James A. Rising

The highest good is like water.
Water gives life to the ten thousand things and does not strive.
It flows in places men reject and so is like the Tao.

– Tao Te Ching (Lao Tzu)

Water is such a fascinating resource because it’s at the center of things: absolutely necessary, but generally given no value. This the fundamental enigma that has motivated a huge growth in the study of “water-energy-food systems” (WEF systems or nexus). But the WEF nexus are also defined to dodge the central problems of water.

The first dodge is by framing water as an equal partner with energy and food. As I’ve written before, water plays a very different role than energy or food: energy and food are completely dependent on water, not so much on each other. WEF systems should better be called “Greater Water Systems”.

The second dodge is a common avoidance of the fundamental decisions-making build into water systems. Water availability isn’t really a physical fact of nature: it depends on human decisions. Water grows scarce when we demand more than the natural system can provide. And in most areas of the world now, water supply is the result of our investments in reservoirs, canals, treatment and reuse systems. WEF systems have no static elements; it is constantly being created by us.

A full understanding of the WEF nexus requires an integrated approach, which makes decisions about water use and infrastructure, based on how we can ensure the most beneficial use of water for ourselves and the environment. I presented on these ideas at the 1st International Conference on Water Security, using the AWASH model to understand long-term investment decisions around reservoirs.

The insights from that work were published this week in the new journal Water Security. Take a look:

Decision-making and integrated assessment models of the water-energy-food nexus

Through an expert elicitation involving LSE, Columbia University, and PIK, we have developed a statement for policy-makers on missing risks of climate change. Often the discussion of the risks of climate change focuses on what we know: higher temperatures and sea-levels, biodiversity loss, deaths from heat waves. But scientists are reticent to discuss what we do not yet understand: die-off of the Amazon, loss of the ocean currents that warm Europe, mass migrations and their conflict consequences.

Our paper says that even though we cannot yet quantify these risks, we should be planning for them. Even if the worst scenarios are unlikely to happen, leaving them out of our discussions with policy-makers is the same as claiming that their risk is zero, which is not right either. Governments regularly plan for international security scenarios that only have a 1-in-10,000 chance of happening, and we should treat the worst risks of climate change the same way.

Our document on the missing risks of climate change will be presented at the U.N. General Assembly Climate Action Summit, and we hope it will heat things up!

Dividing an elephant in half does not make two small elephants. It makes one mess.

The same is true of our oceans. Modern management of the natural environment is all about dividing up elephants, assigning the halves to different owners, and blinding ourselves to the activities beyond our halves. But just as with elephants, pieces of an ocean depend on each other: fish and currents do not respect national boundaries.

That is the starting point of a new paper Nandini Ramesh, Kimberly Oremus, and I recently published in Science, entitled “The small world of global marine fisheries: The cross-boundary consequences of larval dispersal“. We wanted to understand how national fisheries depended upon each other.

To study this, we used the same model used to study how debris from the Malaysia Airlines Flight 370 crash ended up halfway around the world:

Instead of looking at airplane debris, we looked at fish spawn. Most marine species spend a stage of their lives as plankton, either in the form of floating eggs or microscopic larvae. They can travel huge distances as they float with the currents, sometimes over the course of several months. We can use those journeys to identify the original spawning grounds of the adult fish that are eventually caught.

These connections are important, because they mean that your national fisheries depend upon neighboring countries. Spawning regions are highly sensitive, and if your national neighbors fail to protect them, the fish in your country can disappear. A country like the UK depends upon plenty of other countries for its many species.

Finally, this is not just an issue for the fishing sector. We also looked at food security and jobs. People around the world depend on the careful environmental management of their neighbors, and it is time we recognized this elephant as a whole.

Shortly after I joined LSE, Stéphane Hallegatte from the World Bank gave a presentation on their new report, “Unbreakable”. The report is about how to measure risk in the face of the potential to fall into poverty, and includes one of my favorite graphs of the last year:

I think it’s an amazing bit of modeling to be able to relate natural events to the excruciatingly chaotic process we call “falling into poverty”. But it’s the scale of the two sides of the graph that blows me away. On the left, earthquakes, storm surge, tsunamis, and windstorms all together account for about 1 million people falling into poverty every year. On the right, floods account for 10x as many, and droughts account for an additional 8x as many.

The reason is that floods and droughts are naturally huge events– covering large areas and affecting millions of people– every time they occur. The second is that they occur all the time.

This gets at the importance of water. Most of the researchers I know don’t spend much time thinking about water. They know it’s important, but in a way that’s so commonplace as to be invisible. We just said that 18 million people fall into poverty each year from floods and droughts; in 2015 there were 736 million people in poverty total. That means that if we magically got everyone out of poverty today, in 41 years, there would have already been 736 million new instances of poverty from floods and drought alone. Water is about enough to explain the stubbornness of extreme poverty all on its own.

At the Sustainable Development Research (SusDeveR) conference this weekend, I offered some simple tools for performing Bayesian Regressions: Jump to the Github Repository.

The point of these templates is to make it possible for anyone who is familiar with OLS to run a Bayesian regression. The templates have a chunk at the top to change for your application, and a chunk at the bottom that uses Gelman et al.’s Stan to estimate the posterior parameter distributions.

In general, the area at the top is just to create an output vector and a predictor matrix. Like this:

The template part has all of the Stan code, which (for a Bayesian regression) always has a simple form:

The last line does all of the work, and just says (in OLS speak) that the error distribution follows a normal distribution. Most of the templates also have a more efficient version, which does the same thing.

I say in the README what Bayesian regressions are and what they do. But why use them? The simple answer is that we shouldn’t expect the uncertainty on our parameters to be well-behaved. It’s nice if it is, and then OLS and Bayesian regressions will give the same answer. But if the true uncertainty on your parameter of interest is skewed or long-tailed or bimodal, the OLS assumption can do some real harm.

Plus, since Bayesian regressions are just a generalization of MLE, you can setup any functional form you like, laying out multiple, nonlinear expressions, estimating intermediate variables, and imposing additional probabilistic constraints, all in one model. Of course, the templates don’t show you how to do all that, but it’s a start.

The water-energy-food nexus has become a popular buzz-word in the sustainability field. It aims to capture the idea that water, energy, and food challenges are intertwined, and that shocks to any one can precipitate problems to all three.

I’ve often wondered how closely these three are intertwined though. Water is certainly needed for energy (for thermoelectric cooling and hydropower), but the reverse link (mostly pumping) seems a lot weaker. Water is also needed for food production, but is food needed for water availability? Energy and food have some links, with a fair amount of energy needed to produce fertilizer, and a some “food” production actually going to biofuelds, but the sizes aren’t clear.

Below is my attempt to show these flows, for the United States:

Water-Energy-Food Flows

It seems to me, based on this, that this is less a nexus than water-centered system. Every drop of water is fought over for energy, food, and urban production. It’s less a interconnected nexus than a hub-with-spokes. A way to recognize that water is at the center of it all.

Sources:
– Hydrological flows: Total water (GW+SW) extractions from USGS. Food system only has irrigation and livestock; energy only has thermoelectric. The rest make up the difference.
– Energy system flows: Food system energy from Canning, P. 2010. Energy Use in the U.S. Food System. USDA Economic Research Report Number 94; “In 2010, the U.S. water system consumed over 600 billion kWh, or approximately 12.6 percent of the nation’s energy according to a study by researchers at the University of Texas at Austin.” from http://www.ncsl.org/research/environment-and-natural-resources/overviewofthewaterenergynexusintheus.aspx “Energy consumption by public drinking water and wastewater utilities, which are primarily owned and operated by local governments, can represent 30%-40% of a municipality’s energy bill.” from https://fas.org/sgp/crs/misc/R43200.pdf; remainder to 100%.
– Biofuels: 18.38e6 m^3 ethanol + 1.7e6 m^3 biodiesel, at a density of 719.7 kg/m^3 is 14.45e6 MT.
– Remainder of food: https://www.ers.usda.gov/topics/international-markets-trade/us-agricultural-trade/import-share-of-consumption.aspx reports 635 billion pounds consumption, 81% of which was domestically produced.