Disentangling the Influence of Landscape Characteristics, Hydroclimatic Variability and Land Management on Surface Water NO3-N Dynamics: Spatially Distributed Modeling Over 30 yr in a Lowland Mixed Land Use Catchment

Songjun Wu*, Doerthe Tetzlaff, Xiaoqiang Yang, Chris Soulsby

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
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Abstract

Nitrate (NO3-N) mobilization is generally controlled by available sources, hydrological connectivity, and biogeochemical transformations along the dominant flow paths. However, their spatial heterogeneity and complex interactions often impede integrated understanding of NO3-N dynamics at the catchment scale. To fully integrate spatiotemporal information for NO3-N simulations, a grid-based model, mHM-Nitrate, was applied to a 68 km2 lowland, mixed land use catchment (Demnitzer Millcreek, DMC) near Berlin. The model successfully captured the spatiotemporal distribution of flow and NO3-N between 2001 and 2019, but was less successful in 1992–2000 due to land management changes. Re-optimization of relative parameters was subsequently conducted for this period to understand management effects. The simulated results revealed landscape characteristics and hydroclimatic variability as the main controlling factors on respective spatial and temporal patterns. The combined effects of vegetation cover and fertilizer inputs dictated the spatial distribution of water and NO3-N fluxes, while wetness condition determined the temporal NO3-N dynamics by regulating hydrological connectivity and NO3-N mobilization. Denitrification was also closely coupled with hydroclimatic conditions, which accounted for ∼20% of NO3-N inputs. In contrast, restoration of riparian wetlands had a modest impact on NO3-N export (∼10% reduction during 2001–2019), suggesting further interventions (e.g., reducing fertilizer application or increased wetland areas) are needed. Our modeling application demonstrated that mHM-Nitrate could provide robust spatially distributed simulations of hydrological and NO3-N fluxes over a long-term period and could successfully differentiate the key controlling factors. This underlines the model's value in contributing to an evidence base to guide future management practices under climate change.

Original languageEnglish
Article numbere2021WR030566
Number of pages20
JournalWater Resources Research
Volume58
Issue number2
Early online date4 Feb 2022
DOIs
Publication statusPublished - Feb 2022

Keywords

  • climatic variablity
  • landscape characteristics
  • management practice
  • nitrate modeling

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