Leveraging pre-storm soil moisture estimates for enhanced land surface model calibration in ungauged hydrologic basins

Authors: Crow, Wade; DONG, J. - Massachusetts Institute Of Technology; REICHLE, R. - Goddard Space Flight Center

Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/15/2022
Publication Date: 8/4/2022
Citation: Crow, W.T., Dong, J., Reichle, R. 2022. Leveraging pre-storm soil moisture estimates for enhanced land surface model calibration in ungauged hydrologic basins. Water Resources Research. 58. Article e2021WR031565. https://doi.org/10.1029/2021WR031565.
DOI: https://doi.org/10.1029/2021WR031565

Interpretive Summary: Accurately forecasting the fraction of rainfall that runs off into streams, as opposed to infiltrates into the soil, is critical for flash-flood prediction, water-resource monitoring, and tracking the transport of nutrients from agricultural fields into local waterways. Such forecasting is typically performed by hydrologic models that attempt to represent the physical processes responsible for surface runoff generation. However, to provide accurate streamflow forecasts, these models typically need to be tuned (i.e., calibrated) against actual streamflow observations. This is problematic given that the majority of medium-scale (i.e., 1000- to 10,000-km2) basins in the United States lack adequate streamflow gauging. In response, this paper presents a novel model calibration strategy that uses remotely sensed surface soil moisture retrievals in place of streamflow observations. This switch has significant practical advantages since, unlike streamflow observations, these retrievals are continuously available in both space and time. Our results demonstrate that this new approach can significantly improve hydrologic models within basins lacking sufficient ground-based instrumentation for traditional model calibration. The strategy developed here will eventually be used by operational hydrologists to improve the quality of streamflow forecasts in agricultural regions of the United States.

Technical Abstract: Despite several decades of effort, hydrologists still lack robust tools for calibrating land surface model (LSM) streamflow estimates within ungauged basins. Using surface soil moisture estimates from the Soil Moisture Active Passive (SMAP) Level 4 Soil Moisture (L4_SM) product, we measure the temporal rank correlation between antecedent (i.e., pre-storm) surface soil moisture (ASM) and storm-scale runoff coefficients (RC; the fraction of storm-scale precipitation accumulation converted into streamflow) obtained from gauges for 617 medium scale (200-10,000 km2) basins in the contiguous United States. In humid and semi-humid basins, the strength of this rank correlation is shown to be sufficient to allow for the substitution of storm-scale RC observations (available only in basins that are both lightly regulated and gauged) with high-quality ASM values (available quasi-globally from L4_SM) in streamflow calibration procedures based on a rank correlation metric. Using this principle, we define a new LSM streamflow calibration approach based on L4_SM alone and successfully apply it to identify LSM configurations providing precise RC estimates (i.e., high Spearman rank correlation with observed RC). However, the approach is markedly less successful in identifying LSM formulations with high RC accuracy (i.e., low mean-absolute error versus observed RC) due to its inability to detect RC bias.