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 with numerous similarly situated small depressional wetlands, suggesting comparative spatial homogeneity of solute composition, and are hydrologically dynamic, suggesting significant temporal heterogeneity. 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. We found an even balance between spatial and temporal variance for ions and organic solutes, but dominance of spatial variance for nutrients, implying local source heterogeneity is at least as important as hydrological and seasonal variation in controlling landscape solute patterns. Models predicting temporal variation based on wetland 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 than increasing sampling frequency (temporal density). We compared wetlandscape-scale variance patterns with water quality observations synthesized at larger scales and observed increasing spatial variation with larger extent, but surprisingly consistent temporal variance at all scales. This framework underscores the utility of low-frequency, high density measurements for maximizing information content from water quality monitoring programs.