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Excess non-point nutrient loading continues to impair urban surface waters. Because of the potential contribution of tree litterfall to nutrient pollution in stormwater, street sweeping is a promising management tool for reducing eutrophication in urban and suburban regions. However, nutrient concentrations and loads of material removed through street sweeping have not been well characterized, impeding the development of pollution reduction credits and improvement of models for stormwater management. We evaluated the role of canopy cover over streets, street sweeper type, season, and sweeping frequency in contributing to variation in concentrations and loads of nitrogen (N), phosphorus (P), and solids recovered in street sweepings, using analyses of samples collected during regular street sweeping operations in five cities in the Minneapolis-St. Paul Metropolitan Area, Minnesota, USA. We expected that nutrient concentrations and loads would be highest in seasons and places of higher tree litterfall. We also expected that regenerative-air sweepers would recover higher loads compared to mechanical broom sweepers. Total N and P concentrations in sweepings increased most strongly with canopy cover in June, October, and November. Total N and P recovered in street sweepings similarly increased with canopy cover in June, October, and November, and peaked in early summer and autumn, times of high litterfall. In contrast, total dry mass in sweepings was greatest in early spring, following winter snowmelt. However, nutrient loads and concentrations did not differ between sweeper types. Our results add to growing evidence of the importance of street trees in contributing nutrient pollution to urban surface waters. Street sweeping focused on high-canopy streets during early summer and autumn is likely an effective management tool for stormwater nutrient pollution. 

Understanding the drivers of food chain length in natural communities has intrigued ecologists since Elton publicized “food cycles” in the early 20th century. Proposed drivers of food chain length have included productivity, disturbance regime, ecosystem size, and trophic omnivory. However, current theories have largely assumed simple, two‐dimensional habitat architectures and may not be adequate to predict food chain length in ecosystems with a complex, branching structure. Here, we develop a spatially explicit theoretical model that provides an integrated framework for understanding variation in food chain length in branching networks. We show independent, positive influences of ecosystem size and complexity (as indicated by branching properties) on food chain length. However, the effects of ecosystem size and complexity were contingent upon other factors, appearing more clearly in high‐disturbance and high‐productivity regimes. Our results suggest that ecosystem complexity is an important yet overlooked driver of food chain length that may increase the resilience to anthropogenic environmental changes.

 

River networks regulate carbon and nutrient exchange between continents, atmosphere, and oceans. However, contributions of riverine processing are poorly constrained at continental scales. Scaling relationships of cumulative biogeochemical function with watershed size (allometric scaling) provide an approach for quantifying the contributions of fluvial networks in the Earth system. Here we show that allometric scaling of cumulative riverine function with watershed area ranges from linear to superlinear, with scaling exponents constrained by network shape, hydrological conditions, and biogeochemical process rates. Allometric scaling is superlinear for processes that are largely independent of substrate concentration (e.g., gross primary production) due to superlinear scaling of river network surface area with watershed area. Allometric scaling for typically substrate-limited processes (e.g., denitrification) is linear in river networks with high biogeochemical activity or low river discharge but becomes increasingly superlinear under lower biogeochemical activity or high discharge, conditions that are widely prevalent in river networks. The frequent occurrence of superlinear scaling indicates that biogeochemical activity in large rivers contributes disproportionately to the function of river networks in the Earth system.

 

Despite decades of policy that strives to reduce nutrient and sediment export from agricultural fields, surface water quality in intensively managed agricultural landscapes remains highly degraded. Recent analyses show that current conservation efforts are not sufficient to reverse widespread water degradation in Midwestern agricultural systems. Intensifying row crop agriculture and increasing climate pressure require a more integrated approach to water quality management that addresses diverse sources of nutrients and sediment and off-field mitigation actions. We used multiobjective optimization analysis and integrated three biophysical models to evaluate the cost-effectiveness of alternative portfolios of watershed management practices at achieving nitrate and suspended sediment reduction goals in an agricultural basin of the Upper Midwestern United States. Integrating watershed-scale models enabled the inclusion of near-channel management alongside more typical field management and thus directly the comparison of cost-effectiveness across portfolios. The optimization analysis revealed that fluvial wetlands (i.e., wide, slow-flowing, vegetated water bodies within the riverine corridor) are the single-most cost-effective management action to reduce both nitrate and sediment loads and will be essential for meeting moderate to aggressive water quality targets. Although highly cost-effective, wetland construction was costly compared to other practices, and it was not selected in portfolios at low investment levels. Wetland performance was sensitive to placement, emphasizing the importance of watershed scale planning to realize potential benefits of wetland restorations. We conclude that extensive interagency cooperation and coordination at a watershed scale is required to achieve substantial, economically viable improvements in water quality under intensive row crop agricultural production.

 

Intensification of brown color in surface waters has been observed over several decades in many areas. We examined a 64‐yr daily record (1947–2010) of visual water color, a measure of chromophoric dissolved organic matter (CDOM), in the Mississippi River at Minneapolis, Minnesota. Although no monotonic trends in daily or mean annual color were evident, our analyses revealed trends in seasonal metrics, for example, mean winter color, on decadal scales related to changes in flow (hence climatic conditions). A pattern of high color (CDOM) in late spring and summer, corresponding with higher flow, was found across the period. Daily flow accounted for ~ 50% of the variance in color, and a lag of four days was found between peak responses of flow and color, supporting a CDOM source from wetlands in northern parts of the basin. The slope of the color‐flow relationship increased over the 64 yr, driven by increased CDOM flushing in late summer‐early fall. Based on trends in seasonally aggregated color and discharge, minimum and mean color and flow increased during winter over the 64 yr, potentially due to higher temperatures. Summer months did not show increases, but color became less variable. As a result, the color difference between summer and winter became smaller over the study period. During high flow events (ice‐out or high precipitation), some hysteretic color patterns were observed consistent with observations on other large rivers. Our results indicate that long‐term color (CDOM) trends in the Mississippi Headwaters reach are related to seasonally dominant changes in climatic conditions.