A Methodological Framework for Characterizing the Spatiotemporal Variability of River Water-Quality Patterns Using Dynamic Factor Analysis

Water quality monitoring programs in many river basins have recorded data during several decades, but dealing with such environmental datasets is not an easy task. Uneven sampling frequency, missing observations and changes in monitoring strategies challenge the most basic data quality requirements and statistical assumptions of most time-series analysis methods. Lack of data consistency forces scientists to lean towards site-by-site approaches, avoiding simultaneous analysis of multiple time-series containing missing data. By using the appropriate tools and methods, however, common water quality patterns in a river basin can be identified and characterized in time and space. We introduce a collection of methodological steps for the detection and characterization of the spatiotemporal variability of river water-quality patterns in the context of global environmental change. Dynamic factor analysis (DFA) is used to extract underlying common patterns from sets of time-series with data gaps. The extracted patterns are further characterized using complementary methods such as frequency and trend analyses for the temporal dimension, together with regression and clustering analyses for the spatial dimension. We show how the application of these methods tackles the challenges of identifying patterns that act at different temporal and spatial scales, and we illustrate three case studies in Mediterranean basins where riverine nutrient patterns are unraveled and related to environmental drivers of change. Our methodological framework seeks to serve as a hypothesis-generation tool for further analyses of drivers of environmental change at the river basin scale.