Coupling novel low cost spatially distributed nutrient sensors and National Water Model output to forecast nutrient loading and inform state implementation of EPA mandated nutrient reduction targets—The Lake Champlain Basin Test Bed

Objective:

Develop nutrient loading forecasts forced by the NWM to predict nutrient loading at focal Lake Champlain Basin watersheds

Abstract:

While not included in the current version of the National Water Model (NWM), there
is vast potential and associated demand to expand the model’s capacity to forecast water
quality. Here, we are focused on leveraging NWM flow forecasts to force novel nutrient loading
forecasts for select basins within the Lake Champlain Basin. This is a particularly relevant use
case, as the Lake Champlain Basin has been mandated to reduce phosphorus loading through
the Total Maximum Daily Load (TMDL) framework to improve impaired waters as mandated by
Section 303a of the Clean Water Act. To develop these forecasts, we will primarily utilize long-
term and high frequency concurrent flow and phosphorus concentration time series and
machine learning algorithms to develop robust predictive models of nutrient loading. Initial
studies will focus on developing models in two small watersheds of distinct landcover
monitored with sensors by our group since 2014 that have high frequency observational time
series (nutrient concentration and flow measurements taken every 15 minutes). We will then
expand our models to larger watershed systems within the basin that have flow measurements
by USGS gages and long-term phosphorus monitoring by the Vermont Department of
Environmental Conservation (grab water samples going back to 1991). These will constitute the
first NWM-forced nutrient loading models. As loading model development is ongoing, we will
also develop low-cost electrochemical phosphate sensors to be distributed spatially within our
focal watershed across different phosphate source environments. Ultimately, we plan to use
these distributed sensor data with NWM forcing and machine learning to forecast not only the
riverine load of phosphate in test case watersheds during storms, but also the source
environments of the phosphate. Beyond research papers and presentations, one of the most
significant outcomes of this work will be providing a template for others to use the NWM with
water quality monitoring data to produce water quality forecasts across the country. We intend
to develop an approach that will be fully compatible with NWM Nextgen that is also NWM
version agnostic, thus being a useful long-term Research to Operations tool that expands the
operations context and community. Given that there are over 50,000 impaired waters governed
by TMDLs in the United States, the potential for this research to expand the operations
community and NWM utility is substantial.