Enhancing Water Supply Forecasting for Systems Management

Objective:

Develop an open-source model evaluation resource to evaluate regionally dominant hydrological prediction capabilities and identify where/how to better represent specific processes, focusing on supply-oriented water resources management.

Abstract:

Season to season water supply estimates is a critical tool for managing water supply and
controlled environmental systems, with flow and volume estimates guiding decisions concerning
source allocation, conservation measures, and ecosystem health. The NOAA National Water
Model (NWM) provides a short-term forecast (1-day), medium-term (10-day), and a long-term
(30-day) forecast for nearly 2.7 million reaches. While impressive, the forecasting horizon does
not support the season-to-season volume estimates necessary for water supply forecasting. With
the introduction of the Next Generation (NexGen) hydrological modeling framework, we have an
opportunity to develop methods to extend the existing long-term streamflow products. The
project will leverage stakeholder relationships with western water resources management to
develop novel methods for extending NWM outputs to a season-to-season product while
accounting for the strong influences of extensive water resources. Using the Great Salt Lake basin
as a case study area, research activities will explore the coupling of the NWM with advanced
water system management models and novel post-processing methods leveraging the power of
deep learning algorithms. We anticipate the project to yield broad-reaching impacts on water
resources management that support management decisions surrounding beneficial use nexuses
such as municipal use, hydropower, and environmental quality.