The calibration of a rainfall-runoff model
Since the inception of SCS (now USDA) runoff prediction model in 1954, worldwide researchers reported inconsistent study results. Non parametric inferential statistics was used to guide the numerical analysis to search for optimum results for the model calibration in this article. The technique was...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Conference or Workshop Item |
| Published: |
2015
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/61448/ http://10times.com/acee-penang |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Teknologi Malaysia |
| Summary: | Since the inception of SCS (now USDA) runoff prediction model in 1954, worldwide researchers reported inconsistent study results. Non parametric inferential statistics was used to guide the numerical analysis to search for optimum results for the model calibration in this article. The technique was tested in a case study and a significant improved runoff prediction model was formulated with Nash-Sutcliffe of 0.82 and 16% less residual sum of squares (RSS) than the conventional model. The methodology proposed herewith also addressed the common selection dilemma between mean and median. It identified optimum value used to calibrate the conventional model and also formulated a better runoff predictive model with statistical significance than those by either mean or median. An adjustment equation was also introduced to amend the conventional USDA runoff prediction model. |
|---|