Landscape heterogeneity drives spatial and temporal patterns of nitrate export within agricultural watersheds
Year: 2026
Type: article
Abstract: For watershed management, it is important to monitor the impact of mitigation practices on water quality. Effective monitoring can foster further adoption of conservation actions by demonstrating success. However, the spatial resolution of most agricultural watershed monitoring is low, and linking field-scale management or the influence of individual watershed features to a single downstream monitoring station is difficult. We investigated how surface flow path sampling can be used in low-order agricultural streams to gain a better understanding of processes controlling nitrate-nitrogen (NO 3 − -N) export. We characterize spatial patterns in stream NO 3 − -N concentrations and the temporal stability of these patterns in two contrasting agricultural stream reaches. In a watershed region with homogenous land use, NO 3 − -N concentrations are stable through time and spatial dependence persists across large distances. In a heterogeneous region of the watershed which includes wetlands, spatial patterns are variable through time and spatial dependence persists across variable distances during contrasting stream conditions. Our results indicate that surface flow path sampling is most valuable in landscapes with multiple, changing influences on analytes. In these landscapes, surface flow-path sampling can identify control points and capture how the influence of these control points change through time. In homogenous landscapes, surface flow-path sampling can be most valuable for establishing the location of control points to inform where fixed-site monitoring should occur. By coupling surface flow-path sampling with traditional fixed-site monitoring, researchers and watershed managers can develop a deeper understanding of watershed processes which can help plan for more effective management.
Source: Biogeochemistry
Language: English
Subfield: Environmental Chemistry
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,390
Award: G1928192306
OpenAlex ID: W7166292669
