 Good morning, good afternoon, good evening and welcome. My name is Anne Van Damme and I'm with the IHE Delft Institute for Water Education in the Netherlands. In this presentation I would like to report on the progress we have made in developing a global model for ecosystem services of inland wetlands. This project is a joint effort led by PBL, the Netherlands Environmental Assessment Agency, in collaboration with the Universities of Wageningen and Utrecht and IHE Delft Institute for Water Education, all in the Netherlands. The continuing loss and degradation of wetland ecosystems, particularly in some regions of the world, are well documented. Our understanding of the direct and indirect drivers that cause loss and degradation is increasing, for example agricultural and urban expansion that is driven by population growth and economic development. And despite an increasing awareness of the importance of wetland ecosystem services, they have often been neglected in global assessments and models, although this is now changing. Different types of valuation studies have demonstrated the importance and the high value of wetlands for biodiversity, for nature and for people. Here you see some examples of the ecosystem services of wetlands. In this study the ecosystem services printed in red will receive most attention. Often the value of wetlands is not taken into account sufficiently in decision making about development projects that may have an impact on wetlands. This applies especially to the regulating ecosystem services that include processes like hydrological regulation, nutrient retention and carbon storage. These are all difficult to quantify because of their dynamics, their natural variation and their complexity. Therefore it is difficult to make them part of the trade-off analysis or cost-benefit analysis. This is where models come in. They can help describe these processes, deal with their dynamics and complexity and make quantitative estimates of regulating services that can be used in decision making. So the objectives of this study are, first of all, the development of a process-based wetland model that can be parameterized regionally for different wetland characteristics and that can be driven by global climate and land use data. The focus of this model is on the regulating ecosystem services like water and nutrient storage, water quality regulation and carbon and greenhouse gases. In particular we want to calibrate the model for different wetland types in different climate zones. In the longer term we aim at applying the model in global studies and assessments of wetland ecosystem services. So how did we do this? First we reviewed wetland modeling studies that had been done before and we defined what we wanted our model to do. This was published in our paper in 2019. We decided for now, for practical reasons, to include only inland wetlands. Then we built the wetland model and I will explain more about that in the next slides. The wetland model needed to be driven by a hydrological model which would predict wetland area, water levels and flow. For this we used the grid-based global hydrology model PCR GLOBWP which was developed at UTEC University. We used global databases, particularly the global lakes and wetlands database, to verify the model estimates. We also used the global nutrient model GNM which is part of the image modeling framework of PBL to quantify nutrient input into the grid cells based on global land use data. In this way we could estimate the water and nutrient balance of each cell and use these to drive the wetland model. In the model a distinction is made between isolated or ponded wetlands that are fed by rainwater or groundwater and floodplain wetlands that are connected to the river network. Grid cells receive water with dissolved and suspended nutrients from upstream cells. Inside cells, nutrients are delivered via diffuse and point sources based on the global nutrient model and distributed to both wetland types. The hydrology model calculates the fraction and area of each cell that is occupied by wetlands and of course the river network also exports nutrients to downstream cells. The wetland model itself is a relatively small model with 9 input variables, 10 state variables and 54 parameters. Inputs consist of river discharge, water body area and depth, a fraction of open water in each cell and nutrient loads. The model itself includes three vegetation types, emergent, floating and submerged vegetation, soil carbon, dissolved oxygen and nutrients. The growth of submerged and floating vegetation in the permanently flooded zone is determined by competition for nutrients and light. While the emergent vegetation in the intermittent zone is regulated by the phosphorus concentration in the poor water. Output of the model includes greenhouse gas fluxes, biomass accumulation and carbon storage, nutrient and water retention or release and the concentration of nutrients in the water. Many of these processes such as for example organic matter degradation or methane production are strongly influenced by the dry wet alternations in the wetlands. In this way, the model captures the hydrological dynamics and how they influence wetland functioning. To calibrate the model, we started reviewing the literature to compile data sets on different types of wetlands in widely varying climatic zones. These data sets form a database on vegetation, biomass, growth rates, nutrient concentrations, nutrient content, carbon storage and emissions and water quality, particularly of nutrients. We will use this database initially to calibrate the model, but once we have enough independent data, we can also use it for model validation. On the map you can see the three wetlands in Sweden, Spain and Kenya for which we can show some initial results, but we are collecting data on other wetlands as well. So now I will show you some initial results of model runs that we did for these individual wetlands sites. I should say that these are part of research that is still ongoing and therefore should be considered unvalidated. Nevertheless, for the Nyando Wetland in Kenya, we were able to do some comparison of model simulations with observed values from the field, and this shows that the model captures the growth of the vegetation and water quality in a realistic way. The Nyando Wetland is dominated by emergent papyrus vegetation, and on the left you can see that emergent vegetation in the intermittent zone dominates the vegetation. In the permanently flooded zone, the floating vegetation is dominant. In the same way, the simulated nutrient concentrations in the water are realistic when compared to observed values. The short-term variation you see in the graphs is caused mostly by seasonal wet-dry changes. We are doing similar comparisons of simulated and observed values for other wetland sites. Based on the simulations of these dynamic processes driven by climate and land use scenarios, we can then use the model to quantify ecosystem services and compare these among different wetlands. This is shown here for nutrient retention and greenhouse gas emissions in the three wetlands. Without going into the details here, we can see that the model calculates different values for ponded and floodplain wetlands of different latitudes of nutrient retention on the left and of greenhouse gas fluxes in the middle. The bars show overall averages for the 25-year simulation period, but of course the underlying time series for these values are available from the model. On the right, we have calculated the overall net greenhouse gas emissions for the wetlands over the whole period. The main point here is that the model can calculate these ecosystem functions and services based on realistic climate hydrology and wetland process dynamics. The last part of this talk I would like to use for some reflection on the model and on how it could be applied. The unique features of the wetland model are that it allows the calculation of wetland processes in a dynamic way, capturing the differences between permanently and intermittently flooded zones and the difference between isolated and river floodplain wetlands. Based on realistic climate and land use scenarios, this leads to clear differences in nutrient retention and greenhouse gas emissions. We are currently trying to validate the model for individual wetland sites that have enough data so that we can check if the model captures differences among wetland types and climate zones correctly. Of course this leads to challenges with the availability of data, but in principle it is possible and the initial results look promising. What does this mean for application of the model? First of all, once the model is validated for enough wetland types and climate zones, we could quantify wetland ecosystem services for different global scenarios and look at more policy-oriented implications such as, for example, the contribution of wetlands to the sustainable development goals, or to determine wetland restoration priorities, which could be interesting for the Ramso convention or for the CBD. We could use global climate scenarios to drive the model and look at the implications for wetland ecosystem functions and services in different regions. But we could also use the model the other way around, for example, to provide input into models that predict the biodiversity in techness in certain regions. An example is the globio-aquatic model, which currently uses land use and habitat data to predict biodiversity in techness, but the wetland model could provide this input in a dynamic process-based way to evaluate different scenarios for their biodiversity impacts. You can see that model development is still ongoing and the most important in this process right now is to calibrate and validate the model with observed data. We have shown the feasibility of coupling a general wetland model to global hydrology and nutrient models. One thing we would like to do is create regional parameter sets that capture differences, for instance, in vegetation types. For example, the emerging vegetation in different regions is typically dominated by species with distinct growth characteristics, and the model can take these regional differences into account to improve model estimates. We've already done some sensitivity analysis with the current version of the model, but this needs to be extended once the model is developed further. Then we can also look at other scenarios, for instance, for different nutrient loads or land use change or wetland management. For example, the model could look at the implications of wetland conversion to urban or agricultural use. This could be done for certain region or on a global scale, and this could be interesting for assessments such as the global wetland outlook. We are preparing a manuscript with a detailed description of the model for publication next year. To summarize and conclude, we have created a process-based dynamic model that incorporates wetland area, hydrology, biogeochemistry and vegetation to predict the main regulating ecosystem services of wetlands. The main objective of this model is a global scale analysis, such as the comparison of regions or wetland types on the basis of global hydrology, land use and climate. The first results show that it is possible to obtain realistic model results compared to observed data. These realistic results then lead to clear differences in ecosystem services outcomes for wetlands of different types in different regions, which demonstrates the potential of the model. We will now proceed with further testing, calibration and validation of the model, and we welcome discussion on its potential applications. With that, I conclude this presentation and I thank you for your attention. Don't hesitate to contact us if you would like more information. Thank you.