Clustering as a tool for identifying drought-prone regions: A Swedish example 

<p>The increasing availability of large data sets has fuelled the application of clustering approaches for discovering and interpreting spatio-temporal patterns in hydroclimatic data. Clustering can be particularly powerful for grouping catchments that span across various climate zones or hydrologic regimes into homogeneous clusters of similar hydrological or climatic behavior.</p> <p>Here, we provide a practical example of how clustering can facilitate comprehensive analyses of streamflow drought characteristics across 50 Swedish catchments spanning three climate zones and ranging from snow-melt driven streamflow regimes in the North to rainfall-driven regimes in the South. To this end, the k-means clustering was applied to generate homogeneous clusters of catchments based on their similarity in streamflow anomalies (detected by using the standardized streamflow index) over the past 60 years. Five geographically distinct regions emerged from the clustering, linking the streamflow anomalies to the hydroclimatic conditions (following the north-south and elevation gradients), and to landscape characteristics, which strongly affect streamflow-generating processes at the catchment scale. Each cluster also featured &#8211; in line with its geographical location &#8211; a distinct hydrological regime.</p> <p>Facilitated by the clustering, a clear north-south gradient emerged for many of the analysed drought statistics, including, e.g., drought duration, annual number of drought days and number of drought days in spring and summer, as well as standardized deficit volumes. Similarly, trends and changes in streamflow anomalies over the past 60 years also varied across clusters, with clusters in northern Sweden exhibiting wetting trends and clusters in southern Sweden drying trends.</p> <p>This case study serves as an illustration of how clustering can be a valuable tool for improving our understanding and potential prediction of hydrological processes. Clustering enabled us to identify drought-prone areas and illuminated various drought behaviors, prevailing drought typologies, and seasonal differences that can be linked to the underlying streamflow regimes.&#160;</p>