Biogeosciences (Jul 2015)

Detection and attribution of global change effects on river nutrient dynamics in a large Mediterranean basin

  • R. Aguilera,
  • R. Marcé,
  • S. Sabater

DOI
https://doi.org/10.5194/bg-12-4085-2015
Journal volume & issue
Vol. 12, no. 13
pp. 4085 – 4098

Abstract

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Attributing changes in river water quality to specific factors is challenging because multiple factors act at different temporal and spatial scales, and it often requires examining long-term series of continuous data. Data consistency is sometimes hindered by the lack of observations of relevant water quality variables and the low and uneven sampling frequency that characterizes many water quality monitoring schemes. Nitrate and dissolved phosphate concentration time series (1980–2011) from 50 sampling stations across a large Mediterranean river basin were analyzed to disentangle the role of hydrology, land-use practices, and global climatic phenomena on the observed nutrient patterns, with the final aim of understanding how the different aspects of global change affected nutrient dynamics in the basin. Dynamic factor analysis (DFA) provided the methodological framework to extract underlying common patterns in nutrient time series with missing observations. Using complementary methods such as frequency and trend analyses, we sought to further characterize the common patterns and identify the drivers behind their variability across time and space. Seasonal and other cyclic patterns were identified as well as trends of increase or decrease of nutrient concentration in particular areas of the basin. Overall, the impact of global change, which includes both climate change and anthropogenic impacts, on the dynamics of nitrate concentration across the study basin was found to be a multifaceted process including regional and global factors, such as climatic oscillations and agricultural irrigation practices, whereas impacts on phosphate concentration seemed to depend more on local impacts, such as urban and industrial activities, and less on large-scale factors.