TY - JOUR
T1 - Advances in Quantifying Streamflow Variability Across Continental Scales
T2 - 1. Identifying Natural and Anthropogenic Controlling Factors in the USA Using a Spatially Explicit Modeling Method
AU - Alexander, Richard B.
AU - Schwarz, Gregory E.
AU - Boyer, Elizabeth W.
N1 - Funding Information:
supporting information ; data sources are referenced and documented in the digital archive). Thanks to Lillian Gorman Sanisaca (USGS Maryland‐Delaware‐DC Water Science Center) for computer‐related support on the R‐based software used to estimate the SPARROW models, Dave Wolock of USGS for discussions on water balance methods and for providing access to national water balance results, and to Dave Anning of USGS for assistance with the final selections of the streamflow monitoring stations, based on his extensive knowledge of site locations. Thanks to the journal reviewers for their helpful comments and suggestions. This research was funded by the National Water Quality Assessment Project of the USGS National Water Quality Program; support was also provided by the Integrated Modeling and Prediction Division (IMPD) of the USGS Water Mission Area. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. We appreciate additional research support at Penn State University from the U.S. Department of Agriculture National Institute of Food and Agriculture. The accompanying this manuscript provides supplementary model results in tabular and graphical form. A digital archive of the models presented in this study is publicly available as a U.S. Geological software release (Alexander & Schwarz,
Funding Information:
The supporting information accompanying this manuscript provides supplementary model results in tabular and graphical form. A digital archive of the models presented in this study is publicly available as a U.S. Geological software release (Alexander & Schwarz,; data sources are referenced and documented in the digital archive). Thanks to Lillian Gorman Sanisaca (USGS Maryland-Delaware-DC Water Science Center) for computer-related support on the R-based software used to estimate the SPARROW models, Dave Wolock of USGS for discussions on water balance methods and for providing access to national water balance results, and to Dave Anning of USGS for assistance with the final selections of the streamflow monitoring stations, based on his extensive knowledge of site locations. Thanks to the journal reviewers for their helpful comments and suggestions. This research was funded by the National Water Quality Assessment Project of the USGS National Water Quality Program; support was also provided by the Integrated Modeling and Prediction Division (IMPD) of the USGS Water Mission Area. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. We appreciate additional research support at Penn State University from the U.S. Department of Agriculture National Institute of Food and Agriculture.
Publisher Copyright:
© 2019. The Authors.
PY - 2019/12/1
Y1 - 2019/12/1
N2 - Despite considerable progress in hydrological modeling, challenges remain in the interpretation and accurate transfer of hydrological information across watersheds and scales. In the conterminous United States (CONUS), these limitations are related to spatial inconsistencies and constraints in hydrological model structures, including a lack of spatially explicit process components (streams, reservoirs, and watershed development) and restricted estimation of model parameters across watersheds. Collectively, such limitations can impede identification of the causes of streamflow variations across the diversity of watershed sizes and land uses in the CONUS and contribute to model imprecision and spatial inconsistencies in prediction uncertainties. We addressed these concerns with a new approach, the first hybrid (statistical-mechanistic) SPARROW (SPAtially Referenced Regression On Watershed attributes) model of long-term mean annual streamflow, applied across diverse environmental settings of the CONUS. The hybrid model coupled previous catchment-scale (1 km) water balance predictions of “natural” unit area runoff, which are inclusive of major water cycling processes, with additional explanatory variables (e.g., soils, vegetation, land use, topography, water losses in streams, and reservoirs) that account for the effects of natural and cultural water supply and demand processes that operate over large spatial scales and explain streamflow variability across CONUS river basins. Accounting for these statistically unique effects, including a nonlinear surface area-dependent scaling of water loss in river networks, significantly improved the accuracy of mean streamflow predictions in CONUS basins. Our hybrid modeling approach provides new methods for transferring hydrological information to ungauged locations in river networks, especially those in larger and more culturally diverse CONUS watersheds.
AB - Despite considerable progress in hydrological modeling, challenges remain in the interpretation and accurate transfer of hydrological information across watersheds and scales. In the conterminous United States (CONUS), these limitations are related to spatial inconsistencies and constraints in hydrological model structures, including a lack of spatially explicit process components (streams, reservoirs, and watershed development) and restricted estimation of model parameters across watersheds. Collectively, such limitations can impede identification of the causes of streamflow variations across the diversity of watershed sizes and land uses in the CONUS and contribute to model imprecision and spatial inconsistencies in prediction uncertainties. We addressed these concerns with a new approach, the first hybrid (statistical-mechanistic) SPARROW (SPAtially Referenced Regression On Watershed attributes) model of long-term mean annual streamflow, applied across diverse environmental settings of the CONUS. The hybrid model coupled previous catchment-scale (1 km) water balance predictions of “natural” unit area runoff, which are inclusive of major water cycling processes, with additional explanatory variables (e.g., soils, vegetation, land use, topography, water losses in streams, and reservoirs) that account for the effects of natural and cultural water supply and demand processes that operate over large spatial scales and explain streamflow variability across CONUS river basins. Accounting for these statistically unique effects, including a nonlinear surface area-dependent scaling of water loss in river networks, significantly improved the accuracy of mean streamflow predictions in CONUS basins. Our hybrid modeling approach provides new methods for transferring hydrological information to ungauged locations in river networks, especially those in larger and more culturally diverse CONUS watersheds.
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U2 - 10.1029/2019WR025001
DO - 10.1029/2019WR025001
M3 - Article
AN - SCOPUS:85076792973
SN - 0043-1397
VL - 55
SP - 10893
EP - 10917
JO - Water Resources Research
JF - Water Resources Research
IS - 12
ER -