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.

A document by Shiqi Fang. Click on the document to view its contents.

The increasing occurrences of global flood events, amidst climate change, highlight the need for hydrological data availability over large geographical domains for robust decision-making. Hydrological rating curves translate fluvial stage to streamflow and play a pivotal role in various applications, including flood inundation modeling and river geomorphology. Power law is an appropriate proxy for the nonlinear relationship between stage and discharge in natural systems. This study aims to develop a hierarchical data-driven approach to compute the power-law rating curve parameters (α, β) across the stream networks of CONtiguous United States (CONUS). The development of rating curve models is motivated by our interest in exploring a unifying solution linking rating curve parameters with hydro-climatological and geomorphological characteristics across CONUS. These can be applied to operational hydrological forecastins, such as the NOAA Office of Water Prediction NextGen framework, to enhance river routing and flood inundation mapping efforts. We used HYDRoacoustics in support of the Surface Water Oceanographic Topography (HYDRoSWOT), National Hydrography (NHDPlus v2.1), and STREAM-CATCHMENT (STREAMCAT) datasets for model development. Four empirical models—Multivariate regression, eXtreme Gradient boosting (XGBoost), Random Forest, Support Vector regression are compared. The first tier of models offers high accuracy but is limited to gauges, while the second-tier models offer a good compromise between accuracy and applicability across CONUS. We found XGBoost yielded R² of 0.67 and 0.55 for α, and 0.74 and 0.70 for β in the first and second-tier models. The spatial distribution of predicted α and β indicates sensitivity to elevation, aridity, and rainfall patterns.

Rating curves are commonly developed through direct observation, open channel flow models, or mechanical methods, each relying on in-situ measurement. As part of a U.S. effort to provide high resolution, continental scale, flood mapping, synthetic rating curves (SRCs) were developed across the National Hydrography Dataset (NHDPlusV2) to translate flows, like those generated by the NOAA National Water Model, into river depths. This approach uses Digital Elevation Models (DEM) to define the necessary cross-sectional properties for Manning’s equation. A significant limitation, alongside an opportunity for broad improvement, has been assigning suitable roughness without local information. We applied the DEM based methodology to generate SRCs at 7,270 locations with known USGS rating curves, and calibrated roughness to minimize the error between predicted and observed flow. Subsequently, we tested several approaches based on land cover, stream order, and the hydrographic network to estimate the optimized values in a manner that can be extended to ungauged catchments. Among these, a predictive Machine Learning (ML) model based on the NHDPlusV2 network attributes demonstrated superior ability to estimate the optimized roughness with a Spearman correlation of 0.89. Sensitivity analysis showed improving accuracy of DEM and roughness is crucial for accurate estimation of the lower and mid/upper parts of SRC, respectively. Finally, we applied the predictive model over the NHDPlusV2, generating reach-level roughness estimates that can directly support national flood mapping efforts. The method is generalizable to any hydrofabric network that contains topology, however the generated values are dependent on the DEM and hydrofabric used.

Floods cause hundreds of fatalities and billions of dollars of economic loss each year in the United States. To mitigate these damages, accurate flood prediction is needed for issuing early warnings to the public. This situation is exacerbated in larger model domains for high flows, particularly in ungauged basins. To improve flood prediction for both gauged and ungauged basins, we propose a spatio-temporal hierarchical model (STHM) to improve high flow estimation using a 10-day window of modeled National Water Model (NWM) streamflow and a variety of catchment characteristics as input. The STHM is calibrated (1993-2008) and validated (2009-2018) in controlled, natural, and coastal basins over three broad groups, and shows significant improvement for the first two basin types. A seasonal analysis shows the most influential predictors are the previous 3-day average streamflow and the aridity index for controlled and natural basins, respectively. To evaluate the STHM in improving streamflow in ungauged basins, 20-fold cross-validation is performed by leaving 5% of sites. Results show that the STHM increases predictive skill in over 50% of sites by 0.1 Nash-Sutcliffe efficiency (NSE) and improves over 65% of sites’ streamflow prediction to an NSE>0.67, which demonstrates that the STHM is one of the first of its kind and could be employed for flood prediction in both gauged and ungauged basins.