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Adaptive management is a widely used approach for supporting decision making in the face of uncertainty, but its success relies on the timely refinement of management strategies in response to monitoring outcomes. In this study, we demonstrate the role of modeling to inform implementation of an adaptive management plan for flow management in the urbanized San Gabriel River, California, USA. We applied modeling techniques to assess the impact of reduced discharge from water reclamation plants on the condition of riparian vegetation. Using stem water potential and canopy volume as indicators of plant stress, we examined their responses to changes in managed discharge over 5 years. Our results showed that stem water potential was more responsive based on environmental conditions than canopy volume, although no significant relationship was found between reduced discharge and overall plant stress. This lack of significant relationship is likely due to inherent variability within the system, including differences among riparian species and their proximity to the managed discharge and other stressors, which may obscure potential effects of flow reduction. By incorporating these findings into the adaptive management plan, we refined the monitoring strategy early on, optimizing resource reallocation and focusing on the most sensitive indicators of plant stress. Our study demonstrates the value of early implementation of modeling in adaptive management, allowing for more timely decision-making and improved management outcomes in flow-regulated systems.
This study presents the most extensive and first region-wide data set to date on microplastic (MP) treatment by stormwater biofilters (also known as biofiltration/bioretention systems), based on paired influent and effluent flow-weighted composite samples collected across seven southern California installations. MPs were quantified and characterized by five size fractions (20 μm to >500 μm), morphology, color, and polymer type. Across 18 storm events, the median MP event-mean concentration (EMC) reduction was 72%, and for the 14 events with runoff volume data, the median total MP load removal was 93%. EMC reduction was generally consistent across size fractions, ranging from 68% to 100% for different size fractions. Physical filtration (i.e., straining) was identified as the dominant removal mechanism for MPs in biofilters, supported by the close correspondence between the MPs and all particles remaining after extraction. The alignment in occurrence across a wide concentration range (linear regression R2=0.91 overall and R2 > 0.75 for individual fractions) indicates that all particle counts may serve as a practical surrogate for estimating MP occurrence and biofilter treatment in urban runoff. Quality assurance and quality control results further demonstrate that automated samplers can be effectively used for stormwater MP monitoring when appropriate blanks are collected and best practices are applied.
The San Diego/Tijuana (US/Mexico) coastal region has immense water quality issues due to untreated wastewater sources. Regular ddPCR Enterococcus sampling provides same-day water quality information for beachgoers but does not provide forecasts for decision making. We describe the Pathogen Forecast Model (PFM) V1.0, providing daily 5 day forecasts of swimmer illness risk along 30 km of shoreline on a web dashboard. PFM is based upon a hydrodynamic model using forecasted waves, winds, offshore currents, and Tijuana River flow as inputs. PFM accurately forecasts tides and waves and forecasts depth-averaged shelf currents moderately well. Modeled untreated wastewater concentration tracer C evolves with a constant decay rate representing Norovirus. Over 13 km of shoreline, forecasted C and observed EnterococcusE are moderately log-correlated (≈0.5) for day-1 and -5 forecasts. The C and E power-law relationship has an exponent of ≈0.5, possibly suggesting the tracer decays 2× slower than Enterococcus DNA, among other potential explanations. Using the cutoff C = 10-5, PFM classified advisory exceedances with nearly 80% raw accuracy, high sensitivity, and lower specificity. This is the first forecast model that resolves estuarine, surfzone, and shelf regions. PFM is a useful public health tool and is constantly being improved.
