Effect of water level on bird habitat at lake Maggiore
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Number of Parts | 351 | |
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License | CC Attribution 3.0 Unported: You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor. | |
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Transcript: English(auto-generated)
00:02
Okay, good morning again. Now I am the speaker of this next presentation. I'm Clara Tatone, I am a researcher at the University of Insubria. And today I will present a work carried out together with BirdLife Switzerland and
00:22
Fundazione Bolle di Magadino about the effect of water level on migratory birds' habitat at Lake Maggiore. Lake Maggiore is the second largest Italian lake, even though it's not completely Italian. Here we are at Florence, about 500 kilometers north, lays Lake Maggiore,
00:48
that is divided between Italy and Switzerland. Lake Maggiore, despite being a natural lake of glacial origin, is regulated by two dams.
01:06
One upstreams the Verzasca Dam, who is in Switzerland, and it is a dam mainly devoted to hydropower production and electricity. Downstream on Ticino River, in the Italian territory, we have the Miorina Dam,
01:27
who is a dam that regulates the level of the lake, with in mind the rice fields that we have in Italy, that are in the plain downstream of the lake. In 1940 it was supposed to establish an international commission that regulates the water level
01:49
and reached an agreement between the two countries. We are still waiting for that. The current management is mainly driven by the Miorina Dam. Not surprisingly, this situation led to water wars,
02:05
because of the different interests at stake between the two nations that share the lake. So, in summer, like this one, where we are experiencing a drought, the water level drops because the water is needed for agriculture, so Miorina Dam is open,
02:23
and water is needed for electricity, so Verzasca Dam is closed, so the water level lowers, putting at risk even the boat navigation, because the boat cannot moor any longer. On the other hand, in winter and autumn, the problem is the reverse.
02:47
Italy is trying to capitalize water for the spring irrigation of the rice field, so the water level rises, and some people have to go to work canoeing.
03:00
So, those are examples of the title of newspaper and media. In order to not solve this problem, but at least provide information about this problem, the Parque Verbano Ticino Interreg project was developed,
03:20
a project between Switzerland and Italy with many partners involved. The aim of this project is to research about the different needs of people and interest at stake about the water of this lake, and try to find useful guidelines, because I mentioned agriculture,
03:46
but there is also the flooding risk, and tourism that are at stake in this project. Today, I'm going to focus on biodiversity, because, of course, water is very important for biodiversity. In fact, Lake Maggiore is on one of the main migratory routes for birds coming from Africa in spring,
04:08
and going back to Africa in autumn. And the area called Bolle di Maggadino is a protected area in the Swiss part of the lake at the north, that is a recognized stopover site for birds by the Ramsar conversion and by the bird habitat directive.
04:28
This is our study area in large, so for this project, we asked, what is the effect of water level management on the habitat for birds at Bolle di Maggadino?
04:43
In order to try to answer this question, we wanted to calculate the flooded habitat when the water level rises or lowers, because the slope is very mild in this area, so even a tiny increment or decrement in the water level can have sensible effect.
05:02
To do that, we tried to calculate the inundated area from two sources, the water level measured hourly by the hydrometer near our study area, and by remote sensing using Sentinel-1 imagery.
05:21
Then we wanted to compare the area derived from Sentinel-1 and from simulation based on water level, and finally evaluate what effect it has on bird migration. So we have to collect a lot of data. Concerning birds, we have a long time series since 2001 collected with traditional ways,
05:44
that is, capturing the birds with nets, marking them with rings, and let them free to fly where they want. Within this project, the University in Subria bought this nice gadget, that is land radar, especially designed for bird observation.
06:07
I cannot show web pages in this convention, but if you want to see real-time migration, scan the code and you will connect to our web page, where the radar is outputting continuously the passage of the birds.
06:28
Our monitoring times where both systems were deployed were three, two in spring and one in autumn. This is the workflow that we followed for this work.
06:43
First of all, I will speak about the simulation done with GRASS GIS with Air Lake package. We took the daily water level from Agrometer, Switzerland, and a DTM with lake bathymetry with 0.5 meter resolution,
07:03
and we calculated the inundated area. Then we match it with a habitat map developed by Fondazione Bolle di Magadino from fieldwork, and we were able to have a time series of flooded habitat. On the other hand, we took Sentinel-1 SAR imagery that is not available on a daily basis,
07:26
and in Google Earth Engine, we used the Edge OTSU algorithm to calculate the inundated area for the available time of recording. We did the same with the habitat map, and finally, we analyzed how well the time series matched with R.
07:47
Finally, we crossed everything with our data about birds. Sentinel-1 is a project from Copernicus. It provides imagery of synthetic aperture radar,
08:05
and it works even in every meteorological condition, so we processed over 2,000 images, and we reclassified the inundated habitat. The thresholds were calculated for each of the periods.
08:22
Going to the results, we see that the simulation and Sentinel data don't really match. In gray, you have the water level, and in red, the simulated inundated area,
08:40
and they match perfectly, of course. SAR has a different pattern that is more linked to rainfall. This is an example. This is our study area, Bolle di Magadino, and this is what happens, according to GRASS, when the water levels rise. You see that the protected area is completely flooded.
09:07
For comparison, I'm showing you what happens with the simulation based on satellite imagery. As you see, satellite imagery is able to capture water also inland, like multi-paddles that can develop around the study area.
09:27
So, we compared the inundated habitat, and we find that there are significant differences between the methods we used. So, which is the right one?
09:41
And which of the methods provides the results that are useful for understanding bird migration? We have to match the data with birds. So, first, we do also some cross-correlations to see what happens between simulated flooded area and satellite imagery
10:07
that we suspect is the ground truth. And we found out that the two systems don't match at all in spring when the dam is open for irrigating the rice field.
10:22
Whereas, in autumn and winter, there is a better match. So, when the dam is not operational, satellite and water simulation goes well. In spring, when there is rainfall and dam operational, the two systems don't match.
10:42
Then we match our results with bird data. And we find some significant temporal correlation with rainfall, which is known in literature and also by common sense that birds don't like to fly when it rains. Who would like to do that? Come on.
11:01
And so, we have a negative peak of birds' capture when we have a peak of raining. I'm showing you the results only for one of the periods because they are more alike, and this is spring 2001. Then we found that the number of birds
11:24
are correlated with the flooded area calculated by simulation from water level. We also found that there is a correlation that is not shown in this graph
11:41
between the inundated water vegetation and the data derived from Sentinel. And this also makes sense because we have also water bird migrating, and when the water level is too low and the water vegetation is dry, birds don't like it.
12:01
So, to come to the conclusion, we have found out that the two methods yielded significantly different results for all habitat types. And that is because of the fact of the management of the dam
12:21
and the fact that our water level simulation does not include rainfall that is captured by SAR. But SAR is available every two or maximum six days for our study area. That is a two-course resolution because we have daily and even hourly data of bird migration.
12:43
And we know that the decision of the birds to stay or live is made not in six days, but in the same day. We observed seasonal patterns and we see that there is a significant agreement between the two methods,
13:03
so we have to find a way to include the rainfall in our simulation or to find some other source for the satellite imagery. During spring, as I mentioned, the two methods are completely uncorrelated
13:21
and provide opposite trends. And, I didn't mention before, the data derived from satellite I've mentioned has a higher standard deviation.
13:41
So, concerning the time series analysis, we found cross-correlation that are significant and positive and are supported by biological data and observation from literature, especially the negative correlation with rainfall
14:03
and the negative correlation when the water vegetation is dry. As future development, we wanted to try to find a way
14:20
to select one of the two methods or to improve the classification of SAR imagery. And this is also why I'm here because there are a lot of experts in hydrology and I hope to have some good inputs from you. I didn't show here the data from the radar because we are beginning to analyze them
14:44
and it's also one of our future developments. So, if you want to reuse our code, it is available. You can find it here. If you want to see immigration in real time, you can scan the MTR code,
15:02
thanks to Damiano Prettoni, one of the co-authors who developed these Shiny apps. And I want to thank Francesca Gianetti, who introduced me to Google Earth Engine and Marco Cioli for their support. So, I'm looking forward for your suggestion. Thank you.