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Discharge values from the National Water Model (NWM) were compared to USGS stream gage discharge observations for the suburban Red Clay Creek watershed (drainage area ~140 km2 and mixed land-use), in Pennsylvania and Delaware, from 2016 to 2018. 18-hour retrospective simulations from the NWM were used with concurrent hourly USGS discharge observations from three locations along the Red Clay Creek. Results indicate that the mean of discharge estimates from the NWM and from USGS observations significantly differed and that the NWM generally underestimates low-flow conditions and overestimates high-flow conditions. Watershed size also impacted NWM performance (with performance degrading in smaller watersheds). A meteorological analysis determined that convective rainfall events were associated with 66% of the largest differences between NWM discharge estimates and USGS observations while mid-latitude cyclone stratiform precipitation events accounted for the other 34%. Lastly, of the largest 15 differences between the NWM and observations, 13 occurred with pre-cursor soil moisture that was below the mean (dry soil conditions), in conjunction with heavy rainfall. Given the NWM’s recent operational implementation, and its status as Prototype guidance, the results of this study present specific geographical and climatological findings that can aid in the NWM’s continued validation and improvement for similar regions.
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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 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.
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- Award ID(s):
- 2033607
- PAR ID:
- 10481735
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Atmospheres
- Volume:
- 128
- Issue:
- 24
- ISSN:
- 2169-897X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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