A comparison of different Ammonia production technologies is made in a post on ‘Comparative studies of ammonia production, combining renewable hydrogen with Haber-Bosch’ by Trevor Brown on the Ammonia Industry blog.

It also features this these Sankey diagrams from an Italian research study by Fratelli et.al.


(published under CC BY 4.0)

All diagrams relate to the production of 1 kg of ammonia (NH3). The authors in their “research examined three cases for renewable hydrogen production, including biomass gasification (Case A), electrolysis of water using solar or wind power (Case B), and biogas reforming (Case C), and compared these sustainable hydrogen sources against the traditional steam methane reformation of natural gas (Case 0)”.

Blue flows represent electrical energy, red flows are heat energy, including the losses (off-heat). Green flows show chemical energy embodied in the product and the feedstock.

For the original study check Fratelli et al: A system approach in energy evaluation of different renewable energies sources integration in ammonia production plants. In: Renewable Energy, Volume 99, December 2016, Pages 472-482.

Couldn’t help but laugh, despite the seriousness of the topic.

Lazaro Gamio of Axios, a “new media company delivering vital, trustworthy news (…) with expertise, voice AND smart brevity”, has created this Sankey diagram infographic to illustrate the Twitter attacks by Trump and who they were targeting. Shown in a blog post by Axios’ Stef Knight.


via Dataviz blog

Very comical use of Sankey diagrams. I love the red tie and just imagine how it will continue to move out to the right shoulder as POTUS’ Twitter attacks on the media continue. Great!

A rather rare find is this Sankey diagram from Iran, posted on the e!Sankey forum.

Those who don’t read Persian, like me, can just enjoy the visual aspect of this Sankey diagram. The diagram most likely depicts energy flows as I can identify the word energy انرژی

What is landscape of climate finance? A paper published December 2016 by I4CE tells us that “Landscapes of climate finance are comprehensive studies mapping financial flows dedicated to climate change action and the energy transition. Covering both end-investment and supporting financial flows from public and private stakeholders, [they] draw the picture of how the financial value chain links sources, intermediaries, project managers and the end investment.”

The paper by Hadrian Hainaut (I4CE), Andreas Barkman (EEA) and Ian Cochran (I4CE) titled ‘Landscapes of domestic climate finance in Europe: Supporting and improving climate and energy policies for a low-carbon, resilient economy’ features two interesting Sankey diagrams.

This is the ‘Landscape of Climate Finance in France 2014’:


Flows are in billion Euro. Sources and receiving sectors indicated with distinctive black boxes. The authors opted for strictly horizontal/vertical arrow routing. There are no individual quantities at each arrow, so the actual numbers can only be estimated from the arrow proportions.

This is the ‘National Climate Finance in Belgium 2013’:


Flows are in million Euros. Some muddle here at the exit of the top light blue box where the arrows overlap instead of showing the sum of roughly 2000 m€ spending. This coincides with three overemphasized arrow heads for the arrows leading to “Public Investments”, “Policy Incentives” and “Grants”. Arriving arrows at the box “Climate Mitigation” overlap and the Sankey diagram could benefit from clearing up here.

Not sure about the ESDC voting: “France: huit points, La Belgique: dix points” maybe 😉

I had reported on climate finance diagrams back in 2014 when the concept was first presented by Climate Policy Initaitive (CPI) but had since lost sight of them. I am happy to see that the idea is still alive and being taken up in a number of countries in Europe. Also good to see that the diagrams are not yet regulated by a standard and there is some “diversity” among these diagrams.

From a paper ‘Integration of deep geothermal energy and woody biomass conversion pathways in urban systems’ by Stefano Moret, Emanuela Peduzzi, Léda Gerber and François Maréchal published at Researchgate, this figure of the energy balance of the city of Lausanne (Switzerland).

Flows are in GWh for the year 2012.

Figure 1: Energy flow Sankey diagram of the city of Lausanne (Switzerland) for the year 2012 (adapted from [18])

Something in this technically well-crafted distribution diagram caught my attention, and at first I didn’t really know what it was…

This is from the Start Fund Report 2017. The Start Network is made up of 42 humanitarian aid agencies and “enables member NGOs and their partners to respond quickly to crises that would traditionally slip under the radar of humanitarian response”. Get more information on the Start Network here and on the Start Fund here.

This is a breakdown of “activated alerts” by geography, by crisis type and by activities that were triggered in the reporting period 01 April 2016 to 31 March 2017. Out of 74 alerts that year 43 resulted in activations (i.e. release of funds for immediate response to a humanitarian crisis).

So what made me stumble and raised my scepticism while looking at this diagram? Can you spot it?
[If you wish to find out yourself, stop here and do not read my thoughts below!]

I had intuitively tried to figure out the number of activated alerts in Africa. But how many alerts correspond to 50.7% out of 43? So I got out my pocket calculator and tried to get some integer numbers for the other values represented by the streams (or ‘bands’). I figured that the percentage values would probably be rounded to one decimal digit, but nevertheless could not get to any meaningful numbers for alerts per geography (nor per crisis type or per activities).

Next I thought that maybe this Sankey diagram doesn’t visualize absolute number of alerts, but rather the money that the fund distributed for these cases. But then, the sub-headline clearly states that it “shows activated alerts” and not “money distributed in response to activated alerts”.

The note “data is from alerts 77 to 150” under the diagram made me think: Hey, what if that was just a small typo and they are actually showing the distribution of 73 alerts instead of 43 alerts. Well, unfortunately none of the percentage values match with a base of 73 cases either and would yield absolute numbers.

My best guess, at present, is that we are actually looking at 150 alerts (the total number of cases the Start Fund has actually been activated over the last three years) and not at 43. Taking into account that the percentage values are rounded (50.66% > 50.1%) this seems to be the best match producing mostly integer numbers. But I might be wrong…

Maybe the author of the graphic or the report wishes to shed some light on this…

From a post ‘Cape Town’s water crisis : Towards a more water secure future’ on the Future Cape Town blog comes this Sankey diagram on the water use in the city of Cape Town (South Africa).

The author of the diagram, Rebecca Cameron, is with MCA Urban and Environmental Planners and looks at how Cape Town could transition towards a more water secure future. This Sankey diagram was originally published in her article Cameron, R and Katzschner, T. 2016. The role of spatial planning in enhancing Integrated Urban Water Management in the City of Cape Town. South African Geographical Journal. 99(2), pp. 196 – 216.

Absolute flow values are not given in this version of the Sankey diagram. Flows are in million cubic metres per year (Mm³/a). Water from five different sources outside the municipality feed the city of Cape Town, as well as five sources within the city. A breakdown of water supplied by the municipal water works is shown. Additional color coding of the arows indicate water quality (dark green = sewage, light green = treated water).

The author explains:

“This diagram is helpful in that it places all aspects of the water system in to one diagram. Here, water supply, water use, wastewater treatment and stormwater have been considered as a single system where too often the urban water cycle is fragmented when addressed within different sectors. The arrows of flow follow a key to represent the quantity and quality of water. The size of the arrow of flow is proportionally indicative of the quantity of water that flows from one process to one another. The colour of the arrows indicates the quality of the water flow; this includes non-potable, potable, sewage, treated sewage, and treated sewage for reuse. This is important to represent as, to intervene in an urban water cycle, both quantity and quality of water must be considered and used appropriately to move towards a more efficient and sustainable water system.”

From the rivers most of the water goes to the ocean. Through evaporation and precipiation it (hopefully) replenishes the reservoirs again that feed the city (this last part not shown in the diagram).

The matplotlib.sankey module doesn’t get much attention, at least that is my impression when browsing the web for new Sankey diagrams. Maybe this is due to the fact that it primarily targets a tech audience, or at least people able to code in Python.

I have reported on this package back in 2011.

Here are two new examples from a Japanese blog.

And a top-down oriented Sankey diagram with arrows branching out to the right.

The project is on sourcefourge: API documentation, demos