 Welcome to groundwater hydrology and management course. This is week 12 lecture two. In this week, we are completing the data requirements and access from the WRIS website. Most importantly, we are looking at hydro-metrological data that we have for the water balance equation to understand groundwater hydrology. We would like to also look at why each parameter is important, just a refresh. I'll do why this parameter is important in the hydrological water balance. So in the last lecture, we looked at the soil moisture. So how rainfall goes in and part of the water is kept in the soil moisture. The remaining goes down to groundwater. So now we know like in the storage, groundwater storage, not all rainfall goes in after it infiltrates. There is some soil moisture that has to be removed. And this will be also used for the remote sensing data that we will be using later, especially the GRACE data. So the evapotranspiration, which is two words, evaporation and transpiration. Evaporation is from the land surface or water body surface, open surface, where water is being evaporated, okay? And the transpiration is the process where the roots take water and goes through the plants and then comes out during the photosynthesis activity. So both evaporation and transpiration start with liquid water, but then it converts it into vapor and then goes up. So these two are kind of losses in the hydrological balance system, whereas one loss, for example, the photosynthesis loss, the transpiration is needed. But again, as a water, it is a loss, right? All the water is not kept in the system. It is taken up and not fully used and then sent into the atmosphere. So once we know how much is sent back to the atmosphere, we would know how much we need to save water for groundwater recharge and for surface water storage. Again, evapotranspiration is, as I mentioned, there is land and then there's plant. Maybe the land you could put some monitoring devices and then assess based on the radiation, incoming radiation, how much evaporation can happen and based on the land type and stuff. But for plants, it's very difficult, right? Because each plant might look different. The biomass is different, even within the same species. The leaf area is different and all these would impact how much water they take and pump. Think about leaves as factories where it pumps the water out of the system. Plustameter, okay? So water comes through the roots, goes through the stem and then goes to the leaf and from the leaf it transpires. So there is a lot of studies that we have done in school where we put the bottle and inside the bottle, we put the plant and we see the transpiration happening, et cetera. It depends on the biomass and the leaf, et cetera. So if you have one tree, it is easier to estimate the evapotranspiration compared to a forest. Because forest, you have multiple things that can go about, right? So we will look at that and that is where, similar to the soil moisture, we are going to use the variable infiltration capacity model, WIC. And the WIC is again driven by remote sensing data because as I said, your incoming radiation is important. The leaf area is important. The barren land, open surface is important. So all these have to be clubbed together in one model and then the result should be how much evaporation plus transpiration happens. And that model which the NRSE uses is called the variable infiltration capacity model called WIC. It has been successfully used across India where a lot of advisories have been built for farmers on this. For example, if they know the evapotranspiration rate, ET, from the crop, then the farmers are advised to say, this is how much volume is lost per day, millimeters per day, you multiply it by the area you get the volume. And so now the farmer knows, okay, if so much water is pumped, would I be able to sustain the whole crop or should I be pumping more water or asking the water policy makers and the engineers to at least more water in the channels? So all these discussions, advisories are based on this model output which is heating based and also remote sensing driven data. There could be some data that has been used for ground roofing, observation data, ground data, but most of the model is driven by your, the remote sensing data. Like earlier, I won't get into the full details of a model, but because here we are just looking at the data and where it comes from. So it comes from a model and the model is driven by remote sensing data. This is how it looks like when I made the slide and there are some changes in the WRIS evapotranspiration link. The link is same, but how it opens, how it visualizes is slightly different and that keeps updating, okay? And so, but the overall arrangement is the same, wherein you have your right side with the focus area, the time and the data and the center part is for the map and the left hand side is to tweak the model outputs, how you want to see it. Okay, so now let us go to the WRIS website where we'll be looking at the different methods and the availabilities of different data. One thing we need to understand that is we do have multiple data, but we need to understand that because it is a government led data, this WRIS data is used widely, okay? So please just understand that it's not one data we are promoting because this is from the government, we are going to use this data, okay? Good, so I'm going to share the screen with the WRIS website and this is how it looks, just to show how you get here, I'm going to come back, so it is WRIS, the home page can be taken up and from the home page, you go back to the hydrometrology and then evapotranspiration, okay? So to save time, I have already downloaded, so I go to water data, come down to hydrometrology and evapotranspiration. When you click it, this page opens and it does take some time, you could see and then it populates. So in the WRIS, you get evapotranspiration, okay? The right hand side says it is India scale, the whole scale of India is taken and you have daily average between the two dates, which is 28 actually three dates, 28 or 9.30, so three dates is taken and the model uses the big model. So the average transpiration across India is 0.81 millimeters, okay, per day. Now, here's the question, is it okay to see the whole India ET rate? No, because at the end of the day, you're going to manage this water and supply water to farmers and people for water management, because this is groundwater, we look at how much goes back to the aquifer, okay? And if you see that the aquifer changes across India, so there is no point of having one value for India as ET. You can differentiate as regions and those regions are called hydroclimatic regions we have here, agroclimatic ecological regions. Those are regions with similar land type, geology type and climate. So you will have a better way of putting this in as an average value for hydroclimatic zones. But for India level, it is kind of stretching power because we will not be using this value per se for water management or groundwater recharge activities. However, this is the default scale, the India scale comes up and a particular date comes up from the VIC model. You can come down, the base layer has been black, which is okay. I can change the base layer if you like. Base map, we will say that I'll put streets so that it might be faster. So yeah, it's faster. You can put imagery, layer list. You want the street boundary, so let me layer list, okay? So the print and data download is there. We will come back to that later. What I would suggest is we will stick to the Maharashtra one because of the evapotranspiration that occurs, okay? So let's do this. The right side, as I said, has your average evapotranspiration in millimeters for that particular time period, three days, okay? And per day is there, the unit is per day, okay? So please don't say that the per day is not put, so I'm not going to do it. It is a kind of a known value because when you say evapotranspiration for what? For a day or for a month, you have to specify that. And when you specify for a day, then the day is gone in the unit, it's just millimeters, okay? So make sure that you know that it is per day and I'll show you the time series of graph also. As initially done, we do have some states. Not all states would be able to be fit on the graph. So you will see down, there are different dates, okay? We have different states here and those states can be put up in this graph when you download it as an Excel sheet. And then there is a daily evapotranspiration rate from 28 to 30. So automatically there is a daily ETI rate given for India for a period of a month. And you can see that it comes down, goes up, comes down and slightly goes up in the March period. So the coming down is also because of less availability of water. I'm reminding you, ETI is the process of evaporating the water, but for that you need to have water. So would there be high ETI during the rainy season after the rainy season? Yes, because you will have water to evaporate. You'll have water for the soil for the plants to take up. There won't be much ETI losses in the summer because already the water is gone, okay? It quickly evaporates and after evaporation there's nothing more to evaporate in the land. So understand that part and it is very carefully to be understood and taken why it is high in a rainy season and why it is low in non rainy season. So coming down, you also have the other states. And again, it's my duty to explain that. It is not that Andaman has no ETI. It does have a lot of forest on it. So Andaman will have ETI. However, because of the size of Andaman, which is smaller, you can see here, it is smaller to the size of the satellite or remote sensing data. You won't have data. So no data should have been there rather than zero. Zero is kind of misleading. I'm going to click India again and show another island state, which is your luxury pilot. Yeah, I clicked India. So I think it's taking some time to go back to the initial stage and looks like the base layer, the black layer is taking a lot of time. So what I'm going to do is I'm going to teach you how to just change the base layer. See, it's not even populating, but when you change the base layer, base map gallery of the streets, and then quickly it will come comparatively, yeah. So let it populate. While it populates, I'm going to come down all these data is already there. You can see luxury. So luxury is zero. So this is what I'm trying to say. There is no zero ETI, it cannot be zero ETI. There should have been some ETI, but it's not there, okay? Because it is not measured, sure. So it should have been no data rather than no zero ETI, because zero is a value, right? Good. And I hope zero was not taken in for the averaging because it pulls down the average value also. So 0.81. So that could be an exercise. You can download this data and then for all the states and then estimate the average. Now you could remove Andaman and remove luxury zeros and then do the average. If the average is same, then it is correct. If the average is different, then it is wrong because Lakshadeep and Andaman should not have zero ETI. By physics, by the land use land cover that we know, there should be some evaporation and transpiration. Always there is some evaporation transpiration. It's not zero. Okay, so now you see the district boundaries and because we have picked here as admin boundaries we want to see. And here we will go back to the full view of India, okay? So then what we do is we want to see if a unit value selection, okay? So then we would going to see for India how it looks like in a particular state, okay? So let us go to state thing. And then as I said, your ETI can be a sum of ETI for a month or average. Average is good because then later you can sum it. So let's do an average. And then there's only one more NRSE VIC model. We're going to do Maharashtra again. Let's pick Jalgo and again. Because Jalgo and we used for the soil moisture. You can see it is very low, but we will increase the date to have more, you know, spread of the data, okay? So Jalgo and it's slightly increased and then the time step is daily is fine and then we're going to go to a month or January. So while it is doing this, you can also see India how ETI happens. And ETI is high in Rajasthan and Gujarat. But the rainfall value and groundwater value is low. So now here's the deal. If it is low in, let's do that quickly before we look at the Maharashtra data. So if this you can see, let me see if I could pull down my pointer. So what do you see here is the dark blues are in the Rajasthan part, Punjab and Gujarat region, which also has less rainfall and less groundwater. So what are they doing is they're pumping more water into the system by using extra groundwater and letting it evaporate and transpire. This is done for crop growth. They're just not simply pumping and putting it on the ground and evaporating. They want to do more and more agriculture. So for that agriculture, there's a lot of groundwater use and that relates beautifully to the evapotranspiration pattern. You will see less evapotranspiration on the water bodies because moving water evaporates slowly compared to a standstill water. For example, you have a dam. The dam water would evaporate faster than a moving water because while moving, it does cool down, okay? So you have these kind of effects and also you have a detailed view of all of India where the ET is high and ET is low for that particular day. So we have picked Zalko one and that one month data is taken. So you could see that it is going down and then slight blip and then goes down again. So, but if you take like your soil moisture from your June month, which is your monsoon month, there's no summit button. So when you do the trick is when you do the final date button, it automatically happens. And beautifully you could see it go up and come down. So here's where the ruby crop picks up and the curry is here, which means during the rainfall season, the ET is slowly building up because people would put crops and then wait for it to grow. And while it is growing, you have the crop water requirements and those water requirements are converted to evapotranspiration, okay? So then what happens is you have also the data on crop type, crop area and acreage. So all these are put together in one model and then the evapotranspiration is taken up, rainfall, radiation, crop area, all these things, right? So you could see that it's slowly picks up and then the growing period is attained, water is there, it grows and then it starts to stagnate. Okay, it comes down and then one low value is attained. This could be different for different districts. You can zoom in to the district if you want. This and double clicking and zooming in. And you could see how the boundary is put for gel go on and you are getting these data. You could see it is like a box, box type. Why is like a box, box on the edges is because it is a pixel, a pixel is a box, right? All these are remote sensing driven data. So the boundaries are not set like original boundaries, it is set as pixel. So if you have one pixel, you draw it. So you have a pixel which is coming in, okay? Like this. So you can have part of this data is going to the other district but part of it is, most of it is within your district. So you'll keep that major part of the data. So there is some averaging that is done at a district level, which is beyond the scope of this class. So I will not be teaching it, but please understand that the boundaries are kept for the pixel data also. So now you have this data and you can download it as an CSV file or you can take the image as a PNG draft. So you can just quickly put this in your reports and other work or just take this as an image. But you can also download it as Excel file CSV file and then you can work on it as a table and do more calculations. Then when you come down, you have the average value per date and you have different dates here where you can put different values. And then you have the data downloaded options are available. So this is how you could see the data for evapotranspiration. It is hard to now break the evaporation and transpiration. All you could do is, you could say that this is kept within, one second, I'll just close this district because it is actually causing some confusion. It is just an example. It says, do you want to see a morality? And then when you click a morality, it goes to a morality and this changes. Okay, so don't get confused by this name. Our district is to gel go on. So whenever you want to assess the data, go back to this name and then you have it as gel go on. Okay, so here is where you could download different data for evapotranspiration. And most importantly, looking at it at India scale does help for seeing the spread of evapotranspiration. And evapotranspiration is not going to be the same across India because of the princess in rainfall, land use land cover type, soil type, and also the plant type, which is part of the land use land cover data. Okay, so we do have again, the default dates coming. So now here's the point. So 30 is there and today is six. So within seven days, you get this data. And this data can be used to understand how much your land is consuming in a district, okay? So for example, you take a district where you have majority of the land as one type of crop. Now you can tell the farmers saying that per day, you're losing this volume of water, which is for example, it is one millimeter times 100 meters square. So you get a volume, right? And 100 meters square is too small, but let's keep it for the calculation purpose. And the one millimeter is 0.001 meter, right? So you're losing one cubic, 0.1 cubic meter per meter square area. So then what happens is the volume is now calculated and you tell the farmer, this is how much you're losing per meter cube, meter cube, meter square of water. And then now they will understand that, okay, do I have that much water to sustain my crop? Because now you know that the crop is going to grow for three more months. And do you have water for the three more months? For example, this is the average value you get, okay? Let's assume this is a state and a district. So there is a growing period. Do you have this sustainable water resources to sustain this growing period? Do you have the ground water? If not, it's better to stop today rather than putting water and then losing the crop and the water. Remember, the crop has to be fully grown and then you harvest it for the market. You cannot harvest half the crop, half grown crop for the market, it is useless. And a lot of times this happens, they cannot do anything so they just let the cattle feed on the crop because the crop is lost, there's no water. So these type of advisories through the remote sensing platforms helped these farmers in assessing the dates for irrigation and also what type of irrigation and how much acre they can irrigate, okay? So these things can be combined together in one platform which can help these farmers tremendously for setting up a good water budget for the ground water. With this, I will conclude today's lecture. I will see you in the next lecture on more data for the water management. Thank you.