 speaker is Chris Vernon. He's at the Pacific Northwest National Laboratory and he will be talking about modeling and land use projections with the major project model. Chris. Yes, I just done muted. Can you hear me okay? Yes, we can. Okay, great. Thanks. Yes, thank you very much for the introduction and I'm a geospatial data scientist at Pacific Northwest National Laboratory. I am the task lead for the enabling and foundational capabilities portion of the global change intersectoral modeling system as well as the lead software engineer for another effort named the integrated multi-sector multi-scale modeling science focus areas. Both of these are multi-institutional efforts funded by the U.S. DOE's Office of Science as a part of the multi-sector dynamics program area. First, I would like to acknowledge my co-authors Pralip Patel and for who has been the lead developer of GCAM for over five years now as well as Min Chen and Kate Calvin for their contributions to this work. So today I would like to talk to you for a few minutes about two models that are developed within the projects that I just mentioned to demonstrate how GCAM and Demeter can be used to incorporate a global integrated human earth systems perspective to modeling land projections. So let's start by introducing the models. So first GCAM or the global change analysis model represents the behavior of and the complex interactions between five major systems which are energy, water, land, climate and the economy. And so they do that at both global and regional scales. The model simulates changes in these systems for decades moving into the future. And so GCAM uses a market equilibrium operating principle where representative agents in GCAM use information on prices, cost and other relevant factors to make decisions about the allocation of resources. GCAM has its roots way back in the Edmonds and Riley model which was developed in the late 70s and early 80s and it's been under active development ever since. So over this time you know scientific questions have become more complex and GCAM has also evolved in complexity transitioning from a focus that you know solely based on energy and CO2 emissions to also an examination of questions at the intersection of energy, water, land and climate. So the model represents all of these systems in a single integrated computational platform that's written in C++. Although some components like the climate system for instance can be ran individually. This allows insights to be derived that are not possible when conducting single sector or single system modeling. Models such as GCAM are really designed to answer what if questions about the future and they really help us to understand how the future will evolve under a particular set of conditions and how the system will change under the influence of external factors. So for example users can examine the influence of changes in socio-economics or policy on energy, water and land systems within GCAM. The model can also be used to explore the implication of changes in one region on other regions. So GCAM is as well computationally inexpensive which also enables the exploration of multiple scenarios and large ensembles to really develop a robust gain robust insights given the significant uncertainty within future conditions. So individual components models within GCAM were designed to capture the key characteristics of all these underlying systems. However because its focus is on the interactions among the systems it does not include the level of detail that you generally find in sector or process specific models. So stepping forward to Demeter. Demeter is a land use and land cover change disaggregation model that downscales coarser land region projections from GCAM to produce a high resolution gridded representation of land use and land cover. So we can see in panels A and B an illustrative comparison of the coarser regionally uniform GCAM allocation of crop land. This is for a specific target year as compared to the higher resolution downscaled product that Demeter produces. So the downscaling is accomplished using a geospatial algorithm that applies a regional land projection from GCAM to a gridded observed data by intensifying land where it already exists and extensifying land where it is more likely to exist. So due to Demeter's scale flexibility in combination with the ability to vary scenario level assumptions within GCAM we're actually able to produce representations of land and how it transitions under different scenario assumptions into the future. So thanks for developments by publication says it's Chen et al. we're able to actually calibrate key parameters for Demeter's geospatial algorithm to capture the implications of human decision making on land management as well as being able to provide input for earth systems models such as CLM to generally explore how the earth system responds to a variety of scenario driven land representations. And then ultimately we're able to provide a linkage between integrated multi sector models by downscaling global and land projections to gridded irrigated and rain fed crops and other land cover types on a scale required by ESMs or you know independent research needs. So I want to give you a quick example of this type of modeling and practice. My co-author Min Chen and others have a paper currently in review describing the use of GCAM to generate all possible shared socioeconomic pathway and representative concentration pathway scenario combinations when forced by five global climate models from the EasyMap project. So the outcome of this research are datasets that we can provide to the community of global gridded land cover downscaled by Demeter for the period of 2015 to 2100 at a 0.05 degree resolution for a five-year time steps. So these are generated for each of the 15 SSP-RCP scenarios driven by five GCAMs which results in 75 possible GCAM SSP-RCP combinations. For this research Demeter used the CLM-5 base map to provide output functional land types that are common to many of the earth system models that are that are being used today. Ongoing work with this will allow us to also use these projections to provide inputs to CLM-5 to evaluate the implications of future bioenergy crop expansion on water scarcity under different scenarios through the year 2100. So in closing I would like to thank Greg, first thank Greg, Glen and all CSDMS scientists and developers for giving us the time to present this work and continuing to develop to develop and promote resources that really benefit our community. We also do have plans to create a BMI for both GCAM and Demeter. We started looking at and actually implementing those now and so hope we're hopeful that in the near future we can have those ready to go to be used by the CSDMS community. As always our all of our code is open source and publicly available. You can find that at those repositories as well as the projects I described they're both available by those two websites. I'd like to thank everyone for giving us time to talk here today. Thank you.