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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Autores principales:	Pankaj Upreti, C. S. P. Ojha
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	antecedent runoff condition (arc) maximum potential retention optimization scs-cn method surface runoff estimation us watersheds Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
Acceso en línea:	https://doaj.org/article/3f7f03d3c9224a368e52f32e4df94e86
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spelling	oai:doaj.org-article:3f7f03d3c9224a368e52f32e4df94e862021-11-06T07:18:35ZComparison of antecedent precipitation based rainfall-runoff models1606-97491607-079810.2166/ws.2020.315https://doaj.org/article/3f7f03d3c9224a368e52f32e4df94e862021-08-01T00:00:00Zhttp://ws.iwaponline.com/content/21/5/2122https://doaj.org/toc/1606-9749https://doaj.org/toc/1607-0798The 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.;Pankaj UpretiC. S. P. OjhaIWA Publishingarticleantecedent runoff condition (arc)maximum potential retentionoptimizationscs-cn methodsurface runoff estimationus watershedsWater supply for domestic and industrial purposesTD201-500River, lake, and water-supply engineering (General)TC401-506ENWater Supply, Vol 21, Iss 5, Pp 2122-2138 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	antecedent runoff condition (arc) maximum potential retention optimization scs-cn method surface runoff estimation us watersheds Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
spellingShingle	antecedent runoff condition (arc) maximum potential retention optimization scs-cn method surface runoff estimation us watersheds Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506 Pankaj Upreti C. S. P. Ojha Comparison of antecedent precipitation based rainfall-runoff models
description	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.;
format	article
author	Pankaj Upreti C. S. P. Ojha
author_facet	Pankaj Upreti C. S. P. Ojha
author_sort	Pankaj Upreti
title	Comparison of antecedent precipitation based rainfall-runoff models
title_short	Comparison of antecedent precipitation based rainfall-runoff models
title_full	Comparison of antecedent precipitation based rainfall-runoff models
title_fullStr	Comparison of antecedent precipitation based rainfall-runoff models
title_full_unstemmed	Comparison of antecedent precipitation based rainfall-runoff models
title_sort	comparison of antecedent precipitation based rainfall-runoff models
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/3f7f03d3c9224a368e52f32e4df94e86
work_keys_str_mv	AT pankajupreti comparisonofantecedentprecipitationbasedrainfallrunoffmodels AT cspojha comparisonofantecedentprecipitationbasedrainfallrunoffmodels
_version_	1718443789269336064

Comparison of antecedent precipitation based rainfall-runoff models

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