Evaluation of a WEHY-HCM Seasonal Forecasting system of monthly flows for the Yuba River Basin in California.

Monday, May 19, 2025

3:15 PM – 3:30 PM Alaska Daylight Time (AKDT)

Room: Room 1

Author(s)

Felipe Ulloa Henriquez (he/him/his)

Ph.D. (c)
University of California, Davis, California, United States

Co-Author(s)

Levent M. Kavvas

Distinguished Professor
University of California, Davis
UC Davis, California, United States
Yoshihiko Iseri

International Platform for Dryland Research and Education, Tottori University, Japan
Naditha D. Imbulana

Postdoctoral Scholar
UC Davis, California, United States
Zeynep Ozcan (she/her/hers)

Postdoctoral Scholar
University of California Davis, California, United States
Emily Snider

Postdoctoral Researcher
University of California, Davis, United States
Benjamin Daniels

Graduate Student Researcher
University of California Davis, United States
Michael L. Anderson, PhD, PE

State Climatologist
California Department of Water Resources, California, United States

Abstract Submission: We present the results and evaluation of a Seasonal Forecasting system based on the Watershed Environmental Hydrology – Hydroclimate Model (WEHY-HCM) applied to the Yuba River Basin. The WEHY-HCM was previously calibrated and validated, and now only the forecasting system is tested. We define the years 2020 to 2023 as the hindcasting period, and we compare our forecasts to the Bulletin 120 (B120) forecasts, an official forecast from the Department of Water Resources of California based on snow observations. Each year, we take 15 members of January to June CFSv2 forecasts. Results in the hindcast period are mixed, with 2020, 2021, and 2022 having good predictions, while the 2023 forecast underestimates flows, which we think is due to 2023 being a specially wet year. For the forecast of 2024, we evaluated the deterministic forecast and developed a statistical updating model using the errors from the hindcasting process. The deterministic forecast of 2024 showed reasonable results compared to B120, without a clear trend of over or underestimation. The statistically updated results improve the 2024 forecast to an accuracy level comparable to B120.

Learning Objectives/Expected Outcome (Optional) :