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ABSTRACT The deterioration of stream water quality threatens ecosystems and human water security worldwide. Effective risk assessment and mitigation requires spatial and temporal data from water quality monitoring networks (WQMNs). However, it remains challenging to quantify how well current WQMNs capture the spatiotemporal variability of stream water quality, making their evaluation and optimisation an important task for water management. Here, we investigate the spatial and temporal variability of concentrations of three constituents, representing different input pathways: anthropogenic (NO₃⁻), geogenic (Ca²⁺) and biogenic (total organic carbon, TOC) at 1215 stations in three major river basins in Germany. We present a typology to classify each constituent on the basis of magnitude, range and dominance of spatial versus temporal variability. We found that mean measures of spatial variability dominated over those for temporal variability for NO₃⁻and Ca²⁺, while for TOC they were approximately equal. The observed spatiotemporal patterns were robustly explained by a combination of local landscape composition and network‐scale landscape heterogeneity, as well as the degree of spatial auto‐correlation of water quality. Our analysis suggests that river network position systematically influences the inference of spatial variability more than temporal variability. By employing a space–time variance framework, this study provides a step towards optimising WQMNs to create water quality data sets that are balanced in time and space, ultimately improving the efficiency of resource allocation and maximising the value of the information obtained. 

Abstract Nutrient impacts on productivity in stream ecosystems can be obscured by light limitation imposed by canopy cover and water turbidity, thereby creating uncertainties in linking nutrient and productivity regimes. Evaluations of nutrient limitations are often based on a response ratio (RR) quantifying productivity stimulation above ambient levels given augmented nutrient supply. This metric neglects the primacy of light effects on productivity. We propose an alternative approach to quantify nutrient limitations using a “decline ratio” (DR), which quantifies the productivity decline from the maximum established by light availability. The DR treats light as the first‐order control and nutrient depletion as a disturbance causing productivity decline, allowing separation of nutrient and light influences. We used DR to assess nutrient diffusing substrate (NDS) experiments with three nutrients (nitrogen [N], phosphorus [P], iron [Fe]) from five Greenland streams during summer, where light is not limited due to the lack of canopy and low turbidity. We tested two hypotheses: (a) productivity maximum (i.e., highest chlorophyll‐aamong NDS treatments) is controlled by light and (b) DR depends on both light and nutrients. The productivity maximum was strongly predicted by light (R² = 0.60). The productivity decline induced by N limitation (i.e., DR_N) was best explained by light availability when parameterized with either dissolved inorganic nitrogen concentration (R² = 0.79) or N:Fe ratio (R² = 0.87). These predictions outperformed predictions of RR for which light was not a significant factor. Reversing the perspective on nutrient limitation from “stimulation above ambient” to “decline below maximum” provides insights into both light and nutrient impacts on stream productivity. 

Abstract The temporal structures of gross primary production (GPP) and ecosystem respiration (ER) vary across time scales in response to complex interactions among dynamic drivers (e.g., flow, light, temperature, organic matter supply). To explore emergent patterns of river metabolic variation, we applied frequency‐domain analysis to multiyear records of metabolism across 87 US rivers. We observed a dominant annual periodicity in metabolic variation and universal fractal scaling (i.e., power spectral density inversely correlated with frequency) at subannual frequencies, suggesting these are foundational temporal structures of river metabolic regimes. Frequency‐domain patterns of river metabolism aligned best with drivers related to energy inputs: benthic light for GPP and GPP for ER. Simple river metabolism models captured frequency‐domain patterns when parameterized with appropriate energy inputs but neglecting temperature controls. These results imply that temporal variation of energy supply imprints directly on metabolic signals and that frequency‐domain patterns provide benchmark properties to predict river metabolic regimes.