 Hello everyone. Welcome to NPTEL course on groundwater hydrology and management. This is week 11, lecture 3. In this week, we have been looking at important data that we would need and require to manage groundwater properly. The explanation about the parameters and data has already been done in the previous weeks. In this week, we'll be looking at exactly where we can get these data for better analysis. So we will start with the groundwater parameters on WRIS website, after which we also identified groundwater quality data that can be taken from these websites. I also want to urge that there are a lot of reports and publications from which data can be taken. This is called kind of data mining and data research where you would read papers and then find data that is suitable for your work. You would also take data like groundwater levels, storage, etc. from government reports, which can later be incorporated into your work. So since we've already seen the most important groundwater data parts, now we'll be focusing on the hydroclimate data. Why do we need hydroclimate data? We already saw in the hydrological water balance slide that there is a storage change, which is estimated from your precipitation plus your Q in, which is your discharge coming in, you have it here, and then minus your Q out, which is your discharge going out. We also would take the negative part in the equation coming from your ET because it is a loss. So evapotranspiration is taken as a loss and ET, and you have G in, G out. So groundwater in, groundwater out. So this can be your net storage inside the basin or inside the watershed along the surface, or it could be a storage in the groundwater. So there could be either a surface storage, which is given at the bottom, or a groundwater storage. So if you make the equation in such a way that you are focusing on the groundwater as the parameter, then this would be precipitation converted into your infiltration and coming in, recharge, but all these would be captured here also. So please understand that the storage is a combination of your surface water plus groundwater. And then we would look into more focussily on the groundwater for this course because it is a groundwater course. Still you would need to understand the dynamics of all the other parameters for which you need to get the data. So let's look, one of the most important, we won't go to all the data sources because all of them are in the WRIS website. But I'll just show you the major ones, which include your precipitation, your discharge and evapotranspiration. Groundwater levels have already been seen. So rainfall data is collected by IMD as a point observation data. IMD stands for Indian Meteorological Department. It is the key nodal agency that is responsible for collecting these weather parameters and hydro climate parameters, most importantly, rainfall, air temperature, humidity, etc. Then what happens is because the location of IMD may not be fully covered, you would include ISRO data. And ISRO data is a satellite data, Indian Space Research Organization. And then you have the state agency. So NRSC is a wing under ISRO, which is responsible for collecting the data and providing it back to the community as a product. NRSC stands for National Remote Sensing Center. This is a national level agency under ISRO. Then you also have RRSC, which is the Regional Remote Sensing Center. Then you have the SRSC, which is your state remote sensing center, and SAC, which is your space application center. So you could see that just the umbrella of ISRO has some satellite manufacturing designs, etc. On the other side, there is launching all the hardware, software, the rockets and all. And they also have a wing where they process this data and give it to the public in open source platforms. We will look at the remote sensing data platforms also while we close up this course. And as I said, NGOs also give data, and all of this data can be housed in one location, which is the WRINs website. There are other state agency websites also, which would take these data out and store it individually in their own archives and database. But the government has given the provision to all states to host it on WRIS, because groundwater doesn't have state boundaries, nor your water basins have state boundaries. So there is no point in restricting the data to just one state. You will have to share it to better manage it. And this novel idea has been used across many countries, wherein one rainfall portal is available and all the data is stored there. So slowly, all these state agencies are also putting their data. Now, one should understand that the IMD takes observation as a point and then converts it into a smooth surface, which is a raster, a pixel. It takes it at a point location, but then it converts it into an area, area of influence or area where it interplays it into an area. Whereas the analysis by default, a satellite image or a satellite data, which comes as a raster or a grid. The state agencies are point data, again, which are very useful for locations, specific rainfall. And these points need not be the same as the IMD. For example, if IMD is putting a station along one street or in a village, the state agency might have it on the 10th street in the village. So there could be some differences in the rainfall calculation and estimation. Just broadly view it as there are multiple agencies. One is at a national level, observation data. One is at a national level satellite data. And then you have the state agencies and NGOs. So without further ado, I'm going to start the website for the data that we can show for at least rainfall. So what we have here is we are going to show that your rainfall data can be taken from the WRIS website. Since we already work on the WRIS a lot, and you know how to navigate it from the previous data sources, we will continue to look at the new data sets that we are going to show. And that includes your rainfall. So I hope you could see it. It is coming up slowly because of the internet. And as I said, you can have this in a fashion where you can have the data coming and then later you can have the remote sensing satellite data all coming in in the same webpage. What we need to understand is there might be some differences in the data that is collected at a point and location. So now we have this WRIS website set up. What I'm going to do is I'm going to go back to home where we had the initial data. And then we go to water data come down to hydro metrological and first option is rainfall. So hydro means water and metrological means the atmospheric data parameters, etc. So you have a set given here rainfall evapotranspiration soil moisture and agro climatic ecological regions. We're not going to talk about that the last one now. We'll just focus on the rainfall and the other data. So I'm clicking rainfall. It does take a little bit of time depending on the internet speed because it has to pull a lot of data to make this webpage open and it's opening now. You could see that while it populates it automatically by default takes a data range which is 01 June 2021 to 29 March 2022 using IMD grid data. As I said IMD is a point data at a location they collect data and then they merge it with other products to give us space, a grid. The grid is nothing but let me draw it. It is like your graph paper. So you have this as a point data. This is your point location where the measurements are taken and now this is going to be converted into a grid. So they'll make it as a grid. And so what happens is whatever is within this grid is taking the value of this point. So this is 50 millimeters of rainfall and this whole grid is given 50 millimeters. Like that all of the grids have will take one value based on the point data they have. If they don't have a point data in the box then it is interpolated. And this creates a beautiful map. So it doesn't mean that every inch of India is monitored for rainfall using IMD but it is interpolated. Those who would like to have more information can search what interpolation means and understand the part. So as you know there is a right panel which is similar to the groundwater data. In the right panel you have the India focus and then the date and then the normal rainfall NRF or RF which is your average rainfall for the past years. And then you have the actual rainfall and how much deviation. So right now you could see that 115 millimeter rainfall is there for a normal and the actual is a little bit lesser. So it is if you do the calculation it is 5 percent lesser. When you come down you could see that there is a graph which says clearly which months is below the average. You could see June, July, August 2021 has been below the long-term average and the other months have picked up more rainfall than the average time. So it's slightly a shift or a double peak happening. Instead of one peak happening you have a double peak happening which is also a concern. Because for example if a farmer says I'm going to plant my June crop like June 6 the rainfall comes in Maharashtra western Ghat region. So if they say I'm going to plant my crops and then suddenly there's a good rainfall which fits the crops and then a dip in the rainfall then there is a loss. So this is what this graph shows in the overall trend how does your year perform for the data that we have. So what I'm going to do is I'm going to say all agencies because I want all the data to be present and if you click the time step there is daily, monthly and annual. Please be informed that daily and monthly are good to understand the variations in the rainfall whereas yearly will just be a big number okay and we won't know like these double peaks or these things happening. So it will be good to understand the differences in the scale resolution. The data, the point data is collected every hourly or sub-daily or at least daily intervals and then it is summed to a month and all the months are summed or added to an annual rainfall. Remember rainfalls are normally given as annuals or seasonal okay so then so for that you need to understand daily which is converted to monthly and then monthly converted to annual and season. The other important aspect about rainfall data is the millimeters unit. It comes in millimeters which is given here and you could see that overall average is given as 115 normal rainfall and most of the regions don't get that rainfall so if it is below the average normally it's zero to 600, 600 to 1000 are in the red region and then your 1000 to 1400 are the yellow region and the blue and the green are where good rainfall is happening which is about the average rainfall. You could see here above the average rainfall. There are some regions with more than 6000 millimeters as they mentioned and I would assume mostly it is here where Chirapunji and etc where it is one of the most bettest part of the planet we have. So that's where the beauty of India where we have the extreme high rainfalls in the planet and also a deserted region where there's not much rainfall all in the same country. Okay so let's do one study. I'm going to take the annual rainfall for 2021 so let's go Jan to December. So you could see that I've taken a year 2021. For some reason the long-term data is not showing right now but most of the time the long-term data does show up so don't worry about why it's not showing up so just check the website often and you will eventually get it. You can also give a feedback maybe they will reply to you by email if you can contact them why the data is not coming. So I'll just show you how long the data exists. You could see here that the data if you click the same as the groundwater. If you click years and then go back it goes up to 1970. It's the first year that you have this monthly or daily data taken from rainfall and then the latest as it's given here is 29 March just two days before this recording. So I'm going to leave it at the same date which I am 2021 December 2021 and I want the some not the average. Average would give me only the monthly average of the whole of India which there's not much use of it because monthly average is different for different regions. The monsoon period is different for different regions. Let's take for example Kerala and Maharashtra both are getting the monsoon from along the western guards but the Kerala monsoon first comes and then after that some days later the rainfall happens in Maharashtra. So let's do some and then advanced filter doesn't work. This is all stations or telemetry or manual. So you're just going to say all see telemetry is where data is collected through the instrument and GSM box relays the data back to the computer and then you have the manual where a person goes and collects the data every day and then records it and to the database. Let's say all just for the clarity and I've clicked submit. Now you have a beautiful picture of the how the rainfall has occurred and where it is almost zero and where it is increasing and decreasing based on the monsoon onset. You could see that in across India how many stations are there. So there's 23,000 plus stations recording data throughout India. There's not many here in the Kashmir part but then there is a total taken per year for that data range and then you average it and then take the long-term average and actual rainfall is for this period 1 Jan to 31 Jan, December 2021. So that's exactly one year of data and this is how much rainfall we get 2006-72. That is a 119% more positive than the overall trend which shows that 2021 has been a blessed year with good rainfall. There's also been a lot of floods but at least most of the rainfall has helped in recharging your groundwater system which is important for this lecture. So now I'm also going to show you how to zoom into a particular location and a particular station. So now you can download this data, you can convert it into a line graph by clicking the line. You can download the data by clicking this button. We have already seen this in the groundwater class and then all the states are here. Since IIT Bombay is from Maharashtra, let me just type Maharashtra and Maharashtra comes. Once I click Maharashtra, what happens is similar to the rainfall groundwater data that we do. See groundwater also, I'll just check if Maharashtra has been selected. So here you can see that Maharashtra has been selected because of the dust it's not clearly visible. So this is Maharashtra selected and that comes here. Now what has happened is the total number of stations come down. Even though you see all of India's stations, what has been used for this average is 1,473 and the deviation is plus 61 percent. Then the data has come. Now I also want to focus on eight particular districts. So let me delete this to see all the districts and I'm going to say maybe Amaravati, I will take the data. You'll be surprised to see that once I click on the data sets, there will be other data that we never looked at in this exercises. Because most of the time we are told which data is available on the slides. But here what happens is they don't tell you exactly where the data sample is taken on the drop down menu. But when you look at the station, it is coming. So I've selected Nagpur. You can see Nagpur district has been selected and these are the stations. So in Nagpur, how many 34 stations? So if you come down and count all this, there will be 34 stations in Nagpur. 35 is the total. So here is where the source is given. You could see here that Maharashtra state, the state government body has sponsored a lot of stations and they are monitoring it. And then you have the CWC, which is the Central Water Commission, another agency which is responsible for water data, especially the discharge data. Then you have, I'll just see what else IMD data is there, but it's not in this at least picture. So I'm going to click one here and look at it. When you have a dash, when a dash is put at the actual millimeter rainfall column, that means that station is there, but it is not collecting data. Some issues there. Maybe the person didn't go and collect data or the instrumentation is broken under repair maintenance. So for now, let's take number 30 and the station name is Thimbuddo. Thimbuddo and you could see that in June, July, the rainfall picks up and then comes down in September and there's not much rainfall. You don't see other data in the first Jan to 31st December, only this much you see. And we'll have to go with that rainfall because here we just go giving an exercise of how to identify the stations and stuff. So for example, if you want that station in that particular district, you can also click on it. So I'm going to click on another station to see if there is good data. And as I said, mostly the data is not available. But here, luckily, we do have data. Now, if you come down, you can download this data into your Excel PDF or tabular format. And it is also giving you the lat log, which means exact location of the station. Please understand the format of that number. It is very useful to understand that these decimals are given to correctly position you at that point. And that is what is happening here. We have selected that point. And we have the lat log, which gives you the exact location. And the actual rainfall, which is the total rainfall in that period is given as 128.6, which the division is giving here. And then there's a small report, if needed, generated by these websites to support the data. So now we have started with India Rainfall, come to Maharashtra State, then from, say, to Nagpur and Nagpur to Kamalthi Kiran, which is the flow chart, what we want. Kamthi Kairi. Kamthi Kairi is the Kamthi Kairi. We have come from India, Maharashtra, Nagpur to Kamthi Kairi. So please understand, this is the format in which this website houses the data. India State District and then the station. You could download this and keep it for your analysis. And further, you could also select which locations you want. Most of the data will not be available, as we saw, not all data is available. And it's also good to understand which is the agency that is supporting this data. And also the basin in which the data is placed, which is the Gujarati Basin, Maharashtra State, the agency's Maharashtra State. Okay, so with this, I've showed you how to take rainfall, all the other data aspects are similar as your locations and ground water data that we discussed. I will see you in the next class for the next data set. Thank you.