 Good afternoon all and thanks for introducing me Daniel and I'll see that I'll keep up to the time and Since you've already spelled my long title. I won't waste time to spell it out I'll just set the tone by this picture from the from last year climate vulnerability forum in Bangladesh Dhaka where the UN Secretary-General said that addressing climate change is imperative and we have to do that in all the circumstances My my talk will be focusing on three major elements around climate change Variability I believe that climate change variability Understanding is a precursor to understand climate change in general. So I'll be talking about vulnerability Now now that in the room that we all of us will agree that climate change is a wicked problem And so climate variability is as complex We are very familiar with the term vulnerability and I have used a similar similar definition by IPCC Prominently the gradient of exposure and promise to damage mainly considering the extreme climate events Now we there are several studies already in place and the challenge is to move the next step We we have a lot of vulnerability assessments worldwide I've taken the reference of the latest British from Maplecroft came up with top 10 countries The revealing thing is Bangladesh and India are a number of position 1 and 2 and the most striking factor is that 60% of the Countries in this top 10 are from Asia and Southeast Asia Now my third point of setting the stage is Transforming adaptive capacity to adaptation adaptive capacity is not new to humankind Adaptation is something that we have to move on from adaptive capacity We already know that how we how to respond and cope with change and The challenge is today. Is it the right way to? To cope or is the right way to adapt is a question that we are facing There are several scenarios in place Which are which are duly referenced while taking ahead climate change research But my research has taken account of social economic scenarios presented by IPCC and Here and highlighted in red are the components that you will hear and see throughout the talk is more on population density GDP land use change water agricultural labor markets and Marine diversity I've taken a case study approach to explain these factors from different regions at different scales and in different systems The three main case studies reflect a fishery sector in Bangladesh human migration in Bangladesh and Agrarian transformation in India all induced by climate variability The first segment of my script of my paper is to see is to address climate change monitoring and the second segment is climate change assessment in order to Assess the change of climate variability is first Equally pertinent to see what are the factors that we are talking about what sort of data real-time data Archive data available models can be used We are very familiar with these scenarios I've taken the scenario in red which is the extreme scenario in South Asia if our business as usual and The green scenario more positive scenario fuel efficient economy reduced emissions My point of concern is the little red bar that you see as a thermometer is that? There is going to be a decline in winter Rainfall and so this is a point that I will explore and analyze through the case studies The second thing is to study the patterns and the pattern of our Monsonal rainfall in last around 140 years The bars in green are drought conditions and the bars in red are flood conditions and the down is anomaly of Deviation from the mean now we see that there is more of dry periods that one is expecting in last third, I would say rather 40 years maybe Once our agriculture evolution or green evolution in India after that we have observed more of drought cases than a flood So it's more of dry spears that we should be worried about This is post monsoon The monsoon or the winter monsoon also says a similar condition of reduced rainfall and Of our increasing rainfall, so this is a positive condition as it was highlighted before Similarly for monsoonal rainfall we see that we see more of drought events during during JJ. That's July June July August Post monsoon will also see a slight decline Let's look at the temperature trends in four seasons Winter autumn spring and rain. We see that there is a constant rise of temperature in last hundred years in Winter is the the in some of the magnitude is one point five and in winter is one point two seven The down graph shows anomaly which is more to us positive as you see more of the bars are above the zero margin Sorry Similar tense are observed for minimum temperature throughout the region in India Let's see how it looks like when we study that spatial variation of rainfall throughout the country when you see more of the dark blue dark blue color and Is reduced rainfall and when you see more of this light blue color in the center of India here It was more and it's reduced to dark blue less Now this was trends, but what what are what is the way forward? We have number of models but more possibly what interesting Interesting way forward is to use ensembles that collects specific parameters from different models and tailored it to your requirement If you for example, if you want to use a model information for agriculture forecast Then you build your assembly in such a way that you take those climate models into account Which have more variability which have more diversity in terms of rainfall temperature or other parameters that affect crop productivity Take V2. I took a model assembled by APCC that's Asia Pacific climate center For crop productivity, and they have used the variables that impact crop productivity mainly temperature That's minimum and maximum temperature and annual precipitation and the line that you see in black Here is observed rainfall Precipitation and the line that you see in green is from the model at certain points The model is able to capture The exact extreme condition as in the as in the observation But as several other points is not able to comprehend with the original condition in the atmosphere The challenge is that if we are not able to see a significant overlap in the high-end cost data That's of last 20 around 20 years Then how do we say that the models the forecast model will give us equal amount of certainty? But certainly we can't lose hope. We really have to see what are the parameters is forecasting well, and what are the parameters is losing out And this was to just build a base of what sort of tools techniques data and is available for Taking into different discipline disciplinary research Now the second segment of the paper is more on climate change assessment where I'm going to use the data So so well talked about in the part one to study the socio-economic consequences of climate variability in different systems My first case study is climate variability vis-a-vis fisheries in Bangladesh. Why fisheries and why Bangladesh a Recent report on climate vulnerability profiling shows that in next 40 years number million number of people affected and And by climate change would almost double from 13 million to 27 million. So it's a concerning situation Why fisheries? There are a number of facts on the slice which prove that fisheries is one of the most vulnerable sector demanding attention This is a marine my focus will be on marine fisheries and this is a territorial marine territorial division of Bangladesh Where number of fishermen dip go fishing each day and their main livelihood is fishing in this marine zone Bangladesh has seen a constant rise of fish production in the recent years from 1979 when it got mechanized But the contribution very very surprisingly the contribution of inland fisheries Though Bangladesh is a small country was much higher than compared to marine fisheries Why is that that with all the technologies in place with all the equipment in place? Marine fisheries has not project has not performed as projected Here here comes the application of satellite systems. I've used the satellite sensor sea waves Which projects the chlorophyll distribution in this marine zone? It gives us how the chlorophyll is distribution distributed in this whole region and chlorophyll is the main Component that drives fish productivity. That's biomass chlorophyll is a surrogate of biomass Biomass distribution. I've done a seasonal analysis, which is very crucial because Marine fisheries at large is also very much related now that I bring the human migration into perspective to people who shift from being marine Fishers to being something else because they are not able to get the amount of economic gains that they used to from marine being marine fishers and Marine fish and when we talk about the season Post monsoon that's September October November, which is regarded in Bangladesh as the most lucrative period to catch fish marine fish over a period of 10 years it has Seen a decline of chlorophyll content from being around 0.9 to 0.5 That's half 50% decline in the chlorophyll content and one can conclude that if chlorophyll has declined So has the fish and the fish productivity in the region Now what are the factors the thing is to be before we conclude something is is is rather interested to go into a science-based evidence the the factor is Sea surface temperature. There has been a gradual rise of temperature in the marine division and as the temperature rises in the same season the chlorophyll declines You see a decline here, and then you see a rise Sorry, you see a rise here and then You see your decline. Sorry. I'm just confused Yeah There are three things here one is chlorophyll sea surface temperature and sea surface height when chlorophyll declines As a result of an increase in sea surface temperature sea surface temperature increases as a result of Increase in sea surface height so sea surface height and sea surface temperature are directly inter interlinked Whereas chlorophyll is inversely linked interlink to these climatic variables. So one has to conclude The correlation between the climatic variables and its direct impact on the livelihood sector or on the productivity sector How does the spatial variation looks like when you see the red color it shows that there these are the regions We still have good good productivity coefficient. That means that they still have good Fish fish content where people can go and fish whereas the blue blue regions indicate a total negative Correlation these are these are the regions which have very less fish diversity fish Fish for fish numbers And so if people try to go and fish here because they're not aware that they can go and fish in other zones Which is dark green or like green which are comparatively much better and they come out with very less produced They don't want to go and fish Again in the in the in this region and they want to opt for any other parallel livelihood, which gives them more economic gains The adaptation measure which we talked about with the local agencies is to see that they are given information on the On those zones which still have high and good amount of fish diversity fish numbers and so that same Same can be applied in issuing permits of how many fishermen can actually go and fish and how many fishes available and in What zones instruments like? Geographical positioning systems or GPS have played a good role in Informing fishermen that these are the regions that they can reach over and And go and fish and still sustain on fisheries marine fisheries as a livelihood measure I'm not concluding much about it because there are experts who are using from social science or Economists who are using this data to build a decision support system and to inform policymakers that instead of Worrying about people who want to migrate off on being fishers to being something else Why don't why don't concentrate on the science-based evidence to see if they can be re-rooted or they can be informed about the resource and its diversity My second study is about climate change and human migration again in Bangladesh The previous speaker has already set the stage for migration To my understanding I've used the concept of push-and-pull factors and climate change has been a push pushing factor and extreme climate events has been a triggering factor of people Migrating within or outside the national boundaries One important observation or conclusion one can made is disaster-dry migration both in case of internally displaced people and refugees Internally displaced people I refer to those people who migrate within the national boundaries from being fishers to bring Labor's within the zone of Bangladesh refugees are people who see it Shelter beyond the national borders who go to different countries or different regions outside the national border Let's look at a very interesting observation. This has been This has been correlated by taking number of available records. It's very difficult to segregate information on migration and Climate-induced human migration and migration as a whole package So we have used certain parameters or certain observations from different research studies to see what can we conclude broadly about it The bar this histogram the bar the bar reflects the number of people dead or as a result of for extreme climate event and the line represents the number of people affected as The as there has been a significant decline in the number of people who who die as a result of a disaster But on the other hand there are there are still cases and that can be more quite quite quite a number who are still affected by disasters and in most of the cases as Some of the experts have concluded these total number of people affected are Somewhere or to some extent equal to the total number of people migrated there are other observations which also Point out the fact that there are several drivers of this migration climate-induced migration in specific and One can conclude. I mean it's a striking conclusion But that 50% of the total population of Bangladesh is projected to get displayed displaced by 2020 and The major contributing factor would be flooding Why do I say that? let's see the spatial distribution of different extreme events and Flood is dominating certainly. There are severe and severe to moderate river flooding represented in red Low river flooding represented in light orange flash floods as gray So flood in general and then there are coastal surges more in the coastal zone exposed to exposed to the sea This is a national picture, but mostly There are concerns that Tractation and vulnerability assessments are not taking local level conditions into account Not taking local data local level data into account while profiling and while making a decision about who is affected and in what way So we collected some information from the from the stakeholders and the partners at a district level which is the administrative boundary in Bangladesh equal to a province in in other countries and and we used agriculture labor households to total households as a comparative measure To study the exposure of climate events as a surrogate of ecological and biophysical vulnerability To just submit a simple way. You wanted to see There was a general consensus that these are the people who who are internally displaced as a result of these events So one would see that Dhaka is a capital. So it's in 2008 Dhaka has seen people the percentage of agriculture labor household is not moving to Dhaka because there aren't any More opportunities to survive, but they are moving to other places like Raj Shahi Which which is not so much affected is in this wise zone and this wise zone is like Moderately affected or not affected comparatively less affected They are moving to those places where agriculture is still not much affected by a climatic events And they still offer them job or livelihood opportunity as agriculture labor So this is the case to study internally displaced people as a result of extreme events At the second step we wanted to see that what is a surrogate that we should select for Analyzing social vulnerability and then we selected Poverty and how did we analyze that we took total households We took the stratification of total households into urban households and rural households and total landlifts now This region is more power. It is more poverty prone But in the other case the total landlifts in this total rural households are more in this region So that means the rural households are into agriculture because this is an agriculture belt And since there is more agriculture dominated it still offers the probability The more proportion to people who come and are seeking from work from other countries from other sorry from other regions Which are highly affected. So this is I don't want to get into numbers, but let's see that this We have taken based on based on I've not got into details of this But based on several experts we have Quantified them and the then we see that this Constitutional division stands on a high vulnerability profiling and whereas the the Rajshahi division Which I was just talking about is has more poverty gradient But has also more adaptive capacity because it still is Is so not not so much affected by extreme events is less vulnerable Now case study 3 which I'm going to just run down briefly small about a gradient transformation in India It was really difficult to combine all these studies into one presentation But I've just made an effort so that we get a flavor of how data can be used And this is about the southern province in India a small district where now which was which is or which used to be rather Let me put it that way or rice dominated landscape Again, I've chosen the second level of administrative division for analysis. That's called a block That's the case in 1970. We used to have this much of rice not again going to the numbers in 2005 we have this much rice, but this is interesting and From 1970 to 2005 this block has lost around 40 percent of his rice growing area to not growing nothing or growing other Crops which makes more economic rationality if they don't want to grow a rice anymore What are the main determinants and we asked? We wanted to see we wanted to focus on rice So there are two two crops of rice winter rice and summer rice winter rice is a major contribution to the total production of rice Both at state level and at the provincial level Now expo when we talk to the people they said that rainfall is a rainfall water availability and Of conditions being more warm. They are not able to get the yield they expect from rice So it's not making any economic rationality to grow rice As most of studies take temperature into account. I've taken Diarrheal temperature range that's maximum and minimum temperature because literature says that crop core simulations That rice yields Decreases by nine percent for every one degree increase in season average temperature Now let's talk about the condition That with a increase in one degree temperature. We're expecting a decrease of nine percent So there was a there was a question read this morning that now we are tending to a Four-degree rise. So I think there is still is is still a concerning situation and what I did I took the temperature anomaly thus taking a difference between maximum and minimum temperature and seeing the deviation from the mean while in summer there used to be high and high Deviation Deviation from the mean up to four degrees. It has declined a bit the condition a negative anomaly in this case It's not suitable suitable for rice growth whereas the positive anomaly anomaly between the range of one to three is So the winter situation looks much better as highlighted in green than the summer situation There are several conclusions to draw, but I've got I'm going to a limited to three With my case studies, I've highlighted the fact that integration of scientifically delineated climate information in decision-making is crucial and And but certainly is upper is a possible and a potential way to address uncertainty to a certain extent Other important factor is to see that we incorporate and integrate scale season systems and focus on regions to assess vulnerability and address adaptation There are there are a lot of studies that focus on global profiling. It's time that we start looking at regional and local details and Last but not the least trans-disciplinarity is a point to ponder I am a special geospatial analyst by training but to get to learn how different systems work was rather challenging And I still I'm still not an expert on that, but I try to weave different Knowledge from different sectors so that it can make sense to number of stakeholders Thank you for your attention