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Abstract Protecting surface water quality can be complicated by high spatiotemporal variability. Pollutant sources and transport pathways may be identified through sufficiently high‐density monitoring sites and high‐frequency sampling, but practical considerations necessitate tradeoffs between spatial and temporal resolution in water quality monitoring network design. We examined how tradeoffs in sampling density and frequency affect measures of spatiotemporal variability in water quality, emphasizing pattern stability over time. We quantified the spatial stability of stream water quality across >250 monitoring sites in the intensively monitored watershed draining to Lake Okeechobee, FL using Spearman's rank correlations between instantaneous observations and site long‐term means for each parameter. We found that water quality spatial patterns for geogenic, biogenic, and anthropogenic parameters were generally stable on decadal timescales for all solutes, and that sampling densely in space yields more information than sampling frequently in time. Variations in spatial stability decreased with increased sampling density but not with greater sampling frequency, attesting to the dominance of spatial variability over temporal variability. For nutrients, the spatial coefficient of variation (CV) was approximately double the temporal CV. Spatial stability of most solutes was similar across flow conditions, but high‐flow monitoring allows for more sites that effectively capture the long‐term spatial patterns of nutrient sources. Water quality monitoring regimes can be optimized for efficiency in capturing water quality patterns and should be adjusted to focus more on spatial variation. We discuss potential improvements for water quality monitoring, particularly in watersheds where scarce resources necessitate tradeoffs between sampling density and frequency. 

Abstract The mechanisms underlying observed global patterns of partitioning precipitation () to evapotranspiration () and runoff () are controversially debated. We test the hypothesis that asynchrony between climatic water supply and demand is sufficient to explain spatio‐temporal variability of water availability. We developed a simple analytical model forthat is determined by four dimensionless characteristics of intra‐annual water supply and demand asynchrony. The analytical model, populated with gridded climate data, accurately predicted global runoff patterns within 2%–4% of independent estimates from global climate models, with spatial patterns closely correlated to observations (). The supply‐demand asynchrony hypothesis provides a physically based explanation for variability of water availability using easily measurable characteristics of climate. The model revealed widespread responsiveness of water budgets to changes in climate asynchrony in almost every global region. Furthermore, the analytical model using global averages independently reproduced the Budyko curve () providing theoretical foundation for this widely used empirical relationship.