 NPTEL course on groundwater hydrology and management. This is the last week, week 12 of groundwater hydrology and management. I hope all these lectures have been in a coercive way where we started with understanding what is groundwater. We understood the issues in groundwater management and then we understood all we discussed about the important parameters for groundwater management. And then we looked at finally the data that is needed for groundwater management. So on the data side, we looked in the two weeks, week 11 and week 12, we will be looking at what is the data that needs to be collected to assess groundwater damage and groundwater resilience. It also is needed to understand what data is important for helping farmers in managing groundwater properly. On that note, in week 11, let's do a recap of week 11 and how it's been to week 12. Week 11, we looked at groundwater recharge structures determined by the government and public private data where these structures are present and how are they helping the groundwater recharge. We also looked at groundwater quality as a tool to understand groundwater depletion scenarios and other aspects, but also groundwater quality was very important to understand the usability of water because if water has a lot of arsenic, you cannot use it for crops or use it for industry and drinking. So quantity and quality are both important and have to be managed for sustainable development in rural India. So the groundwater quality standards we looked at using the CPCB board, Central Pollution Control Board data. And following that, we looked at different hydroclimatic data that is needed for understanding the groundwater resources. The hydroclimate data starts with your rainfall and we looked at other data that includes your storage structures, river discharge and other aspects. Now we are coming to week 12, which is the second part of the groundwater data or data needed in the water balance. So in all that mode, we'll be looking at soil moisture data and evapotranspiration data, which form a bulk in the groundwater budget equation and we'll be looking at groundwater reports, data, et cetera. We'll be looking at remote sensing data in this week. Remote sensing is defined as the method in which you collect data from an object without touching the object, okay? So because a lot of times you cannot have the luxury of measuring the parameter physically like using a meter or a measurement device like groundwater, sometimes you will have to depend on remote sensing because of low cost, high spatial and temporal resolution. On that note, we will go through some remote sensing data, especially GRACE data, which is one of the most important satellites across the world, which is monitoring groundwater depletion. And finally, we will look into a conceptual model idea or framework where we pull all the data that we collected into a single model and that model will explain at time zero to time one what has happened. Again, this cannot be done for a real life scenario or even a theoretical scenario fully because every time has to be accounted for your conceptual model will therefore give you a time snap and that could be used in a automated model to run for different time zones. We will look into the conceptual model in detail before we close this week. And the soil motion evapotranspiration are also driven by remote sensing data. It is not physical data always and that is what we will cover in this week lecture also. So without further delay, let us jump into the week 12 groundwater data aspects. As I said, this is the balance equation that we started with. We understood what DELEIS is, which is the chain in storage, either groundwater storage or soil surface water storage, river discharge, et cetera, as Q. ET we will discuss in this week and your soil moisture, which is part of your storage, water storage component, we will look at in detail in this week, soil moisture. So soil moisture we already defined in the previous classes, but let us give us a very small brief introduction. So we have soil and within the soil we have materials and space. The pore space, which is in between the soil material differs by different type of soil types. So for now we can understand that there are soils with high porosity and low porosity and that pore space can be filled by vacuum, air or water. Vacuum is not always that easy, so it doesn't form that widely in nature. So most of the time it is either air or water. When it is fully filled with water, which is the pore space, the volume of void inside the soil, if it is filled with water, we call it 100% saturated or saturation. And when it is only air present or vacuum present, which would mean that there is no soil moisture, it is called unsaturated, totally dry or unsaturated soil. And unsaturated can be anywhere from 0 to 99 point percentage because even if you have some particles it calls any saturated soil. So what soil moisture is, is like a percentage, percentage of the void which has water in it, okay? Not the soil particles, just the void and it can range from 0 to 100, as I mentioned. So which is helpful for plants, let's have a quick diagram on it. We could see that you have time on your x-axis and soil moisture value on your y-axis. So if the soil moisture is high and low, it goes like sinusoidal because of the seasonality, rainfall, plant update, okay? So if the soil moisture is high, 0 to high, then there is no need for the farmer to put water, okay? And when it is low, yes, the soil moisture has to be enhanced by supplying water. So there's water being supplied at this stage and that is why your soil moisture goes up and then down after harvest, okay? But during harvest, you would notice that farmers do not irrigate the feed, which is when they're cutting the paddy, sugar cane, et cetera. Whereas the crop is grown, all they have to do is cut it, take it to the market or mills or sugar cane factories. But in that phase, you don't have to irrigate the feed, but other phases, yes, you do. Okay, so this is what soil moisture variation is very important for. And then the other aspect is at what depth do you take soil moisture? Let's say this is zero soil and then you go down deep. So from zero, you go to 10, 15, 20, 30 centimeters down. They will say 30 centimeters. And we know that most of the plants, okay? Most of the plants have root zone within the 15 centimeters. This is your root zone, the roots spreading down into the soil. So for that reason, mostly soil moisture is measured up to zero to 40 centimeters or 30 centimeters. Let's say here it is 15 centimeters is what the government reports to the NRC. So soil moisture can be taken from data physically like zero, 10, 15 centimeters deep, you put soil probes and take the data. But as I was saying, it is very expensive time consuming. There are a lot of methods that use satellite data for driving soil moisture output. And that is what we'll be looking at today through the government's data portal, okay? So soil moisture data we're going to look at and it is driven by remote sensing data, wherein you're not touching the soil and collecting data, but using satellites or remote sensing objects to collect data. So how it collects data is it sends a pulse, a pulse of energy, electromagnetic wave, which penetrates into the soil and that penetration level is correlated with the soil moisture, okay? So all this we have seen from other research in other works, but since the class is not going to go deep into remote sensing, I will stop here and say that is it about remote sensing. This for you, you can understand that it is a data source that is used to collect soil moisture without touching the soil, okay? And most data satellites. So because of the satellite, it comes under the National Remote Sensing Center, which is an arm of the Indian Space Research Organization. So ISRO is big and within ISRO, you have multiple agencies that work for soil water and other aspects. So basically multiple agencies that work on data and the key is the National Remote Sensing Center, NRSC. Then you have the SAC, which is the Space Application Center and then you have the RRSC, which is the Regional Remote Sensing Center. So the national is at a bigger scale and within the national, there are regional centers. For example, there's one for south, west, east, north and also the central part of India. And these centers work on specific problems for that area using remote sensing data. So where does the NRSC come here? So NRSC, what they do, they collect the remote sensing data and then put it into a model which gives soil moisture as an output. And that model is called the Variable Infiltration Capacity Model, which has been developed by a very well-known team. So that team model is open source. Anyone can use it. And the NRSC, since it is driving it with their own data, it is calling it as an NRSC VIC model or WIC model. So all this data is housed in the WRIS website. So we'll be looking at that website in detail in this upcoming lecture. This is how it looks. The snapshot I'm showing, why I'm showing the snapshot is because it was having some issues to capture it in your class videos. And as a result, I was even thinking of taking some other material for the class, but luckily it is working, so I am recording the session. But please understand that there will always be issues with the website because of the data getting populated often. And it is operated on a lesser budget than compared to the NASI, ESI, et cetera. So you cannot expect as high bandwidths for this website. So there is some slow part, however, it is good data. So please try it often if it doesn't work. And one time you can find it work and then download the data. Don't try to use this to operate with data and et cetera. Download the data, fix your location, download the data and use GIS software like QGIS, open source to run your models and estimates. So let us go to the website. Okay. So now we are at the home website, WRIS. And then I go to water data and then I go to hydro meteorology. In the last lectures, we have looked at rainfall. So now we are looking at soil moisture. As I said, I'll have to click and then wait for some time for the data to populate. The snapshot I showed you was for entire India and similar analysis is running behind now. So you could see that the date is being fixed up to two date ranges, which is 22 year, March 29th and March 30th of the same year. So just two days, this current population is happening, the data population. And it is at an India scale, okay? So you can see slowly the data is coming up and they have taken only till 15 centimeters of depth. So I'm just concentrating on the right hand side now to show you what the data is and how it is being stacked up. We have the India scale and India scale is a spatial scale. Temporal is only two days. You have March 29th and March 30th of 2022 year, which is just a week ago of this recording. And the average volumetric soil moisture of these two dates is 25%, which is pretty low compared to the planned requirements. And then if you look at soil moisture as statewide, only some states are given. You can add more states by going now and then going to the next page, et cetera. And let's come back to zero, let's come back to zero because it's still typically okay, but practically it's not possible. So some issues that we see what issue. But other states are recording a good soil moisture of the best in this lot is Kerala with 45.49. And then you have Manipur, Megalaya region also high. Why is soil moisture high in Kerala region? As I mentioned in my Western Guard rainfall precipitation aspects, the Western Guard is there. So you have a lot of rainfall just pouring in on the Kerala side. And with water and slopey land, there is always degradation of soil, formation of new soil, et cetera. And then decomposition happens at a faster rate. We have thick soil and that thick soil can hold a lot of water. But because water is also available a lot. So you have a good soil moisture profile in Kerala. Moving down, you have to, so you can download this data by just clicking on this and a CSV Excel or an image. If you like the graph, you can just download it as an image for your report. If you want to work on it, you can download it as an Excel or CSV file and then apply other algorithms to it for better graphing. Then now we are down to daily volumetric soil moisture. This is a point data average for the entire country. So you could see that on 29th, it was 25.67. And then on 30th, it was 25.45. And the average of these two dates is given here as 25.56. So what we've seen here is on the right hand side, the data for the entire country and per date as an average. When you come down to see how the states perform, you would see that Andaman has zero. As I was saying, there is no practical zero value. You can have a theoretical zero value. And here it's more likely like theoretical because Andaman Islands for sure would have good rainfall and soil formation. Less industrialization has happened. So there is less groundwater abstraction. So what is happening there is because here, so here is your Andaman. Because your Andaman is smaller in size than the pixel of the satellite, it cannot drive a model. See, your pixel is the size, lens, the image per pixel, per grid of what the satellite can do or higher the resolution of the satellite. And if the resolution is bigger than the actual size of the land, then it cannot capture the data because you'll have more noise coming. That is why Andaman is removed. And the other similar land is your Lakshadeep. You can see Lakshadeep is also zero for those two dates. It's not because of high evaporation, low water availability, there is no water, you can just think on that. So this is the right hand side. Now we will go slowly to the left hand side part. Before that, we just have a look at the coloring that has been used. So this is the legend. The legend says the blue colors are higher in water content regions, whereas red and orange are lower in water content. You see that most of the central India has issues with the soil moisture because there is no blue color in that region. You would see blue only mostly in the western black region and other regions, okay? And we'll click India again so that the India scale comes up in the second. And then we'll move slowly to the left hand side panel where we had data on the tables, what data is, et cetera. Because I clicked India, I think it has refreshed its page. So let it come. While it's coming, I'm going to explain these points. So you can take the unit, the unit of your analysis as state or basin. As I said in my previous classes, the state is more important here because the rules regulations apply at state level, not at basin level, and the state has its own missionaries to supply water, like your state water department, groundwater department, which can supply water for agricultures. That is why we will keep the state as a unit-wise selection. And then the source of the model is always going to be an RSC, VIC model. There's not much we can pick, but you can pick the state. So I'm going to say, because we know that Rajasthan and Gujarat are in the red. So let me select Gujarat. And then one district we can pick later. Gujarat we want, and so yes, Gujarat is selected. And you can see the selection is here. For some reason, it is on the other side of the webpage, which is fine, we can bring it back later. Then you can see which region we'll do. Okay, let's do Maharashtra. There is a reason why we do Maharashtra is because you have sugarcane growing more than a year. So soil moisture properties can vary from between the season, but also because of the data on rainfall and other things. So I'm selected Maharashtra. Let's give it a second. It is populating, there you go. So Maharashtra, it is 24.29, which means it is lower than the national average. Not much you can deal with the national average is because the soil type is different, rainfall is different, and the management is different. So comparing it with a national average might be an over stretch, but let's see. And then we come here to select the district. I'm going to select Jalgaon because Jalgaon and Parbani, there are a lot of agriculture happening using sugarcane. And so we're going to see how this can be captured in this website using remote sensing data. Now you can see that the coloring scheme has come up. And then, as I said, we'll select Jalgaon. So now we're zooming in. So hopefully the other areas populating with data may not be, and then we're going to see this. So here you see what you see is the data for Jalgaon, which is populating here with this 29.49 percentage. And you don't see the line graph of the states because that is irrelevant here. And you have the district-wise two data points which is average to 29.49. And then the next aspect is the time step. Do you want daily, monthly, or yearly? See monthly and yearly is of less use because a farmer wants to see the soil moisture and then apply water. What are they going to do with the monthly estimate or last month soil moisture water is going to be helping? So daily is good, but because of the way the website is slowing down with data, it may take time to run larger time stamps or time period. So just make sure you run little by little and then add the data. So I'm going to do a one-time zone. Let's do one month just to showcase how it is. And you can see that in April, there's no data. It's only five days in April. So let's take March one to March 30th. Okay, there's no summit, it just runs by itself. Okay, so now you could see that while it's still running, the data has already populated. So first of March, end of March and you could see that the soil moisture content was almost stationary and suddenly it went up. Maybe there was some water application in the area or a big rain that can push the percentage up by 5% and then slowly decreasing. So why is it decreasing? It is decreasing because the summer has already kicked in. So by the end of March, the summer is at full swing, which means that this evaporation is happening less water is going into the soil profile and so the soil moisture is less. Overall, on a national average, it might be healthy, but technically at least more than for 50% the soil moisture is needed for all crops to grow. So still there is some need for irrigation. So how do you know that the full potential for that you need the whole time series of soil moisture, right? So what I'm going to do is I'm going to go up to the month where you have rainfall, which is June. And then we'll map it till the end of today date availability, which is March 30. So leave the image, the images of not much use because it's just going to be a color for a district. What we're worried about is this pattern, this up and down pattern which is happening. So we have a soil moisture. When does it go up when water goes into the soil? And as I said, there has been a good rainfall season from June a month till August, September also we had good rainfall. So that actually increases the soil moisture value. You could see that the peak values of 64, 64.91 all were available during the monsoons season. So once the rainfall stopped, the farmer still grows the crop and he or she will use other resources if there's no groundwater or surface water. Here what he is, they've been waiting patiently every time and then rainfall comes saves them and then rainfall comes saves them. But after this period, you don't see that fluctuation much, why? Because there's no recharge into the soil moisture to enhance the soil moisture. It is purely coming down because of your climatic factors especially your weather patterns of summer and spring. So your spring had good water, your water soil moisture levels were up but then after that it slowly comes down. So most of these regions, you won't see much farming in summer season because they cannot afford to pump and put the water into the soil and that is part of the game in understanding these properties. Okay, you can download this data using the same tool as CSV or Excel and then you can make a different analysis using this data and you can come down to CD date per date value. So this is also an Excel sheet which you could download or a table. So most of India has been blue in that time showing that the soil moisture was healthy. However, it doesn't make sense to compare between states because every state has their own different soil and crop. So it is very important to compare within the state within the districts how the soil moisture is changing and then based on that some help from the government can be given to farmers in growing crops sustainably. Otherwise there's just invest in groundwater and keep on pumping these water until it is depleted with no more love for agriculture. Okay, so we have seen all these data aspects and then I will close this and come out a little bit. So it will have the whole India picture and you can see that slowly the other data is popping. It's not like the South or the North doesn't have data measured. It is a satellite data so the whole of India is measured using these products. So with this I will end today's lecture on soil moisture. I will see you in the next class on a different data book. Thank you.