 and are the main consumptive water user through evaporation losses about 10 10 9 m3. Environmental flow constraints are represented at the level of the main wetlands Kafue flats, Baroste plain, and Mana pools and the Zambezi delta.

Thermal power is represented as aggregated production units per country. A power market per country is represented, including South Africa, with corresponding power demands. The power transmission network is represented with a transport model considering aggregated transmission lines between countries.

Scenarios and projects were defined and developed jointly with stakeholders in these studies. In Payet-Burin et al. The difference with this study, is that here: 1 investments are evaluated individually and included in the objective function, 2 investments from the power sector are included, 3 uncertainties are evaluated using a Monte-Carlo analysis, 4 nexus and silo frameworks are compared. The investments considered are irrigation development, reservoirs, and hydropower plants from World Bank ; power transmission lines, coal, gas, wind and solar power plants from IRENA Six reservoir projects might add up to 6, Mm3 of storage capacity, 13 hydropower projects 6, MW of power capacity, these investments represent Irrigation investments represent kha currently kha are irrigated , for a cost of 3.

Eight transmission projects can add up to 8, MW of international transmission capacity in the South African Power Pool, almost doubling the current capacity for transmission at a cost of 2. Power plant investments are not represented at the scale of particular projects, but using generic investments IRENA, ; Taliotis et al. In the model, investments are selected every 5 years, hydropower, reservoirs, and transmission lines are represented as binary investments all or nothing , while irrigation and power plants can be scaled to the optimal amount.

Irrigation investments are limited to the investment plan in World Bank but can be renewed every 5 years, while lifetime is assumed to be 20 years. Irrigation investments are assumed to replace rainfed area, thus investments costs are the cost of equipping rainfed areas. Wind and Solar power availability are based on Wright et al.

In total, those represent 92 different potential investments, for a total capital cost of The full list of investments with their individual capital costs, operating costs, lifetime, and construction time is available in the Supplementary Material. For the planning horizon — we consider key parameter projections demands, yields, capital costs, climate change, rainfed area from Payet-Burin et al. All uncertainties considered here are parametric uncertainties, except the discount rate which is an objective uncertainty, see Dobson et al.

We consider three frameworks reflecting different perspectives about the planning problem: the nexus framework is the integrated planning approach considering all systems and potential investments jointly; while the Water-Energy and Water-Food silo frameworks do not consider all sectors and investments. The Water-Energy framework considers investments related to reservoirs, hydropower, transmission lines, thermal and renewable power and ignores the food system, including irrigation water abstraction.

The Water-Food framework considers irrigation development investments and ignores the energy system, reservoirs are considered, but not hydropower production. For both silo frameworks, the water system and environmental flows are considered. We perform a Monte-Carlo analysis by sampling randomly scenarios, which are a combination of the described uncertainties, and we compare the outcomes for the silo and nexus frameworks. As irrigated agriculture and hydropower compete for the same water resource, considering them jointly leads to less investments as the resource constraint becomes binding.

In particular, irrigation investments in the upper and lower Kafue are perceived as beneficial only in the silo framework Figure 8. The water in Kafue is particularly valuable as several hydropower plants depend solely on it Ithezi thezi, Kafue Gorge Upper, Kafue Gorge lower , others depend partially on it Cahora Bassa and Mphanda Nkuwa , and environmental flow requirements force releases in the Kafue Flats.

Table 3. Average investment capacity per investment type for the silo and nexus frameworks. Figure 8. Investments in irrigation and hydropower for the nexus and silo frameworks. Dots represent hydropower plants, catchments represent irrigation. The investment sequence Figure 9 shows it is optimal to invest in hydropower, reservoir, and transmission capacity as early as possible. Thermal power investments are found to decrease progressively with time while renewable energy investments increase; this is because we assume the progressive deployment of a carbon tax in some scenarios.

In general, power investments are higher in the early phases, because currently power demand is not fully satisfied, and power curtailments are frequent in various countries. Figure 9. Investment sequence A and evolution of key indicators B in the Zambezi River Basin for the nexus framework.

Boxplots show the variability among scenarios. Early investments in hydropower plants should lead to higher hydropower production in the decade, however production is likely to decline in the decade Figure 9.

This decline can be attributed to climate change and consequent reduction in runoff. There is an important variability among scenarios which increases with time: average hydropower production varies with a factor 2 among the best and worst scenarios in , but with a factor 6 in the decade. Irrigation consumptive water use should continue to increase in the river basin, but a strong variability exists among scenarios: in the decade, irrigation consumption is evaluated between 6, and 12, Mm3 per year.

The main production of crops is ensured by rainfed crops, and their total market value is about 7 times higher than irrigated crops. The perceived sensitivity profile is similar for the nexus and silo frameworks, except that irrigation and hydropower investments are higher in the silo frameworks. This shows that the agriculture and energy systems are relatively decoupled in terms of uncertainty analysis.

However, hydropower investments are sensitive to the parameters of the energy system: energy demand, carbon tax, and capital costs of renewable energies. The discount rate is found to be a driving factor for all investments. Only thermal investments are favored by an increasing discount rate. This is because thermal investments have lower capital costs followed by high operational costs use of fuel , while renewable energies have high capital costs followed by low operational costs and are thus penalized by a higher discount rate.

Figure Sensitivity of average invested capacity to uncertain parameters. The y-axis shows the aggregated capacity per type of selected investments, the x-axis shows the variation in an uncertain parameter. Ansar et al. We perform an additional analysis adding an uncertainty factor on the capital costs of reservoir-hydropower projects that is randomly selected between 0.

We find that the Mphanda Nkuwa and Kholombidzo hydropower dams are found beneficial even with large cost over-runs, however the Kafue Gorge Low, Rumakali, and particularly the Batoka Gorge dams are sensitive to cost over-runs Figure Other risks highlighted by Ansar et al.

Sensitivity analysis of the main hydropower dams to capital investments costs. Based on the previous results, in a stakeholder dialogue process, a few alternative investment plans could be formulated, taking into account the robustness evaluation, as well as other technical or political considerations.

In particular, investments might have different impacts on livelihood, especially for the poorest. Overlooking this in a practical case, might lead to an exclusive focus on resource efficiency while neglecting important equity aspects Allouche et al. For investments that can be scaled the average invested capacity is selected. We re-analyze the Monte-Carlo scenarios by implementing the selected investment plans.

Little difference can be observed between the methodologies with respect to the NPV resulting from the re-analysis of the investment plan Figure The main difference is that the NPV of irrigation investments is lower in the silo framework, as some irrigation projects were selected while ignoring the use of water for hydropower production.

Investment plan reanalysis. Hydropower investments contain hydropower reservoirs. We investigated the benefits of planning infrastructure under uncertainty in a nexus compared to a sector-centered silo perspective. We developed an investment selection module for the open-source WHAT-IF hydroeconomic model by integrating investment decisions in the economic objective function. The framework was demonstrated in two study cases. In the synthetic case, the silo frameworks lead to the selection of investments that generate more trade-offs, while the nexus framework selects investments to maximize synergies across sectors.

For example, the nexus framework leads to co-investments in renewable energy and desalinization, irrigation efficiency measures generating co-benefits to other water users, and power investments that fit the seasonality of hydropower resources and demand. Several investments are found sensitive to uncertainties not related to their respective sectors: in the nexus framework desalinization is sensitive to a carbon tax, solar and wind power investments are sensitive to an environmental flow policy, and the dam extension is sensitive to yields.

This shows that cross-sectoral linkages and uncertainties are related. The synthetic case is simplistic on purpose to expose some effects observed in real world applications. Other studies highlight different types of effects. Vinca et al. However, across the sectors, cooperation is found beneficial for all countries. Each sink is additionally treated with protective Stone Guard coating, You will be amazed at the customer service you receive after the sale.

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