资源环境科技发展态势分析平台

Climate change can significantly affect water quality, in addition contributing non-stationarity and deep uncertainty that complicates water quality management. But most of the total maximum daily load (TMDL) implementation plans crafted to meet water quality standards in the USA are developed assuming stationary climate and at best a small number of land use futures, although neither assumption seems reliably justified. To address this challenge, this study employs robust decision making (RDM) methods, commonly used to help develop water supply plans, to stress test the proposed Enhanced Watershed Management Plan (EWMP), a TMDL implementation plan, for the Tujunga Wash, the largest subwatershed of the Los Angeles River, over a wide range of climate and land use futures. We find that climate change could significantly reduce the ability of the Tujunga EWMP to meet water quality goals; however, meeting the city's goals for increasing permeable land cover offsets the risk of non-compliance in the face of climate change uncertainties. This study also introduces innovations in RDM analyses, including: treatment of the deeply uncertain incidence of extreme precipitation events, an explicit link between RDM scenario discovery methods and the specification of signposts for adaptive policy pathways, and the use of (imprecise) probabilistic climate projections to inform the choice among robust adaptive policy pathways. The paper also contributes to a larger debate over how to address climate and other uncertainties in regulatory processes involving water quality.