Investigating the potential benefits of assimilating USGS-NGWOS hydrologic measurements for enhancing operational streamflow forecasting

Objective:

Develop a hydrologic DA framework for real-time streamflow forecasting using a dense network of NGWOS observations

Approach:

Data assimilation (DA) is a crucial component of operational forecasting systems. Assimilating observed data into hydrologic models allows improvement in forecasting skills. The quality of observations assimilated within a DA framework is the key factor in describing the effectiveness of DA in operational systems. The USGS Next Generation Water Observing System (NGWOS) program has selected five mid-sized watersheds across the US, the Delaware, Upper Colorado, Illinois, Willamette, and Tranity-San Jacinto River basins for scientific purposes. In these basins, a dense array of modern sensors is used to measure hydrological variables such as streamflow, evapotranspiration, snowpack and soil moisture. Having access to a dense network of USGS measurements within the Integrated Water Science basins, we design a set of experiments to evaluate the merit of assimilating these observations into hydrologic models and estimate the potential benefits of such implementation in operational streamflow forecast systems. The project aims to assess how the number, location and density of these measurements impact the performance of DA-hydrological streamflow forecasting systems. We anticipate that the results of the study will provide valuable insights into the necessity of investment in developing intensive monitoring networks and enhancing forecasting skills in operational systems.

Abstract:

Data assimilation (DA) is a crucial component of operational forecasting systems. Assimilating observed data into hydrologic models allows improvement in forecasting skills. The quality of observations assimilated within a DA framework is the key factor in describing the effectiveness of DA in operational systems. The USGS Next Generation Water Observing System (NGWOS) program has selected five mid-sized watersheds across the US, the Delaware, Upper Colorado, Illinois, Willamette, and Tranity-San Jacinto River basins for scientific purposes. In these basins, a dense array of modern sensors is used to measure hydrological variables such as streamflow, evapotranspiration, snowpack and soil moisture. Having access to a dense network of USGS measurements within the Integrated Water Science basins, we design a set of experiments to evaluate the merit of assimilating these observations into hydrologic models and estimate the potential benefits of such implementation in operational streamflow forecast systems. The project aims to assess how the number, location and density of these measurements impact the performance of DA-hydrological streamflow forecasting systems. We anticipate that the results of the study will provide valuable insights into the necessity of investment in developing intensive monitoring networks and enhancing forecasting skills in operational systems.