 In this group, we try to address question number three, what alternative exists to address scaling out primary water management practices? First of all, we try to understand where we want to scale out or scale out. And finally, we agree that the scaling out of scaling out is within the Nile basin within the Ethiopian environment. And then we try to also understand what criteria to continue to scale out. If the practice works at the landscape level, how can we scale out or scale up to the basin? And we discussed that both biophysical and socio-economic parameters under which certain practice or process works in the landscape should be considered. And then try to identify those parameters at the basin scale. But that should not be the only criteria. And that can also be limited by data availability at the basin scale. I think that's the only thing we could figure out from this. Thank you. Look at the last question, what are the draw shows? We have to link biophysical and livelihood issues. And so what actually is needed, the toilet is linked to be effective. And the link actually requires that we follow a few steps. So first of all, we need biophysical data. So we need data on rainfall, for example, sediment, nutrient loss and availability. And according to those feedback, then we can produce data on productivity at a level of crop, livestock and trees. And then according to those data, we can come up with an economic model that will give us data on the benefit and the cost. So the point is that we have in front of us some challenges, like the fact that the establishment of this link between biophysical and livelihood required that we have to define this link before actually we add the data available. So what are the options that we have in front of us? For example, we come up with this alternative, which is like the use of secondary data. And also another alternative could be like combine secondary data with survey. In this way, we can strengthen the validation of our model. The point is that our secondary data would be available at a large scale and administrative unit that would be probably not especially referencing. But these will allow us somehow to test and to validate our model. And when the data actually at the local level would be available, then that would be ready to include that in the model. Okay, we also addressed the question of the alternative options for linking biophysical and livelihoods. And I think our first and most important observation is similar to what that other group said, which was that a lot of this is going to be done in the way that you choose the rainwater ranking strategies. Because everything else in the modeling is going to flow from what scenarios you choose to model. And so we need to make sure that we have, that we consider the likely that options are interests and issues when those are chosen. So that should be a careful process and also a lot of people. And they should not just be the ones in this room possibly. Then the second thing that we talked about was that we could be linking models that have biophysical with models that have social data. In fact, that was covered in some of the presentations, but we concluded that you need to be very cautious about that because the extent to which some of these models are flexible enough to really include those things and the ease with which they can actually be integrated is not very high, especially as you get to the high details. So while that's something that's important to keep in consideration, it's probably not going to be the solution to all the linkage issues. And then the final thing that we talked about was how we need to make sure that we are getting feedback from and validating the results from these models in the communities. So we want to be both using the data from the communities to help choose the strategies and also using the results to help kind of to feed into the discussions that are going on locally. And some of those will be innovation platforms in the field sites, but probably we'll need to think beyond that. Because if we're going from basic-level modeling then down to a few conversations that are going on locally, you may be missing out on some important areas. So we might need to think about how we expand some of that community work, not with the same intensity that it goes on in the innovation platform areas, but we'll need to be doing something different. What about? What I've discussed on the what alternative exists to address scaling out of awareness and sending practice at a basic level. And from our discussion, we came up with that the entry point for scaling out should be the productivity or production gain. So the changes in our next practices should be probably mainly with productivity gains. And the other point to which we raised that this will add to its productivity analysis would help us in scaling out, but we should also come up with a good method, the level of adoption. This level of adoption probably could be decided or could come from the innovation process or community conversation. The other thing which we discussed is the impact of this RMS has a temporal variability and the temporal variability is more important than aggregated impact. So the scaling out should also consider this temporal variability of different impacts. And the other point with the group, we should start by modeling existing practices. We said that there are a lot of existing practices in the indigenous practices of our rainwater management system. So we should start our modeling from existing practices by characterizing the existing, even if they are small in number, we should characterize them spatially and we should start by modeling the existing practices that we can scale out properly to the base and level. And the final point is that the scaling out should build upon existing experiences, experiences of other similar projects and experiences of the water does and the government institution because they have this rotation management and they are talking about scaling out so we should also build on their experiences and their plan of scaling out RMS practices. First question is what we discussed and we also discussed basically what types of data we were talking about and now the two groups, hydrometric data and socio-economic data. And basically the issues are the things I want to mention, where it's needed. There's sometimes a say in getting data to models and the quality of data basically varies from place to place. But the first thing to acknowledge is that there is actually a lot of data available, so particularly for socio-economic data, for instance, there's a lot of surveys that have been done, the central statistical agencies called masses of data and response surveys and things and this can be used at least as a starting point for a lot of the modeling that we want to do. So some of the solutions we came up with were for hydrometric data, perhaps, correlating data between locations and between sites and then this is one way of extending short data series into longer data series that you can do and things like there isn't data available, not just throwing up our hands in despair but thinking about sensitivity analysis and doing model ones initially with guesstimated data and thinking about if you tweak that data, if you're not sure whether it's good data it's high quality data then tweaking it and seeing whether it makes a big difference if you change the data in the model solutions and this is one way of identifying actually which data is important in the model series. So again, that will now down what data you actually need to re-focus on and handle it. Thank you. A question about the scenarios and we started off with a few remarks on the impacts. We said there are many different impacts economic, environmental impact, problem versus short term and they all have to be taken into account when we talk about priority decision and then we had a bit of a longer discussion about the scenarios. We started off with thinking that scenarios are some kind of combinations of interventions and practices that are targeted to specific areas in the vision but then we started thinking more thoroughly or deeper what this means and how to build them and we had a bit of a discussion like do we base them on just biophysical suitabilities or how do we maybe involve the community before it was probably important to also involve the communities through the IPs or in any other process and then we went on with talking about what are the components of these scenarios. We definitely have to look at the institutional environment and the social aspects but also the actual practices and how they change and then we came up with well there are many different aspects you can kind of consider and then using those aspects in access to comment with different scenarios but how to decide which aspects to consider is not an easy task and we ended up with a revised one to look at the similarity analysis to get some inspiration on which aspects to consider in these scenarios. We are testing the challenge one the data gaps and how to fill the data gaps while we don't have a solution. First of all we categorize the types of data and we would leave being hydrological data mostly streamflow and meteorology crop data in terms of crop distribution types and productivity lots of different types of livelihood data at household level for example about size, income sources food security status, access to markets access to water those data would need to be aggregated according to the scales at which we are working from study sites to sub basins to the full basin level talking in hydrological units here we are aware that lots of data exist they are spread out not easy to find and even if they are identified people may not be willing to share them so there is an issue of finding a mechanism within the project to make institutions that hold the data with NVDC and then we looked at possibilities places for data where they might be one suggestion was to look for phase one data and it turned out that these are also not very well consolidated or harmonized and maybe some in the ITIS database mostly lower data sets of which one would have to still clip the respective sub-basin or base net which one would like to look at national institutions hold data but may also not easily share them like ministries of water and agriculture and the IWAS mentioned in other networks research institutions and national agencies and we didn't get into details within that time for that it was suggested that there is a need for central data coordination within the project and somebody mentioned that this would be taken care of by N2 and it seems there is no central facility in the IWAS facility that would have the task of harmonizing making stations because of all those different types of data that we mentioned that's as far as we got