2023 article

Comprehensive analysis of the NOAA National Water Model: A call for heterogeneous formulations and diagnostic model selection

Johnson, J. M., Fang, S., Sankarasubramanian, A., Rad, A. M., Cunha, L. K., Clarke, K. C., … Yeghiazarian, L. (2023, January 19).

By: J. Johnson, S. Fang  n, A. Sankarasubramanian  n, A. Rad, L. Cunha, K. Clarke*, A. Mazrooei*, L. Yeghiazarian *

co-author countries: United States of America πŸ‡ΊπŸ‡Έ
Source: ORCID
Added: January 20, 2023

With an increasing number of continental-scale hydrologic models, the ability to evaluate performance is key to understanding uncertainty in prediction and making improvements to the model(s). In 2016, the NOAA National Water Model (NWM) was put into operations to improve the spatial and temporal resolution of hydrologic prediction in the U.S. Here, we evaluate the NWM 2.0 historical streamflow record in natural and controlled basins using the Nash Sutcliffe Efficiency metric decomposed into relative error, conditional, and unconditional bias. Each of these is evaluated in the contexts of categorized meteorologic, landscape, and anthropogenic characteristics to assess model performance and diagnose error types. Broadly speaking greater rainfall and snow coverage leads to improved performance while larger potential evapotranspiration (PET), aridity, and phase correlation reduce performance. More rainfall and phase correlation reduce overall bias, while increasing PET, aridity, snow coverage/fraction increase model bias. With respect to landscape traits, more barren and agricultural land yeild improved performance while more forest, shrubland, grassland and imperviousness tend to decrease performance. Lastly, more barren and herbaceous land tend to decrease bias, while greater imperviousness, urban, forest, and shrubland cover increase bias. The insights gained can help identify key hydrological factors in NWM predictions; enforce the need for regionalized physics and modeling; and help develop hybid post-processing methods to improve prediction. Finally, we demonstrate how the NOAA Next Generation Water Resource Modeling Framework can help reduce the structural bias through the application of heterogenous model processes and highlight opportunities for ongoing development and evaluation.