Characterizing Catchment-Scale Nitrogen Legacies and Constraining Their Uncertainties

Fanny J. Sarrazin, Rohini Kumar, Nandita B. Basu, Andreas Musolff, Michael Weber, Kimberly J. Van Meter, Sabine Attinger

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Improving nitrogen (N) status in European water bodies is a pressing issue. N levels depend not only on current but also past N inputs to the landscape, that have accumulated through time in legacy stores (e.g., soil, groundwater). Catchment-scale N models, that are commonly used to investigate in-stream N levels, rarely examine the magnitude and dynamics of legacy components. This study aims to gain a better understanding of the long-term fate of the N inputs and its uncertainties, using a legacy-driven N model (ELEMeNT) in Germany's largest national river basin (Weser; 38,450 km2) over the period 1960–2015. We estimate the nine model parameters based on a progressive constraining strategy, to assess the value of different observational data sets. We demonstrate that beyond in-stream N loading, soil N content and in-stream N concentration allow to reduce the equifinality in model parameterizations. We find that more than 50% of the N surplus denitrifies (1480–2210 kg ha−1) and the stream export amounts to around 18% (410–640 kg ha−1), leaving behind as much as around 230–780 kg ha−1 of N in the (soil) source zone and 10–105 kg ha−1 in the subsurface. A sensitivity analysis reveals the importance of different factors affecting the residual uncertainties in simulated N legacies, namely hydrologic travel time, denitrification rates, a coefficient characterizing the protection of organic N in source zone and N surplus input. Our study calls for proper consideration of uncertainties in N legacy characterization, and discusses possible avenues to further reduce the equifinality in water quality modeling.

Original languageEnglish (US)
Article numbere2021WR031587
JournalWater Resources Research
Volume58
Issue number4
DOIs
StatePublished - Apr 2022

All Science Journal Classification (ASJC) codes

  • Water Science and Technology

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