 Hello, everyone. I would like to welcome you all to week two of our Water Productivity Masterclass series. We had a really nice showing last week and we're excited to see everyone here again this week. My name is Lauren Zelinski. I work on the water pip project team at IET Delft. Today I'll be moderating the webinar along with Abraham Abishek from Mesa Mesa. And he's also our technical guide. So if you have any questions about your connection or audio or video, please message him and he can help you. Just to remind you that this Masterclass series is brought to you by the Water Pip Project, which is the Water Productivity Improvement in Practice. And that is a group of organizations focused on water science and water management. And we're looking at improving water productivity in the agricultural sector. So I put it in the chat, but we would like you all to introduce yourselves in the chat box with your name, where the institute that you work for in the country that you're living in, it really helps us understand who is attending these webinars and how we can engage with you better in the future. So today is week two of sixth of the webinar series. Last week we focused on introducing the concept of water productivity and how to monitor it. This week we'll be focusing on the WAPOR portal and how to monitor different parameters of water productivity using that data. This week we'll focus more on how to use the portal online on the web. And next week we'll get more advanced into topics on how to use the data and analyze the data in GIS and Python. The following week we will look at water productivity and sugar cane production and the specific aspects with that crop. And then followed by looking at different socioeconomic water productivity parameters. And the final week we'll be using aquacrop to monitor different parameters as well. So if you would like to rewatch the recording from today or look at the presentations that were made, or from this week or previous week, you can go to the project website that is waterpip.un-ihe.org. And that website should go live later this week, so that's a good central location for that information. And you can also go to the water channel at www.thewaterchannel.tv. And all the PowerPoints and videos will be there. So our agenda for today is we will start with a short video introducing WAPOR that comes from the water channel. And then our colleague at IHE Delft, IHE Delft, Marluse Mool, will talk about the WAPOR portal in more details, the ins and outs of WAPOR. That will be followed by Pulad Karimi, also from IHE Delft. We'll talk about different WAPOR-based indicators. And then Vic Tran, another colleague from IHE Delft, will show a video about how to use the tool in the portal. And then at the end, we'll have about 30 minutes for a question and answer session. So we will not answer questions in between the different presentations, but we encourage you to ask them in the chat box. And Abraham will be collecting the questions and then we will answer them all at the end. So please be engaged in that chat box and we'll try and answer as many questions as possible. If we don't get to your question, we'll also collect them and answer them separately and put them on the website. So you still have access to that information. So I think with that, we can start with the first video. FAO has developed WAPOR, a remote sensing database which allows for the monitoring of agricultural water productivity. WAPOR gives users access to a wide variety of real-time satellite data on water productivity collected over a span of 10 years. This information can be used to propose solutions to increase water productivity while respecting the environment and making equitable use of water resources. Welcome, my name is Sam Bastiaze of Metameta and welcome to these how to use WAPOR tutorials. WAPOR is an FAO platform and stands for Water Productivity through Open Access of Remotely Sensitive Drive Data. WAPOR has a lot of data to offer and in these videos, I will show you how to collect this data. First, you need a WAPOR account. You can create an account right here. Mine says my WAPOR, that means that I'm already signed in. WAPOR is currently two versions. I'm using the latest version, version 2.1 now. The WAPOR database is divided into three skill levels, continental, national and sub-national. Continental has a pixel resolution of 250 meters and it's available all of Africa and the Middle East. Then you have national skill level. This is the pixel resolution of 100 meters, which is only available in some countries in Africa and the Middle East. At last, you have sub-national level, a pixel resolution of 30 meters. However, this one is only available for some areas. In Ethiopia, Lebanon, Kenya, Sudan, Mozambique, Mali and Egypt. If you want to download and collect WAPOR data, you go to the left corner. The first option is analysis. Here you can download and do your own analysis for your own area. I'll show you this in the next videos. The second option is locates. With locates, you can search for your own Pacific area. You can search for country, city, street or place of interest. The third option is layers. The layers will activate the layer what you see at this moment. For now, the cross-bioMOS water productivity of 2019 is activated, the layer. If you want to remove this layer, you can click on the bin button. Now there's no layer active. If you want to active another layer, you go to layers, then you click on theme and you can activate a layer what you want. And also you can choose the period what you want. What kind of data has WAPOR to offer? All the databases are shown in the catalog. If I click on catalog, you can see all the databases. You can see all the databases per scale level. The databases are going from evapotranspiration to net primary protection to biomass, to precipitation, to a land surface temperature. This one is for the continental level. If you go to national and subnational, you can also search what's available for those scale levels. If you click on back to the map, you go back to the main page. This was a very short introduction of how to use WAPOR. In the next videos, I will show you how to collect this data. Thank you very much and see you next time. That was just a short introduction on how to use the WAPOR portal. If you would like to re-watch that video or there's more videos as well, you can go to the waterchannel.tv and they have more videos there. I think now we'll pass it on to Marluse and she'll give us more information about the ins and outs of WAPOR. Thank you very much, Lauren. Thanks, Bastian, also for the introduction into WAPOR. That's a brief idea of the portal that we're going to talk about today, which is the WAPOR database that is hosted by FEO. I need to click here to move this slide. What is WAPOR? WAPOR is a database of remote sensing derived data. I had a nice video here. I don't know why it's not playing, but that's the same one in the video. It's a database of remote sensing derived information that's related to water productivity. The database was developed as a tool to monitor improvements in water productivity for Africa and the Middle East initially. It's accessible through a portal. You already saw some showing the portal and where to access the data. I'll go to a little bit more detail later on, but that is WAPOR. You also see a link here to the website of the project that developed the WAPOR database, and it's had a lot of information about what the database is and how it's being used. I'll just explain a little bit more of the technical aspects of this database. I don't know what's happening here. We have three different levels at different resolutions. We've got level one, which covers the whole of Africa and the Middle East, and that's available at 250-meter data. The source of data is MODIS, which is available at a one-day interval. It passes by, but in the portal it's combined into a decadal time step. What do we mean by decadal? That's actually a time step of 10 days, but there's a small caveat. It covers three decades in a month. If a month is 31 days, then you've got the last decade is actually 11 days, or for February the last decade is eight days. That's a bit of a trick that you need to know when you're using the data. The second level is 100-meter resolution data. That's available at decadal monthly and annual time scales, but also at seasonal time scales, and later in the presentation I will explain a bit what the seasonal is about. This is available for 21 countries. The list is provided here, but you can also find it on the website, and also for five river basins, the Jordan, Litany, Nile, Awash, and Niger river basins. This is partly based on satellite information. Also the MODIS data and re-sampled prior to 2014, and after 2014 it's derived from the Probeve, which is an European satellite at 100-meter resolution. Then we have in specific locations for irrigation schemes and for Awash for instance, at sub-basin level. We have 30-meter resolution data at decadal monthly and seasonal time scale, and that's available for specific locations. Currently it is available for locations in Lebanon, Ethiopia, Mali, Egypt, Kenya, Mozambique, and Sudan. This is derived from the Landsat data or the satellite that's available at 30-meter resolution. This is what it looks like. This is the portal. You see the areas where there is data. The left-hand side is the map of Africa. This is the 250-meter resolution data. You see it covers the whole extent of Africa in the Middle East. Then on the right-hand side you see the national level, which is the 100-meter level where the countries are, with the colors are the countries that have data at that level. Currently the website and the database offers WAPOR version 2, but how did we get to this version 2? The project that developed the database started in 2015 or 2016, and they started developing the data sets. There was a beta version that was available from 2017. That was evaluated by the project partners, and there were some improvements made, and version 1 was launched in February 2018. That was much improved from the beta version, and it was available open access for everybody. A lot of analysis, et cetera, were done with the database. Also, again, internally the project did quality assessments on the database. Based on their recommendations, there were improvements made on the database, and some major improvements were made regarding the underlying soil moisture product that is informing the ET data. There was also a tweet towards ET and biomass production irrigated area, which were underestimated from the first version. There was also another adjustment made based on the recommendation, whereby the first version showed only the above-ground biomass production with a fixed ratio, and we recommended that they should just report the total biomass production and then based on what you know on the ground, or you can calculate yourself the above-ground portion. Since June 2019, the version 2 with all these adjustments has been made online, and it was officially launched during the International Seminar on Drought and Agriculture, which was called Counting Crops and Drops. Let's grow the future together, which was held in Rome last year. So, that's what is currently available, and that's what we are using now for all the analysis. So, the brief overview of the databases or the datasets that are available on the VAPOR dataset or on the portal. The main map or the main data that had why the database was developed is this gross biomass water productivity. But there are also underlying datasets that have been used for these analysis, like the actual evapotranspiration and interception data, which is available as a composite, but there's also a split between where the individual evaporation, transpiration, and interception layers are also available. Then there is information on net primary production and above ground biomass production. There's also a layer on the land cover classification, and there's information on the phenology, and there's some other quality layers that are also available. These are the main layers that are required for water productivity analysis. In the previous webinar, we talked a lot about water productivity. This is a reminder I'm putting up the equation for water productivity, which is the amount of biomass, yield, but it could even be economic output against how much input, and in this case, how much water is being used to produce that. The VAPOR portal is separating it into two components or two indicators, both gross water productivity and net water productivity, and the gross is by dividing it with the total ET, and the net is dividing the biomass by just the transpiration, so just the amount of water that the plant is using. Ideally, you want to see what is the crop production, but at this stage, the VAPOR portal is providing biomass water productivity. We received a lot of comments at the earliest stages about what is the value of the VAPOR database, and since the Version 1 has been launched, there have been a various amount of studies being done on the value of the database, and these are three of the key publications that provide input into how well does the database work against observed data. We have the VAPOR quality assessment report that was published last year, and that actually evaluates each individual layer, and we have two key publications that came out this year about, in particular, the evaporation product. These are also the data sets or the reports that I'm referring to when I'm continuing with this presentation. There are results and analysis that come from these publications that I'm summarizing in this presentation. Just the background, VAPOR is not the only product and database that is providing evaporation. On the evaporation side, there are many, many projects, and there is a large variability between the products, and you also see that there is a difference in spatial and temporal resolutions between the different products. Why do we need this new database and how much does it really add and what is the quality compared to these other products? In this report, the quality assessment report from IHE and FAO, we evaluated a number of different products using a variety of methods. One of the methods was looking at the water balance of large river basins in Africa. We evaluated the water balance, so we evaluated precipitation minus the discharge that we received from a global dataset, and we compared that to the ET from VAPOR. The graph shows the comparison, which is probably not very clear, but if we look at the statistics, we compared the average of the different products and the one with the precipitation minus the discharge. We also did a weighted average, so we gave more weight to the larger basins compared to the smaller basins, and we looked at the correlation. You see that all the correlations are about 0.9 compared to the observed data, but VAPOR is together with, in this case, the highest in terms of correlation. What is most important, what you see most is that the difference between the observed data and some of these ET products is quite large, like GLEEM and MODIS have more than 200 mm out of 700 or 800 mm difference, so that's almost 25% difference of an absolute value. You see here that SABOP and VAPOR provide the closest match with the observed data, so that's already a very nice indication that VAPOR is actually performing amongst the best of the other ET products. We also evaluated against the discharge data in the river basin in Lebanon, the Litani River Basin, and you see, I don't know if it's very clear, but you see the two different colors, dark green and dark blue, and we initially wanted to evaluate the flow, which is the dark green one, against the line, which is the T-minus ET data from VAPOR, and it didn't match at all. But later we realized that there was an intubation transfer from one of the reservoirs there outside of the basin. If we added that MAP, we actually see that the water balance is closing very nicely. The other report or the other paper from Megan Blatchford, she also evaluated a number of point measurements and observed eddy covariance towers towards the VAPOR database, and you see that there isn't general agreement, but there's also a large scatter around the average. So what are the main conclusions from the ET products? We see that VAPOR is one of the highest spatial resolution compared to the other products, the other product products are one kilometer or even higher. The data is provided near real time, which is also a major advantage, and at basin level, VAPOR is generally performing very well. But we also saw that the report from Megan shows that VAPOR is overestimating when ETA is low, VAPOR overestimate ET in irrigated fields, and VAPOR misrepresent ETA when ETA is high in humid conditions. And especially the humid conditions, yeah, it's probably related to the quality of the input data in terms of cloud cover that is affecting the data. Also what I would like to say is that if you look at the water balance, ET is not the only product that is included here. It's also the uncertainty in the rainfall that affects whether or not you can close the water balance. So it's not only the ET data that is affecting how well a water balance can close. But the other product that we need for the water productivity evaluation is the biomass product on the VAPOR database. Unfortunately, there's not a lot of in-situ data to evaluate whether or not the database is providing good results. So we did something else. We collected the biomass production from the VAPOR database, and we collected for a number of fields and a number of sites. We collected information on the crop types, the crop calendar, the beginning and the end of the cropping season. And we looked at literature to identify the harvest index and the moisture content to be able to calculate what is the harvestable and fresh product. So that's basically the crop yield. And we compared that with observed yields. And we checked whether or not this comes in the right range. So we did that for a case study in Ethiopia. So this is a large irrigation system for sugarcane, where you see here the biomass production in the area. And the green part is the irrigation field. And the surrounding areas are the red areas with very low biomass production. So we converted that to yields. And you see in the top hand, on the right hand, the parameters we used for the analysis. And we compared that to the observed yields. So our analysis showed that the average is around 100 ton per hectare. And we also found from the field managers that indeed the yield is on average about 100 ton per hectare. So this is a very good indication that the data layer for the biomass is doing very well. We did a similar analysis in Egypt on the Fayoumi irrigation system, where there are two main seasons, winter wheat and summer maize. And here the average from Wauport we calculated, for instance, for wheat, 5 ton per hectare. And the literature gave 5 ton per hectare. Maze was a bit underestimated at 3 ton per hectare, but also the literature value was slightly higher. But we also realized that we assumed that the whole area was wheat and the whole area was maize. And that's probably not the case. I mean, it's very likely that the large part is also fellow. So if you need quite a lot of field information to be able to make these analysis, but at least it's in the right order of magnitude. And it comes out quite nicely. So what do we actually see from Wauport? Because Wauport provides their own water productivity layer. At the first level, we get the water productivity layer is an annual water productivity layer. So it just looks at the whole year what is water productivity. At level 2 and 3, it uses a seasonal water productivity. And it even is able to provide two different season in a year. So how does Wauport derived this seasonality? Because it assumes the beginning and the end of the season. And it uses what they call the phenology layer, which is a layer which is based on the NDVI, which is the normalized vegetation index, to estimate the crop season. And you see here this image where during the cropping season there's more biomass, more vegetation growing, and it actually picks up this change in vegetation as an indication of the start of a growing season and the reduction of this vegetation at the end of the growing season. So it's using that layer as an input to identify what is the season and then it calculates the water productivity for that particular season. But we also found that if you do have local information and begin in the end of the cropping season, it's better to use that than to use the phenology layer. Because that phenology layer is highly dependent on the cloud cover or it's affected by the cloud cover, the quality of it. So this is the one that we already saw. So this is the portal that we're talking about where we have, if you go to the left bottom corner, you have a button that sells layers and you can select the different layers and the different information that is available. So you can get it visualized on your screen for the different layers. So that's how you find the different layers. In the catalog, you can see all the different layers and a small introduction of what each of these layers stand for. And if you click on one of these layers, you also find much more technical information and there's also an option to download individual maps. One thing that I also want to point out is that it's always good to read these descriptions, to really clearly understand what it is about. And sometimes some of these layers have conversion factors. For instance, the ET layer has a conversion factor of 0.1. So when you download it, you need to multiply it by 0.1 to get the right unit. So also you need to check what is the unit. And I think even with the ET data maybe big and also confirm, I think it's meaningly to per day. So it's a decade. It's an average value over a decade. So if you want to know the total amount of unit over that decade, you need to multiply by the number of days in a decade, which I earlier also explained how that is not necessarily 10 in each of the decade. There's a couple of videos of the demonstrations of these videos. So even the two videos that some refer to an introduction video, they are available on the water channel. I think we can make them available on our website. And then our project website will maintain updated list of videos and resources. So yeah, this is just a very short list of the available videos, but there will definitely be more. If you want to see the latest ones in the next couple of days, go to our website and keep track of what we're posting there and what we're uploading there. Thank you very much. At least that was a really nice overview about WAPOR, how it started and the different ways that it's evolved over time. And I think it's always good to talk about the specifics. So you know when you use this data, there are things you have to keep in mind that when you compare it, the final results to other results. It's good to know. So thank you very much for your nice presentation. Now we'll move on to Pulat, and he will talk about WAPOR-based performance indicators. Hello. Hi. Thank you, Lauren. And thank you, Marjus, for providing such a good introduction. It makes my life very easy to pick up. I always go back to what you described in detail. So basically one of the uses that we can have for the WAPOR database is actually to use them to do what we call performance assessment using performance indicator. Why do we use this indicator? It's generally about when we have an irrigation system, we would like to improve system operations. We would like to maybe know how far are we from our strategy goals. I mean, at the beginning, we have some goals. But then throughout the years, we have to see whether we have been able to achieve those goals or are we drifting away from the where we started. We could also use this to assist our management. I mean, as a day-to-day operation of the irrigation scheme, we can use these kind of indicators and performance assessment to guide those. We can also use that to kind of look into the general health system and also maybe do intervention before doing so. We can also see what are the impacts of those interventions. It can also be used for, say, other uses, especially what we call benchmarking. Benchmarking is a practice through which we compare a system with other systems. If you imagine you have 10 irrigation schemes, you want to know where your irrigation scheme A stands in comparison with the other nine, or how irrigation system A compares with its own performance over the years ago. So that is a practice in irrigation engineering or irrigation management we call benchmarking. The overall goal of doing all these is to actually improve the productivity of the use of land and water and also make sure that the quality of service delivery of irrigation is there. So these are why we do these sort of indicator-based operations and indicator-based corrections. However, there are challenges. I mean, everybody likes to have all this information, but in reality, there are a lot of resources needed to collect this extensive data. Those resources include time, labor, and also budget. There's also an issue of the lack of continuity. We can do one irrigation performance once, and then the next one may be in 10 years if the irrigation scheme actually has enough funding and enough resources to carry on that. Also, we don't have or we have limited knowledge of the past. We know a bit about today through this performance assessment, but we don't know how was the scheme five years ago. So we cannot really understand how the system has changed. And also, what we do in physically doing it, it would be limited for a small geographical extent, basically for a irrigation scheme or if you have a large budget for a district or a problem at most. But what WAPR has to offer here is that it offers freely available data on land and water and climate. So speaking to one of the issues that I was about the budget and then the time that it needs to collect that data. WAPR offers continuous data. Data comes a decade old and then for seasonal annual and then you can continuously keep monitoring these situations. It can provide a glimpse of the past. It goes to data goes back to 2009. So we would, at the moment, we would have 10 years of data that to build up on. And also it covers a large geographical extent. It covers continental gale analysis. So these are what WAPR would have to offer us to carry on irrigation performance assessment in a new way. The way it works is that you have buffer data. You have the land use land cover map coming from the same database. You have the, you can use your irrigation system boundary to extract information that you need for performance indicators. So they explain by thick to some extent today and then, of course, continued by a detailed training and by a better which actually goes through all these steps of how we extract this information. What I want to show is a couple of indicators that are something that we can quickly do and then use for this kind of analysis. This includes water consumption. We get it from through evapotranspiration, crop area, yield reference evapotranspiration, beneficial fraction, equity, relative water deficit, relative yield reduction, and water productivity. These are the, some of the, some of the indicators that we can quickly calculate with using vapor. And if you want to, we want to show this with the, with a case study in, in Deco Valley in Lebanon. The first and most important to us as, as agro-hydrologists are evapotranspiration. It helps us to track water consumption. That can be done through space and through time. The graph to the, to the left shows seasonal ETA or actual evapotranspiration variations for winter week in Deco Valley. You see, the numbers are changing. So you don't have a continued or continuous number that is repeated every year. So that helps you to understand how evapotranspiration actually has changed over the years. You can also look into this from a spatial point of view. So you can pick up a year, pick up a season, pick up an average of a couple of years to see how spatially water consumption through evapotranspiration has changed. These are a lot of important information because you would know which part of your scheme is actually consuming more water or which part of your scheme has not received enough water to be able to do, to provide the crops with the required water. So that information you can benchmark and you can understand through this, through tracking of evapotranspiration. Another indicator is crop area. It helps us to track the extent of the land with the use of land. So you would know in any particular season or year how many, how many hectares of land has been under cultivation for the, for major crops. I mean, at the moment this crop map is provided by Vapor at 30 meters resolution for a selected area. But at times we can have that information also coming in from the secondary sources that we can use to support our analysis. Again, that can be looked upon as the inter-annual changes or different seasons to see how it has changed. And if you look into the graph, you see how it is dynamic. I mean, say in 2013 and 2014, the area under cultivation of winter wheat in Vapor Valley has about, it's about 5,700 hectares. Whereas the year before is about 2,500. This is how dynamic some of the decisions are. And then as irrigation engineers, as planners, as managers, we really need to know about these things. We really need to know whether the area has increased, the area has decreased, why they are happening, and then what are the reasons behind that. And hopefully that would help us to guide our intervention to improve the situation. The other important indicator is the yield. I mean, yield is the ultimate information that a farmer requires, because that speaks directly to how much livelihood they can make out of a hectare of land. And tracking yield is important because it also is one of the SDGs, like we want to increase the double crop production tree. And then that has to happen in hand-in-hand when increase of the yield and the land productivity. So when we actually look into the yield, again, you see a lot of variations. So it's not that the yield is constant. An area could be going through different yields, different years. And then when we do this kind of analysis, we would understand, we could take a step towards understanding what are the reasons behind it. And also especially looking at the special distribution of yield also gives us an idea of what are the areas or spots that produce more yield and which are the areas that produce that yield. And then we can go ahead and then look into the reasons why some areas lag behind and then how can we help them to improve. ET reference is another important indicator because it also tells us the guidance through water allocations. Because crop water demand traditionally has been decided using crop coefficients times the ET reference. So in that case, when we actually make those decisions, it would be good for us to see how the reference evapotranspiration has changed over time and how that correlates with the change of evapotranspiration actual in the same area. Another important indicator is beneficial fraction. It tracks the system's efficiency in turning water consumption into benefit because it looks into the transpiration part of the ET. As opposed to evaporation. For definition, evaporation happens from soil surface and transpiration through plant. And only transpiration contributes to biomass production which is our beneficial use. So through this doing and then tracking beneficial fraction we understand how efficient is our system towards translating water used to biomass production which gives us direct benefits. We could also look into equity. Equity is a very important indicator. It tells us how fair is water distribution. How equitable has been our plans and operation of irrigation towards making sure that all the farmers have access to water with the amount that they need. If you see this graph shows how equity has changed over time in Beka Valley for irrigated winter week. The area in red is the area that is rather critical. You don't want to go there. These decisions are where the critical point starts. If that are arbitrary you have to take on you can consider your local situation, local target or you can look into the general literature. Generally we say something between below 10 is the target between 10 to 20 or 17 or that range is the reality unacceptable. But anything past that is the critical point that we would like to have improved. We could also look into relative water deficit which tracks the irrigation water adequacy. It shows us how much of water in comparison with the maximum developed transportation has available through different places in an irrigation scheme. Here you see again the changes of the relative water deficit through years. You see in some years the water has been a huge deficit in water. In other years there have been less deficit. You can also look into this especially and see what are the areas that are more affected by the lack of availability of water. You could also go back to calculating the yield reduction. I'm glad that Pasquale is also joining us here in this webinar. This work comes from his publication of FAO 66 where the simple equation has been introduced to relate relative water deficit to relative yield reduction. If you look into the graph to the right you see how a linear relationship can be made between the relative yield reduction and relative water deficit. KY is actually our prop yield factor and then if you compare that to the behalf of Bicca Valley through this practice 1.06 and interestingly enough the number that has been suggested as a global average in FAO 66 is 1.05 which shows how some of these empirical relationships are actually quite accurate and how can they be used to represent to be used to understand how yield reduction may happen because of water deficit. These kind of relationships can be used easily if we know that next year we have 20% water reduction than what we need. What would be really our yield reduction? Very important factors for our planning and for decision making when it comes to allocation and deciding how much deficit may have to be given in different sectors. And last but not least is the water productivity and that tracks productivity of use of water and generally it changes through years. In Bicca Valley we see a general increasing trend in water productivity from 2009 to 2019. And then also you can see how especially this distribution is within the Bicca Valley in Lebanon. To help us to understand where we should focus and what we will learn from them and what kind of things or interventions can be used to improve the spots that are having lower water productivity. In summary, WAPR offers free continuous and data that covers a very large geographic area and starts in 2009. This data can be used to track irrigation performance both suspiciously and temporally through years, through seasons and to show us where the locations for the hot spots and bright spots and also that information of the irrigation performance assessment can be used for benchmarking and planning actions for improvement. There are sources that are used in this presentation the open source EME and FAO publications you can just click on the blue links and then get all the the documents to kind of give you a little bit more insight into the theory that is behind the indicators. Thank you so much. Thanks a lot. I think that was a nice extension from your talk last week about how parameters change at different scales and now we talk about how we can measure those parameters using the WAPR data. So thank you. And now I think we'll move on to Bich and I'll give her a few moments to introduce herself in the video and what we'll be learning. Hello everyone. I think you can just play the video. I will explain more in the video. Yeah. Hello everyone. My name is Bich and in this video I will show you how to use the analysis tool on VAPL portal. You can use this to extract point time series, area time series or water productivity raster of an area. I will now demonstrate how to extract time series data for point on VAPL portal. We can do that using the analysis tool on the left corner here. Before clicking on the analysis button we need to change to the data layers that we want to extract time series. For example now we are looking at the gross biomass water productivity. If you want to extract monthly actual evapochinspiration and interception you will need to click on the layers button and select actual evapochinspiration and interception monthly. To extract time series data you don't need to change the month layer so we can leave it for now. After that we can open the analysis tool and select point time series. Now under the place tab click on select point. You can either select a point that you have saved before or add a new point. Under the new point tab you can fill in the latitude and longitude coordinates of the point you want to extract time series data. For example I have this point and then click select. To save this point for later use click on save in my VAPL. You will need to sign up for VAPL account and login to do this. Then give the point a name and save it. Under the time period tab you need to specify the time period you want to extract time series. For example let's extract data from 2009 until the end of 2010. Similarly you can save in my VAPL this time period let's name it period 2. After that click run operation it might take a while to process depends on the internet connection then you can have this graph of monthly data for the selected period. To save this time series data you can click on the button on the right corner of the plot and choose download csv and then a csv file is downloaded you can click on this to open in excel for example. Here we see the date and the value separated by comma so I will convert text to column in delimited mode to separate the date and the value by comma. After that you will have two columns of data. Now go back to the portal in case you don't have the exact coordinates of the location you want to extract time series you can use the locate tool to search for a name of city or place of interest. For example now I will search for Beka Valley in Lebanon. After that the portal will show a point in Lebanon Valley sorry in Beka Valley another way is to click on any point on the map for example I want to select different points in Beka Valley whenever whichever point you click you can choose point time series to open the same analysis window and follow the same step to generate time series. I will now demonstrate how to extract time series data for an area on VapoPortal. First you need to make sure that you are selecting the data layer that you want to extract time series. In my case I want to extract time series data for actual evapotranspiration and interception monthly. Then we can open the analysis tool and select area time series. Under the place tab click select area. You can choose an area that you have saved before in the my areas tab or adding a new area. You have two options one is to draw and second is to upload a shape file. If you want to draw an area click on draw. Here you will start drawing the area that you want to extract time series. When you click you can select a point and drag to draw new lines and continue until the last point is connected to the first point creating a closed boundary. You can also save this area for later use using save in my Vapo button. If you have a shape file of your area you can choose uploading a shape file. Let's try that by select a new area. Here we need a zip file that contains a valid shape file. For example I have these shape files with the mandatory extension files I will select all of them and send to a zip file. After that back to the portal click choose and select the zip file and upload it to the Vapo portal. Here I am asked to confirm the geometry. You will need to check the shape and the location on the map to make sure that the shape file is correctly uploaded. After that we can click click confirm. Now I will save this new area as Xenavan. After that you will need to select time period to extract data. This is similar to point time series analysis. For this I choose the time period that I have saved before. Then I can click run operation. On the right corner you can see the status of processing. Once it show completed click on the green button. Here you will receive the time series data for the area so there will be 3 values average and arrange from minimum to maximum. Similarly you can click on the button next to the save csv file. I will now demonstrate how to calculate crop water productivity rasters for specific crop area on Vapo portal. You can do this for an area of crop where you know the crop specific parameters. For this example I am going to use the national 100 meter data layer. I will open the analysis tool and select area water productivity. In the place tab click select area. You can create a new area or use an area you have saved. In this case I will use a sheaf file of sugar cane crop in Xenavan. Here you can see the plots of sugar cane. Now in the time period tab you can choose the start and the end date of crop season. For example in my area the crop season starts approximately from 1st October until the last day of September of the next year. Therefore I will select the period from 1st October 2009 until 30th September 2010 for the crop season starting in 2009. I will save it as season 2. Under the advanced option tab you can customize the crop specific parameters to calculate crop water productivity. These are light use efficiency LE harvest index above graph over total biomass ratio and moisture content ratio. You can also refer to the explanatory notes for details of the computation. In these notes you will find the definitions of these crop parameters and the formulas used to calculate crop yield and water productivity rasters. Back to the portal in the select crops list you will find some common crop types If you choose one of those crop the reference values for crop specific parameters will be automatically filled. For example in my area the crop is sugar cane. So when I select sugar cane you can see the common values for sugar cane crop parameters are filled. Here the LE value for sugar cane is 1.8 since it is a C4 crop. If you cannot find your crop in the select crop list you will need to determine the crop parameters of your crop type either from literature or field measurements. After filling the crop parameters I can click run operation. Once the processing is completed you can click on the green button here and you will see that there are three links to three rasters. In the explanatory notes you will find the explanation of these results. The raster with AETI code is the total actual evapotranspiration and interception of the crop during the selected season in cubic meter per hectare. The raster with TBP code is the yield generated from the growing season based on the specific crop parameters in kilogram per hectare. And lastly the raster with GBWP code is the crop water productivity. Notice that this has a scaling factor of 1000 which means after downloading the raster you will need to divide the raster by 1000 to get the value in kilogram per cubic meter. For example now I will download the crop water productivity rasters. I can open this raster in QGS here I add a raster layer and select a downloaded crop water productivity raster. After that I can use QGS raster calculator to convert the downloaded raster into kilogram per cubic meter by divided by 1000. Then I will save the result raster. And that is the end of this demonstration. If you have some questions please ask in the Q&A session. Thank you. Thank you big for that nice video. I think it was nice to see a step by step explanation of how to use the portal and how to download different data types where to find things. So we encourage people if you have not used the portal before go check it out and try and find data for your area and different parameters that you're interested in. Practice makes perfect so you can always practice and if you have questions you can contact us or the WAPOR team. So I think now we will move on to the question and answer session so you guys have been very active in the chat which is great it's really great to see. I think some people have had their questions answered by some of the presenters in real time but I think some will be nice to open for a group discussion. So let's start with a question for Vick from Kaiji. Can we add new points at the sub national level and extract the data through a shape file or CSV file? I think Livia from FAO has the answer for the question in the chat box. So the level 3 data product is not in longitude and latitude special reference system but it's in UTM. So I think if you put longitude and latitude in the toolbox it will show invalid coordinate. So I think can you go back to the previous chat box? I mean the new one. I think Livia has the answer to this question. Is it possible to add Livia presenter so she can answer this question? Yes, I'm just trying to do that. Livia, right? Livia, yeah. In the chat box she said that there is no coordinate on level 3 yet because it's in UTM. I have made Livia presenter. Livia if you could please activate your webcam and your microphone using the webcam and the microphone buttons at the top of the screen at the top of the webinar window. It's just about where you see the speakers. In the meantime I will try and pull up the latest chat window. While we're waiting I see a lot of questions on where we can find the recording where can we find the presentation different references that were made all of that will be available on the project website as well as waterchannel.tv You can find those a few hours after we finish the webinar those resources will be available. Hour 9? I think Livia... Can you hear me? Yes. Sorry, I was clicking buttons randomly. I'll make it short. Coordinates entry at level 3 is not available at the moment because that's because level 3 data come in UTM specific projection for the specific level 3 area so there was no standard coordinate system that we could use but it's not too difficult to implement if user finds it necessary so the idea was that at that level we normally know the area already so it was less relevant at level 3 to find points based on coordinates that's on level 2 and level 1 but we have a priority list of functionalities to add to the portal so please send those requests to vapor at file.org and we will prioritize and try to apply those changes when we can. Thank you. In addition to Libya's answer I think for level 3 you can check the reference system on the catalog. If you go to catalog sub-nation in the description there will be the information on the spatial reference system. If you have the coordinate in this system you can use this on the portal instead of latitude and longitude. Great thanks Bik and thanks Libya for clarifying the answer on that it's always nice to have additional capabilities so if you use the portal please let the FAO team know how things can be improved. Okay we have a few questions that are related to the equity indicator so I think I put a small note on the chat box but anyway I mean we actually calculate equity based on the coefficient of variation of evapotranspiration. So basically we are in the old ways you look into the service delivery and delivery to the fields at the farm gates to understand equity however we actually when you actually look at this from the consumption point of view you can use evapotranspiration actual as a replacement because if a farmer has access to water the likelihood that that's the evapotranspiration of people would be much higher than the situation and there is lack of water. So we use evapotranspiration actual per field as a proxy to calculate our equity and to do that we look into coefficient of variation. Oh yeah absolutely yeah there are other resources that people wanted to read up on this indicator. Okay great and we'll put that on the website also so people can see that. Okay next question is for Marluse to avoid the over or underestimation WAPO is a suitable tool in less tropical regions without irrigation systems and where evapotranspiration is high. Well I mean WAPO works better in other areas so that's indeed true. I think one of the key things is that you need to realize that the ET data and also the biomass is dependent on cloud cover images and what I also showed in the higher but the lower resolution levels so level 1 and 2 there are more overpass times so one image can be replaced by another if you have one cloud cover image you know within the 10 days you at least have one clear image whereas in the 30 meter resolution data you have only once and every 16 days overpass time if that one turns out to be cloudy it's not that filling taking place so in general in more humid climates you get more cloudy images and images with missing data and there's a lot of interpolation taking place so there's a bit of a challenge in terms of the data so that's one of the reasons why the humid climates are more problematic than the drier climates I also mentioned in the Jetbox it depends also on your application do you need the absolute value or do you need to have a trend Pulat showed in his presentation that a lot of the analysis are about trends so if it's all 20% higher than the absolute value the trend would still show the same response so there are in any case you need to use remote sensing data with a bit of need in mind that it's not an absolute value and it's not 100% accurate but you do still can use it even if there's a bit of a bias but you need to keep that in mind when you're using it thanks Marlits, appreciate that answer this is a question for BIC to make crop maps we need to validate classification and I consider the WAPOR data sufficient to validate a crop map sounds more like a question for Pulata I would say actually did answer this one in the chat box the crop ground validation is important so these maps are made you have been validated and some of the points are used to train the model but it would be interesting for your own area to actually do this and then let us know the results who would be very much interested to see how your validation results look like and then how accurate do you find these maps it's always difficult to get on the ground and do ground-true things so it's really nice when you have a big network of people in different places that we can all work together and improve the data set a question from Yusef currently it's difficult to upload shapefiles directly into the database and then download the data is this the common problem I think I can answer this question I sometimes have problems with uploading shapefiles but the problem is that the shapefile is not valid so I often use QTS to verify the shapefile before uploading and in the video you will see that you need to compress not only the shapefile but also all the mandatory extension files with it in the Z file before uploading so I hope this will help solve your difficulty and it also seems like you can save that shapefile so of course you better save it so you don't have to upload it again okay I think we just answered this question maybe Abraham we can get a new question let's discuss the oh here's a good one does the quality of water matter when using Wacour? sure yeah and not at the moment quality of water is actually very important it's not one of the one of the indicators that or it's not among the information that you can get from WAPour WAPour is largely remote sensing based and remote sensing has its own I mean there are things that you can measure through remote sensing about water quality but it's yet I mean not that accurate and operational that we can use to this extent but it's a very important factor and likelihood is that you would have to add that information from your own ground for water quality into your own specific use thanks for that clarification on water quality a new question why are these data only available I think we have to ask Livia now Livia you want to say would you like to answer that question Livia are you still with us she's typing we can take the next question we can take the next question Livia you would need to to click on the microphone button again when it turns green then you can speak and we'll be able to hear you okay I'll just take the next question we'll send in all two data we use in WAPour in the future so the current project that developed the WAPour database is ending this year and there have been discussions already on this the second phase yes we can hear you let me finish this question and you take it from there the second phase is coming up and there are and maybe it's even related to the question that we asked Livia there talks about extending the aerial coverage as well as using Sentinel-2 data for these analysis because the PROBA-V satellite is having some issues so there's some challenges with the current satellite input data and yeah one of the discussions is about Sentinel-2 data okay Livia thank you Livia would you like to respond to the previous question now no can you yeah okay sorry I still failed to understand how this works but no Marlos replied already and that's correct we are looking for additional partners for well and also in general we would like to grow to other continents and also of course higher integration of Copernicus data in the system so we are working on it and of course more demands we have from users from countries the better it is for us to understand what is useful so again if you have any specific sounds request on having vapor data in other areas you can try direct that to vapor.org and it will help us shape our new strategies thank you okay and I also saw the comment from Jop the focus was on what the scarce areas in the first place Jop was one of the bright ideas behind the project so he was there at the beginning so he knows best so the focus was on what the scarce areas in the first place so that's why they started with Africa in the Middle East we did have a few other questions but they were answered in the chat so perhaps so we could wrap this up sounds good we had really nice discussions we appreciate both the discussions in the chat and also the questions that were answered as a group I want to thank everyone who attended today and I would like to thank also the presenters and Livia who came in as a presenter at the end for your really nice work so before we log off I would like to remind people that we are gathering information on a survey and this survey will just gives us a chance to find out more information about who you are how you use vapor and any ideas you have for future webinars that we can go into more detail with so the link to the survey we will put in the chat box but as you exit the webinar will also automatically take you there via web page so thank you again to everyone and thank you Abraham for being our tech support and we hope to see you again next week same day on Wednesday at 12 p.m. in Central Eastern