 Okay, it's seven o'clock. We've got 19 people online, so looking forward to talking to all of you. I am just going to invite Elizabeth Gadget to say a few words. Yes, hello. Can you hear me? Yes, we can. Perfect. So hello, everyone. Welcome to all GIZ colleagues online, but also to partner institutions and everyone else interested in the webinar today. So my name is Elizabeth Gadget, and I'm the Speaker of the Mediterranean Environmental Framework. So the network is actually a network of GIZ projects that work on energy, water, climate, environment that are based in the minor region. And our aim is actually to exchange, learn from others, experience, work on papers and other activities together, but also organize other formats of exchange like today's webinar. So our aim is actually to keep ourselves updated so that we can later also provide an up-to-date advisory for our partner institutions as they call this in the sectors we're in. And in actually this network, we have five issues that we're working on more specifically right now. So we have five task forces created. One is working on green jobs, another on sanitation, one on digitalization, one on NDC implementation, and the fifth one on the water energy and food nexus. And this is also the task force that organizes this webinar today. So it's actually a link, of course, between the water and the energy sector. And this webinar today is also open not only to GIZ projects from the minor region, but also beyond the minor region and also to other target groups like partner institutions and others interested. So we're also very happy that we have Stockholm Environmental Institute on board for this webinar. And we hope that it's an entry point for more exchange later on, since several projects in the minor region but also beyond are working on integrated sector planning and modeling. And we hope to also later on have more exchange and also learn from others' experiences on it. So I wish us now all an interesting couple of hours. And please do not hesitate to contact me if you have any questions later on. Thank you. Thank you very much. And now we'll start discussing, start with the presentation. First, I just wanted to make sure that everyone was able to download and install the software on their own computer. And hopefully you have all had an opportunity to run the LeapWeep linking tutorial. That will at least help you follow what we're presenting today a little bit better, but we're going to go through all of that. I'm going to start with an introduction, very brief introduction to Leap and then to Weep, in order to orient people who might not know about one or the other of those models. I'm assuming that at least some people might be energy experts working with Leap and the others, water experts working with Weep. Some may not know either very well, but if you are familiar with some energy system models or water planning models then this should look familiar. So first, about Leap. This is software that was developed at the Stockholm Environment Institute, as was Weep, the long-range energy alternatives planning system. And I am being reminded that there are some further introductory words. I do apologize. And here they come from Falkmobil. Please. Oh, wow. Okay. Thank you very much. Now feel bad. No need to stop your presentation for me. Sorry for that. Yeah, my name is Falkmobil. I work in the department for water and wastewater and GIZ in headquarters. Okay, so now I'm a bit out of concept as I thought we would already have the presentation. I guess what I'm just going to say is that, I mean, we all know what Nexus is. I guess first political impulse. Just a second. Sorry, sorry, I'm on the call. The first political impulse we saw was given in Bonn 2011. And I think sort of since then we're seeing that the supply security of the three different sectors, food, energy and water. And they are underlying natural resources as well as the ecosystems are interdependent and we cannot look at one sector by themselves. So I think the key questions we are facing is how can decisions in one sector be shaped in such a way that the possible consequences for the other sectors are taking into account. And then also for our work, how can the, let's say, sexual interests promote each other in an integrated approach. So I think for these two questions, the webinar today is very relevant. I'm definitely looking forward to it. And yeah, thanks again for allowing me to speak. Thank you very much. So a bit more on leap. If you have been using weep, this will look slightly familiar the data browsing area and an area to enter formulas or view outputs. It's a scenario based modeling tool. The idea is to look at how energy consumption and supply can change. And that was the original design for leap that was an energy supply and demand modeling systems. And at the time that it was developed that it was first developed the real novel feature was to focus on the demand side as well as the supply side demand side measures were were somewhat actually as a concept before that it was just forecast and build forecast demand and build without much thought to what could be done to lower demands. And so this was one of the one of the early tools allowing you to really focus on on both sides. It's scenario based so you can explore a range of possibilities for infrastructure provision or or on assumptions about what might happen in terms of demands. It was intended for use in a wide range of applications including both data scarce and data rich conditions. And so it allows you to start with few assumptions. And limited data and then build out. Over time, a number of methods have been added from early on you could add econometric demand models. Now on the supply side you can add optimization models and simulation models it's very widely used and at different scales from city on on up. It takes a long range perspective, which means that the details are not it's not it doesn't have the fine resolution of a of a daily operations energy system model. But those are very hard to use for long range planning it's a long range planning model with with broader scope. And it is now possible to link to the SEI's wheat model which I will discuss in a moment. And I'll be talking about a number of different ways that you can make that link. And more broadly, there's an application programming interface that allows links to MS Office but other other modeling tools as well. It's Windows based, but if you can write a script in a scripting language that can access the API and the most modern scripting languages can, then you can link them to other modeling tools. For example, the team has used crystal ball the crystal ball software for Monte Carlo analysis, and we've added emission impacts as as a new component in this. So that was done using the API as well. Leap this is the overall flow or logic demographic and macroeconomic variables enter into the demand analysis. There are standard components from energy balances listed here and basically what it does is it tracks those balances over time. And, and through through assumptions about demands and intensities of use and and different options for supply. It then changes those balances resulting in environmental loadings impacts, and then allows you to do a cost benefit analysis, taking into account environmental externalities. In many ways, we've is quite similar. It's also scenario based. It's also meant for long run range planning. It was first created in 1988 actually is a DOS program rather than a Windows program and the programmer spent the same programmer who's working on it now developed that the same graphical drag and drop interface which I'll show in just a moment at that time in DOS it was pretty remarkable. The idea is to enable integrated water resource planning so this was really built around the idea of IWRM integrated water resources management. It was intended from the start to enable engagement of different stakeholders. The idea just like leap is built around an energy balance. We've is built around a water balance. And, and then it tracks changes in those. It does allow for a simulation of some hydrologic processes. And then for different options for supply and demand, you can then explore policy scenarios. So again, it was taking this demand side management approach. It has a user friendly interface and can integrate to other models through the API and through linking to DLLs. And we've done that with a number of different pieces of software. Some of them are now built in including water quality modeling with Qual2k, groundwater modeling with ModFlow, calibration with PEST, and then other models using the API or scripting. We've worked by allocating water by priority. So the water available in one time step may not satisfy all the potential demand. This is fundamental issue with water supply. So demand nodes are assigned to priority. And then from those demands, you can assign priorities to water sources. And then in each time step, what we does under the hood is it first maximizes the degree of coverage of desired demand or potential demand for the first priority. Starting with their preferred sources. And then holding those fixed, it maximizes the coverage for second priority demands and third priority and so on. And so that's basically the algorithm. And each time step, it does this stepwise process from first priority onward. And just to show the software, I will very briefly just show what it looks like. And you have with LEAP, you have the demand side. This is just the tutorial, the standard tutorial. On the supply side, you have energy transformation. Electricity transmission and distribution and pipeline transmission distribution electricity generation. In this particular example, there's charcoal production, but it's flexible what you can define depending on what's going on in the country. Oil refining coal mining. And then resources. So primary fuels. And then resources that might be available outside of the transformation sector. So that is LEAP and WEAP. The opening screen is a drag and drop interface to show the network. You have a number of different possible nodes that you can add, demand sites, catchments. There aren't examples in this tutorial, but that's possible to look at some hydrologic processes, the river itself, groundwater, and so on. So now I'd like to talk about linking LEAP and WEAP and start just with some foundations. And the basic foundations are indeed what Falk was just mentioning, which is the water energy and food nexus. And here we're emphasizing the water and energy links. In an earlier project, SEI carried out a literature review on the water energy food nexus and found that there are really two key factors driving a water energy food nexus analysis. First, if there happens to be in the area scarcity of either water, energy, or food, but it's primarily scarcity of water. And then scarcity of one system is tied to activity in another system. So it's not just that it scares, but there are knock on effects from that scarcity. And the scarcity leads to some notable outcome and the most common one is economic losses for consumers. So this is the motivation on the scarcity side. The alternative is a threat. Infrastructure and other developments that might impact other systems. Hydro-power operation versus irrigation, for example, putting in the dam by itself can affect stream flow and fisheries, wetlands downstream, aside from the area where the dam, where the impoundment happens. And then there's trade-offs between hydropower and irrigation and flood control. Biofuel plantations can compete with food and can compete for water if they are irrigated. Climate change is a major threat that was mentioned in these studies with changing availability of water. So possibly the onset of scarcity if it was not there before or at any rate less availability than there was than there is currently and therefore a change in the system. Reliance on hydropower and rain-fed agriculture and then what will happen under climate change. And in this literature review, this is from a few years back, but this is the distribution of studies with scarcity-dominated ones unsurprisingly in drier areas or otherwise resource-limited on islands, for example, and threat-dominated where climate change is particularly being felt. And then there were some other cases, other examples as well, other reasons to do the study such as harmonizing policy or broader sustainability goals. And so with that as a context, when would you want to use, when would you want to link up leaf and wheat? So what uses can you put them to? Because beyond the broad questions about the water energy food nexus, what specifically are leaf and wheat capable of? Typically we have found in the case studies that we've been involved with that wheat provides one way inputs to leap rather than the other way around. And in fact, that's what we're going to do in the example we show today. So from wheat to leap, energy demand, which pumping, desalination, constraints on hydropower for thermal or thermal cooling water, whether from drought or from optimizing reservoir operation for non-energy use, these kinds of demands and constraints on production flow from wheat to leap. In some cases, you can go back the other way from leap to weep. For example, if hydropower demand takes priority, then you cannot time your releases for agriculture, for example. There may be water for fossil fuel extraction. This is at least the case here. We're seeing an increase in fracking where water is injected into the ground in order to break up the formation underground to allow oil to flow, oil and gas. Cold washing, so other water requirements where they can become significant in some area because of the scale of production. And just a quick example here in one of the studies that we've been working on, there's a mismatch between the timing of agricultural needs for water in Lesotho versus they have an agreement to export electricity to South Africa from hydropower. And so there's a mismatch in the timing there. And when is it worth linking leap and wheat? Well, there's two questions to ask. And the thing is, as we're going to show later, it's not easy to link these two models. And it's not just these two models. It's really the way that water and energy models are structured. Because both of them were drawing on and are similar to other models within the same realm. So before linking a water and an energy model, first ask, is it relevant? Does, in fact, water or energy present constraints to the other, one to the other? And if there is a constraint, make sure it's the most important or one of the most important for the planning and decisions that you need to make now. And the lesson from the water energy food nexus literature is that, yes, often it is important, but it's also good to recognize that sometimes it is not or it is not the most important. It takes time to build a harmonized model. And so make sure that the use of your time is well spent. But then when it is relevant, go for it. And is it feasible? Are the needed data available? Do the leap and wheat models have the same time resolution? And if not, in the example we're going to show in a moment, they did not, but we changed it. And that's okay for this purpose. And do they both include the relevant geographic area? So I'm going to go through just a couple of examples here of where we've done studies like this. One in Mindanao in the Philippines and one in Bangalore. And if there's time, I'll show a couple of others that we set aside because we thought it would be, we likely wouldn't be able to get to them. So I'm just going to say that there are actually two of us on the call. Myself, I'm Eric Kemp Benedict and my colleague is Emily Gosh. Emily is going to actually show you the leap and wheat demo for Morocco. Once I'm through with the slides, Emily worked on this Mindanao project. So Mindanao derives over a third of its electricity from hydropower. And El Nino activity, the ships in El Nino is showing its vulnerability to dry conditions. And so here you can see that hydropower, these dips that occurred in 2010 and 2014 with a rising demand, mean that they have to ensure that there's stable supply and be prepared for that supply. And you can see just how problematic this is that although it's diesel or oil and coal that's growing. So these are quite fossil fuel intensive. And so the objectives for this study. First, how will climate change affect hydropower availability? The energy and water systems and prospects for development and mitigation in Mindanao. Because right now it's the growing demand with constraints on hydropower is actually exacerbating fossil fuel emissions. So look forward, look at different climate scenarios and quantify changes in hydrology and the hydropower availability. Look at long run technical and cost impacts. What additional capacity is needed? What is needed in terms of resource utilization and what are the impacts on emissions? Look at both a business as usual case and a case where national mitigation plans were put in place to compare those two case studies and compare to the hydropower assumptions in national and regional policies. So ultimately this comes back to a critique or a critical review of policies. How realistic are they in terms of building a future in a changing climate? It's a joint modeling exercise of energy and water systems. So first of all, the link from Weep to Leap, from Weep. Simulate hydropower availability under various climate scenarios. A baseline, a lower RCP 4.5, a medium high and then which is RCP 6. And they consider this high emission scenario RCP 8.5. And under those, then see what happens to the energy system where you have different capacity development scenarios. National Renewable Energy Program, I mean aside from the baseline, National Renewable Energy Program, a limited hydro development differentiating solar and wind costs to look at the allocations between those for renewables and then carbon pricing. And so sorry, that is the study that's being undertaken at the moment. A further example in Bangalore, in India. So water is a constraint. Bangalore is built on a hill. It was basically an old hill fort. It gets water from a nearby river. Bangalore is experiencing a boom because that's where it's basically the part of the Silicon Valley. So high tech firms are locating to Bangalore. And so it's growing rapidly and it's placing strains on the city's resources, withdrawals from the nearby Calvary River, which also serves another state in India, and increasingly from groundwater. Water is often brought in by trucks rather than pumped. And SEI has been working with people in Bangalore for several years with the Bangalore Urban Metabolism Project, where the metabolism that is the use of resources within the city is mostly focused on water. And so the difficulty is that the details really matter. And so SEI has been collaborating with teams on the ground using multiple tools, including water use surveys, some borewell surveys. And basically asking people going in and taking a look and seeing what the groundwater level is with people's private boreholes. And then providing some ways to explore what comes out of the project through interactive means. And one of the outputs is to link because pumping is so important as a source of water on this hilltop that electricity consumption and therefore emissions are important considerations. The Bangalore Urban Metabolism Project has an online website that contains some GIS layers, but also it has this online scenario explorer. And in fact behind the scenario explorer is a weak model. And so you can change settings, for example, water demand assumptions, private groundwater supply. And so this private groundwater supply has an impact on electricity consumption and therefore CO2 emissions. And then you can see the consequences on the graphs over here. There's a number of challenges, large sets of water wells to be monitored, seasonal variation of groundwater depths. There's a great deal of industry there as well as just household pollution and rapid city expansion with increasing water and energy demands. And so it's difficult to extrapolate the data. We do have a couple of more minutes. So I'm actually going to jump down to another example that we had here. I mean a couple more minutes on this section. A project that we worked on in UAE and so I know a bit about the Bangalore case study and Emily knows about the Mindanao study. Neither of us worked on the UAE study. So I'm just going to read what's here. But the goal was to assess national regional water energy dynamics. And here there was a definite link from water to energy and vice versa. So building linked water energy modules models to address the issue of groundwater overuse and therefore high energy costs of water supply and possible impacts on energy water demand growth and climate change. So we just have a few results here, water demands by sector are substantial and energy demand in the UAE substantial amount from just direct electricity demand, but also desalination a significant chunk for desalination. There were several constraints on the project difficulty obtaining estimates of available groundwater as everywhere. Irrigated areas not well documented, which was a little more unusual, but fluctuating population and limited energy data. We're also factors of constraining the study. But again, like I said, we were not part of that study. Okay, I will now continue to talk about linking leap and weep in practice and some of the options. So broadly speaking, there are three ways to link leap and weep. One of them is a direct software connection that is built into both leap and weep. And that is what we're going to demonstrate later. That's what Emily will show. Another way though, tried and true. This works especially well if there's only a one way link. For example, you really just want to take weep outputs and put them into leap and you're not interested in what comes back. Then you can simply export to Excel and then import from Excel and get the models working that way. And that's one of the most straightforward lowest cost ways to do it. And then a third one is to do what this direct connection does, but in some ways, but if you have needs that go beyond what the built-in tool will do, you can build your own. So any scripting language that can access the APIs you can use to run these models from an external controller. So direct connection and then you connect to, so here in the expression builder, you then have access not just to the weep branches, but to leap branches when you go over to weep and you can put in your formula a leap value and then get it from that branch. So this linking tool is really quite powerful when it's appropriate and when it all works. It has strict requirements on aligning the two models in their structure. And that's the main reason to use one of the other methods, either the manual method or building your own tool that accesses the APIs. But if you can get the structures to align, then it's quite powerful and flexible. And so then you can run weep and it gets that input. And so you have cooling water demand brought from leap over into weep and that then becomes a demand site so you know that you've got to transfer that water. Even if it comes back through a return link so it's a once through passage still you need the water to flow. And so that becomes a constraint in weep. And so that's one that this is this is the way to build this connection that is built into weep and leap for major restrictions. Both areas have to have the same base and end years. Leap areas have to have only a single year of data in their current accounts data. So weep only allows for one current accounts year. Leap will allow for multiple ones to develop trends trend estimates. Leap areas can only have a single region. And leap and weep must have exactly matching time slices and almost always that means doing it monthly. Although weep will go down as low as as fine as a week. It will not go down to the daily or hourly time slices that leap will allow. So weep the shortest time is a week. Usually these are matched up on a monthly basis. Then there's the manual connection. So you start with weep. You've got your results. And then you just export. This is this is exporting. You can do just by clicking on this Excel button. So you export the table that you want. And then you set up in. This is hydropower generation and you simply import it into Leap. And so hydropower generation is now fixed. And then you can run the scenario with with that given. Supply from hydropower. So again, this is not. This doesn't give you the same flexibility of bringing in multiple variables and so on. You have to do a bit more work. But if this if it's a one way connection from week to leap and you're really not interested in going back the other way. Then you can run weep export and import. And then you can you can run your scenario. And this can be a very straightforward and useful way to link weep and leap models. And then you can link through a script. If you have any programming capability or can bring a programmer onto your team. This can be a very powerful usage. Here you've got weep and then you this particular example is using Python but there's there's you can use visual basic you can use a version of JavaScript called J script you can use all kinds of things. But here you see there's a an object leap object is created using Windows libraries that are available in Python. You open up the application and then you have a leap object and you can start to manipulate it. So you say, let's set the active view to analysis. And you can start working with setting a scenario. So the active scenario can be set to the scenarios that you've identified in this in this range of this this set of scenarios. And then you can access branches and variables and expressions. So this is this is the most flexible and the most time consuming because you have to build your own script. But you have complete control and so you're running weep. You're running weep from your script and you can just tell them what to do at each time step. And this is something we've done many times we actually built Emily and I together with with with their colleague Jason built a macroeconomic model for Morocco that used the the scripting language Julia, which is used for modeling we used Julia and built a Julia application to to create a macroeconomic model into link that to leap. So this scripting approach to control weep and or leave or both is is a very powerful but technically demanding and time consuming way to go. Now, before I hand over to Emily to have her go through the demo. I'm I'm just going to go through some of the steps that she had to do in order to convert the existing models leap and weep models into ones that could that were suitable for the leap weep linking tool. So, here's the case study. We have a. We have already through another project. We have a recent application for the sus masa on the west coast of Morocco and sus masa is actually a region in Morocco, but but we're talking about the basin that includes these two rivers the sus and the masa which is smaller than than the whole region. It's as a region it's among the top for Moroccan agriculture so it's very important, and some of this agriculture is high value crops. So it's actually worthwhile putting in more expensive water supply sources because, for example, it's it's greenhouses greenhouse production. And, and this region produces the large majority of Morocco's vegetable exports and, and a majority of their citrus exports so these are the kinds of high value crops that that can take more expensive water supply sources. And the sus masa river basin makes up about a third of the sus masa region we it works much better to work within a hydrologically defined unit. And so the wheat model is for the sus masa basin, which is in the sus masa region, which is within Morocco, the country. And they are currently planning to put in what is called the agadir desalination plant. When built, it will be the largest seawater diesel plant in Africa. The starting date is planned to be 2021. We don't supply drinking water, but we're going to focus on irrigation of 13,600 hectares in the Stuka area. And the initial capacity is is going to start at this 275,000 cubic meters per day, which is what we assume for this example there are plans to ramp it up. The plant has the option to run entirely on renewable energy. And so this is a way to link potentially leap back to weave, although really all that all that can all be taken care of within the the leap model. Once the wheat calculations for electricity demand from the plant have been calculated and then on the leap side you can look at different electricity supply options. So we're taking this as the example to put into the model. And here's the thing. This was the starting point for leap and we we it was actually an historical model built for calibration purposes. So it starts in 1991 this first scenario year is 92 and the end year is 2010 and everything was being calibrated against historical data on a monthly time step. On the other hand as a planning model for for the energy ministry base year is 2004 first scenario years 2014 and the end year is 2050. And because of the needs of the study and the available data, depending on the supply, they're hourly daily and seasonal time slices. So this these are very different. You may remember that to use the matching tool, you have to have the same start and end year. There can only be one base year here. There's, you know, the base year is 2004 but actually that is number of historical years 2004 to 2013 and leap. That's not good. And the end year we want to look forward we have to at least include 2021. And also the the time resolution has to be the same in the two models so Emily had some work to do. So she harmonized the models by setting the base years and the first scenario year and the final year to the same value. She also set kept the monthly time step and leap we've I'm sorry but in leap had to change to monthly time slices. And this meant two changes so that historical data we needed to project into the future. Fortunately, there's an option in the read from file function in wheat to say cycle. So after it read through the historical data up to the end of the data series, it then just began again. So that's what we had doing we we we said it so that the historical hydrology over the past past two decades, 91 to, yeah, two decades would just be leaped, looped through. And so we would just keep repeating that historical pattern. In leap the change that had to happen is all the load curves were set for those hourly, daily and seasonal time slices and so Emily had to change the load curves to fit a monthly pattern. Next, the actual planning that we want to do, add the new desalination plant and most of this was in wheat. So add the agadir desalination plant as an other source that's one of the possible nodes in wheat. Then add a transmission link between agadir and stuka and set them to start operation in 2021. Set a maximum capacity for the agadir plant and create new variables for electricity demand, both monthly and annually. Now, as I said before, I hope that you had an opportunity to run the tutorial if you did. This last step should be familiar, because in the tutorial in wheat, you create new variables for electricity demand. But it's only monthly, and that's because in the tutorial, you're linking up to an energy demand variable in leap with a monthly time slice. It already had a monthly time slice. But in the Morocco leap model, that is not the case. The time slice for demands is actually annual. It was not time sliced. It's just an annual total. And then the allocation is done using an overall aggregate load curve for all demands. That's the way the model was structured. That's the way a lot of leap models are structured. So what we did, what Emily did, is to create new variables for electricity demand on a monthly time scale, because that's how we is calculating it, and then add them up to get an annual total. And so a lot of that work had to be done on the wheat side. And so that was a bit of the harmonization as well. So on the wheat side, and then on the leap side, all you have to do is add Agadir as a demand note. Now it's possible to link. So open up the leap linking tool and associate the models. So you say I want to link to this model and the scenarios to match up. An enormous number of scenarios in the leap model, and there's a small number in the wheat model, but we only wanted to associate one scenario in each one. A new scenario that was created for this Agadir plan. In leap, also open the week leap linking tool and associate models and scenarios and then do one further thing. Add a week variable with annual electricity demand for the Agadir plant. And that was done with. I showed the screenshot of the tool where in leap in in wheat, there was a reference to a leap variable. In this case in leap, there's a reference to a wheat variable and all of this Emily will show you actually on screen using the software. And the final step is to run it. And the next step here is to go to the demonstration. And so this will take about half an hour or so. And then after that we're going to have half an hour of Q&A. So please record your questions. While Emily is I'm switching over to Emily and she's getting set up. Take a look at the interface for go to webinar and you'll see that there's a way to raise your hand. So, so just keep an eye there and we're going to use that raising the hand. Feature for allowing people to talk once we get to the Q&A session. Okay, so Emily, I am now going to hand over to you. Can you hear me? Yes. Okay, great. All right. And can you see my screen? Yes. Yes, can see it fine. Okay, so. Thanks Eric for going through the Agadir desalination plant case study. Now I'm just going to show exactly what Eric said in words in the actual LEAP and WEAP models. So, as was mentioned, for this particular case study we actually used LEAP and WEAP models which were developed by SEI, which were developed for different projects independent of each other. So we didn't initially have the intention of connecting the two models to each other. So as Eric had mentioned, there were some challenges that we experienced while connecting the WEAP model and the LEAP model. And so this is kind of a real life example of how the two models could be connected using a real life example of a new, the installation of a new desalination plant. And so in this study, the intent is to have a one-way direct link between the WEAP model and the LEAP model. And so what I'm going to walk you through is what we first did in the WEAP model and then how we linked that to LEAP next. So the first thing we did is we added the plant, the agadir desalination plant to our WEAP model. So what I'm showing you here is the model for the Seuss-Massa River basin. And now I'm going to zoom in to the area where we added the plant. So what you can see here is that what we first did is we added a new node, an other supply node, and we named this the agadir desalination plant. And then we added a transmission link from the plant to the tachuca catchment. And so as was described by Eric, this plant will primarily be serving the tachuca catchment because it has major water demand needs within the Seuss-Massa region. And then what we did next was we added data related to the desalination plant. And so next I'm going to switch to the data view and show you where the agadir desalination plant is located. So it's under supply and resources, under other supply, and here's the plant. What we did next is that we added a new scenario and we named it the new desalination plant scenario. So you can see that under current accounts, we set it so that the agadir desalination plant is not active. Since this plant is only commissioned in 2021, we only set it up so that it wasn't active in the current accounts. And so now if we go to the new desalination plant scenario, we can see that there's some expressions that we included within that scenario. So one of the first things we added was the plant capacity and we included the plant capacity under the inflow variable. So you can see here we have connected, we have referenced a key assumption variable for the agadir desalination plant and we linked it to the capacity key assumption. So now I'm just going to take a quick look through to show you what we included under a key assumptions branch. And as some of you may be familiar with, the key assumptions is where we store constants or things that are maybe referenced in multiple places throughout the model or just a place to store some input data. So in this case, we have three variables which we have stored under the key assumptions for the agadir desalination plant. We have the startup year, which is 2021. We have the capacity of 275,000 cubic meters per day and we converted that into cubic meters per second as shown in the equation shown here. And so this is equivalent to around 3.2 cubic meters per second. And then we also included a variable called the electricity consumption per cubic meter. And so this is basically the energy intensity of the desalination plant. We used a value of 5 kilowatt hours per cubic meter. We took this value directly from the leap model. This was the value that was used, that was found to be relevant for desalination plants. And so we use this value as well for the agadir desalination plant. And so now go back to our actual, the variables which we use for the agadir model and go back through what we had inputted there. So as you can see, we have the key assumptions for years prior to the start year. The inflow or in this case the capacity is zero and then in 2021 it jumps to 3.2 cubic meters per second. We also inputted the startup year or the commissioning year of 2021 here as well. I'll show you a bit more about the electricity demand next, but I'm just going to wrap up. First wrap up what we included on the water side for the agadir desalination plant. And so in addition to the plant, we had also added a transmission link to the model. And so if we go down to where that transition link was added to, it was to the Chuka demand node. We can see that there's actually two transmission links that are going there. So if we go back to our schematic, we can see that the Chuka demand node has in fact the agadir desalination plant, as well as the Chuka groundwater supply source connected to the demand, to the catchment node. And so given that there's two different supply nodes connected to the system, we also need to ensure that our supply preferences are set. In this particular case, we decided to set both of them or leave both of them set to one. So what this basically does is that when WEAP is deciding which supply source to use, it doesn't differentiate or prioritize over one or the other. It uses the one that it decides is the most reasonable to want one to use in the model. So in addition to setting the supply preference, you may use a different supply preference in your model. But for ours, we're just leaving it at one. The other variable which we had changed in this example is the startup year. So again, we indicated a startup year of 2021. So given the inclusion of the data for the agadir desalination plant and the transmission link, what are the impacts to adding this new desalination plant to the system? So if we take a quick look at the results, so as it loads. So I already have a view here that shows exactly what happens. So we have our demand site, the Chuka demand site, and we have our new desalination plant scenario. And we can see that just by adding the desalination plant, we see that prior to 2021, the system is primarily sourced by groundwater, but then after the commissioning of the desalination plant, we see that the supply is primarily dominated by this desalination plant. With some additional groundwater being sourced, but now we have now the groundwater that went to Chuka before, it can now be diverted to other water scarce catchments or demand nodes. Another interesting thing we see from this example is how the actual amount of water that is delivered to the demand node actually increases. And this is interesting because it shows how water scarce this particular demand node is. And so if we jump to another view, so I already have a favorite view set up so that I can easily go to it. If we go see the coverage, we can see how the coverage, the demand coverage for this particular node improves significantly through the addition of the new desalination plant. We have around 15% prior to the installation of the desalination plant and that reduces the coverage improves to almost 80% to 100%. And we can also take another look at this compared to the reference scenario. So if we compare this to the reference scenario, we can see that, in the reference scenario, the demand coverage was pretty poor throughout. So by adding this new desalination plant, and if we also compare it to the reference through this other way, we can see that the coverage improves upwards of 75% to 80% throughout the years. So now that we have an understanding of the value of the desalination plant for this particular demand node, what does it mean in terms of electricity demands? So now I'm going to jump back to the data view and show you how we calculated the electricity demands for this particular plant. And so for some of you that did the tutorial, you'll find some similarity with what we did here with the tutorial itself. And so WEAP doesn't typically have any electricity variables, but this is something you can add to the model. And so we added a new user variable to the model. In fact, the first one we added was electricity per cubic meter variable, and this is directly linking to our key assumption for the electricity intensity for the plant. So you can see here, because of our startup year starting in 2021, after 2021, we can see that the value jumps to the value which we had assigned of 5 kilowatt hours per cubic meter. So now that we have the electricity intensity, we can now use that value to calculate what the total electricity demands are for that particular demand node. So we did this by adding a total electricity, a variable. So now I'm just going to walk you through exactly what we did when we added this variable. So, sorry, if I go back, you can see that when you right click, there is a create, but create option and that allows you, that's where you can create new user variables. So by clicking on the edit option, I can see exactly what I did when I created the new variable. So here we have a total electricity variable, and this goes under the category called electricity, which is a category which I had created when I made this variable. What this variable does is it calculates the total electricity requirements per month for the agadir desalination plant. And because the data in Weep is time-sliced, we can obtain the monthly electricity generation for the agadir plant. So, like, I kept the scope as monthly, I changed the units to kilowatt hour, and then I added an expression for this particular variable. And this expression to calculate total electricity, it uses the electricity per cubic meter. So electricity per cubic meter as per the variable, other variable, and it multiplies that by the actual total amount of water treated by the desalination plant. And the amount that is treated is represented by this variable called the total node outflow. So what that means is the outflow or the amount of water treated for the node called the agadir desalination plant. So that's how it calculates the total electricity in each time step. And we left this as a read-only variable because this cannot, so that it cannot be changed. So as you can see here, here are the results of that variable. And we can see that the monthly based on the amount of water that is treated, the amount of electricity that is required can be upwards of 40 million kilowatt hours per month. And so up to this point, this kind of follows the logic that was used in the LeapWeep tutorial that some of you may have done. So in addition to this variable, we added a new variable called annual total electricity. And what this does is it adds up the electricity demanded by the plant for each year. And the reason why we had to do this is because within Leap the demand side is not time-sliced. And we'll be including the agadir desalination plant as a demand branch within Leap. And the thing is is that this is quite typical in a Leap model where the time-slicing that is used is mainly used for the electricity load curve, a single electricity load curve which applies to the entire electricity generation system. Otherwise, if you were to have load curves on the demand side, you would have to specify a separate load curve for each and every single device that uses electricity within the model. And so you can see how that can be kind of cumbersome and if you don't have the data to create load curves for each device, it is challenging. So in light of that, we had to take this total electricity data for each time-slicing aggregated to sum it up for each year. And so similarly, we created this new variable. We changed the scope from monthly to annual for this particular variable. And then we added an expression that takes the time-sliced value from the previous time step. It takes the total electricity variable. It takes the data from the first time slice of zero which is equal to January and to the 11th time slice which is December and then it sums up the values in those time slices and sends it back. And so that's what it does here as you can see in the bottom, you can see for each year what the monthly electricity demands are for the Agadir desalination plant. And so we can also, now that we have this variable within Weep, we can see what the results are for electricity demands within Weep itself. So if I jump to my favorites, I can see I created a new view for this total electricity demand. But in order to, the way that you navigate to this is actually through the input data because this is a new user-defined variable, it would be found under the input data results selection box and then under supply and resources, other supply electricity, and here we have the three variables that we created. So if we, what we can see here is our annual total for electricity for each year for the Agadir desalination plant. And so now that we have the electricity demands calculated in Weep, we then took this and implemented it into LEAP. So I'm just going to open up the LEAP model and show you exactly what we did. So this Weep, so this, sorry, LEAP model has various sectors and the demand side, but to keep things simplified, we just added a new separate sector for the Agadir desalination plant. And then we created a new, a branch, a total energy branch. And within that total energy branch, we added the expression linking the LEAP value to the Weep value. But as Eric has mentioned before, we can't just automatically link the two together. We actually have to create that manual link from LEAP to Weep. And so first what we had to do was we had to create this new scenario within the LEAP model and that's what we did. We created a new scenario called the new desalination plant because this is what we would like to link to the LEAP, the Weep model, sorry. And then after creating that, we had to make sure that our years in the model aligned, so within LEAP, we'd go into the basic params dialogue box and we changed the base year from 2004 to 2011. We changed the first scenario year from 2014 to 2012 because as was mentioned, you can only have one historical year within the LEAP model in order to link the two together. And the end year was already set to 2050. In the Weep model, we had done a, went through a similar process in the, in the general screen under years and times, under general menu under years and time steps, we changed the current accounts year from 1991 to 2011. And we changed the last year of scenarios to 2050 to align with the end year in LEAP. By doing this in the LEAP model, or sorry, in the Weep model, we had to take some certain steps. We had to make sure that the data that was in the model actually worked with the new times that were assigned. And so, for example, for the stream flow data, it was only pulling in data to cover a certain number of years. And so we had to add this term to the file that was reading in the flow data. We had to add a term called cycle, which would then allow from for the Weep model to cycle back to the top of the Excel file that it was reading the data from, or the CSV file that was reading the data from and cycle through that data again so that it can, so that we had data for the years assigned in the model. As you can see here, in the bottom here by adding the cycle function, we see kind of a repetition in the stream flow data from the prior years to the future years. We can see the pattern repeating. And so we had to go through and replace any of the read from file expressions to include this term called cycle at the end of it. And so we had to go through and replace it for anywhere it was reading in data. And then from the leap side by changing the base year, we had to go through the model and change any references to that that were hard coded to a certain year to make sure that that it aligned with the changes that we had implemented to this model. And then the other thing we had to do was make sure that the that the time slices align so here in our general and time slices set up box. Originally we had about 600 time slices set up for this model, and we had to aggregate. We had to change that here to to 12 in order to have a hard link to the Weep model. And then, and any of the yearly shapes that were created, we had to change those shapes to fit into the new time slices that we had that we that we had assigned to this model. And so that took a bunch of time we had about, there's about 15 different yearly shapes to those to go through each of them, recreate them and put them into leap took some effort. But that's what we had to do to get the models to talk to each other. And so after that we actually linked the two models together so through the advanced menu in leap you have the function which allows you to connect the leap model to weep. And so here we have the areas shown the years we can see that they align, and we have our new new desalination plant a scenario in the leap model linked to the weep model. We did a similar linking process in Weep. So we went to the advanced menu linked the model from from leap to weep, and you should see a similar dialogue box as shown here. Okay, so now that we have the two models linked. We were able to actually reference this weep value. And, and here we can see that it is directly linked to the Agadir desalination plant annual total electricity variable. And if we go into our results view, we can also see that here. And so when we compare the the electricity demands in in leap, we can see that it basically matches what's shown in weep so that's a good sign that we know that it's linked and that it's working. And so another. So the reason why we we linked the two together was to understand by adding this desalination plant to to, you know, on the on the water side how does it affects the energy side. What are the implications of adding this plant and it's such a large plant as well. And so if we go through go to our are on our supply side we found a very interesting result on the supply side on the electricity production module. We found our production to electricity so we found here if we look at our capacity added. We can see and in this situation what it's showing is showing the the new desalination plant compared to or the differences between this plant and and the baseline scenario. And we find that the differences show that the capacity added in in this scenario. The capacity is added slightly before when it would have been added in the baseline scenario. So what it's showing is that in 2034 we have 1000 megawatts of capacity added in the new desalination plant scenario capacity that would have only been added in 2035 in the in the baseline scenario. So we show that even this desalination plant actually changed how the demands in the system to so much that we actually had to add additional supply in advance of when it would have been added otherwise. And we see something similar in 2042 where we see some new capacity being added then as well as again in 2046 where it is added a year before when it would have been added in the baseline scenario. And so yeah that's basically all I wanted to show today hopefully it gives you a better sense of how you can link the leap model to weep and vice versa. And some of the challenges that you might have to consider when you are doing this sort of linkage but also the powerful results that it can provide. And so if you have any questions please let us know I'm going to hand it back to Eric to close it off. Thank you. Thank you.