Dr Eric Pohl is a senior scientist at the Alpine Cryosphere and Geomorphology Group, at the UNIFR’s Department of Geosciences. As a geo-ecologist, his interest lies in water: where it comes from (meltwater, precipitation) and how it is distributed on land. He has often worked in Central Asia’s Kyrgyzstan and Tajikistan to trace and measure the meltwaters of different glaciers. It is this research focus and on-site experience, coupled with that of his colleagues, notably Dr Martina Barandun, Dr Tomas Saks, and Prof. Martin Hölzle, which led the University of Fribourg to be involved in the Horizon Europe “WE-ACT” project. This four-year project involves 13 different institutions, NGOs and SMEs across Europe and Central Asia. It will tackle a crucial problem for Central Asian mountainous regions having to grapple with the consequences of ongoing climate change: how to allocate often scarce water resources within and across borders in the glacier- and snow-fed river basins of Central Asia? What amounts should be devoted to hydropower, and what to irrigation? The project partners will notably provide a reliable and intuitive Decision Support System (DSS) which will help allocate water to sectors where it is most needed, alleviating water stress for communities, the economy, and the environment on the short- and long-term.

In order to build such a system and to maintain its efficacy, one needs reliable data arising from real-time monitoring. This is the backbone of the project, the first work package on which all others will rely, and the one in which Dr Pohl will be involved. Indeed, he is very well versed in the importance of data gathering, and the difficulties involved in so doing. One such problem is the potential loss of instruments and data due to on-site happenings. Before this project, Dr Pohl had to return every year to the instruments he had installed from 2019 onwards in the Central Asian mountainous field sites in order to retrieve the small boxes that housed the previous year’s data. Last summer, due to a river course changing, the instruments of one of these sites were lost, together with a year’s worth of data. Data loss is obviously most frustrating for a scientist who requires that data to conduct research, and the EU project’s funding, allowing for new equipment that will either provide near real-time monitoring or robust permanent installation, is a godsend in that sense. This should remove the need to regularly visit a site or reduce the stress wondering whether instruments will still be where they were installed. In the long run, it should solve also one of the most complicated affairs, namely hoping for a sufficient number of donkeys available each time to carry instruments. Dr Pohl confides that donkey logistics can be nerve-racking, as donkeys are a much sought-after and a non-negotiable necessity on which the success of the work depends. Their availability needs to be planned before arrival with local people who are often difficult to contact.

In addition, real-time, more comprehensive data shall allow for much more precise predictions in the long-term. The idea of the DSS is to combine all available data sources with calculations from numerical hydrological and water demand models, using machine learning (artificial intelligence) routines under consideration of water stakeholder needs at regional to local levels. Ultimately, this novel interconnection of data and methods shall allow to make the right decisions regarding water allocation at the earliest stage possible to allow for the best allocation strategy. The DSS shall identify such a strategy thanks to the training on all information with machine learning already through certain measurements of meteorological conditions like snow or rainfall, through measured glacier runoff, or the water level of a reservoir or of a river.

With this project, it will become easier to understand and predict the variable water supply of the two transboundary rivers Syr Darya and Amu Daryain Kyrgyzstan and Tajikistan. The DSS that will arise from this research will lead to better a water allocation system that might ease transboundary political and economic tensions.

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