Dataset Information

Concentration-discharge relationships derived from a larger regional dataset as a tool for watershed management.

ABSTRACT: Concentration-discharge (C-Q) relationships have been widely used to assess the hydrochemical processes that control solute fluxes from streams. Here, using a large regional dataset we assessed long-term C-Q relationships for total phosphorus (TP), soluble reactive phosphorus (SRP), total Kjeldahl nitrogen (TKN), and nitrate (NO₃ ) for 63 streams in Ontario, Canada, to better understand seasonal regional behavior of nutrients. We used C-Q plots, Kruskal-Wallis tests, and breakpoint analysis to characterize overall regional nutrient C-Q relationships and assess seasonal effects, anthropogenic impacts, and differences between "rising" and "falling" hydrograph limbs to gain an understanding of the dominant processes controlling overall C-Q relationships. We found that all nutrient concentrations were higher on average in catchments with greater levels of anthropogenic disturbance (agricultural and urban land use). TP, SRP, and TKN showed similar C-Q dynamics, with nearly flat or gently sloping C-Q relationships up to a discharge threshold after which C-Q slopes substantially increased during the rising limb. These thresholds were seasonally variable, with summer and winter thresholds occurring at lower flows compared with autumn and greater variability during snowmelt. These patterns suggest that seasonal strategies to reduce high flows, such as creating riparian wetlands or reservoirs, in conjunction with reducing related nutrient transport during high flows would be the most effective way to mitigate elevated in-stream concentrations and event export. Elevated rising limb concentrations suggest that nutrients accumulate in upland parts of the catchment during drier periods and that these are released during rain events. NO₃ C-Q patterns tended to be different from the other nutrients and were further complicated by anthropogenic land use, with greater reductions on the falling limb in more disturbed catchments during certain seasons. There were few significant NO₃ hydrograph limb differences, indicating that there was likely to be no dominant hysteretic pattern across our study region due to variability in hysteresis from catchment to catchment. This suggests that this nutrient may be difficult to successfully manage at the regional scale.

SUBMITTER: D'Amario SC

PROVIDER: S-EPMC9285382 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Anthropogenic nutrient inputs have led to nutrient enrichment in many waterbodies worldwide, including Chesapeake Bay (USA). River water quality integrates the spatial and temporal changes of watersheds and forms the foundation for disentangling the effects of anthropogenic inputs. We demonstrate with the Chesapeake Bay Non-Tidal Monitoring Network that machine learning approaches - i.e., hierarchical clustering and random forest (RF) classification - can be combined to better understand the regional patterns and drivers of total nitrogen (TN) trends in large monitoring networks, resulting in information useful for watershed management. Cluster analysis revealed regional patterns of short-term TN trends (2007-2018) and categorized the stations into three distinct trend clusters, namely, V-shape (n = 23), monotonic decline (n = 35), and monotonic increase (n = 26). RF models identified regional drivers of TN trend clusters by quantifying the effects of watershed characteristics (land use, geology, physiography) and major N sources on the trend clusters. Results provide encouraging evidence that improved agricultural nutrient management has resulted in declines in agricultural nonpoint sources, which in turn contributed to water-quality improvement in our period of analysis. Moreover, water-quality improvements are more likely in watersheds underlain by carbonate rocks, reflecting the relatively quick groundwater transport of this terrain. By contrast, water-quality improvements are less likely in Coastal Plain watersheds, reflecting the effect of legacy N in groundwater. Notably, results show degrading trends in forested watersheds, suggesting new and/or remobilized sources that may compromise management efforts. Finally, the developed RF models were used to predict TN trend clusters for the entire Chesapeake Bay watershed at the fine scale of river segments (n = 979), providing fine spatial information that can facilitate targeted watershed management, including unmonitored areas. More broadly, this combined use of clustering and classification approaches can be applied to other regional monitoring networks to address similar water-quality questions.

Dataset Information

Concentration-discharge relationships derived from a larger regional dataset as a tool for watershed management.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets