Simplified SMA-inspired 1-parameter SCS-CN model for runoff estimation
(2018) In Arabian Journal of Geosciences 11(15).- Abstract
This study proposes a simplified 1-parameter SCS-CN model (M5) based on Mishra-Singh (2002) model and soil moisture accounting (SMA) procedure for surface runoff estimation and compares its performance with the existing SCS-CN method (SCS, 1956) (M1), Michel 1-P model (Water Resour Res 41:1-6, 2005) (M2), Sahu 1-P model (Hydrol Process 21:2872-2881, 2007) (M3), and Ajmal et al. model (J Hydrol 530:623-633, 2015) (M4) using large rainfall–runoff dataset of 48,763 events from123 USDA-ARS watersheds. The performance of models was evaluated using three statistical error indices such as Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), percentage bias (PBIAS), and rank and grading system (RGS). Based on the results obtained,... (More)
This study proposes a simplified 1-parameter SCS-CN model (M5) based on Mishra-Singh (2002) model and soil moisture accounting (SMA) procedure for surface runoff estimation and compares its performance with the existing SCS-CN method (SCS, 1956) (M1), Michel 1-P model (Water Resour Res 41:1-6, 2005) (M2), Sahu 1-P model (Hydrol Process 21:2872-2881, 2007) (M3), and Ajmal et al. model (J Hydrol 530:623-633, 2015) (M4) using large rainfall–runoff dataset of 48,763 events from123 USDA-ARS watersheds. The performance of models was evaluated using three statistical error indices such as Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), percentage bias (PBIAS), and rank and grading system (RGS). Based on the results obtained, the models can be ranked as follows: M5 > M4 > M3 > M1 > M2, i.e., model M5 outperformed all the remaining four models M1–M4 and hence is recommended for field applications.
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- author
- Verma, Sangeeta ; Singh, Pushpendra Kumar ; Mishra, Surendra Kumar ; Jain, Sanjay Kumar ; Berndtsson, Ronny LU ; Singh, Anju and Verma, Ravindra Kumar
- organization
- publishing date
- 2018-08-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Curve number, SCS-CN method, Soil moisture accounting, Surface runoff
- in
- Arabian Journal of Geosciences
- volume
- 11
- issue
- 15
- article number
- 420
- publisher
- Springer
- external identifiers
-
- scopus:85051145988
- ISSN
- 1866-7511
- DOI
- 10.1007/s12517-018-3736-7
- language
- English
- LU publication?
- yes
- id
- fc348bcd-c288-4504-b95f-281b543023e8
- date added to LUP
- 2018-08-22 13:34:47
- date last changed
- 2023-09-08 05:08:25
@article{fc348bcd-c288-4504-b95f-281b543023e8, abstract = {{<p>This study proposes a simplified 1-parameter SCS-CN model (M5) based on Mishra-Singh (2002) model and soil moisture accounting (SMA) procedure for surface runoff estimation and compares its performance with the existing SCS-CN method (SCS, 1956) (M1), Michel 1-P model (Water Resour Res 41:1-6, 2005) (M2), Sahu 1-P model (Hydrol Process 21:2872-2881, 2007) (M3), and Ajmal et al. model (J Hydrol 530:623-633, 2015) (M4) using large rainfall–runoff dataset of 48,763 events from123 USDA-ARS watersheds. The performance of models was evaluated using three statistical error indices such as Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), percentage bias (PBIAS), and rank and grading system (RGS). Based on the results obtained, the models can be ranked as follows: M5 > M4 > M3 > M1 > M2, i.e., model M5 outperformed all the remaining four models M1–M4 and hence is recommended for field applications.</p>}}, author = {{Verma, Sangeeta and Singh, Pushpendra Kumar and Mishra, Surendra Kumar and Jain, Sanjay Kumar and Berndtsson, Ronny and Singh, Anju and Verma, Ravindra Kumar}}, issn = {{1866-7511}}, keywords = {{Curve number; SCS-CN method; Soil moisture accounting; Surface runoff}}, language = {{eng}}, month = {{08}}, number = {{15}}, publisher = {{Springer}}, series = {{Arabian Journal of Geosciences}}, title = {{Simplified SMA-inspired 1-parameter SCS-CN model for runoff estimation}}, url = {{http://dx.doi.org/10.1007/s12517-018-3736-7}}, doi = {{10.1007/s12517-018-3736-7}}, volume = {{11}}, year = {{2018}}, }