Audacious Water
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Audacious Water

Trinity River, Fort Worth, Texas. Credit: Tim Cummins/Flickr through a Creative Commons license.

Visualizing Optimized Water Solutions for Texas

Earth Genome and Future H2O are using big data to help bring many stakeholders together for win-win water sustainability outcomes in five Texas river basins

Texas provides a unique opportunity for to advance water sustainability — through a state law that mandates enough water be left in state waterways to provide ecosystem functionality.

But because Texas has so many water stakeholders with different and often competing goals about water use, this opportunity is also a challenge.

An ongoing collaboration between ASU’s Future H2O and Earth Genome, a California-based non-profit dedicated to providing actionable insights from big environmental data, is tackling this opportunity/challenge in unique ways. As Michelle Lapinski, vice-president of Earth Genome, tells us: “These water systems are incredibly complex, with thousands of users on any single system. Gathering the data, processing that data using science, and then putting that in the decision context that helps optimize desired impacts: no one has ever tried to assemble those pieces before in Texas into a tool.”

Lapinski and John Sabo, director of Future H2O, sat down to discuss the project and its implications for big data and water moving forward.

Q: Earth Genome started in 2015, and you’ve tackled with partners a wide range of water projects since then — GRAT, a groundwater recharge assessment tool in California; BASIT, a basin assessment scenario and intervention tool in Pakistan sponsored by Levi-Strauss; and GIST, a green infrastructure support tool with The Dow Chemical Company in Texas.

Now you’re partnering with ASU-Future H2O and others in Texas again, with three new projects that are pushing the boundaries for how big data can contribute to water stewardship decision-making involving a lot of stakeholders and tradeoffs. What’s at the heart of this effort?

Michelle Lapinski: These three projects are pilots of new decision support tools in three distinct Texas river basins — the Brazos, the Trinity and the San Antonio. The fundamental goal for all of these projects is to enable new strategies to meet environmental flows, trying them out first in a basin with partners, then scaling them statewide.

What’s driving them is Texas’s Senate Bill 3 (SB3), which mandates what sounds like a pretty common-sense directive to me and you: determine how much water must be left in our rivers, streams, and in our estuaries to ensure great ecosystem functionality.

Trinity River wetlands. Credit: Kathleen Phillips/Texas A&M Agrilife.

John Sabo: Texas has been unique: it’s been very forward thinking about water in its state law and also in developing — through interactions with universities in Texas — its water management tool portfolio. They’re focused on understanding water allocation in the context of variability and precipitation, understanding what they owe as allocation to different people who have water rights and in the context of SB3.

Michelle Lapinski: But how do we find win-win situations in these three basins and throughout the state of Texas, where multiple parties can come together, agree on a transaction that leaves more water in the river, and that makes more water available in just the right places? It’s easier said than done, because these water systems are incredibly complex, with thousands of users on any single system. Gathering the data, processing that data using science, and then putting that in the decision context that helps optimize desired impacts: no one has ever tried to assemble those pieces before in Texas into a tool.

John Sabo: Together, Earth Genome and Future H2O are improving the visualization and understanding of what the science says about optimized decisions for different stakeholders in order to find transactions and interventions that allow water allocations to be made, but also allow the environment to be served along the way.

Q: How did Earth Genome and Future H2O get involved?

Michelle Lapinski: The George and Cynthia Mitchell Foundation was the catalyst. They do so much work in Texas, uniquely on water. They helped convene a large group of about 20 to 30 different organizations in 2016 — the state’s three environmental agencies, some river authorities, NGOs, civil society, local scientists, and our groups — to talk about what was required to make the work already done on Senate Bill 3 actionable, and to enable voluntary proactive strategies.

We helped bring other people to that conversation, including Dow and Coca-Cola, and we will lead a series of convenings in Texas over the next two years that will help us figure out what are the needs and who are the appropriate end users and how could we create the kinds of tools that would enable multiple users to see these win-win opportunities.

We want to start with the outcome and work backwards in a very robust, high-fidelity way that’s faster and more nimble than conventional science.

— John Sabo

Q: What are some good examples of what these tools allow people to do to advance water stewardship in ways they didn’t imagine possible?

John Sabo: First, understanding how to co-manage the groundwater and surface water needs of industry, agriculture and cities and balance all that with what the environment needs. Those things haven’t been done — haven’t been even thought of — because the science to model the two together isn’t there. Frankly, the two domains have been thought about so separately that mixing them hasn’t even seemed an option. But it is in Texas. That’s one thing that I think we’ll be able to advance.

Second: understanding how to strategically rebuild green infrastructure and couple that with existing gray in order to offset the need for big, expensive gray infrastructure projects that have big impacts on the environment. We can go back the other way and say how can we actually rebuild the environment to function in a way that benefits all the stakeholders for water, including the environment. An NSF CRISP grant has funded this work.

Texas is a dream test bed for this kind of stuff, because it has eight parallel watersheds across the state that represent a gradient in what we call hydro-climate, which is just the difference between rainfall and evaporation. So, arid in the west and humid in the east. And each of those basins has a different need based on industry, ag and cities that are present there, as well as a different set of environmental challenges. I don’t think there’s any other place in the U.S. that would provide that kind of experimental test bed where science could be performed.

Brazos River, Knox County, Texas. Credit: Nicolas Henderson/Flickr through a Creative Commons license.

Q: Are there specific basin insights the tools are providing you can talk about?

Michelle Lapinski: In the Brazos River Basin, there’s been a long contentious history of contention over water — that’s gone all the way up to the Texas Supreme Court. Water is finite, and Brazos River water can nearly run dry before it reaches lower Brazos users like Dow’s Freeport plant, which is its largest chemical plant worldwide. So how do can we help optimize the best use of the finite amount of water, and meet the needs of a variety of water users, including river species?

The tool we’ve co-developed will eventually give not just Dow, but the Brazos River Authority and other lower Brazos water users a way of seeing opportunities to transact with upstream users of water where it’s a true win-win-win. Where and when you implement a strategy matters, and our tools consider the local conditions, the cost of various strategies, and other social and environmental impacts in order to help the user determine the best investments for the greatest impact. For instance, if Dow pays farmers upstream not to farm and use water that season, it’s beneficial for Dow, it’s financially beneficial for the upstream farmers who were likely to struggle through a drought, and it’s a win for the environment because flows are maintained throughout the river.

If we do ground water recharge in California, or in Pakistan, it doesn’t allow me to solve water quality issues in Bangladesh. But the underlying water quantity modeling that is done can then be reused to then do modeling of water quality. That building block mentality — piecing together different components of the hydrological system — when done with coherency and integration is going to allow a whole suite, a whole portfolio of water tools that has never existed before — so that nearly any decision context, nearly any problem anywhere by any local stakeholder will one day be able to be addressed by big data and applied science.

— Michelle Lapinski

John Sabo: In the Trinity River basin, we’re working with Coca-Cola on identifying ways to strategize the rollout of better or best management practices in agriculture in the basin to improve water quality and contribute to environmental flows. But looking at all the different ways leading land-use management techniques can impact nutrients and sediments in streams and figuring out which one is best would be a two-year venture, with a lot of back and forth with the end-user.

Instead, we’re going to use deep learning algorithms to speed up the process and create a tool so Coca-Cola, Trinity water managers, and other Trinity river stakeholders could sit together and say “Okay, we want this water quality in Dallas, without reducing flow — how do we determine the most effective best management practices to use?” We want to start with the outcome and work backwards in a very robust, high-fidelity way that’s faster and more nimble than conventional science.

Q: In that vein: scenario optimization is a crucial differentiator for these tools, isn’t it?

Michelle Lapinski: It is, and that’s what Earth Genome does differently from other tool builders. Most tools just visualize — either current situation or risk. But they don’t help you run scenarios of what you can do to address a problem — and to see the impacts of those options across multiple criteria, from environmental to social and financial outcomes.

Q: This project has a P4 funding model (public-private-philanthropy partnership). Is that how these projects will be funded going forward?

Michelle Lapinski: The Mitchell Foundation’s most valuable contribution in my opinion has been the credibility and social capital that they bring. They have a long history of helping solve Texas water challenges. The co-funding model is really innovative — but their ability to convene the community is the number one thing.

Having said that, we are funded by philanthropy to do water data projects in many places. But philanthropy doesn’t want to pay for everything. There isn’t enough money, unfortunately, in water philanthropy to pay for everything to solve water challenges globally. No one wants to pay the entire R&D costs.

What’s innovative about the Texas funding model is that you have co-funding from Mitchell, tool users, the National Science Foundation. When you put all those things together, it creates a very innovative funding model. I’m not sure there are many parallels in the environmental data space.

John Sabo: And in P4, “partnership” is more than just funding. It’s about convening. A convener is essential, but it might not be philanthropy in every case. It might be a WWF country program, as it is for us in Pakistan. It might be a multilateral that’s done a lot of work in a particular place. It might be a quasi-philanthropy group.

Michelle Lapinski: Mitchell’s convening was crucial here. Local tools need local data, but they also need local stakeholders as well and a local decision context. And Mitchell helped do that for us.

Q: Final question: Can what you’re building for these basins in Texas scale elsewhere?

John Sabo: Water is local for decision making, and these tools need to be driven by a narrative that’s relevant to the end user. You can’t just put a bunch of data in a warehouse and expect that it’s going to get used.

Having said that, let’s say that all three basins we’re working in want a different end use. Those end uses could be shared across the basins to improve social capital and to improve innovation. When one basin sees somebody else doing something in another basin, and it actually works, they might also want it. And I think there’s a scaling that could happen that could actually improve the management of water across the state while adding to the toolbox and making it more relevant to other contexts as well. By not-taking a one-off approach to local decision-making, we’re broadening the solution horizon for all local decision-making.

Michelle Lapinski: If we do ground water recharge in California, or in Pakistan, it doesn’t allow me to solve water quality issues in Bangladesh. It just doesn’t. But the underlying water quantity modeling that is done can then be reused to then do modeling of water quality. That building block mentality — piecing together different components of the hydrological system — when done with coherency and integration is going to allow a whole suite, a whole portfolio of water tools that has never existed before.

It doesn’t exist today, but it’s what we’re trying to build — so that nearly any decision context, nearly any problem anywhere by any local stakeholder will one day be able to be addressed by big data and applied science.

For more information about ASU Future H2O’s work and research on creating opportunities for global water abundance, visit our website and subscribe to our newsletter.

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Bob Lalasz

Bob Lalasz

Founder & principal, Science+Story. Guiding researchers to become public experts & research organizations to share their expertise publicly.