EGU2020-8346
https://doi.org/10.5194/egusphere-egu2020-8346
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

An approach to accommodate and estimate antecedent moisture in runoff curve number methodology- An experimental study

Surendra Kumar Mishra1, Ishan Sharma2, Ashish Pandey3, and Shailendra Kumar Kumre4
Surendra Kumar Mishra et al.
  • 1Indian Institute of Technology Roorkee, Indian Institute of Technology Roorkee, Water Resources Development and Management, India (skm61fwt@gmail.com)
  • 2Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee, India. (ishan2033@gmail.com)
  • 3Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee, India (ashisfwt@gmail.com)
  • 4Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee, India ( skumre159@gmail.com)

Modelling of the event-based rainfall-runoff process has considerable importance in Hydrology, especially for assessment of water yield potential of a watershed, planning of soil and water conservation measures, reducing sedimentation, and flooding hazards downstream. Antecedent moisture (M) plays a significant role in governing the rainfall-runoff modelling process. It has been the focal point of research in the last decade for improving the Soil Conservation Service Curve Number (SCS-CN) method (also known as NRCS-CN method) for surface runoff computation. In this study, an innovative procedure is proposed to accommodate M in the basic structure of the SCS-CN methodology which otherwise was incorporated externally; to compute M using rainfall-runoff data and verify its applicability by comparing M with the in-situ soil moisture.

Natural rainfall, runoff, and soil moisture data from 6 small experimental farms with different land-use viz. Maize, Finger Millet, and Fallow land, located at Roorkee, India, are utilized. The M is computed by optimizing two parameters, i.e., absolute maximum potential retention (Sabs) and initial abstraction ratio (λ), and the optimization is accomplished by minimizing the root mean square error (RMSE). Results show that there exists a good correlation between theoretical M and measured in-situ moisture. Also, the optimized value of λ has the less error in computing M than the other standard values of λ (λ = 0.2; λ= 0.03). This study not only improves the SCS-CN method but also widens its application horizon in soil moisture studies.

How to cite: Mishra, S. K., Sharma, I., Pandey, A., and Kumre, S. K.: An approach to accommodate and estimate antecedent moisture in runoff curve number methodology- An experimental study, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8346, https://doi.org/10.5194/egusphere-egu2020-8346, 2020

Display materials

Display file

Comments on the display material

AC: Author Comment | CC: Community Comment | Report abuse

Display material version 1 – uploaded on 02 May 2020, no comments