 Can you see my screen? Yes, perfect. Thank you. Okay, yep. Thank you, Albert. Hello, everyone. Thanks for coming. So this is Yu Zhang, and today I'm going to share some of my research work on coastal wetland hydrology. So as a background picture shows at here, this is one of my study areas, the Delaware Bay, at here, the Delaware Bay, at the east coast of the US. So I'm looking at the surface and the subsurface interaction and also the freshwater and the saltwater interaction of coastal wetlands under climatic disturbances such as sea level rise and changes in precipitation temperature by considering the coupled hydro-ecogemophologic framework and feedback and also its regional scale hydrologic connectivity with upstream basins such as the Delaware River Basin connected with the Delaware Bay and all the way up to the inland connected with the Susquehanna River Basin. So today I will present my research findings in coastal hydrology and ecogemophology from these perspectives. So the first question you may have is, what wetlands are? Yes, wetland is actually wetland. It is a landscape with groundwater table within the 0.3 meters from the land surface for at least two weeks in its green season. So from the global distribution, the wetlands mainly located at the high latitude area and also the humid tropical region. And if we zoom into the US, the wetlands also follow this distribution pattern and you may notice that there are more than 50% of the wetlands, the green colors in the map are in the southeast of the US. And why are wetlands important? Because people call them the kidneys of the earth because they can help future contaminants provide hepatitis for wild life and they can help stabilize the coastal line and provide the storage room for carbon. So nowadays the climate change is observed and expected to increase in intensity and frequency such as the changes in the frequency and the intensity of precipitation and increases in temperature and the sea level rise. The climate change is a big threat for coastal ecosystem functions and it affects the wetland, not only coastal, all the wetland ecosystem through a faxes hydrological function in terms of the capability of wetlands in storing and releasing water. And with the change of surface and subsurface water storage and also the surface and subsurface flow, the biochemical and the geomorphological function will change too. So the hydrological function is a primary driver of other ecosystem function. Therefore, understanding how the wetlands hydrologic responds to climate disturbances is a very important first step to understand how the wetland ecosystem responds to different climatic disturbances. So in terms of the hydrological control on wetland, what I'm showing you here is the hydrological control across the environmental gradient from the high land terrain to the coastal low plan. So the high steep terrain on the left side plus the same soil and the low permeability of the shallow bedrock favors surface runoff and local precipitation, which can lead to the precipitation and also local runoff wetlands. And if we go all the way down to the ocean side, the flat terrain on the right side over deep soil not favors local runoff and also despite the lack of the local sources, wetland can still form because of the regional scale sources from the river convergence through the river network on land and as well as the groundwater through the regional scale aquifer. So this is pretty much a surface water fat by the groundwater supported wetland. For my own research work, I focused more on the coastal wetlands near the type B wetlands. And also the type B and type C wetlands can be the A wetland or D wetland depends on the local geology and climate and the topographical condition and some other settings there. So I will zoom into the coastal region. Since the wetland is determined by its groundwater level relative to the land surface, I'm interested in understanding how the water storage change under climate disturbances as a function of precipitation, temperature change driven evapotranspiration as well as water flow. So as you may know, this is a very basic water balance equation. We just assume that we are going to estimate the water budget components on the right side of the equation. We can obtain fairly accurate measurements of precipitation and ET from in situ measurements or from a high resolution remote sensing product or analysis data. But there is still a large uncertainty estimating water flow. Even we have a relatively good measurement of water flowing in rivers, but we are still not sure how much water flows through the ground surface and also through the subsurface. Therefore, my research work focused more on improving the prediction of the water flow part. So for the coastal wetland region, the uncertainties in estimating water flow comes from two parts. The first one is the spatial uncertainty. It includes the hydrologic interaction among the upland area, the coastal wetlands and also the ocean, which also includes the spatial heterogeneity across this domain. So most of the current studies near the in the coastal area only focuses on the local surface and the subsurface water supply without considering the impacts, excuse me, the impacts from the inland region and also the interaction with the coastal processes. For example, the sea level rise, tide, tide change and so water intrusion and hurricanes. For example, the historical hurricanes and storms trajectories indicated by different lines with different colors in the Chesapeake Bay and the Delaware Bay area, mainly went through two regions. One was near the coast and the other one was at the inland. So more than half of the inland storms directly results in coastal flooding due to the large amount of water inputs from the inland basins, not actually from the storm in the coastal area. However, the impact of regional skill hydrologic interaction on coastal water flow is not well understood. And another uncertainty is a temporal uncertainty, comes from coastal landscape change on the sea level rise through time, which would affect coastal water flow rate and also water flow path. So what if we don't consider coastal landscape change on the sea level rise? For example, in Livingston 2017 study, they predicted coastal flooding from the present day to the end of the 21st century without considering coastal landscape change. So you can see that a large portion of the coastal zone is submerging in water due to sea level rise. However, coastal wetland is actually a dynamic system. Its surface topography changes under sea level rise as a function of coastal sediment erosion, deposition, vegetation growth and hydrodynamics. For example, one nature paper shows that the Y axis is the equation rate of coastal wetland and the X axis is the relative sea level rise rate. So you can see that the equation rate of coastal low marsh indicated by the blue squares was observed to be at the same or higher rate than the relative sea level rise rate, which means that the coastal marsh is very likely to keep pace with the future sea level rise rate. However, we are still lacking the knowledge of understanding if coastal ecological change really matters in influencing coastal water flow in the decadal and the sanctuary scales. Therefore, my overarching research goal is to understand coastal wetland hydrology response to climate disturbances by addressing the spatial uncertainty in representing the regional scale hydrological interaction and also the temporal uncertainty in considering coastal marsh evolution and its influence on coastal hydrology. So I will start with the first part to understand a coastal wetland regional scale hydrological interaction and to understand how that would affect the water flow path and the storage. Based on the conceptual framework of coastal hydrological cycle discussed before in the previous slides, I developed a coastal wetland hydrological model called PIM Wetland based on the Penn State integrated hydrological model PIM. PIM Wetland considers the surface and the subsurface hydrological processes of upland basins and also coastal wetlands as well as the coastal processes, including tide, civil rights and solar intrusion. The reason for using PIM model because it provides a very good and a clear defined model interface to add other model modules. And they are right now some of well-developed PIM modules available. And also the Livermore National Lab developed the Sundial ODE server that can very well support the numerical simulation. So for example, I listed several of the, it's called the PIM family models or we call it a multi-module models. It builds up on the PIM 2.3 version and with the national data platform and the data process tool called PIM JS. There are several models including the sediment transport PIM model and the land surface energy balance PIM model and the groundwater age model, biogeochemical cycle model and a landscape evolution PIM model as well as the reactive transport PIM and catchment lake coupled hydrological model and the farmland nitrogen cycle model called the cycle PIM. So these are the right now already published and we developed the PIM model. It provides the opportunity to couple these different models under the same structure for different purpose of a watershed scale simulation. And PIM wetland attracts the change of water storage of vegetation canopy, snow covered surface and the subsurface water as well as the soil water and also includes the process of canopy interception, evapotranspiration, precipitation, infiltration, recharge as well as surface and subsurface water flow and soil water movement. So in particular PIM wetland simulates the freshwater and the sore water interaction by tracking the displacement of the freshwater and the sore water interface. So the model was first tested and used for the coastal wetlands in North Carolina. But due to the time limit, I won't show that part to you, but if you have any question, I'm happy to show you that North Carolina study later. But I will focus more on our study after the North Carolina study to understand the groundwater system of the entire Southeast the US on the climate change. The reason we choose this region because the coastal wetland, more than 50% of coastal wetlands are located in the Southeast the US. So this is a large area and how to select the domain, we considered the approaching mountain as the upland boundary. So from this land cover map, you can see that the green color is the coastal woody, not maybe coastal, the woody wetlands at the Southeast US. And the dark green color is the herbaceous wetland. And the Northeast boundary is the upland mountain. So by using this boundary, we can make sure that almost the upland the water supply came from all the water supply from the upland can flow to the coastal plain. And also we considered the water supply from the ocean, the sea water, also of influence the hydrodynamics and the hydrological cycle of the coastal wetland and also the wetland in this area. Since this is a very big region and we use the 146 river basins to represent the domain and we use the pyro computing to simulate these river basins simultaneously and have the exchange of water flows between the basins at every time step of one million time step. And we decompose the domain into triangular mesh and with a size from tens of meters to hundreds of meters. And we also integrated a different level of data with different heterogeneity and the data from land and ocean. The goal is to analyze the annually average groundwater table change between the historical and the future climate projection. For the historical simulation, we consider the simulation between 1995 to 2014 and a future simulation is in the period of between 2050 to 2069. And we consider different level of land cover, soil, DEM, and soil sickness data, but I use the different tide observation for the historical simulation from NOAA and the CESM future sea water height prediction for the future simulation as well as the different meteorological forcing. So for the future simulation, we consider the CESM meteorological, the future climate projection under the RCPA.5 scenario. And also this simulation is the model simulation is well calibrated and validated by using the GRACE and USGS stream flow and the groundwater observation. So right now I'm going to show you the results of the different annual groundwater table depth between the historical simulation and the future. So this is the average groundwater table and the yearly average data. And you can see that the yellow colors and the positive value means the groundwater table in the future increases. And the dark blue color and the negative value means in the future the groundwater table decreases. So in general in the coastal plan area, the groundwater table increases for most of the area except some area in Florida with the dark blue colors. And if we connected the area with the higher increase of groundwater table and the lower increase of groundwater table and maybe the decrease of a groundwater table, it will show this kind of pattern. So we're wondering what process caused this pattern. So we analyze the water balance component. The first one is P minus ET. We also call it the water availability. So you can see that in the future the water availability actually increases for most of the area indicated by the yellow color and the positive number. And only some decrease in the Florida area is the dark blue color and the negative number. So probably the decrease of water availability in Florida can explain the lower increase or decrease of a groundwater table in Florida. But it still cannot explain the patterns of the rest of the area. So we went to analyze another water balance component is water fluxes. So we introduced the concept of hydraulic connectivity. So it defines the connection of one basin with its upstream water basin. So for example, the darker colors means the basin has a good hydrologic connection with the upstream basin and also have some water supply from the upstream basin. But the white colors means it has a weak hydraulic connectivity with the upstream basins or maybe no upstream basins. So if we connected the basins with higher hydraulic connectivity and lower hydraulic connectivity in the flood plan, so it shows this kind of pattern. This will align with the pattern of the groundwater table change in the future, which means that the area with good regional scale hydraulic connectivity, or we call them the regional hydraulic connectivity controlled wetland, they have the higher increase of groundwater table because they have the water supply from upstream basins. But the regions in blue actually are pretty much the water availability controlled wetlands. They can only get water from the local P minus ET. So that's why the groundwater table increase is much less than the yellow regions. So from this regional scale framework, what can we use this information for other studies? We want to take a look at the coastal sewer intrusion. So previously, many studies assess the risk of sea level rise on sewer intrusion by only considered the local hydrological interaction like this study showed many years ago. And here, a smaller gradient actually indicate a higher sewer intrusion. But by using our regional scale framework, it brings us some opportunities to reevaluate the risk of sewer intrusion. And also I plotted the sewer intrusion with the largest increase and the largest decrease. So you can see that the yellow color means the largest decrease, and the blue color means the area with the largest decrease of sewer intrusion in the future. Especially for the area in the circle region in the previous study, they predicted increase of sewer intrusion. But from the regional scale framework, actually this process is very likely to have a decrease of sewer intrusion due to the sufficient upland water supply to contract with sewer intrusion. So there are some other factors may also affect the prediction, but the regional scale framework provide a new insight for this understanding coastal sewer intrusion. So I would like to wrap up the first part. So the regional scale hydrologic connectivity is critical for understanding the coastal wetland flow path and the storage. And PIM wetland is an effective tool for coastal hydrological modeling. And after introducing the importance of coastal regional scale hydrologic interaction, I will move to understand the coastal hydro-ecogemophological feedback. And which is a very important step to understand how the ecogemophological impact on coastal hydrology. So we were interested in understanding how the current coastal marsh landscape would evolve in the future on the sea level rise. Will it turn into the open water or will it survive? Actually the coastal geomorphological community has been working on understanding this question for decades from the point of scale prediction that treated the coastal marsh landscape as a lumped system and two 1D studies that are considered a spatial variation of coastal marsh and also the 2D studies that incorporate coastal landscape, land channel and ocean interaction. So these studies share the same framework of process interaction in this figure with sediment, pungent water, vegetation and that affects the vertical accretion rate of coastal marsh indicated by the yellow box at here which is a function of mineral sediment flux and also the production of organic matter. And the tidal sedimentation on the white box in the middle is a key parameter in this complex system. The tidal range and the depth of inhalation, the vegetation density and the rate of particle titling are the major factors affecting the inorganic suspended sediment transfer and the deposition. And the deposition also affected by the vegetation which orders the hydrodynamics and also vegetation by a production that can directly cause sedimentation due to the organic soil production. So quantitatively, we can use this equation to represent the erosion and the sedimentation and the erosion can break down to the bad erosion due to the tidal current and also the erosion due to wave breaking and the sedimentation includes the sediment settling, sediment trapping and organic soil production due to vegetation. So based on this very basic ecogenmophological framework the representation of each process and the interaction between the processes can be very different and complex. However, we are simulating the ecogenmophological change on the sea level rise. It's not a well known if the key processes and the same way of parameterization will remain the same under sea level rise in our simulation. So which is very important for accurately predicted coastal marsh evolution under sea level rise. So we wanted to review the process representation of several models and also selected some well used widely used equations to represent each term in the governing equation. So I cannot go to the details of each equation and some other equations to calculate the parameters but I would like to highlight the 11 parameters. They are shared in many coastal marsh evolution models including the parameters for erosion, sedimentation, meteorological, the coastal forcing and biomass prediction. In particular, there is a large uncertainty in predicting vegetation biomass. So it is the last term here. So the vegetation biomass change involves complex processes and various for different species. So we still, there is still no sophisticated way to quantify coastal vegetation biomass. And the pioneer work by Morris et al. 2002 quantified the vegetation biomass by using the in situ primary production measurement of the Spartana, the vegetation here. And from this plot on the right side, the bio productive of the low marsh and the high marsh indicated by the two different colors changes with different levels of the innovation. And many models predicted the change of the vegetation biomass by using a linear function with the innovation depth and also the nonlinear function with the innovation depth just assume it's the Spartana dominant coastal landscape. However, some models also found that this linear function and the nonlinear fraction cannot hold if they are multiple vegetation species. The biomass of which increases with lower innovation level and reaches its maximum when the marsh reaches its minimum innovation. So the previous studies I just showed you used this equation very often, but there is no study to better understand what are the topographic outcome from these different representation of vegetations under sea level rise rate. So we selected this 11 parameters under the three vegetation biomass schemes and we use the one de-simplified transect at a data way to as a prototype to understand the topographic outcome of the different representation of the vegetation, dynamic processes and also the parametric sensitivity involved in the ecological process under sea level rise. So you can see the red line is the simplified transect. It almost at the same level of the means a high sea level, which means the current landscape is at kind of equilibrium under the current sea level and the tidal amplitude. And we also selected two moderate and aggressive sea level rise scenarios and they can represent the future sea level rise change. And we used the DOPAS et al. 2007 model as the model tool that included the equations and the processes we discussed before and also we integrated the three vegetation schemes into this model. I cannot present you all the results, but I want to use the simulation under the Spartan dominant on linear function as an example to show you the topographic change and the vegetation biomass change through time under different sea level rise rate and also the different sea level rise rate. And through the evolution process, we can see the surface elevation increases with sea level with a higher increase near the ocean side on the left and with a lower increase at landward. This is caused by different sedimentation rate and also the vegetation biomass at the three locations probably you can see there are three dots. One is near the ocean boundary, one is a little bit landward and another one is at the upland changes through time and with the change of surface elevation. At the end of the simulation of 400 years, for the lower sea level rise case on the left, the elevation near the ocean boundary is at the similar level with the mean height at the tidal level. And the upland, the marsh are also within the tidal range which indicates that the entire marshland can keep pace with the future sea level rise rate. And also the biomass first the increase and then reach a equilibrium state without any degradation. And the vegetation biomass is the highest near the upland because of due to the higher innovation and the lowest near the ocean boundary due to a less innovation because the elevation increase is much quicker. And also for the high sea level rise case on the right, the marsh elevation near the ocean boundary can keep pace with the sea level rise rate. However, due to a fast increase of sea level, the inland area at here cannot keep pace with the sea level rise. Thus a large portion of the inland marsh is submerging in water and turn into open water. So since the inland elevation is below the mean sea level, which is beyond the gross range of vegetation. So we observe a clear dramatic drop of vegetation biomass after about 50 years and for the upland area. And similarly, the case with the spotting a linear function and also mixed vegetation linear function, they showed a similar topographic profile under the lower sea level rise rate. And also you can see the mixed vegetation scheme predicted the highest marsh elevation because the lower sea level rise rate caused by a lower innovation depth which is a favorable condition for marsh species that can grow better with less innovation. And also under the higher sea level rise rate, these three schemes also predicted a similar topographic profile. But we can see that the trajectory of vegetation at different locations are very different for these three schemes. And this reflected the assumptions in each of these equations. And also for the spotting a linear function, we can see that it predicted the longest vegetated land at the end of our simulation. It's stopped at about 200 meters from the upland. And after examining the ecological response of sea level rise from these specific cases, we also conducted more than 4,000 simulations with different combination of model parameters and also consider to understand how the ecological control on the marsh evolution. And with the lower sea level rise, you can see that the most sensitive parameters are the sediment concentration and the sediment settling velocity. And the third ones are the maximum and immediate pounding depths for vegetation growth. But the maximum organic production of vegetation and the maximum biomass are the least sensitive parameter under the lower sea level rise rate. However, with a higher sea level rise rate, the most sensitive parameters become the maximum organic production rate and also the maximum biomass. And the third ones are the sediment concentration and the settling velocity, which means that the vegetation related parameters are more influential for marsh evolution with the increase of sea level rise rate. And this also highlighted the importance to better parameterize vegetation related parameters, which is highly dependent on the coastal vegetation measurements in the future, which is not well measured right now. And also I want to wrap up this part. So the three vegetation schemes predicted a similar topographic outcome spot with different vegetation dynamic trajectories. And the spot in the linear scheme predicted the highest vegetation and the un-vegetation ratio. And the models becomes more sensitive to vegetation related parameters for increasing sea level rise rate. Okay, so with a better understanding about the ecological modeling and model sensitivity, it's good for us to develop the coastal ecological model with the coupling with coastal hydrology. So the last part, I will show you how the coastal marsh evolution would in turn affect the surface hydrodynamics and also the saltwater intrusion under sea level rise. We developed, we call it the next generation of coastal model, including the eco-geomorphologic model and also surface and the subsurface freshwater and the saltwater model into the ATS advanced terrestrial simulator model because we improved the surface hydrodynamics and also with the physically based vegetation dynamic model. But right now the vegetation dynamic model is still under developing. So I will show you a case with the simplified vegetation dynamic process as I showed you before. Here I'm using the Spartina dominant linear scheme at here. So you can see that we have a good improved representation of the hydrodynamics on the complex terrain. And also we include the subsurface 3D density dependent groundwater flow. And to examine the effect of marsh evolution on coastal hydrology, we focused more on the smaller domain from the regional conceptual framework, which included the domain of coastal marsh and also the upland forest or the upland terrestrial river basins. So this simplified 2D transect was designed based on the average slope of coastal marsh and the upland from the Delaware Bay and the Chesapeake Bay area. And the sickness also from the sediment core observation from the same area. And the entire domain is about 3,000 meters and with a tidal boundary condition near the right side of the domain and upland hydrostatic groundwater boundary condition on the left side. So under the current tidal and topographic condition during high tide, you can see the surface water propagation indicated by the light blue colors. It can reach about 1500 meters from the upland. And also we are interested in tracking the displacement of the freshwater and the shore water interface through time and under different sea level. So you can see the interfaces between the blue color and some other colors. And we first use the same way to predict the marsh evolution on this domain. As I showed you before, this study also predicted a similar future landscape profile with a higher increase near the ocean boundary and the lower increase near the inland area and the middle of the marsh which formed a depression room on the coastal marsh landscape. And accordingly, the marsh vegetation increases and expanded it to upland due to the increase of the sea level indicated by the two dashed black lines. And also the marsh evolution, elevation change was started from the initial marsh land elevation indicated by the gray dashed line at here. And how would the shore water intrusion looks like in the future? So we did a comparison. We first assimilated the shore water intrusion without marsh evolution. So we can see that the marsh landscape is still based on the current landscape and we see the shore water interface. It stopped at about 1500 meters under current sea level but it moved the landward about more than a one kilometer under the higher sea level rise rate and also a little bit shorter under the low sea level rise rate. And with marsh evolution, we observed a distinct salinity distribution in the sub surface. So the toe of the freshwater and shore water interface move the landward. However, the upland parts of the aquifer has much less shore water intrusion. So what was the major factor causing this difference? The key factors comes from the surface hydrodynamics. For example, without marsh evolution, we calculated the maximum inflow of the sea water on marsh surface and the maximum concentration. The x-axis is the distance from the upland and you can see that with marsh evolution, due to the elevation increase near the boundary, the hydraulic gradient between the land and the ocean is smaller, which significantly reduced the sea water inflow to an order of magnitude in our test case. So accordingly, the surface water concentration significantly decreased too. But for the water propagation in the case without marsh evolution, water propagates landward under the sea level, a new sea level in the future and it can propagate almost to the upland, but it can flow back to the ocean during the low tides. However, in the case with marsh evolution, the depression zone in the middle of the domain actually can hold a lot of the shore water as well as some fresh water in the middle of the domain without drainage during the low tide. So the depression zone actually significantly increased the residence time of the surface water, surface ponding water. And if we have the sufficient fresh water from the upland, the ponded fresh water can better compete with the shore water and also dilute the shore water. That's why we didn't see too much increase of the subsurface salinity at the upper layer of the soil zone. So the marsh evolution could intensify the future fresh water and shore water interaction on the surface as well as subsurface, which also indicated that how much groundwater supply from the upland becomes more critical. For example, when we also conducted some other experiments with different upland groundwater table. So if the groundwater table is greater than the mean highest tide level, the simulation with marsh evolution indicated by the orange dots shows the least shore water intrusion compared with the current shore water intrusion case, the blue dots and also the future shore water intrusion case without marsh evolution, the CN dots. However, if the upland groundwater table is lower than the mean highest tide level, the negative value, we can see the case with marsh evolution. Actually the shore intrusion just jumps to very, very high and had a similar results with the simulation without marsh evolution, which means that in the future with marsh evolution actually the marsh land could be more sensitive to the upland groundwater supply in the future. So this finding from this study has many ecological implications and I may explore them in the future such as the implication for the vegetation community structures coastal habitat quality as well as the vegetation and the species richness. So in this part, this is the first study to understand the effect of coastal marsh evolution on shore water intrusion. And we see that the marsh evolution could significantly change the sea water inflow pass and also the residence time. And the upland water supply becomes more critical to counteract with shore water intrusion in the future under marsh evolution. So in this talk, I shared some research work with you to discuss the uncertainty to represent the regional scale hydrologic interaction and also the ecogeomorphologic feedback and the impact on coastal hydrology. These are the very important two components that are not well considered right now but are very important to improve our understanding of coastal flow and storage under climate change. So in the future, I would hope to use the modeling framework I'm using right now to bridge the different scale of model. For example, use the PIM wetland regional scale model to link the watershed scale ATS model with some larger scale model and to know how to improve the model process representation and the parameterization as well as to improve the boundary condition by coupling the coastal ocean model as well as consider the human element to add the coastal urban model there. And also as I said, we are developing the physically based vegetation dynamic model that can better consider the subsurface salinity change because right now the vegetation change is only in the function of the ponding depths without any information about the salinity change. So this is the work we are doing right now. So I would stop at here and want to thank my mentors as well as the collaborators for this study and the support from different funding agencies. And thank you. If you have any questions, please, I will take the question and maybe you can email me or we can chat us through Twitter. Thank you. Wonderful, wonderful. Thank you. Sorry, it's a little long. No worries, you're... Well, we scheduled roughly an hour for this webinar and so there is some time for questions. Okay. So you covered quite a bit of groundwater, coastal zone, interaction between terrestrial water sources and the coastal zone. So I can only imagine there are some questions. Okay. Maybe it's best for people, you can either put your question in the chat or you can unmute yourself and we can see from there. And if nobody starts in the chat or with a question then I can maybe start off here. You? Okay. So I wanna go to the first part of your presentation. So the regional skill, hydrologic interaction with the groundwater component. Right, right. And you showed for the Florida Peninsula that there is a low water connectivity, right? Right. But that connectivity does not really change over time as far as I can see, right? So if you look at the connectivity now and in the future, that should not impact how much groundwater is available, right? Or do I misunderstand that? Yeah, I think you are right. And this is a very important point. I mean, for this study, we didn't actually consider the landscape change, especially the change of the river network in the future, which I believe should be a very important part that affects the water flow and the hydrologic connectivity. Yeah, so I guess one reason is that we didn't actually have a very good tool ready to simulate the larger scale river network change with good confidence because we don't have a good data to validate or to compare with some more observation. Yeah, but that could definitely be a factor in the future will control the hydrologic connectivity, right? And so what you do show some change, right? In groundwater table in the future, it's less for the Florida peninsula. Is that correct? And is that due to precipitation patterns then that change and there, or the evaporation that increases over time? Is it one of those components that affecting the lower groundwater table for the future for the peninsula? Yeah, so actually, I didn't hear about, for most of the area, actually the precipitation increases, but for the decrease of the groundwater table in the Florida peninsula actually is pretty much due to the increase of ET at this area. But I would want to point out that this is just based on one model prediction, it's a CSM prediction. So we are considering maybe we need to include more model to do some ensemble simulation with different GCM models. But I think with this only one model, we showed something about the importance of the regional scale hydrological interaction in the coastal area. Yeah, and I like the patterns that you find and that you have in this figure with the areas where there's high connectivity versus low connectivity. Okay. I was, yeah, I know that beautiful. Do you see a change in, if you look at future weather patterns, there, some studies say there will be more erratic, there will be longer dry periods and then intense rain or snowfall. Do you think that has an impact on your groundwater? In other words, will that changes the way water flows towards the ocean? Will there be more runoff versus groundwater component or does the model capture something like that or? Oh, that's a good point. Yeah, for this one, we don't have the very detailed kind of the comparison between the overland flow and the groundwater flow here. But our model simulation with a very, kind of very fine scale mesh and a high resolution simulation captured the change of even the snow and precipitation change. Yeah, that's a good point. I would go to take a look at the surface flow and the subsurface flow to the ocean. Yeah. Wonderful. Let's see, you've got a, many people are raving about your presentation, so very good. Thank you. If there are any questions from the audience, don't be shy, try to unmute yourself. Let me know if you cannot unmute, but I think you can and feel free to ask a question. Otherwise, I have one, other one. If that's okay with you, you. Yep. And oh, hold on. I see a question from Gary. Gary, would you be able to unmute yourself? I can ask you to unmute. Okay. Hi, I'm not a modeler, I'm a biologist, but I'm wondering if your model includes below ground biomass in the marshes? Yeah, we consider the, I should say this, when we predicted the biomass change, actually we pretty much just predicted above ground biomass. And for the soil production term, actually that is the soil production based on the below ground biomass. Right, but we didn't actually explicitly simulate the below ground biomass here. Yeah, I think that is a little complicated process and we consider that in the future version of the ADS model by coupling with the FIS model, which will have detailed subsurface vegetation components. For example, the decomposition and some other process, maybe we can better understand the subsurface biomass, right? And also it's very localized and you're more regional, but this is an incredible presentation, thank you. Yeah, thank you. Are there other questions? Greg noticed that you cannot unmute yourself, but if you raise your hand, I can ask you to unmute. I'm reaching also the end, but I have one other question for the coastal eco simulation you showed, the eco morphological study. So you run scenarios with different sea level, right? To see if the vegetation, if the biomass could keep in pace with sea level, right? And I was wondering, so if you increase mean sea level, you also increased the wave height, I think. So... Yeah, that's true, right. So with an increased wave height, instead of just looking at the mean sea level and see if the vegetation can handle that, are there parameters, or can you model, handle the impact of higher wave heights, higher wave frequencies and see what the impact, if the vegetation basically can survive and also if it can, if they're with higher wave height, you would expect less sediment deposition. So would your model be able to capture that? Yeah, that's a very good question. So for this sensitivity analysis study, actually we didn't consider the change of wave and about with the sea level, the tidal level also change. I mean, with the same tide amplitude, but with the increase of sea level, actually the high tide level also changes, but we didn't actually increase the process about the erosion due to wave and also the wave breaking. We tested that before by using the mayorality and I think 2010 model and it looks like it's kind of a little different from the Doppel 2007 model. So we want to kind of compare something at the same stage. So we just turn off the wave model in the mayorality model. Yeah, actually we did some work also in the mayorality model with the same simulations. Yeah, but that will be a very interesting thing to test in the future to include the wave. But another thing is I think wave will have a larger impact on the sediment transport, especially under the large wave because if the just regular wave, the vegetation can will mitigate the wave speed and some other things. That's why we didn't consider wave at this point. Yeah, no, that makes sense. Okay.