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

Abstract

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.

Improved National-Scale Flood Prediction for Gauged and Ungauged Basins using a Spatio-temporal Hierarchical Model

“…They also found basin characteristics such as total contributing drainage area and path length also influence the NWM performance. Further, variables indicating anthropogenic activities -percent imperviousness and upstream storage in dams-also influence the bias and NSE in predicting the flood flows (Johnson et al, 2023). This indicates that the NWM performance depends on basin and hydroclimatic information, thereby exhibiting a regional/spatial error structure in predicting flood flows.…”

“…Continental-scale hydrology studies have evaluated parsimonious mechanistic models (Archfield et al, 2015), lumped-hydrological models , and hybrid (statistical-mechanistic) models for estimating streamflow at different time scales (Evenson et al, 2021). Utilizing distributed hydrological models provide a viable alternative to estimating daily streamflow at ungauged locations, but challenges remain in accurately predicting streamflow over continental scale (Johnson et al, 2023). Frame et al (2021) evaluated the National Water Model (NWM) for selected virgin basins and found that the performance of NWM is poorer compared to the post-processing models.…”

“…Post-Processing methods can be data-driven (e.g. Johnson et al, 2023) or physically informed (Wu et al, 2019). Recently, Frame et al (2021) applied a long short-term memory (LSTM) machine learning approach to improve daily NWM streamflow prediction across CONUS and compared its performance with streamflow prediction using LSTM with just atmospheric forcings.…”

“…However, this analysis has been performed only for virgin basins and did not focus on controlled basins. Johnson et al (2023) used random forest models to identify the basin characteristics and hydroclimatic information that influence the NWM performance over the CONUS. They found that exist in arid basins and basins with moisture and energy being out of phase exhibit significant bias and reduced Nash-Sutcliffe Coefficient (NSE) (Johnson et al, 2023).…”

“…Johnson et al (2023) used random forest models to identify the basin characteristics and hydroclimatic information that influence the NWM performance over the CONUS. They found that exist in arid basins and basins with moisture and energy being out of phase exhibit significant bias and reduced Nash-Sutcliffe Coefficient (NSE) (Johnson et al, 2023). They also found basin characteristics such as total contributing drainage area and path length also influence the NWM performance.…”