Archetypal flow regime change classes and their associations with anthropogenic drivers of global streamflow alterations

Virkki, V. ORCID: https://orcid.org/0000-0002-2603-3420, Sahu, R.K. ORCID: https://orcid.org/0000-0003-0681-0509, Smilovic, M. ORCID: https://orcid.org/0000-0001-9651-8821, Láng-Ritter, J., Porkka, M., & Kummu, M. (2024). Archetypal flow regime change classes and their associations with anthropogenic drivers of global streamflow alterations. Environmental Research Communications 6 (11) e111007. 10.1088/2515-7620/ad9439.

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Abstract

Streamflow—a key component of the water cycle—is experiencing drastic alteration due to human actions. The global extent and degree of this change have been widely assessed, but understanding of its drivers remains limited because previous global-scale approaches have largely relied on modelled hypothetical scenarios. Here, we advance this understanding by providing an observation-based association analysis of streamflow change and its drivers. We use observed streamflow data in 3,293 catchments globally and combine them with data on precipitation, evapotranspiration, water use, and damming. Building on a robust annual trend analysis covering years 1971–2010, we first determine flow regime change (FRC) classes, and then use them to investigate associations between streamflow change and its drivers. We find that 91% of all catchments are assigned to four main FRCs, which indicates globally consistent flow regime changes. By associating driver trends with the FRCs, we further characterise them by trends and changes in the four investigated drivers. We find that FRCs depicting decreasing streamflow quantity and variability are strongly associated with direct human drivers, either water use or damming. In contrast, associations with indirect drivers (precipitation and evapotranspiration) are more dominant in FRCs that depict increasing streamflow quantity and variability. Our key advance is that our comprehensive, observation-based association analysis substantiates the model-based findings of previous global-scale studies, and thus adds detail and validation to their interpretations. This may further support developing and adopting efficient measures to mitigate streamflow change and its subsequent impacts across scales.

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