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United States Department of Agriculture

Agricultural Research Service

Research Project: Understanding Water-Driven Ecohydrologic and Erosion Processes in the Semiarid Southwest to Improve Watershed Management

Location: Southwest Watershed Research

Title: Comment on “Beyond the SCS-CN method: A theoretical framework for spatially lumped rainfall-runoff response” by M.S. Bartlett, A.J. Parolari, J.J. McDonnell and A. Porporato

Author
item Ogden, F.l.
item Hawkins, R.
item Walter, T.m.
item Goodrich, David - Dave

Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/21/2017
Publication Date: N/A
Citation: N/A

Interpretive Summary: To understand rainfall-runoff processes conceptual and computer models have been developed. The “Curve-Number” (CN) model in one such model that was developed in the 1950s by the Soil Conservation Service (now the Natural Resources Conservation Service). It is an empirical model based on observations from small agricultural watersheds. Bartlett et al. [2016] performed a re-interpretation and modification of the (CN) method. This forward looking discussion of the Bartlett et al. paper urges the research communities in hydrologic science and engineering to consider the CN method as a stepping stone that has outlived its usefulness in research. The CN method fills a narrow niche in certain settings as a regression based method to estimate runoff from a given amount of rainfall. Sixty five years of use and multiple reinterpretations have not resulted in improved hydrological predictability using the method. We suggest that the research community should move forward by (1) identifying appropriate dynamic hydrological model formulations for different hydrogeographic settings, (2) specifying needed model capabilities for solving different classes of problems (e.g. flooding, erosion/sedimentation, nutrient transport, water management, etc.) in different hydro-geographic settings, and (3) expanding data collection and research programs to help ameliorate the so-called “over-parameterization” problem in contemporary modeling. Many decades of advances in geo-spatial data and processing, computation, and understanding are being squandered on continued focus on the static CN regression method. It is time to truly “move beyond” the Curve Number method.

Technical Abstract: Bartlett et al. [2016] performed a re-interpretation and modification of the space-time lumped USDA NRCS (formerly SCS) Curve Number (CN) method to extend its applicability to forested watersheds. We believe that the well documented limitations of the CN method severely constrains the applicability of the modifications proposed by Bartlett et al. [2016] in key ways. This forward-looking comment urges the research communities in hydrologic science and engineering to consider the CN method as a stepping stone that has outlived its usefulness in research. The CN method fills a narrow niche in certain settings as a parsimonious method having utility as a regression equation to estimate runoff from a given amount of rainfall, which originated as a static functional form that fits rainfall-runoff data sets. Sixty five years of use and multiple reinterpretations have not resulted in improved hydrological predictability using the method. We suggest that the research community should move forward by (1) identifying appropriate dynamic hydrological model formulations for different hydrogeographic settings, (2) specifying needed model capabilities for solving different classes of problems (e.g. flooding, erosion/sedimentation, nutrient transport, water management, etc.) in different hydro-geographic settings, and (3) expanding data collection and research programs to help ameliorate the so-called “over-parameterization” problem in contemporary modeling. Many decades years of advances in geo-spatial data and processing, computation, and understanding are being squandered on continued focus on the static CN regression method. It is time to truly “move beyond” the Curve Number method.

Last Modified: 07/20/2017
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