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The physical parameterization of key processes in land surface models (LSMs) remains uncertain, and new techniques are required to evaluate LSMs accuracy over large spatial scales. Given the role of soil moisture in the partitioning of surface water fluxes (between infiltration, runoff, and evapotranspiration), 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 correlation between SSM and surface runoff for multiple Noah-Multiple Physics (Noah-MP) LSM parameterization cases. Results demonstrate that Noah-MP surface runoff parameterizations often underestimate the correlation between prestorm SSM and the event-scale runoff coefficient (RC; defined as the ratio between event-scale streamflow and precipitation volumes). This bias can be quantified against an observational benchmark calculated using streamflow observations and SMAP_L4 SSM and applied to explain a substantial fraction of the observed basin-to-basin (and case-to-case) variability in the skill of event-scale RC estimates from Noah-MP. Most notably, a low bias in LSM-predicted SSM/RC correlation squanders RC information contained in prestorm SSM and reduces LSM RC estimation skill. Based on this concept, a novel case selection strategy for ungauged basins is introduced and demonstrated to successfully identify poorly performing Noah-MP parameterization cases.

Plain Language 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 column prior to a storm event (e.g., more prestorm 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 prestorm soil moisture and runoff. Using newly available data from the NASA Soil Moisture Active/Passive (SMAP) mission, this paper demonstrates that land surface models often misrepresent the impact of prestorm surface soil moisture on runoff generation. This misrepresentation is shown to have a strong negative impact on the ability of models to accurately estimate runoff. A new calibration technique, based on the SMAP Level 4 soil moisture product, is introduced for eliminating this bias. Overall, results demonstrate how remotely sensed soil moisture can potentially play an important role in enhancing the operational forecasting of streamflow.