Comparison of antecedent precipitation based rainfall-runoff models
The Soil Conservation Service Curve Number (SCS-CN) method is one of the popular methods for calculating storm depth from a rainfall event. The previous research identified antecedent rainfall as a key element that controls the non-linear behaviour of the model. The original version indirectly uses...
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| Main Authors: | , |
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| Format: | article |
| Language: | EN |
| Published: |
IWA Publishing
2021
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| Subjects: | |
| Online Access: | https://doaj.org/article/3f7f03d3c9224a368e52f32e4df94e86 |
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| Summary: | The Soil Conservation Service Curve Number (SCS-CN) method is one of the popular methods for calculating storm depth from a rainfall event. The previous research identified antecedent rainfall as a key element that controls the non-linear behaviour of the model. The original version indirectly uses five days antecedent rainfall to identify the land condition as dry, normal or wet. This leads to a sudden jump once the land condition changes. To obviate this, the present work intends to improve the performance of antecedent rainfall-based SCS-CN models. Two forms of SCS-CN model (M1 and M2), two recently developed P-P5 based models (M3 and M4), and an alternate approach of considering P5 in the SCS-CN model (M5 and M6), as proposed here, were investigated. Based on the evaluation of several error metrics, the new proposed model M6 has performed better than other models. The performance of this model is evaluated using rainfall-runoff events of 114 watersheds located in the USA. The median value of Nash-Sutcliffe Efficiency was found as 0.78 for the M6 model followed by M5 (0.75), M3 (0.73), M4 (0.72), M2 (0.63) and M1 (0.61) model. HIGHLIGHTS
The study has been done on significant runoff producing events for which runoff coefficient is greater than 0.12.;
Research supports the superior performance of the proposed model in US watersheds.;
It acknowledges the applicability of five days antecedent rainfall (P5) on runoff prediction and shows improvement in model performance under all statistical indices.; |
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