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Disentangling the Influence of Landscape Characteristics, Hydroclimatic Variability and Land Management on Surface Water NO 3 ‐N Dynamics: Spatially Distributed Modeling Over 30 yr in a Lowland Mixed Land Use Catchment
Author(s) -
Wu Songjun,
Tetzlaff Doerthe,
Yang Xiaoqiang,
Soulsby Chris
Publication year - 2022
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2021wr030566
Subject(s) - environmental science , riparian zone , biogeochemical cycle , hydrology (agriculture) , spatial ecology , wetland , land cover , spatial heterogeneity , spatial variability , drainage basin , land management , spatial distribution , vegetation (pathology) , watershed , land use , ecology , geography , habitat , geology , medicine , statistics , geotechnical engineering , mathematics , cartography , pathology , biology , remote sensing , machine learning , computer science
Nitrate (NO 3 ‐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 NO 3 ‐N dynamics at the catchment scale. To fully integrate spatiotemporal information for NO 3 ‐N simulations, a grid‐based model, mHM‐Nitrate, was applied to a 68 km 2 lowland, mixed land use catchment (Demnitzer Millcreek, DMC) near Berlin. The model successfully captured the spatiotemporal distribution of flow and NO 3 ‐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 NO 3 ‐N fluxes, while wetness condition determined the temporal NO 3 ‐N dynamics by regulating hydrological connectivity and NO 3 ‐N mobilization. Denitrification was also closely coupled with hydroclimatic conditions, which accounted for ∼20% of NO 3 ‐N inputs. In contrast, restoration of riparian wetlands had a modest impact on NO 3 ‐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 NO 3 ‐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.