Quantitative Classification of Spring Discharge Patterns: A Cluster Analysis Approach

Abstract

Springs provide critical water resources that are sensitive to changing climate and catchment processes. In many regions, understanding the temporal variability and spatial distribution of spring discharge is therefore crucial for sustainable water management. Knowledge of these discharge characteristics, organised in a coherent framework, is essential for protecting spring water and preventing shortages. To establish such a framework, we conducted a comparative analysis of long-term discharge records from 96 springs across Austria. Based on discharge seasonality and autocorrelation, we derived a broad-scale classification through cluster analysis and explored associations between individual clusters. The identified similarities in discharge patterns were grouped into four distinct spring categories, each demonstrating common behaviour. To determine the main factors influencing discharge across these four groups, we compared their spatial and temporal patterns with regional climate and catchment characteristics. They align with physical drivers of spring discharge, including precipitation frequency and intensity, snow cover duration, and dominant aquifer type. As these factors were not included in the classification procedure, their alignment supports the validity of our statistical approach. We conclude that the quantitative information derived from this analysis provides a valuable complement to traditional spring classification schemes, which are often based on qualitative knowledge. Our proposed strategy refines these classification approaches, enhances objectivity and reproducibility, and promotes conformity across hydrological disciplines.