A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Runoff Prediction Errors with Conjugate Curve Number
The curve number (CN) rainfall-runoff model is widely adopted for runoff prediction. However, its practitioners seldom verify its validity according to the rainfall-runoff dataset. Conjugate CN was often used by mistake in the CN runoff predictive model for runoff prediction. This study compared runoff predictions of a calibrated CN model to the existing CN modelling approach and simulated the runoff prediction results from the inaccurate use of the conjugate CN. The calibrated CN model outperformed the other 2 models with the highest Nash–Sutcliffe model efficiency index of 0.917 without runoff over and under prediction tendency. When conjugate CN was wrongly used for runoff prediction, the model had the largest runoff depth under-prediction concern. Contrarily, the existing CN approach over-predicted runoff amount when it is not calibrated. On average, the model over-predicted runoff volume by 5 million liters/km2 while the inaccurate use of conjugate CN under-predicted runoff volume by nearly 7.6 million liters/km2 in Peninsula Malaysia.
Runoff Prediction Errors with Conjugate Curve Number
The curve number (CN) rainfall-runoff model is widely adopted for runoff prediction. However, its practitioners seldom verify its validity according to the rainfall-runoff dataset. Conjugate CN was often used by mistake in the CN runoff predictive model for runoff prediction. This study compared runoff predictions of a calibrated CN model to the existing CN modelling approach and simulated the runoff prediction results from the inaccurate use of the conjugate CN. The calibrated CN model outperformed the other 2 models with the highest Nash–Sutcliffe model efficiency index of 0.917 without runoff over and under prediction tendency. When conjugate CN was wrongly used for runoff prediction, the model had the largest runoff depth under-prediction concern. Contrarily, the existing CN approach over-predicted runoff amount when it is not calibrated. On average, the model over-predicted runoff volume by 5 million liters/km2 while the inaccurate use of conjugate CN under-predicted runoff volume by nearly 7.6 million liters/km2 in Peninsula Malaysia.
Runoff Prediction Errors with Conjugate Curve Number
Lecture Notes in Civil Engineering
Awang, Mokhtar (editor) / Ling, Lloyd (editor) / Emamian, Seyed Sattar (editor) / Ling, Lloyd (author) / Yusop, Zulkifli (author) / Tan, Wei Lun (author)
2022-03-01
7 pages
Article/Chapter (Book)
Electronic Resource
English
Improved curve number selection for runoff prediction
| British Library Online Contents | 1998
The Revised Curve Number Rainfall–Runoff Methodology for an Improved Runoff Prediction
| DOAJ | 2023
Hydrotechnical engineering - Improved curve number selection for runoff prediction
| Online Contents | 1998
Runoff Curve Number: Has it Reached Maturity?
| Online Contents | 1997
Runoff Curve Number: Has It Reached Maturity?
| Online Contents | 1996