Land Cover Effects on Selected Nutrient Compounds in Small Lowland Agricultural Catchments

The influence of landscape on nutrient dynamics in rivers constitutes an important research issue because of its significance with regard to water and land management. In the current study spatial and temporal variability of N-NO<sub>3</sub> and P-PO<sub>4</sub> concentration...

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Bibliographic Details
Main Authors: Maksym Łaszewski, Michał Fedorczyk, Sylwia Gołaszewska, Zuzanna Kieliszek, Paulina Maciejewska, Jakub Miksa, Wiktoria Zacharkiewicz
Format: Article
Language:English
Published: MDPI AG 2021-02-01
Series:Land
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Online Access:https://www.mdpi.com/2073-445X/10/2/182
Description
Summary:The influence of landscape on nutrient dynamics in rivers constitutes an important research issue because of its significance with regard to water and land management. In the current study spatial and temporal variability of N-NO<sub>3</sub> and P-PO<sub>4</sub> concentrations and their landscape dependence was documented in the Świder River catchment in central Poland. From April 2019 to March 2020, water samples were collected from fourteen streams in the monthly timescale and the concentrations of N-NO<sub>3</sub> and P-PO<sub>4</sub> were correlated with land cover metrics based on the Corine Land Cover 2018 and Sentinel 2 Global Land Cover datasets. It was documented that agricultural lands and forests have a clear seasonal impact on N-NO<sub>3</sub> concentrations, whereas the effect of meadows was weak and its direction was dependent on the dataset. The application of buffer zones metrics increased the correlation performance, whereas Euclidean distance scaling improved correlation mainly for forest datasets. The concentration of P-PO<sub>4</sub> was not significantly related with land cover metrics, as their dynamics were driven mainly by hydrological conditions. The obtained results provided a new insight into landscape–water quality relationships in lowland agricultural landscape, with a special focus on evaluating the predictive performance of different land cover metrics and datasets.
ISSN:2073-445X