Published November 2017 | Version v1
Journal article

An enhanced SMA based SCS-CN inspired model for watershed runoff prediction

  • 1. National Institute of Industrial Engineering-Mumbai, Environmental Engineering and Management Group (India)
  • 2. Indian Institute of Technology, Department of Water Resources Development and Management (India)
  • 3. National Institute of Hydrology, Water Resources Systems Division (India)
  • 4. G. B. Pant National Institute of Himalayan Environment and Sustainable Development, National Mission of Himalayan Studies (India)

Description

Incorporation of initial soil moisture (V0) in the Soil Conservation Service Curve Number (SCS-CN) methodology helps to avoid the sudden jumps in Curve Number (CN) and, in turn, in computed runoff. It invoked the development of an enhanced (yet simple) Soil Moisture Accounting (SMA) procedure-based-SCS-CN inspired model, by incorporating initial moisture (V0). Its performance is tested using a dataset of 152 small to large watersheds of USDA (total 38,169 storm events), and compared with original SCS-CN method, Mishra and Singh (Acta Geophys Polon 50(3):457–477, 2002), Michel et al. (Water Resour Res 41(2):W02011, 2005) and Singh et al. (Water Resour Manag 29(11): 4111–4127, 2015) model using four statistical indices (RMSE, R2, PBIAS and NSE) and rank grading system (RGS). The proposed model scores highest (= 691 marks out of maximum 2280 marks) (Rank I) followed by Singh et al. (Water Resour Manag 29(11):4111–4127, 2015) model with 642 marks (Rank II), Michel et al. (Water Resour Res 41(2):W02011, 2005) model with 376 marks (Rank III) and Mishra and Singh model with 362 marks (= Rank IV). The original SCS-CN model, however, performs the poorest of all with 209 marks (Rank V).

Additional details

Identifiers

Publishing Information

Journal Title
Environmental Earth Sciences
Journal Volume
76
Journal Issue
21
Journal Page Range
p. 1-20
ISSN
1866-6280

INIS

Country of Publication
Germany
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
51019239
Subject category
S54: ENVIRONMENTAL SCIENCES;
Descriptors DEI
DATASETS; FORECASTING; MOISTURE; RUNOFF; SOIL CONSERVATION; SOILS; STORMS; WATERSHEDS
Descriptors DEC
DOCUMENT TYPES; ENVIRONMENTAL TRANSPORT; MASS TRANSFER; RESOURCE CONSERVATION

Optional Information

Copyright
Copyright (c) 2017 Springer-Verlag GmbH Germany