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This dataset contains dissolved organic carbon concentrations from surface water samples collected at 100 urban stream locations in the greater Salt Lake City, Utah metropolitan area. Samples were collected four times (July 2022, October 2022, February 2023, and May 2023) to capture spatial and seasonal variation in DOC concentrations. Filtered stream samples were analyzed for dissolved organic carbon concentration. These data were collected as part of the Carbon in Urban Rivers Biogeochemistry (CURB) Project. Detailed field data and site data are published separately and can be linked using the “curbid” and “synoptic_event” columns in each dataset. 

This dataset contains field measurements taken during water sampling from 100 urban stream locations in the greater Salt Lake City, Utah (USA) metropolitan area. Field collection took place during four synoptic sampling events (July 2022, October 2022, February 2023, and May 2023) to capture spatial and seasonal variation in stream conditions (specific conductivity, water temperature, dissolved oxygen, pH, ORP). Filtered stream samples were analyzed for dissolved organic carbon concentration and characteristics, available in a separate dataset. These data were collected as part of the Carbon in Urban Rivers Biogeochemistry (CURB) Project. Detailed field data and site data are published separately and can be linked using the “curbid” and “synoptic_event” columns in each dataset. 

Abstract Stream dissolved organic matter (DOM) is a globally important carbon flux and a locally important control on stream ecosystems, and therefore understanding controls on stream DOM fluxes and dynamics is crucial at both local and global scales. However, attributing process controls is challenging because both hydrological and biological controls on DOM are integrated and may vary over time and throughout stream networks. Our objective was to assess the patterns and corresponding controls of diel DOM cycles through a seasonal flow recession by using reach‐scale in situ sensors in a non‐perennial stream network. We identified five characteristic diel variations in DOM with differing phase and amplitude. During snowmelt flows, diel variations in DOM were consistent among sites and reflected diel flowpath shifts and photodegradation. Evapotranspiration‐driven diel stage oscillations emerged at two upstream sites, shaping diel DOM patterns indirectly, by creating conditions for instream DOM processing. At a spring‐fed site, minimal diel variation was observed throughout the summer whereas at an intermittent reach, daily drying and rewetting created biogeochemical hot moments. This research demonstrates that controls on DOM vary over time and space, even in close proximity, generating asynchronous fDOM patterns during low flows, illuminating shifts in biogeochemical processes and flowpaths. 

Geologic, geomorphic, and climatic factors have been hypothesized to influence where streams dry, but hydrologists struggle to explain the temporal drivers of drying. Few hydrologists have isolated the role that vegetation plays in controlling the timing and location of stream drying in headwater streams. We present a distributed, fine-scale water balance through the seasonal recession and onset of stream drying by combining spatiotemporal observations and modeling of flow presence/absence, evapotranspiration, and groundwater inputs. Surface flow presence/absence was collected at fine spatial (~80 m) and temporal (15-min) scales at 25 locations in a headwater stream in southwestern Idaho, USA. Evapotranspiration losses were modeled at the same locations using the Simultaneous Heat and Water (SHAW) model. Groundwater inputs were estimated at four of the locations using a mixing model approach. In addition, we compared high-frequency, fine-resolution riparian normalized vegetation difference index (NDVI) with stream flow status. We found that the stream wetted and dried on a daily basis before seasonally drying, and daily drying occurred when evapotranspiration outputs exceeded groundwater inputs, typically during the hours of peak evapotranspiration. Riparian NDVI decreased when the stream dried, with a ~2-week lag between stream drying and response. Stream diel drying cycles reflect the groundwater and evapotranspiration balance, and riparian NDVI may improve stream drying predictions for groundwater-supported headwater streams. 

Abstract Since the 1987 Clean Water Act Section 319 amendment, the US Government has required and funded the development of nonpoint source pollution programs with about $5 billion dollars. Despite these expenditures, nonpoint source pollution from urban watersheds is still a significant cause of impaired waters in the United States. Urban stormwater management has rapidly evolved over recent decades with decision-making made at a local or city scale. To address the need for a better understanding of how stormwater management has been implemented in different cities, we used stormwater control measure (SCM) network data from 23 US cities and assessed what physical, climatic, socioeconomic, and/or regulatory explanatory variables, if any, are related to SCM assemblages at the municipal scale. Spearman’s correlation and Wilcoxon rank-sum tests were used to investigate relationships between explanatory variables and SCM types and assemblages of SCMs in each city. The results from these analyses showed that for the cities assessed, physical explanatory variables (e.g. impervious percentage and depth to water table) explained the greatest portion of variability in SCM assemblages. Additionally, it was found that cities with combined sewers favored filters, swales and strips, and infiltrators over basins, and cities that are under consent decrees with the Environmental Protection Agency tended to include filters more frequently in their SCM inventories. Future work can build on the SCM assemblages used in this study and their explanatory variables to better understand the differences and drivers of differences in SCM effectiveness across cities, improve watershed modeling, and investigate city- and watershed-scale impacts of SCM assemblages.