CW3E Hydrologists Ming Pan and Taylor Dixon Participate in an Exchange with DWR

March 19, 2024

On Tuesday, March 5th, DWR’s Hydrology Section hosted CW3E employees, Ming Pan and Taylor Dixon, to get better acquainted with each other’s hydrologic forecasting work. Even though the two teams have already developed a strong partnership in improving hydrologic runoff models together, the in-person meeting proved to be extremely beneficial.

DWR’s staff shared with CW3E how the legacy system of forecasting water supply runoff volumes for the Bulletin 120 have been produced. This included the technical details of the various multi-linear equations and other methods that have historically been used to forecast April through July runoff volumes for nearly two dozen watersheds in California. DWR further discussed how the 80 percent confidence range is calculated and what innovations the DWR team has produced over the past few years to help improve the median forecasts and the confidence range. DWR staff highlighted which innovations have come from their work with CW3E which includes advancements in the West-WRF forecasts, S2S forecasts, and CW3E’s development of WRF-Hydro models. Through rich back and forth discussions, the CW3E representatives learned more about the long-standing but evolving statistical forecast procedures and gained a deep appreciation for the institutional dedication to and value of the DWR forecasting platform. The CW3E representatives asked many questions and helped identify potential future collaboration opportunities between the two groups.

The discussion later was focused on the WRF-Hydro model where CW3E gave an update on the model products produced the same week and a deeper dive into the modeling framework. This included discussions on fundamental concepts such as model pre-processing, spatial/temporal resolution on the hydrometeorological data used, modeling calibration methods, and post processing algorithms. The discussions were also expanded to include how to leverage the benefit of both physical (process-based) and statistical (data-driven) modeling, as well as the rich collection of observation records, in the context of Machine Learning and how that may apply to problems like data assimilation/fusion and impact of climate change and landcover change (e.g. wildfire). The teams went through the WRF-Hydro “sandbox” established by CW3E on DWR’s Linux workstation, including the JupyterHub/JupyterLab platform, WRF-Hydro model, and hydrometeorological forcing data in order to help DWR implement innovations made by the CW3E team into water supply forecasts. The meeting was extremely valuable, it brought the two teams’ relationship closer together, and both teams look forward to the next opportunity.