Premium
Comment on “Beyond the SCS‐CN method: A theoretical framework for spatially lumped rainfall‐runoff response” by M . S. Bartlett et al.
Author(s) -
Ogden Fred L.,
Hawkins Richard “Pete”,
Walter M. Todd,
Goodrich David C.
Publication year - 2017
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2016wr020176
Subject(s) - surface runoff , predictability , runoff curve number , computer science , flooding (psychology) , interpretation (philosophy) , computation , hydrology (agriculture) , environmental science , mathematics , algorithm , geology , statistics , ecology , geotechnical engineering , psychology , psychotherapist , biology , programming language
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). 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 an empirical 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 hydro‐geographic 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 “overparameterization” 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.