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Processes driving nutrient retention in stormwater green infrastructure (SGI) are not well quantified in water-limited biomes. We examined the role of plant diversity and physiochemistry as drivers of microbial community physiology and soil N dynamics post precipitation pulses in a semi-arid region experiencing drought. We conducted our study in bioswales receiving experimental water additions and a montane meadow intercepting natural rainfall. Pulses of water generally elevated soil moisture and pH, stimulated ecoenzyme activity (EEA), and increased the concentration of organic matter, proteins, and N pools in both bioswale and meadow soils. Microbial community growth was static, and N assimilation into biomass was limited across pulse events. Unvegetated plots had greater soil moisture than vegetated plots at the bioswale site, yet we detected no clear effect of plant diversity on microbial C:N ratios, EEAs, organic matter content, and N pools. Differences in soil N concentrations in bioswales and the meadow were most directly correlated to changes in organic matter content mediated by ecoenzyme expression and the balance of C, N, and P resources available to microbial communities. Our results add to growing evidence that SGI ecological function is largely comparable to neighboring natural vegetated systems, particularly when soil media and water availability are similar. 

R code for Hastings, Y. D. (2022). Green Infrastructure Microbial Community Response to Simulated Pulse Precipitation Events in the Semi-Arid Western United States (Master's thesis, The University of Utah). This study was supported by a grant from the US National Science Foundation (DEB 2006308). R code for and Hastings, Y. D., et al. Green Infrastructure Microbial Community Response to Simulated Pulse Precipitation Events in the Semi-Arid Western United States. In review. Abstract: Nutrient retention in urban stormwater green infrastructure (SGI) of water-limited biomes is not well quantified, especially when stormwater inputs are scarce. We examined the role of plant diversity and physiochemistry as drivers of microbial community physiology and soil N pools and fluxes in bioswales subjected to simulated precipitation and a montane meadow experiencing natural rainfall within a semi-arid region during drought. Precipitation generally elevated soil moisture and pH, stimulated ecoenzyme activity, and increased the concentration of organic matter, proteins, and N pools in both bioswale and meadow soils; but the magnitude of change differed between events. Microbial community growth was static and N assimilation into biomass was limited across precipitation events. Unvegetated SGI plots had greater soil moisture, yet effects of plant diversity treatments on microbial C:N ratios, organic matter content, and N pools were inconsistent. Differences in soil N concentrations in bioswales and the meadow were most directly correlated to changes in organic matter content mediated by ecoenzyme expression and the balance of C, N, and P resources available to microbial communities. Our results add to growing evidence that ecological function of SGI is comparable to neighboring natural vegetated systems, particularly when soil media and water availability are similar. The file and R code structure is as follows: Data - Contains all data used for the analysis Results - Contains all figures, RMANOVA, and Piecewise Structural Equation Modeling results. renv - R environment used for project EEA_Vector_Analysis.R - R code used to analyze coenzyme (EEA) responses, including RMANOVA to look for significant differences in EEA response to simulated pulse events and Vector Analysis to determine the nutrient resource acquisition. Gravimetric_soil_moisture_pH.R - R code used for RMANOVA of gravimetric soil moisture and pH responses to simulated pulse events. MicrobialBiomass_EEA.Rproj - Downloaded R project Microbial_biomass.R - R code used for RMANOVA of microbial biomass carbon, nitrogen, and C:N responses to simulated pulse events. OM_protien_N_pools_fluxes.R - R code used for RMANOVA of organic matter content, proteins, and N pools and fluxes responses to simulated pulse events. PSEM_final.R - R code used for Pearson Correlation and Piecewise Structural Equation Modeling. Rclimate.R - R code used to obtain summary statistics of climate data from GIRF and TM climate and soil sensors. 

There are challenges in monitoring and managing water quality due to spatial and temporal heterogeneity in contaminant sources, transport, and transformations. We demonstrate the importance of longitudinal stream synoptic (LSS) monitoring, which can track combinations of water quality parameters along flowpaths across space and time. Specifically, we analyze longitudinal patterns of chemical mixtures of carbon, nutrients, greenhouse gasses, salts, and metals concentrations along 10 flowpaths draining 1,765 km 2 of the Chesapeake Bay region. These 10 longitudinal stream flowpaths are drained by watersheds experiencing either urban degradation, forest and wetland conservation, or stream and floodplain restoration. Along the 10 longitudinal stream flowpaths, we monitored over 300 total sampling sites along a combined stream length of 337 km. Synoptic monitoring along longitudinal flowpaths revealed: (1) increasing, decreasing, piecewise, or no trends and transitions in water quality with increasing distance downstream, which provide insights into water quality processes along flowpaths; (2) longitudinal trends and transitions in water quality along flowpaths can be quantified and compared using simple linear and non-linear statistical relationships with distance downstream and/or land use/land cover attributes, (3) attenuation and transformation of chemical cocktails along flowpaths depend on: spatial scales, pollution sources, and transitions in land use and management, hydrology, and restoration. We compared our LSS patterns with others from the global literature to synthesize a typology of longitudinal water quality trends and transitions in streams and rivers based on hydrological, biological, and geochemical processes. Applications of LSS monitoring along flowpaths from our results and the literature reveal: (1) if there are shifts in pollution sources, trends, and transitions along flowpaths, (2) which pollution sources can spread further downstream to sensitive receiving waters such as drinking water supplies and coastal zones, and (3) if transitions in land use, conservation, management, or restoration can attenuate downstream transport of pollution sources. Our typology of longitudinal water quality responses along flowpaths combines many observations across suites of chemicals that can follow predictable patterns based on watershed characteristics. Our typology of longitudinal water quality responses also provides a foundation for future studies, watershed assessments, evaluating watershed management and stream restoration, and comparing watershed responses to non-point and point pollution sources along streams and rivers. LSS monitoring, which integrates both spatial and temporal dimensions and considers multiple contaminants together (a chemical cocktail approach), can be a comprehensive strategy for tracking sources, fate, and transport of pollutants along stream flowpaths and making comparisons of water quality patterns across different watersheds and regions. 

Streams and rivers are significant sources of nitrous oxide (N₂O), carbon dioxide (CO₂), and methane (CH₄) globally, and watershed management can alter greenhouse gas (GHG) emissions from streams. We hypothesized that urban infrastructure significantly alters downstream water quality and contributes to variability in GHG saturation and emissions. We measured gas saturation and estimated emission rates in headwaters of two urban stream networks (Red Run and Dead Run) of the Baltimore Ecosystem Study Long-Term Ecological Research project. We identified four combinations of stormwater and sanitary infrastructure present in these watersheds, including: (1) stream burial, (2) inline stormwater wetlands, (3) riparian/floodplain preservation, and (4) septic systems. We selected two first-order catchments in each of these categories and measured GHG concentrations, emissions, and dissolved inorganic and organic carbon (DIC and DOC) and nutrient concentrations biweekly for 1 year. From a water quality perspective, the DOC : NO₃⁻ ratio of streamwater was significantly different across infrastructure categories. Multiple linear regressions including DOC : NO₃⁻ and other variables (dissolved oxygen, DO; total dissolved nitrogen, TDN; and temperature) explained much of the statistical variation in nitrous oxide (N₂O, r² =  0.78), carbon dioxide (CO₂, r² =  0.78), and methane (CH₄, r² =  0.50) saturation in stream water. We measured N₂O saturation ratios, which were among the highest reported in the literature for streams, ranging from 1.1 to 47 across all sites and dates. N₂O saturation ratios were highest in streams draining watersheds with septic systems and strongly correlated with TDN. The CO₂ saturation ratio was highly correlated with the N₂O saturation ratio across all sites and dates, and the CO₂ saturation ratio ranged from 1.1 to 73. CH₄ was always supersaturated, with saturation ratios ranging from 3.0 to 2157. Longitudinal surveys extending form headwaters to third-order outlets of Red Run and Dead Run took place in spring and fall. Linear regressions of these data yielded significant negative relationships between each gas with increasing watershed size as well as consistent relationships between solutes (TDN or DOC, and DOC : TDN ratio) and gas saturation. Despite a decline in gas saturation between the headwaters and stream outlet, streams remained saturated with GHGs throughout the drainage network, suggesting that urban streams are continuous sources of CO₂, CH₄, and N₂O. Our results suggest that infrastructure decisions can have significant effects on downstream water quality and greenhouse gases, and watershed management strategies may need to consider coupled impacts on urban water and air quality. 

Abstract The 2085 km²Jordan River Basin, and its seven sub‐catchments draining the Central Wasatch Range immediately east of Salt Lake City, UT, are home to an array of hydrologic, atmospheric, climatic and chemical research infrastructure that collectively forms the Wasatch Environmental Observatory (WEO). WEO is geographically nested within a wildland to urban land‐use gradient and built upon a strong foundation of over a century of discharge and climate records. A 2200 m gradient in elevation results in variable precipitation, temperature and vegetation patterns. Soil and subsurface structure reflect systematic variation in geology from granitic, intrusive to mixed sedimentary clastic across headwater catchments, all draining to the alluvial or colluvial sediments of the former Lake Bonneville. Winter snowfall and spring snowmelt control annual hydroclimate, rapid population growth dominates geographic change in lower elevations and urban gas and particle emissions contribute to episodes of severe air pollution in this closed‐basin. Long‐term hydroclimate observations across this diverse landscape provide the foundation for an expanding network of infrastructure in both montane and urban landscapes. Current infrastructure supports both basic and applied research in atmospheric chemistry, biogeochemistry, climate, ecology, hydrology, meteorology, resource management and urban redesign that is augmented through strong partnerships with cooperating agencies. These features allow WEO to serve as a unique natural laboratory for addressing research questions facing seasonally snow‐covered, semi‐arid regions in a rapidly changing world and an excellent facility for providing student education and research training.