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

Content Provider	Springer Nature Link
Author	Verma, S. Mishra, S. K. Singh, A. Singh, P. K. Verma, R. K.
Copyright Year	2017
Abstract	Incorporation of initial soil moisture (V $_{0}$) 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 (V $_{0}$). 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, R $^{2}$, 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).
Starting Page	1
Ending Page	20
Page Count	20
File Format	PDF
ISSN	18666280
Journal	Environmental Earth Sciences
Volume Number	76
Issue Number	21
e-ISSN	18666299
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2017-10-28
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	SCS-CN method Initial soil moisture Soil moisture accounting Rank grading system Geology Hydrology/Water Resources Geochemistry Environmental Science and Engineering Terrestrial Pollution Biogeosciences
Content Type	Text
Resource Type	Article
Subject	Global and Planetary Change Earth-Surface Processes Soil Science Environmental Chemistry Pollution Geology Water Science and Technology

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Raster-based derivation of a flood runoff susceptibility map using the revised runoff curve number (CN) for the Kuantan watershed, Malaysia

A threshold artificial neural network model for improving runoff prediction in a karst watershed

The soil leakage ratio in the Mudu watershed, China

Development of a Modified SMA Based MSCS-CN Model for Runoff Estimation

A case study for ANN-based rainfall–runoff model considering antecedent soil moisture conditions in Imha Dam watershed, Korea

Assessment of hydrologic impacts of climate change on the runoff trend in Klang Watershed, Malaysia

Scenario-based runoff prediction for the Kaidu River basin of the Tianshan Mountains, Northwest China

Snowmelt-runoff estimation using radiation SRM model in Taleghan watershed

Comparison of SRM and WetSpa models efficiency for snowmelt runoff simulation

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

Similar Documents

Raster-based derivation of a flood runoff susceptibility map using the revised runoff curve number (CN) for the Kuantan watershed, Malaysia

A threshold artificial neural network model for improving runoff prediction in a karst watershed

The soil leakage ratio in the Mudu watershed, China

Development of a Modified SMA Based MSCS-CN Model for Runoff Estimation

A case study for ANN-based rainfall–runoff model considering antecedent soil moisture conditions in Imha Dam watershed, Korea

Assessment of hydrologic impacts of climate change on the runoff trend in Klang Watershed, Malaysia

Scenario-based runoff prediction for the Kaidu River basin of the Tianshan Mountains, Northwest China

Snowmelt-runoff estimation using radiation SRM model in Taleghan watershed

Comparison of SRM and WetSpa models efficiency for snowmelt runoff simulation

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