Diagnosing bias in modeled soil moisture/runoff coefficient correlation using the SMAP Level 4 soil moisture product

Authors: Crow, Wade; CHEN, F. - Science Systems And Applications, Inc; REICHELE, R. - Goddard Space Flight Center; XIA, Y. - National Oceanic & Atmospheric Administration (NOAA)

Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/15/2019
Publication Date: 8/31/2019
Citation: Crow, W.T., Chen, F., Reichele, R., Xia, Y. 2019. Diagnosing bias in modeled soil moisture/runoff coefficient correlation using the SMAP Level 4 soil moisture product. Water Resources Research. 55:7010-7026. https://doi.org/10.1029/2019WR025245.
DOI: https://doi.org/10.1029/2019WR025245

Interpretive Summary: Land surface models are commonly tasked with determining what fraction of incoming rainfall infiltrates into the soil versus runs off into stream channels. The key factor determining this partitioning is the amount of water in the soil prior to a storm event (e.g., more pre-storm soil moisture is generally associated with decreased amounts of infiltration and increased surface runoff). However, due to a lack of soil moisture observations available at large scales, it has generally been difficult to assess whether existing models are accurately capturing the true relationship between pre-storm soil moisture and runoff. Using a newly available data product from the NASA Soil Moisture Active/Passive (SMAP) mission, this paper demonstrates that land surface models often underestimate the impact of pre-storm soil moisture on runoff generation. This underestimation is shown to have a clear negative impact on the ability of models to accurately estimate runoff. A new calibration technique, based on the Level 4 SMAP soil moisture product, is introduced for eliminating this bias. Overall, results suggest that remotely sensed soil moisture can play an important role in enhancing operational hydrologic forecasting of streamflow.

Technical Abstract: The physical parameterization of key processes in land surface models (LSMs) remains uncertain and new techniques are required to evaluate LSM accuracy over coarse spatial scales. Given the role of soil moisture in the partitioning of surface water fluxes (between infiltration, runoff and evapotranspiration components), satellite-based surface soil moisture (SSM) estimates represent an important observational benchmark for such evaluations. Here, we apply SSM estimates from the NASA Soil Moisture Active Passive Level 4 product (SMAP_L4) to diagnose bias in the coupling between soil moisture and surface runoff for multiple Noah-Multiple Physics (Noah-MP) LSM parameterization cases. Results demonstrate that Noah-MP surface runoff parameterizations often underestimate the coupling strength between pre-storm SSM and the event-scale runoff coefficient (RC; defined here as the ratio between event-scale streamflow and precipitation volumes). This underestimation squanders RC information contained in pre-storm SSM content and is associated with reduced RC skill. Furthermore, coupling bias can be quantified against an observational benchmark calculated using streamflow observations and SMAP_L4 SSM and used to explain substantial fraction of the observed basin-to-basin and case-to-case variability in the skill of event-scale Noah-MP RC estimates. Based on this concept, a novel case selection strategy for ungauged basins is introduced and demonstrated to successfully identify poor-performing Noah-MP parameterization cases.