Assessing and improving the outcomes of nonpoint source water quality trading policies in urban areas: A case study in Virginia.

Nonpoint source (NPS) water quality trading (WQT) is a market-based approach to improving water quality. Past work has shown that these programs could increase localized pollutant loadings, in part by exporting water quality controls from urban to rural areas. Virginia's NPS WQT program has enabled thousands of transactions and may provide a model for other programs, but its impacts on urban water quality have not been thoroughly assessed. We quantify the impact of NPS WQT purchases in Virginia on water quality and hydrology in an urban catchment. We go on to assess outcomes of a policy alternative where buyers and sellers are collocated in the urban catchment. Simulation results show that NPS WQT increased total phosphorus (TP) loading by an average of 0.8 lbs TP/year for each 1.0 offsite credits purchased in the analyzed catchment. The TP loading increased in years with greater rainfall, such that TP loads were increased by up to 1.2 lbs TP/year for each offsite credit purchased. These loading increases may or may not be acceptable, depending on the cumulative number of purchases within an urban catchment and existing local water quality issues. In our policy alternative with buyers and sellers collocated in the catchment, we found that the TP increase from development was completely offset at the catchment scale, with a decrease of 4.3 lbs TP/year for each 1.0 credits purchased. This suggests that credits awarded for urban mitigation practices are undervalued compared with water quality requirements for credit purchasers. This undervaluation is a result of the Virginia trading program using one approach to compute the credit value for buyers and a different approach to compute the credit value for sellers. We demonstrate how using a single model to determine both buyer and seller credit values in urban areas could provide greater transparency and mitigate the risk of urban pollution hot spots. This work demonstrates the importance of consistency in the scale of pollutant load calculations between buyers and sellers for NPS WQT, and contributes novel insight into the implications of WQT for urban NPS pollution.

Leveraging Big Data Towards Functionally-Based, Catchment Scale Restoration Prioritization.

The persistence of freshwater degradation has necessitated the growth of an expansive stream and wetland restoration industry, yet restoration prioritization at broad spatial extents is still limited and ad-hoc restoration prevails. The River Basin Restoration Prioritization tool has been developed to incorporate vetted, distributed data models into a catchment scale restoration prioritization framework. Catchment baseline condition and potential improvement with restoration activity is calculated for all National Hydrography Dataset stream reaches and catchments in North Carolina and compared to other catchments within the river subbasin to assess where restoration efforts may best be focused. Hydrologic, water quality, and aquatic habitat quality conditions are assessed with peak flood flow, nitrogen and phosphorus loading, and aquatic species distribution models. The modular nature of the tool leaves ample opportunity for future incorporation of novel and improved datasets to better represent the holistic health of a watershed, and the nature of the datasets used herein allow this framework to be applied at much broader scales than North Carolina.