Stochastic Water Quality Modeling of an Impaired River Impacted by Climate Change

Abstract

A new stochastic water quality modeling tool was applied to quantify potential climate change effects on a nutrient impaired reach in the Pacific Northwest. This tool allows for multiple stochastic inputs for steady state river water quality simulations. A previously published calibrated deterministic model of the targeted reach was adapted for this study. This model simulates steady-state nutrient, algae, and dissolved oxygen dynamics with both point and nonpoint pollutant loadings. It also includes simulation of diurnally varying water temperature, calculated as a function of air temperature, shading, and streamflow using heat budget equations. For this study, local summer air temperature and critical low flows were treated stochastically in the model. Parameterization of these inputs was based on analysis of multiple global climate model (GCM) projections for the study area corresponding to a 2060 planning horizon. Continuous probability distribution functions were fitted to ensemble GCM data sets grouped according to two different greenhouse gas emission scenarios (best case, worst case). Climate projections were translated into summer low flows using a simple empirical regression hydrologic model that was developed on the basis of observed historical data. Model outputs are provided probabilistically, helping to quantify levels of climate model consensus and capturing a portion of the large uncertainty associated with the forecasts. This type of framework is valuable in its support of planning decision making. Results specific to this study indicate that, whereas reach dissolved oxygen and algae biomass levels are relatively insensitive to projected climate change, simulated stream water temperature changes could have an adverse effect on native salmon populations in the region. The demonstrated methods are believed to be generally transferable to other river water quality studies and are recommended as an option for planning studies.