Abstract: I applaud your enthusiasm and, may I say, audacity to decide to form the Indian Youth Water Network and through the network to bring new energy to help solve water problems in India. I doubt that there is a formula about how best to run a volunteer network like this nor any recorded lessons learned from previous experiences from other networks. I am much older than you and I had not done anything like this when I was your age. So I am not sure I can present you with a formula that will work. It seems like you have to learn from your own successes and failures. This should not be such a bad thing – this is how we all learned to walk and talk and play when we were toddlers and children. In this spirit, I want to share some of my experiences in leading community activities. Well into my career, when I was in my 50s and later, I had the opportunity to lead community efforts such as the Predictions in Ungauged Basins (PUB) initiative and later the socio-hydrology movement, both at a global level. I also led a few small-scale activities, such as synthesis activities, where I used the lessons learned from PUB with somewhat mixed results. In my talk, I will share some of these experiences and will be delighted to engage in a discussion with you to see any of these relate to your own expectations and experiences. As someone who hails from Sri Lanka with a similar background to you in India, I will also discuss my views on how to go about choosing the kinds of problems you want to work on, the methods that you might use, how to benefit from the community efforts, and how to keep up with and now be overawed by what is happening in the rest of the world. This is meant as a lecture but I like to think of it as a conversation, which I am looking forward to.

Abstract: India is one of the most water stressed countries in the world. We urgently need good science and science-based decision making. The question is what does “good science” look like?A typical graduate student studying water science might answer “by building simulation models”. Models are indeed valuable because they help make predictions about the future. But how is model building science? After all, a model only gives you what you put into it. A model does not create new knowledge about processes. New knowledge about how the world works is still generated by collecting and analyzing data to test hypotheses. But where do we get data and what hypotheses do we test? Here we are presented with a paradox. On one hand, India has a paucity of water data in the traditional sense. On the other hand, we are in the midst of a data revolution; 2.5 quintillion bytes of data is created every day with new means and opportunities to collect data. How do we then take advantage of this? How do we then ask useful questions? How do we design field data collection studies? How do we use data to answer those questions and then build models to extend the knowledge to predict and inform possible futures?