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

doi:10.1029/2021WR030566

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

Geography & Environment

Research output: Contribution to journal › Article › peer-review

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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 language	English
Article number	e2021WR030566
Number of pages	20
Journal	Water Resources Research
Volume	58
Issue number	2
Early online date	4 Feb 2022
DOIs	https://doi.org/10.1029/2021WR030566
Publication status	Published - Feb 2022

Bibliographical note

Funding Information:
Songjun Wu is funded by the Chinese Scholarship Council (CSC). Contributions from Chris Soulsby were supported by the Leverhulme Trust through the ISO‐LAND project (grant no. RPG 2018 375). The authors thank the German Weather Service (DWD) for providing meteorological data set. The staff of the IGB chemical analytics and biogeochemistry lab were appreciated for NO‐N analyses, and for compiling the long‐term DMC data set. The authors also thank Aaron Smith for supports in terms of comparing results between mHM‐Nitrate and EcHO‐iso.

Data Availability Statement

The data used are presented in the tables, figures and Supporting Information. For source codes please refer to https://doi.org/10.5281/zenodo.3891629.

Keywords

climatic variablity
landscape characteristics
management practice
nitrate modeling

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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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. / Wu, Songjun; Tetzlaff, Doerthe; Yang, Xiaoqiang et al.
In: Water Resources Research, Vol. 58, No. 2, e2021WR030566, 02.2022.

Research output: Contribution to journal › Article › peer-review

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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.",

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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