 Thank you. And good morning. Thank you for the invitation from Systems. This is actually my first time at the annual meeting and it's been a very pleasurable and interesting first two days. This is work I've been doing at CUNY in New York. We have a new science research center, the advanced science research center and environmental science is one of our five new research areas. We have colleagues at City College and James and Effie, well obviously here and Effie I did a week last night. I'm going to be speaking about relative civil rights and which we've heard some of us before this meeting, but also connecting into the outcomes for the communities and the people who live in these systems. We find out at the global scale can we see different patterns and separate out different drivers from the from each other and see our different systems more sensitive to particular environmental changes. So this won't come as a surprise to anybody in this room. The relative civil rights is one of the major challenges facing Delta's. If we look at the global mean civil rights it's substantially lower than the total relative civil rights being experienced in many Delta systems. And the MS 1.8 is actually quite out of date. More recent estimates closely to three millimeters per year and with even more recent estimates of changes in sea ice melt rates this number is probably needing to soon be revised up. And then when we talk about people who live in these systems substantial areas of these Delta's are below sea level and this is I wouldn't call this sustainable but people can live here and aren't swimming every day because there are protected webbies and there are coastal infrastructure in place to maintain the liveability of these areas for the time being. So we're going to start asking how sustainable is this and can we draw any conclusions about what different future scenarios might hold for these systems. So a lot of this work has been inspired by a paper from 2006 from Erikson et al. And they looked at a suite of Delta's across the globe and tried to divide up the major driving factors of relative civil rights in these different systems. And we can kind of divide these up into three different boxes. We have sediment slapping or upstream influences so we'll do dams and reservoirs slap sediment to reduce sediment boxes to the Delta's. What's going on in the Delta itself so here accelerated subsidence refers to pumping of groundwater out of the sediments extraction of hydrocarbon and then there's offshore factors particularly civil rights. So the consequences of a given amount of civil rights for a particular Delta though is not going to be the same everywhere. So we can imagine two different scenarios. If you are in this Delta with very shallow relief and a lot of development in a population very close to the shore I would say I think this is a riskier place to live than a Delta that is more like the one on the right. So to first order I would prefer to live here probably less risky. We can also talk about the hazards that different systems are exposed to. These are storm facts and so the Mississippi is exposed to a different suite of hazardous events than say the Amazon. So it's another way that we can consider the importance of relative civil rights in terms of changing the risk outcome for the communities to live here. And then we can also consider that different systems are able to protect themselves proactively or reactively. In the event of a hazardous event some communities are able to respond better at their infrastructure in their hospitals. So we have the Ganges which has lots of personal levies and folders and then in the Netherlands billions and billions of dollars of engineering infrastructure that is preventing flooding on a regular basis. So the communities living in these systems experience the effects of relative civil rights very differently. In our work we're taking a suite of 48 Deltas across the globe and trying to make it realistic estimates of both roads of civil rights and how that is changing the risk in these systems. One of the constraints working on the global scale for comparative purposes is we're limited to global scale datasets. So for most of how we're using remote sensing, global and American modeling, some global datasets that are derived from administrative social data such as population, GDP. And I'm also going to talk a bit about the distinction between risk and vulnerability. Those two terms are somewhat overloaded and even within the community they're often used for different purposes. So I'll get to it in just one second. But these systems when we consider the social aspects and the vulnerability of the communities here can get very complicated very quickly. So it's hard to see but we have multiple spatial scales here from the upstream basin to Delta and then multiple sub-Delta regions. We have the multiple hazards that they're exposed to so natural and climactic hazards and anthropogenic hazards even. And then the impacts are across various social system scales ranging from provinces up to the national level and international levels. In all of these terms and factors come together to concept the stage for risk which I'll define all these terms in just one moment. And so in the literature there's a large number of indicators that have been used for assessing the vulnerability of a particular community to a hazardous event. So how, how impactful is a particular hazardous event and in what ways might this vary between different communities? And if you squint you can see we're looking at urbanization, population density, infrastructure and economic variables like water supply, transportation, and then demographics of the community or people's well-being, what is the age distribution, how sensitive are people's income streams and their livelihoods to climate relevant or climate sensitive industries. And then food and waterborne diseases there's a whole suite of indicators that have been used. At the global scale we're somewhat restricted because we don't have much of this data at the global scale. So we're doing using a smaller subset of this to try to get in a comparative sense across these systems. So when we talk about risk we're modeling risk as an expected loss. So if we consider one particular hazardous event we can say that the expected loss from that hazardous event is the product of the exposure. So how many people are exposed to hazardous events from that, hazardous conditions by that event and then the vulnerability. So if you are exposed how much of loss do you experience? Are you, is your home damaged? Are you injured? Are people killed or displaced? And that's going to very dramatically across different systems and different events. So because these terms are often used in different ways I like to think of the hazardous event term as these strong tracks. So how likely is the storm to affect you? Exposure is where do you live? So if there is there's a two meter storm surge do you live half a meter above sea level right on the coast or do you live five meters above sea level further inland? So this would be population distributions in a number of people affected by an event. And then finally vulnerability I think of the three little pigs right they all have, it's the same work going on all of those houses but the stone maybe grid house is much more resilient and does not experience the same loss as the poor pig in the straw house. So this would be the vulnerability term. And then we're not just concerned about one particular hazardous event or we're concerned about all the potential hazardous events. And so this has a probability distribution and if we take this probability distribution we sum over all events we can get this estimate of risk. So the question becomes what do these functions look like? What is the probability distribution across all hazardous events not just storm surge but also fluvial flooding and any other event you could think of? What is the exposure? So as we go to higher larger events or more dangerous events presumably more people would be exposed. These two terms can be estimated empirically right we can look at the historical record and say well this many swarms at this particular location and when a swarm of this size hit you know X number of people were exposed. So we can try to start estimating these functions. The vulnerability function is more difficult so different types of events are going to have very distinct effects on the communities. If you are exposed to a large flood, so if it's a two meter storm surge and you with a half meter elevation and you're flooded versus you're living a half meter elevation and it's a one meter storm surge, you're flooded both times. So are you going to damage and harm more in one event? It's somewhat unclear. So these terms can be estimated um roughly limited by empirical data so some deltas we have lots of a very long historical record of impacts and other systems we even don't. So if we're working at the global scale we're getting a very simplistic view by just coming up with a representative value for each of these terms. So each delta we're saying is it in an highly hazardous area or not? Is it the people there likely to be exposed? Are there a lot of people living close to the shoreline or not? And is the community more flexible and mobile than relative to another delta? So how are we estimating these terms? We're using an indicator based approach. So for hazard we're looking at tropical cyclone intensity and frequency. We're looking at tidal amplitude, river discharge and extreme wave energy. So for each of these deltas and you're using global models and global data sets to extract these terms and then we're constructing an index from these indicators by normalizing each of them across all of the different delta systems. So this is a heuristic model. We're not modeling each of these individual processes but by putting them all together we hope we can come up with a reasonable estimate of which delta systems are exposed to more hazardous conditions in a hot spot type argument. So the vulnerability we saw before, many, many variables have been used. We are using two GDP based indicators and a government effectiveness indicator. So we can think of per capita GDP as if people individually are wealthy they can build homes out of brick instead of homes out of straw so they can protect themselves. If the delta as whole is wealthy they can construct large scale infrastructure in the Netherlands or Mississippi that was in a coastal master plan. And then the question is well even if you have GDP and if you have some well are you able to effectively utilize that for risk reduction? And so that we're looking at a government effectiveness index. So how capable and how willing would a community be to invest in these large scale infrastructure projects? Our exposure, we're actually interested in how relative sea level rise is impacting the change in risk. So we're skipping over exposure directly and we're instead looking at the change in exposure associated with relative sea level rise. So relative sea level rise effectively lowers your elevation above sea level. So everywhere within a delta that is experiencing relative sea level rise we can say exposure is increasing. So we're not going to decrease anywhere. Consequently, a realistic estimate of relative sea level rise with these delta indicators, some upstream basin indicators, population density of the surface area to the proxies for development, ground weather vacation and hydrocarbon extraction, wetland disconnectivity is a measure of the disconnection between the river network and the flood plain. We extract that from datasets of locations of wetlands and locations of agricultural land. And then we also include a sea level rise offshore. And it's not a perfect relationship but it's reasonable when we compare it to estimates from the literature. So what do these industries end up looking like? Well, if we stack up all of these different indicators we see and construct what we're calling an anthropogenic conditioning index. So this is how the human communities on the deltas and in the upstream basins are changing the natural environment of. We see on the left we have the Ganges on the far left and just to see you highlight a few, the Mekong is somewhere in the middle, the Amazon is on the right and if you squint either several high-latitude deltas, the Mekong, the Mackenzie, and the Burlina. Third index, the same deltas are rearranged. These are very different terms and they're not correlated with the anthropogenic conditioning index at all. And the investment capacity, this is an inverse of vulnerability. So low vulnerability, the Rhine and the city, the Shaw-Fried, they're able to have the GDP and the governments to presumably protect themselves from hazardous events. Whereas on the far side we have the Irrawaddy, the Lamal Ode. No, we're just looking at these. So if we put these different terms together, according to the risk model, expected loss is exposure times vulnerability times hazard, we can estimate this rate of change of risk associated with relative sea level rise. So relative sea level rise is different amongst all these deltas and that is proxied by this horizontal axis. Both of them is how exposed they are to hazard events and the size of the dot here is the vulnerability. So larger dots here are more vulnerable. So this quadrant 2 is going to be the deltas that are experiencing large amounts of environmental change, exposed to frequent or large hazardous events and have the least capacity and the large delts there are the ones with the least capacity to respond to those proactively or reactively. So several to highlight a few with the Ganges which ends up quite high at the list. So these are quite capital estimates. If we look at populations at risk, the Ganges would be by far the highest as well as the population of the next nearest, which would be the Nile, the Minranges. The Newcombe, which we looked at before, and the Amazon. So they're scattered about this different risk space. One thing to note is that several of these deltas that are either in or near quadrant 2, so the systems that we would expect based on their environmental characteristics and their exposure to hazardous events, we would expect these to be quite risky. But solely because they have very, very low vulnerability, they end up in the bottom of the list. They're less sensitive to changing relative to civil rise because they're able to actively defend against it, which is very good for them. And it's certainly valuable right now that they can do that. There are people who live there and they're benefiting every day because of the infrastructure that they're able to protect, to construct. So because these systems are so dependent on their vulnerability, we're thinking, well, how sustainable is that? They're investing lots and lots of money to maintain these structures. And is that sustainable in the long run? So if we look at energy price forecasts, which are at the global scale, we think we can make some reasonable estimates in the short or medium term. And energy prices are expected to rise faster than GDP. And if the global community gets more serious about limiting costs of fuel use, I think it's reasonable in the short term that that this could even be underestimating based on the economics of the energy system. So if energy prices are rising faster than available funds to buy that to construct infrastructure and support and maintain that infrastructure, we would expect that maintaining a certain level of economic, a certain level of reduced vulnerability is going to become increasingly expensive. So I would say that this is the very definition of not sustainable, because it's kind of more expensive to maintain the same amount of infrastructure or for the same amount of money you get less things to buy. We can reweight our vulnerability index to consider how this might affect the long term risk outcomes. Let's skip this guy. So on the left, we have this rate of change of risk. If we're only considering the geophysical context, this is the environmental change and the hazards events. And we can see the Ganges, the Yanks, and the Mississippi are the top. When we also consider reduced vulnerability, these wealthy deltas are permanent. They have greatly reduced risk outcomes. But if we imagine the streets where energy prices are more expensive and the infrastructure that they're currently investing in becomes less effective, those particular deltas rebound into a higher risk state. And relative sea level rise becomes increasingly more important in those deltas as it is elsewhere. So a contemporary map of sensitivity to relative sea level rise in terms of risk highlights South Asia, where there's a lot of environmental change and less infrastructure to defend against these hazards. But the deltas that are likely to be most impacted by potentially faster fuel constraints future are predominantly in the Mississippi and Europe and in East Asia. So we're considering how to extend this through additional future scenarios beyond just the economic vulnerability argument. Part of the challenge is that we're not including any direct physical modeling. So we're starting to incorporate that. I have a number of plots that I'm going to skip right on over. So here we're starting with a number of geophysical and GIS type inputs, reservoirs. We're using a sediment flux model and a relative sea level rise model and kind of coupling in a very loose sense to a closer risk model to try to look at differences in populations at risk currently and in the future across all of these systems. So just as these two different colors or two different potential scenarios is very simplistic scenarios, one we're just turning off population, we're turning off dams, we're turning off groundwater extraction. So this in red is a more pristine type of situation in the blue is a contemporary situation. And the spread of this color represents different population growth scenarios. So a high population growth scenario and a low population growth scenario. And the first thing that jumps out is population is a major driver. In many cases it's far more important than any environmental changes for some of these systems. This is the the economy in the Mississippi on the right. And we can look at across different deltas and start to see how can we balance these different environmental impacts and what does this mean for the long term populations at risk in all of these different systems. And how does that compare to the uncertainty in population estimates? So this is a contemporary in blue and here is just a higher sea level rise rate. So certain deltas like the Limpopo really pop up between the two. So we're starting to be able to ask these kind of what is questions in a very broad sense. There's a lot of uncertainty here. But I think it's very important to note that a lot of the uncertainty is associated with the social questions. So I think one of the major conclusions here is that collaborations with our social science colleagues, our economists and our demographers are very, very important owner incorporating trying to answer any of these risk questions, certainly in the same order of importance as the environmental and geophysical change questions. And so in terms of the changing risk, we're able to identify many of the deltas that are most sensitive to increased sea level rise relative to sea level rise contemporary in a contemporary situation and also identifying some of the challenges that the more wealthy deltas are going to be facing likely in the near to medium term future. So thank you. And this is a part of our maps and data sets are available online. So if you would like to either contact me or at this website, we have all of the data that went into this. So global sea level rise is the melting of the polar ice sheets going to be very non-uniform on a global scale and especially exaggerated on the low latitudes. So it's a standard incorporate. Yes, Indy. This is a local silverized trend. It's driving satellite altimetry. It's contributed the trends up. So now there's no forecasting in this. So it doesn't include potential future local and regional differences in sea level rise. But over the past 30 years, that's incorporated here. Thank you.