Abstract 071

Abstract ID: 071

The Subseasonal Forecasting of Hydrological Variables: Improvement Strategies Inferred from a Water Balance Model Analysis

Lead Author: Randal Koster
Global Modeling and Assimilation Office, NASA/Goddard Space Flight Center, United States of America

Keywords: Subseasonal hydrological forecasts, Soil moisture, Streamflow, Land model development

Abstract: Past work has shown that a land surface model’s (LSM’s) implicit (not explicitly coded) relationships between soil moisture and both evapotranspiration (ET) and runoff largely determine the LSM’s mean hydrological behavior. Here we estimate the relationships that appear to be operating in the real world and compare them to those of the LSM component of a state-of-the-art S2S forecast system, focusing on how both sets affect our ability to forecast 10-day-averaged hydrological variables (soil moisture and streamflow) at leads of 11-20 days and 21-30 days. The two sets of relationships are determined by calibrating them within a simple water balance model (WBM): once using stream gauge observations from small, unregulated rivers over the eastern half of the U.S., and once using the runoffs generated by the LSM as part of a state-of-the-art atmospheric reanalysis. Subseasonal hydrological forecasts performed with the two calibrated versions of the WBM provide two key results. First, the version calibrated to the LSM-generated runoffs does successfully reproduce, to first order, the behavior of the full LSM within its S2S forecast system environment in terms of both the magnitude and temporal variability of forecast hydrological variables. Second, of the two WBM versions, the one calibrated to the observations indeed produces more accurate forecasts of a broad collection of fully independent streamflow observations as well as a similarly broad collection of in-situ soil moisture measurements. Taken together, the two results suggest that the observations-calibrated ET and runoff efficiency functions do successfully represent, to first order, soil moisture controls over decad-scale hydrological variability in Nature. They can thus serve as potentially useful targets for further LSM development aimed at improving our ability to forecast hydrological variables at subseasonal leads.

Anthony DeAngelis (GMAO, NASA/GSFC and SSAI, Inc.)
Qing Liu (GMAO, NASA/GSFC and SSAI, Inc.)
Siegfried Schubert, (GMAO, NASA/GSFC and SSAI, Inc.)
Andrea Molod (GMAO, NASA/GSFC