 Actually, as you can see on the first slide, this talk is actually not my talk. I'm kind of substituting with Charles Weirsmarty here, and I really regret that he couldn't come. Normally, when I step in for him, I make a mockery of his presentation, but unfortunately he has two serious reasoning not to come here. So I will try to stay as close to his presentation as I can. In many ways, I cannot really step in a sense that he is a very different kind of sinker. He looks at things very differently than I do, so I may not be able to represent everything the way he would say, but I'll do my best. And these are slides that I got from him. All I did was pretty much reduce the number of slides. And although the title says global hydrology, but I think it's largely more than hydrology. It goes way beyond hydrology. So evidently, when we look at hydrology, climate change is always kind of occupying our thinking. And my understanding is that Charlie has participated in this NRC committee on hydrological sciences, which recognizes that climate change is already happening, so basically the precipitation patterns are changing globally, and we are witnessing numerous changes affecting the hydrological cycle. Secondly, when you look at discharge data records, for instance, in the United States, we seem to have a better climate, so we are getting more precipitation, which actually shows up in discharge gauges. But according to this figure, actually it doesn't mean that we have more extreme events. Basically what this graph shows you is the number of stations, all the 500 stations where there is a significant trend in either way, either in terms of increasing discharge or decreasing discharge. And what you see is pretty much at the lower echelon, the low discharges do have a tendency to increase. But at the maximum Q actually, we don't necessarily see, let me see kind of same amount of increase and decrease. So it's not getting more extreme according to our discharge records. And actually when we look at climate, we keep talking about climate change, but for some reason climate change is actually not that strong of a signal. And this is a study that Charlie actually carried out in 2000 and still I guess it was just kind of recently repolished a little bit for the National Intelligence Council, where we looked at the impacts of climate change versus the impact of population growth on water resources globally. And what you can see is when you look at climate change, climate change clearly has winners and losers. So basically it's a signal, it's something that's happening to us, which there are places where it doesn't really make a big difference and there are places where things are going to be worse and there are places where things are going to be better. When you look at just population growth, population growth everywhere puts pressure on water resources. So first of all it's always one directional, it's always kind of negative. And the signal is actually way higher. So population growth actually puts on hydrography way more pressure than climate change apparently. And the last figure is actually when you combine the two, you get this expectation for the future. And here I would like to put, this is a bit I put in, and this is actually a follow-up to the presentation from the civil science engineer from yesterday where she talked about this UNESCO new study where we looked at Constitue and Foxes to the oceans. And my role in that effort was providing the hydrography for present and future climate. And what you see here is on the left side, this column is basically the present. And basically this is 100 year century, air temperature variation by latitude. These outer curves are the maximum and minimums according to the CRU, grid air temperature data record. The dotted line is actually the standard deviation of the last 100 years. So this is the kind of climate variation we experienced in the last 100 years. This is the same thing for precipitation. These are the minimum and maximum curves throughout the 20th century. And these are the forcing data that went into our model. And in terms of air temperature, I would say that up until 2030, we're not going to see anything beyond what we have seen before. So basically all the temperature here are within this bracket of the maximum temperature we recorded. And we will start to step out by 2050. When you look at precipitation, it's even less. Basically the precipitation is within the standard deviation noise. So we're not going to see much, the drastic changes in my mind in terms of precipitation. And the big question is when you look at this going up precipitation, going down precipitation places accompanied by temperature change, what wins out in terms of a runoff generation? Evidently higher temperature means more evapotranspiration, more precipitation on the other hand should mean more runoff. And basically this is a signal of what we would expect to see in terms of a runoff change. And one of the striking figures to me on this figure is that we have these four storyline, really kind of catching stories. That doesn't really show much difference. It looks like to me that regardless of what we do, it's kind of be heading to the same direction. If I wanted to be cynical, I would say that all we can do with different policies is just kind of delay achieving a climate near Vana. If I like, I personally kind of like warmer temperature and my dream is actually move to Canada and have a beachfront property on the Hudson Bay. So on the other hand, what's striking to me is that this lower figure is basically running our water balance, water transport model with turning on and off human impacts. So basically this is a contemporary impact that humans have in terms of picking up, picking up water for irrigation. So this by large is in the same magnitude that we are anticipating in the future climate. So humans are already altering significantly the hydrological cycle in a really, really strong way. And this is just the water amount. If you factor in water quality, then it's even more striking. So to some degree, I'm kind of puzzled why climate change kind of sealed the agenda of all the other issues. And this is another figure that I put in, it's extra beyond what Charlie gave to me. This is a paper by Ruxim et al, what I find really intriguing in terms of our planetary boundaries. And yes, in here, you have climate change as one of the planetary boundaries, but basically we are reaching planetary boundaries in a number of ways. For instance, humans are now fixating more nitrogen than natural ecosystems. So basically we are completely altering the nitrogen cycle of this planet. And these have a severe impact on where we are heading. And when we look at water, this is basically Charlie's main message is that water traditionally is viewed as a kind of regional local issue, local resource and local problem. And but when we look at all over the globe, basically a kind of global pattern emerges which we believe we need global attention. So basically what he argues that we should somehow find ways to elevate water related issues, and I'm going to say actually a person I would like to go beyond water, a number of other issues which are just as severe as climate change, maybe even more. And one of the attempts with what he and a team of researchers tried recently was quantifying this impact on water resources in a way where you can express what we are seeing globally. And finally he managed to get this paper published in Nature which was kind of interesting that original Nature editors flatly rejected the paper without sending out a review. So I mean Charlie had to write a really strong rebuttal and ultimately it was reviewed. One of the reviewers admired, simply admired the paper to the degree that it ended up being the front page story for Nature. So in this work, we looked at what we tried to do and it was a really, I would say, painstaking, painful exercise in many, many ways, is to collect as much data as we can on the state of the water system. As you can see there, we had data on watershed disturbance like cropland, imperviousness, livestock density, etc. Water resources development like small dam, large dams, river network fragmentations. And we had a group of data on various pollutants, soil salinization, nitrogen loads, phosphorous loads, etc. Biotic stress. Basically we had ultimately 23 global data fields that first of all, we needed to collect. We needed to go through a really painful exercise to actually validate them whether they are meaningful, they are really representative of what we were expecting. And then basically put it into, I mean this is one of the data set, the nitrogen pollution. As I mentioned before, we are already putting more nitrogen into the water system than a natural system would. And there were a couple of other data that we put together. This is the reservoir database that we used for this work. We worked together with a number of international collaborating partner to assemble this global reservoir database called GRAND, which was basically consolidating existing global data sets. We worked together with a number of teams who tried to assemble global reservoir data sets before and we took all of them and tried to consolidate them into a unified database. And this is where I would stop for a minute and say that these are the kind of work where I'm not sure the scientific committee always have the right value judgment. This work took like three years for us to produce and another three years to publish. Basically we went through I think three or four journals before one accepted it because it was always regarded, this is a technicality. Every reviewer said this is a fantastic data set, very much needed, but there's no science producing such a data. And ultimately the way we were able to publish was we put some flag-living about how reservoirs are destroying everything. So I kind of question why just presenting the data was not enough. So I wish because we are living in this world of publish or burial I wish there were mechanisms for producing data like this and get acknowledgement for this. And if I go back to the 23 data set what I mentioned before, one of the striking features is that many of the data sets were actually fairly obsolete, fairly old, 10, 15 years old. It looks like producing a data set first has some merit. Basically that's something you can publish. But doing an update, getting a better nitrogen loading data set, getting a better methane loading data set is not that fancy anymore. It's not that sexy in terms of sciences and basically we are lacking these new versions, improved resolutions. So that's kind of my critique of this whole effort. And I find it repeatedly that it's kind of difficult to justify spending effort to make our data better. So there was a lot of interest in this work in the sense that everyone loves the result. But basically putting energy to get the data right kind of gets less emphasis. In this work what we did was with the 23 data sets, we put them into this fairly complicated kind of waiting scheme which allowed us to first of all do a waiting according to the local waiting versus upstream contributions for the different parameters. And we kind of put each variable into a normalized form. And at the end of the day we put together some sort of waiting, working with a number of experts to express what these different data sets altogether tell us in terms of threats to biodiversity versus threats to water security. And the rationale for actually to look at biodiversity particular for freshwater, fresh waters are the pride domain for biodiversity. So if you look at the number of species per square kilometers by area, basically fresh waters are the one where you see the most biodiversity. So despite of inland waters account only for 1% of the continental land, 10% of the known animal species live in those waters. So fresh waters are really the home for biodiversity. So that's why looking at biodiversity in fresh water seems to be very important. The water resources of water security is obvious. It's the water security for us. And I think that the surprise of this work was that when you look at two aspects, you get two very different words. So basically if you are a human, you're better to live in Europe. I mean, in Europe we are able to provide secure water to everyone. If you live in Africa, you have bad luck. As a human and as a way of accessing water. If you're a fish, you're still probably better in Africa. And as a fish, it's still more pristine, it has more biodiversity. And developed nations, developed countries like the United States or Europe shows up as real red where basically we already destroyed biodiversity significantly. So this difference in the two words is kind of striking. And the evident question is why it's so different? And one of the differences actually comes from that in the developed world, we have the resources to secure water for ourselves. So we have the money to provide water regardless of what happens. In the developed world, basically 1 billion people lack access to drinking water. And one thing that I would like to add to it is these people largely live on a dollar per day annual income. Which is like my run trip ticket was about 300 bucks. So that's their annual income. So if you are sympathizing with the Occupy Wall Street, the difference between our five to six digit salaries versus the ones who live on seven plus digit is about the same as those people, these 1 billion people living in three plus digit income. So and that's again 1.7 million people die today just on water related illnesses. And I sometimes have trouble why we're talking about migrations in future climate serious when we have such serious issues today. So, oops, this is a, actually I will skip this slide, I want to go this one. Basically, this shows the same concept of the issue what I mentioned that. In both words, we actually have the money to secure water resources. And basically what happens typically is as income goes up, first we start to destroy things. So basically we put in infrastructure that destroys biodiversity, destroys water quality, etc. But then as we get rich, basically we are able to kind of bring it back by investing more. So if you have higher GDP, you will start to have money to reduce threats. Typically what we do is reduce threats to water resources, but typically we don't really have the resources to restore biodiversity. So for the challenge for the future is I guess is to find out how people in the developing world could kind of take this path without destroying the environment. So I think that our role would be to scientists to find alternatives, alternative development strategies for them to develop into the future without destroying the environment. And these are the conclusions on this particular paper. So basically the human impact is already pandemic, it's global, and you can read these conclusions. I guess he provided a couple of slides in terms of scaling, which I kind of skipped to the point where this was a study, this was an effort for the Arctic, Arctic system science looking at scaling issues in the Arctic. But I think some of the findings in terms of scaling applies to not only the Arctic, and these were the key findings in terms of scaling. To me the striking feature is that scales has very different meanings for different disciplines. And basically with different disciplines we have difficulties communicating between scales. And in some sense my addition to it would be sometimes I find difficult when people talk about scales as if processes were different at different scales. And I would have to object that. So I would like to think hydrology and think about what we are doing in a way where I think the same laws should apply to each every scale. And if there are differences that's because we lump things together. It's probably more of our inefficiency in terms of dealing with details. Maybe when we do work at large scales then being the process is very different. And I also normally have difficulties when I see heavy use of calibration because in our models we always try to put meaningful values for every parameter. I don't like the idea to tune a model into way outside of the realm of plausible meanings or values. And I will stop here. Gisier Mawal, BOM. Have you or anyone you know looked at the impact of environmental impact of different policies and legislation on let's say hydrology, vegetation and similar issues? I wouldn't say we did anything comprehensively. Like Francis, this global news work with Sebel Manchin yesterday indeed looked at the impact of basically we used scenarios where there were different policies implemented. How we are going to address, how humanity is going to address water quality issues. And basically we tried to simulate how that translates in hydrological. In terms of what kind of hydrology emerges and what kind of water quality responses you see. In that particular word I would say I personally did not try to do any of the scenarios myself. So basically everything was given to me. One of the interesting work for instance was I had to place reservoirs into the future because our model needed reservoir locations. Basically I was given reservoir capacity according to the image model and my role was simply just trying to place those reservoirs. I didn't really try to play what happens if you put reservoirs here versus there. So in that sense we didn't really do the policy analysis per se but we did work with the scenarios given to us as different outcomes. Yeah thank you. My question was more aligned with I know there are countries on the international border between two countries that have different policies on protection of environment. And you can see from satellite pictures how the vegetation for instance dramatically changes at the border. And that affects of course the amount of nutrients in the nearby rivers and so on. That was more or less the intent of my question. Yeah I mean we incorporate that I think into our analysis just because we use data that would come from different countries. So we would see those signals. We actually did work with UNESCO World Water Assessment Program where we provided water budgets or water balances per countries and tabulate how much water is coming originates from country how water is traded between exchange between countries etc. But again it's more what we do is typically more of the reactive way. We just take whatever data is given to us and we kind of repackage it. But I wouldn't say we looked at what's the policy impact of being more cautious about using less water and more water. Thank you. Maybe I have a question for you Balash. Maybe you could take a minute and tell us a little bit about the water balance model. Our water balance model is actually first of all it's definitely was the first global water balance model. So when I started to work with Charlie I thought it was actually crazy to attempt hydrological modeling at that scale. Historically the whole story of how it emerged was kind of interesting from a point of view. There was some imaginary room back in the 80s when the Woods Hole of marine biologically laboratory people and some people of Q and H wanted to develop a terrestrial ecosystem model and basically the motivation to develop a global hydrological model was the support tab. And there was this room where the question was raised that who would want to write the water balance model and nobody raised their hand and Charlie ended up saying okay I'll do it. So that's how he got into large scale water balance modeling. And this originally it was a very simple water balance calculations on a half degree resolution and kind of flow routing at a half resolution grid. And over time it kind of emerged as a useful tool to access water quantity globally. And later on it was sometimes criticized for being too simplistic. We tend to do what we do. Our model is definitely amongst the simpler water balance models but we participated in modern intercom business with the EU Watch program and what I felt was that a BBM was kind of a solid performer. It didn't do work spectacularly or well in any region but it wasn't really stupid in others either. The other participants were models which were heavily tuned for one region and where they did really well but then did poorly somewhere else. So our philosophy with water balance model is actually to try to minimize the complexity partly because we think if you add more complexity you end up having difficulty with parameterization getting all the uncertainty, the input data. So in the simplest form that water balance model we use air temperature and precipitation as the driver, end of story. We have means to incorporate more complicated solutions which would take vapor pressure, wind field, etc. But basically once you do that you have to acknowledge that all of those variables comes with your error terms. So it's not necessarily an improvement to make water balance calculation more complex. So that's pretty much our philosophy. Okay, thank you, Balash, very much.