Year: 2026
Type: article
Abstract: ABSTRACT Quantifying the relative importance of spatial vs. temporal variance informs efficient water quality measurements at all scales. We examined water quality variability across three US coastal plain wetlandscapes to understand when and where solutes vary in these headwater landscapes. These wetlandscapes (< 10 km 2 ) are minimally impacted forested systems composed of numerous similarly situated small depressional wetlands. Given uniformity in land use within each landscape, we predicted commensurate spatial homogeneity of solute composition, while we predicted that dynamic hydrologic forcing would result in dominant temporal variation. We quantified spatial and temporal variance in water quality across 16 wetlands in each wetlandscape using repeated ( n = 4 to 6) field measurements of > 20 solutes—including anions, cations, nutrients, organic matter quality metrics, and physio‐chemical parameters. Major ions and organic solutes showed comparable spatial and temporal variance, whereas nutrients were dominated by spatial variance, indicating local source heterogeneity is at least as important as hydrological and seasonal variation in controlling landscape‐scale solute patterns. Models predicting temporal variation based on landscape hydrologic and seasonal drivers (mean R 2 = 0.61) outperformed models predicting spatial variation using landscape/network position and geomorphic attributes (mean R 2 = 0.22). This implies consistently and markedly larger unexplained variance in space than in time, suggesting that increasing sampling locations (spatial density) is more consequential for capturing environmental variation in these wetlandscapes than increasing sampling frequency (temporal density). Wetlandscape‐scale variance patterns contrasted with water quality observations synthesized at larger scales. Spatial variation dominated at all larger extents, but temporal variance remained surprisingly consistent across scales. This approach underscores the utility of low‐frequency, high‐density measurements for maximizing information obtained from water quality monitoring programs.
Source: Hydrological Processes
DOI: 10.1002/hyp.70637
Institutions: Handong Global University, University of Florida
Language: English
Subfield: Environmental Chemistry
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7167736609