Comprehensive Analysis of the NOAA National Water Model: A Call for Heterogeneous Formulations and Diagnostic Model Selection

With an increasing number of continental-scale hydrologic models, the ability to evaluate performance is key to understanding uncertainty and making improvements to the model(s). We hypothesize that any model, running a single set of physics, cannot be "properly" calibrated for the range of hydroclimatic diversity as seen in the contenintal United States. Here, we evaluate the NOAA National Water Model (NWM) version 2.0 historical streamflow record in over 4,200 natural and controlled basins using the Nash-Sutcliffe Efficiency metric decomposed into relative performance, and conditional, and unconditional bias. Each of these is evaluated in the contexts of meteorologic, landscape, and anthropogenic characteristics to better understand where the model does poorly, what potentially causes the poor performance, and what similarities systemically poor performing areas share. The primary objective is to pinpoint traits in places with good/bad performance and low/high bias. NWM relative performance is higher when there is high precipitation, snow coverage (depth and fraction), and barren area. Low relative skill is associated with high potential evapotranspiration, aridity, moisture-and-energy phase correlation, and forest, shrubland, grassland, and imperviousness area. We see less bias in locations with high precipitation, moisture-and-energy phase correlation, barren, and grassland areas and more bias in areas with high aridity, snow coverage/fraction, and urbanization. The insights gained can help identify key hydrological factors underpinning NWM predictive skill; enforce the need for regionalized parameterization and modeling; and help inform heterogenous modeling systems, like the NOAA Next Generation Water Resource Modeling Framework, to enhance ongoing development and evaluation. Water-related issues challenge societies ability to respond to extreme events and plan for the future. Hydrologic models can help better understanding changing water supply and extreme events. To this end, NOAA has implemented a National Water Model (NWM) to forecast the real-time conditions of U.S. waterways and the hydrologic fluxes on the landscape. Here, we evaluate the performance of the NWM version 2.0 streamflow outputs by comparing a 26-year historic simulation to observed data. We diagnose where the model is performing well (and poorly) in the contexts of landscape, climate conditions, and human influence using a large sample basin set. The insights gained identify key factors driving NWM skill and suggest different model formulations are needed in different places. Lastly, we show that understanding why the NWM performs the way it does can help diagnostically select different physics options within the NOAA Next Generation Water Resource Modeling Framework to reduce error in the model output through a more deliberate process representation. The relative error and biases in the National Water Model 2.0 streamflow are evaluated in the contexts of categorized basin characteristicsAridity, moisture-energy phase correlation, forest and grass cover limit model skill suggesting challenges in modeling evapotranspirationSimilar understandings can inform regionally heterogeneous models while large biases present opportunity for post-processing model output