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Landowners and natural resource agencies are seeking to better understand the benefits of best management practices (BMPs) for addressing water quality issues. Using edge-of-field and edge-of-farm runoff analysis, we compared runoff volumes and water quality between small watersheds where BMPs (e.g., prescribed grazing, silvicultural practices) were implemented and control watersheds managed using conventional practices (i.e., continuous grazing, natural forest revegetation). Flow-weighted samples, collected over a 2-year period using automated samplers, were analyzed for nitrate/nitrite nitrogen (NNN), total Kjeldahl nitrogen (TKN), total phosphorus (P), ortho-phosphate phosphorous (OP), total suspended solids (TSS), and Escherichia coli (E. coli). Comparison of silvicultural planting to conventional reforestation practices showed a significant decrease in NNN loads (p < 0.05) but no significant differences in TKN, P, OP, TSS, or E. coli. Continuously grazed sites yielded >24% more runoff than sites that were under prescribed grazing regimes, despite receiving less total rainfall. Likewise, NNN, TSS, and TKN loadings were significantly lower under prescribed grazing management than on conventionally grazed sites (p < 0.05). Data suggests that grazing BMPs can be an effective tool for rapidly improving water quality. However, silvicultural BMPs require more time (i.e., >2 years) to establish and achieve detectable improvements.
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Effects of Antecedent Precipitation Amount and COVID-19 Lockdown on Water Quality along an Urban Gradient
Water quality is affected by multiple spatial and temporal factors, including the surrounding land characteristics, human activities, and antecedent precipitation amounts. However, identifying the relationships between water quality and spatially and temporally varying environmental variables with a machine learning technique in a heterogeneous urban landscape has been understudied. We explore how seasonal and variable precipitation amounts and other small-scale landscape variables affect E. coli, total suspended solids (TSS), nitrogen-nitrate, orthophosphate, lead, and zinc concentrations in Portland, Oregon, USA. Mann–Whitney tests were used to detect differences in water quality between seasons and COVID-19 periods. Spearman’s rank correlation analysis was used to identify the relationship between water quality and explanatory variables. A Random Forest (RF) model was used to predict water quality using antecedent precipitation amounts and landscape variables as inputs. The performance of RF was compared with that of ordinary least squares (OLS). Mann–Whitney tests identified statistically significant differences in all pollutant concentrations (except TSS) between the wet and dry seasons. Nitrate was the only pollutant to display statistically significant reductions in median concentrations (from 1.5 mg/L to 1.04 mg/L) during the COVID-19 lockdown period, likely associated with reduced traffic volumes. Spearman’s correlation analysis identified the highest correlation coefficients between one-day precipitation amounts and E. coli, lead, zinc, and TSS concentrations. Road length is positively associated with E. coli and zinc. The Random Forest (RF) model best predicts orthophosphate concentrations (R2 = 0.58), followed by TSS (R2 = 0.54) and nitrate (R2 = 0.46). E. coli was the most difficult to model and had the highest RMSE, MAE, and MAPE values. Overall, the Random Forest model outperformed OLS, as evaluated by RMSE, MAE, MAPE, and R2. The Random Forest was an effective approach to modeling pollutant concentrations using both categorical seasonal and COVID data along with continuous rain and landscape variables to predict water quality in urban streams. Implementing optimization techniques can further improve the model’s performance and allow researchers to use a machine learning approach for water quality modeling.
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- Award ID(s):
- 1758006
- PAR ID:
- 10393019
- Date Published:
- Journal Name:
- Hydrology
- Volume:
- 9
- Issue:
- 12
- ISSN:
- 2306-5338
- Page Range / eLocation ID:
- 220
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/hydrology9120220
