Feather River Hydrologic Observatory: Improving Hydrological Snowpack Forecasting for Hydropower Generation Using Intelligent Information Systems

Changing climatic conditions and a lack of representative snow cover data challenge the ability to accurately model and fully utilize the water resources of montane snowpack on which California depends. This project aims to improve scientific understanding of snowpack modeling and streamflow forecasting techniques. Though relevant across industries and the state, this research focuses on hydropower applications in the North Fork of the Feather River. Central components of the project are four wireless sensor networks, installed in locations that are representative of vegetation and topography patterns across the basin. The networks collect snow, temperature, relative humidity, soil moisture, and soil temperature data every 15 minutes. By blending this information with remote sensing data and historical maps, we estimate the spatial distribution of snow water resources at high resolution. These outputs can help improve runoff forecasting tools such as the Precipitation-Runoff Modeling System (PRMS). Preliminary results show that wireless sensor networks can successfully track hydrologic states and fluxes in real time and provide a more representative picture of snowpack accumulation and melt than traditional index stations. Network data are also used to gain insight into rain-on-snow events, which are a key streamflow generation mechanism for the Feather River. The data show that the current calibration of PRMS on the Feather River could overestimate snowfall and underestimate liquid precipitation. Future steps include a sensitivity analysis of PRMS to identify its dominant parameters and conceptual limitations, as well as a full, dynamic recalibration of the model. Blended maps of snow water equivalent will also be assimilated into the model. These results will be included in the decision-support suite for PG&E, California’s largest electric and natural gas utility, and an analysis will be performed to assess results’ economic value for stakeholders in the energy and water-supply sectors.