Intra-catchment comparison and classification of long-term streamflow variability in the Alps using wavelet analysis

Abstract

Understanding the temporal and spatial variability of river discharge of alpine hydrological systems is of particular interest due to their relevance for water uses including water provisioning, hydropower production and touristic activities. Streamflow variability is highly heterogeneous both in time and space due to several reasons, such as a differentiated response to climate change, differences in catchment morphology and geographic location. Therefore, catchment classification for these systems is challenging. A suitable tool to determine the crucial scales of variability of a non-stationary time series is wavelet transform. In this work, we compute the wavelet coherence between fifty selected gauging stations located within the Inn River catchment to classify them by runoff behavior, focusing only on long term variability, between one and eight years scales. This choice allows us to filter out the effect of local meteorological patterns and the effects of hydropower production. In addition, we decompose the streamflow signals in three levels (256 days, three years, six years) using Discrete Wavelet Transform to further understand the detected alterations in the streamflow signal. Three main runoff behaviors (referred as three classes) are found at the yearly scale. Focusing on two-four years scales a loss of coherence between time series located at different elevations becomes significant in the 1980s. Prior to 1980 we detect four different behaviors, while after 1980 we detect eleven different classes. At larger scales the stations are clustered in four classes. Our analysis highlights that catchment classification may depend on the scale of the analyzed signal and it may vary both in time and space. This research contributes to the development of new methods for catchment classification, which is highly relevant for many hydrological applications such as prediction in ungauged basins, model parameterization, understanding the potential impact of environmental and climatic changes, and transferring information from gauged catchments to the ungauged ones.