Presentation of a Rainfall–Runoff Retention Model (3RM) Based on Antecedent Effective Retention for Estimating Runoff in Seven Basins in Iran

Author:

Shamohammadi Shayan1,Ghasemi Ahmad Reza1,Ostad-Ali-Askari Kaveh23,Izadi Saeedeh1

Affiliation:

1. Water Engineering Department, College of Agriculture, Shahrekord University, Shahrekord 8818634141, Iran

2. Department of Water Engineering, College of Agriculture, Isfahan University of Technology, Isfahan 8415683111, Iran

3. Department of Natural Sciences, Manchester Metropolitan University, Chester Street, John Dalton Building, Manchester M1 5GD, UK

Abstract

This study aims to correct and assess the SCS-CN model. In this research, the 3RM model (written by Shamohammadi) has been modified in such a way that the maximum primary retention (I), maximum secondary retention (Fmax), and basin potential retention (Smax) can be calculated using precipitation (Pa). The purpose of this study is to evaluate the total retention model (St=f(Fmax,Smax,pa)) and the runoff model (Q=f(St,pa)) using the mountain basins of Iran, including Emameh, Kasilian, Navrood, Darjazin, Kardeh, Khanmirza, and Mashin. The results showed that the primary retention, maximum secondary retention, and retention capacity are, respectively, 2.3, 30.4, and 32.7 mm in Imamah, 2.5, 48.6, and 51.1 mm in Kasilian, 2.4, 26.7, and 29.1 mm in Navrood, 3.2, 21.5, and 24.7 mm in Darjazin, 1.7, 15.0, and 16.7 mm in Kardeh, 2.5, 33.2, and 38.1 mm in Khanmirza, and 4.9, 44.5, and 50.6 mm in Mashine. Additionally, the λ (ratio of primary retention to potential retention) values for all basins are less than 0.2 (suggested by SCS) and vary between 0.05 in Kasilian and 0.1 in the Darjazin, Kardeh, and Mashine basins. The results of fitting the model to the rainfall-runoff data showed that the evaluation indices, including the coefficient of determination (R2), Nash–Sutcliffe (NS), and root mean square error (RMSE), for predicting the runoff in the basins varied between 0.78 to 0.96, 0.78 to 0.961, and 0.86 to 2.28, respectively. According to the obtained results, it can be concluded that the model has an acceptable ability to predict runoff for all the studied basins.

Publisher

MDPI AG

Subject

Safety, Risk, Reliability and Quality,Civil and Structural Engineering

Reference30 articles.

1. Mosavi, A., Ozturk, P., and Chau, K.W. (2018). Flood Prediction Using Machine Learning Models: Literature Review. Water, 10.

2. Evolution of the SCS Runoff Curve Number Method and its Application to Continuous Runoff Simulation;Williams;J. Hydrol. Eng.,2012

3. Improving the Efficency of SCS Runoff Curve Number;Buszney;J. Irrig. Drain. Eng.,1989

4. Use of Soil Moisture Data and Curve Number Method for Estimating Runoff in the Loess Plateau of China;Huang;Hydrol. Process. Int. J.,2007

5. Estimation of Precipitation Runoff Using SCS and GISApproach in Puzhal Watershed;Nandhakumar;Int. J. Civ. Eng. Technol.,2019

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3