Science Inventory

Salish Sea ecosystem services decision-making support via basin scale modeling of terrestrial inputs into the aquatic habitats

Citation:

Halama, J., R. McKane, A. Brookes, K. Djang, V. Phan, AND S. Chokshi. Salish Sea ecosystem services decision-making support via basin scale modeling of terrestrial inputs into the aquatic habitats. To be Presented at Salmonid Restoration Conference, Fortuna, CA, April 25 - 28, 2023.

Impact/Purpose:

The main objective of this research has been to establish a scientifically sound watershed simulation model that can help inform stormwater management decisions by communities, tribes, and government agencies seeking green infrastructure solutions for reducing runoff of 6PPD-quinone and other toxic chemicals in urban runoff impacting salmonids and other sensitive species. Key component of this research is a holistic perspective through a multi-model integration to provide a better understanding of ecosystem services under alternative scenario plans. Results from these simulations will be fed into the Salish Sea Model for nutrient and contaminant cycling that will be fed into the Atlantis food web model.

Description:

Visualizing Ecosystem Land Management Assessments (VELMA) is an ecohydrology model being leveraged in a recently-initiated effort to identify practical, proactive watershed restoration strategies that can be initiated now to lessen long-term impacts of climate change on Puget Sound communities. This effort is particularly pertinent to tribal communities and the salmonid populations essential to their sustenance, health, and culture. This work focuses on Salish Sea salmonid-bearing watersheds that the tribes and State of Washington co-manage for salmon recovery and habitat protection. VELMA will model alternative future land use and climate change scenarios out to 2100 for all major Puget Sound basins (~30,000 km2). Simulations will evaluate impacts of changing terrestrial nutrient loads on observed historical salmonid declines and potential future degradation vs. Best Management Practice (BMP) based trajectories for marine nutrients and sensitive biota. VELMA is one of four models involved in this ambitious research. Overarching goals will be accomplished through model coupling among the Environmental Protection Agency VELMA (terrestrial ecohydrology), Pacific Northwest National Laboratory Salish Sea Model (estuarine circulation and biogeochemistry), National Oceanic and Atmospheric Administration Atlantis (estuarine food web) and Land Cover Change (Urban Ecology Research Lab, University of Washington) models. The modeling spatial extent spans the Puget Sound and its contributing watershed basins to support a holistic, whole-ecosystem approach to regional planning and restoration decision-making. This approach can assess land and water ecosystem service trade-offs and co-benefits under alternative climate, population, and land use futures for a broad range of regional objectives; e.g., from restoring orcas populations to enhancing human wellbeing. Within this coupled modeling framework, VELMA terrestrial water quality outputs under alternative land use and climate scenarios are input to the Salish Sea Model which, in turn, feeds marine water quality outputs to Atlantis estuarine food web model (microbes to orcas). The Land Cover Change Model will inform VELMA’s alternative scenarios. Results from this research will assist communities, tribes, and state and federal decision makers in determining how much, where, and what kinds of urban and rural BMPs are required to achieve target nutrient load reductions to the Puget Sound estuary, now and in the future. This progressive modeling effort will provide enhanced decision support regarding revitalization of community health and well-being, specifically for Puget Sound tribes reliant on declining Endangered Species Act listed salmonid populations for their subsistence and cultural well-being.

Record Details:

Record Type:DOCUMENT( PRESENTATION/ POSTER)
Product Published Date:04/28/2023
Record Last Revised:07/26/2023
OMB Category:Other
Record ID: 357974