Globally, we have enough fresh water resources, but water is not evenly distributed. Today 663 million people in the world live without clean water. While food production (agriculture) triggers 70% of all our fresh water use, mainly for irrigation. Particular in regions where water is scarce this becomes problematic for humans and the environment, as we consume their water through the products we purchase. To help everyone, we need to reduce the worldwide dependency on scarce water by 50%. Displaying the amount of scarce water on a per product and per country basis, Eaternity enables optimizations and complete transparency on the issue in a useful way.
Eaternity provides water scarcity footprint for a product or a menu and its ingredients dependent on the fresh water footprint of the product in that region and the water scarcity of the region. The stars display if the value is good compared to the average.
The average is calculated based on a weighted average of over 76'000 menus with their actual sales numbers collected over the years from over 150 restaurants. The value therefore corresponds to our current diet in Europe (see: Benchmark for the water value).
The water scarcity footprint of a product depends on two elements. First, the amount of fresh water (surface- and groundwater, not including rainwater or water pollution) that is used for the product in that particular region of production. And second, the relative water scarcity of the same respective region.
Our water footprint calculations sets apart in that it explicitly includes the water stress of a region as a weighing factor for the amount of fresh water used. Water stress depends on the amount of water consumed in that region compared to the amount of water that is naturally provided by rainfall and other precipitation. Note, that the amount of consumed water is corrected for that some of the withdrawn water is returned to the water shed, e.g. after use for cooling in electricity production. Our datasets allow us to differentiate this water scarcity factor between 162 countries.
Globally, the water we consume comes from regions with an average water scarcity index of 0.51, which means that on average, we are consuming water under moderate to high water scarcity. However, the average person lives in an area with water scarcity of 0.32. This means that many products are transported from water scarce areas to areas with lower water scarcity.
For example, a tomato that is produced in Spain requires 44 times more irrigation water than in Switzerland. Because water in Spain is scarcer than in Switzerland, the scarcity footprint of an average Spanish tomato is 2400 times higher than an average Swiss tomato.
Depending on what food is consumed and where the food comes from, every country has a unique list of food products that are typically problematic and contribute most to the national water scarcity footprint. In Switzerland, the consumption of olives, nuts, chocolate, coffee, milk products, rice and beef contribute most to the Swiss national water scarcity footprint. It does not mean that we should avoid these products altogether, but that we should be more careful in their use and that it matters where these foods were produced.
We use the background data for water scarcity footprint data as published by Scherer and Pfister (Scherer & Pfister 2016b). The data set consists of water consumption (m3/kg product) and water scarcity footprint (m3 H2Oeq/kg) for 142 products in 162 countries where production is relevant.
The water consumption only considers water used for irrigation.
The irrigation water consumption was weighed with the water stress index (WSI). The WSI was calculated using the consumption-to-availability ratio accounting for monthly variation in precipitation and flow regulation (Pfister et al. 2009; Scherer & Pfister 2016b). The WSI is estimated using six global models for river discharge, four for ground water recharge, six for precipitation and three for water use. Also here the spatial resolution is 10x10 km.
The WSI values ranges from 0.01 to 0.99 and are interpreted as followed (Pfister et al.2009):