Scholarlink

<p>Estimating total runoff discharge in arid and semi-arid regions poses a significant challenge, particularly in data-scarce environments where real-time hydrological measurements are unavailable. Using a significant rainfall event in November 2018, this paper assesses the SCS-CN approach and its revised models, such as the Mishra-Singh, Woodward, and Huang models, for estimating wadi runoff in Kuwait’s ephemeral (arid) catchments. It was believed to be the heaviest rainfall event Kuwait had seen in a century. The GIS’s HEC-GeoHMS hydrological modeling tools were utilized to determine Curve Number (CN) values based on watershed attributes such as soil type, land use, and digital elevation model (DEM). The uniqueness of this work lies in the integration of HEC-GeoHMS hydrological modeling within GIS to precisely determine CN values in a high-resolution gridded raster map, along with the application of four SCS-CN methods that are well-suited to Kuwait’s catchments and do not require extensive datasets. The performance of the original SCS-CN model and its modified versions is rigorously assessed using multiple statistical metrics to determine the most reliable approach for runoff estimation. In November 2018, runoff volumes were estimated at 95.04 MCM (SCS-CN), 85.94 MCM (Huang), and 158.26 MCM (Mishra-Singh).The Mishra-Singh model achieved the highest coefficient of determination (R² = 0.8374) and the lowest error values, including a Mean Absolute Deviation (MAD) of 1.852, Mean Squared Error (MSE) of 5.079, Root Mean Squared Error (RMSE) of 2.254, Mean Absolute Percentage Error (MAPE) of 63.029, normalized Root Mean Squared Error (nRMSE) of 1.747, Percent Bias (PBIAS) of 0.373, and Nash-Sutcliffe Efficiency (NSE) of 0.991. The Mishra-Singh technique yielded a runoff-rainfall ratio of 21.4% in November 2018 compared to 13.4% using the original SCS-CN method; the observed ratio of 20.8% closely matched the Mishra-Singh model. The findings provide a valuable framework for selecting optimal runoff estimation techniques in wadi systems with limited hydrological data, contributing to improved water resource management and flood mitigation strategies in arid regions.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Assessing SCS-CN and its revised models for Wadi runoff estimation in data-scarce Kuwait catchments

  • 期刊论文

  • 发表日期：

    2026年1月16日

查看全文
  • Ahmed E. M. Al-Juaidi,
  • Amjad S. Aliewi

摘要

Estimating total runoff discharge in arid and semi-arid regions poses a significant challenge, particularly in data-scarce environments where real-time hydrological measurements are unavailable. Using a significant rainfall event in November 2018, this paper assesses the SCS-CN approach and its revised models, such as the Mishra-Singh, Woodward, and Huang models, for estimating wadi runoff in Kuwait’s ephemeral (arid) catchments. It was believed to be the heaviest rainfall event Kuwait had seen in a century. The GIS’s HEC-GeoHMS hydrological modeling tools were utilized to determine Curve Number (CN) values based on watershed attributes such as soil type, land use, and digital elevation model (DEM). The uniqueness of this work lies in the integration of HEC-GeoHMS hydrological modeling within GIS to precisely determine CN values in a high-resolution gridded raster map, along with the application of four SCS-CN methods that are well-suited to Kuwait’s catchments and do not require extensive datasets. The performance of the original SCS-CN model and its modified versions is rigorously assessed using multiple statistical metrics to determine the most reliable approach for runoff estimation. In November 2018, runoff volumes were estimated at 95.04 MCM (SCS-CN), 85.94 MCM (Huang), and 158.26 MCM (Mishra-Singh).The Mishra-Singh model achieved the highest coefficient of determination (R² = 0.8374) and the lowest error values, including a Mean Absolute Deviation (MAD) of 1.852, Mean Squared Error (MSE) of 5.079, Root Mean Squared Error (RMSE) of 2.254, Mean Absolute Percentage Error (MAPE) of 63.029, normalized Root Mean Squared Error (nRMSE) of 1.747, Percent Bias (PBIAS) of 0.373, and Nash-Sutcliffe Efficiency (NSE) of 0.991. The Mishra-Singh technique yielded a runoff-rainfall ratio of 21.4% in November 2018 compared to 13.4% using the original SCS-CN method; the observed ratio of 20.8% closely matched the Mishra-Singh model. The findings provide a valuable framework for selecting optimal runoff estimation techniques in wadi systems with limited hydrological data, contributing to improved water resource management and flood mitigation strategies in arid regions.