 Hello, everyone. Welcome to NPTEL course on rural water resource management. This is week 11, lecture 4. In this week, we have been looking at data to collect for understanding rural water resource management. And all these data follow the same teaching that was focused in class, which is the key parameters, the hydrological cycle, and how these parameters are related to each other. In the last class, we looked at rainfall. And the key agencies that collect rainfall are IMD, which is the Indian Meteorological Department, and ISRO. The space agency, which is the predominant agency for India is Indian Space Research Organization, ISRO. And they have multiple agencies, as I mentioned, of which NRSE does these rainfall products for India. Sometimes IMD and NRSE data are merged as one data set, or you can also download IMD separately, ISRO separately, and then work on it. There are other state agencies also in the WRIS website. In the last class, we looked at this website and some norms that we discussed what is normal rainfall, what does the actual rainfall mean, and what does percentage deviation mean. We also looked at the key tools which are on the left side of your window. And we looked at the basin boundaries, admin boundaries, etc. In this section, we will focus on downloading one data set for rainfall. So let's start by going to the website, because it does take some time to download and assess these data. Please make sure you have good internet when you have to download this data. As I said, it starts with a monthly cumulative rainfall information, which is given from 1 June to 27 June 2022, using IMD grades. And what we will do today is focus on one good rainfall area, which is Maharashtra on the western guards, and also one maybe in Rajasthan where the rainfall is low. So what you see here is a rainfall heat map. What does the heat map show you is when it's red, like a heat, and also a map of where it is happening. The red indicates very low rainfall, 0 to 600 mm per year, whereas blue indicates a good rainfall. So let's look at one thing is the sources. As I said, there is IMD grade, there is NRAC grade, and you could download and see what do these mean by the manuals and about page. You can also give feedback here. So my point is let's do some understanding of the data set. In this, if you type in, let's say, Thani, a district name, it will not come because it is at a state level. I'll show you how to get at district and block loads. So first let's do this APWRIMS, so AP stands for Andhra Pradesh. And then I'm going to say from June to July, what is it? Let's just leave it as is and then hit the summit. Anything else? Just change the location source of APWRIMS. So what you find is that suddenly the AP, which is Andhra Pradesh, state has been highlighted, and the stations where the data is collected has been highlighted, located, and the data has been plotted. So you could see that compared to the previous India scale, Andhra Pradesh is getting a little bit more rainfall annually because we saw it was somewhere around 1200s at an average. So just if you want to see, you can go back to this button. You could go back one more time and say all data. All agencies and then it just populates everything. Hit submit. For whole India it will take some little bit time, but then it will populate it. Okay, so all of India has given and then for this period, June to 2021 to 2021, this is the average rainfall and this is between two months, right? So June and July, it will give you also the date as June 1 to July 31st. Okay, cumulative data. Now we're going to focus on only one data set which is the state data set. I'm not going to use that for entire India. What I try to say here is, if you click on the date and let's say Jan to July, you get some data points for the AP. And you could see that, oh wow, all this state has been covered. A lot of stations have been covered and you can come down to see the district. Now the district names come up. Okay, and if you say Anantapur district, you just click on the district, then the station names will come. It will also zoom into that particular district and all these station names come. So you could see that this is Anantapur and you could see all the station names. And what are their measurements? Actual means the current and the normal is the normal for that period. So actual is the one Jan to 23 July, which is what you put as actual date, whereas normal is the 10-year average kind of. Okay, all long-term averages, keep it long-term average. So for now, let's go back to all agencies. If you want to quickly to come up, you could just hit refresh the page. Sometimes if you go too much into the webpage, it does get stuck or it's better to just refresh the or clear all the filters that you would have put. So what filters would we use? We used the state as AP and also a time, right? So I hit refresh and the other AP was having around 1800 whereas here you see 1100. So overall AP is higher in rainfall compared to the overall India. Normally you don't compare overall India for rainfall, why? Because we are blessed with a lot of diversity in rainfall. For example, if you could see there are high, high rainfall regions in the north. And then one of the most wetter points on the planet is also in the east, whereas the west has some desert and so less rainfall. Whereas the central India is kind of average to above average rainfall. Okay, so let's do this. So this is the date given from 1 June to 27 March 2022. You could see this is the values. What I'm going to do now is focus on one particular state. So first I'll click the state to focus on it, Maharashtra. Let's go step by step, right? So here I'm going to click all agencies. I want all agencies for Maharashtra, not IMD, not NRAC. So when you download the data, sometimes you will be given this is IMD data. This is NRAC data or sometimes it'll be clubbed as one data because IMD also uses a lot of satellite products for rainfall assessment. So moving on, what we've seen is a monthly time step. So the data is collected daily. Okay, so all rainfall data is collected daily. For example, let's say if you do monthly, like groundwater or evapotranspiration and other parameters, what will happen is sometimes your rainfall happens only in three, four days in a month. So if you wake up and say, okay, every 15th of the month I'm going to collect, what happens if there's no rainfall on that day? Does that mean like, for example, March 15 you collect data and if there is zero rainfall, does that mean the average for month is zero? No. It just tells that that day there was no rainfall. So for that, it is always good to do a daily estimate. And in this website, all the data is clubbed and made as a cumulative monthly data. Okay, so you can see the graph here is June, July, it doesn't go every day because it's too much space and it will slow down the internet, slow down your computer. So they will, at the backhand, they have already accumulated it and cumulative as months, some dipped up, not average. Because again, if it's average, then you will say that March 15 there was no rainfall and so average it is zero. So for that, it is always important to have a daily rainfall data. The data comes in daily and sub-daily levels like hourly or multiple times in a day. But just because of this availability, we will be doing it as daily and daily is converted to monthly. Then monthly to annual, it goes by this line. Daily data is collected, it is cumulative as months and in the month is accumulated as yearly. It means some, you add it up for a month. So every day, the data is populated. If you miss the data, that data is some statistics is used to fill the data. And then a month summation is done, and then you have an annual summation. Then if you divide the average, then you get the normal rainfall. So Maharashtra I have clicked. Just for this period, you could see that 1,114 millimeters rainfall. Actual rainfall is slightly higher, 10% higher than the normal rainfall. I'm going to do monthly. When you do monthly, what happens is you get access to the months. For example, if I do yearly, the month is gone. So you could see that if I click this, there's no months. It's only years. If I click 2015, for example, that comes. So let's do monthly. And then I have to select the start date. Let's do a 10-year average. So each time we cannot press this, it might be time consuming. So when you want to jump years, just click on the year. And then you can see it's jumping. What did I say? I want 2012. So you can click it again. If it doesn't move, go to the arrow mark and then push it. Now it has been pushed 1998. What is the earliest data we have? Just keep on pushing. And these dates are not highlighted. It's not black. It's grayed out. So when I move the pointer, it doesn't turn to a finger. So only 1970 terms. So from 1970 to 2022, we have data more than 50 years of groundwater data we have. So I'm not, again, this can be done. But as I said, okay, let's do it for a year. Okay. So 1970 to 2021. Okay. And then average. There's also advanced filters we'll do, but see. So this is the rainfall that happens. And then you could see it is going up. So every year annual rainfall is there. And suddenly the annual rainfall has picked up across India. And across India, all the stations that is available on the website is shown. This does not reflect the actual or all the total rainfall gauges. Because as I said, the state also does monitoring. If you do not include that in this website. Does that does not mean that there's no gauges. So there is a lot of gauges which are not present here. Okay. So what I've done is a 1970 to 2021. Every year data is there and then suddenly there is a jump 1974 to 2021. Okay. So let's say, sum it up. Still it is showing like this. There is some issue in the data we could look at the line graphs if we can find it. And then there is a break here. So that happens when there's no data or data was not input properly in the hardware system hardware and software. So let's ignore it for now. We're going to just jump on to one agency. Sorry, one total. And then why is it better to have all agencies is because you have the possibility of filling a gap. If there is a data gap in AMD, for example, the analysis data can be used to fill it up. Okay. So we're going to do monthly just for the Maharashtra state. So I'm going to click Maharashtra. Okay. So accidentally it went to one location. So I'm just going to click this Maharashtra. You can, if it's too cumbersome, just go here and clear. Clear map selections. Now we are back. And all agencies monthly. I'm going to just click on Maharashtra. You can just come here and do Maharashtra. One more time. Okay. Yes. Now Maharashtra is picking up. There's no, well, I selected. And then let's go into do monthly. So now monthly. Again, let's do 2010. So I'm going to click on the year. I'm going to go back. 2010. Jan. Okay, let's do two years. Let's take 2009. Jan. Jan 2009 to 2010. 2010. Okay. And December. So I'm taking two years. So two years data. I'm going to take. One year because we're going to just see how to set it up. Okay. So any point you have questions, you can come back and see how I'm using my mouse, changing the dates, clicking 2010. Jan. So I didn't put a date, but you will see soon that it is going to do it. So some is what we need. Advanced filter has other aspects. But I think most of it is here. So I'm going to click submit. When you hit submit, this movement should happen. Okay. Which means that. The data is being populated and it's working. Okay. So now you could see it is one. Jan 2010 to 31st. Jan. 2010. So as I mentioned, I did never give the date. As only the month and the year I gave, I did not give a day. So because the data is collected every day. Automatically it has taken from first Jan and summed every day. So Jan one plus Jan two, like that it goes until December 31. And that is the total you get is one, one, four, three millimeters of rainfall in the normal scenario. But for this actual scenario, the rainfall is missing. Maybe the data is not available. Okay. So that is the first thing you should see. Okay. Let me check another state for example. Okay. Sometimes as I said, you will not have the data for that region. It's okay. And now I'm going to click UP. Or Madhya Pradesh. Okay. Yes. Yeah. So Madhya Pradesh is selected. Okay. Now I'm going to hit submit. It's India slash Madhya Pradesh. Again, that rainfall year is not available. So now you can clearly say that there is some issue with the, with that particular data set, which is fine. So now let us go to year the current year. Okay. Last year we'll go Jan 2021. And then this has to be December, at least one month post Jan. Right. So, and then I get submit. I'm going to just do Madhya Pradesh. Let's see if the data is available. It is searching for the data. Okay. And it says one Jan to 31st Jan 2021. Okay. So again, it didn't get a little bit stuck. So I'm just going to do a refresh and whatever data they are going to give us, let's take it. I have downloaded the data for this period. But as I said, I wanted to give you a live demo of this website. So it's better to show you how the data is taken. So for example, right now it says monthly from one June to 27 is available. Right. So let's do Jan 2021. Just to check if it is available. And then we have December 2021. I click submit. Yes. The data is available. Jan, March every month is available. And you could see that it is below normal during the monsoon, but then the monsoon is shifting. Or kind of the peak is happening at the site. And there are two peaks, which is kind of not okay. Okay. So now I'm going to click on Maharashtra. To see if that data still changes. Yes. Now it has changed a little bit more. We all know from in Maharashtra starts in June. And then peaks in July and hours. So you do have a good spread of rainfall that is happening in, in this region. Right. Then I'm going to zoom in. Okay. Now you see the districts. So, and also down when you come, you can see the district. So let's take one of the, this, this areas, right? So if I click on this, which is one of the blue areas that is good rainfall, you can see it is Tani. Okay. So Tani district has good rainfall. And that's why it's blue. It's around 3000 nearing. So it's 2500 millimeters of rainfall. And the actual is 3000 for that year. Last year was really good rainfall, 11% deviation. Okay. So then what can happen is you can also go and zoom into a particular data point. That you want to take and download. So this data can be downloaded. This image can be downloaded as an image. For example, if you hit download, it may ask you what format you want the data. Is it CSV or an image or a JPEG? See PDF or CSV. So I'll click CSV and then you'll get the data. Okay. So the other one, you can also take the table as a CSV. So you can collect and says CSV or what it is. And then you have the, we have to give for what purpose you're going to download the data, the name and email, hit submit. You'll get the data. Okay. So now what I'm going to do is I'm going to add there. And correct. So I just zoomed out. Right. So this is a good rainfall district. Let's take this district, for example, which is I still don't know. So it's still at Maharashtra. Okay. I'm zooming in that way till the background loads and this finger comes up. So the finger has come up. I'll click it. So the whole district is Nasek. So Nasek has a little bit less rainfall, even though it is adjoining to a high rainfall area. You can see a red color. So it is 658 normal. Almost double the rainfall has been taken. So this is how you would look at data and assess it. Okay. So let's look at it down. You can also go and find the district and collect the data. And most importantly, you can also take the individual data out. So I've just clicked average to show you what is the difference. And you can see the average rainfall for that period. Monthly average. Okay. Monthly average is 55 millimeters, West 1000, et cetera. Let's do a daily, a very short time. Okay. Let's do one month in February to February end. And you can see the actual rainfall. Okay. So still populating. So there has been no rainfall. Okay. Zero normal, zero actual percentage deviation. You could see very small above, which is not actually rainfall because it is almost zero. For February, not much rainfall is happening. That is what it's saying. See IMD grid. So IMD grid has all these different data sets. Now I'm going to go back to all agencies. Okay. I'm going to zoom out to full extent. I can click India, for example. Okay. So the whole of India has come. Now I'm just going to click the month. I don't want daily. I want monthly. And then I'm just going to do one year, which is Jan to December. And then apply a summation. Monthly sum. What you see now is you do see the points coming up. Rainfall stations. For IMD grid, you will not get the rainfall station because IMD grid is a interpolated to a grid to a pixel. If you want the point, you have to do all agencies and then you will get the points. So now I'm going to zoom into a high rainfall region. I'm going to take out the data. So let's do a Tane, for example. So this is Tane. And let's click one point in Tane. Now you will see a yellow color turning on. And you will see that this is the monthly rainfall information for first Jan to 31st Jan. And it is at the Wagi Wali station. There is a station name. The station's location is given as a lat long. So those who know GIS and GPS, this is the location of the station on the planet. And you could see that that location has data. You could now, this is no data display, but the actual is given fine. We will take another point. So this is how it works. Not all data points, you will get data. If you're lucky, you will get one or two. So there you are. So this I've been just clicking on different stations. And Mukda, Mukda station has the rainfall from April. April it picks up and then December goes down. Why April? Very less the other months. Why are they missing? There's no rainfall data. Okay. So the actual rainfall is what we are interested in, not the average. And for that period, you could see that it is made in by CWC is the source. It is a West flowing river basin. Okay. Where the basin is located. And this is that long and the average rainfall, actual rainfall for that year is given. So almost 3000 millimeters of rainfall in that particular year. One thing you notice here is it is a CWC station, not an IMD, not an NRC. That is why you have to click all agencies. Okay. All agencies would include every agency that has stations. IMD grid is a station and ISRO product together. So you'll have a map. NRC is a map. Only all agencies, if you click, you will see the different agencies that are collecting rainfall. So here in this location, it is CWC. Okay. Now you could go to download data. Download a CSV. I'm just going to see how it is. Okay. Okay. Okay. So for today, I have shown you how to exactly take the location of one station. You need to be careful about this location. Lack long. Lack long. And then I'm going to give my email. I let it download, but sometimes it does take a long time. Since we click monthly. It might be quick. And download the data. Okay. So for today, I have shown you how to exactly take the Lack long. Okay. Let me do it again. Oh yeah. So once you give the government or not, you will have to do it again. Okay. Okay. There it is. It says it is trying to download. Give it some more time. Okay. So all this is given here, India, Maharashtra, Tane, Mukda. So every step has to be followed. Okay. And you could download with it. And there it is. The download is happening. It's very slow for some reason, but it's fine. So I'm just going to store it. And as a PDF. So I'll be opening the PDF. And because I wanted to download as a PDF. And you can have the data shown as a PDF on your screen. So with this, I will conclude today's lecture. You could see that the data has been taken. A map has been created. You can also download it as an Excel and then work it on Excel. I will stop here for today's lecture. I'll see you in the next lecture. Thank you.